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The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model

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A seasonal forecast system is presented, based on the global coupled climate model MPI-ESM as used for CMIP5 simulations. We describe the initialisation of the system and analyse its predictive skill for surface temperature. The presented system is initialised in the atmospheric, oceanic, and sea ice component of the model from reanalysis/observations with full field nudging in all three components. For the initialisation of the ensemble, bred vectors with a vertically varying norm are implemented in the ocean component to generate initial perturbations. In a set of ensemble hindcast simulations, starting each May and November between 1982 and 2010, we analyse the predictive skill. Bias-corrected ensemble forecasts for each start date reproduce the observed surface temperature anomalies at 2–4 months lead time, particularly in the tropics. Niño3.4 sea surface temperature anomalies show a small root-mean-square error and predictive skill up to 6 months. Away from the tropics, predictive skill is mostly limited to the ocean, and to regions which are strongly influenced by ENSO teleconnections. In summary, the presented seasonal prediction system based on a coupled climate model shows predictive skill for surface temperature at seasonal time scales comparable to other seasonal prediction systems using different underlying models and initialisation strategies. As the same model underlying our seasonal prediction system—with a different initialisation—is presently also used for decadal predictions, this is an important step towards seamless seasonal-to-decadal climate predictions.

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  • Alessandri A, Borrelli A, Navarra A, Arribas A, Déqué M, Rogel P, Weisheimer A (2010) Evaluation of probabilistic quality and value of the ENSEMBLES multimodel seasonal forecasts: comparison with DEMETER. Mon Weather Rev 139(2):581–607. doi:10.1175/2010MWR3417.1

    Article  Google Scholar 

  • Arribas A, Glover M, Maidens A, Peterson K, Gordon M, MacLachlan C, Graham R, Fereday D, Camp J, Scaife A, Xavier P, McLean P, Colman A, Cusack S (2010) The GloSea4 ensemble prediction system for seasonal forecasting. Mon Weather Rev 139(6):1891–1910. doi:10.1175/2010MWR3615.1

    Article  Google Scholar 

  • Baehr J, Piontek R (2013) Ensemble initialization of the oceanic component of a coupled model through bred vectors at seasonal-to-interannual time scales. Geosci Model Dev Discuss 6:5189–5214. doi:10.5194/gmdd-6-5189-2013

    Article  Google Scholar 

  • Balmaseda MA, Mogensen K, Weaver AT (2013) Evaluation of the ECMWF ocean reanalysis system ORAS4. Quart J R Meteorol Soc 139(674):1132–1161. doi:10.1002/qj.2063

    Article  Google Scholar 

  • 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, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The era-interim reanalysis: configuration and performance of the data assimilation system reanalysis: configuration and performance of the data assimilation system. Quart J R Meteorol Soc 137(656):553–597. doi:10.1002/qj.828

    Article  Google Scholar 

  • Doblas-Reyes FJ, García-Serrano J, Lienert F, Biescas AP, Rodrigues LRL (2013) Seasonal climate predictability and forecasting: status and prospects. Wiley Interdiscip Rev Clim Change 4(4):245–268. doi:10.1002/wcc.217

    Article  Google Scholar 

  • Domeisen DIV, Butler AH, Fröhlich K, Bittner M, Müller WA, Baehr J (2014) Seasonal predictability over Europe arising from El Nino and stratospheric variability in the MPI-ESM seasonal prediction system. J Clim. doi:10.1175/JCLI-D-14-00207.1

  • Du H, Doblas-Reyes FJ, García-Serrano J, Guemas V, Soufflet Y, Wouters B (2012) Sensitivity of decadal predictions to the initial atmospheric and oceanic perturbations. Clim Dyn 7–8:2013–2023. doi:10.1007/s00382-011-1285-9

    Article  Google Scholar 

  • Fetterer F, Knowles K, Meier W, Savoie M (2002) Sea ice index. National Snow and Ice Data Center, Boulder. doi:10.7265/N5QJ7F7W

    Google Scholar 

  • Giorgetta MA, Jungclaus JH, Reick CH, Legutke S, Brovkin V, Crueger T, Fieg MEK, Glushak K, Gayler V, Haak H, Hollweg HD, Kinne TIS, Kornblueh L, Matei D, Mauritsen T, Mikolajewicz U, Mueller WA, Notz D, Raddatz R, Rast S, Redler R, Schmidt ERH, Schnur R, Segschneider J, Six K, Stockhause M, Wegner J, Widmann H, Wieners KH, Claussen M, Marotzke J, Stevens B (2013) Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J Adv Model Earth Syst 5:572–597. doi:10.1002/jame.20038

    Article  Google Scholar 

  • Hagemann S, Loew A, Andersson A (2013) Combined evaluation of MPI-ESM land surface water and energy fluxes. J Adv Model Earth Syst 5:259–286. doi:10.1029/2012MS000173

    Google Scholar 

  • Hazeleger W, Wang X, Severijns C, Ştefănescu S, Bintanja R, Sterl A, Wyser K, Semmler T, Yang S, Hurk B, Noije T, Linden E, Wiel K (2012) EC-Earth V2.2: description and validation of a new seamless earth system prediction model. Clim Dyn 39(11):2611–2629

    Article  Google Scholar 

  • Ho C, Hawkins E, Shaffrey L, Broecker J, Hermanson L, Murphy J, Smith D, Eade R (2013) Examining reliability of seasonal to deacadal sea surface temperature forecasts: the role of ensemble dispersion. Geophys Res Lett 40:5770–5775. doi:10.1002/2013GL057630

    Article  Google Scholar 

  • Hsu WR, Murphy AH (1986) The attributes diagram: a geometrical framework for assessing the quality of probability forecasts. Int J Forecast 2:285–293

    Article  Google Scholar 

  • Jungclaus J, Fischer N, Haak H, Lohmann K, Marotzke J, Matei D, Mikolajewicz U, Notz D, von Storch J (2013) Characteristics of the ocean simulations in MPIOM, the ocean component of the MPI-Earth System Model. J Adv Model Earth Syst 422–446: doi:10.1002/jame.20023

  • Jungclaus JH, Keenlyside N, Botzet M, Haak H, Luo JJ, Latif M, Marotzke J, Mikolajewicz U, Roeckner E (2006) Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM. J Clim 19:3952–3972. doi:10.1175/JCLI3827.1

    Article  Google Scholar 

  • Keller JD, Hense A (2011) A new non-gaussian evaluation method for ensemble forecasts based on analysis rank histograms. Meteorol Zeitschrift 20(2):107–117. doi:10.1127/0941-2948/2011/0217

    Article  Google Scholar 

  • 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. doi:10.1007/s00382-012-1364-6

  • Kirtman B, Anderson D, Brunet G, Kang IS, Scaife AA, Smith D (2013a) Prediction from weeks to decades. In: Asrar GR, Hurrell JW (eds) Climate science for serving society. Springer, Berlin. doi:10.1007/978-94-007-6692-1_8

    Google Scholar 

  • Kirtman BP, Min D, Infanti JM, Kinter JL, 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 YK, Tribbia J, Pegion K, Merryfield WJ, Denis B, Wood EF (2013b) The North American Multi-Model Ensemble (NMME): phase-1 Seasonal to interannual prediction, Phase-2 Toward developing intra-seasonal prediction. Bull Am Meteorol Soc. doi:10.1175/BAMS-D-12-00050.1

  • Kröger J, Müller WA, von Storch JS (2012) Impact of different ocean reanalyses on decadal climate prediction. Clim Dyn 39(3–4):795–810. doi:10.1007/s00382-012-1310-7

    Article  Google Scholar 

  • MacLachlan C, Arribas A, Peterson KA, Maidens A, Fereday D, Scaife AA, Gordon M, Vellinga M, Williams A, Comer RE, Camp J, Xavier P, Madec G (2014) Global seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Quart J R Meteorol Soc. doi:10.1002/qj.2396

  • Meehl GA, Goddard L, Boer G, Burgman R, Branstator G, Cassou C, Corti S, Danabasoglu G, Doblas-Reyes F, Hawkins E, Karspeck A, Kimoto M, Kumar A, Matei D, Mignot J, Msadek R, Pohlmann H, Rienecker M, Rosati T, Schneider E, Smith D, Sutton R, Teng H, van Oldenborgh GJ, Vecchi G, Yeager S (2013) Decadal climate prediction: an update from the trenches. Bull Am Meteorol Soc. doi:10.1175/BAMS-D-12-00241.1

  • Merryfield WJ, Lee WS, 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(8):2910–2945. doi:10.1175/MWR-D-12-00216.1

    Article  Google Scholar 

  • Müller WA, Baehr J, Haak H, Jungclaus JH, Kröger J, Matei D, Notz D, Pohlmann H, von Storch JS, Marotzke J (2012) Forecast skill of multi-year seasonal means in the decadal prediction system of the Max Planck Institute for Meteorology. Geophys Res Lett 39(22). doi:10.1029/2012GL053326

  • Munoz E, Kirtman B, Weijer W (2011) Varied representation of the Atlantic Meridional Overturning across multidecadal ocean reanalyses. Deep Sea Res Part II Top Stud Oceanogr 58(17–18):1848–1857. doi:10.1016/j.dsr2.2010.10.064

    Article  Google Scholar 

  • Notz D, Haumann A, Haak H, Jungclaus JH, Marotzke J (2013) Arctic sea-ice evolution as modeled by max planck institute for meteorology’s earth system model. J Adv Model Earth Syst 5:173–194. doi:10.1002/jame.20016

    Article  Google Scholar 

  • Palmer TN, Doblas-Reyes FJ, Hagedorn R, Alessandri A, Gualdi S, Andersen U, Feddersen H, Cantelaube P, Terres JM, Davey M, Graham R, Délécluse P, Lazar A, Déqué M, Guérémy JF, Díez E, Orfila B, Hoshen M, Morse AP, Keenlyside N, Latif M, Maisonnave E, Rogel P, Marletto V, Thomson MC (2004) Development of a european multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bull Am Meteorol Soc 85(6):853–872. doi:10.1175/BAMS-85-6-853

    Article  Google Scholar 

  • Palmer TN, Doblas-Reyes FJ, Weisheimer A, Rodwell MJ (2008) Toward seamless prediction: calibration of climate change projections using seasonal forecasts. Bull Am Meteorol Soc 89(4):459–470. doi:10.1175/BAMS-89-4-459

    Article  Google Scholar 

  • Pohlmann H, Müller WA, Kulkarni K, Kameswarrao M, Matei D, Vamborg FSE, Kadow C, Illing S, Marotzke J (2013) Improved forecast skill in the tropics in the new MiKlip decadal climate predictions. Geophys Res Lett 40. doi:10.1002/2013GL058051

  • 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 longrange prediction of European and North American winters. Geophys Res Lett 41. doi:10.1002/2014GL059637

  • Schmidt H, Rast S, Bunzel F, Esch M, Giorgetta MA, Kinne S, Krismer T, Stenchikov G, Timmreck C, Tomassini L, Walz M (2013) The response of the middle atmosphere to anthropogenic and natural forcing in the CMIP5 simulations with the MPI-ESM. J Adv Model Earth Syst 5:98–116. doi:10.1002/jame.20014

    Article  Google Scholar 

  • Schubert JJ, Stevens B, Crueger T (2013) Madden–Julian Oscillation as simulated by the MPI Earth System Model: over the last and into the next millennium. J Adv Model Earth Syst 5:71–84. doi:10.1029/2012MS000180

    Article  Google Scholar 

  • Smith D, Eade R, Pohlmann H (2013) Seasonal to decadal climate prediction: full field initialization; anomaly initialization. Clim Dyn. doi:10.1007/s00382-013-1683-2

  • Smith DM, Scaife AA, Kirtman BP (2012) What is the current state of scientific knowledge with regard to seasonal and decadal forecasting? Environ Res Lett 7(015602). doi:10.1088/1748-9326/7/1/015602

  • Stevens B, Giorgetta M, Esch M, Mauritsen T, Crueger T, Rast S, Salzmann M, Schmidt H, Bader J, Block K, Brokopf R, Fast I, Kinne S, Kornblueh L, Lohmann U, Pincus R, Reichler T, Roeckner E (2013) The atmospheric component of the MPI-M Earth System Model: ECHAM6. J Adv Model Earth Syst 5:146–172. doi:10.1002/jame.20015

    Article  Google Scholar 

  • Stockdale T, Anderson D, Balmaseda M, Doblas-Reyes F, Ferranti L, Mogensen K, Palmer T, Molteni F, Vitart F (2011) ECMWF seasonal forecast System 3 and its prediction of sea surface temperature. Clim Dyn 37(3–4):455–471

    Article  Google Scholar 

  • Stockdale TN (1997) Coupled ocean–atmosphere forecasts in the presence of climate drift. Mon Weather Rev 125(5):809–818. doi:10.1175/1520-0493(1997)125<0809:COAFIT>2.0.CO;2

  • Talagrand O, Vautard R, Strauss B (1997) Evaluation of probabilistic prediction systems. In: ECMWF workshop on predictability, pp 1–25

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. doi:10.1175/BAMS-D-11-00094.1

    Article  Google Scholar 

  • Tietsche S, Notz D, Jungclaus JH, Marotzke J (2013) Assimilation of sea-ice concentration in a global climate model—physical and statistical aspects. Ocean Sci 9(1):19–36. doi:10.5194/os-9-19-2013

    Article  Google Scholar 

  • Valcke S (2006) OASIS3 user guide (prism\_2-5). PRISM support initiative report. Technical report, PRISM Support Initiative Report, CERFACS, Toulouse, France

  • Weisheimer A, Doblas-Reyes FJ, Palmer TN, Alessandri A, Arribas A, Déqué M, Keenlyside N, MacVean M, Navarra A, Rogel P (2009) ENSEMBLES: a new multi-model ensemble for seasonal-to-annual predictions—skill and progress beyond DEMETER in forecasting tropical Pacific SSTs. Geophys Res Lett 36(21). doi:10.1029/2009GL040896

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We wish to thank Tim Stockdale for many helpful discussions. We thank Helmuth Haak, Jürgen Kröger, Modali Kameswarrao, and Sebastian Rast for technical help with the model. All simulations were carried out at the German Climate Computing Center (DKRZ) in Hamburg, Germany. This research is supported through the Cluster of Excellence ’CliSAP’ (EXC177), University of Hamburg, funded through the German Science Foundation (DFG) (JB, DD, WM, RP), by the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 308378 ENV.2012.6.1-1: Seasonal-to-decadal climate predictions towards climate services [] (JB, WM, DD), and by the German Federal Ministry for Education and Research (BMBF) project MiKlip ( (HP, WM).

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Baehr, J., Fröhlich, K., Botzet, M. et al. The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model. Clim Dyn 44, 2723–2735 (2015).

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