21070
Article
Space-time variability of summer hydroclimate in the United States Prairie Pothole Region
The Prairie Pothole Region (PPR) experiences considerable space–time variability in temperature and precipitation, and this variability is expected to increase. The PPR is sensitive to this variability—it plays a large role in the water availability of the region. Thousands of wetlands in the region, sometimes containing ponds, provide habitats and breeding grounds for various species. Many wildlife management decisions are planned and executed on subseasonal-to-seasonal time scales and would benefit from knowledge of seasonal conditions at longer lead times. Therefore, it is important to understand potential driving mechanisms and teleconnections behind space–time climate variability in the PPR. We performed principal component analysis on summer precipitation of the southeastern PPR (SEPPR) to determine the leading principal components (PCs) of variability. These PCs were used to establish teleconnections to large-scale climate variables and indices. They were also used to determine potential mechanisms driving the precipitation variability. There were teleconnections to Pacific and Atlantic Ocean sea surface temperatures (SST) resembling the Pacific decadal oscillation and El Niño–Southern Oscillation, low 500-hPa heights over the western United States, and the Palmer drought severity index over the SEPPR. A large-scale low pressure region over the northwestern United States and a pattern like the Great Plains low-level jet, observed in the 500- and 850-hPa heights and winds, are a potential mechanism of the precipitation variability by increasing precipitation during wet PC1 years. These findings can inform management actions to maintain and restore wildlife habitat and the resources used for those actions in the PPR.
2022-1
Earth Interact.
26
39-51
0
10.1175/EI-D-21-0004.1
The Prairie Pothole Region (PPR) experiences considerable space–time variability in temperature and precipitation, and this variability is expected to increase. The PPR is sensitive to this variability—it plays a large role in the water availability of the region. Thousands of wetlands in the region, sometimes containing ponds, provide habitats and breeding grounds for various species. Many wildlife management decisions are planned and executed on subseasonal-to-seasonal time scales and would benefit from knowledge of seasonal conditions at longer lead times. Therefore, it is important to understand potential driving mechanisms and teleconnections behind space–time climate variability in the PPR. We performed principal component analysis on summer precipitation of the southeastern PPR (SEPPR) to determine the leading principal components (PCs) of variability. These PCs were used to establish teleconnections to large-scale climate variables and indices. They were also used to determine potential mechanisms driving the precipitation variability. There were teleconnections to Pacific and Atlantic Ocean sea surface temperatures (SST) resembling the Pacific decadal oscillation and El Niño–Southern Oscillation, low 500-hPa heights over the western United States, and the Palmer drought severity index over the SEPPR. A large-scale low pressure region over the northwestern United States and a pattern like the Great Plains low-level jet, observed in the 500- and 850-hPa heights and winds, are a potential mechanism of the precipitation variability by increasing precipitation during wet PC1 years. These findings can inform management actions to maintain and restore wildlife habitat and the resources used for those actions in the PPR.
Abel
B. D.
Rajagopalan
B.
Ray
A. J.
21078
Article
Demonstrating a Probabilistic Quantitative Precipitation Estimate for Evaluating Precipitation Forecasts in Complex Terrain
Accurate quantitative precipitation estimates (QPEs) at high spatial and temporal resolution are difficult to obtain in regions of complex terrain due to the large spatial heterogeneity of orographically enhanced precipitation, sparsity of gauges, precipitation phase variations, and terrain effects that impact the quality of remotely sensed estimates. The large uncertainty of QPE in these regions also makes the evaluation of high-resolution quantitative precipitation forecasts (QPFs) challenging, as it can be difficult to choose a reference QPE that is reliable at both high and low elevations. In this paper we demonstrate a methodology to combine information from multiple high-resolution hourly QPE products to evaluate QPFs from NOAA’s High-Resolution Rapid Refresh (HRRR) model in a region of Northern California. The methodology uses the quantiles of monthly QPE distributions to determine a range of hourly precipitation that correspond to “good,” “possible,” “underestimated,” or “overestimated” QPFs. In this manuscript, we illustrate the use of the methodology to evaluate QPFs for seven atmospheric river events that occurred during the 2016–17 wet season in Northern California. Because the presence of frozen precipitation is often not captured by traditional QPE products, we evaluate QPFs both for all precipitation, and with likely frozen precipitation excluded. The methodology is shown to provide useful information to evaluate model performance while taking into account the uncertainty of available QPE at various temporal and spatial scales. The potential of the technique to evaluate changes between model versions is also shown.
2022-1
Wea. Forecasting
37
45-64
0
10.1175/WAF-D-21-0074.1
Accurate quantitative precipitation estimates (QPEs) at high spatial and temporal resolution are difficult to obtain in regions of complex terrain due to the large spatial heterogeneity of orographically enhanced precipitation, sparsity of gauges, precipitation phase variations, and terrain effects that impact the quality of remotely sensed estimates. The large uncertainty of QPE in these regions also makes the evaluation of high-resolution quantitative precipitation forecasts (QPFs) challenging, as it can be difficult to choose a reference QPE that is reliable at both high and low elevations. In this paper we demonstrate a methodology to combine information from multiple high-resolution hourly QPE products to evaluate QPFs from NOAA’s High-Resolution Rapid Refresh (HRRR) model in a region of Northern California. The methodology uses the quantiles of monthly QPE distributions to determine a range of hourly precipitation that correspond to “good,” “possible,” “underestimated,” or “overestimated” QPFs. In this manuscript, we illustrate the use of the methodology to evaluate QPFs for seven atmospheric river events that occurred during the 2016–17 wet season in Northern California. Because the presence of frozen precipitation is often not captured by traditional QPE products, we evaluate QPFs both for all precipitation, and with likely frozen precipitation excluded. The methodology is shown to provide useful information to evaluate model performance while taking into account the uncertainty of available QPE at various temporal and spatial scales. The potential of the technique to evaluate changes between model versions is also shown.
Bytheway
J. L.
Hughes
M.
Cifelli
R.
Mahoney
K. M.
English
J. M.
21080
Article
Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations
Thermodynamic profiles are often retrieved from the multi-wavelength brightness temperature observations made by microwave radiometers (MWRs) using regression methods (linear, quadratic approaches), artificial intelligence (neural networks), or physical iterative methods. Regression and neural network methods are tuned to mean conditions derived from a climatological dataset of thermodynamic profiles collected nearby. In contrast, physical iterative retrievals use a radiative transfer model starting from a climatologically reasonable profile of temperature and water vapor, with the model running iteratively until the derived brightness temperatures match those observed by the MWR within a specified uncertainty.
In this study, a physical iterative approach is used to retrieve temperature and humidity profiles from data collected during XPIA (eXperimental Planetary boundary layer Instrument Assessment), a field campaign held from March to May 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility. During the campaign, several passive and active remote sensing instruments as well as in situ platforms were deployed and evaluated to determine their suitability for the verification and validation of meteorological processes. Among the deployed remote sensing instruments were a multi-channel MWR as well as two radio acoustic sounding systems (RASSs) associated with 915 and 449 MHz wind profiling radars.
In this study the physical iterative approach is tested with different observational inputs: first using data from surface sensors and the MWR in different configurations and then including data from the RASS in the retrieval with the MWR data. These temperature retrievals are assessed against co-located radiosonde profiles. Results show that the combination of the MWR and RASS observations in the retrieval allows for a more accurate characterization of low-level temperature inversions and that these retrieved temperature profiles match the radiosonde observations better than the temperature profiles retrieved from only the MWR in the layer between the surface and 3 km above ground level (a.g.l.). Specifically, in this layer of the atmosphere, both root mean square errors and standard deviations of the difference between radiosonde and retrievals that combine MWR and RASS are improved by mostly 10 %–20 % compared to the configuration that does not include RASS observations. Pearson correlation coefficients are also improved.
2022-1
Atmos. Meas. Tech.
15
521-537
0
10.5194/amt-15-521-2022
Thermodynamic profiles are often retrieved from the multi-wavelength brightness temperature observations made by microwave radiometers (MWRs) using regression methods (linear, quadratic approaches), artificial intelligence (neural networks), or physical iterative methods. Regression and neural network methods are tuned to mean conditions derived from a climatological dataset of thermodynamic profiles collected nearby. In contrast, physical iterative retrievals use a radiative transfer model starting from a climatologically reasonable profile of temperature and water vapor, with the model running iteratively until the derived brightness temperatures match those observed by the MWR within a specified uncertainty.
In this study, a physical iterative approach is used to retrieve temperature and humidity profiles from data collected during XPIA (eXperimental Planetary boundary layer Instrument Assessment), a field campaign held from March to May 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility. During the campaign, several passive and active remote sensing instruments as well as in situ platforms were deployed and evaluated to determine their suitability for the verification and validation of meteorological processes. Among the deployed remote sensing instruments were a multi-channel MWR as well as two radio acoustic sounding systems (RASSs) associated with 915 and 449 MHz wind profiling radars.
In this study the physical iterative approach is tested with different observational inputs: first using data from surface sensors and the MWR in different configurations and then including data from the RASS in the retrieval with the MWR data. These temperature retrievals are assessed against co-located radiosonde profiles. Results show that the combination of the MWR and RASS observations in the retrieval allows for a more accurate characterization of low-level temperature inversions and that these retrieved temperature profiles match the radiosonde observations better than the temperature profiles retrieved from only the MWR in the layer between the surface and 3 km above ground level (a.g.l.). Specifically, in this layer of the atmosphere, both root mean square errors and standard deviations of the difference between radiosonde and retrievals that combine MWR and RASS are improved by mostly 10 %–20 % compared to the configuration that does not include RASS observations. Pearson correlation coefficients are also improved.
Djalalova
I.
Turner
D. D.
Bianco
L.
Wilczak
J. M.
Duncan
J.
Adler
B.
Gottas
D. J.
21085
Article
A Hybrid Bulk Algorithm to Predict Turbulent Fluxes over Dry and Wet Bare Soils
Measurements made in the Columbia River basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modeled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow, which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin–Obukhov similarity; and (ii) a modified Priestley–Taylor (P-T) algorithm with physically based ecophysiological constraints, which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various time scales based on the modeled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP2, a hybrid approach can be used for similar flux-tower sites and field campaigns.
2022-4
J. Appl. Meteor. Climatol.
61
393-414
0
10.1175/JAMC-D-20-0232.1
Measurements made in the Columbia River basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modeled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow, which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin–Obukhov similarity; and (ii) a modified Priestley–Taylor (P-T) algorithm with physically based ecophysiological constraints, which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various time scales based on the modeled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP2, a hybrid approach can be used for similar flux-tower sites and field campaigns.
Grachev
A. A.
Fairall
C. W.
Blomquist
B. W.
Fernando
H. J. S.
Leo
L. S.
Otárola-Bustosc
S. F.
Wilczak
J. M.
McCaffrey
K.
21089
Article
Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0
The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these polar regions covered by sea ice, the wind is relatively strong due to the absence of obstructions and redistributes a large part of the deposited snow mass, which complicates estimates for precipitation hardly distinguishable from blowing or drifting snow. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove the snow mass balance uncertainties (i.e., snow transport contribution) in the Arctic environment. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open-source fluid dynamics software OpenFOAM. We combine the numerical simulations with terrestrial laser scan observations of surface dynamics to simulate snow deposition in a MOSAiC (Multidisciplinary Drifting Observatory for the Study of Arctic Climate) sea ice domain with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against scanner measurements. However, the approximations imposed by the numerical framework, together with potential measurement errors (precipitation), give rise to quantitative inaccuracies, which should be addressed in future work. The modeling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic.
2022-8
Geosci. Model Dev.
15
6429-6449
0
10.5194/gmd-15-6429-2022
The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these polar regions covered by sea ice, the wind is relatively strong due to the absence of obstructions and redistributes a large part of the deposited snow mass, which complicates estimates for precipitation hardly distinguishable from blowing or drifting snow. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove the snow mass balance uncertainties (i.e., snow transport contribution) in the Arctic environment. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open-source fluid dynamics software OpenFOAM. We combine the numerical simulations with terrestrial laser scan observations of surface dynamics to simulate snow deposition in a MOSAiC (Multidisciplinary Drifting Observatory for the Study of Arctic Climate) sea ice domain with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against scanner measurements. However, the approximations imposed by the numerical framework, together with potential measurement errors (precipitation), give rise to quantitative inaccuracies, which should be addressed in future work. The modeling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic.
Hames
O.
Jafari
M.
Wagner
D. N.
Raphael
I.
Clemens-Sewall
D.
Polashenski
C.
Shupe
M. D.
Schneebeli
M.
Lehning
M.
21090
Article
The reanalysis for the Global Ensemble Forecast System, version 12
NOAA has created a global reanalysis dataset, intended primarily for initialization of reforecasts for its Global Ensemble Forecast System, version 12 (GEFSv12), which provides ensemble forecasts out to +35-days lead time. The reanalysis covers the period 2000–19. It assimilates most of the observations that were assimilated into the operational data assimilation system used for initializing global predictions. These include a variety of conventional data, infrared and microwave radiances, global positioning system radio occultations, and more. The reanalysis quality is generally superior to that from NOAA’s previous-generation Climate Forecast System Reanalysis (CFSR), demonstrated in the fit of short-term forecasts to the observations and in the skill of 5-day deterministic forecasts initialized from CFSR versus GEFSv12. Skills of reforecasts initialized from the new reanalyses are similar but slightly lower than skills initialized from a preoperational version of the real-time data assimilation system conducted at the higher, operational resolution. Control member reanalysis data on vertical pressure levels are made publicly available.
2022-1
Mon. Wea. Rev.
150
59-79
0
10.1175/MWR-D-21-0023.1
NOAA has created a global reanalysis dataset, intended primarily for initialization of reforecasts for its Global Ensemble Forecast System, version 12 (GEFSv12), which provides ensemble forecasts out to +35-days lead time. The reanalysis covers the period 2000–19. It assimilates most of the observations that were assimilated into the operational data assimilation system used for initializing global predictions. These include a variety of conventional data, infrared and microwave radiances, global positioning system radio occultations, and more. The reanalysis quality is generally superior to that from NOAA’s previous-generation Climate Forecast System Reanalysis (CFSR), demonstrated in the fit of short-term forecasts to the observations and in the skill of 5-day deterministic forecasts initialized from CFSR versus GEFSv12. Skills of reforecasts initialized from the new reanalyses are similar but slightly lower than skills initialized from a preoperational version of the real-time data assimilation system conducted at the higher, operational resolution. Control member reanalysis data on vertical pressure levels are made publicly available.
Hamill
T. M.
Whitaker
J. S.
Shlyaeva
A.
Bates
G. T.
Fredrick
S.
Pegion
P.
Sinsky
E.
Zhu
Y.
Tallapragada
V.
Guan
H.
Zhou
X.
Woolen
J.
21092
Article
Record Low 2020 North American Monsoon Rains Reignites Drought
N/A
2022-1
Bull. Amer. Meteor. Soc.
Explaining Extreme Events from a Climate Perspective
103
S26–S32
0
10.1175/BAMS-D-21-0129.1
N/A
Hoell
A.
Quan
X.-W.
Hoerling
M. P.
Fu
R.
Mankin
J.
Simpson
I.
Seager
R.
He
C.
Lisonbee
J.
Livneh
B.
Sheffield
A.
21093
Article
Changes in extreme Integrated water Vapor Transport on the U.S. west coast in NA-CORDEX, and relationship to mountain and inland precipitation
Western U.S. (WUS) rainfall and snowpack vary greatly on interannual and decadal timescales. This combined with their importance to water resources makes future projections of these variables highly societally relevant. Previous studies have shown that precipitation events in the WUS are influenced by the timing, positioning, and duration of extreme integrated water vapor transport (IVT) events (e.g., atmospheric rivers) along the coast. We investigate end-of-21st-century projections of WUS precipitation and IVT in a collection of regional climate models (RCMs) from the North American Coordinated Regional Downscaling Experiment (NA-CORDEX). Several of the NA-CORDEX RCMs project a decrease in cool season precipitation at high elevation (e.g., across the Sierra Nevada) with a corresponding increase in the Great Basin of the U.S. We explore the larger-scale controls on this terrain-related precipitation change in a subset of the NA-CORDEX RCMs through an examination of IVT-events. Projected changes in frequency and duration of IVT-events depend on the event’s extremity: by the end of the century extreme IVT-events increase in frequency whereas moderate IVT-events decrease in frequency. Furthermore, in the future, total precipitation across the WUS generally increases during extreme IVT-events, whereas total precipitation from moderate IVT-events decreases across higher elevations. Thus, we argue that the mean cool season precipitation decreases at high elevations and increases in the Great Basin are largely determined by changes in moderate IVT-events which are projected to be less frequent and bring less high-elevation precipitation.
2022-2
Clim. Dyn.
59
973–995
0
10.1007/s00382-022-06168-6
Western U.S. (WUS) rainfall and snowpack vary greatly on interannual and decadal timescales. This combined with their importance to water resources makes future projections of these variables highly societally relevant. Previous studies have shown that precipitation events in the WUS are influenced by the timing, positioning, and duration of extreme integrated water vapor transport (IVT) events (e.g., atmospheric rivers) along the coast. We investigate end-of-21st-century projections of WUS precipitation and IVT in a collection of regional climate models (RCMs) from the North American Coordinated Regional Downscaling Experiment (NA-CORDEX). Several of the NA-CORDEX RCMs project a decrease in cool season precipitation at high elevation (e.g., across the Sierra Nevada) with a corresponding increase in the Great Basin of the U.S. We explore the larger-scale controls on this terrain-related precipitation change in a subset of the NA-CORDEX RCMs through an examination of IVT-events. Projected changes in frequency and duration of IVT-events depend on the event’s extremity: by the end of the century extreme IVT-events increase in frequency whereas moderate IVT-events decrease in frequency. Furthermore, in the future, total precipitation across the WUS generally increases during extreme IVT-events, whereas total precipitation from moderate IVT-events decreases across higher elevations. Thus, we argue that the mean cool season precipitation decreases at high elevations and increases in the Great Basin are largely determined by changes in moderate IVT-events which are projected to be less frequent and bring less high-elevation precipitation.
Hughes
M.
Swales
D.
Scott
J. D.
Alexander
M. A.
Mahoney
K. M.
McCrary
R.
Cifelli
R.
Bukovsky
M.
21097
Article
Evaluating multiple canopy-snow unloading parameterizations in SUMMA with time-lapse photography characterized by citizen scientists
Canopy-snow unloading is the complex physical process of snow unloading from the canopy through meltwater drip, sublimation to the atmosphere, or solid snow unloading to the snowpack below. This process is difficult to parameterize due to limited observations. Time-lapse photographs of snow in the canopy were characterized by citizen scientists to create a data set of snow interception observations at multiple locations across the western United States. This novel interception data set was used to evaluate three snow unloading parameterizations in the Structure for Unifying Multiple Modeling Alternatives (SUMMA) modular hydrologic modeling framework. SUMMA was modified to include a third snow unloading parameterization, termed Wind-Temperature (Roesch et al., 2001, https://doi.org/10.1007/s003820100153), which includes wind-dependent and temperature-dependent unloading functions. It was compared to a meltwater drip unloading parameterization, termed Melt (Andreadis et al., 2009, https://doi.org/10.1029/2008wr007042), and a time-dependent unloading parameterization, termed Exponential-Decay (Hedstrom & Pomeroy, 1998, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4). Wind-Temperature performed well without calibration across sites, specifically in cold climates, where wind dominates unloading and rime accretion is low. At rime prone sites, Wind-Temperature should be calibrated to account for longer interception events with less sensitivity to wind, otherwise Melt can be used without calibration. The absence of model physics in Exponential-Decay requires local calibration that can only be transferred to sites with similar unloading patterns. The choice of unloading parameterization can result in 20% difference in SWE on the ground below the canopy and 10% difference in estimated average winter canopy albedo. These novel observations shed light on processes that are often overlooked in hydrology.
2022-6
Water Resour. Res.
58
e2021WR030852
0
10.1029/2021WR030852
Canopy-snow unloading is the complex physical process of snow unloading from the canopy through meltwater drip, sublimation to the atmosphere, or solid snow unloading to the snowpack below. This process is difficult to parameterize due to limited observations. Time-lapse photographs of snow in the canopy were characterized by citizen scientists to create a data set of snow interception observations at multiple locations across the western United States. This novel interception data set was used to evaluate three snow unloading parameterizations in the Structure for Unifying Multiple Modeling Alternatives (SUMMA) modular hydrologic modeling framework. SUMMA was modified to include a third snow unloading parameterization, termed Wind-Temperature (Roesch et al., 2001, https://doi.org/10.1007/s003820100153), which includes wind-dependent and temperature-dependent unloading functions. It was compared to a meltwater drip unloading parameterization, termed Melt (Andreadis et al., 2009, https://doi.org/10.1029/2008wr007042), and a time-dependent unloading parameterization, termed Exponential-Decay (Hedstrom & Pomeroy, 1998, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4). Wind-Temperature performed well without calibration across sites, specifically in cold climates, where wind dominates unloading and rime accretion is low. At rime prone sites, Wind-Temperature should be calibrated to account for longer interception events with less sensitivity to wind, otherwise Melt can be used without calibration. The absence of model physics in Exponential-Decay requires local calibration that can only be transferred to sites with similar unloading patterns. The choice of unloading parameterization can result in 20% difference in SWE on the ground below the canopy and 10% difference in estimated average winter canopy albedo. These novel observations shed light on processes that are often overlooked in hydrology.
Lumbrazo
C.
Bennett
A.
Currier
W. R.
Nijssen
B.
Lundquist
J. D.
21098
Article
The Press and Pulse of Climate Change: Extreme Events in the Colorado River Basin
2022-12
J. Am. Water Resour. Assoc.
58
1076-1097
0
10.1111/1752-1688.13021
McCoy
A.
Jacobs
K.
Vano
J. A.
Wilson
J.
Martin
S.
Cifelli
R.
21100
Article
Rain drop size distributions estimated from NOAA Snow-Level Radar data
Using NOAA’s S-band High-Power Snow-Level Radar (HPSLR), a technique for estimating the rain drop size distribution (DSD) above the radar is presented. This technique assumes the DSD can be described by a four parameter, generalized gamma distribution (GGD). Using the radar’s measured average Doppler velocity spectrum and a value (assumed, measured, or estimated) of the vertical air motion w, an estimate of the GGD is obtained. Four different methods can be used to obtain w. One method that estimates a mean mass-weighted raindrop diameter Dm from the measured reflectivity Z produces realistic DSDs compared to prior literature examples. These estimated DSDs provide evidence that the radar can retrieve the smaller drop sizes constituting the “drizzle” mode part of the DSD. This estimation technique was applied to 19 h of observations from Hankins, North Carolina. Results support the concept that DSDs can be modeled using GGDs with a limited range of parameters. Further work is needed to validate the described technique for estimating DSDs in more varied precipitation types and to verify the vertical air motion estimates.
2022-3
J. Atmos. Oceanic Technol.
39
353–366
0
10.1175/JTECH-D-21-0049.1
Using NOAA’s S-band High-Power Snow-Level Radar (HPSLR), a technique for estimating the rain drop size distribution (DSD) above the radar is presented. This technique assumes the DSD can be described by a four parameter, generalized gamma distribution (GGD). Using the radar’s measured average Doppler velocity spectrum and a value (assumed, measured, or estimated) of the vertical air motion w, an estimate of the GGD is obtained. Four different methods can be used to obtain w. One method that estimates a mean mass-weighted raindrop diameter Dm from the measured reflectivity Z produces realistic DSDs compared to prior literature examples. These estimated DSDs provide evidence that the radar can retrieve the smaller drop sizes constituting the “drizzle” mode part of the DSD. This estimation technique was applied to 19 h of observations from Hankins, North Carolina. Results support the concept that DSDs can be modeled using GGDs with a limited range of parameters. Further work is needed to validate the described technique for estimating DSDs in more varied precipitation types and to verify the vertical air motion estimates.
Johnston
P. E.
Williams
C. R.
White
A. B.
21108
Article
Validation and Bias Correction of Forecast Reference Evapotranspiration for Agricultural Applications in Nevada
Accurate estimates of reference evapotranspiration (ET0) are critical for estimating actual crop evapotranspiration and agricultural water use. This study uses observations from the Nevada Integrated Climate and Evapotranspiration Network (NICE Net) to validate forecasts of ET0 and its driving variables from the National Weather Service’s National Digital Forecast Database (NDFD). Daily NDFD ET0 at lead times of 1 to 6 days were compared against 18 NICE Net stations. Correlations between NDFD and observations generally ranged between 0.4 and 0.9, with lower correlations at longer leads and a notable drop in skill during July and August. Systematic arid biases (high bias for temperatures and low bias for humidity) were found in NDFD with a strong warm minimum temperature bias and low vapor pressure bias most prominent during the growing season. Some of the largest relative biases were found in wind speed, although they were systematic and varied greatly by location. A case study revealed that NDFD consistently underestimates the variability found in observed minimum temperature, solar radiation, wind speed, and ET0. Cloudy days during summer were not well represented in the NDFD estimated solar radiation, which had a cascading impact on temperature, vapor pressure, and ET0 estimates. A monthly ratio-based bias-correction was applied to NDFD ET0, which reduced the root-mean squared error by 5%–30% for most locations. Bias-corrected ET0 forecasts from NDFD or other forecast systems show potential as a guide to develop weekly irrigation schedules for agricultural producers, with the ultimate goal of reducing applications of excess irrigation water.
2022-11
J. Water Resour. Plann. Manage.
148
04022057
0
10.1061/(ASCE)WR.1943-5452.0001595
Accurate estimates of reference evapotranspiration (ET0) are critical for estimating actual crop evapotranspiration and agricultural water use. This study uses observations from the Nevada Integrated Climate and Evapotranspiration Network (NICE Net) to validate forecasts of ET0 and its driving variables from the National Weather Service’s National Digital Forecast Database (NDFD). Daily NDFD ET0 at lead times of 1 to 6 days were compared against 18 NICE Net stations. Correlations between NDFD and observations generally ranged between 0.4 and 0.9, with lower correlations at longer leads and a notable drop in skill during July and August. Systematic arid biases (high bias for temperatures and low bias for humidity) were found in NDFD with a strong warm minimum temperature bias and low vapor pressure bias most prominent during the growing season. Some of the largest relative biases were found in wind speed, although they were systematic and varied greatly by location. A case study revealed that NDFD consistently underestimates the variability found in observed minimum temperature, solar radiation, wind speed, and ET0. Cloudy days during summer were not well represented in the NDFD estimated solar radiation, which had a cascading impact on temperature, vapor pressure, and ET0 estimates. A monthly ratio-based bias-correction was applied to NDFD ET0, which reduced the root-mean squared error by 5%–30% for most locations. Bias-corrected ET0 forecasts from NDFD or other forecast systems show potential as a guide to develop weekly irrigation schedules for agricultural producers, with the ultimate goal of reducing applications of excess irrigation water.
McEvoy
D. J.
Roj
S.
Dunkerly
C.
Huntington
J. L.
Hobbins
M. T.
Ott
T.
21115
Article
A multivariate index for tropical intraseasonal oscillations based on seasonally-varying modal structures
The spatial structure and propagation characteristics of tropical intraseasonal oscillations vary substantially by season. In this study, these seasonal variations are identified using a multivariate sliding-window Empirical Orthogonal Function (EOF) analysis. The two modes comprising the leading EOF pair have equal variances and depict the propagation of intraseasonal oscillations in convection and low-level circulation over the Indian Ocean, the Maritime Continent, and the western Pacific region in the equatorial summer hemisphere. In contrast, the upper tropospheric circulation shows more structure in the winter hemisphere. It is suggested that this variation in seasonality with height is an inherent feature of intraseasonal oscillations. A new multivariate index for tropical intraseasonal oscillations (MII) is developed based on the leading EOFs and represents the three-dimensional structure of intraseasonal variability in all seasons. The MII is computed by projecting intraseasonal anomalies onto the leading EOFs pair, and it exhibits clearly delineated but smooth seasonal transitions and rich meridional structure. The real-time version of this new index, rMII, is shown to be similar to MII, with a correlation of 0.9. Compared to the widely used Real-time Multivariate MJO (RMM) index, the power spectrum of rMII represents substantially greater intraseasonal variance, and the application of rMII in dynamical forecast models indicates rMII is skillfully predicted for an additional week compared to RMM.
2022-2
J. Geophys. Res. Atmos.
127
e2021JD035961
0
10.1029/2021JD035961
The spatial structure and propagation characteristics of tropical intraseasonal oscillations vary substantially by season. In this study, these seasonal variations are identified using a multivariate sliding-window Empirical Orthogonal Function (EOF) analysis. The two modes comprising the leading EOF pair have equal variances and depict the propagation of intraseasonal oscillations in convection and low-level circulation over the Indian Ocean, the Maritime Continent, and the western Pacific region in the equatorial summer hemisphere. In contrast, the upper tropospheric circulation shows more structure in the winter hemisphere. It is suggested that this variation in seasonality with height is an inherent feature of intraseasonal oscillations. A new multivariate index for tropical intraseasonal oscillations (MII) is developed based on the leading EOFs and represents the three-dimensional structure of intraseasonal variability in all seasons. The MII is computed by projecting intraseasonal anomalies onto the leading EOFs pair, and it exhibits clearly delineated but smooth seasonal transitions and rich meridional structure. The real-time version of this new index, rMII, is shown to be similar to MII, with a correlation of 0.9. Compared to the widely used Real-time Multivariate MJO (RMM) index, the power spectrum of rMII represents substantially greater intraseasonal variance, and the application of rMII in dynamical forecast models indicates rMII is skillfully predicted for an additional week compared to RMM.
Wang
S.
Martin
Z. K.
Sobel
A. H.
Tippett
M. K.
Dias
J.
Kiladis
G. N.
21118
Article
Assessing the Use of Climate Change Information in State Wildlife Action Plans
Assessing how climate change information is used in conservation planning is an important part of meeting long-term conservation and climate adaptation goals. In the United States, state agencies responsible for fish and wildlife management create State Wildlife Action Plans (SWAPs) to identify conservation goals, prioritize actions, and establish plans for managing and monitoring target species and habitats. We created a rubric to assess and compare the use of climate change information in SWAPs for 10 states in the Intermountain West and Great Plains. Interviews with SWAP authors identified institutional factors influencing applications of climate change information. Access to professional networks and climate scientists, funding support for climate change vulnerability analysis, Congressional mandates to include climate change, and supportive agency leadership facilitate using climate change information. Political climate could either support or limit options for using this information. Together, the rubric and the interview results can be used to identify opportunities to improve the use of climate information, and to identify entry points to support conservation planning and natural resource managers in successful adaptation to climate change. This research is directly relevant to future SWAP revisions, which most states will complete by 2025, and more broadly to other conservation planning processes.
2022-3
Conservation Sci. Practice
4
e608
0
10.1111/csp2.608
Assessing how climate change information is used in conservation planning is an important part of meeting long-term conservation and climate adaptation goals. In the United States, state agencies responsible for fish and wildlife management create State Wildlife Action Plans (SWAPs) to identify conservation goals, prioritize actions, and establish plans for managing and monitoring target species and habitats. We created a rubric to assess and compare the use of climate change information in SWAPs for 10 states in the Intermountain West and Great Plains. Interviews with SWAP authors identified institutional factors influencing applications of climate change information. Access to professional networks and climate scientists, funding support for climate change vulnerability analysis, Congressional mandates to include climate change, and supportive agency leadership facilitate using climate change information. Political climate could either support or limit options for using this information. Together, the rubric and the interview results can be used to identify opportunities to improve the use of climate information, and to identify entry points to support conservation planning and natural resource managers in successful adaptation to climate change. This research is directly relevant to future SWAP revisions, which most states will complete by 2025, and more broadly to other conservation planning processes.
Yocum
H.
Metivier Sassorossi
D.
Ray
A. J.
21124
Article
Blasts from the past: Reimagining historical storms with model simulations to modernize dam safety and flood risk assessment
Accurate estimation of the potential “upper limit” for extreme precipitation is critical for dam safety and water resources management, as dam failures pose significant risks to life and property. Methods used to estimate the theoretical upper limit of precipitation are often outdated and in need of updating. The rarity of extreme events means that old storms with limited observational data are often used to define the upper bound of precipitation. Observations of many important old storms are limited in spatial and temporal coverage, and sometimes of dubious quality. This reduces confidence in flood hazard assessments used in dam safety evaluations and leads to unknown or uncertain societal risk. This paper describes a method for generating and applying ensembles of high-resolution, state-of-the-art numerical model simulations of historical past extreme precipitation events to meet contemporary stakeholder needs. The method was designed as part of a research-to-application-focused partnership project to update state dam safety rules in Colorado and New Mexico. The results demonstrated multiple stakeholder and user benefits that were applied directly into storm analyses utilized for extreme rainfall estimation, and diagnostics were developed and ultimately used to update Colorado state dam safety rules, officially passed in January 2020. We discuss how what started as a prototype research foray to meet a specific user need may ultimately inform wider adoption of numerical simulations for water resources risk assessment, and how the historical event downscaling method performed offers near-term, implementable improvements to current dam safety flood risk estimates that can better serve society today.
2022-2
Bull. Amer. Meteor. Soc.
103
E266–E280
0
10.1175/BAMS-D-21-0133.1
Accurate estimation of the potential “upper limit” for extreme precipitation is critical for dam safety and water resources management, as dam failures pose significant risks to life and property. Methods used to estimate the theoretical upper limit of precipitation are often outdated and in need of updating. The rarity of extreme events means that old storms with limited observational data are often used to define the upper bound of precipitation. Observations of many important old storms are limited in spatial and temporal coverage, and sometimes of dubious quality. This reduces confidence in flood hazard assessments used in dam safety evaluations and leads to unknown or uncertain societal risk. This paper describes a method for generating and applying ensembles of high-resolution, state-of-the-art numerical model simulations of historical past extreme precipitation events to meet contemporary stakeholder needs. The method was designed as part of a research-to-application-focused partnership project to update state dam safety rules in Colorado and New Mexico. The results demonstrated multiple stakeholder and user benefits that were applied directly into storm analyses utilized for extreme rainfall estimation, and diagnostics were developed and ultimately used to update Colorado state dam safety rules, officially passed in January 2020. We discuss how what started as a prototype research foray to meet a specific user need may ultimately inform wider adoption of numerical simulations for water resources risk assessment, and how the historical event downscaling method performed offers near-term, implementable improvements to current dam safety flood risk estimates that can better serve society today.
Mahoney
K. M.
McColl
C.
Hultstrand
D. M.
McCormick
B.
Compo
G. P.
21139
Article
Measurements from the University of Colorado RAAVEN Uncrewed Aircraft System during ATOMIC
Between 24 January and 15 February 2020, small uncrewed aircraft systems (sUASs) were deployed to Morgan Lewis (Barbados) as part of the Atlantic Tradewind Ocean–Atmosphere Mesoscale Interaction Campaign (ATOMIC), a sister project to the ElUcidating the RolE of Cloud-Circulation Coupling in ClimAte (EUREC4A) project. The observations from ATOMIC and EUREC4A were aimed at improving our understanding of trade-wind cumulus clouds and the environmental regimes supporting them and involved the deployment of a wide variety of observational assets, including aircraft, ships, surface-based systems, and profilers. The current paper describes ATOMIC observations obtained using the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle – Endurant Nimble) sUAS. This platform collected nearly 80 h of data throughout the lowest kilometer of the atmosphere, sampling the near-shore environment upwind from Barbados. Data from these platforms are publicly available through the National Oceanic and Atmospheric Administration's National Center for Environmental Intelligence (NCEI) archive. The primary DOI for the quality-controlled dataset described in this paper is https://doi.org/10.25921/jhnd-8e58 (de Boer et al., 2021).
2022-1
Earth Syst. Sci. Data
14
19-31
0
10.5194/essd-2021-175
Between 24 January and 15 February 2020, small uncrewed aircraft systems (sUASs) were deployed to Morgan Lewis (Barbados) as part of the Atlantic Tradewind Ocean–Atmosphere Mesoscale Interaction Campaign (ATOMIC), a sister project to the ElUcidating the RolE of Cloud-Circulation Coupling in ClimAte (EUREC4A) project. The observations from ATOMIC and EUREC4A were aimed at improving our understanding of trade-wind cumulus clouds and the environmental regimes supporting them and involved the deployment of a wide variety of observational assets, including aircraft, ships, surface-based systems, and profilers. The current paper describes ATOMIC observations obtained using the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle – Endurant Nimble) sUAS. This platform collected nearly 80 h of data throughout the lowest kilometer of the atmosphere, sampling the near-shore environment upwind from Barbados. Data from these platforms are publicly available through the National Oceanic and Atmospheric Administration's National Center for Environmental Intelligence (NCEI) archive. The primary DOI for the quality-controlled dataset described in this paper is https://doi.org/10.25921/jhnd-8e58 (de Boer et al., 2021).
de Boer
G.
Borenstein
S.
Calmer
R.
Cox
C. J.
Rhodes
M.
Choate
C.
Hamilton
J.
Osborn
J.
Lawrence
D.
Argrow
B.
Intrieri
J. M.
21141
Article
Assimilation of a coordinated fleet of uncrewed aircraft systems observations in complex terrain: Observing System Experiments
Uncrewed aircraft system (UAS) observations from the Lower Atmospheric Profiling Studies at Elevation–A Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting (WRF) Model. The impact of assimilating targeted UAS observations in addition to surface observations was compared to that obtained when assimilating surface observations alone using observing system experiments (OSEs) for a terrain-driven flow case and a convection initiation (CI) case observed within Colorado’s San Luis Valley (SLV). The assimilation of UAS observations in addition to surface observations results in a clear increase in skill for both flow regimes over that obtained when assimilating surface observations alone. For the terrain-driven flow case, the UAS observations improved the representation of thermal stratification across the northern SLV, which produced stronger upvalley flow over the eastern half of the SLV that better matched the observations. For the CI case, the UAS observations improved the representation of the pre-convective environment by reducing dry biases across the SLV and over the surrounding terrain. This led to earlier CI and more organized convection over the foothills that spilled outflows into the SLV, ultimately helping to increase low-level convergence and CI there. In addition, the importance of UAS capturing an outflow that originated over the Sangre de Cristo Mountains and triggered CI is discussed. These outflows and subsequent CI were not well captured in the simulation that assimilated surface observations alone. Observations obtained with a fleet of UAS are shown to notably improve high-resolution analyses and short-term predictions of two very different mesogamma-scale weather events.
2022-10
Mon. Wea. Rev.
150
2737–2763
0
10.1175/MWR-D-22-0090.1
Uncrewed aircraft system (UAS) observations from the Lower Atmospheric Profiling Studies at Elevation–A Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting (WRF) Model. The impact of assimilating targeted UAS observations in addition to surface observations was compared to that obtained when assimilating surface observations alone using observing system experiments (OSEs) for a terrain-driven flow case and a convection initiation (CI) case observed within Colorado’s San Luis Valley (SLV). The assimilation of UAS observations in addition to surface observations results in a clear increase in skill for both flow regimes over that obtained when assimilating surface observations alone. For the terrain-driven flow case, the UAS observations improved the representation of thermal stratification across the northern SLV, which produced stronger upvalley flow over the eastern half of the SLV that better matched the observations. For the CI case, the UAS observations improved the representation of the pre-convective environment by reducing dry biases across the SLV and over the surrounding terrain. This led to earlier CI and more organized convection over the foothills that spilled outflows into the SLV, ultimately helping to increase low-level convergence and CI there. In addition, the importance of UAS capturing an outflow that originated over the Sangre de Cristo Mountains and triggered CI is discussed. These outflows and subsequent CI were not well captured in the simulation that assimilated surface observations alone. Observations obtained with a fleet of UAS are shown to notably improve high-resolution analyses and short-term predictions of two very different mesogamma-scale weather events.
Jensen
A.
Jacob
J.
Bailey
S.
Sobash
R. A.
Romine
G.
de Boer
G.
Houston
A.
Chilson
P. B.
Bell
T.
Smith
S.
Lawrence
D.
Dixon
C.
Lundquist
J.
Jacob
J.
Elston
J.
Waugh
S.
Brus
D.
Steiner
M.
21144
Article
The Impact of Forest-Controlled Snow Variability on Late-Season Streamflow Varies by Climatic Region and Forest Structure
Previous studies have documented how forests influence snow at fine spatial scales, but none have documented the influence that existing forest-snow variability has on streamflow. To test how much forest-controlled snow variability influences streamflow, a tiling parameterization based on classifications from high-resolution (1–3 m) vegetation maps was incorporated into the Distributed Hydrology and Soil Vegetation Model (DHSVM). Within each grid cell (90–150 m), the tiling parameterization simulated forest-snow variability with four independently evolving snowpacks. Each tile had unique radiation conditions to represent conditions underneath the canopy, in exposed areas, and along north- and south-facing forest edges. This tiled parameterization was used to test where and when detailed forest-snow modelling should be considered further and where and when the impacts are too small to be worth the effort. To test this, tiled model simulations of streamflow were compared to non-tiled model simulations in the Sierra Nevada, CA, the Jemez Mountains, NM, and the Eastern Cascades, WA. In Tuolumne, CA, the tiled model simulated little difference in grid cell average SWE, and late-season streamflow decreased by only 3%–4% compared to the non-tiled model. In Jemez, NM, the tiled model decreased late-season streamflow by 18% due to increased sublimation. In Chiwawa, WA, the tiled model increased late-season streamflow by 15% due to high shortwave radiation attenuation and less longwave radiation enhancement from the forest. Furthermore, within the Chiwawa, a substantial silvicultural practice was synthetically implemented to increase the north-facing edge's fractional area. This silvicultural experiment, which used the same fractional forest area in all simulations increased late-season streamflow by 35% compared to tiled model simulations that did not represent forest edges. In conclusion, representing forest-SWE variability had an effect on late-season streamflow in some watersheds but not in others based on the fractional area of the forest edges, forest characteristics, and climate conditions.
2022-5
Hydrol. Process.
36
e14614
0
10.1002/hyp.14614
Previous studies have documented how forests influence snow at fine spatial scales, but none have documented the influence that existing forest-snow variability has on streamflow. To test how much forest-controlled snow variability influences streamflow, a tiling parameterization based on classifications from high-resolution (1–3 m) vegetation maps was incorporated into the Distributed Hydrology and Soil Vegetation Model (DHSVM). Within each grid cell (90–150 m), the tiling parameterization simulated forest-snow variability with four independently evolving snowpacks. Each tile had unique radiation conditions to represent conditions underneath the canopy, in exposed areas, and along north- and south-facing forest edges. This tiled parameterization was used to test where and when detailed forest-snow modelling should be considered further and where and when the impacts are too small to be worth the effort. To test this, tiled model simulations of streamflow were compared to non-tiled model simulations in the Sierra Nevada, CA, the Jemez Mountains, NM, and the Eastern Cascades, WA. In Tuolumne, CA, the tiled model simulated little difference in grid cell average SWE, and late-season streamflow decreased by only 3%–4% compared to the non-tiled model. In Jemez, NM, the tiled model decreased late-season streamflow by 18% due to increased sublimation. In Chiwawa, WA, the tiled model increased late-season streamflow by 15% due to high shortwave radiation attenuation and less longwave radiation enhancement from the forest. Furthermore, within the Chiwawa, a substantial silvicultural practice was synthetically implemented to increase the north-facing edge's fractional area. This silvicultural experiment, which used the same fractional forest area in all simulations increased late-season streamflow by 35% compared to tiled model simulations that did not represent forest edges. In conclusion, representing forest-SWE variability had an effect on late-season streamflow in some watersheds but not in others based on the fractional area of the forest edges, forest characteristics, and climate conditions.
Currier
W. R.
Sun
N.
Wigmosta
M.
Cristea
N.
Lundquist
J. D.
21145
Article
Subseasonal Meteorological Drought Development over the Central United States during Spring
Diagnosis of rapidly developing springtime droughts in the central United States has mostly been made via numerous individual case studies rather than in an aggregate sense. This study investigates common aspects of subseasonal “meteorological drought” evolution, here defined as persistent precipitation minus evapotranspiration (P − ET) deficits, revealed in early (1 April–15 May) and late (16 May–30 June) spring composites of 5-day running mean JRA-55 reanalysis data for three different central U.S. regions during 1958–2018. On average, these droughts are initiated by a quasi-stationary Rossby wave packet (RWP), propagating from the western North Pacific, which arises about a week prior to drought onset. The RWP is related to a persistent ridge west of the incipient drought region and strong subsidence over it. This subsidence is associated with low-level divergent flow that dries the atmosphere and suppresses precipitation for roughly 1–2 weeks, and generally has a greater impact on the local moisture budget than does reduced poleward moisture transport. The resulting “dynamically driven” evaporative demand corresponds to a rapid drying of the root-zone soil moisture, which decreases around 40 percentiles within about 10 days. Anomalous near-surface warmth develops only after the P − ET deficit onset, as does anomalously low soil moisture that then lingers a month or more, especially in late spring. The horizontal scale of the RWPs, and of the related drought anomalies, decreases from early to late spring, consistent with the climatological change in the Pacific Rossby waveguide. Finally, while this composite analysis is based upon strong, persistent P − ET deficits, it still appears to capture much of the springtime development of “flash droughts” as well.
2022-4
J. Climate
35
2525-2547
0
10.1175/JCLI-D-21-0435.1
Diagnosis of rapidly developing springtime droughts in the central United States has mostly been made via numerous individual case studies rather than in an aggregate sense. This study investigates common aspects of subseasonal “meteorological drought” evolution, here defined as persistent precipitation minus evapotranspiration (P − ET) deficits, revealed in early (1 April–15 May) and late (16 May–30 June) spring composites of 5-day running mean JRA-55 reanalysis data for three different central U.S. regions during 1958–2018. On average, these droughts are initiated by a quasi-stationary Rossby wave packet (RWP), propagating from the western North Pacific, which arises about a week prior to drought onset. The RWP is related to a persistent ridge west of the incipient drought region and strong subsidence over it. This subsidence is associated with low-level divergent flow that dries the atmosphere and suppresses precipitation for roughly 1–2 weeks, and generally has a greater impact on the local moisture budget than does reduced poleward moisture transport. The resulting “dynamically driven” evaporative demand corresponds to a rapid drying of the root-zone soil moisture, which decreases around 40 percentiles within about 10 days. Anomalous near-surface warmth develops only after the P − ET deficit onset, as does anomalously low soil moisture that then lingers a month or more, especially in late spring. The horizontal scale of the RWPs, and of the related drought anomalies, decreases from early to late spring, consistent with the climatological change in the Pacific Rossby waveguide. Finally, while this composite analysis is based upon strong, persistent P − ET deficits, it still appears to capture much of the springtime development of “flash droughts” as well.
Jong
B.-T.
Newman
M.
Hoell
A.
21148
Article
Improved representation of horizontal variability and turbulence in mesoscale simulations of an extended cold-air pool event
Cold-air pools (CAPs), or stable atmospheric boundary layers that form within topographic basins, are associated with poor air quality, hazardous weather, and low wind energy output. Accurate prediction of CAP dynamics presents a challenge for mesoscale forecast models, in part because CAPs occur in regions of complex terrain, where traditional turbulence parameterizations may not be appropriate. This study examines the effects of the planetary boundary layer (PBL) scheme and horizontal diffusion treatment on CAP prediction in the Weather Research and Forecasting (WRF) model. Model runs with a one-dimensional (1D) PBL scheme and Smagorinsky-like horizontal diffusion are compared to runs that use a new three-dimensional (3D) PBL scheme to calculate turbulent fluxes. Simulations are completed in a nested configuration with 3 km/750 m horizontal grid spacing over a 10-day case study in the Columbia River basin, and results are compared to observations from the Second Wind Forecast Improvement Project. Using event-averaged error metrics, potential temperature and wind speed errors are shown to decrease both with increased horizontal grid resolution, and with improved treatment of horizontal diffusion over steep terrain. The 3D PBL scheme further reduces errors relative to a standard 1D PBL approach. Error reduction is accentuated during CAP erosion, when turbulent mixing plays a more dominant role in the dynamics. Lastly, the 3D PBL scheme is shown to reduce near-surface overestimates of turbulence kinetic energy during the CAP event. The sensitivity of turbulence predictions to the master length scale formulation in the 3D PBL parameterization is also explored.
2022-6
J. Appl. Meteor. Climatol.
61
685–707
0
10.1175/JAMC-D-21-0138.1
Cold-air pools (CAPs), or stable atmospheric boundary layers that form within topographic basins, are associated with poor air quality, hazardous weather, and low wind energy output. Accurate prediction of CAP dynamics presents a challenge for mesoscale forecast models, in part because CAPs occur in regions of complex terrain, where traditional turbulence parameterizations may not be appropriate. This study examines the effects of the planetary boundary layer (PBL) scheme and horizontal diffusion treatment on CAP prediction in the Weather Research and Forecasting (WRF) model. Model runs with a one-dimensional (1D) PBL scheme and Smagorinsky-like horizontal diffusion are compared to runs that use a new three-dimensional (3D) PBL scheme to calculate turbulent fluxes. Simulations are completed in a nested configuration with 3 km/750 m horizontal grid spacing over a 10-day case study in the Columbia River basin, and results are compared to observations from the Second Wind Forecast Improvement Project. Using event-averaged error metrics, potential temperature and wind speed errors are shown to decrease both with increased horizontal grid resolution, and with improved treatment of horizontal diffusion over steep terrain. The 3D PBL scheme further reduces errors relative to a standard 1D PBL approach. Error reduction is accentuated during CAP erosion, when turbulent mixing plays a more dominant role in the dynamics. Lastly, the 3D PBL scheme is shown to reduce near-surface overestimates of turbulence kinetic energy during the CAP event. The sensitivity of turbulence predictions to the master length scale formulation in the 3D PBL parameterization is also explored.
Arthur
R. S.
Juliano
T. W.
Adler
B.
Krishnamurthy
R.
Lundquist
J. K.
Kosovic
B.
Jimenez
P. A.
21154
Article
Snowfall and snow accumulation processes during the MOSAiC winter and spring season
Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38 mm SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio2 pluviometer and the OTT Parsivel2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based Ka-band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the Ka-band radar-derived snowfall as an upper limit and the present weather detector on RV Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 mm and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47 % and 68 %, for the time period between 31 October 2019 and 26 April 2020. Extending this period beyond available snow cover measurements, we suggest a cumulative snowfall of 98–114 mm.
2022-6
Cryosphere
16
2373–2402
0
10.5194/tc-16-2373-2022
Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38 mm SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio2 pluviometer and the OTT Parsivel2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based Ka-band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the Ka-band radar-derived snowfall as an upper limit and the present weather detector on RV Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 mm and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47 % and 68 %, for the time period between 31 October 2019 and 26 April 2020. Extending this period beyond available snow cover measurements, we suggest a cumulative snowfall of 98–114 mm.
Wagner
D.
Shupe
M. D.
Persson
P. O. G.
Uttal
T.
al.
et
21155
Article
Relating snowfall observations to Greenland ice sheet mass changes: an atmospheric circulation perspective
Snowfall is the major source of mass for the Greenland ice sheet (GrIS) but the spatial and temporal variability of snowfall and the connections between snowfall and mass balance have so far been inadequately quantified. By characterizing local atmospheric circulation and utilizing CloudSat spaceborne radar observations of snowfall, we provide a detailed spatial analysis of snowfall variability and its relationship to Greenland mass balance, presenting first-of-their-kind maps of daily spatial variability in snowfall from observations across Greenland. For identified regional atmospheric circulation patterns, we show that the spatial distribution and net mass input of snowfall vary significantly with the position and strength of surface cyclones. Cyclones west of Greenland driving southerly flow contribute significantly more snowfall than any other circulation regime, with each daily occurrence of the most extreme southerly circulation pattern contributing an average of 1.66 Gt of snow to the Greenland ice sheet. While cyclones east of Greenland, patterns with the least snowfall, contribute as little as 0.58 Gt each day. Above 2 km on the ice sheet where snowfall is inconsistent, extreme southerly patterns are the most significant mass contributors, with up to 1.20 Gt of snowfall above this elevation. This analysis demonstrates that snowfall over the interior of Greenland varies by up to a factor of 5 depending on regional circulation conditions. Using independent observations of mass changes made by the Gravity Recovery and Climate Experiment (GRACE), we verify that the largest mass increases are tied to the southerly regime with cyclones west of Greenland. For occurrences of the strongest southerly pattern, GRACE indicates a net mass increase of 1.29 Gt in the ice sheet accumulation zone (above 2 km elevation) compared to the 1.20 Gt of snowfall observed by CloudSat. This overall agreement suggests that the analytical approach presented here can be used to directly quantify snowfall mass contributions and their most significant drivers spatially across the GrIS. While previous research has implicated this same southerly regime in ablation processes during summer, this paper shows that ablation mass loss in this circulation regime is nearly an order of magnitude larger than the mass gain from associated snowfall. For daily occurrences of the southerly circulation regime, a mass loss of approximately 11 Gt is observed across the ice sheet despite snowfall mass input exceeding 1 Gt. By analyzing the spatial variability of snowfall and mass changes, this research provides new insight into connections between regional atmospheric circulation and GrIS mass balance.
2022-2
Cryosphere
16
435–450
0
10.5194/tc-16-435-2022
Snowfall is the major source of mass for the Greenland ice sheet (GrIS) but the spatial and temporal variability of snowfall and the connections between snowfall and mass balance have so far been inadequately quantified. By characterizing local atmospheric circulation and utilizing CloudSat spaceborne radar observations of snowfall, we provide a detailed spatial analysis of snowfall variability and its relationship to Greenland mass balance, presenting first-of-their-kind maps of daily spatial variability in snowfall from observations across Greenland. For identified regional atmospheric circulation patterns, we show that the spatial distribution and net mass input of snowfall vary significantly with the position and strength of surface cyclones. Cyclones west of Greenland driving southerly flow contribute significantly more snowfall than any other circulation regime, with each daily occurrence of the most extreme southerly circulation pattern contributing an average of 1.66 Gt of snow to the Greenland ice sheet. While cyclones east of Greenland, patterns with the least snowfall, contribute as little as 0.58 Gt each day. Above 2 km on the ice sheet where snowfall is inconsistent, extreme southerly patterns are the most significant mass contributors, with up to 1.20 Gt of snowfall above this elevation. This analysis demonstrates that snowfall over the interior of Greenland varies by up to a factor of 5 depending on regional circulation conditions. Using independent observations of mass changes made by the Gravity Recovery and Climate Experiment (GRACE), we verify that the largest mass increases are tied to the southerly regime with cyclones west of Greenland. For occurrences of the strongest southerly pattern, GRACE indicates a net mass increase of 1.29 Gt in the ice sheet accumulation zone (above 2 km elevation) compared to the 1.20 Gt of snowfall observed by CloudSat. This overall agreement suggests that the analytical approach presented here can be used to directly quantify snowfall mass contributions and their most significant drivers spatially across the GrIS. While previous research has implicated this same southerly regime in ablation processes during summer, this paper shows that ablation mass loss in this circulation regime is nearly an order of magnitude larger than the mass gain from associated snowfall. For daily occurrences of the southerly circulation regime, a mass loss of approximately 11 Gt is observed across the ice sheet despite snowfall mass input exceeding 1 Gt. By analyzing the spatial variability of snowfall and mass changes, this research provides new insight into connections between regional atmospheric circulation and GrIS mass balance.
Gallagher
M. R.
Shupe
M. D.
Chepfer
H.
L'Ecuyer
T. S.
21156
Article
Overview of the MOSAiC expedition: Snow and Sea Ice
Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.
2022-2
Elementa Sci. Anthrop.
10
00046
0
10.1525/elementa.2021.000046
Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.
Nicolaus
M.
Perovich
D. K.
Spreen
G.
Granskog
M. A.
. .
.
de Boer
G.
. .
.
Shupe
M. D.
al.
et
21157
Article
Overview of the MOSAiC expedition: Atmosphere
With the Arctic rapidly changing, the needs to observe, understand, and model the changes are essential. To support these needs, an annual cycle of observations of atmospheric properties, processes, and interactions were made while drifting with the sea ice across the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. An international team designed and implemented the comprehensive program to document and characterize all aspects of the Arctic atmospheric system in unprecedented detail, using a variety of approaches, and across multiple scales. These measurements were coordinated with other observational teams to explore cross-cutting and coupled interactions with the Arctic Ocean, sea ice, and ecosystem through a variety of physical and biogeochemical processes. This overview outlines the breadth and complexity of the atmospheric research program, which was organized into 4 subgroups: atmospheric state, clouds and precipitation, gases and aerosols, and energy budgets. Atmospheric variability over the annual cycle revealed important influences from a persistent large-scale winter circulation pattern, leading to some storms with pressure and winds that were outside the interquartile range of past conditions suggested by long-term reanalysis. Similarly, the MOSAiC location was warmer and wetter in summer than the reanalysis climatology, in part due to its close proximity to the sea ice edge. The comprehensiveness of the observational program for characterizing and analyzing atmospheric phenomena is demonstrated via a winter case study examining air mass transitions and a summer case study examining vertical atmospheric evolution. Overall, the MOSAiC atmospheric program successfully met its objectives and was the most comprehensive atmospheric measurement program to date conducted over the Arctic sea ice. The obtained data will support a broad range of coupled-system scientific research and provide an important foundation for advancing multiscale modeling capabilities in the Arctic.
2022-2
Elementa Sci. Anthrop.
10
00060
0
10.1525/elementa.2021.00060
With the Arctic rapidly changing, the needs to observe, understand, and model the changes are essential. To support these needs, an annual cycle of observations of atmospheric properties, processes, and interactions were made while drifting with the sea ice across the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. An international team designed and implemented the comprehensive program to document and characterize all aspects of the Arctic atmospheric system in unprecedented detail, using a variety of approaches, and across multiple scales. These measurements were coordinated with other observational teams to explore cross-cutting and coupled interactions with the Arctic Ocean, sea ice, and ecosystem through a variety of physical and biogeochemical processes. This overview outlines the breadth and complexity of the atmospheric research program, which was organized into 4 subgroups: atmospheric state, clouds and precipitation, gases and aerosols, and energy budgets. Atmospheric variability over the annual cycle revealed important influences from a persistent large-scale winter circulation pattern, leading to some storms with pressure and winds that were outside the interquartile range of past conditions suggested by long-term reanalysis. Similarly, the MOSAiC location was warmer and wetter in summer than the reanalysis climatology, in part due to its close proximity to the sea ice edge. The comprehensiveness of the observational program for characterizing and analyzing atmospheric phenomena is demonstrated via a winter case study examining air mass transitions and a summer case study examining vertical atmospheric evolution. Overall, the MOSAiC atmospheric program successfully met its objectives and was the most comprehensive atmospheric measurement program to date conducted over the Arctic sea ice. The obtained data will support a broad range of coupled-system scientific research and provide an important foundation for advancing multiscale modeling capabilities in the Arctic.
Shupe
M. D.
Blomquist
B. W.
Persson
P. O. G.
Schmale
J.
Uttal
T.
Althausen
D.
Angot
H.
Archer
S.
Bariteau
L.
. .
.
Costa
D. M.
Cox
C. J.
. .
.
de Boer
G.
. .
.
Gallagher
M. R.
. .
.
Hamilton
J.
. .
.
Morris
S. M.
. .
.
Osborn
J.
. .
.
Solomon
A.
al.
et
21162
Article
Overlapping Windows in a Global Hourly Data Assimilation System
The U.S. operational global data assimilation system provides updated analysis and forecast fields every 6 h, which is not frequent enough to handle the rapid error growth associated with hurricanes or other storms. This motivates development of an hourly updating global data assimilation system, but observational data latency can be a barrier. Two methods are presented to overcome this challenge: “catch-up cycles,” in which a 1-hourly system is reinitialized from a 6-hourly system that has assimilated high-latency observations; and “overlapping assimilation windows,” in which the system is updated hourly with new observations valid in the past 3 h. The performance of these methods is assessed in a near-operational setup using the Global Forecast System by comparing forecasts with in situ observations. At short forecast leads, the overlapping windows method performs comparably to the 6-hourly control in a simplified configuration and outperforms the control in a full-input configuration. In the full-input experiment, the catch-up cycle method performs similarly to the 6-hourly control; reinitializing from the 6-hourly control does not appear to provide a significant benefit. Results suggest that the overlapping windows method performs well in part because of the hourly update cadence, but also because hourly cycling systems can make better use of available observations. The impact of the hourly update relative to the 6-hourly update is most significant during the first forecast day, while impacts on longer-range forecasts were found to be mixed and mostly insignificant. Further effort toward an operational global hourly updating system should be pursued.
2022-6
Mon. Wea. Rev.
150
1317-1334
0
10.1175/MWR-D-21-0214.1
The U.S. operational global data assimilation system provides updated analysis and forecast fields every 6 h, which is not frequent enough to handle the rapid error growth associated with hurricanes or other storms. This motivates development of an hourly updating global data assimilation system, but observational data latency can be a barrier. Two methods are presented to overcome this challenge: “catch-up cycles,” in which a 1-hourly system is reinitialized from a 6-hourly system that has assimilated high-latency observations; and “overlapping assimilation windows,” in which the system is updated hourly with new observations valid in the past 3 h. The performance of these methods is assessed in a near-operational setup using the Global Forecast System by comparing forecasts with in situ observations. At short forecast leads, the overlapping windows method performs comparably to the 6-hourly control in a simplified configuration and outperforms the control in a full-input configuration. In the full-input experiment, the catch-up cycle method performs similarly to the 6-hourly control; reinitializing from the 6-hourly control does not appear to provide a significant benefit. Results suggest that the overlapping windows method performs well in part because of the hourly update cadence, but also because hourly cycling systems can make better use of available observations. The impact of the hourly update relative to the 6-hourly update is most significant during the first forecast day, while impacts on longer-range forecasts were found to be mixed and mostly insignificant. Further effort toward an operational global hourly updating system should be pursued.
Slivinski
L. C.
Lippi
D. E.
Whitaker
J. S.
Ge
G.
Carley
J. R.
Alexander
C. R.
Compo
G. P.
21164
Article
Understanding the Dominant Moisture Sources and Pathways of Summer Precipitation in the Southeast Prairie Pothole Region
Summer rainfall in the southeast Prairie Pothole Region (SEPPR) is an important part of a vital wetland ecosystem that various species use as their habitat. We examine sources and pathways for summer rainfall moisture, large-scale features influencing moisture delivery, and large-scale connections related to summer moisture using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Analysis of HYSPLIT back trajectories shows that land is the primary moisture source for summer rainfall events indicating moisture recycling plays an important role in precipitation generation. The Great Plains Low-Level Jet/Maya Express is the most prominent moisture pathway. It impacts events sourced by land and the Gulf of Mexico (GoM), the secondary moisture source. There is a coupling between land, atmosphere, and ocean conveyed by large-scale climate connections between rainfall events and sea surface temperature (SST), Palmer Drought Severity Index, and 850-mb heights. Land-sourced events have a connection to the northern Pacific and northwest Atlantic Oceans, soil moisture over the central U.S., and low-pressure systems over the SEPPR. GoM-sourced events share the connection to soil moisture over the central U.S. but also show connections to SSTs in the North Pacific and Atlantic Oceans and the GoM, soil moisture in northern Mexico, and 850-mb heights in the eastern Pacific Ocean. Both types of events show connections to high 850-mb heights in the Caribbean which may reflect a connection to Bermuda High. These insights into moisture sources and pathways can improve skill in SEPPR summer rainfall predictions and benefit natural resource managers in the region.
2022-3
J. Geophys. Res. Earth Space Sci.
9
e2021EA001855
0
10.1029/2021EA001855
Summer rainfall in the southeast Prairie Pothole Region (SEPPR) is an important part of a vital wetland ecosystem that various species use as their habitat. We examine sources and pathways for summer rainfall moisture, large-scale features influencing moisture delivery, and large-scale connections related to summer moisture using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Analysis of HYSPLIT back trajectories shows that land is the primary moisture source for summer rainfall events indicating moisture recycling plays an important role in precipitation generation. The Great Plains Low-Level Jet/Maya Express is the most prominent moisture pathway. It impacts events sourced by land and the Gulf of Mexico (GoM), the secondary moisture source. There is a coupling between land, atmosphere, and ocean conveyed by large-scale climate connections between rainfall events and sea surface temperature (SST), Palmer Drought Severity Index, and 850-mb heights. Land-sourced events have a connection to the northern Pacific and northwest Atlantic Oceans, soil moisture over the central U.S., and low-pressure systems over the SEPPR. GoM-sourced events share the connection to soil moisture over the central U.S. but also show connections to SSTs in the North Pacific and Atlantic Oceans and the GoM, soil moisture in northern Mexico, and 850-mb heights in the eastern Pacific Ocean. Both types of events show connections to high 850-mb heights in the Caribbean which may reflect a connection to Bermuda High. These insights into moisture sources and pathways can improve skill in SEPPR summer rainfall predictions and benefit natural resource managers in the region.
Abel
B. D.
Rajagopalan
B.
Ray
A. J.
21167
Article
Variations in Wave Slope and Momentum Flux From Wave-Current Interactions in the Tropical Trade Winds
Observations from six Lagrangian Surface Wave Instrument Float with Tracking drifters in January-February 2020 in the northwestern tropical Atlantic during the Atlantic Tradewind Ocean-atmosphere Mesoscale Interaction Campaign are used to evaluate the influence of wave-current interactions on wave slope and momentum flux. At observed wind speeds of 4–12 ms−1, wave mean square slopes are positively correlated with wind speed. Wave-relative surface currents varied significantly, from opposing the wave direction at 0.24 ms−1 to following the waves at 0.47 ms−1. Wave slopes are 5%–10% higher when surface currents oppose the waves compared to when currents strongly follow the waves, consistent with a conservation of wave energy flux across gradients in currents. Assuming an equilibrium frequency range in the wave spectrum, wave slope is proportional to wind friction velocity and momentum flux. The observed variation in wave slope equates to a 10%–20% variation in momentum flux over the range of observed wind speeds (4–12 ms−1), with larger variations at higher winds. At wind speeds over 8 ms−1, momentum flux varies by at least 6% more than the variation expected from current-relative winds alone, and suggests that wave-current interactions can generate significant spatial and temporal variability in momentum fluxes in this region of prevailing trade winds. Results and data from this study motivate the continued development of fully coupled atmosphere-ocean-wave models.
2022-3
J. Geophys. Res. Oceans
127
e2021JC018003
0
10.1029/2021JC018003
Observations from six Lagrangian Surface Wave Instrument Float with Tracking drifters in January-February 2020 in the northwestern tropical Atlantic during the Atlantic Tradewind Ocean-atmosphere Mesoscale Interaction Campaign are used to evaluate the influence of wave-current interactions on wave slope and momentum flux. At observed wind speeds of 4–12 ms−1, wave mean square slopes are positively correlated with wind speed. Wave-relative surface currents varied significantly, from opposing the wave direction at 0.24 ms−1 to following the waves at 0.47 ms−1. Wave slopes are 5%–10% higher when surface currents oppose the waves compared to when currents strongly follow the waves, consistent with a conservation of wave energy flux across gradients in currents. Assuming an equilibrium frequency range in the wave spectrum, wave slope is proportional to wind friction velocity and momentum flux. The observed variation in wave slope equates to a 10%–20% variation in momentum flux over the range of observed wind speeds (4–12 ms−1), with larger variations at higher winds. At wind speeds over 8 ms−1, momentum flux varies by at least 6% more than the variation expected from current-relative winds alone, and suggests that wave-current interactions can generate significant spatial and temporal variability in momentum fluxes in this region of prevailing trade winds. Results and data from this study motivate the continued development of fully coupled atmosphere-ocean-wave models.
Iyer
S.
Thomson
J.
Thompson
E. J.
Drushka
K.
21168
Article
Global Synthesis of Air-Sea CO2 Transfer Velocity Estimates From Ship-Based Eddy Covariance Measurements
The air-sea gas transfer velocity (K660) is typically assessed as a function of the 10-m neutral wind speed (U10n), but there remains substantial uncertainty in this relationship. Here K660 of CO2 derived with the eddy covariance (EC) technique from eight datasets (11 research cruises) are reevaluated with consistent consideration of solubility and Schmidt number and inclusion of the ocean cool skin effect. K660 shows an approximately linear dependence with the friction velocity (u*) in moderate winds, with an overall relative standard deviation (relative standard error) of about 20% (7%). The largest relative uncertainty in K660 occurs at low wind speeds, while the largest absolute uncertainty in K660 occurs at high wind speeds. There is an apparent regional variation in the steepness of the K660-u* relationships: North Atlantic ≥ Southern Ocean > other regions (Arctic, Tropics). Accounting for sea state helps to collapse some of this regional variability in K660 using the wave Reynolds number in very large seas and the mean squared slope of the waves in small to moderate seas. The grand average of EC-derived K660 (−1.47 + 76.67u∗+ 20.48u2∗ or 0.36 + 1.203U10n+ 0.167U210n) is similar at moderate to high winds to widely used dual tracer-based K660 parametrization, but consistently exceeds the dual tracer estimate in low winds, possibly in part due to the chemical enhancement in air-sea CO2 exchange. Combining the grand average of EC-derived K660 with the global distribution of wind speed yields a global average transfer velocity that is comparable with the global radiocarbon (14C) disequilibrium, but is ~20% higher than what is implied by dual tracer parametrizations. This analysis suggests that CO2 fluxes computed using a U210n dependence with zero intercept (e.g., dual tracer) are likely underestimated at relatively low wind speeds.
2022-5
Front. Mar. Sci.
9
826421
0
10.3389/fmars.2022.826421
The air-sea gas transfer velocity (K660) is typically assessed as a function of the 10-m neutral wind speed (U10n), but there remains substantial uncertainty in this relationship. Here K660 of CO2 derived with the eddy covariance (EC) technique from eight datasets (11 research cruises) are reevaluated with consistent consideration of solubility and Schmidt number and inclusion of the ocean cool skin effect. K660 shows an approximately linear dependence with the friction velocity (u*) in moderate winds, with an overall relative standard deviation (relative standard error) of about 20% (7%). The largest relative uncertainty in K660 occurs at low wind speeds, while the largest absolute uncertainty in K660 occurs at high wind speeds. There is an apparent regional variation in the steepness of the K660-u* relationships: North Atlantic ≥ Southern Ocean > other regions (Arctic, Tropics). Accounting for sea state helps to collapse some of this regional variability in K660 using the wave Reynolds number in very large seas and the mean squared slope of the waves in small to moderate seas. The grand average of EC-derived K660 (−1.47 + 76.67u∗+ 20.48u2∗ or 0.36 + 1.203U10n+ 0.167U210n) is similar at moderate to high winds to widely used dual tracer-based K660 parametrization, but consistently exceeds the dual tracer estimate in low winds, possibly in part due to the chemical enhancement in air-sea CO2 exchange. Combining the grand average of EC-derived K660 with the global distribution of wind speed yields a global average transfer velocity that is comparable with the global radiocarbon (14C) disequilibrium, but is ~20% higher than what is implied by dual tracer parametrizations. This analysis suggests that CO2 fluxes computed using a U210n dependence with zero intercept (e.g., dual tracer) are likely underestimated at relatively low wind speeds.
Yang
M.
Bell
T.
Bidlot
J.
Blomquist
B. W.
Butterworth
B. J.
Dong
Y.
Fairall
C. W.
al.
et
21169
Article
A new methodology to produce more skillful United States cool season precipitation forecasts
The water resources of the western United States have enormous agricultural and municipal demands. At the same time, droughts like the one enveloping the West in the summer of 2021 have disrupted supply of this strained and precious resource. Historically, seasonal forecasts of cool-season (November–March) precipitation from dynamical models such as North American Multi-Model Ensemble (NMME) and the Seasonal Forecasting System 5 (SEAS5) from the European Centre for Medium-Range Weather Forecasts have lacked sufficient skill to aid in Western stakeholders’ and water managers’ decision-making. Here, we propose a new empirical–statistical framework to improve cool-season precipitation forecasts across the contiguous United States (CONUS). This newly developed framework is called the Statistical Climate Ensemble Forecast (SCEF) model. The SCEF framework applies a principal component regression model to predictors and predictands that have undergone dimensionality reduction, where the predictors are large-scale meteorological variables that have been prefiltered in space. The forecasts of the SCEF model captures 12.0% of the total CONUS-wide standardized observed variance over the period 1982/83–2019/20, whereas NMME captures 7.2%. Over the more recent period 2000/01–2019/20, the SCEF, NMME, and SEAS5 models respectively capture 11.8%, 4.0%, and 4.1% of the total CONUS-wide standardized observed variance. An important finding is that much of the improved skill in the SCEF, with respect to models such as NMME and SEAS5, can be attributed to better forecasts across most of the western United States.
2022-6
J. Hydrometeor.
23
991-1005
0
10.1175/JHM-D-21-0235.1
The water resources of the western United States have enormous agricultural and municipal demands. At the same time, droughts like the one enveloping the West in the summer of 2021 have disrupted supply of this strained and precious resource. Historically, seasonal forecasts of cool-season (November–March) precipitation from dynamical models such as North American Multi-Model Ensemble (NMME) and the Seasonal Forecasting System 5 (SEAS5) from the European Centre for Medium-Range Weather Forecasts have lacked sufficient skill to aid in Western stakeholders’ and water managers’ decision-making. Here, we propose a new empirical–statistical framework to improve cool-season precipitation forecasts across the contiguous United States (CONUS). This newly developed framework is called the Statistical Climate Ensemble Forecast (SCEF) model. The SCEF framework applies a principal component regression model to predictors and predictands that have undergone dimensionality reduction, where the predictors are large-scale meteorological variables that have been prefiltered in space. The forecasts of the SCEF model captures 12.0% of the total CONUS-wide standardized observed variance over the period 1982/83–2019/20, whereas NMME captures 7.2%. Over the more recent period 2000/01–2019/20, the SCEF, NMME, and SEAS5 models respectively capture 11.8%, 4.0%, and 4.1% of the total CONUS-wide standardized observed variance. An important finding is that much of the improved skill in the SCEF, with respect to models such as NMME and SEAS5, can be attributed to better forecasts across most of the western United States.
Switanek
M. B.
Hamill
T. M.
21171
Article
Rain-Induced Stratification of the Equatorial Indian Ocean and Its Potential Feedback to the Atmosphere
Surface freshening through precipitation can act to stably stratify the upper ocean, forming a rain layer (RL). RLs inhibit subsurface vertical mixing, isolating deeper ocean layers from the atmosphere. This process has been studied using observations and idealized simulations. The present ocean modeling study builds upon this body of work by incorporating spatially resolved and realistic atmospheric forcing. Fine-scale observations of the upper ocean collected during the Dynamics of the Madden-Julian Oscillation field campaign are used to verify the General Ocean Turbulence Model (GOTM). Spatiotemporal characteristics of equatorial Indian Ocean RLs are then investigated by forcing a 2D array of GOTM columns with realistic and well-resolved output from an existing regional atmospheric simulation. RL influence on the ocean-atmosphere system is evaluated through analysis of RL-induced modification to surface fluxes and sea surface temperature (SST). This analysis demonstrates that RLs cool the ocean surface on time scales longer than the associated precipitation event. A second simulation with identical atmospheric forcing to that in the first, but with rainfall set to zero, is performed to investigate the role of rain temperature and salinity stratification in maintaining cold SST anomalies within RLs. Approximately one third, or 0.1°C, of the SST reduction within RLs can be attributed to rain effects, while the remainder is attributed to changes in atmospheric temperature and humidity. The prolonged RL-induced SST anomalies enhance SST gradients that have been shown to favor the initiation of atmospheric convection. These findings encourage continued research of RL feedbacks to the atmosphere.
2022-3
J. Geophys. Res. Oceans
127
e2021JC018025
0
10.1029/2021JC018025
Surface freshening through precipitation can act to stably stratify the upper ocean, forming a rain layer (RL). RLs inhibit subsurface vertical mixing, isolating deeper ocean layers from the atmosphere. This process has been studied using observations and idealized simulations. The present ocean modeling study builds upon this body of work by incorporating spatially resolved and realistic atmospheric forcing. Fine-scale observations of the upper ocean collected during the Dynamics of the Madden-Julian Oscillation field campaign are used to verify the General Ocean Turbulence Model (GOTM). Spatiotemporal characteristics of equatorial Indian Ocean RLs are then investigated by forcing a 2D array of GOTM columns with realistic and well-resolved output from an existing regional atmospheric simulation. RL influence on the ocean-atmosphere system is evaluated through analysis of RL-induced modification to surface fluxes and sea surface temperature (SST). This analysis demonstrates that RLs cool the ocean surface on time scales longer than the associated precipitation event. A second simulation with identical atmospheric forcing to that in the first, but with rainfall set to zero, is performed to investigate the role of rain temperature and salinity stratification in maintaining cold SST anomalies within RLs. Approximately one third, or 0.1°C, of the SST reduction within RLs can be attributed to rain effects, while the remainder is attributed to changes in atmospheric temperature and humidity. The prolonged RL-induced SST anomalies enhance SST gradients that have been shown to favor the initiation of atmospheric convection. These findings encourage continued research of RL feedbacks to the atmosphere.
Shackelford
K.
DeMott
C. A.
van Leeuwen
P. J.
Thompson
E. J.
Hagos
S.
21173
Article
Evaluating convective planetary boundary-layer height estimations resolved by both active and passive remote sensing instruments during the CHEESEHEAD19 field campaign
During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, USA, active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include radar wind profilers, microwave radiometers, atmospheric emitted radiance interferometers, ceilometers, high spectral resolution lidars, Doppler lidars, and collaborative lower-atmospheric mobile profiling systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary layer depth estimations is also investigated.
We found that while all instruments are overall able to provide reasonable boundary layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, radar wind profilers perform well during cloud-free conditions, and microwave radiometers and atmospheric emitted radiance interferometers have a very good agreement during all conditions but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from ceilometers and high spectral resolution lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the collaborative lower atmospheric mobile profiling systems can be constricted to a limited height range in low-aerosol conditions.
2022-4
Atmos. Meas. Tech.
15
2479-2502
0
10.5194/amt-2021-363
During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, USA, active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include radar wind profilers, microwave radiometers, atmospheric emitted radiance interferometers, ceilometers, high spectral resolution lidars, Doppler lidars, and collaborative lower-atmospheric mobile profiling systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary layer depth estimations is also investigated.
We found that while all instruments are overall able to provide reasonable boundary layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, radar wind profilers perform well during cloud-free conditions, and microwave radiometers and atmospheric emitted radiance interferometers have a very good agreement during all conditions but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from ceilometers and high spectral resolution lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the collaborative lower atmospheric mobile profiling systems can be constricted to a limited height range in low-aerosol conditions.
Duncan Jr.
J. B.
Bianco
L.
Adler
B.
Bell
T.
Djalalova
I.
Riihimaki
L.
Sedlar
J.
Smith
E. N.
Turner
D. D.
Wagner
T. J.
Wilczak
J. M.
21176
Article
Ocean bubbles under high wind conditions – Part 1: Bubble distribution and development
The bubbles generated by breaking waves are of considerable scientific interest due to their influence on air–sea gas transfer, aerosol production, and upper ocean optics and acoustics. However, a detailed understanding of the processes creating deeper bubble plumes (extending 2–10 m below the ocean surface) and their significance for air–sea gas exchange is still lacking. Here, we present bubble measurements from the HiWinGS expedition in the North Atlantic in 2013, collected during several storms with wind speeds of 10–27 m s−1. A suite of instruments was used to measure bubbles from a self-orienting free-floating spar buoy: a specialised bubble camera, acoustical resonators, and an upward-pointing sonar. The focus in this paper is on bubble void fractions and plume structure. The results are consistent with the presence of a heterogeneous shallow bubble layer occupying the top 1–2 m of the ocean, which is regularly replenished by breaking waves, and deeper plumes which are only formed from the shallow layer at the convergence zones of Langmuir circulation. These advection events are not directly connected to surface breaking. The void fraction distributions at 2 m depth show a sharp cut-off at a void fraction of 10−4.5 even in the highest winds, implying the existence of mechanisms limiting the void fractions close to the surface. Below wind speeds of 16 m s−1 or a wind-wave Reynolds number of , the probability distribution of void fraction at 2 m depth is very similar in all conditions but increases significantly above either threshold. Void fractions are significantly different during periods of rising and falling winds, but there is no distinction with wave age. There is a complex near-surface flow structure due to Langmuir circulation, Stokes drift, and wind-induced current shear which influences the spatial distribution of bubbles within the top few metres. We do not see evidence for slow bubble dissolution as bubbles are carried downwards, implying that collapse is the more likely termination process. We conclude that the shallow and deeper bubble layers need to be studied simultaneously to link them to the 3D flow patterns in the top few metres of the ocean. Many open questions remain about the extent to which deep bubble plumes contribute to air–sea gas transfer. A companion paper (Czerski et al., 2022) addresses the observed bubble size distributions and the processes responsible for them.
2022-5
Ocean Sci.
18
565–586
0
10.5194/os-18-565-2022
The bubbles generated by breaking waves are of considerable scientific interest due to their influence on air–sea gas transfer, aerosol production, and upper ocean optics and acoustics. However, a detailed understanding of the processes creating deeper bubble plumes (extending 2–10 m below the ocean surface) and their significance for air–sea gas exchange is still lacking. Here, we present bubble measurements from the HiWinGS expedition in the North Atlantic in 2013, collected during several storms with wind speeds of 10–27 m s−1. A suite of instruments was used to measure bubbles from a self-orienting free-floating spar buoy: a specialised bubble camera, acoustical resonators, and an upward-pointing sonar. The focus in this paper is on bubble void fractions and plume structure. The results are consistent with the presence of a heterogeneous shallow bubble layer occupying the top 1–2 m of the ocean, which is regularly replenished by breaking waves, and deeper plumes which are only formed from the shallow layer at the convergence zones of Langmuir circulation. These advection events are not directly connected to surface breaking. The void fraction distributions at 2 m depth show a sharp cut-off at a void fraction of 10−4.5 even in the highest winds, implying the existence of mechanisms limiting the void fractions close to the surface. Below wind speeds of 16 m s−1 or a wind-wave Reynolds number of , the probability distribution of void fraction at 2 m depth is very similar in all conditions but increases significantly above either threshold. Void fractions are significantly different during periods of rising and falling winds, but there is no distinction with wave age. There is a complex near-surface flow structure due to Langmuir circulation, Stokes drift, and wind-induced current shear which influences the spatial distribution of bubbles within the top few metres. We do not see evidence for slow bubble dissolution as bubbles are carried downwards, implying that collapse is the more likely termination process. We conclude that the shallow and deeper bubble layers need to be studied simultaneously to link them to the 3D flow patterns in the top few metres of the ocean. Many open questions remain about the extent to which deep bubble plumes contribute to air–sea gas transfer. A companion paper (Czerski et al., 2022) addresses the observed bubble size distributions and the processes responsible for them.
Czerski
H.
Brooks
I. M.
Gunn
S.
Pascal
R.
Matei
A.
Blomquist
B. W.
21177
Article
Ocean bubbles under high wind conditions – Part 2: Bubble size distributions and implications for models of bubble dynamics
Bubbles formed by breaking waves in the open ocean influence many surface processes but are poorly understood. We report here on detailed bubble size distributions measured during the High Wind Speed Gas Exchange Study (HiWinGS) in the North Atlantic, during four separate storms with hourly averaged wind speeds from 10–27 m s−1. The measurements focus on the deeper plumes formed by advection downwards (at 2 m depth and below), rather than the initial surface distributions. Our results suggest that bubbles reaching a depth of 2 m have already evolved to form a heterogeneous but statistically stable population in the top 1–2 m of the ocean. These shallow bubble populations are carried downwards by coherent near-surface circulations; bubble evolution at greater depths is consistent with control by local gas saturation, surfactant coatings and pressure. We find that at 2 m the maximum bubble radius observed has a very weak wind speed dependence and is too small to be explained by simple buoyancy arguments. For void fractions greater than 10−6, bubble size distributions at 2 m can be fitted by a two-slope power law (with slopes of −0.3 for bubbles of radius <80 µm and −4.4 for larger sizes). If normalised by void fraction, these distributions collapse to a very narrow range, implying that the bubble population is relatively stable and the void fraction is determined by bubbles spreading out in space rather than changing their size over time. In regions with these relatively high void fractions we see no evidence for slow bubble dissolution. When void fractions are below 10−6, the peak volume of the bubble size distribution is more variable and can change systematically across a plume at lower wind speeds, tracking the void fraction. Relatively large bubbles (80 µm in radius) are observed to persist for several hours in some cases, following periods of very high wind. Our results suggest that local gas supersaturation around the bubble plume may have a strong influence on bubble lifetime, but significantly, the gas in the bubbles contained in the deep plumes cannot be responsible for this supersaturation. We propose that the supersaturation is predominately controlled by the dissolution of bubbles in the top metre of the ocean, and that this bulk water is then drawn downwards, surrounding the deep bubble plume and influencing its lifetime. In this scenario, oxygen uptake is associated with deep bubble plumes but is not driven directly by them. We suggest that as bubbles move to depths greater than 2 m, sudden collapse may be more significant as a bubble termination mechanism than slow dissolution, especially in regions of high void fraction. Finally, we present a proposal for the processes and timescales which form and control these deeper bubble plumes.
2022-5
Ocean Sci.
18
587–608
0
10.5194/os-18-587-2022
Bubbles formed by breaking waves in the open ocean influence many surface processes but are poorly understood. We report here on detailed bubble size distributions measured during the High Wind Speed Gas Exchange Study (HiWinGS) in the North Atlantic, during four separate storms with hourly averaged wind speeds from 10–27 m s−1. The measurements focus on the deeper plumes formed by advection downwards (at 2 m depth and below), rather than the initial surface distributions. Our results suggest that bubbles reaching a depth of 2 m have already evolved to form a heterogeneous but statistically stable population in the top 1–2 m of the ocean. These shallow bubble populations are carried downwards by coherent near-surface circulations; bubble evolution at greater depths is consistent with control by local gas saturation, surfactant coatings and pressure. We find that at 2 m the maximum bubble radius observed has a very weak wind speed dependence and is too small to be explained by simple buoyancy arguments. For void fractions greater than 10−6, bubble size distributions at 2 m can be fitted by a two-slope power law (with slopes of −0.3 for bubbles of radius <80 µm and −4.4 for larger sizes). If normalised by void fraction, these distributions collapse to a very narrow range, implying that the bubble population is relatively stable and the void fraction is determined by bubbles spreading out in space rather than changing their size over time. In regions with these relatively high void fractions we see no evidence for slow bubble dissolution. When void fractions are below 10−6, the peak volume of the bubble size distribution is more variable and can change systematically across a plume at lower wind speeds, tracking the void fraction. Relatively large bubbles (80 µm in radius) are observed to persist for several hours in some cases, following periods of very high wind. Our results suggest that local gas supersaturation around the bubble plume may have a strong influence on bubble lifetime, but significantly, the gas in the bubbles contained in the deep plumes cannot be responsible for this supersaturation. We propose that the supersaturation is predominately controlled by the dissolution of bubbles in the top metre of the ocean, and that this bulk water is then drawn downwards, surrounding the deep bubble plume and influencing its lifetime. In this scenario, oxygen uptake is associated with deep bubble plumes but is not driven directly by them. We suggest that as bubbles move to depths greater than 2 m, sudden collapse may be more significant as a bubble termination mechanism than slow dissolution, especially in regions of high void fraction. Finally, we present a proposal for the processes and timescales which form and control these deeper bubble plumes.
Czerski
H.
Brooks
I. M.
Gunn
S.
Pascal
R.
Matei
A.
Blomquist
B. W.
21180
Article
The Role of Seasonality and the ENSO Mode in Central and East Pacific ENSO Growth and Evolution
A cyclostationary linear inverse model (CSLIM) is used to investigate the seasonal growth of tropical Pacific Ocean El Niño–Southern Oscillation (ENSO) events with canonical, central Pacific (CP), or eastern Pacific (EP) sea surface temperature (SST) characteristics. Analysis shows that all types of ENSO events experience maximum growth toward final states occurring in November and December. ENSO events with EP characteristics also experience growth into May and June, but CP events do not. A single dominant “ENSO mode,” growing from an equatorial heat content anomaly into a characteristic ENSO-type SST pattern in about 9 months (consistent with the delayed/recharge oscillator model of ENSO), is essential for the predictable development of all ENSO events. Notably, its seasonality is responsible for the late-calendar-year maximum in ENSO amplification. However, this ENSO mode alone does not capture the observed growth and evolution of diverse ENSO events, which additionally involve the seasonal evolution of other nonorthogonal Floquet modes. EP event growth occurs when the ENSO mode is initially “covered up” in combination with other Floquet modes. The ENSO mode’s slow seasonal evolution allows it to emerge while the other modes rapidly evolve and/or decay, leading to strongly amplifying and more predictable EP events. CP events develop when the initial state has a substantial contribution from Floquet modes with meridional mode–like SST structures. Thus, while nearly all ENSO events involve the seasonally varying ENSO-mode dynamics, the diversity and predictability of ENSO events cannot be understood without identifying contributions from the remaining Floquet modes.
2022-6
J. Climate
35
3195-3209
0
10.1175/JCLI-D-21-0599.1
A cyclostationary linear inverse model (CSLIM) is used to investigate the seasonal growth of tropical Pacific Ocean El Niño–Southern Oscillation (ENSO) events with canonical, central Pacific (CP), or eastern Pacific (EP) sea surface temperature (SST) characteristics. Analysis shows that all types of ENSO events experience maximum growth toward final states occurring in November and December. ENSO events with EP characteristics also experience growth into May and June, but CP events do not. A single dominant “ENSO mode,” growing from an equatorial heat content anomaly into a characteristic ENSO-type SST pattern in about 9 months (consistent with the delayed/recharge oscillator model of ENSO), is essential for the predictable development of all ENSO events. Notably, its seasonality is responsible for the late-calendar-year maximum in ENSO amplification. However, this ENSO mode alone does not capture the observed growth and evolution of diverse ENSO events, which additionally involve the seasonal evolution of other nonorthogonal Floquet modes. EP event growth occurs when the ENSO mode is initially “covered up” in combination with other Floquet modes. The ENSO mode’s slow seasonal evolution allows it to emerge while the other modes rapidly evolve and/or decay, leading to strongly amplifying and more predictable EP events. CP events develop when the initial state has a substantial contribution from Floquet modes with meridional mode–like SST structures. Thus, while nearly all ENSO events involve the seasonally varying ENSO-mode dynamics, the diversity and predictability of ENSO events cannot be understood without identifying contributions from the remaining Floquet modes.
Vimont
D. J.
Newman
M.
Battisti
D. S.
Shin
S.-I.
21182
Article
Including parameterized error covariance in local ensemble solvers: Experiments in a 1D model with balance constraints
Lack of efficient ways to include parameterized error covariance in ensemble-based local volume solvers (e.g. the local ensemble-transform Kalman filter – the LETKF) remains an outstanding problem in data assimilation. Here, we describe two new algorithms: GETKF-OI and LETKF-OI. These algorithms are similar to the traditional optimal interpolation (OI) algorithm in that they use parameterized error covariance to update each of the local volume solutions. However, unlike the traditional OI that scales poorly as the number of observations increases, the new algorithms achieve linear scalability by using either the observational-space localization strategy of the traditional LETKF algorithm or the modulated ensembles of the gain-form (GETKF) algorithm. In our testing with a simple one-dimensional univariate system, we find that the GETKF-OI algorithm can recover the exact solution within the truncation bounds of the modulated ensemble and the LETKF-OI algorithm achieves a close approximation to the exact solution. We also demonstrate how to extend GETKF-OI algorithm to a toy multivariate system with balance constraints.
2022-7
Q. J. R. Meteorol. Soc.
148
2086-2101
0
10.1002/qj.4289
Lack of efficient ways to include parameterized error covariance in ensemble-based local volume solvers (e.g. the local ensemble-transform Kalman filter – the LETKF) remains an outstanding problem in data assimilation. Here, we describe two new algorithms: GETKF-OI and LETKF-OI. These algorithms are similar to the traditional optimal interpolation (OI) algorithm in that they use parameterized error covariance to update each of the local volume solutions. However, unlike the traditional OI that scales poorly as the number of observations increases, the new algorithms achieve linear scalability by using either the observational-space localization strategy of the traditional LETKF algorithm or the modulated ensembles of the gain-form (GETKF) algorithm. In our testing with a simple one-dimensional univariate system, we find that the GETKF-OI algorithm can recover the exact solution within the truncation bounds of the modulated ensemble and the LETKF-OI algorithm achieves a close approximation to the exact solution. We also demonstrate how to extend GETKF-OI algorithm to a toy multivariate system with balance constraints.
Frolov
S.
Whitaker
J. S.
Draper
C.
21183
Article
Overview of the MOSAiC expedition: Physical Oceanography
Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s sampling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present along-drift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling capabilities in the Arctic Ocean.
2022-2
Elementa Sci. Anthrop.
10
00062
0
10.1525/elementa.2021.00062
Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s sampling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present along-drift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling capabilities in the Arctic Ocean.
Rabe
B.
Heuzé
C.
Regnery
J.
Aksenov
Y.
. .
.
Shupe
M. D.
al.
et
21184
Article
Insights on sources and formation mechanisms of liquid-bearing clouds over MOSAiC examined from a Lagrangian framework
Understanding Arctic stratiform liquid-bearing cloud life cycles and properly representing these life cycles in models is crucial for evaluations of cloud feedbacks as well as the faithfulness of climate projections for this rapidly warming region. Examination of cloud life cycles typically requires analyses of cloud evolution and origins on short time scales, on the order of hours to several days. Measurements from the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provide a unique view of the current state of the central Arctic over an annual cycle. Here, we use the MOSAiC radiosonde measurements to detect liquid-bearing cloud layers over full atmospheric columns and to examine the cloud-generating air masses’ properties. We perform 5-day (120 h) back-trajectory calculations for every detected cloud and cluster them using a unique set of variables extracted from these trajectories informed by ERA5 reanalysis data. This clustering method enables us to separate between the air mass source regions such as ice-covered Arctic and midlatitude open water. We find that moisture intrusions into the central Arctic typically result in multilayer liquid-bearing cloud structures and that more than half of multilayer profiles include overlying liquid-bearing clouds originating in different types of air masses. Finally, we conclude that Arctic cloud formation via prolonged radiative cooling of elevated stable subsaturated air masses circulating over the Arctic can occur frequently (up to 20% of detected clouds in the sounding data set) and may lead to a significant impact of ensuing clouds on the surface energy budget, including net surface warming in some cases.
2022-3
Elementa Sci. Anthrop.
10
000071
0
10.1525/elementa.2021.000071
Understanding Arctic stratiform liquid-bearing cloud life cycles and properly representing these life cycles in models is crucial for evaluations of cloud feedbacks as well as the faithfulness of climate projections for this rapidly warming region. Examination of cloud life cycles typically requires analyses of cloud evolution and origins on short time scales, on the order of hours to several days. Measurements from the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provide a unique view of the current state of the central Arctic over an annual cycle. Here, we use the MOSAiC radiosonde measurements to detect liquid-bearing cloud layers over full atmospheric columns and to examine the cloud-generating air masses’ properties. We perform 5-day (120 h) back-trajectory calculations for every detected cloud and cluster them using a unique set of variables extracted from these trajectories informed by ERA5 reanalysis data. This clustering method enables us to separate between the air mass source regions such as ice-covered Arctic and midlatitude open water. We find that moisture intrusions into the central Arctic typically result in multilayer liquid-bearing cloud structures and that more than half of multilayer profiles include overlying liquid-bearing clouds originating in different types of air masses. Finally, we conclude that Arctic cloud formation via prolonged radiative cooling of elevated stable subsaturated air masses circulating over the Arctic can occur frequently (up to 20% of detected clouds in the sounding data set) and may lead to a significant impact of ensuing clouds on the surface energy budget, including net surface warming in some cases.
Silber
I.
Shupe
M. D.
21185
Article
Diagnosing Hawaii’s Recent Drought
Hawaii’s recent drought is among the most severe on record. Wet-season (November–April) rainfall deficits during 2010–19 rank second lowest among consecutive 10-yr periods since 1900. Various lines of empirical and model evidence indicate a principal natural atmospheric cause for the low rainfall, mostly unrelated to either internal oceanic variability or external forcing. Empirical analysis reveals that traditional factors have favored wetness rather than drought in recent decades, including a cold phase of the Pacific decadal oscillation in sea surface temperatures (SSTs) and a weakened Aleutian low in atmospheric circulation. But correlations of Hawaiian rainfall with patterns of Pacific sea level pressure and SSTs that explained a majority of its variability during the twentieth century collapsed in the twenty-first century. Atmospheric model simulations indicate a forced decadal signal (2010–19 vs 1981–2000) of Aleutian low weakening, consistent with recent observed North Pacific circulation. However, model ensemble means do not generate reduced Hawaiian rainfall, indicating that neither oceanic boundary forcing nor a weakened Aleutian low caused recent low Hawaiian rainfall. Additional atmospheric model experiments explored the role of anthropogenic forcing. These reveal a strong sensitivity of Hawaiian rainfall to details of long-term SST change patterns. Under an assumption that anthropogenic forcing drives zonally uniform SST warming, Hawaiian rainfall declines, with a range of 3%–9% among three models. Under an assumption that anthropogenic forcing also increases the equatorial Pacific zonal SST gradient, Hawaiian rainfall increases 2%–6%. Large spread among ensemble members indicates that no forced signals are detectable.
2022-7
J. Climate
35
3997–4012
0
10.1175/JCLI-D-21-0754.1
Hawaii’s recent drought is among the most severe on record. Wet-season (November–April) rainfall deficits during 2010–19 rank second lowest among consecutive 10-yr periods since 1900. Various lines of empirical and model evidence indicate a principal natural atmospheric cause for the low rainfall, mostly unrelated to either internal oceanic variability or external forcing. Empirical analysis reveals that traditional factors have favored wetness rather than drought in recent decades, including a cold phase of the Pacific decadal oscillation in sea surface temperatures (SSTs) and a weakened Aleutian low in atmospheric circulation. But correlations of Hawaiian rainfall with patterns of Pacific sea level pressure and SSTs that explained a majority of its variability during the twentieth century collapsed in the twenty-first century. Atmospheric model simulations indicate a forced decadal signal (2010–19 vs 1981–2000) of Aleutian low weakening, consistent with recent observed North Pacific circulation. However, model ensemble means do not generate reduced Hawaiian rainfall, indicating that neither oceanic boundary forcing nor a weakened Aleutian low caused recent low Hawaiian rainfall. Additional atmospheric model experiments explored the role of anthropogenic forcing. These reveal a strong sensitivity of Hawaiian rainfall to details of long-term SST change patterns. Under an assumption that anthropogenic forcing drives zonally uniform SST warming, Hawaiian rainfall declines, with a range of 3%–9% among three models. Under an assumption that anthropogenic forcing also increases the equatorial Pacific zonal SST gradient, Hawaiian rainfall increases 2%–6%. Large spread among ensemble members indicates that no forced signals are detectable.
Eischeid
J. K.
Hoerling
M. P.
Quan
X.-W.
Diaz
H. F.
21188
Article
Observations of the Lower Atmosphere From the 2021 WiscoDISCO Campaign
The mesoscale meteorology of lake breezes along Lake Michigan impacts local observations of high-ozone events. Previous manned aircraft and UAS observations have demonstrated non-uniform ozone concentrations within and above the marine layer over water and within shoreline environments. During the 2021 Wisconsin's Dynamic Influence of Shoreline Circulations on Ozone (WiscoDISCO-21) campaign, two UAS platforms, a fixed-wing (University of Colorado RAAVEN) and a multirotor (Purdue University DJI M210), were used simultaneously to capture lake breeze during forecasted high-ozone events at Chiwaukee Prairie State Natural Area in southeastern Wisconsin from 21–26 May 2021. The RAAVEN platform (data DOI: https://doi.org/10.5281/zenodo.5142491, de Boer et al., 2021) measured temperature, humidity, and 3-D winds during 2 h flights following two separate flight patterns up to three times per day at altitudes reaching 500 m above ground level (a.g.l.). The M210 platform (data DOI: https://doi.org/10.5281/zenodo.5160346, Cleary et al., 2021a) measured vertical profiles of temperature, humidity, and ozone during 15 min flights up to six times per day at altitudes reaching 120 ma.g.l. near a Wisconsin DNR ground monitoring station (AIRS ID: 55-059-0019). This campaign was conducted in conjunction with the Enhanced Ozone Monitoring plan from the Wisconsin DNR that included Doppler lidar wind profiler observations at the site (data DOI: https://doi.org/10.5281/zenodo.5213039, Cleary et al., 2021b).
2022-5
Earth Syst. Sci. Data
14
2129–2145
0
10.5194/essd-14-2129-2022
The mesoscale meteorology of lake breezes along Lake Michigan impacts local observations of high-ozone events. Previous manned aircraft and UAS observations have demonstrated non-uniform ozone concentrations within and above the marine layer over water and within shoreline environments. During the 2021 Wisconsin's Dynamic Influence of Shoreline Circulations on Ozone (WiscoDISCO-21) campaign, two UAS platforms, a fixed-wing (University of Colorado RAAVEN) and a multirotor (Purdue University DJI M210), were used simultaneously to capture lake breeze during forecasted high-ozone events at Chiwaukee Prairie State Natural Area in southeastern Wisconsin from 21–26 May 2021. The RAAVEN platform (data DOI: https://doi.org/10.5281/zenodo.5142491, de Boer et al., 2021) measured temperature, humidity, and 3-D winds during 2 h flights following two separate flight patterns up to three times per day at altitudes reaching 500 m above ground level (a.g.l.). The M210 platform (data DOI: https://doi.org/10.5281/zenodo.5160346, Cleary et al., 2021a) measured vertical profiles of temperature, humidity, and ozone during 15 min flights up to six times per day at altitudes reaching 120 ma.g.l. near a Wisconsin DNR ground monitoring station (AIRS ID: 55-059-0019). This campaign was conducted in conjunction with the Enhanced Ozone Monitoring plan from the Wisconsin DNR that included Doppler lidar wind profiler observations at the site (data DOI: https://doi.org/10.5281/zenodo.5213039, Cleary et al., 2021b).
Cleary
P.
de Boer
G.
Hupy
J.
Borenstein
S.
Hamilton
J.
Kies
B.
Lawrence
D.
Pierce
R. B.
Tirado
J.
Voon
A.
Wagner
T.
21189
Article
Role of the Tropics and its Extratropical Teleconnections in State-Dependent Improvements of U.S. West Coast UFS Precipitation Forecasts
Boreal-wintertime hindcasts in the Unified Forecast System with the tropics nudged toward reanalysis improve United States (US) West Coast precipitation forecasts at Weeks 3–4 lead times when compared to those without nudging. To diagnose the origin of these improvements, a multivariate k-means clustering method is used to group hindcasts into subsets by their initial conditions. One cluster characterized by an initially strong Aleutian Low demonstrates larger improvements at Weeks 3–4 with nudging compared to the others. The greater improvements with nudging for this cluster are related to model errors in simulating the interaction between the Aleutian Low and the teleconnection patterns associated with the Madden-Julian oscillation (MJO) and El Niño-Southern Oscillation (ENSO). Improving forecasts of tropical intraseasonal precipitation, especially during early MJO phases under non-cold ENSO, may be important for producing better Weeks 3–4 precipitation forecasts for the US West Coast.
2022-3
Geophys. Res. Lett.
49
e2021GL096447
0
10.1029/2021GL096447
Boreal-wintertime hindcasts in the Unified Forecast System with the tropics nudged toward reanalysis improve United States (US) West Coast precipitation forecasts at Weeks 3–4 lead times when compared to those without nudging. To diagnose the origin of these improvements, a multivariate k-means clustering method is used to group hindcasts into subsets by their initial conditions. One cluster characterized by an initially strong Aleutian Low demonstrates larger improvements at Weeks 3–4 with nudging compared to the others. The greater improvements with nudging for this cluster are related to model errors in simulating the interaction between the Aleutian Low and the teleconnection patterns associated with the Madden-Julian oscillation (MJO) and El Niño-Southern Oscillation (ENSO). Improving forecasts of tropical intraseasonal precipitation, especially during early MJO phases under non-cold ENSO, may be important for producing better Weeks 3–4 precipitation forecasts for the US West Coast.
Hsiao
W.-T.
Barnes
E. A.
Maloney
E. D.
Tulich
S. N.
Dias
J.
Kiladis
G. N.
21190
Article
Relationships Between the Eastward Propagation of the Madden-Julian Oscillation and its Circulation Structure
The circulation associated with convection of the Madden-Julian Oscillation (MJO) has been suggested to have an impact on its propagation by a number of previous studies. This circulation contains both flanking Rossby waves to the rear and a Kelvin wave leading the convective center. In this study, individual MJO convective envelopes from a 40-year database are tracked, a technique to scale the MJO by its zonal wavelength is employed, and statistical methods are used to assess how the MJO circulation pattern might impact its eastward propagation downstream. Results suggest that continuous eastward propagation of the MJO is favored when a strong Kelvin wave circulation is present east of MJO convection, indicated by both an easterly zonal wind anomaly and negative geopotential height anomaly. In addition to the known significance of having Kelvin wave easterly wind anomalies, the results of this study highlight that the existence of negative geopotential height is important to supporting moistening and MJO propagation. It is found that importance of the Kelvin wave signal to MJO propagation depends on the region that the MJO is located over. Kelvin wave circulation east of MJO convection enhances moistening to support continuous eastward propagation of the MJO, mainly through meridional moisture advection due to the coupling between the Kelvin wave and Rossby-like disturbances east of the active convection. The roles of boundary layer convergence and vertical moistening are also discussed.
2022-8
J. Geophys. Res. Atmos.
127
e2021JD035806
0
10.1029/2021JD035806
The circulation associated with convection of the Madden-Julian Oscillation (MJO) has been suggested to have an impact on its propagation by a number of previous studies. This circulation contains both flanking Rossby waves to the rear and a Kelvin wave leading the convective center. In this study, individual MJO convective envelopes from a 40-year database are tracked, a technique to scale the MJO by its zonal wavelength is employed, and statistical methods are used to assess how the MJO circulation pattern might impact its eastward propagation downstream. Results suggest that continuous eastward propagation of the MJO is favored when a strong Kelvin wave circulation is present east of MJO convection, indicated by both an easterly zonal wind anomaly and negative geopotential height anomaly. In addition to the known significance of having Kelvin wave easterly wind anomalies, the results of this study highlight that the existence of negative geopotential height is important to supporting moistening and MJO propagation. It is found that importance of the Kelvin wave signal to MJO propagation depends on the region that the MJO is located over. Kelvin wave circulation east of MJO convection enhances moistening to support continuous eastward propagation of the MJO, mainly through meridional moisture advection due to the coupling between the Kelvin wave and Rossby-like disturbances east of the active convection. The roles of boundary layer convergence and vertical moistening are also discussed.
Berrington
A. H.
Sakaeda
N.
Dias
J.
Kiladis
G. N.
21191
Article
Subseasonal-to-seasonal forecast skill in the California Current System and its connection to coastal Kelvin waves
Accurate dynamical forecasts of ocean variables in the California Current System (CCS) are essential decision support tools for advancing ecosystem-based marine resource management. However, model and dynamical uncertainties present a significant challenge when attempting to incorporate these forecasts into a formal decision-making process. To provide guidance on the reliability of dynamical forecasts, previous studies have suggested that deterministic climate processes associated with atmospheric or oceanic teleconnections may provide opportunities for enhanced forecast skill. Recent computational advances have led to the availability of subseasonal-to-seasonal (S2S) forecasts of key oceanic variables such as sea surface height (SSH), which may be leveraged to identify such “forecast opportunities.” In this study, we conduct a S2S forecast skill assessment of SSH anomalies in the CCS using an ensemble of 46-day reforecasts made by the European Center for Medium-range Weather Forecasting (ECMWF) model for the period 2000–2018. We find that the ECMWF model consistently produces skillful dynamical forecasts of SSH, particularly in both the southern and northern CCS at leads of 4–7 weeks. Using a high-resolution ocean reanalysis, we develop a new index designed to characterize the location and intensity of coastally trapped waves propagating through the CCS. We then show that the S2S dynamical forecasts have enhanced skill in forecasts of SSH in weeks 4–7 when initialized with strong or extreme coastally trapped wave conditions, explaining 30–40% more SSH variance than the corresponding persistence forecast.
2022-1
J. Geophys. Res. Oceans
127
e2021JC017892
0
10.1029/2021JC017892
Accurate dynamical forecasts of ocean variables in the California Current System (CCS) are essential decision support tools for advancing ecosystem-based marine resource management. However, model and dynamical uncertainties present a significant challenge when attempting to incorporate these forecasts into a formal decision-making process. To provide guidance on the reliability of dynamical forecasts, previous studies have suggested that deterministic climate processes associated with atmospheric or oceanic teleconnections may provide opportunities for enhanced forecast skill. Recent computational advances have led to the availability of subseasonal-to-seasonal (S2S) forecasts of key oceanic variables such as sea surface height (SSH), which may be leveraged to identify such “forecast opportunities.” In this study, we conduct a S2S forecast skill assessment of SSH anomalies in the CCS using an ensemble of 46-day reforecasts made by the European Center for Medium-range Weather Forecasting (ECMWF) model for the period 2000–2018. We find that the ECMWF model consistently produces skillful dynamical forecasts of SSH, particularly in both the southern and northern CCS at leads of 4–7 weeks. Using a high-resolution ocean reanalysis, we develop a new index designed to characterize the location and intensity of coastally trapped waves propagating through the CCS. We then show that the S2S dynamical forecasts have enhanced skill in forecasts of SSH in weeks 4–7 when initialized with strong or extreme coastally trapped wave conditions, explaining 30–40% more SSH variance than the corresponding persistence forecast.
Amaya
D. J.
Jacox
M. G.
Dias
J.
Alexander
M. A.
Karnauskas
K.
Scott
J. D.
Gehne
M.
21193
Article
The influence of the trend, basin interactions, and ocean dynamics on tropical ocean prediction
The trend, connections between tropical ocean basins and dynamical processes on sea surface temperature (SST) and sea surface height (SSH) forecast skill are investigated using a linear inverse model (LIM) framework. The warming trend has a strong influence on 6-month SST forecast skill in the Indian Ocean, Western Pacific, and north tropical Atlantic, but little effect on El Niño-Southern Oscillation (ENSO) prediction. ENSO strongly impacts the SST forecast skill in all three ocean basins including most of the Indian Ocean and the north tropical Atlantic. Without interactions with the Indian Ocean, the 6-month SST forecast skill is substantially reduced in the western tropical Pacific and SSH skill decreases in the central Pacific. Atlantic and Indian Ocean interactions with the Pacific enhance ENSO forecast skill at 6–12 months leads. The Indian Ocean influences SSH in the eastern Atlantic, while the tropical Atlantic affects SST forecasts in portions of the Indian Ocean.
2022-2
Geophys. Res. Lett.
49
e2021GL096120
0
10.1029/2021GL096120
The trend, connections between tropical ocean basins and dynamical processes on sea surface temperature (SST) and sea surface height (SSH) forecast skill are investigated using a linear inverse model (LIM) framework. The warming trend has a strong influence on 6-month SST forecast skill in the Indian Ocean, Western Pacific, and north tropical Atlantic, but little effect on El Niño-Southern Oscillation (ENSO) prediction. ENSO strongly impacts the SST forecast skill in all three ocean basins including most of the Indian Ocean and the north tropical Atlantic. Without interactions with the Indian Ocean, the 6-month SST forecast skill is substantially reduced in the western tropical Pacific and SSH skill decreases in the central Pacific. Atlantic and Indian Ocean interactions with the Pacific enhance ENSO forecast skill at 6–12 months leads. The Indian Ocean influences SSH in the eastern Atlantic, while the tropical Atlantic affects SST forecasts in portions of the Indian Ocean.
Alexander
M. A.
Shin
S.-I.
Battisti
D. S.
21195
Article
Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems
Over a five-month time window between March and July 2020, scientists deployed two small uncrewed aircraft systems (sUAS) to the central Arctic Ocean as part of legs three and four of the MOSAiC expedition. These sUAS were flown to measure the thermodynamic and kinematic state of the lower atmosphere, including collecting information on temperature, pressure, humidity and winds between the surface and 1 km, as well as to document ice properties, including albedo, melt pond fraction, and open water amounts. The atmospheric state flights were primarily conducted by the DataHawk2 sUAS, which was operated primarily in a profiling manner, while the surface property flights were conducted using the HELiX sUAS, which flew grid patterns, profiles, and hover flights. In total, over 120 flights were conducted and over 48 flight hours of data were collected, sampling conditions that included temperatures as low as −35 °C and as warm as 15 °C, spanning the summer melt season.
2022-7
Nat. Sci. Data
9
439
0
10.1038/s41597-022-01526-9
Over a five-month time window between March and July 2020, scientists deployed two small uncrewed aircraft systems (sUAS) to the central Arctic Ocean as part of legs three and four of the MOSAiC expedition. These sUAS were flown to measure the thermodynamic and kinematic state of the lower atmosphere, including collecting information on temperature, pressure, humidity and winds between the surface and 1 km, as well as to document ice properties, including albedo, melt pond fraction, and open water amounts. The atmospheric state flights were primarily conducted by the DataHawk2 sUAS, which was operated primarily in a profiling manner, while the surface property flights were conducted using the HELiX sUAS, which flew grid patterns, profiles, and hover flights. In total, over 120 flights were conducted and over 48 flight hours of data were collected, sampling conditions that included temperatures as low as −35 °C and as warm as 15 °C, spanning the summer melt season.
de Boer
G.
Calmer
R.
Jozef
G.
Cassano
J.
Hamilton
J.
Lawrence
D.
Borenstein
S.
Doddi
A.
Cox
C. J.
Schmale
J.
Preußer
A.
Argrow
B.
21196
Article
Datagrams: Diagrammatic Metadata for Humans
Creation of metadata (data about data) takes many forms and has many standards, much of which are designed to provide information for computer algorithms to find, access, and distribute data rather than for how humans might ingest data information. The humans (engineers, technicians, operators, scientists, data managers) that are increasingly tasked with being the providers of standard scientific metadata by the data science community also have a critical need for a different kind of metadata: metadata that can be used in the field (often offline) that provide a detailed visual map of the pathway taken by the electronic signal from a measuring device to a finalized, quality controlled geophysical variable. Datagrams presented here have been developed to fill this requirement and are a user-friendly, information-rich, graphical format that outline, record, and detail the critical information and steps involved with origin, collection, dataflow, processing, and archiving of data. Datagrams are designed to provide critical information across engineering, maintenance, data processing, and scientific teams that might speak different languages but are all required to process and maintain the data or instrument. The essential components of datagrams developed for instruments operating at remote Arctic stations are described here, but of course the concept is applicable to any type of observing protocol in any location.
2022-5
Bull. Amer. Meteor. Soc.
103
E1343–E1350
0
10.1175/BAMS-D-21-0219.1
Creation of metadata (data about data) takes many forms and has many standards, much of which are designed to provide information for computer algorithms to find, access, and distribute data rather than for how humans might ingest data information. The humans (engineers, technicians, operators, scientists, data managers) that are increasingly tasked with being the providers of standard scientific metadata by the data science community also have a critical need for a different kind of metadata: metadata that can be used in the field (often offline) that provide a detailed visual map of the pathway taken by the electronic signal from a measuring device to a finalized, quality controlled geophysical variable. Datagrams presented here have been developed to fill this requirement and are a user-friendly, information-rich, graphical format that outline, record, and detail the critical information and steps involved with origin, collection, dataflow, processing, and archiving of data. Datagrams are designed to provide critical information across engineering, maintenance, data processing, and scientific teams that might speak different languages but are all required to process and maintain the data or instrument. The essential components of datagrams developed for instruments operating at remote Arctic stations are described here, but of course the concept is applicable to any type of observing protocol in any location.
Morris
S. M.
Uttal
T.
21197
Article
Scientific Challenges to Characterizing the Wind Resource in the Marine Atmospheric Boundary Layer
2022-11
Wind Energ. Sci.
7
2307-2334
0
10.5194/wes-7-2307-2022
Shaw
W. J.
Berg
L. K.
Debnath
M.
Deskos
G.
Draxl
C.
Ghate
V. P.
Hasager
C. B.
Kotamarthi
R.
Mirocha
J. D.
Muradyan
P.
Pringle
W.
Turner
D. D.
Wilczak
J. M.
21198
Article
Global seasonal forecasts of marine heatwaves
Marine heatwaves (MHWs)—periods of exceptionally warm ocean temperature lasting weeks to years—are now widely recognized for their capacity to disrupt marine ecosystems1,2,3. The substantial ecological and socioeconomic impacts of these extreme events present significant challenges to marine resource managers4,5,6,7, who would benefit from forewarning of MHWs to facilitate proactive decision-making8,9,10,11. However, despite extensive research into the physical drivers of MHWs11,12, there has been no comprehensive global assessment of our ability to predict these events. Here we use a large multimodel ensemble of global climate forecasts13,14 to develop and assess MHW forecasts that cover the world’s oceans with lead times of up to a year. Using 30 years of retrospective forecasts, we show that the onset, intensity and duration of MHWs are often predictable, with skilful forecasts possible from 1 to 12 months in advance depending on region, season and the state of large-scale climate modes, such as the El Niño/Southern Oscillation. We discuss considerations for setting decision thresholds based on the probability that a MHW will occur, empowering stakeholders to take appropriate actions based on their risk profile. These results highlight the potential for operational MHW forecasts, analogous to forecasts of extreme weather phenomena, to promote climate resilience in global marine ecosystems.
2022-4
Nature
604
486-490
0
10.1038/s41586-022-04573-9
Marine heatwaves (MHWs)—periods of exceptionally warm ocean temperature lasting weeks to years—are now widely recognized for their capacity to disrupt marine ecosystems1,2,3. The substantial ecological and socioeconomic impacts of these extreme events present significant challenges to marine resource managers4,5,6,7, who would benefit from forewarning of MHWs to facilitate proactive decision-making8,9,10,11. However, despite extensive research into the physical drivers of MHWs11,12, there has been no comprehensive global assessment of our ability to predict these events. Here we use a large multimodel ensemble of global climate forecasts13,14 to develop and assess MHW forecasts that cover the world’s oceans with lead times of up to a year. Using 30 years of retrospective forecasts, we show that the onset, intensity and duration of MHWs are often predictable, with skilful forecasts possible from 1 to 12 months in advance depending on region, season and the state of large-scale climate modes, such as the El Niño/Southern Oscillation. We discuss considerations for setting decision thresholds based on the probability that a MHW will occur, empowering stakeholders to take appropriate actions based on their risk profile. These results highlight the potential for operational MHW forecasts, analogous to forecasts of extreme weather phenomena, to promote climate resilience in global marine ecosystems.
Jacox
M. G.
Alexander
M. A.
Amaya
D. J.
Becker
E.
Bograd
S. J.
Brodie
S.
Hazen
E. L.
Pozo Buil
M.
Tommasi
D.
21201
Article
Parameterizing the Impact of Unresolved Temperature Variability on the Large-Scale Density Field: 2. Modeling
Ocean circulation models have systematic errors in large-scale horizontal density gradients due to estimating the grid-cell-mean density by applying the nonlinear seawater equation of state to the grid-cell-mean water properties. In frontal regions where unresolved subgrid-scale (SGS) fluctuations are significant, dynamically relevant errors in the representation of current systems can result. A previous study developed a novel and computationally efficient parameterization of the unresolved SGS temperature variance and resulting density correction. This parameterization was empirically validated but not tested in an ocean model. In this study, we implement deterministic and stochastic variants of this parameterization in the pressure-gradient force term of a coupled ocean-sea ice configuration of the community Earth system model-modular ocean model version 6 and perform a suite of hindcast sensitivity experiments to investigate the ocean response. The parameterization leads to coherent changes in the large-scale ocean circulation and hydrography, particularly in the Nordic Seas and Labrador Sea, which are attributable in large part to changes in the seasonally varying upper-ocean exchange through Denmark Strait. In addition, the separated Gulf Stream strengthens and shifts equatorward, reducing a common bias in coarse-resolution ocean models. The ocean response to the deterministic and stochastic variants of the parameterization is qualitatively, albeit not quantitatively, similar, yet qualitative differences are found in various regions.
2022-3
J. Adv. Model. Earth Syst.
14
e2021MS002844
0
10.1029/2020MS002185
Ocean circulation models have systematic errors in large-scale horizontal density gradients due to estimating the grid-cell-mean density by applying the nonlinear seawater equation of state to the grid-cell-mean water properties. In frontal regions where unresolved subgrid-scale (SGS) fluctuations are significant, dynamically relevant errors in the representation of current systems can result. A previous study developed a novel and computationally efficient parameterization of the unresolved SGS temperature variance and resulting density correction. This parameterization was empirically validated but not tested in an ocean model. In this study, we implement deterministic and stochastic variants of this parameterization in the pressure-gradient force term of a coupled ocean-sea ice configuration of the community Earth system model-modular ocean model version 6 and perform a suite of hindcast sensitivity experiments to investigate the ocean response. The parameterization leads to coherent changes in the large-scale ocean circulation and hydrography, particularly in the Nordic Seas and Labrador Sea, which are attributable in large part to changes in the seasonally varying upper-ocean exchange through Denmark Strait. In addition, the separated Gulf Stream strengthens and shifts equatorward, reducing a common bias in coarse-resolution ocean models. The ocean response to the deterministic and stochastic variants of the parameterization is qualitatively, albeit not quantitatively, similar, yet qualitative differences are found in various regions.
Kenigson
J. S.
Adcroft
A.
Bachman
S. D.
Castruccio
F.
Grooms
I.
Pegion
P.
Stanley
Z.
21202
Article
Investigating the impacts of daytime boundary layer clouds on surface energy fluxes and boundary layer structure during CHEESEHEAD19
Studies of land-atmosphere interactions under clear sky and low cumulus cloud conditions are common from long-term observatories like at the southern great plains (SGP). How well the relationships and responses of surface radiative and turbulent heat fluxes determined from these investigations hold for more heterogeneous surfaces in other climate regimes, however, is uncertain. In this study, detailed observations of the surface energy budget and daytime boundary layer properties are analyzed using measurements from the CHEESEHEAD19 field campaign, July-October 2019, across a heterogeneous forested landscape of northern Wisconsin. A cloud regime framework is employed to classify consecutive periods of clear skies from lower atmosphere stratiform and cumulus clouds. A seasonal transition from low cumulus to low stratiform periods occurred, together with a diurnal pattern in cloudy or clear sky period dominance. Radiative forcing was highly dependent on sky condition, leading to changes in the redistribution efficiency of radiative energy by the surface turbulent heat fluxes. During CHEESEHEAD19, small Bowen ratios dominated with daytime latent heat fluxes three times as large as sensible heat fluxes for all sky conditions studied; the forested region therefore falls within an energy-limited regime. The depth of the daytime mixed layer depended upon the sky condition and thermodynamic setting; deeper mixed layers occurred during periods of low cumulus and not clear skies. Profiles of vertical velocity were found to have enhanced variance under low cumulus compared to clear sky periods, suggesting potential for cloud feedbacks on boundary layer structure and surface energy fluxes.
2022-3
J. Geophys. Res. Atmos.
127
e2021JD036060
0
10.1029/2021JD036060
Studies of land-atmosphere interactions under clear sky and low cumulus cloud conditions are common from long-term observatories like at the southern great plains (SGP). How well the relationships and responses of surface radiative and turbulent heat fluxes determined from these investigations hold for more heterogeneous surfaces in other climate regimes, however, is uncertain. In this study, detailed observations of the surface energy budget and daytime boundary layer properties are analyzed using measurements from the CHEESEHEAD19 field campaign, July-October 2019, across a heterogeneous forested landscape of northern Wisconsin. A cloud regime framework is employed to classify consecutive periods of clear skies from lower atmosphere stratiform and cumulus clouds. A seasonal transition from low cumulus to low stratiform periods occurred, together with a diurnal pattern in cloudy or clear sky period dominance. Radiative forcing was highly dependent on sky condition, leading to changes in the redistribution efficiency of radiative energy by the surface turbulent heat fluxes. During CHEESEHEAD19, small Bowen ratios dominated with daytime latent heat fluxes three times as large as sensible heat fluxes for all sky conditions studied; the forested region therefore falls within an energy-limited regime. The depth of the daytime mixed layer depended upon the sky condition and thermodynamic setting; deeper mixed layers occurred during periods of low cumulus and not clear skies. Profiles of vertical velocity were found to have enhanced variance under low cumulus compared to clear sky periods, suggesting potential for cloud feedbacks on boundary layer structure and surface energy fluxes.
Sedlar
J.
Riihimaki
L.
Turner
D. D.
Duncan
J.
Bianco
L.
Adler
B.
Lantz
K. O.
Wilczak
J. M.
21203
Article
An Optimal Precursor of Northeast Pacific Marine Heatwaves and Central Pacific El Niño events
The intensity of Northeast Pacific marine heatwaves (MHWs) has been related to local stochastic atmospheric forcing with limited predictability, but their evolution and persistence may be controlled by large-scale climate influences. A Linear Inverse Model containing both sea surface temperature and sea surface height (SSH) anomalies is used to identify the “optimal” conditions for observed Northeast Pacific MHW events that developed two-to-four seasons later. These optimal initial conditions include SSH anomalies that are responsible for most of the MHW growth, suggesting the relevance of subsurface ocean dynamics. Moreover, Northeast Pacific MHW growth occurs as part of a basin-scale dynamical mode that links the North Pacific to central equatorial Pacific El Niño events, whose subsequent development may lengthen MHW duration.
2022-3
Geophys. Res. Lett.
49
e2021GL097350
0
10.1029/2021GL097350
The intensity of Northeast Pacific marine heatwaves (MHWs) has been related to local stochastic atmospheric forcing with limited predictability, but their evolution and persistence may be controlled by large-scale climate influences. A Linear Inverse Model containing both sea surface temperature and sea surface height (SSH) anomalies is used to identify the “optimal” conditions for observed Northeast Pacific MHW events that developed two-to-four seasons later. These optimal initial conditions include SSH anomalies that are responsible for most of the MHW growth, suggesting the relevance of subsurface ocean dynamics. Moreover, Northeast Pacific MHW growth occurs as part of a basin-scale dynamical mode that links the North Pacific to central equatorial Pacific El Niño events, whose subsequent development may lengthen MHW duration.
Capotondi
A.
Newman
M.
Xu
Tongtong
T.
Di Lorenzo
E.
21204
Article
Nocturnal atmospheric conditions and their impact on air pollutant concentrations in the city of Stuttgart
Meteorological and air pollutant measurements were conducted in the area of Stuttgart during winter and summer seasons. Stuttgart is situated in moderate mountainous terrain in southwestern Germany. We focus on the connection between atmospheric conditions and air pollutants in the urban nocturnal boundary layer. This is done by relating the bulk Richardson number (Rib), turbulence intensity, cloudiness, and winds, as well as NOx and O3 data. Turbulence intensity is inversely related to Rib, with the lower values occurring at Rib >0.33. The coefficient of determination for the exponential regression is only moderate, which partly can be attributed to sporadic turbulence in the transition from dynamically unstable to stable flows. Dynamically unstable flows (Rib <0.33) occur frequently in winter, as a result of the presence of low-level clouds and strong winds, supporting low buoyant suppression and strong shear generation of turbulence. Dynamically stable flows (Rib >1.25) are found preferably under clear skies in summer with the build-up of strong surface inversions, so that buoyant suppression is strong and shear generation of turbulence is weak. The nocturnal NOx concentrations are positively correlated with Rib. The correlation is weak, which is mainly related to the large variability of air pollutant concentrations in a range around Rib = 0.33. In this range, many low-level jets are present that can cause sporadic turbulent coupling between the atmosphere and the surface. Reduced mixing under dynamically stable flows causes NOx values about 3 times higher than under dynamically unstable flows. The overall lowest NOx concentrations occur during winter when low clouds and strong winds are present.
2022-11
Meteor. App.
28
e2037
0
10.1002/met.2037
Meteorological and air pollutant measurements were conducted in the area of Stuttgart during winter and summer seasons. Stuttgart is situated in moderate mountainous terrain in southwestern Germany. We focus on the connection between atmospheric conditions and air pollutants in the urban nocturnal boundary layer. This is done by relating the bulk Richardson number (Rib), turbulence intensity, cloudiness, and winds, as well as NOx and O3 data. Turbulence intensity is inversely related to Rib, with the lower values occurring at Rib >0.33. The coefficient of determination for the exponential regression is only moderate, which partly can be attributed to sporadic turbulence in the transition from dynamically unstable to stable flows. Dynamically unstable flows (Rib <0.33) occur frequently in winter, as a result of the presence of low-level clouds and strong winds, supporting low buoyant suppression and strong shear generation of turbulence. Dynamically stable flows (Rib >1.25) are found preferably under clear skies in summer with the build-up of strong surface inversions, so that buoyant suppression is strong and shear generation of turbulence is weak. The nocturnal NOx concentrations are positively correlated with Rib. The correlation is weak, which is mainly related to the large variability of air pollutant concentrations in a range around Rib = 0.33. In this range, many low-level jets are present that can cause sporadic turbulent coupling between the atmosphere and the surface. Reduced mixing under dynamically stable flows causes NOx values about 3 times higher than under dynamically unstable flows. The overall lowest NOx concentrations occur during winter when low clouds and strong winds are present.
Kiseleva
O.
Kalthoff
N.
Adler
B.
Kossmann
M.
Wieser
A.
Rinke
R.
21206
Article
Developing 4D-Var for Strongly Coupled Data Assimilation Using a Coupled Atmosphere–Ocean Quasigeostrophic Model
Four-dimensional variational (4D-Var) data assimilation (DA) is developed for a coupled atmosphere–ocean quasigeostrophic application. Complications arise in coupled data assimilation (CDA) systems due to the presence of multiple spatiotemporal scales. Various formulations of the background error covariance matrix (B), using different localization strategies, are explored to evaluate their impact on 4D-Var performance in a CDA setting. 4D-Var requires access to tangent linear and adjoint models (TLM/AM) to propagate information about the misfit between the forecast and observations within an optimization window. In practice, particularly for coupled models, the TLM and adjoint are often difficult to produce, and for some models are nonexistent in analytic form. Accordingly, a statistical data-driven alternative is also employed and evaluated to determine its feasibility for a 4D-Var CDA system. Using experiments conducted with a coupled atmosphere–ocean quasigeostrophic model, it is found that ensemble generation of flow-dependent error covariance statistics can increase the accuracy of 4D-Var CDA. When observing all variables, the hybrid climatological/flow-dependent B constructions outperform either independently. The use of a hybrid B matrix combined with a rapid updating ensemble transform Kalman filter (RU-ETKF) using either strongly or weakly CDA resulted in lower overall RMSE. The ocean component achieved its lowest RMSE when using a fully flow-dependent B matrix generated using 4D-ETKF and using weakly CDA. These results show the importance of time scales and analysis update frequencies. The use of a statistically derived TLM/AM generated from the ETKF ensemble perturbations produces results similar to cases using the analytical coupled TLM/AM in 4D-Var.
2022-9
Mon. Wea. Rev.
150
2443–2458
0
10.1175/MWR-D-21-0240.1
Four-dimensional variational (4D-Var) data assimilation (DA) is developed for a coupled atmosphere–ocean quasigeostrophic application. Complications arise in coupled data assimilation (CDA) systems due to the presence of multiple spatiotemporal scales. Various formulations of the background error covariance matrix (B), using different localization strategies, are explored to evaluate their impact on 4D-Var performance in a CDA setting. 4D-Var requires access to tangent linear and adjoint models (TLM/AM) to propagate information about the misfit between the forecast and observations within an optimization window. In practice, particularly for coupled models, the TLM and adjoint are often difficult to produce, and for some models are nonexistent in analytic form. Accordingly, a statistical data-driven alternative is also employed and evaluated to determine its feasibility for a 4D-Var CDA system. Using experiments conducted with a coupled atmosphere–ocean quasigeostrophic model, it is found that ensemble generation of flow-dependent error covariance statistics can increase the accuracy of 4D-Var CDA. When observing all variables, the hybrid climatological/flow-dependent B constructions outperform either independently. The use of a hybrid B matrix combined with a rapid updating ensemble transform Kalman filter (RU-ETKF) using either strongly or weakly CDA resulted in lower overall RMSE. The ocean component achieved its lowest RMSE when using a fully flow-dependent B matrix generated using 4D-ETKF and using weakly CDA. These results show the importance of time scales and analysis update frequencies. The use of a statistically derived TLM/AM generated from the ETKF ensemble perturbations produces results similar to cases using the analytical coupled TLM/AM in 4D-Var.
Goodliff
M.
Penny
S. G.
21213
Article
The surface longwave cloud radiative effect derived from space lidar observations
Clouds warm the surface in the longwave (LW), and this warming effect can be quantified through the surface LW cloud radiative effect (CRE). The global surface LW CRE has been estimated over more than 2 decades using space-based radiometers (2000–2021) and over the 5-year period ending in 2011 using the combination of radar, lidar and space-based radiometers. Previous work comparing these two types of retrievals has shown that the radiometer-based cloud amount has some bias over icy surfaces. Here we propose new estimates of the global surface LW CRE from space-based lidar observations over the 2008–2020 time period. We show from 1D atmospheric column radiative transfer calculations that surface LW CRE linearly decreases with increasing cloud altitude. These computations allow us to establish simple parameterizations between surface LW CRE and five cloud properties that are well observed by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) space-based lidar: opaque cloud cover and altitude and thin cloud cover, altitude, and emissivity. We evaluate this new surface LWCRE–LIDAR product by comparing it to existing satellite-derived products globally on instantaneous collocated data at footprint scale and on global averages as well as to ground-based observations at specific locations. This evaluation shows good correlations between this new product and other datasets. Our estimate appears to be an improvement over others as it appropriately captures the annual variability of the surface LW CRE over bright polar surfaces and it provides a dataset more than 13 years long.
2022-7
Atmos. Meas. Tech.
15
3893-3923
0
10.5194/amt-15-3893-2022
Clouds warm the surface in the longwave (LW), and this warming effect can be quantified through the surface LW cloud radiative effect (CRE). The global surface LW CRE has been estimated over more than 2 decades using space-based radiometers (2000–2021) and over the 5-year period ending in 2011 using the combination of radar, lidar and space-based radiometers. Previous work comparing these two types of retrievals has shown that the radiometer-based cloud amount has some bias over icy surfaces. Here we propose new estimates of the global surface LW CRE from space-based lidar observations over the 2008–2020 time period. We show from 1D atmospheric column radiative transfer calculations that surface LW CRE linearly decreases with increasing cloud altitude. These computations allow us to establish simple parameterizations between surface LW CRE and five cloud properties that are well observed by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) space-based lidar: opaque cloud cover and altitude and thin cloud cover, altitude, and emissivity. We evaluate this new surface LWCRE–LIDAR product by comparing it to existing satellite-derived products globally on instantaneous collocated data at footprint scale and on global averages as well as to ground-based observations at specific locations. This evaluation shows good correlations between this new product and other datasets. Our estimate appears to be an improvement over others as it appropriately captures the annual variability of the surface LW CRE over bright polar surfaces and it provides a dataset more than 13 years long.
Arouf
A.
Chepfer
H.
Vaillant de Guelis
T.
Chiriaco
M.
Shupe
M. D.
Guzman
R.
Feofilov
A.
Raberanto
P.
L'Ecuyer
T. S.
Kato
S.
Gallagher
M. R.
21214
Article
The COMBLE campaign: A study of marine boundary-layer clouds in Arctic cold-air outbreaks
One of the most intense air mass transformations on Earth happens when cold air flows from frozen surfaces to much warmer open water in cold-air outbreaks (CAOs), a process captured beautifully in satellite imagery. Despite the ubiquity of the CAO cloud regime over high-latitude oceans, we have a rather poor understanding of its properties, its role in energy and water cycles, and its treatment in weather and climate models. The Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) was conducted to better understand this regime and its representation in models. COMBLE aimed to examine the relations between surface fluxes, boundary layer structure, aerosol, cloud, and precipitation properties, and mesoscale circulations in marine CAOs. Processes affecting these properties largely fall in a range of scales where boundary layer processes, convection, and precipitation are tightly coupled, which makes accurate representation of the CAO cloud regime in numerical weather prediction and global climate models most challenging. COMBLE deployed an Atmospheric Radiation Measurement Mobile Facility at a coastal site in northern Scandinavia (69°N), with additional instruments on Bear Island (75°N), from December 2019 to May 2020. CAO conditions were experienced 19% (21%) of the time at the main site (on Bear Island). A comprehensive suite of continuous in situ and remote sensing observations of atmospheric conditions, clouds, precipitation, and aerosol were collected. Because of the clouds’ well-defined origin, their shallow depth, and the broad range of observed temperature and aerosol concentrations, the COMBLE dataset provides a powerful modeling testbed for improving the representation of mixed-phase cloud processes in large-eddy simulations and large-scale models.
2022-3
Bull. Amer. Meteor. Soc.
103
E1371–E1389
0
10.1175/BAMS-D-21-0044.1
One of the most intense air mass transformations on Earth happens when cold air flows from frozen surfaces to much warmer open water in cold-air outbreaks (CAOs), a process captured beautifully in satellite imagery. Despite the ubiquity of the CAO cloud regime over high-latitude oceans, we have a rather poor understanding of its properties, its role in energy and water cycles, and its treatment in weather and climate models. The Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) was conducted to better understand this regime and its representation in models. COMBLE aimed to examine the relations between surface fluxes, boundary layer structure, aerosol, cloud, and precipitation properties, and mesoscale circulations in marine CAOs. Processes affecting these properties largely fall in a range of scales where boundary layer processes, convection, and precipitation are tightly coupled, which makes accurate representation of the CAO cloud regime in numerical weather prediction and global climate models most challenging. COMBLE deployed an Atmospheric Radiation Measurement Mobile Facility at a coastal site in northern Scandinavia (69°N), with additional instruments on Bear Island (75°N), from December 2019 to May 2020. CAO conditions were experienced 19% (21%) of the time at the main site (on Bear Island). A comprehensive suite of continuous in situ and remote sensing observations of atmospheric conditions, clouds, precipitation, and aerosol were collected. Because of the clouds’ well-defined origin, their shallow depth, and the broad range of observed temperature and aerosol concentrations, the COMBLE dataset provides a powerful modeling testbed for improving the representation of mixed-phase cloud processes in large-eddy simulations and large-scale models.
Geerts
B.
Giangrande
S. E.
McFarquhar
G. M.
Xue
L.
. .
.
Shupe
M. D.
al.
et
21215
Article
Tethered balloon-borne profile measurements of atmospheric properties in cloudy conditions over Arctic sea ice during MOSAiC: First Results
The tethered balloon-borne measurement system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was deployed over the Arctic sea ice for 4 weeks in summer 2020 as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition. Using BELUGA, vertical profiles of dynamic, thermodynamic, aerosol particle, cloud, radiation, and turbulence properties were measured from the ground up to a height of 1,500 m. BELUGA was operated during an anomalously warm period with frequent liquid water clouds and variable sea ice conditions. Three case studies of liquid water phase, single-layer clouds observed on 3 days (July 13, 23, and 24, 2020) are discussed to show the potential of the collected data set to comprehensively investigate cloud properties determining cloud evolution in the inner Arctic over sea ice. Simulated back-trajectories show that the observed clouds have evolved within 3 different air masses (“aged Arctic,” “advected over sea ice,” and “advected over open ocean”), which left distinct fingerprints in the cloud properties. Strong cloud top radiative cooling rates agree with simulated results of previous studies. The weak warming at cloud base is mostly driven by the vertical temperature profile between the surface and cloud base. In-cloud turbulence induced by the cloud top cooling was similar in strength compared to former studies. From the extent of the mixing layer, it is speculated that the overall cloud cooling is stronger and thus faster in the warm oceanic air mass. Larger aerosol particle number concentrations and larger sizes were observed in the air mass advected over the sea ice and in the air mass advected over the open ocean.
2022-9
Elementa Sci. Anthrop.
10
000120
0
10.1525/elementa.2021.000120
The tethered balloon-borne measurement system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was deployed over the Arctic sea ice for 4 weeks in summer 2020 as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition. Using BELUGA, vertical profiles of dynamic, thermodynamic, aerosol particle, cloud, radiation, and turbulence properties were measured from the ground up to a height of 1,500 m. BELUGA was operated during an anomalously warm period with frequent liquid water clouds and variable sea ice conditions. Three case studies of liquid water phase, single-layer clouds observed on 3 days (July 13, 23, and 24, 2020) are discussed to show the potential of the collected data set to comprehensively investigate cloud properties determining cloud evolution in the inner Arctic over sea ice. Simulated back-trajectories show that the observed clouds have evolved within 3 different air masses (“aged Arctic,” “advected over sea ice,” and “advected over open ocean”), which left distinct fingerprints in the cloud properties. Strong cloud top radiative cooling rates agree with simulated results of previous studies. The weak warming at cloud base is mostly driven by the vertical temperature profile between the surface and cloud base. In-cloud turbulence induced by the cloud top cooling was similar in strength compared to former studies. From the extent of the mixing layer, it is speculated that the overall cloud cooling is stronger and thus faster in the warm oceanic air mass. Larger aerosol particle number concentrations and larger sizes were observed in the air mass advected over the sea ice and in the air mass advected over the open ocean.
Lonardi
M.
Pilz
C.
Akansu
U.
Ehrlich
A.
Griesche
H.
Heymsfield
A. J.
Kirbus
B.
Schmitt
C. G.
Shupe
M. D.
Siebert
H.
Wehner
B.
Wendisch
M.
21216
Article
Atmospheric and Surface Processes and Feedback Mechanisms Determining Arctic Amplification: A review of first results and prospects of the (AC)3 Project
2022-9
Bull. Amer. Meteor. Soc.
0
10.1175/BAMS-D-21-0218.1
Wendisch
M.
Bruckner
M.
Crewell
S.
Ehrlich
A.
. .
.
Shupe
M. D.
al.
et
21217
Article
Toward a more realistic representation of surface albedo in NASA CERES-derived surface radiative fluxes: A comparison with the MOSAiC field campaign: Comparison of CERES and MOSAiC surface radiation fluxes
Accurate multidecadal radiative flux records are vital to understand Arctic amplification and constrain climate model uncertainties. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other surface type and comes from several sources. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes was conducted using in situ measurements from MOSAiC flux stations. The CERES Synoptic 1degree (SYN1deg) product overestimates the downwelling shortwave flux by +11.40 Wm–2 and underestimates the upwelling shortwave flux by –15.70 Wm–2 and downwelling longwave fluxes by –12.58 Wm–2 at the surface during summer. In addition, large differences are found in the upwelling longwave flux when the surface approaches the melting point (approximately 0°C). The biases in downwelling shortwave and longwave fluxes suggest that the atmosphere represented in CERES is too optically thin. The large negative bias in upwelling shortwave flux can be attributed in large part to lower surface albedo (–0.15) in satellite footprint relative to surface sensors. Additionally, the results show that the spectral surface albedo used in SYN1deg overestimates albedo in visible and mid-infrared bands. A series of radiative transfer model perturbation experiments are performed to quantify the factors contributing to the differences. The CERES-MOSAiC broadband albedo differences (approximately 20 Wm–2) explain a larger portion of the upwelling shortwave flux difference than the spectral albedo shape differences (approximately 3 Wm–2). In addition, the differences between perturbation experiments using hourly and monthly MOSAiC surface albedo suggest that approximately 25% of the sea ice surface albedo variability is explained by factors not correlated with daily sea ice concentration variability. Biases in net shortwave and longwave flux can be reduced to less than half by adjusting both albedo and cloud inputs toward observed values. The results indicate that improvements in the surface albedo and cloud data would substantially reduce the uncertainty in the Arctic surface radiation budget derived from CERES data products.
2022-6
Elementa Sci. Anthrop.
10
00013
0
10.1525/elementa.2022.00013
Accurate multidecadal radiative flux records are vital to understand Arctic amplification and constrain climate model uncertainties. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other surface type and comes from several sources. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes was conducted using in situ measurements from MOSAiC flux stations. The CERES Synoptic 1degree (SYN1deg) product overestimates the downwelling shortwave flux by +11.40 Wm–2 and underestimates the upwelling shortwave flux by –15.70 Wm–2 and downwelling longwave fluxes by –12.58 Wm–2 at the surface during summer. In addition, large differences are found in the upwelling longwave flux when the surface approaches the melting point (approximately 0°C). The biases in downwelling shortwave and longwave fluxes suggest that the atmosphere represented in CERES is too optically thin. The large negative bias in upwelling shortwave flux can be attributed in large part to lower surface albedo (–0.15) in satellite footprint relative to surface sensors. Additionally, the results show that the spectral surface albedo used in SYN1deg overestimates albedo in visible and mid-infrared bands. A series of radiative transfer model perturbation experiments are performed to quantify the factors contributing to the differences. The CERES-MOSAiC broadband albedo differences (approximately 20 Wm–2) explain a larger portion of the upwelling shortwave flux difference than the spectral albedo shape differences (approximately 3 Wm–2). In addition, the differences between perturbation experiments using hourly and monthly MOSAiC surface albedo suggest that approximately 25% of the sea ice surface albedo variability is explained by factors not correlated with daily sea ice concentration variability. Biases in net shortwave and longwave flux can be reduced to less than half by adjusting both albedo and cloud inputs toward observed values. The results indicate that improvements in the surface albedo and cloud data would substantially reduce the uncertainty in the Arctic surface radiation budget derived from CERES data products.
Huang
Y.
Taylor
C.
Rose
F. G.
Rutan
D. A.
Shupe
M. D.
Webster
M.
21222
Article
Testing the efficacy of atmospheric boundary layer height detection algorithms using uncrewed aircraft system data from MOSAiC
During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, meteorological conditions over the lowest 1 km of the atmosphere were sampled with the DataHawk2 (DH2) fixed-wing uncrewed aircraft system (UAS). These in situ observations of the central Arctic atmosphere are some of the most extensive to date and provide unique insight into the atmospheric boundary layer (ABL) structure. The ABL is an important component of the Arctic climate, as it can be closely coupled to cloud properties, surface fluxes, and the atmospheric radiation budget. The high temporal resolution of the UAS observations allows us to manually identify the ABL height (ZABL) for 65 out of the total 89 flights conducted over the central Arctic Ocean between 23 March and 26 July 2020 by visually analyzing profiles of virtual potential temperature, humidity, and bulk Richardson number. Comparing this subjective ZABL with ZABL identified by various previously published automated objective methods allows us to determine which objective methods are most successful at accurately identifying ZABL in the central Arctic environment and how the success of the methods differs based on stability regime. The objective methods we use are the Liu–Liang, Heffter, virtual potential temperature gradient maximum, and bulk Richardson number methods. In the process of testing these objective methods on the DH2 data, numerical thresholds were adapted to work best for the UAS-based sampling. To determine if conclusions are robust across different measurement platforms, the subjective and objective ZABL determination processes were repeated using the radiosonde profile closest in time to each DH2 flight. For both the DH2 and radiosonde data, it is determined that the bulk Richardson number method is the most successful at identifying ZABL, while the Liu–Liang method is least successful. The results of this study are expected to be beneficial for upcoming observational and modeling efforts regarding the central Arctic ABL.
2022-7
Atmos. Meas. Tech.
15
4001–4022
0
10.5194/amt-15-4001-2022
During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, meteorological conditions over the lowest 1 km of the atmosphere were sampled with the DataHawk2 (DH2) fixed-wing uncrewed aircraft system (UAS). These in situ observations of the central Arctic atmosphere are some of the most extensive to date and provide unique insight into the atmospheric boundary layer (ABL) structure. The ABL is an important component of the Arctic climate, as it can be closely coupled to cloud properties, surface fluxes, and the atmospheric radiation budget. The high temporal resolution of the UAS observations allows us to manually identify the ABL height (ZABL) for 65 out of the total 89 flights conducted over the central Arctic Ocean between 23 March and 26 July 2020 by visually analyzing profiles of virtual potential temperature, humidity, and bulk Richardson number. Comparing this subjective ZABL with ZABL identified by various previously published automated objective methods allows us to determine which objective methods are most successful at accurately identifying ZABL in the central Arctic environment and how the success of the methods differs based on stability regime. The objective methods we use are the Liu–Liang, Heffter, virtual potential temperature gradient maximum, and bulk Richardson number methods. In the process of testing these objective methods on the DH2 data, numerical thresholds were adapted to work best for the UAS-based sampling. To determine if conclusions are robust across different measurement platforms, the subjective and objective ZABL determination processes were repeated using the radiosonde profile closest in time to each DH2 flight. For both the DH2 and radiosonde data, it is determined that the bulk Richardson number method is the most successful at identifying ZABL, while the Liu–Liang method is least successful. The results of this study are expected to be beneficial for upcoming observational and modeling efforts regarding the central Arctic ABL.
Jozef
G.
Cassano
J.
Dahlke
S.
de Boer
G.
21225
Article
Air-sea trace gas fluxes: Direct and indirect measurements
The past decade has seen significant technological advance in the observation of trace gas fluxes over the open ocean, most notably CO2, but also an impressive list of other gases. Here we will emphasize flux observations from the air-side of the interface including both turbulent covariance (direct) and surface-layer similarity-based (indirect) bulk transfer velocity methods. Most applications of direct covariance observations have been from ships but recently work has intensified on buoy-based implementation. The principal use of direct methods is to quantify empirical coefficients in bulk estimates of the gas transfer velocity. Advances in direct measurements and some recent field programs that capture a considerable range of conditions with wind speeds exceeding 20 ms-1 are discussed. We use coincident direct flux measurements of CO2 and dimethylsulfide (DMS) to infer the scaling of interfacial viscous and bubble-mediated (whitecap driven) gas transfer mechanisms. This analysis suggests modest chemical enhancement of CO2 flux at low wind speed. We include some updates to the theoretical structure of bulk parameterizations (including chemical enhancement) as framed in the COAREG gas transfer algorithm.
2022-7
Front. Mar. Sci.
8
826606
0
10.3389/fmars.2022.826606
The past decade has seen significant technological advance in the observation of trace gas fluxes over the open ocean, most notably CO2, but also an impressive list of other gases. Here we will emphasize flux observations from the air-side of the interface including both turbulent covariance (direct) and surface-layer similarity-based (indirect) bulk transfer velocity methods. Most applications of direct covariance observations have been from ships but recently work has intensified on buoy-based implementation. The principal use of direct methods is to quantify empirical coefficients in bulk estimates of the gas transfer velocity. Advances in direct measurements and some recent field programs that capture a considerable range of conditions with wind speeds exceeding 20 ms-1 are discussed. We use coincident direct flux measurements of CO2 and dimethylsulfide (DMS) to infer the scaling of interfacial viscous and bubble-mediated (whitecap driven) gas transfer mechanisms. This analysis suggests modest chemical enhancement of CO2 flux at low wind speed. We include some updates to the theoretical structure of bulk parameterizations (including chemical enhancement) as framed in the COAREG gas transfer algorithm.
Fairall
C. W.
Yang
M.
Brumer
S. E.
Blomquist
B. W.
Edson
J. B.
Zappa
C. J.
Bariteau
L.
Pezoa
S.
Bell
T.
Saltzmann
E.
21226
Article
Attribution of observed recent decrease in low clouds over the northeastern Pacific to cloud-controlling factors
Marine low clouds cool the Earth's climate, with their coverage (LCC) being controlled by their environment. Here, an observed significant decrease of LCC in the northeastern Pacific over the past two decades is linked quantitatively to changes in cloud-controlling factors. In a comparison of different statistical and machine learning methods, a decrease in the inversion strength and near-surface winds, and an increase in sea surface temperatures (SSTs) are unanimously shown to be the main causes of the LCC decrease. While the decreased inversion strength leads to more entrainment of dry free-tropospheric air, the increasing SSTs are shown to lead to an increased vertical moisture gradient that enhances evaporation when entrainment takes place. While the LCC trend is likely driven by natural variability, the trend-attribution framework developed here can be used with any method in future analyses. We find the choice of predictors is more important than the method.
2022-2
Geophys. Res. Lett.
49
e2021GL096498
0
10.1029/2021GL096498
Marine low clouds cool the Earth's climate, with their coverage (LCC) being controlled by their environment. Here, an observed significant decrease of LCC in the northeastern Pacific over the past two decades is linked quantitatively to changes in cloud-controlling factors. In a comparison of different statistical and machine learning methods, a decrease in the inversion strength and near-surface winds, and an increase in sea surface temperatures (SSTs) are unanimously shown to be the main causes of the LCC decrease. While the decreased inversion strength leads to more entrainment of dry free-tropospheric air, the increasing SSTs are shown to lead to an increased vertical moisture gradient that enhances evaporation when entrainment takes place. While the LCC trend is likely driven by natural variability, the trend-attribution framework developed here can be used with any method in future analyses. We find the choice of predictors is more important than the method.
Andersen
H.
Cermak
J.
Zipfel
L.
Myers
T. A.
21227
Article
Extratropical shortwave cloud feedbacks in the context of the global circulation and hydrological cycle
2022-4
Geophys. Res. Lett.
49
e2021GL097154
0
10.1029/2021GL097154
McCoy
D. T.
Field
P.
Frazer
M. E.
Zelinka
M. D.
Elsaesser
G. S.
Mülmenstädt
J.
Tan
I.
Myers
T. A.
Lebo
Z. J.
21228
Article
Forcing for multidecadal surface solar radiation trends over Northern Hemisphere continents
The long-term variation of North Pacific and North Atlantic sea surface temperatures (SSTs) is shown to be associated with multidecadal trends of surface solar radiation in North America, Europe, and Asia. Long-term, large-scale warm SST anomalies lead to a mid-level planetary wave anomaly pattern of geopotential height ridges over the warm water and dynamically-induced lower heights on either side, sometimes extending over adjacent continents. Geopotential height troughs over the continents encourage more cloud cover and dimming of surface solar radiation. Conversely, cool SST anomalies correspond to a pattern of lower mid-level geopotential heights over the cool water and compensating high pressure on either side that encourages decreasing cloud cover and brightening over the continents, if the wave positioning is favorable. Additionally, these effects are observed to be latitude dependent, showing stronger SST-geopotential height associations in the northern half of the Northern Hemisphere. The change from continental dimming to brightening and the reversal of North Pacific SST trends are nearly simultaneous. A similar SST-geopotential height association is seen in the North Atlantic and leads to brightening and dimming in Europe and North America, but there is a ∼12-year lag between the transition of dimming to brightening and SST reversals there, which is yet unexplained. The next step is to support these connections and their latitudinal dependence with carefully designed numerical experiments that consider variable greenhouse gas and aerosol concentrations.
2022-8
J. Geophys. Res. Atmos.
127
e2021JD036342
0
10.1029/2021JD036342
The long-term variation of North Pacific and North Atlantic sea surface temperatures (SSTs) is shown to be associated with multidecadal trends of surface solar radiation in North America, Europe, and Asia. Long-term, large-scale warm SST anomalies lead to a mid-level planetary wave anomaly pattern of geopotential height ridges over the warm water and dynamically-induced lower heights on either side, sometimes extending over adjacent continents. Geopotential height troughs over the continents encourage more cloud cover and dimming of surface solar radiation. Conversely, cool SST anomalies correspond to a pattern of lower mid-level geopotential heights over the cool water and compensating high pressure on either side that encourages decreasing cloud cover and brightening over the continents, if the wave positioning is favorable. Additionally, these effects are observed to be latitude dependent, showing stronger SST-geopotential height associations in the northern half of the Northern Hemisphere. The change from continental dimming to brightening and the reversal of North Pacific SST trends are nearly simultaneous. A similar SST-geopotential height association is seen in the North Atlantic and leads to brightening and dimming in Europe and North America, but there is a ∼12-year lag between the transition of dimming to brightening and SST reversals there, which is yet unexplained. The next step is to support these connections and their latitudinal dependence with carefully designed numerical experiments that consider variable greenhouse gas and aerosol concentrations.
Augustine
J. A.
Capotondi
A.
21229
Article
Unraveling Forest Complexity: Resource Use Efficiency, Disturbance, and the Structure-Function Relationship
Structurally complex forests optimize resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)-derived canopy structural complexity (CSC) metrics across spatial scales serves as a powerful indicator of ecosystem-scale functions such as gross primary productivity (GPP). However, our understanding of mechanistic links between forest structure and function, and the impact of disturbance on the relationship, is limited. Here, we paired eddy covariance measurements of carbon and water fluxes from nine forested sites within the 10 × 10 km CHEESEHEAD19 study domain in Northern Wisconsin, USA with drone LiDAR measurements of CSC to establish which CSC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at five resolutions (0.25, 2, 10, 25, and 50 m) to determine whether relationships persisted with scale. Vertical heterogeneity metrics were the most influential in predicting productivity for forests with a significant degree of heterogeneity in management, forest type, and species composition. CSC metrics included in the structure-function relationship as well as driver strength was dependent on metric calculation resolution. The relationship was mediated by light use efficiency (LUE) and water use efficiency (WUE), with WUE being a stronger mediator and driver of GPP. These findings allow us to improve representation in ecosystem models of how CSC impacts light and water-sensitive processes, and ultimately GPP. Improved models enhance our capacity to accurately simulate forest responses to management, furthering our ability to assess climate mitigation strategies.
2022-6
J. Geophys. Res. Biogeosci.
127
e2021JG006748
0
10.1029/2021JG006748
Structurally complex forests optimize resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)-derived canopy structural complexity (CSC) metrics across spatial scales serves as a powerful indicator of ecosystem-scale functions such as gross primary productivity (GPP). However, our understanding of mechanistic links between forest structure and function, and the impact of disturbance on the relationship, is limited. Here, we paired eddy covariance measurements of carbon and water fluxes from nine forested sites within the 10 × 10 km CHEESEHEAD19 study domain in Northern Wisconsin, USA with drone LiDAR measurements of CSC to establish which CSC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at five resolutions (0.25, 2, 10, 25, and 50 m) to determine whether relationships persisted with scale. Vertical heterogeneity metrics were the most influential in predicting productivity for forests with a significant degree of heterogeneity in management, forest type, and species composition. CSC metrics included in the structure-function relationship as well as driver strength was dependent on metric calculation resolution. The relationship was mediated by light use efficiency (LUE) and water use efficiency (WUE), with WUE being a stronger mediator and driver of GPP. These findings allow us to improve representation in ecosystem models of how CSC impacts light and water-sensitive processes, and ultimately GPP. Improved models enhance our capacity to accurately simulate forest responses to management, furthering our ability to assess climate mitigation strategies.
Murphy
B. A.
May
J. A.
Butterworth
B. J.
Andresen
C. F.
Desai
A. R.
21231
Article
Sea ice concentration impacts dissolved organic gases in the Canadian Arctic
The marginal sea ice zone has been identified as a source of different climate-active gases to the atmosphere due to its unique biogeochemistry. However, it remains highly undersampled, and the impact of summertime changes in sea ice concentration on the distributions of these gases is poorly understood. To address this, we present measurements of dissolved methanol, acetone, acetaldehyde, dimethyl sulfide, and isoprene in the sea ice zone of the Canadian Arctic from the surface down to 60 m. The measurements were made using a segmented flow coil equilibrator coupled to a proton-transfer-reaction mass spectrometer. These gases varied in concentrations with depth, with the highest concentrations generally observed near the surface. Underway (3–4 m) measurements showed higher concentrations in partial sea ice cover compared to ice-free waters for most compounds. The large number of depth profiles at different sea ice concentrations enables the proposition of the likely dominant production processes of these compounds in this area. Methanol concentrations appear to be controlled by specific biological consumption processes. Acetone and acetaldehyde concentrations are influenced by the penetration depth of light and stratification, implying dominant photochemical sources in this area. Dimethyl sulfide and isoprene both display higher surface concentrations in partial sea ice cover compared to ice-free waters due to ice edge blooms. Differences in underway concentrations based on sampling region suggest that water masses moving away from the ice edge influences dissolved gas concentrations. Dimethyl sulfide concentrations sometimes display a subsurface maximum in ice -free conditions, while isoprene more reliably displays a subsurface maximum. Surface gas concentrations were used to estimate their air–sea fluxes. Despite obvious in situ production, we estimate that the sea ice zone is absorbing methanol and acetone from the atmosphere. In contrast, dimethyl sulfide and isoprene are consistently emitted from the ocean, with marked episodes of high emissions during ice-free conditions, suggesting that these gases are produced in ice-covered areas and emitted once the ice has melted. Our measurements show that the seawater concentrations and air–sea fluxes of these gases are clearly impacted by sea ice concentration. These novel measurements and insights will allow us to better constrain the cycling of these gases in the polar regions and their effect on the oxidative capacity and aerosol budget in the Arctic atmosphere.
2022-2
Biogeosci.
19
1021-1045
0
10.5194/bg-19-1021-2022
The marginal sea ice zone has been identified as a source of different climate-active gases to the atmosphere due to its unique biogeochemistry. However, it remains highly undersampled, and the impact of summertime changes in sea ice concentration on the distributions of these gases is poorly understood. To address this, we present measurements of dissolved methanol, acetone, acetaldehyde, dimethyl sulfide, and isoprene in the sea ice zone of the Canadian Arctic from the surface down to 60 m. The measurements were made using a segmented flow coil equilibrator coupled to a proton-transfer-reaction mass spectrometer. These gases varied in concentrations with depth, with the highest concentrations generally observed near the surface. Underway (3–4 m) measurements showed higher concentrations in partial sea ice cover compared to ice-free waters for most compounds. The large number of depth profiles at different sea ice concentrations enables the proposition of the likely dominant production processes of these compounds in this area. Methanol concentrations appear to be controlled by specific biological consumption processes. Acetone and acetaldehyde concentrations are influenced by the penetration depth of light and stratification, implying dominant photochemical sources in this area. Dimethyl sulfide and isoprene both display higher surface concentrations in partial sea ice cover compared to ice-free waters due to ice edge blooms. Differences in underway concentrations based on sampling region suggest that water masses moving away from the ice edge influences dissolved gas concentrations. Dimethyl sulfide concentrations sometimes display a subsurface maximum in ice -free conditions, while isoprene more reliably displays a subsurface maximum. Surface gas concentrations were used to estimate their air–sea fluxes. Despite obvious in situ production, we estimate that the sea ice zone is absorbing methanol and acetone from the atmosphere. In contrast, dimethyl sulfide and isoprene are consistently emitted from the ocean, with marked episodes of high emissions during ice-free conditions, suggesting that these gases are produced in ice-covered areas and emitted once the ice has melted. Our measurements show that the seawater concentrations and air–sea fluxes of these gases are clearly impacted by sea ice concentration. These novel measurements and insights will allow us to better constrain the cycling of these gases in the polar regions and their effect on the oxidative capacity and aerosol budget in the Arctic atmosphere.
Wohl
C.
Jones
A. E.
Sturges
W. T.
Nightingale
P. D.
Else
B.
Butterworth
B. J.
Yang
M.
21232
Article
Preparing for Long-Term Drought and Aridification
2022-3
Bull. Amer. Meteor. Soc.
103
E821–E827
0
10.1175/BAMS-D-21-0321.1
Lisonbee
J.
Ossowski
E.
Muth
M.
Deheza
V.
Sheffield
A.
21233
Article
The Critical Role of Euro-Atlantic Blocking in Promoting Snowfall in Central Greenland
The Greenland Ice Sheet (GrIS) is losing mass at an increasing rate yet mass gain from snowfall still exceeds the loss attributed to surface melt processes on an annual basis. This work assesses the relationship between persistent atmospheric blocking across the Euro-Atlantic region and enhanced precipitation processes over the central GrIS during June–August and September–November. Results show that the vast majority of snowfall events in the central GrIS coincide with Euro-Atlantic blocking. During June–August, snowfall events are produced primarily by mixed-phase clouds (88%) and are linked to a persistent blocking anticyclone over southern Greenland (84%). The blocking anticyclone slowly advects warm, moist air masses into western and southern Greenland, with positive temperature and water vapor anomalies that intensify over the central GrIS. A zonal integrated water vapor transport pattern south of Greenland indicates a southern shift of the North Atlantic storm track associated with the high-latitude blocking. In contrast, snowfall events during September–November are largely produced by ice-phase clouds (85%) and are associated with a blocking anticyclone over the Nordic Seas and blocked flow over northern Europe (78%). The blocking anticyclone deflects the westerly North Atlantic storm track poleward and enables the rapid transport of warm, moist air masses up the steep southeastern edge of the GrIS, with positive temperature and water vapor anomalies to the east and southeast of Greenland. These results emphasize the critical role of Euro-Atlantic blocking in promoting snowfall processes over the central GrIS and the importance of accurate representation of blocking in climate model projections.
2022-3
J. Geophys. Res. Atmos.
127
e2021JD035776
0
10.1029/2021JD035776
The Greenland Ice Sheet (GrIS) is losing mass at an increasing rate yet mass gain from snowfall still exceeds the loss attributed to surface melt processes on an annual basis. This work assesses the relationship between persistent atmospheric blocking across the Euro-Atlantic region and enhanced precipitation processes over the central GrIS during June–August and September–November. Results show that the vast majority of snowfall events in the central GrIS coincide with Euro-Atlantic blocking. During June–August, snowfall events are produced primarily by mixed-phase clouds (88%) and are linked to a persistent blocking anticyclone over southern Greenland (84%). The blocking anticyclone slowly advects warm, moist air masses into western and southern Greenland, with positive temperature and water vapor anomalies that intensify over the central GrIS. A zonal integrated water vapor transport pattern south of Greenland indicates a southern shift of the North Atlantic storm track associated with the high-latitude blocking. In contrast, snowfall events during September–November are largely produced by ice-phase clouds (85%) and are associated with a blocking anticyclone over the Nordic Seas and blocked flow over northern Europe (78%). The blocking anticyclone deflects the westerly North Atlantic storm track poleward and enables the rapid transport of warm, moist air masses up the steep southeastern edge of the GrIS, with positive temperature and water vapor anomalies to the east and southeast of Greenland. These results emphasize the critical role of Euro-Atlantic blocking in promoting snowfall processes over the central GrIS and the importance of accurate representation of blocking in climate model projections.
Pettersen
C.
Henderson
S. A.
Mattingly
K. S.
Bennartz
R.
Breeden
M. L.
21234
Article
Destructive Interference of ENSO on North Pacific SST and North American Precipitation Associated with Aleutian Low Variability
Identifying the origins of wintertime climate variations in the Northern Hemisphere requires careful attribution of the role of El Niño–Southern Oscillation (ENSO). For example, Aleutian low variability arises from internal atmospheric dynamics and is remotely forced mainly via ENSO. How ENSO modifies the local sea surface temperature (SST) and North American precipitation responses to Aleutian low variability remains unclear, as teasing out the ENSO signal is difficult. This study utilizes carefully designed coupled model experiments to address this issue. In the absence of ENSO, a deeper Aleutian low drives a positive Pacific decadal oscillation (PDO)-like SST response. However, unlike the observed PDO pattern, a coherent zonal band of turbulent heat flux–driven warm SST anomalies develops throughout the subtropical North Pacific. Furthermore, non-ENSO Aleutian low variability is associated with a large-scale atmospheric circulation pattern confined over the North Pacific and North America and dry precipitation anomalies across the southeastern United States. When ENSO is included in the forcing of Aleutian low variability in the experiments, the ENSO teleconnection modulates the turbulent heat fluxes and damps the subtropical SST anomalies induced by non-ENSO Aleutian low variability. Inclusion of ENSO forcing results in wet precipitation anomalies across the southeastern United States, unlike when the Aleutian low is driven by non-ENSO sources. Hence, we find that the ENSO teleconnection acts to destructively interfere with the subtropical North Pacific SST and southeastern United States precipitation signals associated with non-ENSO Aleutian low variability.
2022-6
J. Climate
35
3567–3585
0
10.1175/JCLI-D-21-0560.1
Identifying the origins of wintertime climate variations in the Northern Hemisphere requires careful attribution of the role of El Niño–Southern Oscillation (ENSO). For example, Aleutian low variability arises from internal atmospheric dynamics and is remotely forced mainly via ENSO. How ENSO modifies the local sea surface temperature (SST) and North American precipitation responses to Aleutian low variability remains unclear, as teasing out the ENSO signal is difficult. This study utilizes carefully designed coupled model experiments to address this issue. In the absence of ENSO, a deeper Aleutian low drives a positive Pacific decadal oscillation (PDO)-like SST response. However, unlike the observed PDO pattern, a coherent zonal band of turbulent heat flux–driven warm SST anomalies develops throughout the subtropical North Pacific. Furthermore, non-ENSO Aleutian low variability is associated with a large-scale atmospheric circulation pattern confined over the North Pacific and North America and dry precipitation anomalies across the southeastern United States. When ENSO is included in the forcing of Aleutian low variability in the experiments, the ENSO teleconnection modulates the turbulent heat fluxes and damps the subtropical SST anomalies induced by non-ENSO Aleutian low variability. Inclusion of ENSO forcing results in wet precipitation anomalies across the southeastern United States, unlike when the Aleutian low is driven by non-ENSO sources. Hence, we find that the ENSO teleconnection acts to destructively interfere with the subtropical North Pacific SST and southeastern United States precipitation signals associated with non-ENSO Aleutian low variability.
Larson
S. M.
Okumura
Y.
Bellomo
K.
Breeden
M. L.
21235
Article
Warming in the land of the midnight sun: breeding birds may suffer greater heat stress at high- versus low-Arctic sites
Rising global temperatures are expected to increase reproductive costs for wildlife as greater thermoregulatory demands interfere with reproductive activities. However, predicting the temperatures at which reproductive performance is negatively impacted remains a significant hurdle. Using a thermoregulatory polygon approach, we derived a reproductive threshold temperature for an Arctic songbird—the snow bunting (Plectrophenax nivalis). We defined this threshold as the temperature at which individuals must reduce activity to suboptimal levels (i.e. less than four-time basal metabolic rate) to sustain nestling provisioning and avoid overheating. We then compared this threshold to operative temperatures recorded at high (82° N) and low (64° N) Arctic sites to estimate how heat constraints translate into site-specific impacts on sustained activity level. We predict buntings would become behaviourally constrained at operative temperatures above 11.7°C, whereupon they must reduce provisioning rates to avoid overheating. Low-Arctic sites had larger fluctuations in solar radiation, consistently producing daily periods when operative temperatures exceeded 11.7°C. However, high-latitude birds faced entire, consecutive days when parents would be unable to sustain required provisioning rates. These data indicate that Arctic warming is probably already disrupting the breeding performance of cold-specialist birds and suggests counterintuitive and severe negative impacts of warming at higher latitude breeding locations.
2022-8
Proc. Royal Soc. B
289
20220300
0
10.1098/rspb.2022.0300
Rising global temperatures are expected to increase reproductive costs for wildlife as greater thermoregulatory demands interfere with reproductive activities. However, predicting the temperatures at which reproductive performance is negatively impacted remains a significant hurdle. Using a thermoregulatory polygon approach, we derived a reproductive threshold temperature for an Arctic songbird—the snow bunting (Plectrophenax nivalis). We defined this threshold as the temperature at which individuals must reduce activity to suboptimal levels (i.e. less than four-time basal metabolic rate) to sustain nestling provisioning and avoid overheating. We then compared this threshold to operative temperatures recorded at high (82° N) and low (64° N) Arctic sites to estimate how heat constraints translate into site-specific impacts on sustained activity level. We predict buntings would become behaviourally constrained at operative temperatures above 11.7°C, whereupon they must reduce provisioning rates to avoid overheating. Low-Arctic sites had larger fluctuations in solar radiation, consistently producing daily periods when operative temperatures exceeded 11.7°C. However, high-latitude birds faced entire, consecutive days when parents would be unable to sustain required provisioning rates. These data indicate that Arctic warming is probably already disrupting the breeding performance of cold-specialist birds and suggests counterintuitive and severe negative impacts of warming at higher latitude breeding locations.
O'Connor
R.
Le Pogam
A.
Young
K. G.
Love
O. P.
Cox
C. J.
Roy
G.
Robitaille
F.
Elliott
K. H.
Hargreaves
A. L.
Choy
E. S.
Gilchrist
H. G.
Berteaux
D.
Tam
A.
Vezina
F.
21237
Article
The intricacies of identifying equatorial waves
Equatorial waves (EWs) are synoptic- to planetary-scale propagating disturbances at low latitudes with periods from a few days to several weeks. Here, this term includes Kelvin waves, equatorial Rossby waves, mixed Rossby–gravity waves, and inertio-gravity waves, which are well described by linear wave theory, but it also other tropical disturbances such as easterly waves and the intraseasonal Madden–Julian Oscillation with more complex dynamics. EWs can couple with deep convection, leading to a substantial modulation of clouds and rainfall. EWs are amongst the dynamic features of the troposphere with the longest intrinsic predictability, and models are beginning to forecast them with an exploitable level of skill. Most of the methods developed to identify and objectively isolate EWs in observations and model fields rely on (or at least refer to) the adiabatic, frictionless linearized primitive equations on the sphere or the shallow-water system on the equatorial -plane. Common ingredients to these methods are zonal wave-number–frequency filtering (Fourier or wavelet) and/or projections onto predefined empirical or theoretical dynamical patterns. This paper gives an overview of six different methods to isolate EWs and their structures, discusses the underlying assumptions, evaluates the applicability to different problems, and provides a systematic comparison based on a case study (February 20–May 20, 2009) and a climatological analysis (2001–2018). In addition, the influence of different input fields (e.g., winds, geopotential, outgoing long-wave radiation, rainfall) is investigated. Based on the results, we generally recommend employing a combination of wave-number–frequency filtering and spatial-projection methods (and of different input fields) to check for robustness of the identified signal. In cases of disagreement, one needs to carefully investigate which assumptions made for the individual methods are most probably not fulfilled. This will help in choosing an approach optimally suited to a given problem at hand and avoid misinterpretation of the results.
2022-7
Q. J. R. Meteorol. Soc.
148
2814-2852
0
10.1002/qj.4338
Equatorial waves (EWs) are synoptic- to planetary-scale propagating disturbances at low latitudes with periods from a few days to several weeks. Here, this term includes Kelvin waves, equatorial Rossby waves, mixed Rossby–gravity waves, and inertio-gravity waves, which are well described by linear wave theory, but it also other tropical disturbances such as easterly waves and the intraseasonal Madden–Julian Oscillation with more complex dynamics. EWs can couple with deep convection, leading to a substantial modulation of clouds and rainfall. EWs are amongst the dynamic features of the troposphere with the longest intrinsic predictability, and models are beginning to forecast them with an exploitable level of skill. Most of the methods developed to identify and objectively isolate EWs in observations and model fields rely on (or at least refer to) the adiabatic, frictionless linearized primitive equations on the sphere or the shallow-water system on the equatorial -plane. Common ingredients to these methods are zonal wave-number–frequency filtering (Fourier or wavelet) and/or projections onto predefined empirical or theoretical dynamical patterns. This paper gives an overview of six different methods to isolate EWs and their structures, discusses the underlying assumptions, evaluates the applicability to different problems, and provides a systematic comparison based on a case study (February 20–May 20, 2009) and a climatological analysis (2001–2018). In addition, the influence of different input fields (e.g., winds, geopotential, outgoing long-wave radiation, rainfall) is investigated. Based on the results, we generally recommend employing a combination of wave-number–frequency filtering and spatial-projection methods (and of different input fields) to check for robustness of the identified signal. In cases of disagreement, one needs to carefully investigate which assumptions made for the individual methods are most probably not fulfilled. This will help in choosing an approach optimally suited to a given problem at hand and avoid misinterpretation of the results.
Knippertz
P.
Gehne
M.
Kiladis
G. N.
Kikuchi
K.
Satheesh
A. R.
Roundy
P. E.
Yang
G.-Y.
Dias
J.
Fink
A. H.
Methven
J.
Schlueter
A.
Sielmann
F.
Wheeler
M. C.
21238
Article
Tropical Thermodynamic–Convection Coupling in Observations and Reanalyses
This study examines thermodynamic-convection coupling in observations and re-analyses, and attempts to establish process level benchmarks needed to guide model development. Thermodynamic profiles obtained from the NOAA Integrated Global Radiosonde Archive, COSMIC-1 GPS radio occultations, and several reanalyses are examined alongside Tropical Rain-fall Measuring Mission precipitation estimates. Cyclical increases and decreases in a bulk measure of lower tropospheric convective instability are shown to be coupled to the cyclical amplification and decay of convection. This cyclical flow emerges from conditional-mean analysis in a thermodynamic space comprised of two components: a measure of “undiluted” instability which neglects lower free tropospheric (LFT) entrainment, and a measure of the reduction of instability by LFT entrainment. The observational and reanalysis products examined share the following qualitatively robust characterization of these convective cycles: increases in undiluted instability tend to occur when the LFT is less saturated, are followed by increases in LFT saturation and precipitation rate, which are then followed by decreases in undiluted instability. Shallow, convective and stratiform precipitation are coupled to these cycles in a manner consistent with meteorological expectations. In situ and satellite observations differ systematically from reanalyses in their depictions of lower tropospheric temperature and moisture variations throughout these convective cycles. When using reanalysis thermodynamic fields, these systematic differences cause variations in lower free tropospheric saturation deficit to appear less influential in determining the strength of convection than is suggested by observations. Disagreements amongst reanalyses, as well as between reanalyses and observations, pose significant challenges to process level assessments of thermodynamic-convection coupling.
2022-7
J. Atmos. Sci.
79
1781–1803
0
10.1175/JAS-D-21-0256.1
This study examines thermodynamic-convection coupling in observations and re-analyses, and attempts to establish process level benchmarks needed to guide model development. Thermodynamic profiles obtained from the NOAA Integrated Global Radiosonde Archive, COSMIC-1 GPS radio occultations, and several reanalyses are examined alongside Tropical Rain-fall Measuring Mission precipitation estimates. Cyclical increases and decreases in a bulk measure of lower tropospheric convective instability are shown to be coupled to the cyclical amplification and decay of convection. This cyclical flow emerges from conditional-mean analysis in a thermodynamic space comprised of two components: a measure of “undiluted” instability which neglects lower free tropospheric (LFT) entrainment, and a measure of the reduction of instability by LFT entrainment. The observational and reanalysis products examined share the following qualitatively robust characterization of these convective cycles: increases in undiluted instability tend to occur when the LFT is less saturated, are followed by increases in LFT saturation and precipitation rate, which are then followed by decreases in undiluted instability. Shallow, convective and stratiform precipitation are coupled to these cycles in a manner consistent with meteorological expectations. In situ and satellite observations differ systematically from reanalyses in their depictions of lower tropospheric temperature and moisture variations throughout these convective cycles. When using reanalysis thermodynamic fields, these systematic differences cause variations in lower free tropospheric saturation deficit to appear less influential in determining the strength of convection than is suggested by observations. Disagreements amongst reanalyses, as well as between reanalyses and observations, pose significant challenges to process level assessments of thermodynamic-convection coupling.
Wolding
B.
Powell
S. W.
Ahmed
F.
Dias
J.
Gehne
M.
Kiladis
G. N.
Neelin
J. D.
21239
Article
The Kalman Filter as Post-Processor for Analog Data-Model Assimilation in Paleoclimate Reconstruction
2022-9
J. Climate
35
5501–5518
0
10.1175/JCLI-D-21-0454.1
Wahl
E.
Zorita
E.
Hoell
A.
21240
Article
Soil Moisture Profile Retrievals Using Reflection of Multifrequency Electromagnetic Signals
A method for retrieving soil moisture (SM) profiles using multifrequency, ground-reflected electromagnetic (EM) waves is proposed. In a previous publication, a retrieval technique was developed, which used variations in the angular dependence of the reflectivity to infer the SM profile. In this follow-on work a more practical approach based on the frequency dependence of the modulus of the reflection coefficient for a single incidence angle is extended and its feasibility demonstrated. Two possible approaches are considered: the direct retrieval of the SM profile and the retrieval of the dielectric constant (DC) profile. The former immediately yields the parameter of interest, however, it requires a soil dielectric model linking the DC of the soil to its water content. Such a model, which depends on the type of soil, may not be immediately available. The latter does not require a linking model, but by comparing measured profiles of the DC under dry and wet conditions, the SM profile can be estimated. Both approaches are considered in this article and their feasibility investigated with the help of numerical simulations in the presence of multiplicative noise in the data. For the case of a direct retrieval of the SM profile, a representative soil dielectric model was used. The retrieval procedure was simplified and made more robust and the frequency independence assumption of the DC in the earlier work was removed.
2022-9
IEEE Trans. Geosci. Remote Sens.
60
2006510
0
10.1109/TGRS.2022.3204522
A method for retrieving soil moisture (SM) profiles using multifrequency, ground-reflected electromagnetic (EM) waves is proposed. In a previous publication, a retrieval technique was developed, which used variations in the angular dependence of the reflectivity to infer the SM profile. In this follow-on work a more practical approach based on the frequency dependence of the modulus of the reflection coefficient for a single incidence angle is extended and its feasibility demonstrated. Two possible approaches are considered: the direct retrieval of the SM profile and the retrieval of the dielectric constant (DC) profile. The former immediately yields the parameter of interest, however, it requires a soil dielectric model linking the DC of the soil to its water content. Such a model, which depends on the type of soil, may not be immediately available. The latter does not require a linking model, but by comparing measured profiles of the DC under dry and wet conditions, the SM profile can be estimated. Both approaches are considered in this article and their feasibility investigated with the help of numerical simulations in the presence of multiplicative noise in the data. For the case of a direct retrieval of the SM profile, a representative soil dielectric model was used. The retrieval procedure was simplified and made more robust and the frequency independence assumption of the DC in the earlier work was removed.
Voronovich
A. G.
Lataitis
R.
21241
Article
Ocean Reference Stations: Long-Term, Open-Ocean Observations of Surface Meteorology and Air–Sea Fluxes Are Essential Benchmarks
There is great interest in improving our understanding of the respective roles of the ocean and atmosphere in variability and change in weather and climate. Due to the sparsity of sustained observing sites in the open ocean, information about the air–sea exchanges of heat, freshwater, and momentum is often drawn from models. In this paper observations from three long-term surface moorings deployed in the trade wind regions of the Pacific and Atlantic Oceans are used to compare observed means and low-passed air–sea fluxes from the moorings with coincident records from three atmospheric reanalyses (ERA5, NCEP-2, and MERRA-2) and from CMIP6 coupled models. To set the stage for the comparison, the methodologies of maintaining the long-term surface moorings, known as ocean reference stations (ORS), and assessing the accuracies of their air–sea fluxes are described first. Biases in the reanalyses’ means and low-passed wind stresses and net air–sea heat fluxes are significantly larger than the observational uncertainties and in some case show variability in time. These reanalyses and most CMIP6 models fail to provide as much heat into the ocean as observed. In the discussion and conclusions section, long-term observing sites in the open ocean are seen as essential, independent benchmarks not only to document the coupling between the atmosphere and ocean but also to promote collaborative efforts to assess and improve the ability of models to simulate air–sea fluxes.
2022-8
Bull. Amer. Meteor. Soc.
103
E1968–E1990
0
10.1175/BAMS-D-21-0084.1
There is great interest in improving our understanding of the respective roles of the ocean and atmosphere in variability and change in weather and climate. Due to the sparsity of sustained observing sites in the open ocean, information about the air–sea exchanges of heat, freshwater, and momentum is often drawn from models. In this paper observations from three long-term surface moorings deployed in the trade wind regions of the Pacific and Atlantic Oceans are used to compare observed means and low-passed air–sea fluxes from the moorings with coincident records from three atmospheric reanalyses (ERA5, NCEP-2, and MERRA-2) and from CMIP6 coupled models. To set the stage for the comparison, the methodologies of maintaining the long-term surface moorings, known as ocean reference stations (ORS), and assessing the accuracies of their air–sea fluxes are described first. Biases in the reanalyses’ means and low-passed wind stresses and net air–sea heat fluxes are significantly larger than the observational uncertainties and in some case show variability in time. These reanalyses and most CMIP6 models fail to provide as much heat into the ocean as observed. In the discussion and conclusions section, long-term observing sites in the open ocean are seen as essential, independent benchmarks not only to document the coupling between the atmosphere and ocean but also to promote collaborative efforts to assess and improve the ability of models to simulate air–sea fluxes.
Weller
R. A.
Lukas
R.
Potemra
J.
Plueddemann
A. J.
Fairall
C. W.
Bigorre
S.
21242
Article
Demistify: a large-eddy simulation (LES) and single-column model (SCM) intercomparison of radiation fog
An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the Local and Non-local Fog Experiment (LANFEX) field campaign. Seven of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst three are research-grade SCMs designed for fog simulation, and the LESs are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number concentration (CDNC) conditions. The main SCM bias appears to be toward the overdevelopment of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parameterisation, as it is to the underlying aerosol or CDNC.
2022-1
Atmos. Chem. Phys.
22
319-333
0
10.5194/acp-22-319-2022
An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the Local and Non-local Fog Experiment (LANFEX) field campaign. Seven of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst three are research-grade SCMs designed for fog simulation, and the LESs are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number concentration (CDNC) conditions. The main SCM bias appears to be toward the overdevelopment of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parameterisation, as it is to the underlying aerosol or CDNC.
Boutle
I.
Angevine
W.
Bao
J.-W.
Bergot
T.
Bhattacharya
R.
. .
.
Grell
E. D.
al.
et
21243
Article
GEFSv12 reforecast dataset for supporting subseasonal and hydrometeorological applications
For the newly implemented Global Ensemble Forecast System, version 12 (GEFSv12), a 31-yr (1989–2019) ensemble reforecast dataset has been generated at the National Centers for Environmental Prediction (NCEP). The reforecast system is based on NCEP’s Global Forecast System, version 15.1, and GEFSv12, which uses the Finite Volume 3 dynamical core. The resolution of the forecast system is ∼25 km with 64 vertical hybrid levels. The Climate Forecast System (CFS) reanalysis and GEFSv12 reanalysis serve as initial conditions for the Phase 1 (1989–99) and Phase 2 (2000–19) reforecasts, respectively. The perturbations were produced using breeding vectors and ensemble transforms with a rescaling technique for Phase 1 and ensemble Kalman filter 6-h forecasts for Phase 2. The reforecasts were initialized at 0000 (0300) UTC once per day out to 16 days with 5 ensemble members for Phase 1 (Phase 2), except on Wednesdays when the integrations were extended to 35 days with 11 members. The reforecast dataset was produced on NOAA’s Weather and Climate Operational Supercomputing System at NCEP. This study summarizes the configuration and dataset of the GEFSv12 reforecast and presents some preliminary evaluations of 500-hPa geopotential height, tropical storm track, precipitation, 2-m temperature, and MJO forecasts. The results were also compared with GEFSv10 or GEFS Subseasonal Experiment reforecasts. In addition to supporting calibration and validation for the National Water Center, NCEP Climate Prediction Center, and other National Weather Service stakeholders, this high-resolution subseasonal dataset also serves as a useful tool for the broader research community in different applications.
2022-3
Mon. Wea. Rev.
150
647–665
0
10.1175/MWR-D-21-0245.1
For the newly implemented Global Ensemble Forecast System, version 12 (GEFSv12), a 31-yr (1989–2019) ensemble reforecast dataset has been generated at the National Centers for Environmental Prediction (NCEP). The reforecast system is based on NCEP’s Global Forecast System, version 15.1, and GEFSv12, which uses the Finite Volume 3 dynamical core. The resolution of the forecast system is ∼25 km with 64 vertical hybrid levels. The Climate Forecast System (CFS) reanalysis and GEFSv12 reanalysis serve as initial conditions for the Phase 1 (1989–99) and Phase 2 (2000–19) reforecasts, respectively. The perturbations were produced using breeding vectors and ensemble transforms with a rescaling technique for Phase 1 and ensemble Kalman filter 6-h forecasts for Phase 2. The reforecasts were initialized at 0000 (0300) UTC once per day out to 16 days with 5 ensemble members for Phase 1 (Phase 2), except on Wednesdays when the integrations were extended to 35 days with 11 members. The reforecast dataset was produced on NOAA’s Weather and Climate Operational Supercomputing System at NCEP. This study summarizes the configuration and dataset of the GEFSv12 reforecast and presents some preliminary evaluations of 500-hPa geopotential height, tropical storm track, precipitation, 2-m temperature, and MJO forecasts. The results were also compared with GEFSv10 or GEFS Subseasonal Experiment reforecasts. In addition to supporting calibration and validation for the National Water Center, NCEP Climate Prediction Center, and other National Weather Service stakeholders, this high-resolution subseasonal dataset also serves as a useful tool for the broader research community in different applications.
Guan
H.
Zhu
Y.
Sinsky
E.
Fu
B.
Li
W.
Zhou
X.
Xue
X.
Hou
D.
Peng
J.
Nageswararao
M. M.
Tallapragada
V.
Hamill
T. M.
Whitaker
J. S.
Bates
G. T.
Pegion
P.
Frederick
S.
Rosencrans
M.
Kumar
A.
21244
Article
Objective methods for thinning the frequency of reforecasts while meeting post-processing and model validation needs
This paper utilizes statistical and statistical–dynamical methodologies to select, from the full observational record, a minimal subset of dates that would provide representative sampling of local precipitation distributions across the contiguous United States (CONUS). The CONUS region is characterized by a great diversity of precipitation-producing systems, mechanisms, and large-scale meteorological patterns (LSMPs), which can provide favorable environment for local precipitation extremes. This diversity is unlikely to be adequately captured in methodologies that rely on grossly reducing the dimensionality of the data—by representing it in terms of a few patterns evolving in time—and thus requires data thinning techniques based on high-dimensional dynamical or statistical data modeling. We have built a novel high-dimensional empirical model of temperature and precipitation capable of producing statistically accurate surrogate realizations of the observed 1979–99 (training period) evolution of these fields. This model also provides skillful hindcasts of precipitation over the 2000–20 (validation) period. We devised a subsampling strategy based on the relative entropy of the empirical model’s precipitation (ensemble) forecasts over CONUS and demonstrated that it generates a set of dates that captures a majority of high-impact precipitation events, while substantially reducing a heavy-precipitation bias inherent in an alternative methodology based on the direct identification of large precipitation events in the Global Ensemble Forecast System (GEFS), version 12 reforecasts. The impacts of data thinning on the accuracy of precipitation statistical postprocessing, as well as on the calibration and validation of the Hydrologic Ensemble Forecast Service (HEFS) reforecasts are yet to be established.
2022-5
Wea. Forecasting
37
727–748
0
10.1175/WAF-D-21-0162.1
This paper utilizes statistical and statistical–dynamical methodologies to select, from the full observational record, a minimal subset of dates that would provide representative sampling of local precipitation distributions across the contiguous United States (CONUS). The CONUS region is characterized by a great diversity of precipitation-producing systems, mechanisms, and large-scale meteorological patterns (LSMPs), which can provide favorable environment for local precipitation extremes. This diversity is unlikely to be adequately captured in methodologies that rely on grossly reducing the dimensionality of the data—by representing it in terms of a few patterns evolving in time—and thus requires data thinning techniques based on high-dimensional dynamical or statistical data modeling. We have built a novel high-dimensional empirical model of temperature and precipitation capable of producing statistically accurate surrogate realizations of the observed 1979–99 (training period) evolution of these fields. This model also provides skillful hindcasts of precipitation over the 2000–20 (validation) period. We devised a subsampling strategy based on the relative entropy of the empirical model’s precipitation (ensemble) forecasts over CONUS and demonstrated that it generates a set of dates that captures a majority of high-impact precipitation events, while substantially reducing a heavy-precipitation bias inherent in an alternative methodology based on the direct identification of large precipitation events in the Global Ensemble Forecast System (GEFS), version 12 reforecasts. The impacts of data thinning on the accuracy of precipitation statistical postprocessing, as well as on the calibration and validation of the Hydrologic Ensemble Forecast Service (HEFS) reforecasts are yet to be established.
Kravtsov
S.
Roebber
P.
Hamill
T. M.
Brown
J.
21245
Article
Impaired hatching exacerbates the high CO2 sensitivity of embryonic sand lance, Ammodytes dubius
Rising oceanic partial pressure of CO2 (pCO2) could affect many traits in fish early life stages, but only few species to date have shown direct CO2-induced survival reductions. This might partly be because species from less CO2-variable, offshore environments in higher latitudes are currently underrepresented in the literature. We conducted new experimental work on northern sand lance Ammodytes dubius, a key forage fish on offshore Northwest Atlantic sand banks, which was recently suggested to be highly CO2-sensitive. In 2 complementary trials, we produced embryos from wild, Gulf of Maine spawners and reared them at several pCO2 levels (~400-2000 µatm) in combination with static (6, 7, 10°C) and dynamic (10→5°C) temperature treatments. Again, we consistently observed large, CO2-induced reductions in hatching success (-23% at 1000 µatm, -61% at ~2000 µatm), and the effects were temperature-independent. To distinguish pCO2 effects during development from potential impacts on hatching itself, some embryos were switched between high and control pCO2 treatments just prior to hatch. This indeed altered hatching patterns, consistent with the CO2-impaired hatching hypothesis. High CO2 also delayed the day of first hatch in one trial and peak hatch in the other, where later-hatched larvae were of similar size but with progressively less endogenous energy reserves. For context, we extracted seasonal pCO2 projections for Stellwagen Bank (Gulf of Maine) from regional ensemble simulations, which indicated a CO2-induced reduction in sand lance hatching success to 71% of contemporary levels by 2100. The species’ unusual CO2 sensitivity has large ecological and scientific ramifications that warrant future in-depth research.
2022-4
Mar. Ecol. Prog. Ser.
687
147-162
0
10.3354/meps14010
Rising oceanic partial pressure of CO2 (pCO2) could affect many traits in fish early life stages, but only few species to date have shown direct CO2-induced survival reductions. This might partly be because species from less CO2-variable, offshore environments in higher latitudes are currently underrepresented in the literature. We conducted new experimental work on northern sand lance Ammodytes dubius, a key forage fish on offshore Northwest Atlantic sand banks, which was recently suggested to be highly CO2-sensitive. In 2 complementary trials, we produced embryos from wild, Gulf of Maine spawners and reared them at several pCO2 levels (~400-2000 µatm) in combination with static (6, 7, 10°C) and dynamic (10→5°C) temperature treatments. Again, we consistently observed large, CO2-induced reductions in hatching success (-23% at 1000 µatm, -61% at ~2000 µatm), and the effects were temperature-independent. To distinguish pCO2 effects during development from potential impacts on hatching itself, some embryos were switched between high and control pCO2 treatments just prior to hatch. This indeed altered hatching patterns, consistent with the CO2-impaired hatching hypothesis. High CO2 also delayed the day of first hatch in one trial and peak hatch in the other, where later-hatched larvae were of similar size but with progressively less endogenous energy reserves. For context, we extracted seasonal pCO2 projections for Stellwagen Bank (Gulf of Maine) from regional ensemble simulations, which indicated a CO2-induced reduction in sand lance hatching success to 71% of contemporary levels by 2100. The species’ unusual CO2 sensitivity has large ecological and scientific ramifications that warrant future in-depth research.
Baumann
H.
Jones
L. F.
Murray
C. S.
Siedlecki
S. A.
Alexander
M. A.
Cross
E. L.
21247
Article
A comparison of hybrid-gain versus hybrid-covariance data assimilation for 2 global NWP
Two methods for incorporating a time-invariant, high-rank covariance estimate in an ensemble-based data assimilation system for global weather prediction are compared. The hybrid-covariance approach linearly combines the static and ensemble-based covariance estimate in a four-dimensional variational solver, whereas the hybrid-gain approach blends analysis increments computed separately using a three-dimensional variational solution and an ensemble Kalman filter solution. Results show that the simpler and less expensive hybrid-gain approach performs similarly if the incremental normal-mode balance constraint applied to the ensemble-part of the hybrid-covariance update is turned off.
2022-8
J. Adv. Model. Earth Syst.
14
e2022MS003036
0
10.1029/2022MS003036
Two methods for incorporating a time-invariant, high-rank covariance estimate in an ensemble-based data assimilation system for global weather prediction are compared. The hybrid-covariance approach linearly combines the static and ensemble-based covariance estimate in a four-dimensional variational solver, whereas the hybrid-gain approach blends analysis increments computed separately using a three-dimensional variational solution and an ensemble Kalman filter solution. Results show that the simpler and less expensive hybrid-gain approach performs similarly if the incremental normal-mode balance constraint applied to the ensemble-part of the hybrid-covariance update is turned off.
Whitaker
J. S.
Shlyaeva
A.
Penny
S. G.
21248
Article
Meteorological data rescue: Citizen science lessons learned from Southern Weather Discovery
Daily weather reconstructions (called “reanalyses”) can help improve our understanding of meteorology and long-term climate changes. Adding undigitized historical weather observations to the datasets that underpin reanalyses is desirable; however, time requirements to capture those data from a range of archives is usually limited. Southern Weather Discovery is a citizen science data rescue project that recovered tabulated handwritten meteorological observations from ship log books and land-based stations spanning New Zealand, the Southern Ocean, and Antarctica. We describe the Zooniverse-hosted Southern Weather Discovery campaign, highlight promotion tactics, and replicate keying levels needed to obtain 100% complete transcribed datasets with minimal type 1 and type 2 transcription errors. Rescued weather observations can augment optical character recognition (OCR) text recognition libraries. Closer links between citizen science data rescue and OCR-based scientific data capture will accelerate weather reconstruction improvements, which can be harnessed to mitigate impacts on communities and infrastructure from weather extremes.
2022-5
Patterns
3
100495
0
10.1016/j.patter.2022.100495
Daily weather reconstructions (called “reanalyses”) can help improve our understanding of meteorology and long-term climate changes. Adding undigitized historical weather observations to the datasets that underpin reanalyses is desirable; however, time requirements to capture those data from a range of archives is usually limited. Southern Weather Discovery is a citizen science data rescue project that recovered tabulated handwritten meteorological observations from ship log books and land-based stations spanning New Zealand, the Southern Ocean, and Antarctica. We describe the Zooniverse-hosted Southern Weather Discovery campaign, highlight promotion tactics, and replicate keying levels needed to obtain 100% complete transcribed datasets with minimal type 1 and type 2 transcription errors. Rescued weather observations can augment optical character recognition (OCR) text recognition libraries. Closer links between citizen science data rescue and OCR-based scientific data capture will accelerate weather reconstruction improvements, which can be harnessed to mitigate impacts on communities and infrastructure from weather extremes.
Lorrey
A. M.
Pearce
P. R.
Allan
R.
Wilkinson
C.
Woolley
J.-M.
Judd
E.
Mackay
S.
Rahwat
S.
Slivinski
L. C.
Wilkinson
S.
Hawkins
E.
Quesnel
P.
Compo
G. P.
21249
Article
What
Mainstream and popular science media are rife with misunderstandings about what a “polar vortex” is. The term most aptly describes the stratospheric polar vortex, a single feature dominating the cool-season circulation from ∼15–50 km. Regional jet stream variations dominate the tropospheric circulation, which is not well-described as a polar vortex; indeed, there is no single consistent definition of a tropospheric polar vortex in the literature. Stratospheric polar vortex disturbances profoundly influence extreme weather events, including cold air outbreaks (CAOs). How the stratospheric polar vortex affects tropospheric jets, whose local excursions drive CAOs, is not fully understood. Public-facing parts of publications describing research on this topic are not always clear about how the “polar vortex” is defined; greater clarity could improve communications both within the community and with non-specialist audiences.
2022-5
Geophys. Res. Lett.
49
e2021GL097617
0
10.1029/2021GL097617
Mainstream and popular science media are rife with misunderstandings about what a “polar vortex” is. The term most aptly describes the stratospheric polar vortex, a single feature dominating the cool-season circulation from ∼15–50 km. Regional jet stream variations dominate the tropospheric circulation, which is not well-described as a polar vortex; indeed, there is no single consistent definition of a tropospheric polar vortex in the literature. Stratospheric polar vortex disturbances profoundly influence extreme weather events, including cold air outbreaks (CAOs). How the stratospheric polar vortex affects tropospheric jets, whose local excursions drive CAOs, is not fully understood. Public-facing parts of publications describing research on this topic are not always clear about how the “polar vortex” is defined; greater clarity could improve communications both within the community and with non-specialist audiences.
Manney
G. L.
Butler
A. H.
Lawrence
Z. D.
Wargan
K.
Santee
M. L.
21250
Article
Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems
The stratosphere can be a source of predictability for surface weather on timescales of several weeks to months. However, the potential predictive skill gained from stratospheric variability can be limited by biases in the representation of stratospheric processes and the coupling of the stratosphere with surface climate in forecast systems. This study provides a first systematic identification of model biases in the stratosphere across a wide range of subseasonal forecast systems.
It is found that many of the forecast systems considered exhibit warm global-mean temperature biases from the lower to middle stratosphere, too strong/cold wintertime polar vortices, and too cold extratropical upper-troposphere/lower-stratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each system's climatology with lead time. In the Northern Hemisphere (NH), most systems do not capture the seasonal cycle of extreme-vortex-event probabilities, with an underestimation of sudden stratospheric warming events and an overestimation of strong vortex events in January. In the Southern Hemisphere (SH), springtime interannual variability in the polar vortex is generally underestimated, but the timing of the final breakdown of the polar vortex often happens too early in many of the prediction systems.
These stratospheric biases tend to be considerably worse in systems with lower model lid heights. In both hemispheres, most systems with low-top atmospheric models also consistently underestimate the upward wave driving that affects the strength of the stratospheric polar vortex. We expect that the biases identified here will help guide model development for subseasonal-to-seasonal forecast systems and further our understanding of the role of the stratosphere in predictive skill in the troposphere.
2022-8
Weather Clim. Dynam.
3
977–1001
0
10.5194/wcd-2022-12
The stratosphere can be a source of predictability for surface weather on timescales of several weeks to months. However, the potential predictive skill gained from stratospheric variability can be limited by biases in the representation of stratospheric processes and the coupling of the stratosphere with surface climate in forecast systems. This study provides a first systematic identification of model biases in the stratosphere across a wide range of subseasonal forecast systems.
It is found that many of the forecast systems considered exhibit warm global-mean temperature biases from the lower to middle stratosphere, too strong/cold wintertime polar vortices, and too cold extratropical upper-troposphere/lower-stratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each system's climatology with lead time. In the Northern Hemisphere (NH), most systems do not capture the seasonal cycle of extreme-vortex-event probabilities, with an underestimation of sudden stratospheric warming events and an overestimation of strong vortex events in January. In the Southern Hemisphere (SH), springtime interannual variability in the polar vortex is generally underestimated, but the timing of the final breakdown of the polar vortex often happens too early in many of the prediction systems.
These stratospheric biases tend to be considerably worse in systems with lower model lid heights. In both hemispheres, most systems with low-top atmospheric models also consistently underestimate the upward wave driving that affects the strength of the stratospheric polar vortex. We expect that the biases identified here will help guide model development for subseasonal-to-seasonal forecast systems and further our understanding of the role of the stratosphere in predictive skill in the troposphere.
Lawrence
Z. D.
Abalos
M.
Ayarzagüena
B.
Barriopedro
D.
Barriopedro
D.
Butler
A. H.
. .
.
Perlwitz
J.
al.
et
21251
Article
Global Ocean Monitoring and Prediction at NOAA Climate Prediction Center: 15 Years of Operations
Climate variability on sub-seasonal to interannual time scales has significant impacts on our economy, society, and the earth’s environment. Predictability for these time scales is largely due to the influence of the slowly varying climate anomalies in the oceans. The importance of the global oceans in governing climate variability demonstrates the need to monitor and forecast the global oceans in addition to the El Niño-Southern Oscillation in the tropical Pacific. To meet this need, the Climate Prediction Center (CPC) of the National Centers for Environmental Prediction (NCEP) initiated real-time global ocean monitoring, and a monthly briefing in 2007. The monitoring covers observations as well as forecasts for each ocean basin. In this paper, we introduce the monitoring and forecast products. CPC’s efforts bridge the gap between the ocean observing system and the delivery of the analyzed products to the community. We also discuss the challenges involved in ocean monitoring and forecasting, as well as the future directions for these efforts.
2022-12
Bull. Amer. Meteor. Soc.
103
2701–E2718
0
10.1175/BAMS-D-22-0056.1
Climate variability on sub-seasonal to interannual time scales has significant impacts on our economy, society, and the earth’s environment. Predictability for these time scales is largely due to the influence of the slowly varying climate anomalies in the oceans. The importance of the global oceans in governing climate variability demonstrates the need to monitor and forecast the global oceans in addition to the El Niño-Southern Oscillation in the tropical Pacific. To meet this need, the Climate Prediction Center (CPC) of the National Centers for Environmental Prediction (NCEP) initiated real-time global ocean monitoring, and a monthly briefing in 2007. The monitoring covers observations as well as forecasts for each ocean basin. In this paper, we introduce the monitoring and forecast products. CPC’s efforts bridge the gap between the ocean observing system and the delivery of the analyzed products to the community. We also discuss the challenges involved in ocean monitoring and forecasting, as well as the future directions for these efforts.
Hu
Z.-Z.
Xue
Y.
Kumar
A.
Wen
C.
Xie
P.
Zhu
J.
Pegion
P.
Wang
W.
21252
Article
Risk to Resilience: Climate change, disasters and the WMO-UNDRR Centre of Excellence
N/A
2022-3
WMO Bull.
71
12-19
0
N/A
Pulwarty
R. S.
Hiebert-Girardet
L.
Speck
R. M.
Allis
E.
Honoré
C.
Stander
C.
21253
Article
A mechanism for the skew of ensemble forecasts
It is often not appreciated that forecast ensembles are generally skewed. The skew can arise from the state dependence of the chaotic system dynamics responsible for the ensemble spread. Generation of skew by this mechanism can be demonstrated in even the simplest dynamical system with state-dependent noise, and even when the initial and the asymptotic (i.e., the “climatological”) forecast distributions are both symmetric. Indeed, forecast distributions of systems with state-dependent noise in the dynamical tendencies must in general be both skewed and heavy tailed, with implications for forecasting extreme anomaly risks. Ensemble forecast systems that misrepresent such state-dependent noise have state-dependent errors in their forecast probability distributions. Because such errors depend on both the initial condition and forecast lead time, they cannot be removed by simple a posteriori bias corrections of the forecast distributions. The ensemble standard deviation is often used as a simple metric of ensemble spread even when the forecast distribution is not Gaussian. In a similar spirit, the ensemble skew S may be used as a simple metric of the difference D between the ensemble-mean and most likely forecast, as well as the risk ratio R of extreme positive and negative deviations from the ensemble-mean forecast. This is motivated by the facts that (1) the probability distributions of many geophysical quantities are approximately stochastically generated skewed (SGS) distributions, for which simple analytical relationships exist between these quantities, and (2) Gaussian distributions are a sub-class of SGS distributions. However, S may serve as a useful metric of R and D even when the distributions are not strictly SGS distributions.
2022-4
Q. J. R. Meteorol. Soc.
148
1131-1143
0
10.1002/qj.4251
It is often not appreciated that forecast ensembles are generally skewed. The skew can arise from the state dependence of the chaotic system dynamics responsible for the ensemble spread. Generation of skew by this mechanism can be demonstrated in even the simplest dynamical system with state-dependent noise, and even when the initial and the asymptotic (i.e., the “climatological”) forecast distributions are both symmetric. Indeed, forecast distributions of systems with state-dependent noise in the dynamical tendencies must in general be both skewed and heavy tailed, with implications for forecasting extreme anomaly risks. Ensemble forecast systems that misrepresent such state-dependent noise have state-dependent errors in their forecast probability distributions. Because such errors depend on both the initial condition and forecast lead time, they cannot be removed by simple a posteriori bias corrections of the forecast distributions. The ensemble standard deviation is often used as a simple metric of ensemble spread even when the forecast distribution is not Gaussian. In a similar spirit, the ensemble skew S may be used as a simple metric of the difference D between the ensemble-mean and most likely forecast, as well as the risk ratio R of extreme positive and negative deviations from the ensemble-mean forecast. This is motivated by the facts that (1) the probability distributions of many geophysical quantities are approximately stochastically generated skewed (SGS) distributions, for which simple analytical relationships exist between these quantities, and (2) Gaussian distributions are a sub-class of SGS distributions. However, S may serve as a useful metric of R and D even when the distributions are not strictly SGS distributions.
Penland
C.
Sardeshmukh
P. D.
21254
Article
A Hydrologic Monitoring Dataset for Food and Water Security Applications in Central Asia
2022-7
Earth Syst. Sci. Data
14
3115–3135
0
McNally
A. L.
Jacob
J.
Arsenault
K. R.
Slinski
K.
Sarmiento
D. P.
Hoell
A.
Pervez
S.
Rowland
J.
Budde
M.
Kumar
S.
Peters-Lidard
C.
Verdin
J. P.
21257
Article
The Impact of Incremental Analysis Update on Regional Simulations for Typhoons
The analyses produced by intermittent data assimilation methods can be dynamically inconsistent and unbalanced. By gradually distributing the analysis increment along model integration, the incremental analysis update (IAU) is effective to combat the inconsistences and imbalances. Different implementations of IAU with time constant or time-varying increments using different increment frequencies are systematically evaluated for regional simulations, especially for fast-moving typhoons. Results show that experiments with IAU generally produce smaller forecast errors of temperature, specific humidity, and wind speed than experiment CTRL without initialization. Three-dimensional IAUs (3DIAUs) with time-constant increments have smaller errors than four-dimensional IAUs (4DIAUs) with time-varying increments interpolated from 3-hr and hourly increments. Thus, for regional simulations, 3DIAU that imposes stronger filtering has advantages over 4DIAUs with different increment frequencies. For two typhoon cases, experiments with IAU obtain better intensity and structure of vortex than experiment CTRL; thus, the application of IAU can better retain the observation information and build the improved TC structure. But due to the displacement errors in priors and posteriors, the advantage of 4DIAU that considers the propagation of increment is limited compared to 3DIAU. As a trade-off between the filtering and time-varying increment, 4DIAU with 3-hr increment that considers time-varying increments compared to 3DIAU but imposes stronger filtering than 4DIAU with hourly increment could be preferred for TCs.
2022-10
J. Adv. Model. Earth Syst.
14
e2022MS003084
0
10.1029/2022MS003084
The analyses produced by intermittent data assimilation methods can be dynamically inconsistent and unbalanced. By gradually distributing the analysis increment along model integration, the incremental analysis update (IAU) is effective to combat the inconsistences and imbalances. Different implementations of IAU with time constant or time-varying increments using different increment frequencies are systematically evaluated for regional simulations, especially for fast-moving typhoons. Results show that experiments with IAU generally produce smaller forecast errors of temperature, specific humidity, and wind speed than experiment CTRL without initialization. Three-dimensional IAUs (3DIAUs) with time-constant increments have smaller errors than four-dimensional IAUs (4DIAUs) with time-varying increments interpolated from 3-hr and hourly increments. Thus, for regional simulations, 3DIAU that imposes stronger filtering has advantages over 4DIAUs with different increment frequencies. For two typhoon cases, experiments with IAU obtain better intensity and structure of vortex than experiment CTRL; thus, the application of IAU can better retain the observation information and build the improved TC structure. But due to the displacement errors in priors and posteriors, the advantage of 4DIAU that considers the propagation of increment is limited compared to 3DIAU. As a trade-off between the filtering and time-varying increment, 4DIAU with 3-hr increment that considers time-varying increments compared to 3DIAU but imposes stronger filtering than 4DIAU with hourly increment could be preferred for TCs.
Ge
Y.
Lei
L.
Whitaker
J. S.
Tan
Z.-M.
21258
Article
Two extratropical pathways to forcing tropical convective disturbances
Observational evidence of two extratropical pathways to forcing tropical convective disturbances is documented through a statistical analysis of satellite-derived OLR and ERA5 reanalysis. The forcing mechanism and the resulting disturbances are found to strongly depend on the structure of the background zonal wind. Although Rossby wave propagation is prohibited in easterlies, modeling studies have shown that extratropical forcing can still excite equatorial waves through resonance between the tropics and extratropics. Here this “remote” forcing pathway is investigated for the first time in the context of convectively coupled Kelvin waves over the tropical Pacific during northern summer. The extratropical forcing is manifested by eddy momentum flux convergence that arises when extratropical eddies propagate into the subtropics and encounter their critical line. This nonlinear forcing has similar wavenumbers and frequencies with Kelvin waves and excites them by projecting onto their meridional eigenstructure in zonal wind, as a form of resonance. This resonance is also evidenced by a momentum budget analysis, which reveals the nonlinear forcing term is essential for maintenance of the waves, while the remaining linear terms are essential for propagation. In contrast, the “local” pathway of extratropical forcing entails the presence of a westerly duct during northern winter that permits Rossby waves to propagate into the equatorial east Pacific, while precluding any sort of resonance with Kelvin waves due to Doppler shifting effects. The intruding disturbances primarily excite tropical “cloud plumes” through quasigeostrophic forcing, while maintaining their extratropical nature. This study demonstrates the multiple roles of the extratropics in forcing in tropical circulations and illuminates how tropical–extratropical interactions and extratropical basic states can provide be a source of predictability at the S2S time scale.
2022-10
J. Climate
35
2987–3009
0
10.1175/JCLI-D-22-0171.1
Observational evidence of two extratropical pathways to forcing tropical convective disturbances is documented through a statistical analysis of satellite-derived OLR and ERA5 reanalysis. The forcing mechanism and the resulting disturbances are found to strongly depend on the structure of the background zonal wind. Although Rossby wave propagation is prohibited in easterlies, modeling studies have shown that extratropical forcing can still excite equatorial waves through resonance between the tropics and extratropics. Here this “remote” forcing pathway is investigated for the first time in the context of convectively coupled Kelvin waves over the tropical Pacific during northern summer. The extratropical forcing is manifested by eddy momentum flux convergence that arises when extratropical eddies propagate into the subtropics and encounter their critical line. This nonlinear forcing has similar wavenumbers and frequencies with Kelvin waves and excites them by projecting onto their meridional eigenstructure in zonal wind, as a form of resonance. This resonance is also evidenced by a momentum budget analysis, which reveals the nonlinear forcing term is essential for maintenance of the waves, while the remaining linear terms are essential for propagation. In contrast, the “local” pathway of extratropical forcing entails the presence of a westerly duct during northern winter that permits Rossby waves to propagate into the equatorial east Pacific, while precluding any sort of resonance with Kelvin waves due to Doppler shifting effects. The intruding disturbances primarily excite tropical “cloud plumes” through quasigeostrophic forcing, while maintaining their extratropical nature. This study demonstrates the multiple roles of the extratropics in forcing in tropical circulations and illuminates how tropical–extratropical interactions and extratropical basic states can provide be a source of predictability at the S2S time scale.
Cheng
Y.-M.
Tulich
S. N.
Kiladis
G. N.
Dias
J.
21259
Article
Long-range prediction and the stratosphere
Over recent years there have been concomitant advances in the development of stratosphere-resolving numerical models, our understanding of stratosphere–troposphere interaction, and the extension of long-range forecasts to explicitly include the stratosphere. These advances are now allowing for new and improved capability in long-range prediction. We present an overview of this development and show how the inclusion of the stratosphere in forecast systems aids monthly, seasonal, and annual-to-decadal climate predictions and multidecadal projections. We end with an outlook towards the future and identify areas of improvement that could further benefit these rapidly evolving predictions.
2022-2
Atmos. Chem. Phys.
22
601–2623
0
10.5194/acp-22-2601-2022
Over recent years there have been concomitant advances in the development of stratosphere-resolving numerical models, our understanding of stratosphere–troposphere interaction, and the extension of long-range forecasts to explicitly include the stratosphere. These advances are now allowing for new and improved capability in long-range prediction. We present an overview of this development and show how the inclusion of the stratosphere in forecast systems aids monthly, seasonal, and annual-to-decadal climate predictions and multidecadal projections. We end with an outlook towards the future and identify areas of improvement that could further benefit these rapidly evolving predictions.
Scaife
A. A.
Baldwin
M. P.
Butler
A. H.
Charlton-Perez
A. J.
Domeisen
D. I. V.
. .
.
Perlwitz
J.
al.
et
21260
Article
Drought and all-cause mortality in Nebraska from 1980 to 2014: Time-series analyses by age, sex, race, urbanicity and drought severity
Background: Climate change will increase drought duration and severity in many regions around the world, including the Central Plains of North America. However, studies on drought-related health impacts are still sparse. This study aims to explore the potential associations between drought and all-cause mortality in Nebraska from 1980 to 2014.
2022-9
Sci. Total Environ.
840
156660
0
10.1016/j.scitotenv.2022.156660
Background: Climate change will increase drought duration and severity in many regions around the world, including the Central Plains of North America. However, studies on drought-related health impacts are still sparse. This study aims to explore the potential associations between drought and all-cause mortality in Nebraska from 1980 to 2014.
Abadi
A.
Gwon
Y.
Berman
M.
Bilotta
R.
Hobbins
M. T.
Bell
J.
21261
Article
Getting ahead of flash drought: From early warning to early action
Flash droughts, characterized by their unusually rapid intensification, have garnered increasing attention within the weather, climate, agriculture, and ecological communities in recent years due to their large environmental and socioeconomic impacts. Because flash droughts intensify quickly, they require different early warning capabilities and management approaches than are typically used for slower-developing “conventional” droughts. In this essay, we describe an integrated research-and-applications agenda that emphasizes the need to reconceptualize our understanding of flash drought within existing drought early warning systems by focusing on opportunities to improve monitoring and prediction. We illustrate the need for engagement among physical scientists, social scientists, operational monitoring and forecast centers, practitioners, and policy-makers to inform how they view, monitor, predict, plan for, and respond to flash drought. We discuss five related topics that together constitute the pillars of a robust flash drought early warning system, including the development of 1) a physically based identification framework, 2) comprehensive drought monitoring capabilities, and 3) improved prediction over various time scales that together 4) aid impact assessments and 5) guide decision-making and policy. We provide specific recommendations to illustrate how this fivefold approach could be used to enhance decision-making capabilities of practitioners, develop new areas of research, and provide guidance to policy-makers attempting to account for flash drought in drought preparedness and response plans.
2022-10
Bull. Amer. Meteor. Soc.
103
E2188–E2202
0
10.1175/BAMS-D-21-0288.1
Flash droughts, characterized by their unusually rapid intensification, have garnered increasing attention within the weather, climate, agriculture, and ecological communities in recent years due to their large environmental and socioeconomic impacts. Because flash droughts intensify quickly, they require different early warning capabilities and management approaches than are typically used for slower-developing “conventional” droughts. In this essay, we describe an integrated research-and-applications agenda that emphasizes the need to reconceptualize our understanding of flash drought within existing drought early warning systems by focusing on opportunities to improve monitoring and prediction. We illustrate the need for engagement among physical scientists, social scientists, operational monitoring and forecast centers, practitioners, and policy-makers to inform how they view, monitor, predict, plan for, and respond to flash drought. We discuss five related topics that together constitute the pillars of a robust flash drought early warning system, including the development of 1) a physically based identification framework, 2) comprehensive drought monitoring capabilities, and 3) improved prediction over various time scales that together 4) aid impact assessments and 5) guide decision-making and policy. We provide specific recommendations to illustrate how this fivefold approach could be used to enhance decision-making capabilities of practitioners, develop new areas of research, and provide guidance to policy-makers attempting to account for flash drought in drought preparedness and response plans.
Otkin
J. A.
Woloszyn
M.
Wang
H.
Svoboda
M.
Skumanich
M.
Pulwarty
R. S.
Lisonbee
Hoell
A.
Hobbins
M. T.
Haigh
T.
Cravens
A. E.
21262
Article
Non-Gaussian Detection using Machine Learning with Data Assimilation Applications
In most data assimilation and numerical weather prediction systems, the Gaussian assumption is prevalent for the behavior of the random variables/errors that are involved. At the Cooperative Institute for Research in the Atmosphere theory has been developed for different forms of variational data assimilation schemes that enables the Gaussian assumption to be relaxed. For certain variable types, a lognormally distributed random variable can be combined in a mixed Gaussian-lognormal distribution to better capture the interactions of the errors of different distributions. However, assuming that a distribution can change in time, then developing techniques to know when to switch between different versions of the data assimilation schemes becomes very important. By dynamically changing the formulation of the data assimilation system we are able to assimilate observations in a way that reflects the flow-dependent variability of their distribution.
In this paper, we present results with a machine learning technique (the support vector machine) to switch between data assimilation methods based on the detection of a change in the Lorenz 1963 model's z component's probability distribution. Given the machine learning technique's detection/prediction of a change in the distribution, then either a Gaussian or a mixed Gaussian-lognormal 3DVar based cost function is used to minimize the errors in this period of time. It is shown that switching from a Gaussian 3DVar to a lognormal 3DVar at lognormally distributed parts of the attractor improves the data assimilation analysis error compared to using one distribution type for the entire attractor.
2022-4
Earth Space Sci.
9
e2021EA001908
0
10.1029/2021EA001908
In most data assimilation and numerical weather prediction systems, the Gaussian assumption is prevalent for the behavior of the random variables/errors that are involved. At the Cooperative Institute for Research in the Atmosphere theory has been developed for different forms of variational data assimilation schemes that enables the Gaussian assumption to be relaxed. For certain variable types, a lognormally distributed random variable can be combined in a mixed Gaussian-lognormal distribution to better capture the interactions of the errors of different distributions. However, assuming that a distribution can change in time, then developing techniques to know when to switch between different versions of the data assimilation schemes becomes very important. By dynamically changing the formulation of the data assimilation system we are able to assimilate observations in a way that reflects the flow-dependent variability of their distribution.
In this paper, we present results with a machine learning technique (the support vector machine) to switch between data assimilation methods based on the detection of a change in the Lorenz 1963 model's z component's probability distribution. Given the machine learning technique's detection/prediction of a change in the distribution, then either a Gaussian or a mixed Gaussian-lognormal 3DVar based cost function is used to minimize the errors in this period of time. It is shown that switching from a Gaussian 3DVar to a lognormal 3DVar at lognormally distributed parts of the attractor improves the data assimilation analysis error compared to using one distribution type for the entire attractor.
Goodliff
M.
Fletcher
S. J.
Kliewer
A. J.
Jones
A. S.
Forsythe
J. M.
21264
Article
A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics
A reservoir computer (RC) is a type of recurrent neural network architecture with demonstrated success in the prediction of spatiotemporally chaotic dynamical systems. A further advantage of RC is that it reproduces intrinsic dynamical quantities essential for its incorporation into numerical forecasting routines such as the ensemble Kalman filter—used in numerical weather prediction to compensate for sparse and noisy data. We explore here the architecture and design choices for a “best in class” RC for a number of characteristic dynamical systems. Our analysis points to the importance of large scale parameter optimization. We also note in particular the importance of including input bias in the RC design, which has a significant impact on the forecast skill of the trained RC model. In our tests, the use of a nonlinear readout operator does not affect the forecast time or the stability of the forecast. The effects of the reservoir dimension, spinup time, amount of training data, normalization, noise, and the RC time step are also investigated. Finally, we detail how our investigation leads to optimal design choices for a parallel RC scheme applied to the 40 dimensional spatiotemporally chaotic Lorenz 1996 dynamics.
2022-9
Neural Netw.
153
530-552
0
10.1016/j.neunet.2022.06.025
A reservoir computer (RC) is a type of recurrent neural network architecture with demonstrated success in the prediction of spatiotemporally chaotic dynamical systems. A further advantage of RC is that it reproduces intrinsic dynamical quantities essential for its incorporation into numerical forecasting routines such as the ensemble Kalman filter—used in numerical weather prediction to compensate for sparse and noisy data. We explore here the architecture and design choices for a “best in class” RC for a number of characteristic dynamical systems. Our analysis points to the importance of large scale parameter optimization. We also note in particular the importance of including input bias in the RC design, which has a significant impact on the forecast skill of the trained RC model. In our tests, the use of a nonlinear readout operator does not affect the forecast time or the stability of the forecast. The effects of the reservoir dimension, spinup time, amount of training data, normalization, noise, and the RC time step are also investigated. Finally, we detail how our investigation leads to optimal design choices for a parallel RC scheme applied to the 40 dimensional spatiotemporally chaotic Lorenz 1996 dynamics.
Platt
J. A.
Penny
S. G.
Smith
T. A.
Chen
T.-C.
Abarbanel
H. D. I.
21266
Article
A Year in the Changing Arctic Sea Ice
N/A
2022-8
Oceanography
35
224-225
0
10.5670/oceanog.2022.126
N/A
Shupe
M. D.
Rex
M.
21267
Article
Verification of regional climate model simulations of near-surface variables for the MOSAiC winter period
The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic and yielded an excellent data set to test the parameterizations of ocean/sea-ice/atmosphere interaction processes in regional climate models (RCMs). In the present paper, near-surface data during MOSAiC are used for the verification of the RCM COnsortium for Small-scale MOdel–Climate Limited area Mode (COSMO-CLM or CCLM). CCLM is used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data with around 6 km resolution, sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead fraction data for the winter period. CCLM simulations show a good agreement with the measurements. Relatively large negative biases for temperature occur for November and December, which are likely associated with a too large ice thickness used by CCLM. The consideration of sea ice leads in the sub-grid parameterization in CCLM yields improved results for the near-surface temperature. ERA5 data show a large warm bias of about 2.5°C and an underestimation of the temperature variability.
2022-8
Elementa Sci. Anthrop.
10
00033
0
10.1525/elementa.2022.00033
The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic and yielded an excellent data set to test the parameterizations of ocean/sea-ice/atmosphere interaction processes in regional climate models (RCMs). In the present paper, near-surface data during MOSAiC are used for the verification of the RCM COnsortium for Small-scale MOdel–Climate Limited area Mode (COSMO-CLM or CCLM). CCLM is used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data with around 6 km resolution, sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead fraction data for the winter period. CCLM simulations show a good agreement with the measurements. Relatively large negative biases for temperature occur for November and December, which are likely associated with a too large ice thickness used by CCLM. The consideration of sea ice leads in the sub-grid parameterization in CCLM yields improved results for the near-surface temperature. ERA5 data show a large warm bias of about 2.5°C and an underestimation of the temperature variability.
Heinemann
G.
Schefczyk
L.
Willmes
S.
Shupe
M. D.
21268
Article
The Spring Minimum in Subseasonal 2-m Temperature Forecast Skill over North America
On average, 2-m temperature forecasts over North America for lead times greater than two weeks have generally low skill in operational dynamical models, largely because of the chaotic, unpredictable nature of daily weather. However, for a small subset of forecasts, more slowly evolving climate processes yield some predictable signal that may be anticipated in advance, occasioning “forecasts of opportunity.” Forecasts of opportunity evolve seasonally, since they are a function of the seasonally varying jet stream and various remote forcings such as tropical heating. Prior research has demonstrated that for boreal winter, an empirical dynamical modeling technique called a linear inverse model (LIM), whose forecast skill is typically comparable to operational forecast models, can successfully identify forecasts of opportunity both for itself and for other dynamical models. In this study, we use a set of LIMs to examine how subseasonal North American 2-m temperature potential predictability and forecasts of opportunity vary from boreal winter through summer. We show how LIM skill evolves during the three phases of the spring transition of the North Pacific jet—late winter, spring, and early summer—revealing clear differences in each phase and a distinct skill minimum in spring. We identify a subset of forecasts with markedly higher skill in all three phases, despite LIM temperature skill that is somewhat low on average. However, skill improvements are only statistically significant during winter and summer, again reflecting the spring subseasonal skill minimum. The spring skill minimum is consistent with the skill predicted from theory and arises due to a minimum in LIM forecast signal-to-noise ratio.
2022-10
Mon. Wea. Rev.
150
2617–2628
0
10.1175/MWR-D-22-0062.1
On average, 2-m temperature forecasts over North America for lead times greater than two weeks have generally low skill in operational dynamical models, largely because of the chaotic, unpredictable nature of daily weather. However, for a small subset of forecasts, more slowly evolving climate processes yield some predictable signal that may be anticipated in advance, occasioning “forecasts of opportunity.” Forecasts of opportunity evolve seasonally, since they are a function of the seasonally varying jet stream and various remote forcings such as tropical heating. Prior research has demonstrated that for boreal winter, an empirical dynamical modeling technique called a linear inverse model (LIM), whose forecast skill is typically comparable to operational forecast models, can successfully identify forecasts of opportunity both for itself and for other dynamical models. In this study, we use a set of LIMs to examine how subseasonal North American 2-m temperature potential predictability and forecasts of opportunity vary from boreal winter through summer. We show how LIM skill evolves during the three phases of the spring transition of the North Pacific jet—late winter, spring, and early summer—revealing clear differences in each phase and a distinct skill minimum in spring. We identify a subset of forecasts with markedly higher skill in all three phases, despite LIM temperature skill that is somewhat low on average. However, skill improvements are only statistically significant during winter and summer, again reflecting the spring subseasonal skill minimum. The spring skill minimum is consistent with the skill predicted from theory and arises due to a minimum in LIM forecast signal-to-noise ratio.
Breeden
M. L.
Albers
J. R.
Butler
A. H.
Newman
M.
21270
Article
Megadrought: A Series of Unfortunate La Niña Events?
Megadroughts are multidecadal periods of aridity more persistent than most droughts during the instrumental period. Paleoclimate evidence suggests that megadroughts occur in many parts of the world, including North America, Central America, western Europe, eastern Asia, and northern Africa. It remains unclear to what extent such megadroughts require external forcing or whether they can arise from internal climate variability alone. A novel statistical–dynamical approach is used to evaluate the possibility that such events arise solely as a function of interannual tropical sea surface temperature (SST) variations. A statistical emulator of tropical SST variations is constructed by using an empirical moving-blocks bootstrap approach that randomly samples multiyear sequences of the observational SST record. This approach preserves the power spectrum, seasonal cycle, and spatial pattern of El Niño-Southern Oscillation (ENSO) but removes longer timescale fluctuations embedded in the observational record. These resampled SST anomalies are then used to force an atmospheric model (the Community Atmosphere Model Version 5). As megadroughts emerge in this run, they should, therefore, be solely a consequence of La Niña sequences combined with internal atmospheric variability and persistence driven by soil moisture storage and other land-surface processes. We indeed find that megadroughts in this simulation have an amplitude-duration rate that is generally indistinguishable from the rate documented in paleoclimate records of the western United States. Our findings support the idea that megadroughts may occur randomly when the unforced climate system evolves freely over a sufficiently long period of time, implying that an unforced unusual but statistically plausible series of La Niña events may be sufficient to generate megadrought.
2022-11
J. Geophys. Res. Atmos.
127
e2021JD036376
0
10.1029/2021JD036376
Megadroughts are multidecadal periods of aridity more persistent than most droughts during the instrumental period. Paleoclimate evidence suggests that megadroughts occur in many parts of the world, including North America, Central America, western Europe, eastern Asia, and northern Africa. It remains unclear to what extent such megadroughts require external forcing or whether they can arise from internal climate variability alone. A novel statistical–dynamical approach is used to evaluate the possibility that such events arise solely as a function of interannual tropical sea surface temperature (SST) variations. A statistical emulator of tropical SST variations is constructed by using an empirical moving-blocks bootstrap approach that randomly samples multiyear sequences of the observational SST record. This approach preserves the power spectrum, seasonal cycle, and spatial pattern of El Niño-Southern Oscillation (ENSO) but removes longer timescale fluctuations embedded in the observational record. These resampled SST anomalies are then used to force an atmospheric model (the Community Atmosphere Model Version 5). As megadroughts emerge in this run, they should, therefore, be solely a consequence of La Niña sequences combined with internal atmospheric variability and persistence driven by soil moisture storage and other land-surface processes. We indeed find that megadroughts in this simulation have an amplitude-duration rate that is generally indistinguishable from the rate documented in paleoclimate records of the western United States. Our findings support the idea that megadroughts may occur randomly when the unforced climate system evolves freely over a sufficiently long period of time, implying that an unforced unusual but statistically plausible series of La Niña events may be sufficient to generate megadrought.
Carrillo
C.
Coats
S.
Newman
M.
Herrera
D. A.
Li
X.
Moore
R.
Shin
S.-I.
Stevenson
S.
Lehner
F.
Ault
T. R.
21272
Article
Idealized Simulations of the Tropical Climate and Variability in the Single Column Atmosphere Model (SCAM): Radiative-Convective Equilibrium
To explore the interactions among column processes in the Community Atmosphere Model (CAM), the single-column version of CAM (SCAM) is integrated for 1000 days in radiative-convective equilibrium (RCE) with tropical values of boundary conditions, spanning a parameter or configuration space of model physics versions (v5 vs. v6), vertical resolution (standard and 60 levels), sea surface temperature (SST), and some interpretation-driven experiments. The simulated time-mean climate is reasonable, near observations and RCE of a cyclic cloud-resolving model. Updraft detrainment in the deep convection scheme produces distinctive grid-scale structures in humidity and cloud, which also interact with radiative transfer processes. These grid artifacts average out in multi-column RCE results reported elsewhere, illustrating the nuts-and-bolts interpretability that SCAM adds to the hierarchy of model configurations. Multi-day oscillations of precipitation arise from descent of warm convection-capping layers starting near the tropopause, eventually reset by a burst of convective deepening. Experiments reveal how these oscillations depend critically on an internal parameter that controls the number of neutral buoyancy levels allowed for determining cloud top and computing dilute convective available potential energy in the deep convection scheme, and merely modified a little by disabling cloud-base radiation (heating of cloud base). This strong dependence of transient behavior in 1D on this parameter will be tested in the second part of this work, in which SCAM is coupled to a parameterized dynamics of two-dimensional, linearized gravity wave, and in the 3D simulations in future study.
2022-2
J. Adv. Model. Earth Syst.
14
e2021MS002826
0
10.1029/2021MS002826
To explore the interactions among column processes in the Community Atmosphere Model (CAM), the single-column version of CAM (SCAM) is integrated for 1000 days in radiative-convective equilibrium (RCE) with tropical values of boundary conditions, spanning a parameter or configuration space of model physics versions (v5 vs. v6), vertical resolution (standard and 60 levels), sea surface temperature (SST), and some interpretation-driven experiments. The simulated time-mean climate is reasonable, near observations and RCE of a cyclic cloud-resolving model. Updraft detrainment in the deep convection scheme produces distinctive grid-scale structures in humidity and cloud, which also interact with radiative transfer processes. These grid artifacts average out in multi-column RCE results reported elsewhere, illustrating the nuts-and-bolts interpretability that SCAM adds to the hierarchy of model configurations. Multi-day oscillations of precipitation arise from descent of warm convection-capping layers starting near the tropopause, eventually reset by a burst of convective deepening. Experiments reveal how these oscillations depend critically on an internal parameter that controls the number of neutral buoyancy levels allowed for determining cloud top and computing dilute convective available potential energy in the deep convection scheme, and merely modified a little by disabling cloud-base radiation (heating of cloud base). This strong dependence of transient behavior in 1D on this parameter will be tested in the second part of this work, in which SCAM is coupled to a parameterized dynamics of two-dimensional, linearized gravity wave, and in the 3D simulations in future study.
Hu
I.-K.
Mapes
B. E.
Tulich
S. N.
Neale
R. B.
Gettelman
A.
Reed
K. A.
21273
Article
The DataHawk2 Uncrewed Aircraft System for Atmospheric Research
The DataHawk2 (DH2) is a small, fixed-wing, uncrewed aircraft system, or UAS, developed at the University of Colorado (CU) primarily for taking detailed thermodynamic measurements of the atmospheric boundary layer. The DH2 weighs 1.7kg and has a wingspan of 1.3m, with a flight endurance of approximately 60min, depending on configuration. In the DH2’s most modern form, the aircraft carries a Vaisala RSS-421 sensor for pressure, temperature, and relative humidity measurements, two CU-developed infrared temperature sensors, and a CU-developed fine-wire array, in addition to sensors required to support autopilot function (pitot tube with pressure sensor, GPS receiver, inertial measurement unit), from which wind speed and direction can also be estimated. This paper presents a description of the DH2, including information on its design and development work, and puts the DH2 into context with respect to other contemporary UASs. Data from recent field work (MOSAiC, the Multidisciplinary drifting Observatory for the Study of Arctic Climate) is presented and compared with radiosondes deployed during that campaign to provide an overview of sensor and system performance. These data show good agreement across pressure, temperature, and relative humidity as well as across wind speed and direction. Additional examples of measurementsprovided bytheDH2aregivenfrom a variety of previous campaigns in locations ranging from the continental United States to Japan and northern Alaska. Finally, a look toward future system improvements and upcoming research campaign participation is given.
2022-11
Atmos. Meas. Tech.
15
6789–6806
0
10.5194/amt-15-6789-2022
The DataHawk2 (DH2) is a small, fixed-wing, uncrewed aircraft system, or UAS, developed at the University of Colorado (CU) primarily for taking detailed thermodynamic measurements of the atmospheric boundary layer. The DH2 weighs 1.7kg and has a wingspan of 1.3m, with a flight endurance of approximately 60min, depending on configuration. In the DH2’s most modern form, the aircraft carries a Vaisala RSS-421 sensor for pressure, temperature, and relative humidity measurements, two CU-developed infrared temperature sensors, and a CU-developed fine-wire array, in addition to sensors required to support autopilot function (pitot tube with pressure sensor, GPS receiver, inertial measurement unit), from which wind speed and direction can also be estimated. This paper presents a description of the DH2, including information on its design and development work, and puts the DH2 into context with respect to other contemporary UASs. Data from recent field work (MOSAiC, the Multidisciplinary drifting Observatory for the Study of Arctic Climate) is presented and compared with radiosondes deployed during that campaign to provide an overview of sensor and system performance. These data show good agreement across pressure, temperature, and relative humidity as well as across wind speed and direction. Additional examples of measurementsprovided bytheDH2aregivenfrom a variety of previous campaigns in locations ranging from the continental United States to Japan and northern Alaska. Finally, a look toward future system improvements and upcoming research campaign participation is given.
Hamilton
J.
de Boer
G.
Doddi
A.
Lawrence
D.
21274
Article
Vegetation Type is an Important Predictor of the Arctic Summer Land Surface Energy Budget
Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.
2022-10
Nat. Commun.
13
6379
0
10.1038/s41467-022-34049-3
Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.
Oehri
J.
Schaepman-Strub
G.
Kim
J.-S.
Grysko
R.
. .
.
Cox
C. J.
. .
.
Morris
S. M.
. .
.
Uttal
T.
. .
.
de Boer
G.
al.
et
21275
Article
Observational data from uncrewed systems over Southern Great Plains
Uncrewed Systems (UxS), including uncrewed aerial systems (UAS) and tethered balloon/kite systems (TBS), are significantly expanding observational capabilities in atmospheric science. Rapid adaptation of these platforms and the advancement of miniaturized instruments have resulted in an expanding number of datasets captured under various environmental conditions by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. In 2021, observational data collected using ARM UxS platforms, including seven TigerShark UAS flights and 133 tethered balloon system (TBS) flights, were archived by the ARM Data Center (https://adc.arm.gov/discovery/#/, last access: 11 February 2022) and made publicly available at no cost for all registered users (https://doi.org/10.5439/1846798) (Mei and Dexheimer, 2022). These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research. This paper describes the DOE UAS/TBS datasets, including information on the acquisition, collection, and quality control processes, and highlights the potential scientific contributions using UAS and TBS platforms.
2022-7
Earth Syst. Sci. Data
14
3423–3438
0
10.5194/essd-14-3423-2022
Uncrewed Systems (UxS), including uncrewed aerial systems (UAS) and tethered balloon/kite systems (TBS), are significantly expanding observational capabilities in atmospheric science. Rapid adaptation of these platforms and the advancement of miniaturized instruments have resulted in an expanding number of datasets captured under various environmental conditions by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. In 2021, observational data collected using ARM UxS platforms, including seven TigerShark UAS flights and 133 tethered balloon system (TBS) flights, were archived by the ARM Data Center (https://adc.arm.gov/discovery/#/, last access: 11 February 2022) and made publicly available at no cost for all registered users (https://doi.org/10.5439/1846798) (Mei and Dexheimer, 2022). These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research. This paper describes the DOE UAS/TBS datasets, including information on the acquisition, collection, and quality control processes, and highlights the potential scientific contributions using UAS and TBS platforms.
Mei
F.
Pekour
S.
Dexheimer
D.
de Boer
G.
Cook
R.
Tomlinson
J.
Schmid
B.
Goldberger
L. A.
Newsom
R.
Fast
J. D.
21276
Article
The frictional layer in the observed momentum budget of the trades
Profiles of eddy momentum flux divergence are calculated as the residual in the momentum budget constructed from airborne circular dropsonde arrays (220 km) for 13 days during the EUREC
A/ATOMIC field campaign. The observed dynamical forcing averaged over all flights agrees broadly with European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) forecasts. In the direction of the flow, a mean flux divergence (friction) exists over a 1.5-km deep Ekman layer, and a mean flux convergence (acceleration) is present near cloud tops. The friction is countergradient between 1 and 1.5 km, where vertical wind shear exceeds the observed thermal wind. From the frictional profile, a 10-m momentum flux of 0.1 Nm
is derived, in line with Saildrone turbulence measurements. A momentum flux divergence in the cross-wind direction is pronounced near the surface and acts to veer the wind, opposing the friction-induced cross-isobaric wind turning. Weaker friction and upper-level acceleration of easterly flow are observed when stronger winds and more vigorous convection prevail. Turbulence measurements on board the SAFIRE ATR-42 aircraft and the Uncrewed Aircraft System (UAS) RAAVEN reveal pronounced spatial variability of momentum fluxes, with a non-negligible contribution of mesoscales (5–30 km). The findings highlight the nontrivial impact of turbulence, convection, and mesoscale flows in the presence of diverse cloud fields on the depth and strength of the frictional layer.
2022-10
Q. J. R. Meteorol. Soc.
148
3343-3365
0
10.1002/qj.4364
Profiles of eddy momentum flux divergence are calculated as the residual in the momentum budget constructed from airborne circular dropsonde arrays (220 km) for 13 days during the EUREC
A/ATOMIC field campaign. The observed dynamical forcing averaged over all flights agrees broadly with European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) forecasts. In the direction of the flow, a mean flux divergence (friction) exists over a 1.5-km deep Ekman layer, and a mean flux convergence (acceleration) is present near cloud tops. The friction is countergradient between 1 and 1.5 km, where vertical wind shear exceeds the observed thermal wind. From the frictional profile, a 10-m momentum flux of 0.1 Nm
is derived, in line with Saildrone turbulence measurements. A momentum flux divergence in the cross-wind direction is pronounced near the surface and acts to veer the wind, opposing the friction-induced cross-isobaric wind turning. Weaker friction and upper-level acceleration of easterly flow are observed when stronger winds and more vigorous convection prevail. Turbulence measurements on board the SAFIRE ATR-42 aircraft and the Uncrewed Aircraft System (UAS) RAAVEN reveal pronounced spatial variability of momentum fluxes, with a non-negligible contribution of mesoscales (5–30 km). The findings highlight the nontrivial impact of turbulence, convection, and mesoscale flows in the presence of diverse cloud fields on the depth and strength of the frictional layer.
Nuijens
L.
Savazzi
A.
de Boer
G.
Brilouet
P.-E.
George
G.
Lothon
M.
Zhang
D.
21277
Article
A Central Arctic Extreme Aerosol Event Triggered by a Warm Air-Mass Intrusion
Frequency and intensity of warm and moist air-mass intrusions into the Arctic have increased over the past decades and have been related to sea ice melt. During our year-long expedition in the remote central Arctic Ocean, a record-breaking increase in temperature, moisture and downwelling-longwave radiation was observed in mid-April 2020, during an air-mass intrusion carrying air pollutants from northern Eurasia. The two-day intrusion, caused drastic changes in the aerosol size distribution, chemical composition and particle hygroscopicity. Here we show how the intrusion transformed the Arctic from a remote low-particle environment to an area comparable to a central-European urban setting. Additionally, the intrusion resulted in an explosive increase in cloud condensation nuclei, which can have direct effects on Arctic clouds’ radiation, their precipitation patterns, and their lifetime. Thus, unless prompt actions to significantly reduce emissions in the source regions are taken, such intrusion events are expected to continue to affect the Arctic climate.
2022-9
Nat. Commun.
13
5290
0
10.1038/s41467-022-32872-2
Frequency and intensity of warm and moist air-mass intrusions into the Arctic have increased over the past decades and have been related to sea ice melt. During our year-long expedition in the remote central Arctic Ocean, a record-breaking increase in temperature, moisture and downwelling-longwave radiation was observed in mid-April 2020, during an air-mass intrusion carrying air pollutants from northern Eurasia. The two-day intrusion, caused drastic changes in the aerosol size distribution, chemical composition and particle hygroscopicity. Here we show how the intrusion transformed the Arctic from a remote low-particle environment to an area comparable to a central-European urban setting. Additionally, the intrusion resulted in an explosive increase in cloud condensation nuclei, which can have direct effects on Arctic clouds’ radiation, their precipitation patterns, and their lifetime. Thus, unless prompt actions to significantly reduce emissions in the source regions are taken, such intrusion events are expected to continue to affect the Arctic climate.
Dada
L.
Angot
H.
Beck
I.
Baccarini
A.
. .
.
de Boer
G.
Shupe
M. D.
al.
et
21281
Article
Potential caveats in land surface model evaluations using the US drought monitor: roles of base periods and drought indicators
The US drought monitor (USDM) has been widely used as an observational reference for evaluating land surface model (LSM) simulation of drought. This study investigates potential caveats in such evaluation when the USDM and LSMs use different base periods and drought indices to identify drought. The retrospective national water model (NWM) v2.0 simulation (1993–2018) was used to exemplify the evaluation, supplemented by North American land data assimilation system phase 2 (NLDAS-2). Over their common period (2000–2018), in distinct contrast with the USDM which shows high drought occurrence (>50%) in the western half of the continental US (CONUS) and the southeastern US with low occurrence (<30%) elsewhere, the NWM and NLDAS-2 based on soil moisture percentiles (SMPs) consistently show higher drought occurrence (30%–40%) in the central and southeastern US than the rest of the CONUS. Much of the differences between the LSMs and USDM, particularly the strong LSM underestimation of drought occurrence in the western and southeastern US, are not attributed to the LSM deficiencies, but rather the lack of long-term drought in the LSM simulations due to their relatively short lengths. Specifically, the USDM integrates drought indices with century-long periods of record, which enables it to capture both short-term (<6 months) drought and long-term (⩾6 months) drought, whereas the relatively short retrospective simulations of the LSMs allows them to adequately capture short-term drought but not long-term drought. In addition, the USDM integrates many drought indices whereas the NWM results are solely based on the SMP, further adding to the inconsistency. The high occurrence of long-term drought in the western and southeastern US in the USDM is further found to be driven collectively by the post-2000 long-term warm sea surface temperature (SST) trend, cold Pacific decadal oscillation and warm Atlantic multi-decadal oscillation, all of which are typical leading patterns of global SST variability that can induce drought conditions in the western, central, and southeastern US. Our findings highlight the effects of the above caveats and suggest that LSM evaluation should stay qualitative when the caveats are considerable.
2022-1
Environ. Res. Lett.
17
014011
0
10.1088/1748-9326/ac3f63
The US drought monitor (USDM) has been widely used as an observational reference for evaluating land surface model (LSM) simulation of drought. This study investigates potential caveats in such evaluation when the USDM and LSMs use different base periods and drought indices to identify drought. The retrospective national water model (NWM) v2.0 simulation (1993–2018) was used to exemplify the evaluation, supplemented by North American land data assimilation system phase 2 (NLDAS-2). Over their common period (2000–2018), in distinct contrast with the USDM which shows high drought occurrence (>50%) in the western half of the continental US (CONUS) and the southeastern US with low occurrence (<30%) elsewhere, the NWM and NLDAS-2 based on soil moisture percentiles (SMPs) consistently show higher drought occurrence (30%–40%) in the central and southeastern US than the rest of the CONUS. Much of the differences between the LSMs and USDM, particularly the strong LSM underestimation of drought occurrence in the western and southeastern US, are not attributed to the LSM deficiencies, but rather the lack of long-term drought in the LSM simulations due to their relatively short lengths. Specifically, the USDM integrates drought indices with century-long periods of record, which enables it to capture both short-term (<6 months) drought and long-term (⩾6 months) drought, whereas the relatively short retrospective simulations of the LSMs allows them to adequately capture short-term drought but not long-term drought. In addition, the USDM integrates many drought indices whereas the NWM results are solely based on the SMP, further adding to the inconsistency. The high occurrence of long-term drought in the western and southeastern US in the USDM is further found to be driven collectively by the post-2000 long-term warm sea surface temperature (SST) trend, cold Pacific decadal oscillation and warm Atlantic multi-decadal oscillation, all of which are typical leading patterns of global SST variability that can induce drought conditions in the western, central, and southeastern US. Our findings highlight the effects of the above caveats and suggest that LSM evaluation should stay qualitative when the caveats are considerable.
Wang
H.
Xu
L.
Hughes
M.
Chelliah
M.
DeWitt
D. G.
Fuchs
B. A.
Jackson
D. L.
21282
Article
Projections of North American Snow from NA-CORDEX and their Uncertainties, with a Focus on Model Resolution
Snow is important for many physical, social, and economic sectors in North America. In a warming climate, the characteristics of snow will likely change in fundamental ways, therefore compelling societal need for future projections of snow. However, many stakeholders require climate change information at finer resolutions that global climate models (GCMs) can provide. The North American Coordinated Regional Downscaling Experiment (NA-CORDEX) provides an ensemble of regional climate model (RCMs) simulations at two resolutions (~ 0.5° and ~ 0.25°) designed to help serve the climate impacts and adaptation communities. This is the first study to examine the differences in end of twenty-first-century projections of snow from the NA-CORDEX RCMs and their driving GCMs. We find that the broad patterns of change are similar across RCMs and GCMs: snow cover retreats, snow mass decreases everywhere except at high latitudes, and the duration of the snow covered season decreases. Regionally, the spatial details, magnitude, percent, and uncertainty of future changes vary between the GCM and RCM ensemble but are similar between the two resolutions of the RCM ensembles. An increase in winter snow amounts at high latitudes is a robust response across all ensembles. Percent snow losses are found to be more substantial in the GCMs than the RCMs over most of North America, especially in regions with high-elevation topography. Specifically, percent snow losses decrease with increasing elevation as the model resolution becomes finer.
2022-2
Clim. Change
170
0
10.1007/s10584-021-03294-8
Snow is important for many physical, social, and economic sectors in North America. In a warming climate, the characteristics of snow will likely change in fundamental ways, therefore compelling societal need for future projections of snow. However, many stakeholders require climate change information at finer resolutions that global climate models (GCMs) can provide. The North American Coordinated Regional Downscaling Experiment (NA-CORDEX) provides an ensemble of regional climate model (RCMs) simulations at two resolutions (~ 0.5° and ~ 0.25°) designed to help serve the climate impacts and adaptation communities. This is the first study to examine the differences in end of twenty-first-century projections of snow from the NA-CORDEX RCMs and their driving GCMs. We find that the broad patterns of change are similar across RCMs and GCMs: snow cover retreats, snow mass decreases everywhere except at high latitudes, and the duration of the snow covered season decreases. Regionally, the spatial details, magnitude, percent, and uncertainty of future changes vary between the GCM and RCM ensemble but are similar between the two resolutions of the RCM ensembles. An increase in winter snow amounts at high latitudes is a robust response across all ensembles. Percent snow losses are found to be more substantial in the GCMs than the RCMs over most of North America, especially in regions with high-elevation topography. Specifically, percent snow losses decrease with increasing elevation as the model resolution becomes finer.
McCrary
R. R.
Mearns
L. O.
Hughes
M.
Biner
S.
Bukovsky
M. S.
21286
Article
Wet season rainfall onset and flash drought: The case of the northern Australian wet season
In this paper, we report on the frequency of false onsets of wet season rainfall in the case of the Northern Australian wet season and investigate the role of large-scale tropical climate processes such as the El Niño–Southern Oscillation, Indian Ocean Dipole (IOD) and Madden–Julian Oscillation. A false onset occurs when a wet season rainfall onset criterion is met, but follow-up rainfall is not received for weeks or months later. Our analysis of wet season rainfall data from 1950 through 2020 shows a false onset occurs, on average, between 20 and 30% of wet seasons across all of northern Australia. This increases at a regional and local level such as at Darwin, the Northern Territory (NT), and parts of Queensland's north coast to over 50%. Seasonal climate influences, such as a La Niña pattern and a negative IOD that typically expedite the wet season rainfall onset, also increase the likelihood of a false onset over northern Australia. Our analysis also finds that periods of false onsets can sometimes, but not always, coincide with periods of rapid soil moisture depletion. The false rainfall onsets that develop into flash drought can be potentially disruptive and costly and are of potential significance for agriculture and fire management in northern Australia, and in other monsoonal climates that also typically experience a slow build-up to the seasonal monsoon. In conclusion, effective rainfall indicates that many seasons experience ‘false onsets’ with dry conditions after early rainfall. We propose that false onsets are a physical characteristic of the climate of northern Australia which occurs with relatively high frequency. In addition, these false onsets may sometimes co-occur with a flash drought.
2022-10
Int. J. Climatol.
42
6499-6514
0
10.1002/joc.7609
In this paper, we report on the frequency of false onsets of wet season rainfall in the case of the Northern Australian wet season and investigate the role of large-scale tropical climate processes such as the El Niño–Southern Oscillation, Indian Ocean Dipole (IOD) and Madden–Julian Oscillation. A false onset occurs when a wet season rainfall onset criterion is met, but follow-up rainfall is not received for weeks or months later. Our analysis of wet season rainfall data from 1950 through 2020 shows a false onset occurs, on average, between 20 and 30% of wet seasons across all of northern Australia. This increases at a regional and local level such as at Darwin, the Northern Territory (NT), and parts of Queensland's north coast to over 50%. Seasonal climate influences, such as a La Niña pattern and a negative IOD that typically expedite the wet season rainfall onset, also increase the likelihood of a false onset over northern Australia. Our analysis also finds that periods of false onsets can sometimes, but not always, coincide with periods of rapid soil moisture depletion. The false rainfall onsets that develop into flash drought can be potentially disruptive and costly and are of potential significance for agriculture and fire management in northern Australia, and in other monsoonal climates that also typically experience a slow build-up to the seasonal monsoon. In conclusion, effective rainfall indicates that many seasons experience ‘false onsets’ with dry conditions after early rainfall. We propose that false onsets are a physical characteristic of the climate of northern Australia which occurs with relatively high frequency. In addition, these false onsets may sometimes co-occur with a flash drought.
Lisonbee
J.
Ribbe
J.
Otkin
J. A.
Pudmenzky
C.
21287
Article
Long-term single-column model intercomparison of diurnal cycle of precipitation over midlatitude and tropical land
General Circulation Models (GCMs) have for decades exhibited difficulties in modelling the diurnal cycle of precipitation (DCP). This issue can be related to inappropriate representation of the processes controlling sub-diurnal phenomena like convection. In this study, 11 single-column versions of GCMs are used to investigate the interactions between convection and environmental conditions, processes that control nocturnal convections, and the transition from shallow to deep convection on a diurnal time-scale. Long-term simulations are performed over two continental land sites: the Southern Great Plains (SGP) in the USA for 12 summer months from 2004 to 2015 and the Manacapuru site at the central Amazon (MAO) in Brazil for two full years from 2014 to 2015. The analysis is done on two regimes: afternoon convective regime and nocturnal precipitation regime. Most models produce afternoon precipitation too early, likely due to the missing transition of shallow-to-deep convection in these models. At SGP, the unified convection schemes better simulate the onset time of precipitation. At MAO, models produce the heating peak in a much lower level compared with observation, indicating too shallow convection in the models. For nocturnal precipitation, models that produce most of nocturnal precipitation all allow convection to be triggered above the boundary layer. This indicates the importance of model capability to detect elevated convection for simulating nocturnal precipitation. Sensitivity studies indicate that (a) nudging environmental variables towards observations has a minor impact on DCP, (b) unified treatment of shallow and deep convection and the capability to capture mid-level convection can help models better capture DCP, and (c) the interactions of the atmosphere with other components in the climate system (e.g. land) are also important for DCP simulations in coupled models. These results provide long-term statistical insights on which physical processes are essential in climate models to simulate DCP.
2022-1
Q. J. R. Meteorol. Soc.
148
641-669
0
10.1002/qj.4222
General Circulation Models (GCMs) have for decades exhibited difficulties in modelling the diurnal cycle of precipitation (DCP). This issue can be related to inappropriate representation of the processes controlling sub-diurnal phenomena like convection. In this study, 11 single-column versions of GCMs are used to investigate the interactions between convection and environmental conditions, processes that control nocturnal convections, and the transition from shallow to deep convection on a diurnal time-scale. Long-term simulations are performed over two continental land sites: the Southern Great Plains (SGP) in the USA for 12 summer months from 2004 to 2015 and the Manacapuru site at the central Amazon (MAO) in Brazil for two full years from 2014 to 2015. The analysis is done on two regimes: afternoon convective regime and nocturnal precipitation regime. Most models produce afternoon precipitation too early, likely due to the missing transition of shallow-to-deep convection in these models. At SGP, the unified convection schemes better simulate the onset time of precipitation. At MAO, models produce the heating peak in a much lower level compared with observation, indicating too shallow convection in the models. For nocturnal precipitation, models that produce most of nocturnal precipitation all allow convection to be triggered above the boundary layer. This indicates the importance of model capability to detect elevated convection for simulating nocturnal precipitation. Sensitivity studies indicate that (a) nudging environmental variables towards observations has a minor impact on DCP, (b) unified treatment of shallow and deep convection and the capability to capture mid-level convection can help models better capture DCP, and (c) the interactions of the atmosphere with other components in the climate system (e.g. land) are also important for DCP simulations in coupled models. These results provide long-term statistical insights on which physical processes are essential in climate models to simulate DCP.
Tang
S.
Xie
S.
Guo
Z.
Hong
S.-Y.
al.
et
21288
Article
Projected effects of climate change on Pseudo-nitzschia bloom dynamics in the Gulf of Maine
Worldwide, warming ocean temperatures have contributed to extreme harmful algal bloom events and shifts in phytoplankton species composition. In 2016 in the Gulf of Maine (GOM), an unprecedented Pseudo-nitzschia bloom led to the first domoic-acid induced shellfishery closures in the region. Potential links between climate change, warming temperatures, and the GOM Pseudo-nitzschia assemblage, however, remain unexplored. In this study, a global climate change projection previously downscaled to 7-km resolution for the Northwest Atlantic was further refined with a 1–3-km resolution simulation of the GOM to investigate the effects of climate change on HAB dynamics. A 25-year time slice of projected conditions at the end of the 21st century (2073–2097) was compared to a 25-year hindcast of contemporary ocean conditions (1994–2018) and analyzed for changes to GOM inflows, transport, and Pseudo-nitzschia australis growth potential. On average, climate change is predicted to lead to increased temperatures, decreased salinity, and increased stratification in the GOM, with the largest changes occurring in the late summer. Inflows from the Scotian Shelf are projected to increase, and alongshore transport in the Eastern Maine Coastal Current is projected to intensify. Increasing ocean temperatures will likely make P. australis growth conditions less favorable in the southern and western GOM but improve P. australis growth conditions in the eastern GOM, including a later growing season in the fall, and a longer growing season in the spring. Combined, these changes suggest that P. australis blooms in the eastern GOM could intensify in the 21st century, and that the overall Pseudo-nitzschia species assemblage might shift to warmer-adapted species such as P. plurisecta or other Pseudo-nitzschia species that may be introduced.
2022-6
J. Mar. Syst.
230
103737
0
10.1016/j.jmarsys.2022.103737
Worldwide, warming ocean temperatures have contributed to extreme harmful algal bloom events and shifts in phytoplankton species composition. In 2016 in the Gulf of Maine (GOM), an unprecedented Pseudo-nitzschia bloom led to the first domoic-acid induced shellfishery closures in the region. Potential links between climate change, warming temperatures, and the GOM Pseudo-nitzschia assemblage, however, remain unexplored. In this study, a global climate change projection previously downscaled to 7-km resolution for the Northwest Atlantic was further refined with a 1–3-km resolution simulation of the GOM to investigate the effects of climate change on HAB dynamics. A 25-year time slice of projected conditions at the end of the 21st century (2073–2097) was compared to a 25-year hindcast of contemporary ocean conditions (1994–2018) and analyzed for changes to GOM inflows, transport, and Pseudo-nitzschia australis growth potential. On average, climate change is predicted to lead to increased temperatures, decreased salinity, and increased stratification in the GOM, with the largest changes occurring in the late summer. Inflows from the Scotian Shelf are projected to increase, and alongshore transport in the Eastern Maine Coastal Current is projected to intensify. Increasing ocean temperatures will likely make P. australis growth conditions less favorable in the southern and western GOM but improve P. australis growth conditions in the eastern GOM, including a later growing season in the fall, and a longer growing season in the spring. Combined, these changes suggest that P. australis blooms in the eastern GOM could intensify in the 21st century, and that the overall Pseudo-nitzschia species assemblage might shift to warmer-adapted species such as P. plurisecta or other Pseudo-nitzschia species that may be introduced.
Clark
S.
Hubbard
K. A.
Ralston
D. K.
McGillicuddy
D. J.
Stock
C. A.
Alexander
M. A.
Curchitser
E. N.
21289
Article
Quantitative Precipitation Estimation using X-Band Radar for Orographic Rainfall in the San Francisco Bay Area
In the San Francisco Bay Area, precipitation occurs in the wintertime, mostly as rain. Wintertime rainfall can be further classified into cold or stratiform rain with a typical radar bright band signature and warm orographic rain with absence of a radar bright band. Vertical Pointing S-Band profiler radar and disdrometer measurements from two of NOAA’s Hydrometeorology Testbed (HMT) sites in California are used to study the differences in microphysical properties between these two types of rain and their implications in radar rainfall estimation. A methodology has been developed to discriminate non bright band (NBB) rainfall from bright band (BB) rainfall using reflectivity (Z) and differential reflectivity (Z DR ) computed from disdrometer data. Delineating the two rainfall types in this way allowed for an algorithm to be applied to the radar scans to identify rainfall types and apply appropriate reflectivity based and specific differential phase (K DP ) based rainfall estimators. Recently, a gap-filling X-Band weather radar with dual-polarization capabilities was deployed in the San Francisco Bay Area in Santa Rosa to aid in weather monitoring and provide high resolution Quantitative Precipitation Estimation (QPE) products. When applied to real radar observations, this method shows great potential for improving the QPE compared to traditional operational products which more often tend to underestimate rainfall in the California coastal region.
2022-9
IEEE Trans. Geosci. Remote Sens.
0
10.1109/TGRS.2022.3207829
In the San Francisco Bay Area, precipitation occurs in the wintertime, mostly as rain. Wintertime rainfall can be further classified into cold or stratiform rain with a typical radar bright band signature and warm orographic rain with absence of a radar bright band. Vertical Pointing S-Band profiler radar and disdrometer measurements from two of NOAA’s Hydrometeorology Testbed (HMT) sites in California are used to study the differences in microphysical properties between these two types of rain and their implications in radar rainfall estimation. A methodology has been developed to discriminate non bright band (NBB) rainfall from bright band (BB) rainfall using reflectivity (Z) and differential reflectivity (Z DR ) computed from disdrometer data. Delineating the two rainfall types in this way allowed for an algorithm to be applied to the radar scans to identify rainfall types and apply appropriate reflectivity based and specific differential phase (K DP ) based rainfall estimators. Recently, a gap-filling X-Band weather radar with dual-polarization capabilities was deployed in the San Francisco Bay Area in Santa Rosa to aid in weather monitoring and provide high resolution Quantitative Precipitation Estimation (QPE) products. When applied to real radar observations, this method shows great potential for improving the QPE compared to traditional operational products which more often tend to underestimate rainfall in the California coastal region.
Biswas
S. K.
Cifelli
R.
Chandrasekar
V.
21290
Article
An Optimal Interpolation–Based Snow Data Assimilation for NOAA’s Unified Forecast System (UFS)
Within the National Weather Service’s Unified Forecast System (UFS), snow depth and snow cover observations are assimilated once daily using a rule-based method designed to correct for gross errors. While this approach improved the forecasts over its predecessors, it is now quite outdated and is likely to result in suboptimal analysis. We have then implemented and evaluated a snow data assimilation using the 2D optimal interpolation (OI) method, which accounts for model and observation errors and their spatial correlations as a function of distances between the observations and model grid cells. The performance of the OI was evaluated by assimilating daily snow depth observations from the Global Historical Climatology Network (GHCN) and the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover data into the UFS, from October 2019 to March 2020. Compared to the control analysis, which is very similar to the method currently in operational use, the OI improves the forecast snow depth and snow cover. For instance, the unbiased snow depth root-mean-squared error (ubRMSE) was reduced by 45 mm and the snow cover hit rate increased by 4%. This leads to modest improvements to globally averaged near-surface temperature (an average reduction of 0.23 K in temperature bias), with significant local improvements in some regions (much of Asia, the central United States). The reduction in near-surface temperature error was primarily caused by improved snow cover fraction from the data assimilation. Based on these results, the OI DA is currently being transitioned into operational use for the UFS.
2022-12
Wea. Forecasting
37
2209–2221
0
10.1175/WAF-D-22-0061.1
Within the National Weather Service’s Unified Forecast System (UFS), snow depth and snow cover observations are assimilated once daily using a rule-based method designed to correct for gross errors. While this approach improved the forecasts over its predecessors, it is now quite outdated and is likely to result in suboptimal analysis. We have then implemented and evaluated a snow data assimilation using the 2D optimal interpolation (OI) method, which accounts for model and observation errors and their spatial correlations as a function of distances between the observations and model grid cells. The performance of the OI was evaluated by assimilating daily snow depth observations from the Global Historical Climatology Network (GHCN) and the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover data into the UFS, from October 2019 to March 2020. Compared to the control analysis, which is very similar to the method currently in operational use, the OI improves the forecast snow depth and snow cover. For instance, the unbiased snow depth root-mean-squared error (ubRMSE) was reduced by 45 mm and the snow cover hit rate increased by 4%. This leads to modest improvements to globally averaged near-surface temperature (an average reduction of 0.23 K in temperature bias), with significant local improvements in some regions (much of Asia, the central United States). The reduction in near-surface temperature error was primarily caused by improved snow cover fraction from the data assimilation. Based on these results, the OI DA is currently being transitioned into operational use for the UFS.
Gichamo
T. Z.
Draper
C.
21291
Article
Emerging Technologies and Approaches for In Situ, Autonomous Observing in the Arctic
Understanding and predicting Arctic change and its impacts on global climate requires broad, sustained observations of the atmosphere-ice-ocean system, yet technological and logistical challenges severely restrict the temporal and spatial scope of observing efforts. Satellite remote sensing provides unprecedented, pan-Arctic measurements of the surface, but complementary in situ observations are required to complete the picture. Over the past few decades, a diverse range of autonomous platforms have been developed to make broad, sustained observations of the ice-free ocean, often with near-real-time data delivery. Though these technologies are well suited to the difficult environmental conditions and remote logistics that complicate Arctic observing, they face a suite of additional challenges, such as limited access to satellite services that make geolocation and communication possible. This paper reviews new platform and sensor developments, adaptations of mature technologies, and approaches for their use, placed within the framework of Arctic Ocean observing needs.
2022-9
Oceanogr. Soc.
35
210 - 221
0
10.5670/oceanog.2022.127
Understanding and predicting Arctic change and its impacts on global climate requires broad, sustained observations of the atmosphere-ice-ocean system, yet technological and logistical challenges severely restrict the temporal and spatial scope of observing efforts. Satellite remote sensing provides unprecedented, pan-Arctic measurements of the surface, but complementary in situ observations are required to complete the picture. Over the past few decades, a diverse range of autonomous platforms have been developed to make broad, sustained observations of the ice-free ocean, often with near-real-time data delivery. Though these technologies are well suited to the difficult environmental conditions and remote logistics that complicate Arctic observing, they face a suite of additional challenges, such as limited access to satellite services that make geolocation and communication possible. This paper reviews new platform and sensor developments, adaptations of mature technologies, and approaches for their use, placed within the framework of Arctic Ocean observing needs.
Lee
C. M.
DeGrandpre
M.
Guthrie
J.
Hill
V.
Kwok
R.
Morison
J.
Cox
C. J.
Singh
H.
Stanton
T.
Wilkinson
J.
21295
Article
Attribution of North American Subseasonal Precipitation Prediction Skill
2022-11
Wea. Forecasting
37
2069–2085
0
10.1175/WAF-D-22-0076.1
Sun
L.
Hoerling
M. P.
Jadwiga
R.
Hoell
A.
Kumar
A.
Hurrell
J.
21296
Article
High temporal resolution estimates of Arctic snowfall rates emphasizing gauge and radar-based retrievals from the MOSAiC expedition
This article presents the results of snowfall rate and accumulation estimates from a vertically pointing 35-GHz radar and other sensors deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The radar-based retrievals are the most consistent in terms of data availability and are largely immune to blowing snow. The total liquid-equivalent accumulation during the snow accumulation season is around 110 mm, with more abundant precipitation during spring months. About half of the total accumulation came from weak snowfall with rates less than approximately 0.2 mmh–1. The total snowfall estimates from a Vaisala optical sensor aboard the icebreaker are similar to those from radar retrievals, though their daily and monthly accumulations and instantaneous rates varied significantly. Compared to radar retrievals and the icebreaker optical sensor data, measurements from an identical optical sensor at an ice camp are biased high. Blowing snow effects, in part, explain differences. Weighing gauge measurements significantly overestimate snowfall during February–April 2020 as compared to other sensors and are not well suited for estimating instantaneous snowfall rates. The icebreaker optical disdrometer estimates of snowfall rates are, on average, relatively little biased compared to radar retrievals when raw particle counts are available and appropriate snowflake mass-size relations are used. These counts, however, are not available during periods that produced more than a third of the total snowfall. While there are uncertainties in the radar-based retrievals due to the choice of reflectivity-snowfall rate relations, the major error contributor is the uncertainty in the radar absolute calibration. The MOSAiC radar calibration is evaluated using comparisons with other radars and liquid water cloud–drizzle processes observed during summer. Overall, this study describes a consistent, radar-based snowfall rate product for MOSAiC that provides significant insight into Central Arctic snowfall and can be used for many other purposes.
2022-4
Elementa Sci. Anthrop.
10
00101
0
10.1525/elementa.2021.00101
This article presents the results of snowfall rate and accumulation estimates from a vertically pointing 35-GHz radar and other sensors deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The radar-based retrievals are the most consistent in terms of data availability and are largely immune to blowing snow. The total liquid-equivalent accumulation during the snow accumulation season is around 110 mm, with more abundant precipitation during spring months. About half of the total accumulation came from weak snowfall with rates less than approximately 0.2 mmh–1. The total snowfall estimates from a Vaisala optical sensor aboard the icebreaker are similar to those from radar retrievals, though their daily and monthly accumulations and instantaneous rates varied significantly. Compared to radar retrievals and the icebreaker optical sensor data, measurements from an identical optical sensor at an ice camp are biased high. Blowing snow effects, in part, explain differences. Weighing gauge measurements significantly overestimate snowfall during February–April 2020 as compared to other sensors and are not well suited for estimating instantaneous snowfall rates. The icebreaker optical disdrometer estimates of snowfall rates are, on average, relatively little biased compared to radar retrievals when raw particle counts are available and appropriate snowflake mass-size relations are used. These counts, however, are not available during periods that produced more than a third of the total snowfall. While there are uncertainties in the radar-based retrievals due to the choice of reflectivity-snowfall rate relations, the major error contributor is the uncertainty in the radar absolute calibration. The MOSAiC radar calibration is evaluated using comparisons with other radars and liquid water cloud–drizzle processes observed during summer. Overall, this study describes a consistent, radar-based snowfall rate product for MOSAiC that provides significant insight into Central Arctic snowfall and can be used for many other purposes.
Matrosov
S. Y.
Shupe
M. D.
Uttal
T.
21299
Article
A Prognostic-Stochastic and Scale-Adaptive Cumulus Convection Closure for Improved Tropical Variability and Convective Gray-Zone Representation in NOAA’s Unified Forecast System (UFS)
A prognostic closure is introduced to, and evaluated in, NOAA’s Unified Forecast System. The closure addresses aspects that are not commonly represented in traditional cumulus convection parameterizations, and it departs from the previous assumptions of a negligible subgrid area coverage and statistical quasi-equilibrium at steady state, the latter of which becomes invalid at higher resolution. The new parameterization introduces a prognostic evolution of the convective updraft area fraction based on a moisture budget, and, together with the buoyancy-driven updraft vertical velocity, it completes the cloud-base mass flux. In addition, the new closure addresses stochasticity and includes a representation of subgrid convective organization using cellular automata as well as scale-adaptive considerations. The new cumulus convection closure shows potential for improved Madden–Julian oscillation (MJO) prediction. In our simulations we observe better propagation, amplitude, and phase of the MJO in a case study relative to the control simulation. This improvement can be partly attributed to a closer coupling between low-level moisture flux convergence and precipitation as revealed by a space–time coherence spectrum. In addition, we find that enhanced organization feedback representation and stochastic effects, represented using cellular automata, further enhance the amplitude and propagation of the MJO, and they provide realistic uncertainty estimates of convectively coupled equatorial waves at seasonal time scales. The scale-adaptive behavior of the scheme is also studied by running the global model with 25-, 13-, 9-, and 3-km grid spacing. It is found that the convective area fraction and the convective updraft velocity are both scale adaptive, leading to a reduction of subgrid convective precipitation in the higher-resolution simulations.
2022-12
Mon. Wea. Rev.
150
3211–3227
0
10.1175/MWR-D-22-0114.1
A prognostic closure is introduced to, and evaluated in, NOAA’s Unified Forecast System. The closure addresses aspects that are not commonly represented in traditional cumulus convection parameterizations, and it departs from the previous assumptions of a negligible subgrid area coverage and statistical quasi-equilibrium at steady state, the latter of which becomes invalid at higher resolution. The new parameterization introduces a prognostic evolution of the convective updraft area fraction based on a moisture budget, and, together with the buoyancy-driven updraft vertical velocity, it completes the cloud-base mass flux. In addition, the new closure addresses stochasticity and includes a representation of subgrid convective organization using cellular automata as well as scale-adaptive considerations. The new cumulus convection closure shows potential for improved Madden–Julian oscillation (MJO) prediction. In our simulations we observe better propagation, amplitude, and phase of the MJO in a case study relative to the control simulation. This improvement can be partly attributed to a closer coupling between low-level moisture flux convergence and precipitation as revealed by a space–time coherence spectrum. In addition, we find that enhanced organization feedback representation and stochastic effects, represented using cellular automata, further enhance the amplitude and propagation of the MJO, and they provide realistic uncertainty estimates of convectively coupled equatorial waves at seasonal time scales. The scale-adaptive behavior of the scheme is also studied by running the global model with 25-, 13-, 9-, and 3-km grid spacing. It is found that the convective area fraction and the convective updraft velocity are both scale adaptive, leading to a reduction of subgrid convective precipitation in the higher-resolution simulations.
Bengtsson
L.
Gerard
L.
Han
J.
Gehne
M.
Li
W.
Dias
J.
21300
Article
Sensitivity of the Arctic Sea Ice Cover to the Summer Surface Scattering Layer
The “surface scattering layer” (SSL) is the highly-scattering, coarse-grained ice layer that forms on the surface of melting, drained sea ice during spring and summer. Ice of sufficient thickness with an SSL has an observed persistent broadband albedo of ∼0.65, resulting in a strong influence on the regional solar partitioning. Experiments during the Multidisciplinary drifting Observatory for the Study of the Arctic Climate expedition showed that the SSL re-forms in approximately 1 day following manual removal. Coincident spectral albedo measurements provide insight into the SSL evolution, where albedo increased on sunny days with higher solar insolation. Comparison with experiments in radiative transfer and global climate models show that the sea ice albedo is greatly impacted by the SSL thickness. The presence of SSL is a significant component of the ice-albedo feedback, with an albedo impact of the same order as melt ponds. Changes in SSL and implications for Arctic sea ice within a warming climate are uncertain.
2022-5
Geophys. Res. Lett.
49
e2022GL098349
0
10.1029/2022GL098349
The “surface scattering layer” (SSL) is the highly-scattering, coarse-grained ice layer that forms on the surface of melting, drained sea ice during spring and summer. Ice of sufficient thickness with an SSL has an observed persistent broadband albedo of ∼0.65, resulting in a strong influence on the regional solar partitioning. Experiments during the Multidisciplinary drifting Observatory for the Study of the Arctic Climate expedition showed that the SSL re-forms in approximately 1 day following manual removal. Coincident spectral albedo measurements provide insight into the SSL evolution, where albedo increased on sunny days with higher solar insolation. Comparison with experiments in radiative transfer and global climate models show that the sea ice albedo is greatly impacted by the SSL thickness. The presence of SSL is a significant component of the ice-albedo feedback, with an albedo impact of the same order as melt ponds. Changes in SSL and implications for Arctic sea ice within a warming climate are uncertain.
Smith
M. M.
Light
B.
Macfarlane
A. R.
Perovich
D. K.
Holland
M. M.
Shupe
M. D.
21306
Article
The February 2021 Cold Air Outbreak in the United States: a Subseasonal Forecast of Opportunity
2022-12
Bull. Amer. Meteor. Soc.
103
E2887–E2904
0
10.1175/BAMS-D-21-0266.1
Albers
J. R.
Newman
M.
Hoell
A.
Breeden
M. L.
Lillo
S. P.
Wang
Y.
Lou
J.
21308
Article
Water Year 2021 Compound Precipitation and Temperature Extremes in California and Nevada
Anthropogenically forced-warming and La Niña forced-precipitation deficits caused at least a sixfold risk increase for compound extreme low precipitation and high temperature in California–Nevada from October 2020 to September 2021.
2022-12
Bull. Amer. Meteor. Soc.
103
E2905–E2911
0
10.1175/BAMS-D-22-0112.1
Anthropogenically forced-warming and La Niña forced-precipitation deficits caused at least a sixfold risk increase for compound extreme low precipitation and high temperature in California–Nevada from October 2020 to September 2021.
Hoell
A.
Quan
X.-W.
Hoerling
M. P.
Diaz
H. F.
Fu
R.
He
C.
Lisonbee
J.
Mankin
J.
Seager
R.
Sheffield
A.
Simpson
I.
Wahl
E. R.
21309
Article
An Improved Deep Learning Model for High-Impact Weather Nowcasting
Accurate nowcasting (short-term prediction, 0-6 h) of high-impact weather such as landfalling hurricanes and extreme convective precipitation plays a critical role in natural disaster monitoring and mitigation. A number of nowcasting approaches have been developed in the past few decades such as optical flow and the tracking radar echoes by correlation (TREC) system. Most of these mainstream operational techniques are based on radar echo map extrapolation, which determines the velocity and direction of precipitation systems using historical and current radar observations. However, the skill of the traditional extrapolation method decreases rapidly within the first hour. In order to improve nowcasting skill, recent studies have proposed using deep learning methods such as Convolutional Recurrent Neural Network (ConvRNN) and Trajectory Gate Recurrent Unit (TrajGRU). But none of these methods focuses on high-impact weather events, and the deep learning models trained based on general precipitation events cannot meet the demand of accurate warnings and decision-making at the scales required for high-impact weather events such as hurricanes. Using multi-radar observations, this paper introduces the idea of self-attention and develops a self-attention-based GRU (SaGRU) to enhance its generalization capability and scalability in predicting high-impact weather events. In particular, two types of high-impact weather systems, namely, landfalling hurricanes and extreme convective precipitation events, are investigated. Three models are trained based on hurricane events, heavy rainfall (i.e., non-hurricane) events, and all events combined in the southeast United States during 2015 and 2020. The impacts of different data sources on the nowcasting performance are quantified. The evaluation results of nowcasting products show that our saGRU performs very well in predicting hurricane-induced rainfall. In the new methodology, the data from non-hurricane events are shown to provide u...
2022-9
IEEE J. Select. Topics Appl. Earth Obs. Remote Sens.
15
7400-7413
0
10.1109/JSTARS.2022.3203398
Accurate nowcasting (short-term prediction, 0-6 h) of high-impact weather such as landfalling hurricanes and extreme convective precipitation plays a critical role in natural disaster monitoring and mitigation. A number of nowcasting approaches have been developed in the past few decades such as optical flow and the tracking radar echoes by correlation (TREC) system. Most of these mainstream operational techniques are based on radar echo map extrapolation, which determines the velocity and direction of precipitation systems using historical and current radar observations. However, the skill of the traditional extrapolation method decreases rapidly within the first hour. In order to improve nowcasting skill, recent studies have proposed using deep learning methods such as Convolutional Recurrent Neural Network (ConvRNN) and Trajectory Gate Recurrent Unit (TrajGRU). But none of these methods focuses on high-impact weather events, and the deep learning models trained based on general precipitation events cannot meet the demand of accurate warnings and decision-making at the scales required for high-impact weather events such as hurricanes. Using multi-radar observations, this paper introduces the idea of self-attention and develops a self-attention-based GRU (SaGRU) to enhance its generalization capability and scalability in predicting high-impact weather events. In particular, two types of high-impact weather systems, namely, landfalling hurricanes and extreme convective precipitation events, are investigated. Three models are trained based on hurricane events, heavy rainfall (i.e., non-hurricane) events, and all events combined in the southeast United States during 2015 and 2020. The impacts of different data sources on the nowcasting performance are quantified. The evaluation results of nowcasting products show that our saGRU performs very well in predicting hurricane-induced rainfall. In the new methodology, the data from non-hurricane events are shown to provide u...
Yao
S.
Chen
H.
Thompson
E. J.
Cifelli
R.
21310
Article
Small-Scale Spatial Variations of Air-Sea Heat, Moisture, and Buoyancy Fluxes in the Tropical Trade Winds
Observations from two autonomous Wave Gliders and six Lagrangian Surface Wave Instrument Float with Tracking drifters in the northwestern tropical Atlantic during the January–February 2020 NOAA Atlantic Tradewind Ocean-atmosphere Mesoscale Interaction Campaign (ATOMIC) are used to evaluate the spatial variability of bulk air-sea heat, moisture, and buoyancy fluxes. Sea surface temperature (SST) gradients up to 0.7°C across 10–100 km frequently persisted for several days. SST gradients were a leading cause of systematic spatial air-sea sensible heat flux gradients, as variations over 5 Wm−2 across under 20 km were observed. Wind speed gradients played no significant role and air temperature adjustments to SST gradients sometimes acted to reduce spatial flux gradients. Wind speed, air temperature, and air humidity caused high-frequency spatial and temporal flux variations on both sides of SST gradients. A synthesis of observations demonstrated that fluxes were usually enhanced on the warm SST side of gradients compared to the cold SST side, with variations up to 10 Wm−2 in sensible heat and upward buoyancy fluxes and 50 Wm−2 in latent heat flux. Persistent SST gradients and high-frequency air temperature variations each contributed up to 5 Wm−2 variability in sensible heat flux. Latent heat flux was instead mostly driven by air humidity variability. Atmospheric gradients may result from convective structures or high-frequency turbulent fluctuations. Comparisons with 0.05°-resolution daily satellite SST observations demonstrate that remote sensing observations or lower-resolution models may not capture the small-scale spatial ocean variability present in the Atlantic trade wind region.
2022-10
J. Geophys. Res. Oceans
127
e2022JC018972
0
10.1029/2022JC018972
Observations from two autonomous Wave Gliders and six Lagrangian Surface Wave Instrument Float with Tracking drifters in the northwestern tropical Atlantic during the January–February 2020 NOAA Atlantic Tradewind Ocean-atmosphere Mesoscale Interaction Campaign (ATOMIC) are used to evaluate the spatial variability of bulk air-sea heat, moisture, and buoyancy fluxes. Sea surface temperature (SST) gradients up to 0.7°C across 10–100 km frequently persisted for several days. SST gradients were a leading cause of systematic spatial air-sea sensible heat flux gradients, as variations over 5 Wm−2 across under 20 km were observed. Wind speed gradients played no significant role and air temperature adjustments to SST gradients sometimes acted to reduce spatial flux gradients. Wind speed, air temperature, and air humidity caused high-frequency spatial and temporal flux variations on both sides of SST gradients. A synthesis of observations demonstrated that fluxes were usually enhanced on the warm SST side of gradients compared to the cold SST side, with variations up to 10 Wm−2 in sensible heat and upward buoyancy fluxes and 50 Wm−2 in latent heat flux. Persistent SST gradients and high-frequency air temperature variations each contributed up to 5 Wm−2 variability in sensible heat flux. Latent heat flux was instead mostly driven by air humidity variability. Atmospheric gradients may result from convective structures or high-frequency turbulent fluctuations. Comparisons with 0.05°-resolution daily satellite SST observations demonstrate that remote sensing observations or lower-resolution models may not capture the small-scale spatial ocean variability present in the Atlantic trade wind region.
Iyer
S.
Drushka
K.
Thompson
E. J.
Thomson
J.
21313
Article
The reemergence of the winter sea surface temperature tripole in the North Atlantic from ocean reanalysis data
Multiple ocean reanalyses and objective analyses are used to study the reemergence of the large–scale pattern of winter sea surface temperature anomalies (SSTAs) in the North Atlantic (15° N–70° N 80° W–8° W). The dominant SSTA pattern in winter forms under the North Atlantic Oscillation forcing and have a tripole structure with anomalies of one sign in the subtropics and the opposite sign in the tropics and high latitudes. Empirical orthogonal function (EOF) analysis indicates that the dominant mode of interannual variability in the summer seasonal thermocline (~ 65–90 m in August–September) also has a tripole structure. The reemergence mechanism is evaluated by correlating the time series of the leading pattern of ocean temperature anomalies in the summer seasonal thermocline with SSTAs over the course of the year. It is shown that the tripole in the summer seasonal thermocline is most strongly related to SSTAs in the previous March–April (explains ~ 15% of the variance), when the upper mixed layer (UML) of the North Atlantic is deepest. During summer, the SSTA variance explained by this EOF decreases, reaching a minimum of 5–6% in August–September. With the UML deepening in the subsequent autumn–winter, this value increases, reaching two–thirds of the initial signal.
2022-11
Clim. Dyn.
0
10.1007/s00382-022-06581-x
Multiple ocean reanalyses and objective analyses are used to study the reemergence of the large–scale pattern of winter sea surface temperature anomalies (SSTAs) in the North Atlantic (15° N–70° N 80° W–8° W). The dominant SSTA pattern in winter forms under the North Atlantic Oscillation forcing and have a tripole structure with anomalies of one sign in the subtropics and the opposite sign in the tropics and high latitudes. Empirical orthogonal function (EOF) analysis indicates that the dominant mode of interannual variability in the summer seasonal thermocline (~ 65–90 m in August–September) also has a tripole structure. The reemergence mechanism is evaluated by correlating the time series of the leading pattern of ocean temperature anomalies in the summer seasonal thermocline with SSTAs over the course of the year. It is shown that the tripole in the summer seasonal thermocline is most strongly related to SSTAs in the previous March–April (explains ~ 15% of the variance), when the upper mixed layer (UML) of the North Atlantic is deepest. During summer, the SSTA variance explained by this EOF decreases, reaching a minimum of 5–6% in August–September. With the UML deepening in the subsequent autumn–winter, this value increases, reaching two–thirds of the initial signal.
Sukhonos
P. A.
Alexander
M. A.
21314
Article
The Great Drought of the 21st Century in the American Southwest: Intensity and Causes in Historical Context and Future Implications
2022-9
Nat. Commun. Earth Environ.
3
202
0
10.1038/s43247-022-00532-4
Wahl
E. R.
Zorita
E.
Diaz
H. F.
Hoell
A.
21316
Article
Towards a more realistic representation of surface albedo in NASA CERES-derived surface radiative fluxes: A comparison with the MOSAiC field campaign
Accurate multidecadal radiative flux records are vital to understand Arctic amplification and constrain climate model uncertainties. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other surface type and comes from several sources. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes was conducted using in situ measurements from MOSAiC flux stations. The CERES Synoptic 1degree (SYN1deg) product overestimates the downwelling shortwave flux by +11.40 Wm–2 and underestimates the upwelling shortwave flux by –15.70 Wm–2 and downwelling longwave fluxes by –12.58 Wm–2 at the surface during summer. In addition, large differences are found in the upwelling longwave flux when the surface approaches the melting point (approximately 0°C). The biases in downwelling shortwave and longwave fluxes suggest that the atmosphere represented in CERES is too optically thin. The large negative bias in upwelling shortwave flux can be attributed in large part to lower surface albedo (–0.15) in satellite footprint relative to surface sensors. Additionally, the results show that the spectral surface albedo used in SYN1deg overestimates albedo in visible and mid-infrared bands. A series of radiative transfer model perturbation experiments are performed to quantify the factors contributing to the differences. The CERES-MOSAiC broadband albedo differences (approximately 20 Wm–2) explain a larger portion of the upwelling shortwave flux difference than the spectral albedo shape differences (approximately 3 Wm–2). In addition, the differences between perturbation experiments using hourly and monthly MOSAiC surface albedo suggest that approximately 25% of the sea ice surface albedo variability is explained by factors not correlated with daily sea ice concentration variability. Biases in net shortwave and longwave flux can be reduced to less than half by adjusting both albedo and cloud inputs toward observed values. The results indicate that improvements in the surface albedo and cloud data would substantially reduce the uncertainty in the Arctic surface radiation budget derived from CERES data products.
2022-6
Elementa Sci. Anthrop.
10
00013
0
10.1525/elementa.2022.00013
Accurate multidecadal radiative flux records are vital to understand Arctic amplification and constrain climate model uncertainties. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other surface type and comes from several sources. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes was conducted using in situ measurements from MOSAiC flux stations. The CERES Synoptic 1degree (SYN1deg) product overestimates the downwelling shortwave flux by +11.40 Wm–2 and underestimates the upwelling shortwave flux by –15.70 Wm–2 and downwelling longwave fluxes by –12.58 Wm–2 at the surface during summer. In addition, large differences are found in the upwelling longwave flux when the surface approaches the melting point (approximately 0°C). The biases in downwelling shortwave and longwave fluxes suggest that the atmosphere represented in CERES is too optically thin. The large negative bias in upwelling shortwave flux can be attributed in large part to lower surface albedo (–0.15) in satellite footprint relative to surface sensors. Additionally, the results show that the spectral surface albedo used in SYN1deg overestimates albedo in visible and mid-infrared bands. A series of radiative transfer model perturbation experiments are performed to quantify the factors contributing to the differences. The CERES-MOSAiC broadband albedo differences (approximately 20 Wm–2) explain a larger portion of the upwelling shortwave flux difference than the spectral albedo shape differences (approximately 3 Wm–2). In addition, the differences between perturbation experiments using hourly and monthly MOSAiC surface albedo suggest that approximately 25% of the sea ice surface albedo variability is explained by factors not correlated with daily sea ice concentration variability. Biases in net shortwave and longwave flux can be reduced to less than half by adjusting both albedo and cloud inputs toward observed values. The results indicate that improvements in the surface albedo and cloud data would substantially reduce the uncertainty in the Arctic surface radiation budget derived from CERES data products.
Huang
Y.
Taylor
P. C.
Rose
F. G.
Rutan
D. A.
Shupe
M. D.
Webster
M. A.
Smith
M. M.
21318
Article
Subseasonal precipitation forecasts of opportunity over southwest Asia
Subseasonal forecasts of opportunity (SFOs) for precipitation over southwest Asia during January–March at lead times of 3–6 weeks are identified using elevated expected forecast skill from a linear inverse model (LIM), an empirical dynamical model that uses statistical relationships to infer the predictable dynamics of a system. The expected forecast skill from this LIM, which is based on the atmospheric circulation, tropical outgoing longwave radiation, and sea surface temperatures, captures the predictability associated with many relevant signals as opposed to just one. Two modes of variability, El Niño–Southern Oscillation (ENSO) and the Madden–Julian Oscillation (MJO), which themselves are predictable because of their slow variations, are related to southwest Asia precipitation SFOs. Strong El Niño events, as observed in 1983, 1998, and 2016, significantly increase the likelihood by up to 3-fold of an SFO 3–4 and 5–6 weeks in advance. Strong La Niña events, as observed in 1989, 1999, 2000, also significantly increase the likelihood of an SFO at those same lead times. High-amplitude MJO events in phases 2–4 and 6–8 of greater than one standardized departure also significantly increase the likelihood of an SFO 3–4 weeks in advance. Predictable atmospheric circulation patterns preceding anomalously wet periods indicate a role for enhanced tropical convection in the South Pacific convergence zone (SPCZ) region, while suppressed convection is observed preceding predictable dry periods. Anomalous heating in this region is found to distinguish wet and dry periods during both El Niño and La Niña conditions, although the atmospheric circulation response to the heating differs between each ENSO phase.
2022-10
Weather Clim. Dynam.
3
1183–1197
0
10.5194/wcd-3-1183-2022
Subseasonal forecasts of opportunity (SFOs) for precipitation over southwest Asia during January–March at lead times of 3–6 weeks are identified using elevated expected forecast skill from a linear inverse model (LIM), an empirical dynamical model that uses statistical relationships to infer the predictable dynamics of a system. The expected forecast skill from this LIM, which is based on the atmospheric circulation, tropical outgoing longwave radiation, and sea surface temperatures, captures the predictability associated with many relevant signals as opposed to just one. Two modes of variability, El Niño–Southern Oscillation (ENSO) and the Madden–Julian Oscillation (MJO), which themselves are predictable because of their slow variations, are related to southwest Asia precipitation SFOs. Strong El Niño events, as observed in 1983, 1998, and 2016, significantly increase the likelihood by up to 3-fold of an SFO 3–4 and 5–6 weeks in advance. Strong La Niña events, as observed in 1989, 1999, 2000, also significantly increase the likelihood of an SFO at those same lead times. High-amplitude MJO events in phases 2–4 and 6–8 of greater than one standardized departure also significantly increase the likelihood of an SFO 3–4 weeks in advance. Predictable atmospheric circulation patterns preceding anomalously wet periods indicate a role for enhanced tropical convection in the South Pacific convergence zone (SPCZ) region, while suppressed convection is observed preceding predictable dry periods. Anomalous heating in this region is found to distinguish wet and dry periods during both El Niño and La Niña conditions, although the atmospheric circulation response to the heating differs between each ENSO phase.
Breeden
M. L.
Albers
J. R.
Hoell
A.
21319
Article
Correcting systematic and state-dependent errors in the NOAA FV3-GFS using neural networks
2022-11
J. Adv. Model. Earth Syst.
14
e2022MS003309
0
10.1029/2022MS003309
Chen
T.-C.
Penny
S. G.
Whitaker
J. S.
Frolov
S.
Pincus
R.
Tulich
S. N.
21320
Article
Annual cycle observations of aerosols capable of ice formation in central Arctic clouds
The Arctic is warming faster than anywhere else on Earth, prompting glacial melt, permafrost thaw, and sea ice decline. These severe consequences induce feedbacks that contribute to amplified warming, affecting weather and climate globally. Aerosols and clouds play a critical role in regulating radiation reaching the Arctic surface. However, the magnitude of their effects is not adequately quantified, especially in the central Arctic where they impact the energy balance over the sea ice. Specifically, aerosols called ice nucleating particles (INPs) remain understudied yet are necessary for cloud ice production and subsequent changes in cloud lifetime, radiative effects, and precipitation. Here, we report observations of INPs in the central Arctic over a full year, spanning the entire sea ice growth and decline cycle. Further, these observations are size-resolved, affording valuable information on INP sources. Our results reveal a strong seasonality of INPs, with lower concentrations in the winter and spring controlled by transport from lower latitudes, to enhanced concentrations of INPs during the summer melt, likely from marine biological production in local open waters. This comprehensive characterization of INPs will ultimately help inform cloud parameterizations in models of all scales.
2022-6
Nat. Commun.
13
3537
0
10.1038/s41467-022-31182-x
The Arctic is warming faster than anywhere else on Earth, prompting glacial melt, permafrost thaw, and sea ice decline. These severe consequences induce feedbacks that contribute to amplified warming, affecting weather and climate globally. Aerosols and clouds play a critical role in regulating radiation reaching the Arctic surface. However, the magnitude of their effects is not adequately quantified, especially in the central Arctic where they impact the energy balance over the sea ice. Specifically, aerosols called ice nucleating particles (INPs) remain understudied yet are necessary for cloud ice production and subsequent changes in cloud lifetime, radiative effects, and precipitation. Here, we report observations of INPs in the central Arctic over a full year, spanning the entire sea ice growth and decline cycle. Further, these observations are size-resolved, affording valuable information on INP sources. Our results reveal a strong seasonality of INPs, with lower concentrations in the winter and spring controlled by transport from lower latitudes, to enhanced concentrations of INPs during the summer melt, likely from marine biological production in local open waters. This comprehensive characterization of INPs will ultimately help inform cloud parameterizations in models of all scales.
Creamean
J. M.
Barry
K.
Hill
T. C. J.
Hume
C.
DeMott
P. J.
Shupe
M. D.
. .
.
Persson
P. O. G.
21321
Article
Year-round trace gas measurements in the Central Arctic during the MOSAiC expedition
Despite the key role of the Arctic in the global Earth system, year-round in-situ atmospheric composition observations within the Arctic are sparse and mostly rely on measurements at ground-based coastal stations. Measurements of a suite of in-situ trace gases were performed in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. These observations give a comprehensive picture of year-round near-surface atmospheric abundances of key greenhouse and trace gases, i.e., carbon dioxide, methane, nitrous oxide, ozone, carbon monoxide, dimethylsulfide, sulfur dioxide, elemental mercury, and selected volatile organic compounds (VOCs). Redundancy in certain measurements supported continuity and permitted cross-evaluation and validation of the data. This paper gives an overview of the trace gas measurements conducted during MOSAiC and highlights the high quality of the monitoring activities. In addition, in the case of redundant measurements, merged datasets are provided and recommended for further use by the scientific community.
2022-11
Nat. Sci. Data
9
723
0
10.1038/s41597-022-01769-6
Despite the key role of the Arctic in the global Earth system, year-round in-situ atmospheric composition observations within the Arctic are sparse and mostly rely on measurements at ground-based coastal stations. Measurements of a suite of in-situ trace gases were performed in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. These observations give a comprehensive picture of year-round near-surface atmospheric abundances of key greenhouse and trace gases, i.e., carbon dioxide, methane, nitrous oxide, ozone, carbon monoxide, dimethylsulfide, sulfur dioxide, elemental mercury, and selected volatile organic compounds (VOCs). Redundancy in certain measurements supported continuity and permitted cross-evaluation and validation of the data. This paper gives an overview of the trace gas measurements conducted during MOSAiC and highlights the high quality of the monitoring activities. In addition, in the case of redundant measurements, merged datasets are provided and recommended for further use by the scientific community.
Angot
H.
Blomquist
B. W.
Howard
D.
Archer
S.
Bariteau
L.
. .
.
Shupe
M. D.
al.
et
21323
Article
Developing a framework for an early warning system of seasonal temperature and rainfall tailored to aquaculture in Bangladesh
The occurrence of high temperature and heavy rain events during the monsoon season are a major climate risk affecting aquaculture production in Bangladesh. Despite the advances in the seasonal forecasting, the development of operational tools remains a challenge. This work presents the development of a seasonal forecasting approach to predict the number of warm days (NWD) and number of heavy rain days (NHRD) tailored to aquaculture in two locations of Bangladesh (Sylhet and Khulna). The approach is based on the use of meteorological and pond temperature data to generate linear models of the relationship between three-monthly temperature and rainfall statistics and NWD and NHRD, and on the evaluation of the skill of three operational dynamical models from the North American Multi-Model Ensemble (NMME) project. The linear models were used to evaluate the forecasts for two seasons and 1-month lead time: May to July (MJJ), forecast generated in April, and August to October (ASO), forecast generated in July. Differences were observed in the skill of the models predicting maximum temperature and rainfall (Spearman correlation, Root Mean Square Error, Bias statistics, and Willmott’s Index of Agreement,), in addition to NWD and NHRD from linear models, which also vary for the target seasons and location. In general, the models show higher predictive skill for NWD than NHRD, and for Sylhet than in Khulna. Among the three evaluated NMME models, CanSIPSv2 and GFDL-SPEAR exhibit the best performance, they show similar features in terms of error metrics, but CanSIPSv2 presents a lower interannual standard deviation.
2022-4
Clim. Serv.
26
100292
0
10.1016/j.cliser.2022.100292
The occurrence of high temperature and heavy rain events during the monsoon season are a major climate risk affecting aquaculture production in Bangladesh. Despite the advances in the seasonal forecasting, the development of operational tools remains a challenge. This work presents the development of a seasonal forecasting approach to predict the number of warm days (NWD) and number of heavy rain days (NHRD) tailored to aquaculture in two locations of Bangladesh (Sylhet and Khulna). The approach is based on the use of meteorological and pond temperature data to generate linear models of the relationship between three-monthly temperature and rainfall statistics and NWD and NHRD, and on the evaluation of the skill of three operational dynamical models from the North American Multi-Model Ensemble (NMME) project. The linear models were used to evaluate the forecasts for two seasons and 1-month lead time: May to July (MJJ), forecast generated in April, and August to October (ASO), forecast generated in July. Differences were observed in the skill of the models predicting maximum temperature and rainfall (Spearman correlation, Root Mean Square Error, Bias statistics, and Willmott’s Index of Agreement,), in addition to NWD and NHRD from linear models, which also vary for the target seasons and location. In general, the models show higher predictive skill for NWD than NHRD, and for Sylhet than in Khulna. Among the three evaluated NMME models, CanSIPSv2 and GFDL-SPEAR exhibit the best performance, they show similar features in terms of error metrics, but CanSIPSv2 presents a lower interannual standard deviation.
Montes
C.
Acharya
N.
Hossain
P. R.
Babu
T. S. A.
Krupnik
T. J.
Hassan
S. M. Q.
21324
Article
Seasonal forecasting of tropical cyclones over the Bay of Bengal using a hybrid statistical/dynamical model
The post-monsoon (October–November–December) tropical cyclone (TC) over the Bay of Bengal is one of the most devastating natural disasters causing economic and human losses over India and its neighbouring countries. This study discusses a hybrid statistical/dynamical model developed to forecast the post-monsoon cyclone activities over the Bay of Bengal, where 80% of the TCs of the North Indian Ocean are originated. In the hybrid model, the coupled model CFSv2 predicts the large-scale climate indices, and the principal component regression (PCR) model is used to relate these indices with the TC frequency. A solid concurrent relation between the cyclonic disturbance frequencies and various large-scale variables is noted. The dynamical variable, for example, the zonal wind, acts as a precursor variable. We identified three concurrent predictors (ocean heat content over the Bay of Bengal, sea surface temperature (SST) over the Indian Ocean, and SST over the tropical central Pacific regions) and two precursor predictors (low-level wind at equatorial Indian ocean and strength of upper-level easterly jet over African coast) influencing the cyclonic disturbance frequencies over the Bay of Bengal. The concurrent predictors are calculated from the CFSv2 hindcast/forecast output and the precursor predictors are calculated from the reanalysis data. The predictors influencing the cyclonic disturbance over the Bay of Bengal are also influencing the cyclonic storms. Hence, the same predictors are used for developing a hybrid model for cyclonic disturbance and storm frequencies. A significant inter-correlation among different predictors is observed and the PCR model avoids these inter-correlations and, in this method, PCs are estimated on the predictors to make them orthogonal to each other. The hybrid model achieved a significant skill for seasonal cyclone forecast over the Bay of Bengal. Results suggest the potential for using the hybrid model for the operational seasonal forecasting of post-monsoon cyclone activity over the Bay of Bengal.
2022-11
Int. J. Climatol.
42
7383-7396
0
10.1002/joc.7651
The post-monsoon (October–November–December) tropical cyclone (TC) over the Bay of Bengal is one of the most devastating natural disasters causing economic and human losses over India and its neighbouring countries. This study discusses a hybrid statistical/dynamical model developed to forecast the post-monsoon cyclone activities over the Bay of Bengal, where 80% of the TCs of the North Indian Ocean are originated. In the hybrid model, the coupled model CFSv2 predicts the large-scale climate indices, and the principal component regression (PCR) model is used to relate these indices with the TC frequency. A solid concurrent relation between the cyclonic disturbance frequencies and various large-scale variables is noted. The dynamical variable, for example, the zonal wind, acts as a precursor variable. We identified three concurrent predictors (ocean heat content over the Bay of Bengal, sea surface temperature (SST) over the Indian Ocean, and SST over the tropical central Pacific regions) and two precursor predictors (low-level wind at equatorial Indian ocean and strength of upper-level easterly jet over African coast) influencing the cyclonic disturbance frequencies over the Bay of Bengal. The concurrent predictors are calculated from the CFSv2 hindcast/forecast output and the precursor predictors are calculated from the reanalysis data. The predictors influencing the cyclonic disturbance over the Bay of Bengal are also influencing the cyclonic storms. Hence, the same predictors are used for developing a hybrid model for cyclonic disturbance and storm frequencies. A significant inter-correlation among different predictors is observed and the PCR model avoids these inter-correlations and, in this method, PCs are estimated on the predictors to make them orthogonal to each other. The hybrid model achieved a significant skill for seasonal cyclone forecast over the Bay of Bengal. Results suggest the potential for using the hybrid model for the operational seasonal forecasting of post-monsoon cyclone activity over the Bay of Bengal.
Sabeerali
C. T.
Sreejith
O. P.
Acharya
N.
Surendran
D. E.
Pai
D. S.
21325
Article
XCast: A Python Climate Forecasting Toolkit
Climate forecasts, both experimental and operational, are often made by calibrating Global Climate Model (GCM) outputs with observed climate variables using statistical and machine learning models. Often, machine learning techniques are applied to gridded data independently at each gridpoint. However, the implementation of these gridpoint-wise operations is a significant barrier to entry to climate data science. Unfortunately, there is a significant disconnect between the Python data science ecosystem and the gridded earth data ecosystem. Traditional Python data science tools are not designed to be used with gridded datasets, like those commonly used in climate forecasting. Heavy data preprocessing is needed: gridded data must be aggregated, reshaped, or reduced in dimensionality in order to fit the strict formatting requirements of Python's data science tools. Efficiently implementing this gridpoint-wise workflow is a time-consuming logistical burden which presents a high barrier to entry to earth data science. A set of high-performance, easy-to-use Python climate forecasting tools is needed to bridge the gap between Python's data science ecosystem and its gridded earth data ecosystem. XCast, an Xarray-based climate forecasting Python library developed by the authors, bridges this gap. XCast wraps underlying two-dimensional data science methods, like those of Scikit-Learn, with data structures that allow them to be applied to each gridpoint independently. XCast uses high-performance computing libraries to efficiently parallelize the gridpoint-wise application of data science utilities and make Python's traditional data science toolkits compatible with multidimensional gridded data. XCast also implements a diverse set of climate forecasting tools including traditional statistical methods, state-of-the-art machine learning approaches, preprocessing functionality (regridding, rescaling, smoothing), and postprocessing modules (cross validation, forecast verification, visualization). These tools are useful for producing and analyzing both experimental and operational climate forecasts. In this study, we describe the development of XCast, and present in-depth technical details on how XCast brings highly parallelized gridpoint-wise versions of traditional Python data science tools into Python's gridded earth data ecosystem. We also demonstrate a case study where XCast was used to generate experimental real-time deterministic and probabilistic forecasts for South Asian Summer Monsoon Rainfall in 2022 using different machine learning-based multi-model ensembles.
2022-7
Front. Clim.
4
953262
0
10.3389/fclim.2022.953262
Climate forecasts, both experimental and operational, are often made by calibrating Global Climate Model (GCM) outputs with observed climate variables using statistical and machine learning models. Often, machine learning techniques are applied to gridded data independently at each gridpoint. However, the implementation of these gridpoint-wise operations is a significant barrier to entry to climate data science. Unfortunately, there is a significant disconnect between the Python data science ecosystem and the gridded earth data ecosystem. Traditional Python data science tools are not designed to be used with gridded datasets, like those commonly used in climate forecasting. Heavy data preprocessing is needed: gridded data must be aggregated, reshaped, or reduced in dimensionality in order to fit the strict formatting requirements of Python's data science tools. Efficiently implementing this gridpoint-wise workflow is a time-consuming logistical burden which presents a high barrier to entry to earth data science. A set of high-performance, easy-to-use Python climate forecasting tools is needed to bridge the gap between Python's data science ecosystem and its gridded earth data ecosystem. XCast, an Xarray-based climate forecasting Python library developed by the authors, bridges this gap. XCast wraps underlying two-dimensional data science methods, like those of Scikit-Learn, with data structures that allow them to be applied to each gridpoint independently. XCast uses high-performance computing libraries to efficiently parallelize the gridpoint-wise application of data science utilities and make Python's traditional data science toolkits compatible with multidimensional gridded data. XCast also implements a diverse set of climate forecasting tools including traditional statistical methods, state-of-the-art machine learning approaches, preprocessing functionality (regridding, rescaling, smoothing), and postprocessing modules (cross validation, forecast verification, visualization). These tools are useful for producing and analyzing both experimental and operational climate forecasts. In this study, we describe the development of XCast, and present in-depth technical details on how XCast brings highly parallelized gridpoint-wise versions of traditional Python data science tools into Python's gridded earth data ecosystem. We also demonstrate a case study where XCast was used to generate experimental real-time deterministic and probabilistic forecasts for South Asian Summer Monsoon Rainfall in 2022 using different machine learning-based multi-model ensembles.
Hall
K. J.
Acharya
N.
21326
Article
Subseasonal Forecast Skill Improvement From Strongly Coupled Data Assimilation With a Linear Inverse Model
Strongly coupled data assimilation (SCDA), such as using atmospheric observations to update ocean analyses, is critical for properly initializing Earth System models to predict subseasonal to decadal timescales. We show that a Kalman filter with a linear emulator of the coupled dynamics can be used to efficiently assimilate observations with SCDA. A linear inverse model (LIM), trained on 25 years of Climate Forecast System Reanalysis gridded data, is used to assimilate observations daily during an independent 7-year period. SCDA sea-surface temperature (SST) analysis errors are reduced over 20% in global-mean mean-squared error relative to a control experiment where only SST observations are assimilated with an SST LIM. The analysis improvements enhance forecast skill for leads of at least 50 days. In contrast, extratropical Northern Hemisphere 2 m air temperature forecast errors increase for coupled data assimilation in these experiments, despite reduction during the training period.
2022-6
Geophys. Res. Lett.
49
e2022GL097996
0
10.1029/2022GL097996
Strongly coupled data assimilation (SCDA), such as using atmospheric observations to update ocean analyses, is critical for properly initializing Earth System models to predict subseasonal to decadal timescales. We show that a Kalman filter with a linear emulator of the coupled dynamics can be used to efficiently assimilate observations with SCDA. A linear inverse model (LIM), trained on 25 years of Climate Forecast System Reanalysis gridded data, is used to assimilate observations daily during an independent 7-year period. SCDA sea-surface temperature (SST) analysis errors are reduced over 20% in global-mean mean-squared error relative to a control experiment where only SST observations are assimilated with an SST LIM. The analysis improvements enhance forecast skill for leads of at least 50 days. In contrast, extratropical Northern Hemisphere 2 m air temperature forecast errors increase for coupled data assimilation in these experiments, despite reduction during the training period.
Hakim
G. J.
Snyder
C.
Penny
S. G.
Newman
M.
21331
Article
Feasibility of Adding Twitter Data to Aid Drought Depiction: Case Study in Colorado
The use of social media, such as Twitter, has changed the information landscape for citizens’ participation in crisis response and recovery activities. Given that drought progression is slow and also spatially extensive, an interesting set of questions arise, such as how the usage of Twitter by a large population may change during the development of a major drought alongside how the changing usage facilitates drought detection. For this reason, contemporary analysis of how social media data, in conjunction with meteorological records, was conducted towards improvement in the detection of drought and its progression. The research utilized machine learning techniques applied over satellite-derived drought conditions in Colorado. Three different machine learning techniques were examined: the generalized linear model, support vector machines and deep learning, each applied to test the integration of Twitter data with meteorological records as a predictor of drought development. It is found that the integration of data resources is viable given that the Twitter-based model outperformed the control run which did not include social media input. Eight of the ten models tested showed quantifiable improvements in the performance over the control run model, suggesting that the Twitter-based model was superior in predicting drought severity. Future work lies in expanding this method to depict drought in the western U.S.
2022-9
Water
14
2773
0
10.3390/w14182773
The use of social media, such as Twitter, has changed the information landscape for citizens’ participation in crisis response and recovery activities. Given that drought progression is slow and also spatially extensive, an interesting set of questions arise, such as how the usage of Twitter by a large population may change during the development of a major drought alongside how the changing usage facilitates drought detection. For this reason, contemporary analysis of how social media data, in conjunction with meteorological records, was conducted towards improvement in the detection of drought and its progression. The research utilized machine learning techniques applied over satellite-derived drought conditions in Colorado. Three different machine learning techniques were examined: the generalized linear model, support vector machines and deep learning, each applied to test the integration of Twitter data with meteorological records as a predictor of drought development. It is found that the integration of data resources is viable given that the Twitter-based model outperformed the control run which did not include social media input. Eight of the ten models tested showed quantifiable improvements in the performance over the control run model, suggesting that the Twitter-based model was superior in predicting drought severity. Future work lies in expanding this method to depict drought in the western U.S.
Mukherjee
S.
Wang
S.-Y.
Hirschfeld
D.
Lisonbee
J.
Gillies
R.
21332
Article
Northern Hemisphere Extratropical Cyclone Activity in the Twentieth Century Reanalysis Version 3 (20CRv3) and Its Relationship with Continental Extreme Temperatures
In this study, we detect and track extratropical cyclones using 6-hourly mean sea level pressure data taken from the Twentieth Century Reanalysis version 3 (20CRv3) over the period 1951–2015 and compare them with those in the Interim and fifth generation of ECMWF reanalyses over the period 1979–2018. Three indices were employed to characterize cyclone activity, including cyclone count, cyclone intensity, and a cyclone activity index (CAI) that combines the count and intensity. The results show that the cyclone indices in the three datasets have comparable annual climatologies and seasonal evolution over the northern extratropical land and ocean in recent decades. Based on the cyclone indices over the period 1951–2010 in 80 ensemble members of 20CRv3, cyclone count and intensity are negatively correlated in winter and tend to be positively and weakly correlated in summer. The interannual CAI variability is dominated by the cyclone count variability. Regional mean cyclone activity can be well represented using the ensemble average cyclone index. We then examined the linkage of the cyclone activity in 20CRv3 and observed cold and warm extremes over Eurasia and North America over the period 1951–2010. In winter, the principal components of interannual cold and warm extreme anomalies are more correlated with the regional mean cyclone count index over Eurasia, while they are more correlated with the cyclone intensity index over North America. The temperature anomalies associated with the regional and ensemble mean cyclone count index explain about 10% (20%) of interannual cold (warm) extreme variances averaged over Eurasia. The temperature anomalies associated with the mean cyclone intensity explain about 10% of interannual cold and warm extreme variances over North America. Large-scale atmospheric circulation anomalies in association with cyclone activity and the induced temperature advection drive temperature anomalies over Eurasia and North America. In summer, circulation and thermal advection anomalies associated with cyclone activity are weak over the two continents. Hence, that season’s relationship between cyclone activity and extreme temperature variability is weak.
2022-7
Atmosphere
13
0
10.3390/atmos13081166
In this study, we detect and track extratropical cyclones using 6-hourly mean sea level pressure data taken from the Twentieth Century Reanalysis version 3 (20CRv3) over the period 1951–2015 and compare them with those in the Interim and fifth generation of ECMWF reanalyses over the period 1979–2018. Three indices were employed to characterize cyclone activity, including cyclone count, cyclone intensity, and a cyclone activity index (CAI) that combines the count and intensity. The results show that the cyclone indices in the three datasets have comparable annual climatologies and seasonal evolution over the northern extratropical land and ocean in recent decades. Based on the cyclone indices over the period 1951–2010 in 80 ensemble members of 20CRv3, cyclone count and intensity are negatively correlated in winter and tend to be positively and weakly correlated in summer. The interannual CAI variability is dominated by the cyclone count variability. Regional mean cyclone activity can be well represented using the ensemble average cyclone index. We then examined the linkage of the cyclone activity in 20CRv3 and observed cold and warm extremes over Eurasia and North America over the period 1951–2010. In winter, the principal components of interannual cold and warm extreme anomalies are more correlated with the regional mean cyclone count index over Eurasia, while they are more correlated with the cyclone intensity index over North America. The temperature anomalies associated with the regional and ensemble mean cyclone count index explain about 10% (20%) of interannual cold (warm) extreme variances averaged over Eurasia. The temperature anomalies associated with the mean cyclone intensity explain about 10% of interannual cold and warm extreme variances over North America. Large-scale atmospheric circulation anomalies in association with cyclone activity and the induced temperature advection drive temperature anomalies over Eurasia and North America. In summer, circulation and thermal advection anomalies associated with cyclone activity are weak over the two continents. Hence, that season’s relationship between cyclone activity and extreme temperature variability is weak.
Yu
B.
Wang
X. L.
Feng
Y.
Chan
R.
Compo
G. P.
Slivinski
L. C.
Sardeshmukh
P. D.
Wehner
M.
Wang
X.-Y.
21335
Article
Global decline in ocean memory over the 21st century
Ocean memory, the persistence of ocean conditions, is a major source of predictability in the climate system beyond weather time scales. We show that ocean memory, as measured by the year-to-year persistence of sea surface temperature anomalies, is projected to steadily decline in the coming decades over much of the globe. This global decline in ocean memory is predominantly driven by shoaling of the upper-ocean mixed layer depth in response to global surface warming, while thermodynamic and dynamic feedbacks can contribute substantially regionally. As the mixed layer depth shoals, stochastic forcing becomes more effective in driving sea surface temperature anomalies, increasing high-frequency noise at the expense of persistent signals. Reduced ocean memory results in shorter lead times of skillful persistence-based predictions of sea surface thermal conditions, which may present previously unknown challenges for predicting climate extremes and managing marine biological resources under climate change.
2022-5
Science Adv.
8
eabm3468
0
10.1126/sciadv.abm346
Ocean memory, the persistence of ocean conditions, is a major source of predictability in the climate system beyond weather time scales. We show that ocean memory, as measured by the year-to-year persistence of sea surface temperature anomalies, is projected to steadily decline in the coming decades over much of the globe. This global decline in ocean memory is predominantly driven by shoaling of the upper-ocean mixed layer depth in response to global surface warming, while thermodynamic and dynamic feedbacks can contribute substantially regionally. As the mixed layer depth shoals, stochastic forcing becomes more effective in driving sea surface temperature anomalies, increasing high-frequency noise at the expense of persistent signals. Reduced ocean memory results in shorter lead times of skillful persistence-based predictions of sea surface thermal conditions, which may present previously unknown challenges for predicting climate extremes and managing marine biological resources under climate change.
Shi
H.
Jin
F.-F.
Wills
R. C. J.
Jacox
M. G.
Amaya
D. J.
Black
B. A.
Rykaczewski
R. R.
Bograd
S. J.
García-Reyes
M.
Sydeman
W. J.
21336
Article
Role of ocean dynamics in equatorial Pacific decadal variability
The tropical Pacific exhibits decadal El Niño-Southern Oscillation (ENSO)-like variability, characterized by meridionally broad sea surface temperature anomalies in the eastern Pacific. In this study, we focus on the variability in the equatorial Pacific band (5°S–5°N), termed equatorial Pacific decadal variability (EPDV). While it is known that ocean dynamics plays an essential role in EPDV, the simulations by air-sea thermodynamically coupled slab ocean models (SOM) obscure the nature of the role of ocean dynamics. To confront this issue, we use a mechanically decoupled simulation, which isolates the effects of thermodynamic coupling processes and mean ocean circulation on EPDV. Thus, by comparing the simulation to a SOM, we investigate the role of mean ocean circulation and show that it plays a role in damping EPDV, primarily through mean equatorial Pacific upwelling. By comparing the simulation to a fully coupled dynamic ocean model (DOM), we examine the role of anomalous wind-driven ocean circulation and demonstrate that it plays a role in amplifying EPDV. Further, this amplification strength overwhelms the upwelling damping effect, resulting in the anomalous wind-driven ocean circulation forcing EPDV. Finally, we examine the origin of EPDV in the DOM and show that it originates from a zonal dipole mode in the tropical Pacific, which is strongly associated with decadal modulation of ENSO amplitude. Taking EPDV as an example, our study advances the understanding of the two distinct dynamical systems (SOM and DOM), benefiting the physical interpretation of other climate variabilities.
2022-10
Clim. Dyn.
59
2517–2529
0
10.1007/s00382-022-06312-2
The tropical Pacific exhibits decadal El Niño-Southern Oscillation (ENSO)-like variability, characterized by meridionally broad sea surface temperature anomalies in the eastern Pacific. In this study, we focus on the variability in the equatorial Pacific band (5°S–5°N), termed equatorial Pacific decadal variability (EPDV). While it is known that ocean dynamics plays an essential role in EPDV, the simulations by air-sea thermodynamically coupled slab ocean models (SOM) obscure the nature of the role of ocean dynamics. To confront this issue, we use a mechanically decoupled simulation, which isolates the effects of thermodynamic coupling processes and mean ocean circulation on EPDV. Thus, by comparing the simulation to a SOM, we investigate the role of mean ocean circulation and show that it plays a role in damping EPDV, primarily through mean equatorial Pacific upwelling. By comparing the simulation to a fully coupled dynamic ocean model (DOM), we examine the role of anomalous wind-driven ocean circulation and demonstrate that it plays a role in amplifying EPDV. Further, this amplification strength overwhelms the upwelling damping effect, resulting in the anomalous wind-driven ocean circulation forcing EPDV. Finally, we examine the origin of EPDV in the DOM and show that it originates from a zonal dipole mode in the tropical Pacific, which is strongly associated with decadal modulation of ENSO amplitude. Taking EPDV as an example, our study advances the understanding of the two distinct dynamical systems (SOM and DOM), benefiting the physical interpretation of other climate variabilities.
Zhang
Y.
Yu
S.-Y.
Xie
S.-P.
Amaya
D. J.
al.
et
21337
Article
Northern Hemisphere Stratosphere-Troposphere Circulation Change in CMIP6 Models. Part 1: Inter-Model Spread and Scenario Sensitivity
Projected changes in the Northern Hemisphere stratospheric polar vortex are analyzed using Climate Model Intercomparison Project Phase 6 experiments. Previous studies showed that projections of the wintertime zonally averaged polar vortex strength diverge widely between climate models with no agreement on the sign of change, and that this uncertainty contributes to the regional climate change uncertainty. Here, we show that there remains large uncertainty in the projected strength of the polar vortex in experiments with global warming levels ranging from moderate (SSP245 runs) to large (Abrupt-4xCO2 runs), and that the uncertainty maximizes in winter. Partitioning of the uncertainty in wintertime polar vortex strength projections reveals that, by the end of the 21st century, model uncertainty contributes half of the total uncertainty, with scenario uncertainty contributing only 10%. Regression analysis shows that up to 20% of the intermodel spread in projected precipitation over the Iberian Peninsula and northwestern US, and 20%–30% in near-surface temperature over western US and northern Eurasian, can be associated with the spread in vortex strength projections after accounting for global warming. While changes in the magnitude and sign of the zonally averaged vortex strength are uncertain, most models (>95%) predict an eastward shift of the vortex by 8°–20° degrees in longitude relative to its historical location with the magnitude of the shift increasing for larger global warming levels. There is less agreement across models on a latitudinal shift, whose direction and magnitude correlate with changes in the zonally averaged vortex strength so that vortex weakening/strengthening corresponds to a southward/poleward shift.
2022-9
J. Geophys. Res. Atmos.
127
e2022JD036992
0
10.1029/2022JD036992
Projected changes in the Northern Hemisphere stratospheric polar vortex are analyzed using Climate Model Intercomparison Project Phase 6 experiments. Previous studies showed that projections of the wintertime zonally averaged polar vortex strength diverge widely between climate models with no agreement on the sign of change, and that this uncertainty contributes to the regional climate change uncertainty. Here, we show that there remains large uncertainty in the projected strength of the polar vortex in experiments with global warming levels ranging from moderate (SSP245 runs) to large (Abrupt-4xCO2 runs), and that the uncertainty maximizes in winter. Partitioning of the uncertainty in wintertime polar vortex strength projections reveals that, by the end of the 21st century, model uncertainty contributes half of the total uncertainty, with scenario uncertainty contributing only 10%. Regression analysis shows that up to 20% of the intermodel spread in projected precipitation over the Iberian Peninsula and northwestern US, and 20%–30% in near-surface temperature over western US and northern Eurasian, can be associated with the spread in vortex strength projections after accounting for global warming. While changes in the magnitude and sign of the zonally averaged vortex strength are uncertain, most models (>95%) predict an eastward shift of the vortex by 8°–20° degrees in longitude relative to its historical location with the magnitude of the shift increasing for larger global warming levels. There is less agreement across models on a latitudinal shift, whose direction and magnitude correlate with changes in the zonally averaged vortex strength so that vortex weakening/strengthening corresponds to a southward/poleward shift.
Karpechko
A. Y.
Afargan-Gerstman
H.
Butler
A. H.
Domeisen
D. I. V.
Kretschmer
M.
Lawrence
Z. D.
Manzini
E.
Sigmond
M.
Simpson
I. R.
Wu
Z.
21339
Article
Advanced Quantitative Precipitation Information: Improving Monitoring and Forecasts of Precipitation, Streamflow, and Coastal Flooding in the San Francisco Bay Area
2022-10
Bull. Amer. Meteor. Soc.
0
10.1175/BAMS-D-21-0121.1
Cifelli
R.
21340
Article
Evaluation of Snow and Streamflows Using Noah-MP and WRF-Hydro Models in Aroostook River Basin, Maine
Snow influences land–atmosphere interactions in snow-dominated areas, and snow melt contributes to basin streamflows. However, estimating snowpack properties such as the snow depth (SD) and snow water equivalent (SWE) from land surface model simulations remains a challenge. This is, in part, due to uncertainties in the atmospheric forcing variables, which propagate into hydrological model predictions. This study implements the Weather Research and Forecasting (WRF)-Hydro framework with the Noah-Multiparameterization (Noah-MP) land surface model in the NOAA’s National Water Model (NWM) version 2.0 configuration to estimate snow in a single column and subsequently the streamflow across the Aroostook River’s sub-basins in Maine for water years (WY) 2014–2016. This study evaluates how differences between two atmospheric forcing datasets, the North American Land Data Assimilation version 2 (NLDAS-2) and in situ (Station), translate into differences in the simulation of snow. NLDAS-2 was used as the meteorological forcing in the retrospective NWM 2.0 simulations. The results from the single-column study showed that differences in the simulated SWE and SD were linked to differences in the 2 m air temperature (T2m), which influenced the precipitation partitioning of rain and snow, as parameterized in Noah-MP. The negative mean bias of −0.7 K (during the accumulation period) in T2m for NLDAS-2, compared to the Station forcing, was a major factor that contributed to the positive mean bias of +52 mm on average in the peak SWE in the NLDAS-2-forced Noah-MP simulation during the study period. The higher T2m values at the Station led to higher sensible heat fluxes towards the snowpack, which led to a higher amount of net energy at the snow’s surface and melt events during the accumulation season in Station-forced Noah-MP simulations. The results from the retrospective NWM version 2.0′s simulation in the basin showed that the streamflow estimates were closer to the United States Geological Survey gage observations at the two larger sub-basins (NSE = 0.9), which were mostly forested, compared to the two smaller sub-basins (NSE ≥ 0.4), which had more agricultural land-use. This study also showed that the spring snowmelt timing was captured quite well by the timing of the decline in the simulated SWE and SD, providing an early indication of melt in most sub-basins. The simulated fractional snow cover area (fSCA) however provided less information about the changes in snow or onset of snowmelt as it was mostly binary (full snow cover in winter), which differed from the more realistic fSCA values shown by the Moderate Resolution Imaging Spectroradiometer.
2022-7
Water
14
2145
0
10.3390/w14142145
Snow influences land–atmosphere interactions in snow-dominated areas, and snow melt contributes to basin streamflows. However, estimating snowpack properties such as the snow depth (SD) and snow water equivalent (SWE) from land surface model simulations remains a challenge. This is, in part, due to uncertainties in the atmospheric forcing variables, which propagate into hydrological model predictions. This study implements the Weather Research and Forecasting (WRF)-Hydro framework with the Noah-Multiparameterization (Noah-MP) land surface model in the NOAA’s National Water Model (NWM) version 2.0 configuration to estimate snow in a single column and subsequently the streamflow across the Aroostook River’s sub-basins in Maine for water years (WY) 2014–2016. This study evaluates how differences between two atmospheric forcing datasets, the North American Land Data Assimilation version 2 (NLDAS-2) and in situ (Station), translate into differences in the simulation of snow. NLDAS-2 was used as the meteorological forcing in the retrospective NWM 2.0 simulations. The results from the single-column study showed that differences in the simulated SWE and SD were linked to differences in the 2 m air temperature (T2m), which influenced the precipitation partitioning of rain and snow, as parameterized in Noah-MP. The negative mean bias of −0.7 K (during the accumulation period) in T2m for NLDAS-2, compared to the Station forcing, was a major factor that contributed to the positive mean bias of +52 mm on average in the peak SWE in the NLDAS-2-forced Noah-MP simulation during the study period. The higher T2m values at the Station led to higher sensible heat fluxes towards the snowpack, which led to a higher amount of net energy at the snow’s surface and melt events during the accumulation season in Station-forced Noah-MP simulations. The results from the retrospective NWM version 2.0′s simulation in the basin showed that the streamflow estimates were closer to the United States Geological Survey gage observations at the two larger sub-basins (NSE = 0.9), which were mostly forested, compared to the two smaller sub-basins (NSE ≥ 0.4), which had more agricultural land-use. This study also showed that the spring snowmelt timing was captured quite well by the timing of the decline in the simulated SWE and SD, providing an early indication of melt in most sub-basins. The simulated fractional snow cover area (fSCA) however provided less information about the changes in snow or onset of snowmelt as it was mostly binary (full snow cover in winter), which differed from the more realistic fSCA values shown by the Moderate Resolution Imaging Spectroradiometer.
Sthapit
E.
Lakhankar
T.
Hughes
M.
Khanbilvardi
R.
Cifelli
R.
Mahoney
K. M.
Currier
W. R.
Viterbo
F.
Rafieeinasab
A.
21343
Article
Drivers of Decadal Carbon Fluxes Across Temperate Ecosystems
Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple timescales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO2) across the Chequamegon Ecosystem-Atmosphere Study AmeriFlux core site cluster in the upper Great Lakes region of the USA from 1997 to 2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying levels of canopy sheltering and vegetation (US-Los and US-ALQ), and a very tall (400 m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became weaker with time as non-climatic factors such as local disturbances likely dominated flux time series. Average decadal NEE at the tall tower transitioned from carbon source to sink to near neutral over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. Declining snowfall offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. Higher CO2 increased maximum net assimilation parameters but not total gross primary productivity. Stand-scale sites were larger net sinks than the landscape tower. Clustered, long-term carbon flux observations provide value for understanding the diverse links between carbon and climate and the challenges of upscaling these responses across space.
2022-12
J. Geophys. Res. Biogeosci.
127
e2022JG007014
0
10.1029/2022JG007014
Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple timescales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO2) across the Chequamegon Ecosystem-Atmosphere Study AmeriFlux core site cluster in the upper Great Lakes region of the USA from 1997 to 2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying levels of canopy sheltering and vegetation (US-Los and US-ALQ), and a very tall (400 m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became weaker with time as non-climatic factors such as local disturbances likely dominated flux time series. Average decadal NEE at the tall tower transitioned from carbon source to sink to near neutral over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. Declining snowfall offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. Higher CO2 increased maximum net assimilation parameters but not total gross primary productivity. Stand-scale sites were larger net sinks than the landscape tower. Clustered, long-term carbon flux observations provide value for understanding the diverse links between carbon and climate and the challenges of upscaling these responses across space.
Desai
A.
Wiesner
S.
Thom
J.
Butterworth
B. J.
al.
et
21345
Article
Space-Scale Resolved Surface Fluxes Across a Heterogeneous, Mid-Latitude Forested Landscape
2022-12
J. Geophys. Res. Atmos.
127
e2022JD037138
0
10.1029/2022JD037138
Paleri
S.
Desai
A.
Metzger
S.
Durden
D.
Butterworth
B. J.
Mauder
M.
Kohnert
K.
Serafimovich
A.
21348
Article
It
This acoustics project evolved responding to a challenge to increase the role of analysis in design for 1st year students early in their engineering careers. Student teams must design three musical instruments each producing a single note meeting frequency and sound level specifications using different physical sound generation processes. They are given background material, example analyses, references, and resources. A key requirement is that they create a spreadsheet with equations guiding the design of each instrument before proceeding with construction. Students experience the entire design process: brainstorm, analyze, create, build, test, iterate, present, demonstrate, and report. This introduces the range of resources available to them. An emphasis is on comparing theory and experiment and explaining reasons for any disagreements. As implemented, this project concentrated over about a two week period, provided an introduction to a major design project continuing for a full semester.
2022-2
J. Acoust. Soc. Am.
151
831
0
10.1121/10.0009382
This acoustics project evolved responding to a challenge to increase the role of analysis in design for 1st year students early in their engineering careers. Student teams must design three musical instruments each producing a single note meeting frequency and sound level specifications using different physical sound generation processes. They are given background material, example analyses, references, and resources. A key requirement is that they create a spreadsheet with equations guiding the design of each instrument before proceeding with construction. Students experience the entire design process: brainstorm, analyze, create, build, test, iterate, present, demonstrate, and report. This introduces the range of resources available to them. An emphasis is on comparing theory and experiment and explaining reasons for any disagreements. As implemented, this project concentrated over about a two week period, provided an introduction to a major design project continuing for a full semester.
Bedard
A. J.
21350
Article
An agenda for land data assimilation priorities: Realizing 1 the promise of terrestrial water, energy, and vegetation 2 observations from space
The task of quantifying spatial and temporal variations in terrestrial water, energy, and vegetation conditions is challenging due to the significant complexity and heterogeneity of these conditions, all of which are impacted by climate change and anthropogenic activities. To address this challenge, Earth Observations (EOs) of the land and their utilization within data assimilation (DA) systems are vital. Satellite EOs are particularly relevant, as they offer quasi-global coverage, are non-intrusive, and provide uniformity, rapid measurements, and continuity. The past three decades have seen unprecedented growth in the number and variety of land remote sensing technologies launched by space agencies and commercial companies around the world. There have also been significant developments in land modeling and DA systems to provide tools that can exploit these measurements. Despite these advances, several important gaps remain in current land DA research and applications. This paper discusses these gaps, particularly in the context of using DA to improve model states for short-term numerical weather and sub-seasonal to seasonal predictions. We outline an agenda for land DA priorities so that the next generation of land DA systems will be better poised to take advantage of the significant current and anticipated shifts and advancements in remote sensing, modeling, computational technologies, and hardware resources.
2022-11
J. Adv. Model. Earth Syst.
14
e2022MS003259
0
10.1029/2022MS003259
The task of quantifying spatial and temporal variations in terrestrial water, energy, and vegetation conditions is challenging due to the significant complexity and heterogeneity of these conditions, all of which are impacted by climate change and anthropogenic activities. To address this challenge, Earth Observations (EOs) of the land and their utilization within data assimilation (DA) systems are vital. Satellite EOs are particularly relevant, as they offer quasi-global coverage, are non-intrusive, and provide uniformity, rapid measurements, and continuity. The past three decades have seen unprecedented growth in the number and variety of land remote sensing technologies launched by space agencies and commercial companies around the world. There have also been significant developments in land modeling and DA systems to provide tools that can exploit these measurements. Despite these advances, several important gaps remain in current land DA research and applications. This paper discusses these gaps, particularly in the context of using DA to improve model states for short-term numerical weather and sub-seasonal to seasonal predictions. We outline an agenda for land DA priorities so that the next generation of land DA systems will be better poised to take advantage of the significant current and anticipated shifts and advancements in remote sensing, modeling, computational technologies, and hardware resources.
Kumar
S.
Kolassa
J.
Reichle
R.
Crow
W.
. .
.
Draper
C.
al.
et
21354
Article
Assessing potential of sparse-input reanalyses for centennial-scale land surface air temperature homogenisation
Observations from the historical meteorological observing network contain many artefacts of non-climatic origin which must be accounted for prior to using these data in climate applications. State-of-the-art homogenisation approaches use various flavours of pairwise comparison between a target station and candidate neighbour station series. Such approaches require an adequate number of neighbours of sufficient quality and comparability – a condition that is met for most station series since the mid-20th Century. However, pairwise approaches have challenges where suitable neighbouring stations are sparse, as remains the case in vast regions of the globe and is common almost everywhere prior to the early 20th Century. Modern sparse-input centennial reanalysis products continue to improve and offer a potential alternative to pairwise comparison, particularly where and when observations are sparse. They do not directly ingest or use land-based surface temperature observations, so they are a formally independent estimate. This may be particularly helpful in cases where structurally similar changes exist across broad networks, which challenges current techniques in the absence of metadata. They also potentially offer a valuable methodologically distinct method, which would help explore structural uncertainty in homogenisation techniques. The present study compares the potential of spatially-interpolated sparse-input reanalysis products to neighbour-based approaches to perform homogenisation of global monthly land surface air temperature records back to 1850 based upon the statistical properties of station-minus-reanalysis and station-minus-neighbour series. This shows that neighbour-based approaches likely remain preferable in data dense regions and epochs. However, the most recent reanalysis product, NOAA-CIRES-DOE 20CRv3, is potentially preferable in cases where insufficient neighbours are available. This may in particular affect long-term global average estimates where a small number of long-term stations in data sparse regions will make substantial contributions to global estimates and may contain missed data artefacts in present homogenisation approaches.
2022-1
Int. J. Climatol.
41
E3000-E3020
0
10.1002/joc.6898
Observations from the historical meteorological observing network contain many artefacts of non-climatic origin which must be accounted for prior to using these data in climate applications. State-of-the-art homogenisation approaches use various flavours of pairwise comparison between a target station and candidate neighbour station series. Such approaches require an adequate number of neighbours of sufficient quality and comparability – a condition that is met for most station series since the mid-20th Century. However, pairwise approaches have challenges where suitable neighbouring stations are sparse, as remains the case in vast regions of the globe and is common almost everywhere prior to the early 20th Century. Modern sparse-input centennial reanalysis products continue to improve and offer a potential alternative to pairwise comparison, particularly where and when observations are sparse. They do not directly ingest or use land-based surface temperature observations, so they are a formally independent estimate. This may be particularly helpful in cases where structurally similar changes exist across broad networks, which challenges current techniques in the absence of metadata. They also potentially offer a valuable methodologically distinct method, which would help explore structural uncertainty in homogenisation techniques. The present study compares the potential of spatially-interpolated sparse-input reanalysis products to neighbour-based approaches to perform homogenisation of global monthly land surface air temperature records back to 1850 based upon the statistical properties of station-minus-reanalysis and station-minus-neighbour series. This shows that neighbour-based approaches likely remain preferable in data dense regions and epochs. However, the most recent reanalysis product, NOAA-CIRES-DOE 20CRv3, is potentially preferable in cases where insufficient neighbours are available. This may in particular affect long-term global average estimates where a small number of long-term stations in data sparse regions will make substantial contributions to global estimates and may contain missed data artefacts in present homogenisation approaches.
Gillespie
I. M.
Haimberger
L.
Compo
G. P.
Thorne
P. W.
21355
Article
A decadal intensification in the modulation of spring western tropical Atlantic sea surface temperature to the following winter ENSO after the mid-1980s
This study identifies that the spring sea surface temperature anomalies (SSTA) over the western tropical Atlantic (WTA) has a pronounced negative correlation with the following winter El Niño–Southern Oscillation (ENSO) variability. This negative correlation is stronger than that between the SSTA over each of the Atlantic Niño region, the north tropical Atlantic or the Atlantic warm pool, and the succeeding winter ENSO. Different SST datasets can recognize the strongest correlation, suggesting that the modulation of the WTA SSTA to the following ENSO variability is robust. Moreover, this modulation has a decadal shift, being weak before the mid-1980s but significantly enhanced thereafter. The intensified modulation of the WTA SSTA to the following ENSO after the mid-1980s displays as that the positive WTA SSTA during spring leads to a significant negative SSTA over the central and eastern tropical Pacific during the subsequent summer, which further develops into La Niña events during the following winter. This significant negative SSTA is induced by the strengthened anomalous easterly over the central equatorial Pacific associated with the enhanced WTA precipitation anomalies in response to the WTA SSTA. Further analysis suggests that this intensified modulation tends to be attributed to the increase in climatological mean SST over the WTA after the mid-1980s. The warmer WTA SST mean states is likely to enhance the convective activity and relevant atmospheric circulation in response to the WTA SSTA forcing, resulting in the intensified modulation of the WTA SSTA to ENSO variability after the mid-1980s.
2022-12
Clim. Dyn.
59
3643–3655
0
10.1007/s00382-022-06288-z
This study identifies that the spring sea surface temperature anomalies (SSTA) over the western tropical Atlantic (WTA) has a pronounced negative correlation with the following winter El Niño–Southern Oscillation (ENSO) variability. This negative correlation is stronger than that between the SSTA over each of the Atlantic Niño region, the north tropical Atlantic or the Atlantic warm pool, and the succeeding winter ENSO. Different SST datasets can recognize the strongest correlation, suggesting that the modulation of the WTA SSTA to the following ENSO variability is robust. Moreover, this modulation has a decadal shift, being weak before the mid-1980s but significantly enhanced thereafter. The intensified modulation of the WTA SSTA to the following ENSO after the mid-1980s displays as that the positive WTA SSTA during spring leads to a significant negative SSTA over the central and eastern tropical Pacific during the subsequent summer, which further develops into La Niña events during the following winter. This significant negative SSTA is induced by the strengthened anomalous easterly over the central equatorial Pacific associated with the enhanced WTA precipitation anomalies in response to the WTA SSTA. Further analysis suggests that this intensified modulation tends to be attributed to the increase in climatological mean SST over the WTA after the mid-1980s. The warmer WTA SST mean states is likely to enhance the convective activity and relevant atmospheric circulation in response to the WTA SSTA forcing, resulting in the intensified modulation of the WTA SSTA to ENSO variability after the mid-1980s.
Chen
W.
Lu
R.
Ding
H.
21357
Article
Furthering Understanding of Aerosol–Cloud–Precipitation Interactions in the Arctic
The Arctic has been shown to be particularly sensitive to human-induced climatic change. With observed temperatures having warmed by several degrees over the last decades, surface properties are rapidly evolving, with changes in snow and ice cover and vegetation documented at a variety of Arctic locations (e.g., Moon et al. 2021). These changes to the Arctic surface have supported the ice–albedo feedback, in which a darkening of the Arctic surface facilitates further warming, resulting in a positive feedback loop and accelerating Arctic warming (e.g., Curry et al. 1995). Also thought to be of importance is the lapse-rate feedback (Pithan and Mauritsen 2014) in which the vertical structure of temperature and its changes due to forced warming influence surface and top-of-atmosphere longwave radiation. Clouds (see Fig. 1) play a central and critical role in these feedbacks, driving the surface and top-of-atmosphere energy budgets at high latitudes. Changes to cloud cover influence both shortwave radiation in summer months and surface and longwave radiation throughout the annual cycle. As a result of their importance, Arctic clouds have been the focus of a wide variety of studies working to understand the macrophysical and microphysical drivers of cloud lifetime across a variety of Arctic regimes (e.g., Morrison et al. 2012).
2022-11
Bull. Amer. Meteor. Soc.
103
E2484–E2491
0
10.1175/BAMS-D-22-0168.1
The Arctic has been shown to be particularly sensitive to human-induced climatic change. With observed temperatures having warmed by several degrees over the last decades, surface properties are rapidly evolving, with changes in snow and ice cover and vegetation documented at a variety of Arctic locations (e.g., Moon et al. 2021). These changes to the Arctic surface have supported the ice–albedo feedback, in which a darkening of the Arctic surface facilitates further warming, resulting in a positive feedback loop and accelerating Arctic warming (e.g., Curry et al. 1995). Also thought to be of importance is the lapse-rate feedback (Pithan and Mauritsen 2014) in which the vertical structure of temperature and its changes due to forced warming influence surface and top-of-atmosphere longwave radiation. Clouds (see Fig. 1) play a central and critical role in these feedbacks, driving the surface and top-of-atmosphere energy budgets at high latitudes. Changes to cloud cover influence both shortwave radiation in summer months and surface and longwave radiation throughout the annual cycle. As a result of their importance, Arctic clouds have been the focus of a wide variety of studies working to understand the macrophysical and microphysical drivers of cloud lifetime across a variety of Arctic regimes (e.g., Morrison et al. 2012).
de Boer
G.
McCusker
G. Y.
Sotiropoulou
G.
Gramlich
Y.
Browse
J.
Raut
J. C.
21360
Article
The Role of Extratropical Pacific in Crossing ENSO Spring Predictability Barrier
This paper investigates the impacts of extratropical Pacific on El Niño-Southern Oscillation (ENSO) spring predictability barrier (SPB). Using an empirical dynamical model – Linear Inverse Model (LIM), we find that the dynamics of the northern and southern extratropical Pacific can significantly and equally weaken the Eastern Pacific (EP)-ENSO SPB, while the North Pacific is more important for weakening the Central Pacific (CP)-ENSO SPB. The evolution of the extratropical optimum initial structures illustrates the different roles of the northern and southern extratropical Pacific in crossing EP and CP ENSO SPBs and demonstrates the decisive role that the South Pacific initial condition plays in ENSO diversity. Additionally, the extratropical Pacific greatly influences the forecast skill of El Niño during SPB, while tropical dynamics may be more important for crossing the SPB of La Niña.
2022-8
Geophys. Res. Lett.
49
e2022GL099488
0
10.1029/2022GL099488
This paper investigates the impacts of extratropical Pacific on El Niño-Southern Oscillation (ENSO) spring predictability barrier (SPB). Using an empirical dynamical model – Linear Inverse Model (LIM), we find that the dynamics of the northern and southern extratropical Pacific can significantly and equally weaken the Eastern Pacific (EP)-ENSO SPB, while the North Pacific is more important for weakening the Central Pacific (CP)-ENSO SPB. The evolution of the extratropical optimum initial structures illustrates the different roles of the northern and southern extratropical Pacific in crossing EP and CP ENSO SPBs and demonstrates the decisive role that the South Pacific initial condition plays in ENSO diversity. Additionally, the extratropical Pacific greatly influences the forecast skill of El Niño during SPB, while tropical dynamics may be more important for crossing the SPB of La Niña.
Zhao
Y.
Jin
Y.
Li
J.
Capotondi
A.
21361
Article
Toward Regional Marine Ecological Forecasting Using Global Climate Model Predictions From Subseasonal to Decadal Timescales: Bottlenecks and Recommendations
This perspective paper discusses how the research community can promote enhancement of marine ecosystem forecasts using physical ocean conditions predicted by global climate models (GCMs). We review the major climate prediction projects and outline new research opportunities to achieve skillful marine biological forecasts. Physical ocean conditions are operationally predicted for subseasonal to seasonal timescales, and multi-year predictions have been enhanced recently. However, forecasting applications are currently limited by the availability of oceanic data; most subseasonal-to-seasonal prediction projects make only sea-surface temperature (SST) publicly available, though other variables useful for biological forecasts are also calculated in GCMs. To resolve the bottleneck of data availability, we recommend that climate prediction centers increase the range of ocean data available to the public, perhaps starting with an expanded suite of 2-dimensional variables, whose storage requirements are much smaller than 3-dimensional variables. Allowing forecast output to be downloaded for a selected region, rather than the whole globe, would also facilitate uptake. We highlight new research opportunities in both physical forecasting (e.g., new approaches to dynamical and statistical downscaling) and biological forecasting (e.g., conducting biological reforecasting experiments) and offer lessons learned to help guide their development. In order to accelerate this research area, we also suggest establishing case studies (i.e., particular climate and biological events as prediction targets) to improve coordination. Advancing our capacity for marine biological forecasting is crucial for the success of the UN Decade of Ocean Science, for which one of seven desired outcomes is “A Predicted Ocean”.
2022-8
Front. Mar. Sci.
855965
0
10.3389/fmars.2022.855965
This perspective paper discusses how the research community can promote enhancement of marine ecosystem forecasts using physical ocean conditions predicted by global climate models (GCMs). We review the major climate prediction projects and outline new research opportunities to achieve skillful marine biological forecasts. Physical ocean conditions are operationally predicted for subseasonal to seasonal timescales, and multi-year predictions have been enhanced recently. However, forecasting applications are currently limited by the availability of oceanic data; most subseasonal-to-seasonal prediction projects make only sea-surface temperature (SST) publicly available, though other variables useful for biological forecasts are also calculated in GCMs. To resolve the bottleneck of data availability, we recommend that climate prediction centers increase the range of ocean data available to the public, perhaps starting with an expanded suite of 2-dimensional variables, whose storage requirements are much smaller than 3-dimensional variables. Allowing forecast output to be downloaded for a selected region, rather than the whole globe, would also facilitate uptake. We highlight new research opportunities in both physical forecasting (e.g., new approaches to dynamical and statistical downscaling) and biological forecasting (e.g., conducting biological reforecasting experiments) and offer lessons learned to help guide their development. In order to accelerate this research area, we also suggest establishing case studies (i.e., particular climate and biological events as prediction targets) to improve coordination. Advancing our capacity for marine biological forecasting is crucial for the success of the UN Decade of Ocean Science, for which one of seven desired outcomes is “A Predicted Ocean”.
Minobe
S.
Capotondi
A.
Jacox
M. G.
Nonaka
M.
Rykaczewski
R. R.
21368
Article
Data from the MOSAiC Arctic Ocean drift experiment
MOSAiC resulted in an unprecedented amount and diversity of observation data over the central Arctic sea ice (Fig. 1): over 660 unique sensors or measurement devices with >8200 registered events, resulting in more than 90000 managed parameters in 73 endorsed sub-projects have been recorded (see https://mosaic-expedition.org/). 122 PIs are registered to work on the data. The year-round experiment started in September 2019 -and ended in October 2020 after a long-lasting preparation phase. The Implementation planning meeting in November 2017 in St. Petersburg was the starting point of its data logistics and data management concepts, with its core organizational component being the MOSAiC Data Policy, described further below. As technical core component the MOSAiC Central Storage (MCS) was drafted, to be operated on board of the research icebreaker Polarstern and mirrored on land, leg by leg, allowing for data sharing and collaboration amongst researchers of the MOSAiC consortium.
2022-9
Nat. Sci. Data
9
568
0
10.1038/s41597-022-01678-8
MOSAiC resulted in an unprecedented amount and diversity of observation data over the central Arctic sea ice (Fig. 1): over 660 unique sensors or measurement devices with >8200 registered events, resulting in more than 90000 managed parameters in 73 endorsed sub-projects have been recorded (see https://mosaic-expedition.org/). 122 PIs are registered to work on the data. The year-round experiment started in September 2019 -and ended in October 2020 after a long-lasting preparation phase. The Implementation planning meeting in November 2017 in St. Petersburg was the starting point of its data logistics and data management concepts, with its core organizational component being the MOSAiC Data Policy, described further below. As technical core component the MOSAiC Central Storage (MCS) was drafted, to be operated on board of the research icebreaker Polarstern and mirrored on land, leg by leg, allowing for data sharing and collaboration amongst researchers of the MOSAiC consortium.
Frickenhaus
S.
Ransby
D.
Shupe
M. D.
Jaiser
R.
Nicolaus
M.
21373
Article
Dynamics of ENSO-driven stratosphere-to-troposphere transport of ozone over North America
The El Niño–Southern Oscillation (ENSO) is known to modulate the strength and frequency of stratosphere-to-troposphere transport (STT) of ozone over the Pacific–North American region during late winter to early summer. Dynamical processes that have been proposed to account for this variability include variations in the amount of ozone in the lowermost stratosphere that is available for STT and tropospheric circulation-related variations in the frequency and geographic distribution of individual STT events.
Here we use a large ensemble of Whole Atmosphere Community Climate Model (WACCM) simulations (forced by sea-surface temperature (SST) boundary conditions consistent with each phase of ENSO) to show that variability in lower-stratospheric ozone and shifts in the Pacific tropospheric jet constructively contribute to the amount of STT of ozone in the North American region during both ENSO phases. In terms of stratospheric variability, ENSO drives ozone anomalies resembling the Pacific–North American teleconnection pattern that span much of the lower stratosphere below 50 hPa. These ozone anomalies, which dominate over other ENSO-driven changes in the Brewer–Dobson circulation (including changes due to both the stratospheric residual circulation and quasi-isentropic mixing), strongly modulate the amount of ozone available for STT transport. As a result, during late winter (February–March), the stratospheric ozone response to the teleconnections constructively reinforces anomalous ENSO-jet-driven STT of ozone. However, as ENSO forcing weakens as spring progresses into summer (April–June), the direct effects of the ENSO-jet-driven STT transport weaken. Nevertheless, the residual impacts of the teleconnections on the amount of ozone in the lower stratosphere persist, and these anomalies in turn continue to cause anomalous STT of ozone. These results should prove helpful for interpreting the utility of ENSO as a subseasonal predictor of both free-tropospheric ozone and the probability of stratospheric ozone intrusion events that may cause exceedances in surface air quality standards.
2022-10
Atmos. Chem. Phys.
22
13035–13048
0
10.5194/acp-22-13035-2022
The El Niño–Southern Oscillation (ENSO) is known to modulate the strength and frequency of stratosphere-to-troposphere transport (STT) of ozone over the Pacific–North American region during late winter to early summer. Dynamical processes that have been proposed to account for this variability include variations in the amount of ozone in the lowermost stratosphere that is available for STT and tropospheric circulation-related variations in the frequency and geographic distribution of individual STT events.
Here we use a large ensemble of Whole Atmosphere Community Climate Model (WACCM) simulations (forced by sea-surface temperature (SST) boundary conditions consistent with each phase of ENSO) to show that variability in lower-stratospheric ozone and shifts in the Pacific tropospheric jet constructively contribute to the amount of STT of ozone in the North American region during both ENSO phases. In terms of stratospheric variability, ENSO drives ozone anomalies resembling the Pacific–North American teleconnection pattern that span much of the lower stratosphere below 50 hPa. These ozone anomalies, which dominate over other ENSO-driven changes in the Brewer–Dobson circulation (including changes due to both the stratospheric residual circulation and quasi-isentropic mixing), strongly modulate the amount of ozone available for STT transport. As a result, during late winter (February–March), the stratospheric ozone response to the teleconnections constructively reinforces anomalous ENSO-jet-driven STT of ozone. However, as ENSO forcing weakens as spring progresses into summer (April–June), the direct effects of the ENSO-jet-driven STT transport weaken. Nevertheless, the residual impacts of the teleconnections on the amount of ozone in the lower stratosphere persist, and these anomalies in turn continue to cause anomalous STT of ozone. These results should prove helpful for interpreting the utility of ENSO as a subseasonal predictor of both free-tropospheric ozone and the probability of stratospheric ozone intrusion events that may cause exceedances in surface air quality standards.
Albers
J. R.
Butler
A. H.
Langford
A. O.
Elsbury
D.
Breeden
M. L.
21374
Article
Working toward a National Coordinated Soil Moisture Monitoring Network: vision, progress, and future directions
Soil moisture is a critical land surface variable, impacting the water, energy, and carbon cycles. While in situ soil moisture monitoring networks are still developing, there is no cohesive strategy or framework to coordinate, integrate, or disseminate these diverse data sources in a synergistic way that can improve our ability to understand climate variability at the national, state, and local levels. Thus, a national strategy is needed to guide network deployment, sustainable network operation, data integration and dissemination, and user-focused product development. The National Coordinated Soil Moisture Monitoring Network (NCSMMN) is a federally led, multi-institution effort that aims to address these needs by capitalizing on existing wide-ranging soil moisture monitoring activities, increasing the utility of observational data, and supporting their strategic application to the full range of decision-making needs. The goals of the NCSMMN are to 1) establish a national “network of networks” that effectively demonstrates data integration and operational coordination of diverse in situ networks; 2) build a community of practice around soil moisture measurement, interpretation, and application—a “network of people” that links data providers, researchers, and the public; and 3) support research and development (R&D) on techniques to merge in situ soil moisture data with remotely sensed and modeled hydrologic data to create user-friendly soil moisture maps and associated tools. The overarching mission of the NCSMMN is to provide coordinated high-quality, nationwide soil moisture information for the public good by supporting applications like drought and flood monitoring, water resource management, agricultural and forestry planning, and fire danger ratings.
2022-9
Bull. Amer. Meteor. Soc.
103
E2719–E2732
0
10.1175/BAMS-D-21-0178.1
Soil moisture is a critical land surface variable, impacting the water, energy, and carbon cycles. While in situ soil moisture monitoring networks are still developing, there is no cohesive strategy or framework to coordinate, integrate, or disseminate these diverse data sources in a synergistic way that can improve our ability to understand climate variability at the national, state, and local levels. Thus, a national strategy is needed to guide network deployment, sustainable network operation, data integration and dissemination, and user-focused product development. The National Coordinated Soil Moisture Monitoring Network (NCSMMN) is a federally led, multi-institution effort that aims to address these needs by capitalizing on existing wide-ranging soil moisture monitoring activities, increasing the utility of observational data, and supporting their strategic application to the full range of decision-making needs. The goals of the NCSMMN are to 1) establish a national “network of networks” that effectively demonstrates data integration and operational coordination of diverse in situ networks; 2) build a community of practice around soil moisture measurement, interpretation, and application—a “network of people” that links data providers, researchers, and the public; and 3) support research and development (R&D) on techniques to merge in situ soil moisture data with remotely sensed and modeled hydrologic data to create user-friendly soil moisture maps and associated tools. The overarching mission of the NCSMMN is to provide coordinated high-quality, nationwide soil moisture information for the public good by supporting applications like drought and flood monitoring, water resource management, agricultural and forestry planning, and fire danger ratings.
Baker
C. B.
Cosh
M.
Bolten
J.
Skumanich
M.
. .
.
Woloszyn
M.
21376
Article
Future Projections of Precipitation using Bias–Corrected High–Resolution Regional Climate Models for Sub–Regions with Homogeneous Characteristics in South Korea
Although South Korea has a relatively small area when compared to neighboring countries, there are large differences in precipitation characteristics by region due to its complex topography. Therefore, to effectively respond to disasters caused by precipitation in South Korea, climate change information using a climate model with an improved spatial resolution is required. This study classified sub–regions with homogeneous characteristics in South Korea using transformed gridded precipitation observation datasets. Then, high–resolution regional climate models (RCMs) with a 12.5 km horizontal resolution, which are known to simulate added value well in simulating future projections of South Korea, were bias–corrected, and future changes in the precipitation means and extremes were analyzed using these RCMs. The classified precipitation sub–regions in South Korea reasonably reflected the observed distribution of precipitation, depending on latitude and topography. The future precipitation characteristics of the classified precipitation sub–regions were predicted using bias–corrected RCMs. While the annual precipitation is projected to increase relative to the present in most grids for all future periods, the RCP8.5 scenario for the mid–twenty-first century is projected to decrease in the north of the central region. Intensified warming in the late twenty-first century is predicted to considerably increase the mean precipitation intensity and magnitude of the high–intensity extreme precipitation in all the precipitation sub–regions. As these results can lead to increased hydrological disasters, this study will help to prepare practical countermeasures for precipitation changes on regional and local spatial scales in South Korea.
2022-8
Asia-Pacific J. Atmos. Sci.
58
715–727
0
10.1007/s13143-022-00292-3
Although South Korea has a relatively small area when compared to neighboring countries, there are large differences in precipitation characteristics by region due to its complex topography. Therefore, to effectively respond to disasters caused by precipitation in South Korea, climate change information using a climate model with an improved spatial resolution is required. This study classified sub–regions with homogeneous characteristics in South Korea using transformed gridded precipitation observation datasets. Then, high–resolution regional climate models (RCMs) with a 12.5 km horizontal resolution, which are known to simulate added value well in simulating future projections of South Korea, were bias–corrected, and future changes in the precipitation means and extremes were analyzed using these RCMs. The classified precipitation sub–regions in South Korea reasonably reflected the observed distribution of precipitation, depending on latitude and topography. The future precipitation characteristics of the classified precipitation sub–regions were predicted using bias–corrected RCMs. While the annual precipitation is projected to increase relative to the present in most grids for all future periods, the RCP8.5 scenario for the mid–twenty-first century is projected to decrease in the north of the central region. Intensified warming in the late twenty-first century is predicted to considerably increase the mean precipitation intensity and magnitude of the high–intensity extreme precipitation in all the precipitation sub–regions. As these results can lead to increased hydrological disasters, this study will help to prepare practical countermeasures for precipitation changes on regional and local spatial scales in South Korea.
Park
C.
Shin
S.-W.
Cha
D.-H.
Suh
M.-S.
Hong
S.-Y.
Ahn
J.-B.
Min
S.-K.
Byun
Y.-H.
21377
Article
Spring Land Temperature in Tibetan Plateau and Global-Scale Summer Precipitation: Initialization and Improved Prediction
Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land-atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project.
After using an innovative new land state initialization approach based on observed surface 2-meter temperature over the TP in the LS4P experiment, results from a multi-model ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hot spot” regions identified here; the ensemble means in some “hot spots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.
2022-12
Bull. Amer. Meteor. Soc.
103
E2756–E2767
0
10.1175/BAMS-D-21-0270.1
Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land-atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project.
After using an innovative new land state initialization approach based on observed surface 2-meter temperature over the TP in the LS4P experiment, results from a multi-model ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hot spot” regions identified here; the ensemble means in some “hot spots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.
Xue
Y.
Diallo
I.
Boone
A. A.
Yao
T.
. .
.
Hong
S.-Y.
al.
et
21382
Article
Tethered balloon-borne profile measurements of atmospheric properties in the cloudy atmospheric boundary layer over the Arctic sea ice during MOSAiC: Overview and first results
The tethered balloon-borne measurement system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was deployed over the Arctic sea ice for 4 weeks in summer 2020 as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition. Using BELUGA, vertical profiles of dynamic, thermodynamic, aerosol particle, cloud, radiation, and turbulence properties were measured from the ground up to a height of 1,500 m. BELUGA was operated during an anomalously warm period with frequent liquid water clouds and variable sea ice conditions. Three case studies of liquid water phase, single-layer clouds observed on 3 days (July 13, 23, and 24, 2020) are discussed to show the potential of the collected data set to comprehensively investigate cloud properties determining cloud evolution in the inner Arctic over sea ice. Simulated back-trajectories show that the observed clouds have evolved within 3 different air masses (“aged Arctic,” “advected over sea ice,” and “advected over open ocean”), which left distinct fingerprints in the cloud properties. Strong cloud top radiative cooling rates agree with simulated results of previous studies. The weak warming at cloud base is mostly driven by the vertical temperature profile between the surface and cloud base. In-cloud turbulence induced by the cloud top cooling was similar in strength compared to former studies. From the extent of the mixing layer, it is speculated that the overall cloud cooling is stronger and thus faster in the warm oceanic air mass. Larger aerosol particle number concentrations and larger sizes were observed in the air mass advected over the sea ice and in the air mass advected over the open ocean.
2022-9
Elementa Sci. Anthrop.
10
000120
0
10.1525/elementa.2021.000120
The tethered balloon-borne measurement system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was deployed over the Arctic sea ice for 4 weeks in summer 2020 as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition. Using BELUGA, vertical profiles of dynamic, thermodynamic, aerosol particle, cloud, radiation, and turbulence properties were measured from the ground up to a height of 1,500 m. BELUGA was operated during an anomalously warm period with frequent liquid water clouds and variable sea ice conditions. Three case studies of liquid water phase, single-layer clouds observed on 3 days (July 13, 23, and 24, 2020) are discussed to show the potential of the collected data set to comprehensively investigate cloud properties determining cloud evolution in the inner Arctic over sea ice. Simulated back-trajectories show that the observed clouds have evolved within 3 different air masses (“aged Arctic,” “advected over sea ice,” and “advected over open ocean”), which left distinct fingerprints in the cloud properties. Strong cloud top radiative cooling rates agree with simulated results of previous studies. The weak warming at cloud base is mostly driven by the vertical temperature profile between the surface and cloud base. In-cloud turbulence induced by the cloud top cooling was similar in strength compared to former studies. From the extent of the mixing layer, it is speculated that the overall cloud cooling is stronger and thus faster in the warm oceanic air mass. Larger aerosol particle number concentrations and larger sizes were observed in the air mass advected over the sea ice and in the air mass advected over the open ocean.
Lonardi
M.
Pilz
C.
Akansu
U.
Dahlke
S.
Egerer
U.
. .
.
Shupe
M. D.
al.
et
21384
Article
Modulation of ENSO teleconnections over North America by the Pacific decadal oscillation
In this study, we investigate whether the Pacific decadal oscillation (PDO) can enhance or diminish El Niño Southern Oscillation (ENSO) temperature and precipitation teleconnections over North America using five single model initial-condition large ensembles (SMILEs). The use of SMILEs facilitates a statistically robust comparison of ENSO events that occur during different phases of the PDO. We find that a positive PDO enhances winter and spring El Niño temperature and precipitation teleconnections and diminishes La Niña teleconnections. A negative PDO has the opposite effect. The modulation of ENSO by the PDO is mediated by differences in the location and strength of the Aleutian Low and Pacific Jet during ENSO events under different phases of the PDO. This modulation is a simple combination of the individual effects of the PDO and ENSO over North America. Finally, we show that ENSO and the PDO can be used to evaluate the likelihood of the occurrence of temperature and precipitation anomalies in different regions, but cannot be used as a deterministic predictor of these anomalies due to the large variability between individual events.
2022-10
Environ. Res. Lett.
17
114005
0
10.1088/1748-9326/ac9327
In this study, we investigate whether the Pacific decadal oscillation (PDO) can enhance or diminish El Niño Southern Oscillation (ENSO) temperature and precipitation teleconnections over North America using five single model initial-condition large ensembles (SMILEs). The use of SMILEs facilitates a statistically robust comparison of ENSO events that occur during different phases of the PDO. We find that a positive PDO enhances winter and spring El Niño temperature and precipitation teleconnections and diminishes La Niña teleconnections. A negative PDO has the opposite effect. The modulation of ENSO by the PDO is mediated by differences in the location and strength of the Aleutian Low and Pacific Jet during ENSO events under different phases of the PDO. This modulation is a simple combination of the individual effects of the PDO and ENSO over North America. Finally, we show that ENSO and the PDO can be used to evaluate the likelihood of the occurrence of temperature and precipitation anomalies in different regions, but cannot be used as a deterministic predictor of these anomalies due to the large variability between individual events.
Maher
N.
Kay
J. E.
Capotondi
A.
21387
Article
Sizing ice hydrometeor populations using the dual-wavelength radar ratio
Dual-wavelength (3.2 and 0.32 cm, i.e., X- and W-radar bands) radar ratio (DWR) measurements in ice clouds and precipitation using Canada's National Research Council Institute for Aerospace Research airborne radar are compared to closely collocated particle microphysical in situ sampling data in order to develop relations between DWR and characteristic hydrometeor size. This study uses the radar and in situ data sets collected during the In-Cloud ICing and Large-drop Experiment (ICICLE) campaign in midlatitude frontal clouds. Since atmospheric particle scattering at X band is predominantly in the Rayleigh regime and the W-band frequency is the highest frequency usually used for hydrometeor remote sensing, the X–W-band combination provides a relatively strong dual-wavelength reflectivity difference. This study considers radar and in situ measurements conducted in relatively homogeneous cloud and precipitation conditions. Measurements show that under these conditions, the difference between the X-band radar reflectivities observed with vertical and horizontal pointing of the radar beam are generally small and often negligible. However, W-band reflectivities at vertical beam pointing are, on average, larger than those for horizontal beam pointing by about 4 dB, which is a non-Rayleigh scattering effect from preferentially oriented non-spherical particles. A horizontal radar beam DWR–mean volume particle size relation, Dv, provides robust estimates of this characteristic size for populations of particles with different habits. Uncertainties of Dv retrievals using DWR are around 0.6 mm when Dv is greater than approximately 1 mm. Size estimates using vertical radar beam DWRs have larger uncertainties due to smaller dual-wavelength signals and stronger influences of hydrometeor habits and orientations at this geometry of beam pointing. Mean relations among different characteristic sizes, which describe the entire particle size distribution (PSD), such as Dv, and other sizes used in various applications (e.g., the mean, effective, and median sizes) are derived, so the results of this study can be used for estimating different PSD characteristic sizes.
2022-11
Atmos. Meas. Tech.
15
6373–6386
0
10.5194/amt-15-6373-2022
Dual-wavelength (3.2 and 0.32 cm, i.e., X- and W-radar bands) radar ratio (DWR) measurements in ice clouds and precipitation using Canada's National Research Council Institute for Aerospace Research airborne radar are compared to closely collocated particle microphysical in situ sampling data in order to develop relations between DWR and characteristic hydrometeor size. This study uses the radar and in situ data sets collected during the In-Cloud ICing and Large-drop Experiment (ICICLE) campaign in midlatitude frontal clouds. Since atmospheric particle scattering at X band is predominantly in the Rayleigh regime and the W-band frequency is the highest frequency usually used for hydrometeor remote sensing, the X–W-band combination provides a relatively strong dual-wavelength reflectivity difference. This study considers radar and in situ measurements conducted in relatively homogeneous cloud and precipitation conditions. Measurements show that under these conditions, the difference between the X-band radar reflectivities observed with vertical and horizontal pointing of the radar beam are generally small and often negligible. However, W-band reflectivities at vertical beam pointing are, on average, larger than those for horizontal beam pointing by about 4 dB, which is a non-Rayleigh scattering effect from preferentially oriented non-spherical particles. A horizontal radar beam DWR–mean volume particle size relation, Dv, provides robust estimates of this characteristic size for populations of particles with different habits. Uncertainties of Dv retrievals using DWR are around 0.6 mm when Dv is greater than approximately 1 mm. Size estimates using vertical radar beam DWRs have larger uncertainties due to smaller dual-wavelength signals and stronger influences of hydrometeor habits and orientations at this geometry of beam pointing. Mean relations among different characteristic sizes, which describe the entire particle size distribution (PSD), such as Dv, and other sizes used in various applications (e.g., the mean, effective, and median sizes) are derived, so the results of this study can be used for estimating different PSD characteristic sizes.
Matrosov
S. Y.
Korolev
A.
Wolde
M.
Nguyen
C.
21392
Article
An increase in marine heatwaves without significant changes in surface ocean temperature variability
Marine heatwaves (MHWs)—extremely warm, persistent sea surface temperature (SST) anomalies causing substantial ecological and economic consequences—have increased worldwide in recent decades. Concurrent increases in global temperatures suggest that climate change impacted MHW occurrences, beyond random changes arising from natural internal variability. Moreover, the long-term SST warming trend was not constant but instead had more rapid warming in recent decades. Here we show that this nonlinear trend can—on its own—appear to increase SST variance and hence MHW frequency. Using a Linear Inverse Model to separate climate change contributions to SST means and internal variability, both in observations and CMIP6 historical simulations, we find that most MHW increases resulted from regional mean climate trends that alone increased the probability of SSTs exceeding a MHW threshold. Our results suggest the need to carefully attribute global warming-induced changes in climate extremes, which may not always reflect underlying changes in variability.
2022-12
Nat. Commun.
13
7396
0
10.1038/s41467-022-34934-x
Marine heatwaves (MHWs)—extremely warm, persistent sea surface temperature (SST) anomalies causing substantial ecological and economic consequences—have increased worldwide in recent decades. Concurrent increases in global temperatures suggest that climate change impacted MHW occurrences, beyond random changes arising from natural internal variability. Moreover, the long-term SST warming trend was not constant but instead had more rapid warming in recent decades. Here we show that this nonlinear trend can—on its own—appear to increase SST variance and hence MHW frequency. Using a Linear Inverse Model to separate climate change contributions to SST means and internal variability, both in observations and CMIP6 historical simulations, we find that most MHW increases resulted from regional mean climate trends that alone increased the probability of SSTs exceeding a MHW threshold. Our results suggest the need to carefully attribute global warming-induced changes in climate extremes, which may not always reflect underlying changes in variability.
Xu
Tongtong
T.
Newman
M.
Capotondi
A.
Stevenson
S.
Di Lorenzo
E.
Alexander
M. J.
21393
Article
Radar Retrieval Evaluation and Investigation of Dendritic Growth Layer Polarimetric Signatures in a Winter Storm
This study evaluates ice particle size distribution and aspect ratio φ Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed Kdp and ZDR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using ZDR and Kdp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded −2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While φ radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals.
2022-11
J. Appl. Meteor. Climatol.
61
1685–1711
0
10.1175/JAMC-D-21-0220.1
This study evaluates ice particle size distribution and aspect ratio φ Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed Kdp and ZDR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using ZDR and Kdp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded −2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While φ radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals.
Dunnavan
E. L.
Carlin
J. T.
Hu
J.
Bukovčić
P.
Ryzhkov
A. V.
McFarquhar
G. M.
Finlon
J. A.
Matrosov
S. Y.
Delene
D. J.
21400
Article
Predictability and empirical dynamics of fisheries time series in the North Pacific
Previous studies have documented a strong relationship between marine ecosystems and large-scale modes of sea surface height (SSH) and sea surface temperature (SST) variability in the North Pacific such as the Pacific Decadal Oscillation and the North Pacific Gyre Oscillation. In the central and western North Pacific along the Kuroshio-Oyashio Extension (KOE), the expression of these modes in SSH and SST is linked to the propagation of long oceanic Rossby waves, which extend the predictability of the climate system to ~3 years. Using a multivariate physical-biological linear inverse model (LIM) we explore the extent to which this physical predictability leads to multi-year prediction of dominant fishery indicators inferred from three datasets (i.e., estimated biomasses, landings, and catches). We find that despite the strong autocorrelation in the fish indicators, the LIM adds dynamical forecast skill beyond persistence up to 5-6 years. By performing a sensitivity analysis of the LIM forecast model, we find that two main factors are essential for extending the dynamical predictability of the fishery indicators beyond persistence. The first is the interaction of the fishery indicators with the SST/SSH of the North and tropical Pacific. The second is the empirical relationship among the fisheries time series. This latter component reflects stock-stock interactions as well as common technological and human socioeconomic factors that may influence multiple fisheries and are captured in the training of the LIM. These results suggest that empirical dynamical models and machine learning algorithms, such as the LIM, provide an alternative and promising approach for forecasting key ecological indicators beyond the skill of persistence.
2022-11
Front. Mar. Sci.
9
0
10.3389/fmars.2022.969319
Previous studies have documented a strong relationship between marine ecosystems and large-scale modes of sea surface height (SSH) and sea surface temperature (SST) variability in the North Pacific such as the Pacific Decadal Oscillation and the North Pacific Gyre Oscillation. In the central and western North Pacific along the Kuroshio-Oyashio Extension (KOE), the expression of these modes in SSH and SST is linked to the propagation of long oceanic Rossby waves, which extend the predictability of the climate system to ~3 years. Using a multivariate physical-biological linear inverse model (LIM) we explore the extent to which this physical predictability leads to multi-year prediction of dominant fishery indicators inferred from three datasets (i.e., estimated biomasses, landings, and catches). We find that despite the strong autocorrelation in the fish indicators, the LIM adds dynamical forecast skill beyond persistence up to 5-6 years. By performing a sensitivity analysis of the LIM forecast model, we find that two main factors are essential for extending the dynamical predictability of the fishery indicators beyond persistence. The first is the interaction of the fishery indicators with the SST/SSH of the North and tropical Pacific. The second is the empirical relationship among the fisheries time series. This latter component reflects stock-stock interactions as well as common technological and human socioeconomic factors that may influence multiple fisheries and are captured in the training of the LIM. These results suggest that empirical dynamical models and machine learning algorithms, such as the LIM, provide an alternative and promising approach for forecasting key ecological indicators beyond the skill of persistence.
Navarra
G. G.
Di Lorenzo
E.
Rykaczewski
R. R.
Capotondi
A.
21402
Article
The Global/Regional Integrated Model System (GRIMs): an Update and Seasonal Evaluation
The Global/Regional Integrated Model system (GRIMs) is upgraded to version 4.0, with the advancement of the moisture advection scheme and physics package, focusing on the global model program (GMP) for seasonal simulation and climate studies. Compared to the original version 3.1, which was frozen in 2013, the new version shows no Gibbs phenomenon in the moisture and tracer fields by implementing the semi-Lagrangian advection scheme with a better computational efficiency at higher resolution. The performance of the seasonal ensemble simulation (June–August 2017 and December 2016–February 2017) is significantly improved by new physics and ancillary data. The advancement is largest in the stratosphere, where the cold bias is dramatically reduced and the wind bias of the polar jets is alleviated, especially for the winter hemisphere. Noticeable improvements are also found in tropospheric zonal mean circulation, eddy transport, precipitation, and surface air temperature. This allows GRIMs version 4.0 to be used not only for long-term climate simulations, but also for subseasonal-to-seasonal climate prediction.
2022-10
Asia-Pacific J. Atmos. Sci.
0
10.1007/s13143-022-00297-y
The Global/Regional Integrated Model system (GRIMs) is upgraded to version 4.0, with the advancement of the moisture advection scheme and physics package, focusing on the global model program (GMP) for seasonal simulation and climate studies. Compared to the original version 3.1, which was frozen in 2013, the new version shows no Gibbs phenomenon in the moisture and tracer fields by implementing the semi-Lagrangian advection scheme with a better computational efficiency at higher resolution. The performance of the seasonal ensemble simulation (June–August 2017 and December 2016–February 2017) is significantly improved by new physics and ancillary data. The advancement is largest in the stratosphere, where the cold bias is dramatically reduced and the wind bias of the polar jets is alleviated, especially for the winter hemisphere. Noticeable improvements are also found in tropospheric zonal mean circulation, eddy transport, precipitation, and surface air temperature. This allows GRIMs version 4.0 to be used not only for long-term climate simulations, but also for subseasonal-to-seasonal climate prediction.
Koo
M.-S.
Song
K.
Kim
J.-E. E.
Son
S.-W.
Jeong
J.-H.
Kim
H.
Moon
B.-K.
Park
R. J.
Yeh
S.-W.
Yoo
C.
Hong
S.-Y.
21406
Article
Exploring the effect of waterbodies coupled with other environmental parameters to model PM2.5 over Delhi-NCT in northwest India
The problem of air pollution, especially, the concentration of PM2.5 is a long-standing issue over the national capital territory of India (Delhi- NCT). This study seeks to explore the effect of surface waterbody on the spatial variation of PM2.5 in Delhi-NCT. As moisture determines the concentration of PM2.5 and other pollutants in the atmosphere, the presence of surface waterbody plays a vital role in controlling PM2.5 in the local environment. We use distance to waterbodies, including other covariates of meteorology and surface characteristics, such as the surface greenness and land cover type, to model near the surface concentration of PM2.5. Different machine learning (ML) approaches were applied to predict the spatio-temporal behavior of PM2.5. Out of different ML-based models, the random forest regression produces the highest predictive accuracy (R2 = 0.68), while, gradient boosting, support vector machine and artificial neural net have R2 less than 0.66. The outcome of the feature important score from random forest regression reveals that proximity to the water surface is more important than precipitation and land use and land cover (LULC). The results find that up to a distance of 3 km, the waterbody exerts a significant effect in lowering PM2.5 concentration. The findings suggest that restoring the surface waterbodies – moderate to large in size, including the other prescribed measures could potentially restore the air quality standards for the Delhi-NCT and elsewhere in the world where particulate matter pollution is of great concern.
2022-12
13
101614
0
10.1016/j.apr.2022.101614
The problem of air pollution, especially, the concentration of PM2.5 is a long-standing issue over the national capital territory of India (Delhi- NCT). This study seeks to explore the effect of surface waterbody on the spatial variation of PM2.5 in Delhi-NCT. As moisture determines the concentration of PM2.5 and other pollutants in the atmosphere, the presence of surface waterbody plays a vital role in controlling PM2.5 in the local environment. We use distance to waterbodies, including other covariates of meteorology and surface characteristics, such as the surface greenness and land cover type, to model near the surface concentration of PM2.5. Different machine learning (ML) approaches were applied to predict the spatio-temporal behavior of PM2.5. Out of different ML-based models, the random forest regression produces the highest predictive accuracy (R2 = 0.68), while, gradient boosting, support vector machine and artificial neural net have R2 less than 0.66. The outcome of the feature important score from random forest regression reveals that proximity to the water surface is more important than precipitation and land use and land cover (LULC). The results find that up to a distance of 3 km, the waterbody exerts a significant effect in lowering PM2.5 concentration. The findings suggest that restoring the surface waterbodies – moderate to large in size, including the other prescribed measures could potentially restore the air quality standards for the Delhi-NCT and elsewhere in the world where particulate matter pollution is of great concern.
Gayen
B. K.
Dutta
D.
Acharya
P.
Sreekesh
S.
Kulshrestha
U. C.
Acharya
N.
21410
Article
Validation of the Cloud_CCI cloud products in the Arctic
2022-0
Atmos. Meas. Tech.
16
2903-2918
0
10.5194/amt-16-2903-2023
Vinjamuri
K. S.
Vountas
M.
Lelli
L.
Stengel
M.
Shupe
M. D.
Ebell
K.
Burrows
J. P.
21422
Article
Testing the impact of culturally-relevant communication style on engagement with Hispanic and Latinx adults
Effective science communication for a multilingual population requires more than language translation, it also requires being mindful of cultural communication styles. This study tested the impact that communication style has on feelings of inclusion, learning, and engagement in the Earth sciences for Hispanic and Latinx adults. An online survey with open and closed questions was used to evaluate two science videos (in both Spanish and English) with different communication styles: 1) a traditional, interview-based style, where experts present a science concept, and 2) an informal conversational style, where a scientific message is shared through a casual conversation. Seventy-four participants self-identified as Hispanic and Latinx and were the focus of the data analysis. Both video styles were positively received, with participant feedback emphasizing feelings of inclusion in seeing Latina scientists, easy to understand science concepts, and accessible language. Hispanic and Latinx adults preferred the traditional video, but the conversational video ranked higher in other aspects, which varied based on participants’ primary spoken language at home. For example, the conversational video had a positive impact on the ability to relate information to their own life and increase awareness of Earth science careers for Spanish-language speakers. Findings suggest the use of both video styles could improve feelings of inclusion and engagement for Hispanic and Latinx adults. Additional aspects of culture and demographics may explain some of the language-based results. Future science videos are encouraged to be co-designed by, for, and with Hispanic and Latinx communities to emphasize cultural values while avoiding stereotyping and cultural appropriation.
2022-9
J. Geosci. Educ.
71
0
10.1080/10899995.2022.2120701
Effective science communication for a multilingual population requires more than language translation, it also requires being mindful of cultural communication styles. This study tested the impact that communication style has on feelings of inclusion, learning, and engagement in the Earth sciences for Hispanic and Latinx adults. An online survey with open and closed questions was used to evaluate two science videos (in both Spanish and English) with different communication styles: 1) a traditional, interview-based style, where experts present a science concept, and 2) an informal conversational style, where a scientific message is shared through a casual conversation. Seventy-four participants self-identified as Hispanic and Latinx and were the focus of the data analysis. Both video styles were positively received, with participant feedback emphasizing feelings of inclusion in seeing Latina scientists, easy to understand science concepts, and accessible language. Hispanic and Latinx adults preferred the traditional video, but the conversational video ranked higher in other aspects, which varied based on participants’ primary spoken language at home. For example, the conversational video had a positive impact on the ability to relate information to their own life and increase awareness of Earth science careers for Spanish-language speakers. Findings suggest the use of both video styles could improve feelings of inclusion and engagement for Hispanic and Latinx adults. Additional aspects of culture and demographics may explain some of the language-based results. Future science videos are encouraged to be co-designed by, for, and with Hispanic and Latinx communities to emphasize cultural values while avoiding stereotyping and cultural appropriation.
Morales
A.
Luna
L. M.
Zietlow
D. W.
LeBeau
J. E.
Molina
M. J.
21423
Article
Summertime precipitation extremes and the influence of atmospheric flows on the western slopes of the southern Andes of Perú
Although climatologically dry, the western slopes of the southern Andes of Peru (WSA) can experience precipitation extremes (PEs) during the summer (December–February) resulting in great economic and human losses. Generally, WSA has a positive upslope gradient in precipitation, meaning more rain falls at higher elevations, but observations have shown this gradient can become negative with higher rainfall near the coastal cities. In this study we analyse 2000–2019 regional atmospheric patterns associated with different upslope precipitation gradients and PEs in WSA using principal component analysis methods and surface station observations. Results show important changes in the atmospheric circulation patterns during the occurrence of PE events. A prevailing pattern of negative southerly wind anomalies and regional warming of the southeastern Pacific Ocean leads to significant increases in moisture along the coast of WSA. Eastern moisture flows associated with the presence of the Bolivian High are observed at upper levels of the atmosphere and transport water vapour from the Amazon to the western side of the Andes. Additionally, there is a blocking effect aloft in response to an intense gradient of geopotential height that attenuates the easterly circulations. These large-scale mechanisms act to concentrate high precipitable water amounts and high levels of convective available potential energy in the troposphere which favours the vertical velocities essential to trigger PEs. These results increase our knowledge of the large-scale characteristics of PEs to help with forecasting these impactful events and protecting the more than 1.8 million people living in WSA.
2022-12
Int. J. Climatol.
42
9909-9930
0
10.1002/joc.7871
Although climatologically dry, the western slopes of the southern Andes of Peru (WSA) can experience precipitation extremes (PEs) during the summer (December–February) resulting in great economic and human losses. Generally, WSA has a positive upslope gradient in precipitation, meaning more rain falls at higher elevations, but observations have shown this gradient can become negative with higher rainfall near the coastal cities. In this study we analyse 2000–2019 regional atmospheric patterns associated with different upslope precipitation gradients and PEs in WSA using principal component analysis methods and surface station observations. Results show important changes in the atmospheric circulation patterns during the occurrence of PE events. A prevailing pattern of negative southerly wind anomalies and regional warming of the southeastern Pacific Ocean leads to significant increases in moisture along the coast of WSA. Eastern moisture flows associated with the presence of the Bolivian High are observed at upper levels of the atmosphere and transport water vapour from the Amazon to the western side of the Andes. Additionally, there is a blocking effect aloft in response to an intense gradient of geopotential height that attenuates the easterly circulations. These large-scale mechanisms act to concentrate high precipitable water amounts and high levels of convective available potential energy in the troposphere which favours the vertical velocities essential to trigger PEs. These results increase our knowledge of the large-scale characteristics of PEs to help with forecasting these impactful events and protecting the more than 1.8 million people living in WSA.
Villalobos-Puma
E.
Flores-Rojas
J. L.
Martinez-Castro
D.
Morales
A.
al.
et
21509
Article
Scaling Land‐Atmosphere Interactions: Special or Fundamental?
2022-10
Journal of Geophysical Research: Biogeosciences
127
e2022JG007097
0
10.1029/2022JG007097
Desai
A. R.
Paleri
S.
Mineau
J.
Kadum
H.
Wanner
L.
Mauder
M.
Butterworth
B. J.
al.
et
21550
Article
Rain on Snow understudied in sea ice remote sensing: A multi-sensor analysis of ROS during MOSAiC
2022-10
The Cryosphere
16
4223–4250
0
10.5194/tc-16-4223-2022
Stroeve
J.
...
Gallagher
M. R.
al.
et
21330
Book_Section
Chapter 9. Mitigation and Adaptation Measures
2022-6
Springer
Springer Water
331–360
30
978-981-19-1898-8
10.1007/978-981-19-1898-8_9
Baghban
S.
Bozorg-Haddad
O.
Berndtsson
R.
Hobbins
M. T.
Al-Ansari
N.
21207
Dataset
SnowEx17 Time-Lapse Imagery, Version 1 (SNEX17_TLI)
2022-1
NASA National Snow and Ice Data Center Distributed Active Archive Center
0
10.5067/WYRNU50R9L5R
Raleigh
M. S.
Currier
W. R.
Lundquist
J. D.
Houser
P.
Hiemstra
C.
21278
Dataset
DataHawk2 Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign, raw data
This dataset includes unprocessed raw data from DataHawk2 fixed-wind uncrewed aircraft system (UAS) flights that were conducted in the central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Synchronized and quality controlled data are available in the Arctic Data Center at doi:10.18739/A22Z12Q8X for data provided at their native frequency logged on board the aircraft’s secure digital (SD) card (A1 level files), or at doi:10.18739/A2Z60C34R for data interpolated to a common 10 hertz (Hz) clock (B1 level files). Users are encouraged to primarily use the B1 level data for analysis. Please contact the authors if you plan to use this dataset. More information on data collection with the DataHawk2 can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2022): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, submitted.
2022-1
Arctic Data Center
0
10.18739/A29C6S24C
This dataset includes unprocessed raw data from DataHawk2 fixed-wind uncrewed aircraft system (UAS) flights that were conducted in the central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Synchronized and quality controlled data are available in the Arctic Data Center at doi:10.18739/A22Z12Q8X for data provided at their native frequency logged on board the aircraft’s secure digital (SD) card (A1 level files), or at doi:10.18739/A2Z60C34R for data interpolated to a common 10 hertz (Hz) clock (B1 level files). Users are encouraged to primarily use the B1 level data for analysis. Please contact the authors if you plan to use this dataset. More information on data collection with the DataHawk2 can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2022): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, submitted.
Jozef
G.
de Boer
G.
Cassano
J.
Calmer
R.
Hamilton
J.
al.
et
21279
Dataset
HELiX Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Campaign, A0 level data
2022-1
Arctic Data Center
0
10.18739/A2G44HR8B
Calmer
R.
de Boer
G.
Hamilton
J.
Lawrence
D.
Cox
C. J.
al.
et
21329
Dataset
Snowfall rate estimates using Ka-band radar measurements. ARM Mobile Facility (MOS) MOSAiC (Drifting Obs – Study of Arctic Climate)
2022-0
DOE/ARM
0
10.5439/1853942
Matrosov
S. Y.
Uttal
T.
Shupe
M. D.
21534
Dataset
Harmonized in-situ observations of surface energy fluxes and environmental drivers at 64 Arctic vegetation and glacier sites
2022-0
PANGEA
0
10.1594/PANGAEA.949792
Oehri
J.
..
.
Miller
N.B
..
.
Cox
C. J.
..
.
de Boer
G.
al.
et
21535
Dataset
Upward and downward broadband shortwave and longwave irradiance and downward diffuse and direct solar partitioning during the MOSAiC expedition
2022-0
PANGEA
0
10.1594/PANGAEA.952359
Pirazzini
R.
Henna-Reeta
H.
Shupe
M. D.
Uttal
T.
Cox
C. J.
Costa
D. M.
Persson
P. O. G.
Brasseur
Z.