Benefits of Increasing the Model Resolution for the Seasonal Forecast Quality in EC-Earth
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...
Published in: | Journal of Climate |
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Main Authors: | , , , , , , |
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Format: | Article in Journal/Newspaper |
Language: | English |
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American Meteorological Society
2016
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Online Access: | http://hdl.handle.net/2117/100174 https://doi.org/10.1175/JCLI-D-16-0117.1 |
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ftupcatalunya:oai:upcommons.upc.edu:2117/100174 |
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Open Polar |
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Universitat Politècnica de Catalunya (UPC): Theses and Dissertations Online (TDX) |
op_collection_id |
ftupcatalunya |
language |
English |
topic |
Àrees temàtiques de la UPC::Energies Forecasting--Computer simulation Climate--Research Bias Forecast verification/skill Seasonal forecasting Coupled models Clima--Observacions Previsió del temps |
spellingShingle |
Àrees temàtiques de la UPC::Energies Forecasting--Computer simulation Climate--Research Bias Forecast verification/skill Seasonal forecasting Coupled models Clima--Observacions Previsió del temps Prodhomme, Chloé Batté, L. Massonnet, François Davini, P. Bellprat, Omar Guemas, Virginie Doblas-Reyes, Francisco J. Benefits of Increasing the Model Resolution for the Seasonal Forecast Quality in EC-Earth |
topic_facet |
Àrees temàtiques de la UPC::Energies Forecasting--Computer simulation Climate--Research Bias Forecast verification/skill Seasonal forecasting Coupled models Clima--Observacions Previsió del temps |
description |
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 Postprint (published version) |
author2 |
Barcelona Supercomputing Center |
format |
Article in Journal/Newspaper |
author |
Prodhomme, Chloé Batté, L. Massonnet, François Davini, P. Bellprat, Omar Guemas, Virginie Doblas-Reyes, Francisco J. |
author_facet |
Prodhomme, Chloé Batté, L. Massonnet, François Davini, P. Bellprat, Omar Guemas, Virginie Doblas-Reyes, Francisco J. |
author_sort |
Prodhomme, Chloé |
title |
Benefits of Increasing the Model Resolution for the Seasonal Forecast Quality in EC-Earth |
title_short |
Benefits of Increasing the Model Resolution for the Seasonal Forecast Quality in EC-Earth |
title_full |
Benefits of Increasing the Model Resolution for the Seasonal Forecast Quality in EC-Earth |
title_fullStr |
Benefits of Increasing the Model Resolution for the Seasonal Forecast Quality in EC-Earth |
title_full_unstemmed |
Benefits of Increasing the Model Resolution for the Seasonal Forecast Quality in EC-Earth |
title_sort |
benefits of increasing the model resolution for the seasonal forecast quality in ec-earth |
publisher |
American Meteorological Society |
publishDate |
2016 |
url |
http://hdl.handle.net/2117/100174 https://doi.org/10.1175/JCLI-D-16-0117.1 |
geographic |
Arctic Indian Pacific |
geographic_facet |
Arctic Indian Pacific |
genre |
Arctic Climate change North Atlantic North Atlantic oscillation Sea ice |
genre_facet |
Arctic Climate change North Atlantic North Atlantic oscillation Sea ice |
op_relation |
http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0117.1 info:eu-repo/grantAgreement/EC/H2020/641727/EU/PRocess-based climate sIMulation: AdVances in high resolution modelling and European climate Risk Assessment/PRIMAVERA info:eu-repo/grantAgreement/EC/H2020/641811/EU/IMproving PRedictions and management of hydrological EXtremes/IMPREX info:eu-repo/grantAgreement/EC/H2020/654942/EU/Readdressing Convective-Surface Interaction in Global Climate Models/COGNAC |
op_rights |
Open Access |
op_doi |
https://doi.org/10.1175/JCLI-D-16-0117.1 |
container_title |
Journal of Climate |
container_volume |
29 |
container_issue |
24 |
container_start_page |
9141 |
op_container_end_page |
9162 |
_version_ |
1766349545200943104 |
spelling |
ftupcatalunya:oai:upcommons.upc.edu:2117/100174 2023-05-15T15:19:21+02:00 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 2016-12-05 21 p. http://hdl.handle.net/2117/100174 https://doi.org/10.1175/JCLI-D-16-0117.1 eng eng American Meteorological Society http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0117.1 info:eu-repo/grantAgreement/EC/H2020/641727/EU/PRocess-based climate sIMulation: AdVances in high resolution modelling and European climate Risk Assessment/PRIMAVERA info:eu-repo/grantAgreement/EC/H2020/641811/EU/IMproving PRedictions and management of hydrological EXtremes/IMPREX info:eu-repo/grantAgreement/EC/H2020/654942/EU/Readdressing Convective-Surface Interaction in Global Climate Models/COGNAC Open Access Àrees temàtiques de la UPC::Energies Forecasting--Computer simulation Climate--Research Bias Forecast verification/skill Seasonal forecasting Coupled models Clima--Observacions Previsió del temps Article 2016 ftupcatalunya https://doi.org/10.1175/JCLI-D-16-0117.1 2019-09-29T09:15:56Z 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 Postprint (published version) Article in Journal/Newspaper Arctic Climate change North Atlantic North Atlantic oscillation Sea ice Universitat Politècnica de Catalunya (UPC): Theses and Dissertations Online (TDX) Arctic Indian Pacific Journal of Climate 29 24 9141 9162 |