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

Research output: Contribution to journalArticlepeer-review

22 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

Bibliographical note

Funding Information:
Acknowledgments. We express special thanks to the World Meteorological Organization for fostering the Subseasonal to Seasonal Prediction (S2S) Project and encouraging the activities undertaken by the MJO and Teleconnections Subproject. Support for A. M. J. is from the NOAA Climate and Global Change Postdoctoral Fellowship Program, administered by UCAR’s Cooperative Programs for the Advancement of Earth System Science (CPAESS) under Award NA18NWS4620043B. Wang was supported by NSF Grant AGS-1652289 and the California Department of Water Resources AR Program (Grant 4600013361). Chang was supported by the NOAA Grant NA20OAR4590315. Kim was supported by NSF Grant AGS-1652289. Support from the Swiss National Science Foundation through Projects PP00P2_170523 and PP00P2_198896 to P. Y. and D. D. is gratefully acknowledged. C. I. G. and C. Schwartz are supported by the ISF–NSFC joint research program (Grant 3259/19) and by the European Research Council starting grant under the European Union’s Horizon 2020 research and innovation program (Grant Agreement 677756). Stan was supported by NOAA Grants NA20OAR4590316 and NA18NWS4680069.

Funding Information:
We express special thanks to the World Meteorological Organization for fostering the Subseasonal to Seasonal Prediction (S2S) Project and encouraging the activities undertaken by the MJO and Teleconnections Subproject. Support for A. M. J. is from the NOAA Climate and Global Change Postdoctoral Fellowship Program, administered by UCAR's Cooperative Programs for the Advancement of Earth System Science (CPAESS) under Award NA18NWS4620043B. Wang was supported by NSF Grant AGS-1652289 and the California Department of Water Resources AR Program (Grant 4600013361). Chang was supported by the NOAA Grant NA20OAR4590315. Kim was supported by NSF Grant AGS-1652289. Support from the Swiss National Science Foundation through Projects PP00P2-170523 and PP00P2-198896 to P. Y. and D. D. is gratefully acknowledged. C. I. G. and C. Schwartz are supported by the ISF-NSFC joint research program (Grant 3259/19) and by the European Research Council starting grant under the European Union's Horizon 2020 research and innovation program (Grant Agreement 677756). Stan was supported by NOAA Grants NA20OAR4590316 and NA18NWS4680069.

Publisher Copyright:
© 2022 American Meteorological Society.

Keywords

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

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