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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2018-337
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-337
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model evaluation paper 06 Feb 2019

Model evaluation paper | 06 Feb 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Introducing the Probabilistic Earth-System Model: Examining The Impact of Stochasticity in EC-Earth v3.2

Kristian Strommen1, Hannah M. Christensen1, David MacLeod1, Stephan Juricke2,3, and Tim N. Palmer1 Kristian Strommen et al.
  • 1Department of Atmospheric, Oceanic and Planetary Physics, Oxford University, Oxford, UK
  • 2Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
  • 3Jacobs University Bremen, Bremen, Germany

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 parametrisations 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 ENSO, North Atlantic weather regimes and the Indian monsoon. Stochasticity in the land-component has been shown to improve 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 all three schemes notably change the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to large changes in the model's energy budget. This implies that in order to keep 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.

Kristian Strommen et al.
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Kristian Strommen et al.
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Latest update: 21 Feb 2019
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Short summary
Due to computational limitations, climate models cannot fully resolve the laws of physics below a certain scale, a large source of errors and uncertainty. Stochastic schemes aim to account for this by randomly sampling the possible unresolved states. We develop new stochastic schemes for the EC-Earth climate model and evaluate their impact on model performance. While several benefits are found, the impact is sometimes too strong, suggesting such schemes must be carefully calibrated before use.
Due to computational limitations, climate models cannot fully resolve the laws of physics below...
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