The DOI matches its Crossref record; 103 of the 103 references it cites were found in a registry or an index.
Stochastic Parameterization: Toward a New View of Weather and Climate Models
Open accessAbstract
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.
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Cited by
Schneider, T., Lan, S., Stuart, A., & Teixeira, J. (2017) · Geophysical Research Letters · doi:10.1002/2017gl076101 · Cited by 391
Towards neural Earth system modelling by integrating artificial intelligence in Earth system science
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(2023) · Climate Change 2021 – The Physical Science Basis · doi:10.1017/9781009157896.003 · Cited by 298
Exchange Processes in the Atmospheric Boundary Layer Over Mountainous Terrain
Serafin, S., Adler, B., Cuxart, J., De Wekker, S., Gohm, A., Grisogono, B., Kalthoff, N., Kirshbaum, D., Rotach, M., Schmidli, J., et al. (2018) · Atmosphere · doi:10.3390/atmos9030102 · Cited by 257
AghaKouchak, A., Pan, B., Mazdiyasni, O., Sadegh, M., Jiwa, S., Zhang, W., Love, C. A., Madadgar, S., Papalexiou, S. M., Davis, S. J., et al. (2022) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2021.0288 · Cited by 67
Metadata
- Authors
- Berner, J., Achatz, U., Batté, L., Bengtsson, L., Cámara, A. D. L., Christensen, H. M., Colangeli, M., Coleman, D. R. B., Crommelin, D., Dolaptchiev, S. I., Franzke, C. L. E., Friederichs, P., Imkeller, P., Järvinen, H., Juricke, S., Kitsios, V., Lott, F., Lucarini, V., Mahajan, S., Palmer, T. N., Penland, C., Sakradzija, M., von Storch, J.-S., Weisheimer, A., Weniger, M., Williams, P. D., & Yano, J.-I.
- Venue
- Bulletin of the American Meteorological Society
- Publisher
- American Meteorological Society
- Published
- 2017-03-01
- Volume
- 98
- Issue
- 3
- Pages
- 565-588
- ISSN
- 0003-0007 (ISSN-L)
- 1520-0477
- OpenAlex
- W2237826530
- Cited by
- 411
- References
- 103
- FWCI
- 17.91
- Type
- journal article
- Language
- en
- Open access
- green
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- Berner, J., Achatz, U., Batté, L., Bengtsson, L., Cámara, A. D. L., Christensen, H. M., Colangeli, M., Coleman, D. R. B., Crommelin, D., Dolaptchiev, S. I., Franzke, C. L. E., Friederichs, P., Imkeller, P., Järvinen, H., Juricke, S., Kitsios, V., Lott, F., Lucarini, V., Mahajan, S., . . . Yano, J.-I. (2017). Stochastic Parameterization: Toward a New View of Weather and Climate Models. Bulletin of the American Meteorological Society. https://doi.org/10.1175/bams-d-15-00268.1
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