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Benefits of Increasing the Model Resolution for the Seasonal Forecast Quality in EC-Earth
Prodhomme, Chloé; Batté, L.; Massonnet, François; Davini, P.; Bellprat, Omar; Guemas, Virginie; Doblas-Reyes, Francisco J.
Barcelona Supercomputing Center
Resolution in climate models is thought to be an important factor for advancing seasonal prediction capability. To test this hypothesis, seasonal ensemble reforecasts are conducted over 1993–2009 with the European community model EC-Earth in three configurations: standard resolution (~1° and ~60 km in the ocean and atmosphere models, respectively), intermediate resolution (~0.25° and ~60 km), and high resolution (~0.25° and ~39 km), the two latter configurations being used without any specific tuning. The model systematic biases of 2-m temperature, sea surface temperature (SST), and wind speed are generally reduced. Notably, the tropical Pacific cold tongue bias is significantly reduced, the Somali upwelling is better represented, and excessive precipitation over the Indian Ocean and over the Maritime Continent is decreased. In terms of skill, tropical SSTs and precipitation are better reforecasted in the Pacific and the Indian Oceans at higher resolutions. In particular, the Indian monsoon is better predicted. Improvements are more difficult to detect at middle and high latitudes. Still, a slight improvement is found in the prediction of the winter North Atlantic Oscillation (NAO) along with a more realistic representation of atmospheric blocking. The sea ice extent bias is unchanged, but the skill of the reforecasts increases in some cases, such as in summer for the pan-Arctic sea ice. All these results emphasize the idea that the resolution increase is an essential feature for forecast system development. At the same time, resolution alone cannot tackle all the forecast system deficiencies and will have to be implemented alongside new physical improvements to significantly push the boundaries of seasonal prediction.
The research leading to these results has received funding from the EU Seventh Framework Programme FP7 (2007–2013) under Grant Agreements 308378 (SPECS), 603521 (PREFACE), and 607085 (EUCLEIA), the Horizon 2020 EU program under Grant Agreements 641727 (PRIMAVERA) and 641811 (IMPREX), and the ESA Climate Change Initiative (CCI) Living Planet Fellowship VERITAS-CCI. We acknowledge PRACE for awarding access to Marenostrum3 based in Spain at the Barcelona Supercomputing Center through the HiResClim project. We acknowledge the work of the developers of the s2dverification R-based package (http://cran.r-project. org/web/packages/s2dverification/index.html) and autosubmit workflow manager (https://pypi.python.org/ pypi/autosubmit/3.5.0). Paolo Davini acknowledges the funding from the European Union’s Horizon 2020 research and innovation programme COGNAC under the European Union Marie Sklodowska-Curie Grant Agreement 654942.
Peer Reviewed
Àrees temàtiques de la UPC::Energies
Forecasting--Computer simulation
Climate--Research
Bias
Forecast verification/skill
Seasonal forecasting
Coupled models
Clima--Observacions
Previsió del temps
info:eu-repo/semantics/publishedVersion
Article
American Meteorological Society
         

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