ECMWF seasonal forecast system 3 and its prediction of sea surface temperature

Abstract

The latest operational version of the ECMWF seasonal forecasting system is described. It shows noticeably improved skill for sea surface temperature (SST) prediction compared with previous versions, particularly with respect to El Nino related variability. Substantial skill is shown for lead times up to 1 year, although at this range the spread in the ensemble forecast implies a loss of predictability large enough to account for most of the forecast error variance, suggesting only moderate scope for improving long range El Nino forecasts. At shorter ranges, particularly 3–6 months, skill is still substantially below the model-estimated predictability limit. SST forecast skill is higher for more recent periods than earlier ones. Analysis shows that although various factors can affect scores in particular periods, the improvement from 1994 onwards seems to be robust, and is most plausibly due to improvements in the observing system made at that time. The improvement in forecast skill is most evident for 3-month forecasts starting in February, where predictions of NINO3.4 SST from 1994 to present have been almost without fault. It is argued that in situations where the impact of model error is small, the value of improved observational data can be seen most clearly. Significant skill is also shown in the equatorial Indian Ocean, although predictive skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can be especially high in the Southern Ocean.

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    S2 also had little seasonality in its forecast skill (van Oldenburgh et al. 2005) though it’s skill level was lower than S3.

References

  1. Anderson D, Stockdale T, Balmaseda M, Ferranti L, Vitart F, Molteni F, Doblas-Reyes F, Mogenson K, Vidard A (2007) Development of the ECMWF seasonal forecast System 3. ECMWF Technical Memoranda 503

  2. Balmaseda M, Anderson D (2009) Impact of initialization strategies and observations on seasonal forecast skill. Geophys Res Lett 36:L01701. doi:10.1029/2008GL035561

    Article  Google Scholar 

  3. Balmaseda MA, Dee D, Vidard A, Anderson DLT (2005) A multivariate treatment of bias for sequential data assimilation: application to the tropical oceans. Q J Roy Meteorol Soc 133:167–179

    Article  Google Scholar 

  4. Balmaseda MA, Vidard A, Anderson D (2008) The ECMWF ORA-S3 ocean analysis system. Mon Wea Rev 136:3018–3034

    Google Scholar 

  5. Balmaseda MA, Ferranti L, Molteni F, Palmer TN (2010) Impact of 2007 and 2008 Arctic ice anomalies on the atmospheric circulation: implications for long-range predictions. Q J Roy Meteor Soc 136:1655–1664. doi:10.1002/qj.661

    Google Scholar 

  6. Buizza R, Miller M, Palmer TN (1999) Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Q J Roy Meteor Soc 125:1908–2887

    Article  Google Scholar 

  7. Cane MA, Zebiak SE, Dolan SC (1986) Experimental forecasts of El Nino. Nature 321:827–832

    Article  Google Scholar 

  8. Doblas-Reyes FJ, Hagedorn R, Palmer TN, Morcrette J-J (2006) Impact of increasing greenhouse gas concentrations in seasonal ensemble forecasts. Geophys Res Lett 33:L07708. doi:10.1029/2005GL025061

    Article  Google Scholar 

  9. Hurrell J, Meehl GA, Bader D, Delworth TL, Kirtman B, Wielicki B (2009) A unified modeling approach to climate system prediction. Bull Am Meteorol Soc 90:1819–1832

    Article  Google Scholar 

  10. Jin EK, Kinter JL III, Wang B, Park C-K, Kang I-S, Kirtman BP, Kug J-S, Kumar A, Luo J-J, Schemm J, Shukla J, Yamagata T (2008) Current status of ENSO prediction skill in coupled ocean-atmosphere models. Clim Dyn 31:647–664. doi:10.1007/s00382-008-0397-3

    Article  Google Scholar 

  11. Luo J-J, Behera S, Shingu S, Yamagata T (2005) Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts. J Clim 18:4474–4497

    Article  Google Scholar 

  12. Luo J-J, Masson S, Behera SK, Yamagata T (2008) Extended ENSO predictions using a fully coupled ocean-atmosphere model. J Clim 21:84–93

    Article  Google Scholar 

  13. McPhaden MJ (2003) Tropical Pacific Ocean heat content variations and ENSO persistence barriers. Geophys Res Lett 30:1480. doi:10.1029/2003GL016872

    Google Scholar 

  14. Meehl GA, Arblaster JM, Branstator GW, van Loon H (2008) A coupled air-sea response mechanism to solar forcing in the Pacific region. J Clim 21:2883–2897

    Article  Google Scholar 

  15. Palmer TN, Alessandri A, Andersen U, Cantelaube P, Davey M, Délécluse P, Déqué M, Díez E, Doblas-Reyes FJ, Feddersen H, Graham R, Gualdi S, Guérémy J-F, Hagedorn R, Hoshen M, Keenlyside N, Latif M, Lazar A, Maisonnave E, Marletto V, Morse AP, Orfila B, Rogel P, Terres J-M, Thomson MC (2004) Development of a European multi-model ensemble system for seasonal to inter-annual prediction (DEMETER). Bull Am Meteorol Soc 85:853–872

    Article  Google Scholar 

  16. Palmer TN, Doblas-Reyes FJ, Weisheimer A, Rodwell MJ (2008) Toward seamless prediction: calibration of climate change projections using seasonal forecasts. Bull Am Meteor Soc 89:459–470

    Article  Google Scholar 

  17. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407. doi:10.1029/2002JD002670

    Article  Google Scholar 

  18. Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Clim 15:1609–1625

    Article  Google Scholar 

  19. Saha S, Nadiga S, Thiaw C, Wang J, Wang W, Zhang Q, Van den Dool HM, Pan HL, Moorthi S, Behringer D, Stokes D, Peña M, Lord S, White G, Ebisuzaki W, Peng P, Xie P (2006) The NCEP climate forecast system. J Clim 19:3483–3517

    Article  Google Scholar 

  20. Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363

    Google Scholar 

  21. Stockdale TN (1997) Coupled ocean atmosphere forecasts in the presence of climate drift. Mon Wea Rev 125:809–818

    Article  Google Scholar 

  22. Stockdale TN, Anderson DLT, Alves JO, Balmaseda MA (1998) Global seasonal rainfall forecasts with a coupled ocean atmosphere model. Nature 392:370–373

    Article  Google Scholar 

  23. Stockdale TN, Balmaseda MA, Vidard A (2006) Tropical Atlantic SST prediction with coupled ocean-atmosphere GCMs. J Clim 19:6047–6061

    Article  Google Scholar 

  24. Tompkins AM, Feudale L (2010) Seasonal ensemble predictions of West African monsoon precipitation in the ECMWF System 3 with a focus on the AMMA special observing period in 2006. Weather Forecast 25:768–788

    Article  Google Scholar 

  25. van Oldenburgh GJ, Balmaseda M, Ferranti L, Stockdale T, Anderson D (2005) Did the ECMWF seasonal forecast model outperform statistical ENSO forecast models over the last 15 years? J Clim 18:3240–3249

    Article  Google Scholar 

  26. Vialard J, Vitart F, Balmaseda M, Stockdale T, Anderson D (2005) An ensemble generation method for seasonal forecasting with an ocean-atmosphere coupled model. Mon Weather Rev 133:441–453

    Article  Google Scholar 

  27. Wang B, Lee J-Y, Kang I-S, Shukla J, Park C-K, Kumar A. Schemm J, Cocke S, Kug J.-S, Luo J-J, Zhou T, Wang B, Fu X, Yun W-T, Alves O, Jin EK, Kinter J. Kirtman B, Krishnamurti T, Lau NC, Lau W, Liu P, Pegion P, Rosati T, Schubert S, Stern W, Suarez M, Yamagata T (2009) Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004). Clim Dyn. doi: 10.1007/s00382-008-0460-0

  28. WCRP (2005) The world climate research programme strategic framework 2005–2015: coordinated observation and prediction of the earth system (COPES). WCRP-123, WMO/TD-No. 1291, 65

  29. Webster PJ, Moore A, Loschnigg J, Leban M (1999) Coupled ocean-atmosphere dynamics in the Indian Ocean during 1997–98. Nature 401:356–360

    Article  Google Scholar 

  30. Weisheimer A (2005) SST and wind stess perturbations for seasonal and annual simulations. Available from: http://www.ecmwf.int/research/EU_projects/ENSEMBLES/exp_setup/ini_perturb/index.html

  31. 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:L21711. doi:10.1029/2009GL040896

    Article  Google Scholar 

  32. Zwiers FW, von Storch H (1995) Taking serial correlation into account in tests of the mean. J Clim 8:336–351

    Article  Google Scholar 

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Acknowledgments

The improvements in the atmospheric model used in S3 are due to dedicated work by many individuals at ECMWF.

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Correspondence to Timothy N. Stockdale.

Appendix

Appendix

If a specific season and lead time have been selected because they give the best results, it is necessary to ensure that what is seen is not just a sampling effect. As a simple combinatorial test, define a “poor” forecast as one in which the mean absolute error for the first 3 months exceeds 0.15° C. If we consider all the S3 forecasts from 1961 to 2009 present (49 years), a total of 23 of them are poor, all in the years prior to 1994. If it is assumed, as a null hypothesis, that the poor forecasts are equally likely to occur in any year, the chances of the last 16 years including zero occurrences of the 23 poor forecasts which exist in the sample is (33/49)*(32/48)* …(11/27) or (33!/49!)*(26!/10!) = 0.0000016. Using this method of looking for a change in skill (defining thresholds, counting poor forecasts, and applying combinatorial tests to get a p value), the following selections have been made: the best of 12 start months, the best of 7 possible forecast ranges, and the best of (say) 5 plausible thresholds for the definition of a poor forecast. This multiplies to 420 possible tests for a change in skill, of which this is the highest scoring. Under the null hypothesis, the chances of such a high score are only 0.0007.

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Stockdale, T.N., Anderson, D.L.T., Balmaseda, M.A. et al. ECMWF seasonal forecast system 3 and its prediction of sea surface temperature. Clim Dyn 37, 455–471 (2011). https://doi.org/10.1007/s00382-010-0947-3

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Keywords

  • Indian Ocean Dipole
  • Forecast Skill
  • Anomaly Correlation Coefficient
  • Ensemble Spread
  • Ocean Initial Condition