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Observational evidence of European summer weather patterns predictable from spring.
Ossó, Albert; Sutton, Rowan; Shaffrey, Len; Dong, Buwen.
Afiliação
  • Ossó A; Department of Meteorology, National Centre for Atmospheric Science, University of Reading, Reading RG6 6BB, United Kingdom a.osso@reading.ac.uk.
  • Sutton R; Department of Meteorology, National Centre for Atmospheric Science, University of Reading, Reading RG6 6BB, United Kingdom.
  • Shaffrey L; Department of Meteorology, National Centre for Atmospheric Science, University of Reading, Reading RG6 6BB, United Kingdom.
  • Dong B; Department of Meteorology, National Centre for Atmospheric Science, University of Reading, Reading RG6 6BB, United Kingdom.
Proc Natl Acad Sci U S A ; 115(1): 59-63, 2018 01 02.
Article em En | MEDLINE | ID: mdl-29255052
Forecasts of summer weather patterns months in advance would be of great value for a wide range of applications. However, seasonal dynamical model forecasts for European summers have very little skill, particularly for rainfall. It has not been clear whether this low skill reflects inherent unpredictability of summer weather or, alternatively, is a consequence of weaknesses in current forecast systems. Here we analyze atmosphere and ocean observations and identify evidence that a specific pattern of summertime atmospheric circulation--the summer East Atlantic (SEA) pattern--is predictable from the previous spring. An index of North Atlantic sea-surface temperatures in March-April can predict the SEA pattern in July-August with a cross-validated correlation skill above 0.6. Our analyses show that the sea-surface temperatures influence atmospheric circulation and the position of the jet stream over the North Atlantic. The SEA pattern has a particularly strong influence on rainfall in the British Isles, which we find can also be predicted months ahead with a significant skill of 0.56. Our results have immediate application to empirical forecasts of summer rainfall for the United Kingdom, Ireland, and northern France and also suggest that current dynamical model forecast systems have large potential for improvement.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article