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Title: Stochastic Parameterization: Toward a New View of Weather and Climate Models

Journal Article · · Bulletin of the American Meteorological Society
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  1. National Center for Atmospheric Research, Boulder, CO (United States)
  2. Goethe Univ., Frankfurt (Germany)
  3. CNRM-GAME, Meteo-France/CNRS, Toulouse (France)
  4. Swedish Meteorological and Hydrological Institute, Norrkoping (Sweden)
  5. Univ. of Oxford (United Kingdom)
  6. Gran Sasso Science Institute, L'Aquila (Italy)
  7. Univ. of Amsterdam (Netherlands)
  8. Univ. of Hamburg (Germany)
  9. Univ. of Bonn (Germany)
  10. Humboldt Univ. of Berlin (Germany)
  11. Univ. of Helsinki (Finland)
  12. Commonwealth Scientific and Industrial Research Organization (CSIRO), Aspendale, VIC (Australia)
  13. Ecole Normale Superieure, Paris (France). Lab. de Meteorologie Dynamique (CNRS/IPSL)
  14. Univ. of Hamburg (Germany); Univ. of Reading (United Kingdom)
  15. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  16. Physical Sciences Division, NOAA/Earth System Research Laboratory, Boulder, Colorado
  17. Max Planck Inst. for Meteorology, Hamburg (Germany); Hans-Ertel-Centre for Weather Research, Deutscher Wetterdienst, Hamburg, (Germany)
  18. Max Planck Inst. for Meteorology, Hamburg (Germany)
  19. Univ. of Reading (United Kingdom)
  20. GAME-CNRM, CNRS, Meteo-France, Toulouse (France)
+ Show Author Affiliations

The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1376542
Journal Information:
Bulletin of the American Meteorological Society, Vol. 98, Issue 3; ISSN 0003-0007
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 225 works
Citation information provided by
Web of Science

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