Madden-Julian Oscillation (MJO)

Black and white satellite image of MJO over the Indian Ocean

MJO Overview

The Madden-Julian Oscillation, often referred to as the MJO, is a mode of sub-seasonal atmospheric variability that influences the location and strength of tropical precipitation. It is an eastward moving disturbance of clouds, rainfall, winds, and pressure that traverses the planet in the tropics and returns to its initial starting point in 30 to 60 days, on average.

The MJO impacts extreme weather around the globe. MJO-related weather effects on the United States specifically include extreme rainfall on the west coast related to Atmospheric Rivers as well as hurricane landfalls in the Southeast. These events can have a significant impact on communities, property, and livelihoods. It is NOAA's mission to provide early warning to strengthen the nation's ability to prepare and protect against the damages from extreme weather events.

PSL research aims to better understand, model and predict the MJO and its impacts.

More about the MJO

The MJO was first described in 1971 but a field project held over the west equatorial Pacific during 1992-93 raised awareness of the MJO as a coherent phenomenon, possibly useful for weekly predictions of tropical precipitation and extratropical weather patterns. The MJO is best defined over the oceanic warm pool, which extends from the Indian Ocean to the central Pacific and where complex variations of precipitation occur from day to day. The warm pool is generally defined by sea surface temperatures that are above 28C.

This page provides an overview of current MJO activity, MJO monitoring indices, list of MJO historical MJO events, and a summary of current MJO research activities by PSL.

PSL's MJO research focuses on:

understanding multi-scale processes that impact the strength of the MJO,
improving weather model's representation of the MJO,
developing methods to identify the MJO, and
identifying MJO remote weather impacts over the Western United States.

The surface and upper-atmosphere structure of the MJO for a period when the enhanced convective phase (thunderstorm cloud) is centered across the Indian Ocean and the suppressed convective phase is centered over the west-central Pacific Ocean. Horizontal arrows pointing left represent wind departures from average that are easterly, and arrows pointing right represent wind departures from average that are westerly. The entire system shifts eastward over time, eventually circling the globe and returning to its point of origin. Graphic and Caption Credit: Climate.gov

This graphic shows a map of the impact the Madden-Julian Oscillation has on global weather • Graphic by NOAA's Pacific Marine Environmental Laboratory

Current Status of the MJO

Phase Diagrams

ROMI phase diagram for the latest 90 days

ROMI is computed using OLR only and as such is a purely convective index of the MJO. The EOFs depend on the day of year, latitude and longitude. To plot the diagram in the same phase space as the Wheeler-Hendon RMM index, the sign of ROMI PC1 is reversed and the PC ordering is switched, so that ROMI(PC2) is analogous to RMM(PC1) and -ROMI(PC1) is analogous to RMM(PC2).

RMM phase diagram for the latest 90 days

The RMM is provided by the Bureau of Meteorology Australia . RMM is computed using interpolated OLR from PSL and 850hPa and 200hPa zonal winds from the NCEP reanalysis. The EOFs only depend on longitude.

VPM phase diagram for the latest 90 days

The VPM index was computed using zonal wind and velocity potential based on the NCEP/NCAR Reanalysis Version 1. The VPM PC signs were adjusted to match the RMM index signs for the phase plot.

rMII phase diagram for the latest 90 days

rMII was computed using interpolated OLR and 850hPa and 200hPa zonal winds from ERA5. The EOFs depend on the day of year, latitude and longitude. PC signs are consistent with the RMM index for the phase plots.

Atmosphere

10N-10S: Subseasonal OLR Anomalies

OLR

200mb Zonal Wind Anomalies

R1 200mb uwnd

Precipitation Anomalies latest 90 days

90 day precip anomaly R1 (to be replaced by global cpc pcp)

Satellite

MJO Indices

PSL is creating a set of MJO timeseries that quantify current and historic MJO activity. Links and descriptions are below as well as links to some other MJO timeseries created at other institutions. A description of the timeseries format is available.

NOTE: ROMI is now provided in near real-time. The OMI has been updated through May 20 2024. These PC amplitudes will differ slightly from the previous version since the sample used for the 20-96 day filter is longer (1979-2021), resulting in slightly different values for the filtered OLR used to calculate OMI. In some cases this may also result in a phase difference (by one phase) from the previous versions. The EOFs used are still from 1979-2012 (see below). The previous (1979-2021) version of OMI can be obtained here.

NOTE: When comparing OMI directly with RMM, to obtain the proper phase the sign of OMI PC1 and the PC ordering should be reversed, so that OMI(PC2) is analogous to RMM(PC1) and -OMI(PC1) is analogous to RMM (PC2).

Daily MJO index time series from 1979

Real-time

Index	Description	Obtain timeseries
ROMI The Real-time OLR MJO Index	Projection of 9 day running average OLR anomalies onto the daily spatial EOF patterns of 30-96 day eastward filtered OLR. OLR anomalies are calculated by first subtracting the previous 40 day mean OLR. The running average is tapered as the target date is approached.	ROMI values
RMII The Realtime Multivariate Index for tropical Intraseasonal oscillations	Projection of 9 day running average anomalies onto the daily spatial multivariate EOFs of 20-96 day eastward filtered OLR, U850 and U200. Anomalies are calculated by first subtracting the previous 40 day mean. The running average is tapered as the target date is approached.	rMII data currently unavailable. We are working on having this back on the website in the near future.

Other

Index	Description	Obtain timeseries
OOMI The Original OLR MJO Index	Projection of 30-96 day eastward only filtered OLR onto the spatial EOF patterns of 30-96 day eastward filtered OLR. This results in a smoother index than OMI due to more restrictive filtering.	OOMI values
OMI The OLR MJO Index	Projection of 20-96 day filtered OLR, including all eastward and westward wave numbers onto the daily spatial EOF patterns of 30-96 day eastward filtered OLR.	OMI values
FMO The Filtered OLR MJO index.	Univariate EOF of normalized 20-96 day filtered OLR averaged from 15S-15N, by longitude. The same spatial EOF pattern is used for the entire year (see below).	FMO values.
ERA5 OMI The ERA5 OLR MJO Index	Projection of 20-96 day filtered ERA5 OLR, including all eastward and westward wave numbers onto the daily spatial EOF patterns of 30-96 day eastward filtered OLR from the ERA5 dataset. EOFs are calculated using data from 1940 to "present".	ERA5 OMI values
VPM The Velocity Potential MJO index.	Calculated in the same way as the Wheeler-Hendon RMM, except using 200 hPa Velocity Potential instead of OLR, along with U200 and U850 in a combined EOF (see link to Ventrice et al. 2013 below).	VPM values
REOMI The Rotated EOFs OLR Madden Julian Index.	Projection of 20-96 day filtered OLR, including all eastward and westward wave numbers onto the rotated daily spatial EOF patterns of 30-96 day eastward filtered OLR. EOFs are calculated using OLR from 1979-2012. PCs are calculated from 1979-2022. EOFs are rotated to reduce noise and potential degeneracy issues as detailed in Weidman et al., 2022.	REOMI values
KRMM The Koopman Real-time MultiVariate Madden Julian Index	Calculated following the Wheeler-Hendon RMM, but using Koopman spectral analysis to compute eigenfunctions. The leading mode of intraseasonal variability is rotated to maximize correlation with the standard RMM. See link to Lintner et al. 2023 for further discussion of the Koopman spectral analysis and methodological details.	KRMM values


	MJO Indices Phase Diagram Tool Plot phase diagrams of selected MJO indices.

Other MJO indices

The All-season Real-time Multivariate (RMM) MJO Index (Wheeler-Hendon) from the The Centre for Australian Weather and Climate Research .
Bimodal ISO Index for historical data analysis and real time monitoring from the International Pacific Research Center .
Carl Schreck's analysis of the RMM components .

Click on each section below to expand/collapse

Code

A python routine to calculate the OMI has been developed for use on real-time and model data, and can be accessed via GitHub at: https://github.com/cghoffmann/mjoindices and also at Zenodo: https://doi.org/10.5281/zenodo.3613752.

For the REOMI, code is in the same repository. using the parameter eofs_postprocessing_type="eof_rotation" in the main method for calculating EOFs: omi.omi_calculator.calc_eofs_from_olr(). No other changes should be necessary from the standard OMI calculation.

Details of the implementation of this software as well as the ERA5 based OMI computation Python package are outlined in this article:

Hoffmann CG, Kiladis GN, Gehne M, von Savigny C 2021 A Python Package to Calculate the OLR-Based Index of the Madden-Julian-Oscillation (OMI) in Climate Science and Weather Forecasting . Journal of Open Research Software, 9:9. (PDF)

The EOFs for the ERA5 based OMI were computed using data from 1940-2023. Using the years 1979-2018 to compute ERA5 EOFs gives good agreement with the observed OMI EOFs in terms of spatial correlation (see below). The EOFs for the time period 1940-2023 have less agreement, but still a pattern correlation above 0.9 for most of the year.

The rMII code and MII values are available upon request from the lead author Shuguang Wang .

Python routines to calculate OMI:

MATLAB routines to calculate KRMM:

Courtesy of Dimitrios Giannakis at https://github.com/dg227/NLSA/tree/master/pubs/LintnerEtAl23_GRL

Citations

For more information for all indices other than the VPM and rMII, please read the article A comparison of OLR and circulation based indices for tracking the MJO .

If you use the timeseries in your research, please cite that paper, e.g.:

Kiladis G.N., J. Dias, K.H. Straub, M.C Wheeler, S.N. Tulich, K. Kikuchi, K.M. Weickmann, M.J. Ventrice. A comparison of OLR and circulation based indices for tracking the MJO. Monthly Weather Review, May 2014, 142 1697-1715. https://doi.org/10.1175/MWR-D-13-00301.1

For the VPM, please cite:

Ventrice et al. A Modified Multivariate Madden-Julian Oscillation Index Using Velocity Potential. Monthly Weather Review, December 2013, 141, p. 4197-4120. https://doi.org/10.1175/MWR-D-12-00327.1

For the rMII, please cite:

Wang et al: Multivariate Index for Tropical Intraseasonal Oscillations Based on the Seasonally-Varying Modal Structures: JGR Atmospheres, Feb 2022, Vol 127, pp 1-19. https://doi.org/10.1029/2021JD035961 .

For the KRMM, please cite:

Lintner, B. R., D. Giannakis, M. Pike, J. Slawinska (2023). Identification of the Madden–Julian Oscillation with data-driven Koopman spectral analysis. Geophys. Res. Lett., 50, e2023GL102743. https://doi.org/doi.org/10.1029/2023GL102743 . (PDF)

Composite Patterns

Composite streamfunction and OLR patterns for RMM and OMI based on the events that exceed 1 standard deviation for each phase of the PC combination.

These are based on data from 1979 through 2012, and the number of events in each composite is given as "N= " at the bottom of each plot. Blue shading denotes negative OLR anomalies (regions of convection) and red positive (suppressed), with two levels of shading at +- 10 and +- 6 W/m**2. Streamfunction contour interval is 5 X 10**5 m**2/s at 200 hPa, and 2 X 10**5 m**2/s at 850 hPa. To facilitate comparison with RMM, these composites are constructed by reversing the sign of OMI PC1 and the OMI PC ordering, so that OMI(PC2) is analogous to RMM(PC1) and -OMI(PC1) is analogous to RMM(PC2), as described in Kiladis et al. 2014.

200mb OMI DJF

Other

MJO EOF Patterns

OMI EOF Patterns

The ASCII EOF values are available via ftp/downloads for EOF1 and EOF2 . They can be read using the code read.eof.f. User can retrieve the files via the web or using an (anonymous) ftp client at the address ftp2.psl.noaa.gov. Then, cd to /Datasets.other/MJO/.

OMI EOF Animations

An animation of the daily OMI EOF's (realtime player format).
A javascript animation of the daily OMI EOF's.

VPM EOF Patterns

The VPM EOFs are computed using zonal wind and velocity potential based on NCEP reanalysis version 1 from 1979 to 2012.

FMO EOF Patterns

The ASCII FMO EOF values are available from here.

FMO Spatial EOFs

Comparison of OMI EOFs from ERA5 vs Observed OLR

Pattern correlation between observed OMI EOFs from 1979-2012 and ERA5 OMI EOFs from 1979-2018 for each day of the year. EOF patterns are correlated at 0.95 or above and PC time series are correlated at above 0.97 and are deemed sufficiently similar. The ERA5 based OMI was computed using the python package by: Hoffmann CG, Kiladis GN, Gehne M, von Savigny C 2021 A Python Package to Calculate the OLR-Based Index of the Madden-Julian-Oscillation (OMI) in Climate Science and Weather Forecasting. Journal of Open Research Software, 9:9. DOI: https://doi.org/10.5334/jors.331/ .

MJO Historical Events

MJO DJF Events Tabled

The ERA5 OLR MJO Index (ERA5 OMI) is used to identify historical MJO events from 1940 to 2023. An MJO event is defined as times when OMI amplitude local maxima within a centered 30 day period that also exceeds one standard deviation (as illustrated in the schematic below). Note: Dias et al. 2025 discusses the use of ERA5 to characterize the MJO during the pre-satellite period . Tables with OMI events by phase can be found below.

Table Guide

Column 1: Event date as defined as OMI local max.
Column 2 and 3: OMI PC1 and PC2.
Column 4: OMI amplitude.
Column 5: OMI phase (rotated to be consistent with RMM).
Column 6: Days to demise (number of days subsequent to the OMI peak until OMI<1).
Column 7: OMI phase at the demise lag.
Column 8: Nino 3.4
Column 9: QBO index (based on NCEP equatorial zonal winds at 50 hPa).

Use the dropdown below to select a phase and display the corresponding table:

Schematics

Fig.1 Schematic of MJO events definition based on ERA5 OMI

Fig. 2: Yearly count of MJO events occurring from December through February. Colors correspond to the OMI phase.

MJO Research at PSL

PSL's research on the Madden-Julian Oscillation (MJO) helps improve forecasts for hurricanes, monsoons, atmospheric rivers, and United States weather variability by providing important insights into tropical weather patterns and their impacts on global climate and extreme weather events. This aids the nation's disaster preparedness, water resource management, and seasonal climate outlooks, reducing economic losses and safeguarding lives and property.

Robust Multi-Decadal Variability of Madden-Julian Oscillation Amplitude in the 20th Century

Understanding long-term variability in the Madden-Julian Oscillation (MJO) is crucial for improving sub-seasonal to seasonal predictions around the globe. Observations show that the MJO undergoes multi-decadal variability, with a robust increase in amplitude from the 1960s to the 1990s, followed by a decline in recent decades. In contrast, MJO behavior before 1960 remains more uncertain due to sparse and infrequent tropical observations, limiting our ability to clearly identify its characteristics.

More information in:
Dias, J., Gehne, M., Kiladis, G. N., Wolding, B., & Hoell, A. (2025). Robust multi-decadal variability of Madden-Julian oscillation amplitude in the 20th century. Geophysical Research Letters, 52, e2024GL113303. https://doi.org/10.1029/2024GL113303

line graph of 22-year running mean RMM MJO amplitude

Impacts of Tropical Forecast Errors on Weeks 3–4 Extreme Precipitation Predictions over California during Winter 2022–2023

Accurately predicting extreme precipitation events weeks in advance is crucial for mitigating their destructive impacts and managing water resources. During the winter of 2022–23, multiple extreme precipitation events in California caused severe flooding while also alleviating statewide drought conditions. To improve subseasonal (weeks 3–4) predictions of such events, we conduct model forecast experiments examining the influence of tropical errors. By correcting these errors through nudging to reanalysis, we achieve significant improvements in the precipitation predictions over California compared to free-running control forecasts. Our findings highlight strong impacts of tropical errors associated with the MJO and transient synoptic-scale features on these predictions.

More information in:
Moore, B. J., J. Dias. A. Hoell, S. N. Tulich, M. Gehne, J. Albers, C. F. Baggett, and E. LaJoie, 2024: Impacts of tropical forecast errors on weeks 3–4 extreme precipitation predictions over California during winter 2022–2023., in preparation.

(a) Percentile rank of the observed accumulated precipitation for 30 December 2022 – 13 January 2023 in the 1994–2023 climatology. Ensemble forecast probabilities (%) of accumulated precipitation for 30 December 2022 – 13 January 2023 (days +15–29) exceeding the 80th percentile of the 1994–2023 reforecast-based climatology for (b) the control forecast and (c) the forecast with tropical nudging initialized on 15 December 2022.

The Crucial Role of the Initial State in MJO Prediction

MJO forecast skill is highly sensitive to initial conditions. Using NOAA’s Unified Forecast System, we show that model runs initialized with two independent reanalyses produce significant and persistent differences in MJO-circulation amplitude over 15 days. These differences are linked to initial atmospheric static stability, with less stable conditions enhancing vertical motion and divergent winds. However, a convection-based MJO index shows minimal sensitivity, suggesting that convection and diabatic heating are not the primary drivers of these forecast variations.

More information in:
Bengtsson, L., S. Tulich, J. Dias, B. Wolding, K. Hall, M. Gehene, G. Kiladis, P. Pegion, (2025): The crucial role of initial conditions in MJO prediction. Geophysical Research Letters., under review

Real-Time Multivariate MJO Index (RMM) for UFS simulations initialized from UFS Replay (orange) and GEFSv12 reanalysis (green). The blue and black curves represent observations and/or reanalysis. Shading represents the ensemble difference between the minimum and maximum ensemble for each time. UFS simulations initialized from GEFSv12 reanalysis have a larger amplitude throughout the whole forecast, attributed to a more statically unstable initial state.

Plume Model Assessment of Systematic Errors in Tropical Convective Coupling in the NOAA Unified Forecast System

Accurate representation of tropical convection is essential for improving subseasonal-to-seasonal forecasts, as errors in convective processes can disrupt global weather patterns. We examine the rapid development of systematic errors in NOAA’s Unified Forecast System representation of tropical convection using a plume model, which simulates the interactions between clouds and their surrounding environment. We find that the UFS has systematic errors in the representation of two key cloud types; congestus clouds and mesoscale convective systems. These errors negatively impact teleconnections to higher latitudes, which are crucial for predicting precipitation and temperature over the United States.

More information in:
Maithel, V., B. Wolding,, S. Tulich, M. Gehne, J. Dias, X. Quan, P. Pegion, L. Bengtsson (2025): Plume Model Assessment of Systematic Errors in Tropical Convective Coupling in the NOAA Unified Forecast System, Geophysical Research Letters.,in prep

Graphic showing the errors in the GEFS v12 Day 1 precipitaiton forecast

Errors in the GEFS v12 Day 1 precipitation forecast, as a function of ERA5 lower tropospheric buoyancy (x-axis) and observed IMERG precipitation rate (y-axis). Errors are concentrated in the green and orange boxes, which highlight conditions where enhanced mesoscale convective system (MCS) and cumulus cloud activity is observed respectively.

Plume Model Assessment of the Convective Coupling of Equatorial Waves

In forecast models, convectively coupled equatorial waves (CCEW) and the Madden-Julian Oscillation (MJO) often decay quickly after initialization and or propagate at incorrect speeds, contributing to a rapid degradation of forecast skill of tropical rainfall variability and associated extratropical teleconnections. Systematic errors in model representation of CCEWs and the MJO can arise from unresolved processes which govern both how convection responds to, and impacts, its surrounding environment. In this study, a plume model which simulates how clouds interact with their surrounding environment is used to assess how changes in moisture and temperature impact CCEW and MJO rainfall in the real world. The plume model is then applied to weather forecast and climate models, and used to identify how misrepresentation of interactions between clouds and their environment may ultimately negatively impact representation of CCEWs and the MJO.

More information in:
Wolding, B., J. Dias, M. Gehne, G. Kiladis, F. Ahmed, K. Schiro, and A. Adames (2024), Plume model assessment of the convective coupling of equatorial waves, J. Atm. Sci., submitted 12/14/2024

Color shading represents a measure of how precipitation and plume model buoyancy co-vary in observations (left column), a prototype of the GFS V17 (middle column) and the E3SM V2 (right column). The top row highlights co-variability between precipitation and plume buoyancy when the cloud is not allowed to mix with its surrounding environment. The bottom row highlights co-variability between precipitation and plume buoyancy when the cloud is allowed to mix with its surrounding environment.

Diagnostics of Tropical Variability for Numerical Weather Forecasts

Numerical weather prediction (NWP) models struggle with representing the full range of scales of tropical convective phenomena. Improved skill in the representation of tropical larger-scale features such as convectively coupled equatorial waves (CCEWs) has the potential to reduce forecast error propagation from the tropics to the midlatitudes. We develop diagnostics that are able to assess model skill as a function of lead time and show their utility by applying them to two versions of NOAA’s Unified Forecast System (GFSv15 and GFSv16). The diagnostics are available to the community on GitHub.

More information in:
Gehne, M., B. Wolding, J. Dias, and G. N. Kiladis, 2022: Diagnostics of Tropical Variability for Numerical Weather Forecasts. Wea. Forecasting, 37, 1661–1680, https://doi.org/10.1175/WAF-D-21-0204.1

Symmetric space–time coherence spectra 15°S–15°N between (a) GFSv15 precipitation at FH06 (FH = forecast hour) and IMERG precipitation, (b) GFSv16 precipitation at FH06 and IMERG precipitation, (c) GFSv15 precipitation and divergence at 850 hPa (D850) at FH06, and (d) GFSv16 precipitation and D850 at FH06. Shading shows coherence squared, and arrows show the phase between the two variables. Difference of coherence squared at FH48 and coherence squared at FH06 is shown for (e) GFSv15 precipitation and IMERG precipitation, (f) GFSv16 precipitation and IMERG precipitation, (g) GFSv15 precipitation and D850, and (h) GFSv16 precipitation and D850.

A Data-Driven Approach to Identifying the Dynamical Modes of the Madden-Julian Oscillation

Accurately monitoring and understanding Madden-Julian Oscillation (MJO) variability is crucial for advancing subseasonal-to-seasonal (S2S) predictions and enhancing forecasting systems. To address this, a data-driven filter based on a linear inverse model (LIM) is developed to detect and characterize MJO variability and its links to El Niño-Southern Oscillation (ENSO). This approach represents tropical variability through non-orthonormal dynamical modes, capturing constructive and destructive interference patterns. Observed MJO variability arises from the interplay between a fast and a slow intraseasonal (20–96 days) atmospheric mode (“MJO-modes”), alongside SST-driven variability with similar spatial patterns (“ENSO-modes”). These insights provide a clearer understanding of the sources of predictive skill associated with tropical variability, ultimately improving forecasting capabilities.

Graphic comparing LIM-Filtered RMM Composites and LIM Index Composites

Filtering RMM composites using a “dynamical filter.” The filter reveals that RMM (column (a)) is made up of both fast and slow MJO modes (columns (b) and (c)) as well as a contribution from ENSO.

MJO Research Team

Maria Gehne
PSL | CIRES Associate Scientist

George Kiladis
PSL | Research Scientist

Juliana Dias
PSL | Supervisory Research Scientist

Brandon Wolding
PSL | CIRES Research Scientist

Stefan Tulich
PSL | CIRES Research Scientist

Cathy Smith
PSL | CIRES Senior Associate Scientist

Lisa Bengtsson
PSL | Research Scientist

Benjamin Moore
PSL | Meteorologist

David Marsico
PSL | CIRES Research Scientist

Resources

Tutorials and Monitoring
What is the MJO and why do we care?	A short tutorial on the MJO from the Climate.gov ENSO blog.
MJO monitoring page	From NOAA/CPC. Includes various plots of the current status of the 30-60 day oscillation including OLR anomalies, geopotential height and other animations and satellite photos. Also includes a FAQ on the oscillation mechanism and evolution and its relationship to other climate processes. They also have a MJO daily index.
Wikipedia page on the MJO	Description, behavior, and connection to weather events.
World Climate Service: The Madden Julian Oscillation	Introduction, behavior and measurement.
All-season Real-time Multivariate MJO Index	Forecast and monitoring plots of the MJO that utilize and empirically derived structure of the MJO based on historic data and include predictions for Australia from the Australian Bureau of Meteorology.
Multi-Scale MJO Research Page	From Woods Hole Oceanographic Institution. The tropical Dynamics lab researches the MJO and how it interacts with waves and the mean state of the atmosphere/ocean.
PSL Spotlight Article on the MJO (August 2001)	The MJO and its effect on the onset of an El Niño event.

PSL Interactive Data Plotting and Weather/Climate Monitoring Pages
Plot Phase Diagrams using MJO Indices	Choose from an MJO index to get a phase plot for an input range of dates.
Interactive MJO Composites	Choose MJO to get list of high/low MJO activity days. Use these days on the page daily composite page to obtain composites for different variables.
Time Section Plots	Plots time/latitude and time/longitude plots of NCEP reanalysis and operational data. Plots means and anomalies.
Plot Daily Data	Plots maps or crossections of daily and daily averaged data. Includes anomalies, means and climatologies. Includes OLR.

Related PSL Sites
PSL Map Room	Includes maps of many variables for many different variables and time scales. Includes links to MJO Composites along with many other climate/weather variables.
El Niño/Southern Oscillation (ENSO)	Products, forecasts, and science related to ENSO
Tropical Pacific Precipitation and SST Forecast	Forecasts of tropical variables made using "Model Analogues" are provided.

Links to datasets used in PSL MJO Index Calculations
NCEP/NCAR Reanalysis	NOAA's original global reanalysis starting from 1948
ERA5 Reanalysis	The ECMWF high resolution modern reanalysis from 1940.

Publications and References

Click on a panel below to view the publications from that year. Bold indicates PSL-affiliated author. All links are external

2024

Wolding, B., A. Rydbeck, J. Dias, F. Ahmed, M. Gehne, G. N. Kiladis, et al. (January 2024): Atmosphere-Ocean Coupled Energetics of Shallow and Deep Tropical Convective Discharge-Recharge Cycles. J. Atmos. Sci., 81, 3-29, https://doi.org/10.1175/JAS-D-23-0061.1.

2023

Barpanda, P., S. N. Tulich, J. Dias and G. N. Kiladis (October 2023): The role of subtropical Rossby waves in amplifying the divergent circulation of the Madden Julian Oscillation. J. Atmos. Sci., 80, 2377–2398, https://doi.org/10.1175/JAS-D-22-0259.1.
Klotzbach, P. J., C. J. Schreck III, G. P. Compo, et al. (April 2023): Influence of The Madden-Julian Oscillation on Continental United States Hurricane Landfalls . Geophys. Res. Lett., 50, e2023GL102762, https://doi.org/10.1029/2023gl102762.
Dias, J., M. Gehne, G. N. Kiladis and L. Magnusson (November 2023): The role of convectively coupled equatorial waves in sub-seasonal predictions. Geophys. Res. Lett., 50, e2023GL106198, https://doi.org/10.1029/2023GL106198.
Cheng, Y.-M., J. Dias, G. N. Kiladis, Z. Feng and L. R. Leung (May 2023): Mesoscale convective systems modulated by convectively coupled equatorial waves. Geophys. Res. Lett., 50, e2023GL103335, https://doi.org/10.1029/2023GL103335.

2022

Amaya, D. J., M. G. Jacox, J. Dias, M. A. Alexander, K. Karnauskas, J. D. Scott and M. Gehne (January 2022): Subseasonal-to-seasonal forecast skill in the California Current System and its connection to coastal Kelvin waves. J. Geophys. Res. Oceans, 127 (1), e2021JC017892, https://doi.org/10.1029/2021JC017892..
Bengtsson, L., L. Gerard, J. Han, M. Gehne, W. Li and J. Dias (December 2022): 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). Mon. Wea. Rev., 150 (12), 3211–3227, https://doi.org/10.1175/MWR-D-22-0114.1.
Berrington, A. H., N. Sakaeda, J. Dias and G. N. Kiladis (August 2022): Relationships Between the Eastward Propagation of the Madden-Julian Oscillation and its Circulation Structure. J. Geophys. Res. Atmos., 127 (16), e2021JD035806, https://doi.org/10.1029/2021JD035806.
Cheng, Y.-M., S. N. Tulich, G. N. Kiladis and J. Dias (October 2022): Two extratropical pathways to forcing tropical convective disturbances. J. Climate, 35 (20), 2987–3009, https://doi.org/10.1175/JCLI-D-22-0171.1.
Hsiao, W.-T., E. A. Barnes, E. D. Maloney, S. N. Tulich, J. Dias and G. N. Kiladis (March 2022): Role of the Tropics and its Extratropical Teleconnections in State-Dependent Improvements of U.S. West Coast UFS Precipitation Forecasts. Geophys. Res. Lett., 49 (5), e2021GL096447, https://doi.org/10.1029/2021GL096447.
Knippertz, P., M. Gehne, G. N. Kiladis, K. Kikuchi, A. R. Satheesh, P. E. Roundy, G.-Y. Yang, J. Dias, A. H. Fink, J. Methven, A. Schlueter, F. Sielmann and M. C. Wheeler (July 2022): The intricacies of identifying equatorial waves. Q. J. R. Meteorol. Soc., 148 (747), 2814-2852, https://doi.org/10.1002/qj.4338.
Wang, S., Z. K. Martin, A. H. Sobel, M. K. Tippett, J. Dias and G. N. Kiladis (February 2022): A multivariate index for tropical intraseasonal oscillations based on seasonally-varying modal structures. J. Geophys. Res. Atmos., 127 (4), e2021JD035961, https://doi.org/10.1029/2021JD035961.
Wolding, B., S. W. Powell, F. Ahmed, J. Dias, M. Gehne, G. N. Kiladis and J. D. Neelin (July 2022): Tropical Thermodynamic–Convection Coupling in Observations and Reanalyses. J. Atmos. Sci., 79 (7), 1781–1803, https://doi.org/10.1175/JAS-D-21-0256.1.

2021

Dias, J., S. N. Tulich, M. Gehne and G. N. Kiladis(September 2021): Tropical Origins of Weeks 2–4 Forecast Errors during the Northern Hemisphere Cool Season. Mon. Wea. Rev., 149 (9), 2975–2991, https://doi.org/10.1175/MWR-D-21-0020.1.
Gehne, M., B. Wolding, J. Dias and G. N. Kiladis (September 2021): Diagnostics of Tropical Variability for Numerical Weather Forecasts. Wea. Forecasting, 37 (9), 1661–1680, https://doi.org/10.1175/WAF-D-21-0204.1.
Haynes, P., P. Hitchcock, M. Hitchman, S. Yoden, H. H. Hendon, G. N. Kiladis, K. Kodera and I. Simpson (August 2021): The Influence of the Stratosphere on the Tropical Troposphere. J. Meteor. Soc. Japan, 99 (4), 803-845, https://doi.org/10.2151/jmsj.2021-040.
Hoffmann, C. G., G. N. Kiladis, M. Gehne and C. von Savigny (May 2021): A Python Package to Calculate the OLR-Based Index of the Madden- Julian-Oscillation (OMI) in Climate Science and Weather Forecasting. J. Open Res. Software, 9 (1), 9, https://doi.org/10.5334/jors.331.
Tulich, S. N. and G. N. Kiladis (September 2021): On the Regionality of Moist Kelvin Waves and the MJO: The Critical Role of the Background Zonal Flow. J. Adv. Model. Earth Syst., 13 (9), e2021MS002528, https://doi.org/10.1029/2021MS002528.

2011-2020

2020

Sakaeda, N., J. Dias and G. N. Kiladis (September 2020): The unique characteristics and potential mechanisms of the MJO-QBO relationship. J. Geophys. Res. Atmos., 125 (17), e2020JD033196, https://doi.org/10.1029/2020JD033196.
Wolding, B., J. Dias, G. N. Kiladis, E. Maloney and M. Branson (May 2020): Interactions between Moisture and Tropical Convection. Part II: The Convective Coupling of Equatorial Waves . J. Atmos. Sci., 77 (5), 1801-1819, https://doi.org/10.1175/JAS-D-19-0226.1.
Wolding, B., J. Dias, G. N. Kiladis, F. Ahmed, S. W. Powell, E. Maloney and M. Branson (May 2020): Interactions between Moisture and Tropical Convection. Part I: The Coevolution of Moisture and Convection. J. Atmos. Sci., 77 (5), 1783-1799, https://doi.org/10.1175/JAS-D-19-0225.1.

2019

Camberlin, P., W. Gitau, G. N. Kiladis, E. Bosire and B. Pohl (November 2019): Intraseasonal to Interannual Modulation of Diurnal Precipitation Distribution Over Eastern Africa. J. Geophys. Res. Atmos., 124 (22), 11863-11886, https://doi.org/10.1029/2019JD031167.
Dias, J. and G. N. Kiladis (April 2019): The Influence of Tropical Forecast Errors on Higher Latitude Predictions. Geophys. Res. Lett., 46 (8), 4450-4459, https://doi.org/10.1029/2019GL082812.

2018

Dias, J., M. Gehne, G. N. Kiladis, N. Sakaeda, P. Bechtold and T. Haiden (June 2018): Equatorial Waves and the Skill of NCEP and ECMWF Numerical Weather Prediction Systems. Mon. Wea. Rev., 146, 1763-1784, https://doi.org/10.1175/MWR-D-17-0362.1 .
Hoell A. (November 2018): Middle East and Southwest Asia Daily Precipitation Characteristics Associated with the Madden–Julian Oscillation during Boreal Winter. J. Climate, 31, 8843-8860. doi:10.1175/JCLI-D-18-0059.1
Capotondi A. and P. D. Sardeshmukh (October 2018): The Nature of the Stochastic Wind Forcing of ENSO. J. Climate, 31, 8081-8099. doi:10.1175/JCLI-D-17-0842.1
Vera C. S., P. L. M. Gonzalez, B. Liebmann and G. N. Kiladis (November 2018): Seasonal cycle of precipitation variability in South America on intraseasonal timescales. Clim. Dyn., ONLINE, doi:10.1007/s00382-017-3994-1
Kikuchi K., G. N. Kiladis, J. Dias and T. Nasuno (June 2018): Convectively coupled equatorial waves within the MJO during CINDY/DYNAMO: slow Kelvin waves as building blocks. Clim. Dyn., doi:10.1007/s00382-017-3869-5
Sakaeda N., S. W. Powell, J. Dias and G. N. Kiladis (April 2018): The Diurnal Variability of Precipitating Cloud Populations during DYNAMO. J. Atmos. Sci., 75, 1307-1326. doi:10.1175/JAS-D-17-0312.1

2017

Alvarez M. S., C. S. Vera and G. N. Kiladis (November 2017): MJO Modulating the Activity of the Leading Mode of Intraseasonal Variability in South America. Atmosphere, 8 (12), p. 232. doi:10.3390/atmos8120232
Dias J., N. Sakaeda, G. N. Kiladis and K. Kikuchi (August 2017): Influences of the MJO on the space-time organization of tropical convection. J. Geophys. Res. Atmos., 122 (15), 8012-8032. doi:10.1002/2017JD026526
Sakaeda N., G. N. Kiladis and J. Dias (April 2017): The Diurnal Cycle of Tropical Cloudiness and Rainfall Associated with the Madden-Julian Oscillation. J. Climate, 145, 1401-1412. doi:10.1175/JCLI-D-16-0788.1
Sossa A., B. Liebmann, I. Bladé, D. Allured, H. H. Hendon, P. Peterson and A. Hoell (March 2017): Statistical Connection between the Madden–Julian Oscillation and Large Daily Precipitation Events in West Africa. J. Climate, 30, 1999-2010. doi:10.1175/JCLI-D-16-0144.1
Cannon F. , L. M. V. Carvalho, C. Jones, A. Hoell, J. Norris, G. N. Kiladis and A. A. Tahir (February 2017): The influence of tropical forcing on extreme winter precipitation in the western Himalaya. Clim. Dyn., 48 (3), 1213-1232. doi:10.1007/s00382-016-3137-0

2016

Alvarez M. S., C. S. Vera, G. N. Kiladis and B. Liebmann (January 2016): Influence of the Madden Julian Oscillation on precipitation and surface air temperature in South America. Clim. Dyn., 46 (1), 245-262. doi:10.1007/s00382-015-2581-6.
Cannon F. , L. M. V. Carvalho, C. Jones, A. Hoell, J. Norris, G. N. Kiladis and A. A. Tahir (April 2016): The influence of tropical forcing on extreme winter precipitation in the western Himalaya. Clim. Dyn., ONLINE, doi:10.1007/s00382-016-3137-0
Valéds-Pineda R., J. B. Valdés, H. F. Diaz and R. Pizarro-Tapia (June 2016): Analysis of spatio-temporal changes in annual and seasonal precipitation variability in South America-Chile and related ocean-atmosphere circulation patterns. Int. J. Climatol., 36 (8), 2979-3001. doi:10.1002/joc.4532
van der Linden R., A. H. Fink, J. G. Pinto, T. Phan-Van and G. N. Kiladis (August 2016): Modulation of Daily Rainfall in Southern Vietnam by the Madden–Julian Oscillation and Convectively Coupled Equatorial Waves. J. Climate, 29, 5801-5820. doi:10.1175/JCLI-D-15-0911.1

2015

Chen S., M. Flatau, T. G. Jensen, T. Shinoda, J. Schmidt, P. May, J. Cummings, M. Liu, P. E. Ciesielski, C. W. Fairall, et al. (October 2015): A Study of CINDY/DYNAMO MJO Suppressed Phase. J. Atmos. Sci., 72, 3755-3779. doi:10.1175/JAS-D-13-0348.1
de Szoeke S. P., J. B. Edson, J. R. Marion, C. W. Fairall and L. Bariteau (January 2015): The MJO and Air–Sea Interaction in TOGA COARE and DYNAMO. J. Climate, 28, 597-622. doi:10.1175/JCLI-D-14-00477.1

2014

Moum J. N., S. P. de Szoeke, W. D. Smyth, J. B. Edson, H. L. DeWitt, A. J. Moulin, E. J. Thompson, C. J. Zappa, S. A. Rutledge, R. H. Johnson and C. W. Fairall (August 2014): Air–Sea Interactions from Westerly Wind Bursts During the November 2011 MJO in the Indian Ocean. Bull. Am. Meteorol. Soc., 95 (8), 1185-1199. doi:10.1175/BAMS-D-12-00225.1
Kiladis G. N., J. Dias, K. H. Straub, M. C. Wheeler, S. N. Tulich, K. Kikuchi, K. M. Weickmann and M. J. Ventrice (May 2014): A Comparison of OLR and Circulation-Based Indices for Tracking the MJO. Mon. Weather Rev., 142 (5), 1697-1715. doi:10.1175/MWR-D-13-00301.1
Hamill T. M. and G. N. Kiladis (February 2014): Skill of the MJO and Northern Hemispheric Blocking in GEFS Medium-Range Reforecasts. Mon. Weather Rev., 142 (2), 868-885. doi:10.1175/MWR-D-13-00199.1

2013

Ventrice M. J., M. C. Wheeler, H. H. Hendon, C. J. Schreck III, C. D. Thorncroft and G. N. Kiladis (December 2013): A Modified Multivariate Madden–Julian Oscillation Index Using Velocity Potential. Mon. Weather Rev., 141 (12), 4197-4210. doi:10.1175/MWR-D-12-00327.1
Dias J., S. Leroux, S. N. Tulich and G. N. Kiladis (April 2013): How systematic is organized tropical convection within the MJO? Geophys. Res. Lett., 40 (7), 1420-1425. doi:10.1002/grl.50308

2012

Guan B., D. E. Waliser, N. P. Molotch, E. J. Fetzer and P. J. Neiman (February 2012): Does the Madden-Julian Oscillation influence wintertime atmospheric rivers and snowpack in the Sierra Nevada? Mon. Weather Rev ., 140, 325-345. doi:10.1175/MWR-D-11-00087.1 dx.doi.org/10.1175/MWR-D-11-00087.1

2011

Riley E. , B. Mapes and S. Tulich (December 2011): Clouds associated with the Madden–Julian Oscillation: A new perspective from CloudSat. J. Atmos. Sci., 68, 3032-3051. doi:10.1175/JAS-D-11-030.1

2000-2010

2010

Gottschalck J., . . . ., K. M. Weickmann, et al. (September 2010): A Framework for Assessing Operational Madden-Julian Oscillation Forecasts: A CLIVAR MJO Working Group Project. Bull. Am. Meteorol. Soc., 91 (9), 1247-1258. doi:10.1175/2010BAMS2816.1
Serra Y. L., G. N. Kiladis and K. I. Hodges (September 2010): Tracking and Mean Structure of Easterly Waves over the Intra-Americas Sea. J. Climate, 23 (18), 4823-4840. doi:10.1175/2010JCLI3223.1
Janicot S., F. Mounier, S. Gervois, B. Sultan and G. N. Kiladis (July 2010): The Dynamics of the West African Monsoon. Part V: The Detection and Role of the Dominant Modes of Convectively Coupled Equatorial Rossby Waves. J. Climate, 23 (14), 4005-4024. doi:10.1175/2010jcli3221.1

2009

Kim D., K. R. Sperber, W. Stern, D. E. Waliser, I.S. Kang, E. Maloney, W. Wang, K. M. Weickmann, J. Benedict, M. Khairoutdinov, M.-I. Lee, R. Neale, M. Suarez, K. Thayer-Calder and G. Zhang (December 2009): Application of MJO Simulation Diagnostics to Climate Models. J. Climate, 22 (23), 6413-6436. doi:10.1175/2009JCLI3063.1
Lin J. L., T. Shinoda, B. Liebmann, T. Qian, W. Han, P. E. Roundy, J. Zhou and Y. Zheng (September 2009): Intraseasonal Variability Associated with Summer Precipitation over South America Simulated by 14 IPCC AR4 Coupled GCMs. Mon. Weather Rev., 137 (9), 2931-2954. doi:10.1175/2009MWR2777.1 https://dx.doi.org/10.1175/2009MWR2777.1
Newman M., P. D. Sardeshmukh and M. C. Penland (June 2009): How Important Is Air-Sea Coupling in ENSO and MJO Evolution? J. Climate, 22 (11), 2958-2977. doi:10.1175/2008JCLI2659.1
Waliser D. E., K. R. Sperber, . . . ., K. M. Weickmann and al. et (June 2009): MJO Simulation Diagnostics. J. Climate, 22 (11), 3006-3030. doi:10.1175/2008JCLI2731.1
Weickmann K. and E. Berry (May 2009): The Tropical Madden-Julian Oscillation and the Global Wind Oscillation. Mon. Weather Rev., 137 (5), 1601-1614. doi:10.1175/2008MWR2686.1
Janicot S., F. Mounier, N. M. Hall, S. Leroux, B. Sultan and G. N. Kiladis (March 2009): Dynamics of the West African Monsoon. Part IV: Analysis of 25-90-Day Variability of Convection and the Role of the Indian Monsoon. J. Climate, 22 (6), 1541-1565. doi:10.1175/2008JCLI2314.1

2008

Lin J., B. E. Mapes, K. M. Weickmann, G. N. Kiladis, et al. (June 2008): North American Monsoon and Convectively Coupled Equatorial Waves Simulated by IPCC AR4 Coupled GCMs. J. Climate, 21 (12), 2919-2937. doi:10.1175/2007JCLI1815.1
Shinoda T., P. E. Roundy and G. N. Kiladis (May 2008): Variability of Intraseasonal Kelvin Waves in the Equatorial Pacific Ocean. J. Phys. Oceanogr., 38 (5), 921-944. doi:10.1175/2007JPO3815.1
Haertel P. T., G. Kiladis, A. Denno and T. M. Rickenbach (March 2008): Vertical-Mode Decompositions of 2-Day Waves and the Madden-Julian Oscillation. J. Atmos. Sci., 65 (3), 813-833. doi:10.1175/2007JAS2314.1

2007

Sardeshmukh P. D. and P. Sura (December 2007): Multiscale Impacts of Variable Heating in Climate. J. Climate, 20 (23), 5677-5695. doi:10.1175/2007JCLI1411.1
Weickmann K. and E. Berry (February 2007): A Synoptic-Dynamic Model of Subseasonal Atmospheric Variability. Mon. Weather Rev., 135 (2), 449-474. doi:10.1175/MWR3293.1

2006

Kiladis, G. N. and B. E. Mapes (2006), Convective life cycles and scale interactions in tropical waves, Dynamics of Atmospheres and Ocean, 42(1-4), 1-2, 10.1016/j.dynatmoce.2006.07.001.
Straub K. H., G. N. Kiladis and P. E. Ciesielski (December 2006): The role of equatorial waves in the onset of the South China Sea summer monsoon and the demise of El Niño during 1998. Dyn. Atmos. Oceans, 42 (1-4), doi:10.1016/j.dynatmoce.2006.02.005
Roundy P. E. and G. N. Kiladis (October 2006): Observed relationships between oceanic Kelvin waves and atmospheric forcing. J. Climate, 19 (20), 5253-5272. doi:10.1175/JCLI3893.1
Lin J., G. N. Kiladis, B. Mapes, K. M. Weickmann, K. R. Sperber, W. Lin, M. C. Wheeler, S. Schubert, A. Del Genio, L. J. Donner, S. Emori, J.-F. Gueremy, F. Hourdin, P. J. Rasch, E. Roeckner and J. F. Scinocca (June 2006): Tropical Intraseasonal Variability in 14 IPCC AR4 Climate Models. Part I: Convective Signals. J. Climate, 19 (12), 2665-2690. doi:10.1175/JCLI3735.1

2005

Kiladis, G. N., K. Straub and P. Haertel (2005), Zonal and vertical structure of the Madden-Julian oscillation, J. Atmospheric Sciences, 62(8), 2790-2809, 10.1175/JAS3520.1..
Lin J.-L., M. Zhang and B. Mapes (July 2005): Zonal Momentum Budget of the Madden-Julian Oscillation: The Source and Strength of Equivalent Linear Damping. J. Atmos. Sci., 62 (7), 2172-2188. doi:10.1175/JAS3471.1

2004

Carvalho, L., C Jones and B. Liebmann (2004), The South Atlantic convergence zone: Intensity, form, persistence, and relationships with intraseasonal to interannual activity and extreme rainfall, Journal of Climate, 17(1), 88-108, 10.1175/1520-0442%282004%29017%3C0088:TSACZI%3E2.0.CO;2.
Liebmann, B., G. N. Kiladis, C. Vera, A. Saulo and L. Carvalho (2004), Subseasonal variations of rainfall in South America in the vicinity of the low-level jet east of the Andes and comparison to those in the South Atlantic convergence zone, Journal of Climate, 17(19), 3829-3842, 10.1175/1520-0442%282004%29017%3C3829:SVORIS%3E2.0.CO;2.
Lin, J. L., B. E. Mapes, M. Zhang and M. Newman (2004), Stratiform precipitation, vertical heating profiles, and the Madden-Julian oscillation, J. Atmospheric Sciences, 61(3), 296-309, 10.1175/1520-0469%282004%29061%3C0296:SPVHPA%3E2.0.CO;2.

2003

Lin, J., B. Mapes, M. Zhang, and M. Newman, 2003: Stratiform precipitation, vertical heating profiles, and the Madden-Julian Oscillation. J. Atmos. Sci., in press.

2002

Shinoda, T., and H. H. Hendon, 2002: Rectified wind forcing and latent heat flux produced by the Madden-Julian oscillation. J. Climate., 15, 3500-3508. [Abstract]

2001

Shinoda, T., and H. H. Hendon, 2001: Upper ocean heat budget in response to the Madden-Julian Oscillation in the western equatorial Pacific. J. Climate, 14, 4147-4165. [Abstract]
Lee, M.-I., I.-S. Kang, J.-K. Kim, and B. E. Mapes, 2001: Influence of cloud-radiation interaction on simulating tropical intraseasonal oscillation with an atmospheric general circulation model. J. Geophys. Res., 106, 14,219-14,233. [Abstract]

2000

Hendon, H. H., B. Liebmann, M. E. Newman, J. D. Glick, and J. E. Schemm, 2000: Medium range forecast errors associated with active episodes of the Madden-Julian oscillation. Mon. Wea. Rev., 128, 69-86. [Abstract]

Pre-2000

1999

Hendon, H. H., C. Zhang, and J. D. Glick, 1999: Interannual variation of the Madden-Julian oscillation during austral summer. J. Climate, 12, 2538-2550. [Abstract]

1998

Hendon, H. H., B. Liebmann, and J. D. Glick, 1998: Oceanic Kelvin waves and the Madden-Julian oscillation. J. Atmos. Sci., 55, 88-101. [Abstract]

1997

Weickmann, K. M., G. N. Kiladis, and P. D. Sardeshmukh, 1997: The dynamics of intraseasonal atmospheric angular momentum oscillations.J. Atmos. Sci., 54, 1445-1461. [Abstract]
Zhang, C., and Hendon, H. H., 1997: On the propagating and standing components of the intraseasonal oscillation in tropical convection. J. Atmos. Sci., 54, 741-752. [Abstract]

1995

Hendon, H. H., 1995: Length of day fluctuations associated with the Madden Julian oscillation. J. Atmos. Sci., 52, 2373-2383. [Abstract]

1994

Weickmann, K., and P. Sardeshmukh, 1994: The atmospheric angular momentum budget associated with a Madden-Julian oscillation. J. Atmos. Sci., 51, 3194-3208. [Abstract]
Hendon, H. H., and B. Liebmann, 1994: Organization of convection within the Madden-Julian Oscillation. J. Geophys. Res., 99, 8073-8083. [Abstract]
Liebmann, B., H. H. Hendon, and J. D. Glick, 1994: The relationship between tropical cyclones of the western Pacific and Indian oceans and the Madden-Julian oscillation. J. Meteor. Soc. Japan, 72, 401-412. [Abstract]

Other MJO References

Aiyyer, A. R., and Molinari, J. (2003). Evolution of mixed rossby-gravity waves in idealized MJO environments. J. Atmospheric Sciences, 60(23), 2837-2855.
Aiyyer, A., and Molinari, J. (2002). Evolution of mixed rossby gravity waves in MJO type environments. 45 Beacon St. Boston MA 02108-3693 USA, https://www.ametsoc.org American Meteorological Society.
Bannister, A. J., and Smith, K. J. (1993). The south pacific and southeast indian ocean tropical cyclone season 1990-91. Australian Meteorological Magazine, Canberra, Australia, 42(4), 175-182.
Batstone, C., Matthews, A., and Stevens, D. (2002). Surface heat fluxes during the MJO and their effects on the oceanic mixed layer in the tropical eastern hemisphere. 45 Beacon St. Boston MA 02108-3693 USA, https://www.ametsoc.org American Meteorological Society.
Bergman, J. W., Hendon, H. H., and Weickmann, K. M. (2001). Intraseasonal air-sea interactions at the onset of el nino. Journal of Climate, 14(8), 1702-1719.
Bergman, J. W., Hendon, H. H., and Weickmann, K. M. (2001). Intraseasonal air-sea interactions at the onset of el nino. Journal of Climate, Boston, MA, 14(8), 1702-1719.
Blade, I., and Hartmann, D. L. (1995). The linear and nonlinear extratropical response of the atmosphere to tropical intraseasonal heating. J. Atmospheric Sciences, Boston, MA, 52(24), 4448-4471.
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Carvalho, L. M. V., Jones, C., and Liebmann, B. (2004). The south atlantic convergence zone: intensity, form, persistence, and relationships with intraseasonal to interannual activity and extreme rainfall. Journal of Climate, 17(1), 88-108.
Castro Sr, C., and Cavalcanti, I. (2003). Intraseasonal modes of variability affecting the SACZ (2003 - 7ICSHMO). 45 Beacon St. Boston MA 02108-3693 USA: American Meteorological Society. Retrieved July 12, 2004, from Meteorological and Geoastrophysical Abstracts database.
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Chen, B., and Yanai, M. (2000). Comparison of the madden-julian oscillation (MJO) during the TOGA COARE IOP with a 15-year climatology. Journal of Geophysical Research, Washington, DC, 105(D2), 2139-2149.
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Chen, T., Yen, M., Pfaendtner, J., and Sud, Y. C. (1993). The vertical structure of diabatic heating associated with the madden-julian oscillation simulated by the goddard laboratory for atmospheres climate model. Journal of Geophysical Research, Washington, DC, 98(D5), 8801-8813.
Clayson, C. A., Strahl, B., and Schrage, J. (2002). 2-3-day convective variability in the tropical western pacific. Monthly Weather Review, 130(3), 529-548.
Colon, E., Lindesay, J., and Suarez, M. J. (2002). The impact of surface flux- and circulation-driven feedbacks on simulated madden-julian oscillations. Journal of Climate, 15(6), 624-641.
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Davidson, N., Adug, E., Hassan, W., Nguyen, P., and Prabowo, M. (2002). Diagnosis and prediction of some extreme rain events over southeast asia. 45 Beacon St. Boston MA 02108-3693 USA, https://www.ametsoc.org American Meteorological Society.
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