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Climate Dynamics

, Volume 53, Issue 12, pp 7305–7320 | Cite as

Calibration and combination of monthly near-surface temperature and precipitation predictions over Europe

  • Luis R. L. RodriguesEmail author
  • Francisco J. Doblas-Reyes
  • Caio A. S. Coelho
Article

Abstract

A Bayesian method known as the Forecast Assimilation (FA) was used to calibrate and combine monthly near-surface temperature and precipitation outputs from seasonal dynamical forecast systems. The simple multimodel (SMM), a method that combines predictions with equal weights, was used as a benchmark. This research focuses on Europe and adjacent regions for predictions initialized in May and November, covering the boreal summer and winter months. The forecast quality of the FA and SMM as well as the single seasonal dynamical forecast systems was assessed using deterministic and probabilistic measures. A non-parametric bootstrap method was used to account for the sampling uncertainty of the forecast quality measures. We show that the FA performs as well as or better than the SMM in regions where the dynamical forecast systems were able to represent the main modes of climate covariability. An illustration with the near-surface temperature over North Atlantic, the Mediterranean Sea and Middle-East in summer months associated with the well predicted first mode of climate covariability is offered. However, the main modes of climate covariability are not well represented in most situations discussed in this study as the seasonal dynamical forecast systems have limited skill when predicting the European climate. In these situations, the SMM performs better more often.

Keywords

Climate prediction Multimodel ensemble Forecast quality assessment Forecast assimilation 

Notes

Acknowledgements

The authors thank NOAA, NCEP, IRI and NCAR personnel in creating, updating and maintaining the NMME archive. The NMME project and data dissemination is supported by NOAA, NSF, NASA and DOE. Météo-France and ECMWF are appreciated for making available their seasonal prediction hindcasts. This study was supported by the Seventh Framework Programme SPECS project (contract 308378) and the H2020 EUCP project (contract 776613). CASC was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) processes 304586/2016-1. LRLR and CASC acknowledge the support of FAPESP, process 2015/50687-8 (CLIMAX project). The authors acknowledge two anonymous reviewers for their useful comments and suggestions.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Luis R. L. Rodrigues
    • 1
    Email author
  • Francisco J. Doblas-Reyes
    • 2
    • 3
  • Caio A. S. Coelho
    • 4
  1. 1.Centro de Ciências do Sistema TerrestreInstituto Nacional de Pesquisas EspaciaisCachoeira PaulistaBrazil
  2. 2.Barcelona Supercomputing Center-Centro Nacional de SupercomputaciónBarcelonaSpain
  3. 3.ICREABarcelonaSpain
  4. 4.Centro de Previsão de Tempo e Estudos ClimáticosInstituto Nacional de Pesquisas EspaciaisCachoeira PaulistaBrazil

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