NOAA PSL Climate Data Repository - FACTS Climate Experiment Documentation

FACTS Climate Experiment Documentation

Experiment Forcings
Time Coverage & Ensemble Size
Variables
Model Descriptions
Dataset References

Climate Experiment Forcings

The table below shows the forcings used in the climate experiments that are being made available through the FACTS website.

Experiment Identifiers		Forcings¹
Experiment Name²	File Name ID³	Sea Surface Temperature (SST)	Sea Ice	Greenhouse Gases (GHG)	Ozone
AMIP with Observed Radiative Forcing	amip_obs_rf	Obs	Obs	Obs	Obs
AMIP with Observed Radiative Forcing/ERSST v5	amip_obs_rf_ersstv5	ERSST v5	Obs	Obs	Obs
AMIP with Observed Radiative Forcing/Hurrell SST	amip_obs_rf_hurrell	Hurrell SST	Obs	Obs	Obs
AMIP with 1880s Radiative Forcing	amip_1880s_rf	Obs Detrended to 1880	Present Climatology	Past Climatology	Past Climatology
AMIP with Natural History Forcing	amip_nat_hist	CMIP5-est1⁵	CMIP5-est1⁵	Past Climatology	Past Climatology
AMIP with Observed Radiative Forcing, Climatological Sea Ice and Polar SST	amip_clim_polar	Obs/Present Climatology	Present Climatology	Obs	Obs
Large Ensemble	LENS	Coupled model	Coupled model	Obs/RCP8.5	Obs

¹ Obs - Observed conditions, Present Climatology (varies by forcing, but generally some average conditions between 1981-2010), Past Climatology (1881-1910 climatology, or a specific pre-industrial date). See experiment descriptions for complete details.
² Text for "experiment" global attribute in files
³ Experiment identifier in file and directory names
⁵ Method described in: A benchmark estimate of the effect of anthropogenic emissions on the ocean surface.
⁶ CESM Large Ensemble Project

Single Page

Climate Experiment Time Coverage & Ensemble Size

The table below shows the ensemble size & time coverage for the climate experiments that are being made available through the FACTS website.

Experiment Identifiers		Time Coverage Number of Ensemble Members
Experiment Name	File Name ID	ECHAM5	CAM4	ESRL-GFSv2	GEOS-5	AM3	ESRL-CAM5.1	LBNL-CAM5.1
AMIP with Observed Radiative Forcing	amip_obs_rf	Jan 1979- Feb 2021 50	Jan 1979- May 2015 20	Jan 1979- Feb 2018 50	Jan 1871- Aug 2014 12	Jan 1870- Feb 2014 12	Jan 1900- Feb 2019 40	Jan 1959- Sep 2014 50
AMIP with 1880s Radiative Forcing	amip_1880s_rf	Jan 1979- Feb 2019 50	Jan 1979- Dec 2012 20				Jan 1979- Feb 2019 40
AMIP with Natural History Forcing	amip_nat_hist							Jan 1959- Oct 2014 50
AMIP with Climatological Radiative Forcing	amip_clim_rf	Jan 1979- Dec 2012 10
AMIP with Observed Radiative Forcing, Climatological Sea Ice and Polar SST	amip_clim_polar	Jan 1979- Feb 2018 30	Jan 1979- May 2015 20	Jan 1979- Jun 2015 30			Jan 1979- Apr 2017 20

Single Page

Climate Experiment Variables

The table below shows a list of monthly variables generally being made available through the FACTS website. Each experiment may have only some of the variables available. The netCDF files use the CMIP5 variable names and units where possible and include variable attributes to show the original model variable name and units.

	CMIP5 Variables Names and Units			Model Variables Names
Variable Description (long_name attribute)	Variable Name	Units	CF Standard name	ECHAM5	CAM4/CAM5.1	ESRL-GFSv2	AM3	GEOS-5
Surface Upward Latent Heat Flux	hfls	W m-2	surface_upward_latent_heat_flux	ahfl	LHFLX	lhtfl		lhfx
Surface Upward Sensible Heat Flux	hfss	W m-2	surface_upward_sensible_heat_flux	ahfs	SHFLX	shtfl		shfx
Geopotential Height	zg	m	geopotential_height	geopoth	Z3	hgt		h
Precipitation	pr	kg m-2 s-1	precipitation_flux	precip	PRECT	precip	precip	pr
Surface Runoff	mrro	kg m-2 s-1	runoff_flux	runoff	QRUNOFF
Total Soil Moisture Content	mrso	m	soil_moisture_content	ws	MRSO
Sea Level Pressure	psl	Pa	air_pressure_at_sea_level	slp	PSL	prmsl		slp
Snow Water Equivalent	snw	kg m-2	surface_snow_amount	sn	SNOWHLND
Air Temperature	ta	K	air_temperature	st	T	tmp		t
Daily Maximum Near-Surface Air Temperature	tasmax	K	air_temperature	t2max	TREFMXAV
Daily Minimum Near-Surface Air Temperature	tasmin	K	air_temperature	t2min	TREFMNAV
Near-Surface Air Temperature	tas	K	air_temperature	temp2	TREFHT	t2m	t_ref	t2m
Surface Temperature	ts	K	surface_temperature	tsurf	TS
Eastward Wind	ua	m s-1	eastward_wind	u	U	ugrd		u
Northward Wind	va	m s-1	northward_wind	v	V	vgrd		v

Coordinate Variables

	CMIP5 Variables and Units			Model Variables
Variable Description (long_name attribute)	Variable Name	Units	CF Standard name	ECHAM5	CAM4/CAM5.1	ESRL-GFSv2	AM3	GEOS-5
time	time	days since ?	time	time	time	time	time	time
latitude	lat	degrees_north	latitude	lat	lat	lat	grid_yt	lat
longitude	lon	degrees_east	longitude	lon	lon	lon	grid_xt	lon
pressure	plev	Pa	air_pressure	lev	level	lev		lev

Single Page

Descriptions of the models available in FACTS

AM3

Model:

AM3

Source:

Geophysical Fluid Dynamics Laboratory (GFDL)

Horizontal Resolution:

~1.9^ox1.9^o (192x92)

Vertical Resolution:

48 layers

References:

Donner, Leo J., Bruce Wyman, Richard S Hemler, Larry W Horowitz, Yi Ming, Ming Zhao, J-C Golaz, Paul Ginoux, Shian-Jiann Lin, M Daniel Schwarzkopf, John Austin, G Alaka, W F Cooke, Thomas L Delworth, Stuart Freidenreich, C Tony Gordon, Stephen M Griffies, Isaac M Held, William J Hurlin, Stephen A Klein, Thomas R Knutson, Amy R Langenhorst, H C Lee, Y Lin, B I Magi, Sergey Malyshev, P C D Milly, Vaishali Naik, Mary Jo Nath, R Pincus, Jeff J Ploshay, V Ramaswamy, Charles J Seman, Elena Shevliakova, Joseph J Sirutis, William F Stern, Ronald J Stouffer, R John Wilson, Michael Winton, Andrew T Wittenberg, and Fanrong Zeng, July 2011: The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL Global Coupled Model CM3. Journal of Climate, 24(13), doi:10.1175/2011JCLI3955.1.

Bretherton, Christopher S., James R McCaa, Herve Grenier, 2004: A New Parameterization for Shallow Cumulus Convection and Its Application to Marine Subtropical Cloud-Topped Boundary Layers. Part I: Description and 1D Results. Monthly Weather Review, 132, 864-882.

Donner, Leo J., Charles J Seman, Richard S Hemler, and Song-Miao Fan, 2001: A Cumulus Parameterization Including Mass Fluxes, Convective Vertical Velocities, and Mesoscale Effects: Thermodynamic and Hydrological Aspects in a General Circulation model. Journal of Climate, 14(16), 3444-3463.

Golaz, J-C, M Salzmann, Leo J Donner, Larry W Horowitz, Yi Ming, and Ming Zhao, July 2011: Sensitivity of the Aerosol Indirect Effect to Subgrid Variability in the Cloud Parameterization of the GFDL Atmosphere General Circulation Model AM3. Journal of Climate, 24(13), DOI:10.1175/2010JCLI3945.1.

Ming, Yi, V Ramaswamy, Leo J Donner, and V T J Phillips, 2006: A new parameterization of cloud droplet activation applicable to general circulation models. Journal of the Atmospheric Sciences, 63(4), DOI:10.1175/JAS3686.1.

Wilcox, E M., and Leo J Donner, 2007: The Frequency of Extreme Rain Events in Satellite Rain-Rate Estimates and an Atmospheric General Circulation Model. Journal of Climate, 20(1), DOI:10.1175/JCLI3987.1

CAM4

Model:

CCSM4.0 CAM

Source:

National Center for Atmospheric Research (NCAR)

Horizontal Resolution:

~1.0^oX1.0^o (288x192)

Vertical Resolution:

25 layers

References:

Neale, R. B., et al., (2010a), Description of the NCAR Community Atmosphere Model (CAM 4.0), NCAR Tech. Note NCAR/TN-XXX+STR, 206 pp., Natl. Cent. for Atmos. Res, Boulder, Colo.

CAM5.1

Model:

CAM-5.1.1 (CESM-1.0)

Source:

National Center for Atmospheric Research (NCAR)

Horizontal Resolution:

~1.0^oX1.0^o (288x192)

Vertical Resolution:

25 layers

References:

Neale, R. B., et al., (2012), Description of the NCAR Community Atmosphere Model (CAM 5.0), NCAR Tech. Note NCAR/TN-486+STR, 289 pp., Natl. Cent. for Atmos. Res, Boulder, Colo.

CanESM2

Model:

CanESM2

Source:

Canadian Centre for Climate Modelling and Analysis

Horizontal Resolution:

~2.8^oX2.8^o (128x64)

Vertical Resolution:

35 layers

Model Description:

Second generation Canadian Earth System Model

Large Ensemble Experiment References and Licensing

The Canadian Earth System Model Large Ensembles

CESM1-CAM5

Model:

CESM1.0-CAM5

Source:

National Center for Atmospheric Research (NCAR)

Horizontal Resolution:

~1.0^oX1.0^o (288x192)

Vertical Resolution:

25 layers

References:

Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J., Bates, S., Danabasoglu, G., Edwards, J., Holland, M. Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., Polvani, L., and M. Vertenstein (2015), The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability, Bulletin of the American Meteorological Society, doi: 10.1175/BAMS-D-13-00255.1, 96, 1333-1349.

CESM2 (Large Ensemble)

Model:

CESM2 (Large Ensemble)

Source:

National Center for Atmospheric Research (NCAR)

Horizontal Resolution:

~1.0^oX1.0^o (288x192)

References:

https://www.cesm.ucar.edu/projects/community-projects/LENS2/

Additional Notes:

The CESM2 Large Ensemble (LENS2) consists of 100 members at 1-degree spatial resolution covering the period 1850-2100 under CMIP6 historical and SSP370 future radiative forcing scenarios. We follow the ensemble numbering scheme noted on the CESM2-LE webpage (see above)

Members 1-10: These begin from years 1001, 1021, 1041, 1061, 1081, 1101, 1121, 1141, 1161, and 1181 of the 1400-year pre-industrial control simulation. This segment of the control simulation was chosen to minimize drift.
Members 11-90: These begin from 4 pre-selected years of the pre-industrial control simulation based on the phase of the Atlantic meridional overturning circulation (AMOC). For each of the 4 initial states, there are 20 ensemble members created by randomly perturbing the atmospheric potential temperature field by order 10^-14K. The chosen start dates (model years 1231, 1251, 1281, and 1301) sample AMOC and sea surface height (SSH) in the Labrador Sea at their maximum, minimum, and transition states.
Members 91-100: These begin from years 1011, 1031, 1051, 1071, 1091, 1111, 1131, 1151, 1171, and 1191 of the 1400-year pre-industrial control simulation. This group includes an extensive / comprehensive set of output fields -- referred to as the mother of all runs, "MOAR" outputs, which can be used to drive regional climate models, in addition to COSP output.

LENS2 is divided into two 50-member sub-ensembles: one which uses the original CMIP6 Biomass Burning protocol (BMB) and one which uses a smoothed version of the CMIP6 BMB protocol (11-year running means) that is more comparable to the treatment of CMIP6 BMB emissions used before 1997 and after 2014.

Recent publications document the sizeable effect that the different treatments have on the climate of the model, including the hydrological cycle, Arctic sea ice, climate variability, and global surface temperature. Because the greatest impact is on the recent climate, this difference affects both attribution of extreme events relative to past climates, and the projections of the future climate relative to the present. Therefore we treat these two as separate model ensembles. Data is available for download for those who wish to combine the two biomass burning ensembles into a single 100-member ensemble.

CESM2-CAM6

Model:

CESM2-CAM6

Source:

National Center for Atmospheric Research (NCAR)

Horizontal Resolution:

~1.0^oX1.0^o (288x192)

References:

http://www.cesm.ucar.edu/working_groups/CVC/simulations/cam6-prescribed_sst.html

Additional Notes:

For the Tropical AMIP Ensemble: SST's from 28S:28N are set to time-varying SST's from ERSSTv5. A transition/linear interpolation zone exists from 28:35 degree latitudes. From 35 degrees polewards full-period climatological SSTs (ERSSTv5)/sea-ice (HadISST1) are set.

For the Global AMIP Ensemble: Global SST's (ERSSTv5) and sea-ice (HadISST1) are specified.

All simulations from both ensembles were initialized from the 11th CESM2 historical member on January 1st, 1880, with each ensemble member receiving a small change in the initial air temperature via namelist setting PERTLIM. All CMIP6 time-varying external, natural and anthropogenic forcings were specified in these ensembles.

ECHAM5

Model:

ECHAM5.4

Source:

Max Planck Institute for Meteorology (MPI)

Horizontal Resolution:

0.75^ox0.75^o (480x240)

Vertical Resolution:

31 layers

References:

Roeckner, E., G. Bäuml, L. Bonaventura, R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, I. Kirchner, L. Kornblueh, E. Manzini, A. Rhodin, U. Schlese, U. Schulzweida, and A. Tompkins, 2003: The atmospheric general circulation model ECHAM5. Part I: Model description. Max Planck Institute for Meteorology Rep. 349, 127 pp.

ESRL-CAM5HR

Model:

ESRL-CAM5HR

Source:

National Center for Atmospheric Research (NCAR)

Horizontal Resolution:

~0.5^oX0.5^o (576x384)

Vertical Resolution:

26 layers

References:

ESRL-GFSv2

Model:

GFSv2 run at ESRL

Source:

NOAA/NWS Environmental Modeling Center (EMC)

Horizontal Resolution:

1.0^ox1.0^o (360x181)

Vertical Resolution:

64 layers

References:

Suranjana Saha, Shrinivas Moorthi, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, David Behringer, Yu-Tai Hou, Hui-ya Chuang, Mark Iredell, Michael Ek, Jesse Meng, Rongqian Yang, Malaquías Peña Mendez, Huug van den Dool, Qin Zhang, Wanqiu Wang, Mingyue Chen, and Emily Becker, 2014: The NCEP Climate Forecast System Version 2. J. Climate, 27, 2185–2208. doi: http://dx.doi.org/10.1175/JCLI-D-12-00823.1

GEOS-5

Model:

GEOS-5

Source:

NASA Goddard Space Flight Center (GSFC)

Horizontal Resolution:

1.25^ox1^o (288x181)

Vertical Resolution:

72 layers

References:

Molod, A., L. Takacs, M. Suarez, J. Bacmeister, I. Somg, and A. Eichmann, 2012: The GEOS-5 Atmospheric General Circulation Model: Mean Climate and Development from MERRA to Fortuna. Tech. rep., NASA Technical Report Series on Global Modeling and Data Assimilation, NASA TM2012-104606, Vol. 28, 117 pp.

Siegfried D. Schubert, Hailan Wang, Randal D. Koster, Max J. Suarez, and Pavel Ya. Groisman, 2014: Northern Eurasian Heat Waves and Droughts. J. Climate, 27, 3169–3207.

GFDL-CM3

Model:

GFDL-CM3

Source:

Geophysical Fluid Dynamics Laboratory (GFDL)

Horizontal Resolution:

2.5^ox2.0^o (144x90)

Vertical Resolution:

48 layers

References:

Griffies, S. M., and Coauthors, 2011: GFDL’s CM3 coupled climate model: Characteristics of the ocean and sea ice simulations. J. Climate, 24, 3520–3544, doi: 10.1175/2011JCLI3964.1

Donner, L. J., and Coauthors, 2011: The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component of the GFDL global coupled model CM3. J. Climate, 24, 3484–3519, doi: 10.1175/2011JCLI3955.1

Golaz, J.-C., M. Salzmann, L. J. Donner, L. W. Horowitz, Y. Ming, and M. Zhao, 2011: Sensitivity of the aerosol indirect effect to subgrid variability in the cloud parameterization of the GFDL atmosphere general circulation model AM3. J. Climate, 24, 3145–3160, doi: 10.1175/2010JCLI3945.1

Sun, Lantao, Michael Alexander, and Clara Deser, 2018: Evolution of the global coupled climate response to Arctic sea ice loss during 1990-2090 and its contribution to climate change, J. Climate, 31, 7823-7843, doi: 10.1175/JCLI-D-18-0134.1

GFDL-SPEAR

Model:

GFDL-SPEAR (Seamless System for Prediction and EArth System Research)

Source:

Geophysical Fluid Dynamics Laboratory (GFDL)

Horizontal Resolution:

.5^ox.5^o (576x360)

Vertical Resolution:

33 layers

References:

Delworth,T.L., et al (2020). SPEAR: The Next Generation GFDL Modeling System for Seasonal to Multidecadal Prediction and Projection, Journal of Advances in Modeling Earth Systems, 12(3), e2019MS001895, doi: 10.1029/2019MS001895

Lu, F., et al. (2020). GFDL’s SPEAR seasonal prediction system: initialization and ocean tendency adjustment (OTA) for coupled model predictions. Journal of Advances in Modeling Earth Systems, doi: 10.1029/2020MS002149

PSL CAM5 (.5 degree)

Model:

PSL-CAM5-.5degree

Source:

National Center for Atmospheric Research (NCAR)

Horizontal Resolution:

~0.25^oX0.25^o (1152x768)

Vertical Resolution:

17 layers

References:

PSL CAM5 (1 degree)

Model:

CAM-5.1

Source:

National Center for Atmospheric Research (NCAR)

Horizontal Resolution:

~1.0^oX1.0^o (288x192)

Vertical Resolution:

30 layers

References:

Neale, R. B., et al., (2012), Description of the NCAR Community Atmosphere Model (CAM 5.0), NCAR Tech. Note NCAR/TN-486+STR, 289 pp., Natl. Cent. for Atmos. Res, Boulder, Colo.

(click on a model name to show the details of that model)

Single Page

CITATION REQUEST: When using model or observational data obtained through FACTS in a publication, please provide a citation in the paper to the original underlying data source. This includes both downloading data and creating analysis figures through FACTS.

FACTS Reference

Murray, D., et al., (2020) Facility for Weather and Climate Assessments (FACTS): A Community Resource for Assessing Weather and Climate Variability. Bull. Amer. Meteor. Soc., 101, E1214–E1224, doi: 10.1175/BAMS-D-19-0224.1

ECHAM5

Roeckner, E., G. Bäuml, L. Bonaventura, R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, I. Kirchner, L. Kornblueh, E. Manzini, A. Rhodin, U. Schlese, U. Schulzweida, and A. Tompkins, (2003) The atmospheric general circulation model ECHAM5. Part I: Model description. Max Planck Institute for Meteorology Rep. 349, 127 pp.

CanESM

Second generation Canadian Earth System Model

CAM4

Neale, R. B., et al., (2010a) Description of the NCAR Community Atmosphere Model (CAM 4.0), NCAR Tech. Note NCAR/TN-XXX+STR, 206 pp., Natl. Cent. for Atmos. Res, Boulder, Colo.

CAM5.1

Neale, R. B., et al., (2012) Description of the NCAR Community Atmosphere Model (CAM 5.0), NCAR Tech. Note NCAR/TN-486+STR, 289 pp., Natl. Cent. for Atmos. Res, Boulder, Colo.

CESM1-CAM5

Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J., Bates, S., Danabasoglu, G., Edwards, J., Holland, M. Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., Polvani, L., and M. Vertenstein (2015) The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability, Bulletin of the American Meteorological Society, doi: 10.1175/BAMS-D-13-00255.1, 96, 1333-1349.

GEOS-5

GFDL-AM3

Donner, L. J., and Coauthors (2011) The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component of the GFDL global coupled model CM3. J. Climate, 24, 3484–3519, doi: 10.1175/2011JCLI3955.1

GFDL-SPEAR

GFS

Suranjana Saha, Shrinivas Moorthi, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, David Behringer, Yu-Tai Hou, Hui-ya Chuang, Mark Iredell, Michael Ek, Jesse Meng, Rongqian Yang, Malaquías Peña Mendez, Huug van den Dool, Qin Zhang, Wanqiu Wang, Mingyue Chen, and Emily Becker (2014) The NCEP Climate Forecast System Version 2. J. Climate, 27, 2185–2208. doi: http://dx.doi.org/10.1175/JCLI-D-12-00823.1

20th Century Reanalysis V2

Compo, G.P., J.S. Whitaker, P.D. Sardeshmukh, N. Matsui, R.J. Allan, X. Yin, B.E. Gleason, R.S. Vose, G. Rutledge, P. Bessemoulin, S. Brönnimann, M. Brunet, R.I. Crouthamel, A.N. Grant, P.Y. Groisman, P.D. Jones, M. Kruk, A.C. Kruger, G.J. Marshall, M. Maugeri, H.Y. Mok, Ø. Nordli, T.F. Ross, R.M. Trigo, X.L. Wang, S.D. Woodruff, and S.J. Worley (2011) The Twentieth Century Reanalysis Project. Quarterly J. Roy. Meteorol. Soc., 137, 1-28. doi: 10.1002/qj.776

20th Century Reanalysis V2c

Compo, G.P., J.S. Whitaker, P.D. Sardeshmukh, N. Matsui, R.J. Allan, X. Yin, B.E. Gleason, R.S. Vose, G. Rutledge, P. Bessemoulin, S. Brönnimann, M. Brunet, R.I. Crouthamel, A.N. Grant, P.Y. Groisman, P.D. Jones, M. Kruk, A.C. Kruger, G.J. Marshall, M. Maugeri, H.Y. Mok, Ø. Nordli, T.F. Ross, R.M. Trigo, X.L. Wang, S.D. Woodruff, and S.J. Worley, (2011) The Twentieth Century Reanalysis Project. Quarterly J. Roy. Meteorol. Soc., 137, 1-28. doi: 10.1002/qj.776

20th Century Reanalysis V3

Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese, B. S., McColl, C., Allan, R., Yin, X., Vose, R., Titchner, H., Kennedy, J., Spencer, L. J., Ashcroft, L., Brönnimann, S., Brunet, M., Camuffo, D., Cornes, R., Cram, T. A., Crouthamel, R., Domínguez?Castro, F., Freeman, J. E., Gergis, J., Hawkins, E., Jones, P. D., Jourdain, S., Kaplan, A., Kubota, H., Le Blancq, F., Lee, T., Lorrey, A., Luterbacher, J., Maugeri, M., Mock, C. J., Moore, G. K., Przybylak, R., Pudmenzky, C., Reason, C., Slonosky, V. C., Smith, C., Tinz, B., Trewin, B., Valente, M. A., Wang, X. L., Wilkinson, C., Wood, K. and Wyszy?ski, P. (2019) Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Q J R Meteorol Soc. doi: 10.1002/qj.3598

CERA-20C

Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K. and Janssen, P. (2016) A coupled data assimilation system for climate reanalysis. Q.J.R. Meteorol. Soc., 142: 65-78. 10.1002/qj.2629

ERA-Interim

Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A.C.M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hersbach, H., Hólm, E.V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A.P., Monge?Sanz, B.M., Morcrette, J.?J., Park, B.?K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.?N. and Vitart, F. (2011) The ERA?Interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553-597. doi: 10.1002/qj.828

ERA5

Malardel, S., N. Wedi, W. Deconinck, M. Diamantakis, C. Kuhnlein, G. Mozdzynski, M. Hamrud, and P. Smolarkiewicz, (2015) A new grid for the IFS. Newsletter No. 146 - Winter 2015/16, ECMWF, 6 pp.

JRA55

Kobayashi, S., Y. Ota, Y. Harada, A. Ebita, M. Moriya, H. Onoda, K. Onogi, H. Kamahori, C. Kobayashi, H. Endo, K. Miyaoka, and K. Takahashi (2015) The JRA-55 Reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 5-48, doi: 10.2151/jmsj.2015-001

MERRA

Rienecker, M.M., M.J. Suarez, R. Gelaro, R. Todling, J. Bacmeister, E. Liu, M.G. Bosilovich, S.D. Schubert, L. Takacs, G.-K. Kim, S. Bloom, J. Chen, D. Collins, A. Conaty, A. da Silva, et al. (2011) MERRA: NASA's Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 3624-3648, doi: 10.1175/JCLI-D-11-00015.1

MERRA-2

Gelaro, R., et al., 2017, The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) J. Clim., doi: 10.1175/JCLI-D-16-0758.1

NCEP/NCAR R1

Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K.C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, R. Jenne, and D. Joseph (1996) The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437-471

COBE SST

Ishii, M., A. Shouji, S. Sugimoto, and T. Matsumoto (2005) Objective Analyses of Sea-Surface Temperature and Marine Meteorological Variables for the 20th Century using ICOADS and the Kobe Collection. Int. J. Climatol., 25, 865-879.

COBE2 SST

Hirahara, S., Ishii, M., and Y. Fukuda, (2014) Centennial-scale sea surface temperature analysis and its uncertainty. J of Climate, 27, 57-75. doi: 10.1175/JCLI-D-12-00837.1

GHCN CAMS

Fan, Y., and H. van den Dool (2008) A global monthly land surface air temperature analysis for 1948-present, J. Geophys. Res., 113, D01103, doi:10.1029/2007JD008470

GPCC

Schneider et al (2017) Evaluating the Hydrological Cycle over Land Using the Newly-Corrected Precipitation Climatology from the Global Precipitation Climatology Centre (GPCC). Atmosphere 8(3), 52; doi:10.3390/atmos8030052

GPCP

Adler et al. (2016) An Update (Version 2.3) of the GPCP Monthly Analysis. (in Preparation). Huffman, G.J., R.F. Adler, P. Arkin, A. Chang, R. Ferraro, A. Gruber, J. Janowiak, A. McNab, B. Rudolf, U. Schneider, 1997: The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset. Bull. Amer. Meteor. Soc., 78(1), 5-20.

HadCRUT5 Analysis

Osborn, T.J., Jones, P.D., Lister, D.H., Morice, C.P., Simpson, I.R., Winn, J.P., Hogan, E., and Harris, I.C., (2021) Land surface air temperature variations across the globe updated to 2019: the CRUTEM5 dataset. Journal of Geophysical Research: Atmospheres. 126, e2019JD032352, doi: 10.1029/2019JD032352

HadISST

Rayner, N. A.; Parker, D. E.; Horton, E. B.; Folland, C. K.; Alexander, L. V.; Rowell, D. P.; Kent, E. C.; Kaplan, A. (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century J. Geophys. Res.Vol. 108, No. D14, 4407 doi: 10.1029/2002JD002670

NASA GISTEMP

Lenssen, N., G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D. Zyss, (2019) Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos., 124, no. 12, 6307-6326, doi: 10.1029/2018JD029522

NOAA ERSST V3b

Smith, T.M., R.W. Reynolds, T.C. Peterson, and J. Lawrimore, (2008) Improvements NOAAs Historical Merged Land–Ocean Temp Analysis (1880–2006). Journal of Climate, 21, 2283–2296

NOAA ERSST V5

Boyin Huang, Peter W. Thorne, Viva F. Banzon, Tim Boyer, Gennady Chepurin, Jay H. Lawrimore, Matthew J. Menne, Thomas M. Smith, Russell S. Vose, and Huai-Min Zhang (2017) NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 5. subset used. NOAA National Centers for Environmental Information. doi: 10.7289/V5T72FNM date

NOAA OISST v2.1

Reynolds, R.W., N.A. Rayner, T.M. Smith, D.C. Stokes, and W. Wang, (2002) An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609-1625

NOAA PRECL

Chen, M., P. Xie, J. E. Janowiak, and P. A. Arkin (2002) Global Land Precipitation: A 50-yr Monthly Analysis Based on Gauge Observations, J. of Hydrometeorology, 3, 249-266

U Delaware 5.01

Willmott, C. J. and K. Matsuura (2001) Terrestrial Air Temperature and Precipitation: Monthly and Annual Time Series (1950 - 1999), http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_ts2.html

Single Page