Subseasonal prediction with and without a well-represented stratosphere in cesm1

Jadwiga H. Richter, Kathy Pegion, Lantao Sun, Hyemi Kim, Julie M. Caron, Anne Glanville, Emerson Lajoie, Stephen Yeager, Who M. Kim, Ahmed Tawfik, Dan Collins

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


There is a growing demand for understanding sources of predictability on subseasonal to seasonal (S2S) time scales. Predictability at subseasonal time scales is believed to come from processes varying slower than the atmosphere such as soil moisture, snowpack, sea ice, and ocean heat content. The stratosphere as well as tropospheric modes of variability can also provide predictability at subseasonal time scales. However, the contributions of the above sources to S2S predictability are not well quantified. Here we evaluate the subseasonal prediction skill of the Community Earth System Model, version 1 (CESM1), in the default version of the model as well as a version with the improved representation of stratospheric variability to assess the role of an improved stratosphere on prediction skill. We demonstrate that the subseasonal skill of CESM1 for surface temperature and precipitation is comparable to that of operational models. We find that a betterresolved stratosphere improves stratospheric but not surface prediction skill for weeks 3–4. SIGNIFICANCE STATEMENT: There is a growing demand in society for understanding sources of predictability on subseasonal to seasonal time scales. In this work we demonstrate that the CESM1 research Earth system model can be utilized as a subseasonal prediction model and show that its subseasonal prediction skill is comparable to that of operational models. We also show that the inclusion of a well-resolved stratosphere does not improve the subseasonal (week 3–4 averaged) forecast of temperature and precipitation at the surface.

Original languageEnglish
Pages (from-to)2589-2602
Number of pages14
JournalWeather and Forecasting
Issue number6
StatePublished - Dec 2020

Bibliographical note

Funding Information:
Acknowledgments. This work was supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement 1852977 and by NOAA’s Climate Program Office (CPO) Modeling, Analysis, Predictions and Projections (MAPP). Portions of this study were supported by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the U.S. Department of Energy’s Office of Biological and Environmental Research (BER) via National Science Foundation IA 1844590, and the NOAA Climate Program Office Climate Variability and Predictability Program. We thank Judith Perlwitz for useful discussions in the initial stages of this work. HK was supported by NSF Grant AGS-1652289 and KMA R&D Program Grant KMI2018-03110. KP was supported by NOAA/OWAQ SPC-000940 and NOAA/MAPP NA16OAR4310146.

Publisher Copyright:
© 2020 American Meteorological Society.


  • Ensembles
  • Forecast verification/skill
  • Hindcasts
  • Seasonal forecasting
  • Stratospheric circulation
  • Tropical variability


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