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
Berner, Judith, et al. "Stochastic Parameterization: Toward a New View of Weather and Climate Models." Bulletin of the American Meteorological Society, vol. 98, no. 3, Mar. 2017. https://doi.org/10.1175/BAMS-D-15-00268.1
Berner, Judith, Achatz, Ulrich, Batté, Lauriane, Bengtsson, Lisa, Cámara, Alvaro de la, Christensen, Hannah M., Colangeli, Matteo, Coleman, Danielle R. B., Crommelin, Daan, Dolaptchiev, Stamen I., Franzke, Christian L. E., Friederichs, Petra, Imkeller, Peter, Järvinen, Heikki, Juricke, Stephan, Kitsios, Vassili, Lott, François, Lucarini, Valerio, ... Yano, Jun-Ichi (2017). Stochastic Parameterization: Toward a New View of Weather and Climate Models. Bulletin of the American Meteorological Society, 98(3). https://doi.org/10.1175/BAMS-D-15-00268.1
Berner, Judith, Achatz, Ulrich, Batté, Lauriane, et al., "Stochastic Parameterization: Toward a New View of Weather and Climate Models," Bulletin of the American Meteorological Society 98, no. 3 (2017), https://doi.org/10.1175/BAMS-D-15-00268.1
@article{osti_1376542,
author = {Berner, Judith and Achatz, Ulrich and Batté, Lauriane and Bengtsson, Lisa and Cámara, Alvaro de la and Christensen, Hannah M. and Colangeli, Matteo and Coleman, Danielle R. B. and Crommelin, Daan and Dolaptchiev, Stamen I. and others},
title = {Stochastic Parameterization: Toward a New View of Weather and Climate Models},
annote = {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},
doi = {10.1175/BAMS-D-15-00268.1},
url = {https://www.osti.gov/biblio/1376542},
journal = {Bulletin of the American Meteorological Society},
issn = {ISSN 0003-0007},
number = {3},
volume = {98},
place = {United States},
publisher = {American Meteorological Society},
year = {2017},
month = {03}}
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); 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, Journal Name: Bulletin of the American Meteorological Society Journal Issue: 3 Vol. 98; ISSN 0003-0007
Majda, Andrew J.; Franzke, Christian; Khouider, Boualem
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Williams, Paul D.; Cullen, Michael J. P.; Davey, Michael K.
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 371, Issue 1991https://doi.org/10.1098/rsta.2012.0518
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Journal Article
·
Thu Nov 20 23:00:00 EST 2003
· Published as "Evaluating Parameterizations in General Circulation Models: Climate Simulation Meets Weather Prediction" in the Bulletin of the American Meteorological Society, n/a, n/a, December 1, 2004, pp. 1903-1915
·OSTI ID:1018765