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

doi:10.1175/WAF-D-20-0029.1

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

Department of Science Education

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

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 language	English
Pages (from-to)	2589-2602
Number of pages	14
Journal	Weather and Forecasting
Volume	35
Issue number	6
DOIs	https://doi.org/10.1175/WAF-D-20-0029.1
State	Published - 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.

Keywords

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

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10.1175/WAF-D-20-0029.1

Cite this

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title = "Subseasonal prediction with and without a well-represented stratosphere in cesm1",

abstract = "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.",

keywords = "Ensembles, Forecast verification/skill, Hindcasts, Seasonal forecasting, Stratospheric circulation, Tropical variability",

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

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{\textquoteright}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{\textquoteright}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: {\textcopyright} 2020 American Meteorological Society.",

year = "2020",

month = dec,

doi = "10.1175/WAF-D-20-0029.1",

language = "English",

volume = "35",

pages = "2589--2602",

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publisher = "American Meteorological Society",

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AU - Richter, Jadwiga H.

AU - Pegion, Kathy

AU - Sun, Lantao

AU - Kim, Hyemi

AU - Caron, Julie M.

AU - Glanville, Anne

AU - Lajoie, Emerson

AU - Yeager, Stephen

AU - Kim, Who M.

AU - Tawfik, Ahmed

AU - Collins, Dan

N1 - 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.

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N2 - 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.

AB - 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.

KW - Ensembles

KW - Forecast verification/skill

KW - Hindcasts

KW - Seasonal forecasting

KW - Stratospheric circulation

KW - Tropical variability

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JO - Weather and Forecasting

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ER -

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

Abstract

Bibliographical note

Keywords

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