Stochastic parameterization: towards a new view of weather and climate models

Berner, J., Achatz, U., Batte, L., Bengtsson, L., De La Camara, A., Christensen, H. M., Colangeli, M., Coleman, D. R. B., Crommelin, D., Dolaptchiev, S. I., Franzke, C. L. E., Friederichs, P., Imkeller, P., Jarvinen, H., Juricke, S., Kitsios, V., Lott, F., Lucarini, V. ORCID: https://orcid.org/0000-0001-9392-1471, Mahajan, S., Palmer, T. N., Penland, C., Sakradzija, M., Von Storch, J.-S., Weisheimer, A., Weniger, M., Williams, P. D. ORCID: https://orcid.org/0000-0002-9713-9820 and Yano, J.-I. (2017) Stochastic parameterization: towards a new view of weather and climate models. Bulletin of the American Meteorological Society, 98 (3). pp. 565-588. ISSN 1520-0477 doi: 10.1175/BAMS-D-15-00268.1

Abstract/Summary

Stochastic parameterizations - empirically derived, or based on rigorous mathematical and statistical concepts - have great potential to increase the predictive capability of next generation 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 better represent model inadequacy and 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 longstanding climate biases and relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups which show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface and cryosphere of comprehensive weather and climate models (a) gives rise to more reliable probabilistic forecasts of weather and climate and (b) 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 demonstrated, and future research directions outlined.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/66937
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology Interdisciplinary Research Centres (IDRCs) > Centre for the Mathematics of Planet Earth (CMPE)
Publisher	American Meteorological Society
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