STOCHASTIC PARAMETERIZATION: Toward a New View of Weather and Climate Models

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....

Full description

Saved in:
Bibliographic Details
Published in:Bulletin of the American Meteorological Society 2017-03, Vol.98 (3), p.565-588
Main Authors: Berner, Judith, Achatz, Ulrich, Batté, Lauriane, Bengtsson, Lisa, de la Cámara, Alvaro, Christensen, Hannah M., Colangeli, Matteo, Coleman, Danielle R. B., Crommelin, Daaaan, Dolaptchiev, Stamen I., Franzke, Christian L. E., Friederichs, Petra, Imkeller, Peter, Järvinen, Heikki, Juricke, Stephan, Kitsios, Vassili, Lott, François, Lucarini, Valerio, Mahajan, Salil, Palmer, Timothy N., Penland, Cécile, Sakradzija, Mirjajana, von Storch, Jin-Song, Weisheimer, Antje, Weniger, Michael, Williams, Paul D., Yano, Jun-Ichi
Format: Article
Language:English
Subjects:
Analysis
Atmosphere
Atmospheric models
Climate
Climate models
Climatology
Computer simulation
Cryosphere
Derivation
ENVIRONMENTAL SCIENCES
Gravitational waves
Laboratories
Loads (forces)
Mathematical models
Mathematics
Mechanics
Meteorology
Noise
Numerical weather prediction
Ocean models
Oceans
Parameterization
Physics
Probability theory
Randomness
Reviews
Seasonal forecasting
Statistical mechanics
Stochasticity
Turbulence
Uncertainty
Weather
Weather forecasting
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0003-0007
1520-0477
DOI:10.1175/bams-d-15-00268.1