MJO Propagation Processes and Mean Biases in the SubX and S2S Reforecasts

Hyemi Kim, Matthew A. Janiga, Kathy Pegion

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


The Madden-Julian oscillation (MJO) is the leading source of global subseasonal predictability; however, many dynamical forecasting systems struggle to predict MJO propagation through the Maritime Continent. Better understanding the biases in simulated physical processes associated with MJO propagation is the key to improve MJO prediction. In this study, MJO prediction skill, propagation processes, and mean state biases are evaluated in reforecasts from models participating in the Subseasonal Experiment (SubX) and Subseasonal to Seasonal (S2S) prediction projects. SubX and S2S reforecasts show MJO prediction skill out to 4.5 weeks based on the Real-time Multivariate MJO index consistent with previous studies. However, a closer examination of these models' representation of MJO propagation through the Maritime Continent reveals that they fail to predict the MJO convection, associated circulations, and moisture advection processes beyond 10 days with most of models underestimating MJO amplitude. The biases in the MJO propagation can be partly associated with the following mean biases across the Indo-Pacific: a drier low troposphere, excess surface precipitation, more frequent occurrence of light precipitation rates, and a transition to stronger precipitation rates at lower humidity than in observations. This indicates that deep convection occurs too frequently in models and is not sufficiently inhibited when tropospheric moisture is low, which is likely due to the representation of entrainment.

Original languageEnglish
Pages (from-to)9314-9331
Number of pages18
JournalJournal of Geophysical Research: Atmospheres
Issue number16
StatePublished - 27 Aug 2019

Bibliographical note

Publisher Copyright:
©2019. American Geophysical Union. All Rights Reserved.


Dive into the research topics of 'MJO Propagation Processes and Mean Biases in the SubX and S2S Reforecasts'. Together they form a unique fingerprint.

Cite this