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Advances in the Prediction of MJO Teleconnections in the S2S Forecast Systems

  • Cristiana Stan
  • , Cheng Zheng
  • , Edmund Kar Man Chang
  • , Daniela I.V. Domeisen
  • , Chaim I. Garfinkel
  • , Andrea M. Jenney
  • , Hyemi Kim
  • , Young Kwon Lim
  • , Hai Lin
  • , Andrew Robertson
  • , Chen Schwartz
  • , Frederic Vitart
  • , Jiabao Wang
  • , Priyanka Yadav
  • George Mason University
  • Columbia University
  • University of Lausanne
  • Swiss Federal Institute of Technology Zurich
  • Hebrew University of Jerusalem
  • University of California at Irvine
  • Stony Brook University
  • NASA Goddard Space Flight Center
  • University of Maryland, Baltimore County
  • Environment and Climate Change Canada
  • European Centre for Medium-Range Weather Forecasts
  • University of California at San Diego

Research output: Contribution to journalArticlepeer-review

67 Scopus citations

Abstract

This study evaluates the ability of state-of-the-art subseasonal-to-seasonal (S2S) forecasting systems to represent and predict the teleconnections of the Madden-Julian oscillation and their effects on weather in terms of midlatitude weather patterns and North Atlantic tropical cyclones. This evaluation of forecast systems applies novel diagnostics developed to track teleconnections along their preferred pathways in the troposphere and stratosphere, and to measure the global and regional responses induced by teleconnections across both the Northern and Southern Hemispheres. Results of this study will help the modeling community understand to what extent the potential to predict the weather on S2S time scales is achieved by the current generation of forecasting systems, while informing where to focus further development efforts. The findings of this study will also provide impact modelers and decision-makers with a better understanding of the potential of S2S predictions related to MJO teleconnections.

Original languageEnglish
Pages (from-to)E1426-E1447
JournalBulletin of the American Meteorological Society
Volume103
Issue number6
DOIs
StatePublished - Jun 2022

Keywords

  • Decision making
  • Forecasting
  • Intraseasonal variability
  • Model evaluation/performance
  • Operational forecasting
  • Subseasonal variability

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