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


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.


Climate prediction Multimodel ensemble Forecast quality assessment Forecast assimilation 



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.


  1. Alessandri A, Borrelli A, Navarra A, Arribas A, Déqué M, Rogel P, Weisheimer A (2011) Evaluation of probabilistic quality and value of the ENSEMBLES multimodel seasonal forecasts: comparison with DEMETER. Mon Weather Rev 139:581–607CrossRefGoogle Scholar
  2. Arribas A, Glover M, Maidens A, Peterson K, Gordon M, MacLachlan C, Graham R, Fereday D, Camp J, Scaife AA, Xavier P, McLean P, Colman A, Cusack S (2011) The GloSea4 ensemble prediction system for seasonal forecasting. Mon Weather Rev 139:1891–1910CrossRefGoogle Scholar
  3. Barnett TP, Preisendorfer R (1987) Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis. Mon Weather Rev 115:1825–1850CrossRefGoogle Scholar
  4. Coelho CAS, Pezzulli S, Balmaseda M, Doblas-Reyes FJ, Stephenson DB (2004) Forecast calibration and combination: a simple Bayesian approach for ENSO. J Clim 17:1504–1516CrossRefGoogle Scholar
  5. Coelho CAS, Stephenson DB, Balmaseda M, Doblas-Reyes FJ, van Oldenborgh GJ (2006) Toward an integrated seasonal forecasting system for South America. J Clim 19:3704–3721CrossRefGoogle Scholar
  6. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Holm EV, Isaksen L, Kallberg P, Kohler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J-J, Park B-K, Peubey C, Rosnay P, Tavolato C, Thepaut J-N, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597CrossRefGoogle Scholar
  7. Doblas-Reyes FJ, Déqué M, Piedelievre JP (2000) Multi-model spread and probabilistic seasonal forecasts in PROVOST. Q J R Meteorol Soc 126:2069–2087CrossRefGoogle Scholar
  8. Doblas-Reyes FJ, Hagedorn R, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting. Part II: calibration and combination. Tellus A 57:234–252Google Scholar
  9. Doblas-Reyes FJ, Weisheimer A, Déqué M, Keenlyside N, McVean M, Murphy JM, Rogel P, Smith D, Palmer TN (2009) Addressing model uncertainty in seasonal and annual dynamical seasonal forecasts. Q J R Meteorol Soc 135:1538–1559CrossRefGoogle Scholar
  10. Doblas-Reyes FJ, Garcia-Serrano J, Lienert F, Pinto-Biescas A, Rodrigues LRL (2013) Seasonal climate predictability and forecasting: status and prospects. WIREs Clim Change 4:245–268CrossRefGoogle Scholar
  11. Eden JM, van Oldenborgh GJ, Hawkins E, Suckling EB (2015) A global empirical system for probabilistic seasonal climate prediction. Geosci Model Dev Discuss 8:3941–3970CrossRefGoogle Scholar
  12. EEA (2015) European Environment Agency: Global and European temperatures (CSI 012/CLIM 001) Assessment. Accessed 7 Aug 2015
  13. Gneiting T, Raftery AE, Westveld AH, Goldman T (2005) Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon Weather Rev 133:1098–1118CrossRefGoogle Scholar
  14. Goddard L, Mason SJ, Zebiak SE, Ropelewski CF, Basher R, Cane MA (2001) Current approaches to seasonal to interannual climate predictions. Int J Climatol 21:1111–1152CrossRefGoogle Scholar
  15. Graham RJ, Gordon M, McLean PJ, Ineson S, Huddleston MR, Davey MK, Brookshaw A, Barnes RTH (2005) A performance comparison of coupled and uncoupled versions of the Met Office seasonal prediction general circulation model. Tellus A 57:320–339CrossRefGoogle Scholar
  16. Hagedorn R, Doblas-Reyes FJ, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting-I. Basic concept. Tellus A 57:219–233Google Scholar
  17. Hersbach H (2000) Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather Forecast 15:559–570CrossRefGoogle Scholar
  18. Huffman GJ, Bolvin DT (2013) GPCP Version 2.2 combined precipitation data set documentation, internet publication, pp 1–46. Accessed 16 Nov 2012
  19. Johansson A, Barnston A, Saha S, van den Dool H (1998) On the level and origin of seasonal forecast skill in northern Europe. J Atmos Sci 55:103–127CrossRefGoogle Scholar
  20. Jolliffe IT, Stephenson DB (2012) Forecast verification: a practitioner’s guide in atmospheric science, Second edn. Wiley, ChichesterGoogle Scholar
  21. Kim HM, Webster PJ, Curry JA (2012) Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter. Clim Dyn 39:2957–2973CrossRefGoogle Scholar
  22. Kirtman BP, Min D, Infanti JM, Kinter JL III, Paolino DA, Zhang Q, van den Dool H, Saha S, Mendez MP, Becker E, Peng P, Tripp P, Huang J, DeWitt DG, Tippett MK, Barnston AG, Li S, Rosati A, Schubert SD, Rienecker M, Suarez M, Li ZE, Marshak J, Lim Y-K, Tribbia J, Pegion K, Merryfield WJ, Denis B, Wood EF (2014) The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull Am Meteorol Soc 95:585–601CrossRefGoogle Scholar
  23. Mason SJ (2008) Understanding forecast verification statistics. Meteorol Appl 15:31–40CrossRefGoogle Scholar
  24. Mason SJ, Baddour O (2008) Statistical modeling. In: Troccoli A, Harrison MSJ, Anderson DLT, Mason SJ (eds) Seasonal climate: forecasting and managing risk. Springer Academic Publishers, Dordrecht, pp 167–206Google Scholar
  25. Mason SJ, Goddard L, Graham NE, Yulaeva E, Sun L, Arkin PA (1999) The IRI seasonal climate prediction system and the 1997/98 El Niño Event. Bull Am Meteorol Soc 80:1853–1873CrossRefGoogle Scholar
  26. Merryfield WJ, Lee W-S, Boer GJ, Kharin VV, Scinocca JF, Flato GM, Ajayamohan RS, Fyfe JC, Tang Y, Polavarapu S (2013) The canadian seasonal to interannual prediction System. part i: models and initialization. Mon Weather Rev 141:2910–2945CrossRefGoogle Scholar
  27. Molteni F, Stockdale T, Balmaseda M, Balsamo G, Buizza R, Ferranti L, Magnusson L, Mogensen K, Palmer T, Vitart F (2011) The new ECMWF seasonal forecast system (System 4). ECMWF Technical Memorandum 656. Accessed 20 Dec 2012
  28. Robertson AW, Lall U, Zebiak SE, Goddard L (2004) Improved combination of multiple atmospheric GCM ensembles for seasonal prediction. Mon Weather Rev 132:2732–2744CrossRefGoogle Scholar
  29. Rodrigues LRL, Doblas-Reyes FJ, Coelho CAS (2014a) Multi-model calibration and combination of tropical seasonal sea surface temperature forecasts. Clim Dyn 42:597–616CrossRefGoogle Scholar
  30. Rodrigues LRL, García-Serrano J, Doblas-Reyes FJ (2014b) Seasonal forecast quality of the West African monsoon rainfall regimes by multiple forecast systems. J Geophys Res 119:7908–7930Google Scholar
  31. Saha S, Moorthi S, Wu X, Wang J, Nadiga S, Tripp P, Behringer D, Hou Y-T, Chuang H, Iredell M, Ek M, Meng J, Yang R, Mendez MP, van den Dool H, Zhang Q, Wang W, Chen M, Becker E (2014) The NCEP climate forecast system version 2. J Clim 27:2185–2208CrossRefGoogle Scholar
  32. Scaife AA, Arribas A, Blockley E, Brookshaw A, Clark RT, Dunstone N, Eade R, Fereday D, Folland CK, Gordon M, Hermanson L, Knight JR, Lea DJ, MacLachlan C, Maidens A, Martin M, Peterson AK, Smith D, Vellinga M, Wallace E, Waters J, Williams A (2014) Skillful long-range prediction of European and North American winters. Geophys Res Lett 41:2514–2519CrossRefGoogle Scholar
  33. Stephenson DB, Coelho CAS, Doblas-Reyes FJ, Balmaseda M (2005) Forecast assimilation: a unified framework for the combination of multi-model weather and climate predictions. Tellus A 57:253–264CrossRefGoogle Scholar
  34. Stockdale TN, Molteni F, Ferranti L (2015) Atmospheric initial conditions and the predictability of the Arctic Oscillation. Geophys Res Lett 42:1173–1179CrossRefGoogle Scholar
  35. Vernieres G, Rienecker MM, Kovach R, Keppenne CL (2012) The GEOS-iODAS: Description and evaluation. NASA Technical Report Series on Global Modeling and Data Assimilation TM2012-104606 30Google Scholar
  36. Vitart F, Huddleston MR, Déqué M, Peake D, Palmer TN, Stockdale TN, Davey MK, Ineson S, Weisheimer A (2007) Dynamically-based seasonal forecasts of Atlantic tropical storm activity issued in June by EUROSIP. Geophys Res Lett 34:L16815CrossRefGoogle Scholar
  37. Wang B, Li J-Y, Kang I-S, Shukla J, Park C-K, Kumar A, Schemm J, Cocke S, Kug J-S, Luo J-J, Fu X, Yun W-T, Alves O, Jin E, Kinter J, Kirtman B, Krishnamurti T, Lau N, Lau W, Liu P, Pegion P, Rosati T, Schubert S, Stern W, Suarez M, Yamagate T (2009) Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14 model ensemble retrospective seasonal prediction (1980–2004). Clim Dyn 33:93–117CrossRefGoogle Scholar
  38. Yuan X, Wood EF, Luo L, Pan M (2011) A first look at climate forecast system version 2 (CFSv2) for hydrological seasonal prediction. Geophys Res Lett 38:L1340Google Scholar
  39. Zhang S, Harrison MJ, Rosati A, Wittenberg A (2007) System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon Weather Rev 135:3541–3564CrossRefGoogle Scholar

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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