Progress towards a probabilistic Earth system model: examining the impact of stochasticity in the atmosphere and land component of EC-Earth v3.2
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Published:2019-07-19
Issue:7
Volume:12
Page:3099-3118
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Author:
Strommen KristianORCID, Christensen Hannah M.ORCID, MacLeod Dave, Juricke Stephan, Palmer Tim N.
Abstract
Abstract. We introduce and study the impact of three stochastic schemes in the EC-Earth climate model: two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic Earth system model in atmosphere-only mode. Stochastic parametrization have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as El Niño–Southern Oscillation (ENSO), North Atlantic weather regimes, and the Indian monsoon. Stochasticity in the land component has been shown to improve the variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied. It is shown that the inclusion of all three schemes notably changes the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to significant changes in the model's energy budget and atmospheric circulation. This implies that in order to maintain the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.
Funder
FP7 Environment Natural Environment Research Council Horizon 2020 Deutsche Forschungsgemeinschaft
Publisher
Copernicus GmbH
Reference50 articles.
1. Arakawa, A. and Schubert, W. H.: Interaction of a cumulus cloud ensemble with
the large scale environment, Part I, J. Atmos. Sci., 31, 674–701,
https://doi.org/10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2, 1974. a 2. Baldwin, M. P., Gray, L. J., Dunkerton, T. J., Hamilton, K., Haynes, P. H.,
Randel, W. J., Holton, J. R., Alexander, M. J., Hirota, I., Horinouchi, T.,
Jones, D. B., Kinnersley, J. S., Marquardt, C., Sato, K., and Takahashi, M.:
The quasi-biennial oscillation, Rev. Geophys., 39, 179–229,
https://doi.org/10.1029/1999RG000073, 2001. a 3. Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., van den Hurk, B., Hirschi,
M., and Betts, A. K.: A Revised Hydrology for the ECMWF Model: Verification
from Field Site to Terrestrial Water Storage and Impact in the Integrated
Forecast System, J. Hydrometeorol., 10, 623–643,
https://doi.org/10.1175/2008JHM1068.1,
2009. a, b 4. Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical
weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a 5. Beljaars, A. E. A.: The numerics of physical parametrization, in: Seminar on recent developments in numerical methods for atmospheric and ocean modelling, ECMW, 113–134, 2004. a
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