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Using climate models to estimate the quality of global observational data sets.
Massonnet, François; Bellprat, Omar; Guemas, Virginie; Doblas-Reyes, Francisco J.
Afiliação
  • Massonnet F; Earth Sciences Department, Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS), Barcelona, Spain. Georges Lemaître Centre for Earth and Climate Research (TECLIM), Earth and Life Institute (ELI), Université catholique de Louvain, Louvain-la-Neuve, Belgium. francois.massonnet
  • Bellprat O; Earth Sciences Department, Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS), Barcelona, Spain.
  • Guemas V; Earth Sciences Department, Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS), Barcelona, Spain. Centre National de Recherches Météorologiques (CNRM), Toulouse, France.
  • Doblas-Reyes FJ; Earth Sciences Department, Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS), Barcelona, Spain. ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain.
Science ; 354(6311): 452-455, 2016 10 28.
Article em En | MEDLINE | ID: mdl-27789838
ABSTRACT
Observational estimates of the climate system are essential to monitoring and understanding ongoing climate change and to assessing the quality of climate models used to produce near- and long-term climate information. This study poses the dual and unconventional question Can climate models be used to assess the quality of observational references? We show that this question not only rests on solid theoretical grounds but also offers insightful applications in practice. By comparing four observational products of sea surface temperature with a large multimodel climate forecast ensemble, we find compelling evidence that models systematically score better against the most recent, advanced, but also most independent product. These results call for generalized procedures of model-observation comparison and provide guidance for a more objective observational data set selection.
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Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Ano de publicação: 2016 Tipo de documento: Article
Buscar no Google
Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Ano de publicação: 2016 Tipo de documento: Article