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

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  • 1 National Center for Atmospheric Research,* Boulder, Colorado
  • 2 Institut für Atmosphäre und Umwelt, Goethe-Universität, Frankfurt am Main, Germany
  • 3 CNRM-GAME, Météo-France/CNRS, Toulouse, France
  • 4 Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
  • 5 National Center for Atmospheric Research,* Boulder, Colorado
  • 6 Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, United Kingdom
  • 7 Gran Sasso Science Institute, L’Aquila, Italy
  • 8 National Center for Atmospheric Research,* Boulder, Colorado
  • 9 Centrum Wiskunde en Informatica, and Korteweg-de Vries Institute for Mathematics, University of Amsterdam, Amsterdam, Netherlands
  • 10 Institut für Atmosphäre und Umwelt, Goethe-Universität, Frankfurt am Main, Germany
  • 11 Meteorological Institute, and Centre for Earth System Research and Sustainability, University of Hamburg, Hamburg, Germany
  • 12 Meteorological Institute, University of Bonn, Bonn, Germany
  • 13 Institut für Mathematik, Humboldt-Universität zu Berlin, Berlin, Germany
  • 14 Department of Physics, University of Helsinki, Helsinki, Finland
  • 15 Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, United Kingdom
  • 16 Oceans and Atmosphere Flagship, CSIRO, Aspendale, Victoria, Australia
  • 17 Laboratoire de Météorologie Dynamique (CNRS/IPSL), Ecole Normale Supérieure, Paris, France
  • 18 Meteorological Institute, and Centre for Earth System Research and Sustainability, University of Hamburg, Hamburg, Germany, and Department of Mathematics and Statistics, University of Reading, Reading, United Kingdom
  • 19 Oak Ridge National Laboratory, Oak Ridge, Tennessee
  • 20 Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, United Kingdom
  • 21 Physical Sciences Division, NOAA/Earth System Research Laboratory, Boulder, Colorado
  • 22 Max Planck Institute for Meteorology, and Hans-Ertel-Centre for Weather Research, Deutscher Wetterdienst, Hamburg, Germany
  • 23 Max Planck Institute for Meteorology, Hamburg, Germany
  • 24 National Centre for Atmospheric Science, and Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, and ECMWF, Reading, United Kingdom
  • 25 Meteorological Institute, University of Bonn, Bonn, Germany
  • 26 Department of Meteorology, University of Reading, Reading, United Kingdom
  • 27 GAME-CNRM, CNRS, Météo-France, Toulouse, France
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Abstract

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.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR E-MAIL: Judith Berner,berner@ucar.edu

Abstract

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.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR E-MAIL: Judith Berner,berner@ucar.edu
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