MJO propagation across the maritime continent in the ECMWF ensemble prediction system

Hye Mi Kim, Daehyun Kim, Frederic Vitart, Violeta E. Toma, Jong Seong Kug, Peter J. Webster

Research output: Contribution to journalArticlepeer-review

72 Scopus citations

Abstract

The characteristics of the MJO propagation across the Maritime Continent are investigated using a 20-yr reforecast dataset from the ECMWF ensemble prediction system. Analysis of the MJO events initialized over the Indian Ocean (phase 2) shows that the initial MJO amplitude and prediction skill relationship is not linear, particularly when the predictions start in moderate (between strong and weak) MJO amplitude category. To examine the key factors that determine the prediction skill, reforecasts in the moderate category are grouped into high- and low-skill events, and the differences in their ocean-atmospheric conditions as well as the physical processes during reforecast period are examined. The initial distribution of OLR anomalies in high-skill events shows a clear dipole pattern of convection with an enhanced convective anomalies over the Indian Ocean and strongly suppressed convective anomalies in the western Pacific Ocean. This dipole mode may support the MJO propagation across the Maritime Continent via the Rossby wave response and associated meridional moisture advection. Prominent ocean-atmosphere coupled processes are also simulated during the propagation of high-skill events. However, in low-skill events, the convective signal over the western Pacific is almost absent and less organized, and the ocean-atmosphere coupled processes are not simulated correctly. It is found that in both high- and low-skill events, the amplitude of the convective anomaly decreases significantly after about day 15, possibly due to the systematic mean model bias. A strong wet bias in the vicinity of the Maritime Continent, a cold SST bias in the equatorial Pacific, and associated circulation biases make the west Pacific area unfavorable for MJO propagation, thus limiting its prediction skill.

Original languageEnglish
Pages (from-to)3973-3988
Number of pages16
JournalJournal of Climate
Volume29
Issue number11
DOIs
StatePublished - 1 Jun 2016

Bibliographical note

Publisher Copyright:
© 2016 American Meteorological Society.

Keywords

  • Climate models
  • Forecasting
  • Geographic location/entity
  • Hindcasts
  • Intraseasonal variability
  • Models and modeling
  • Tropics
  • Variability

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