ECMWF seasonal forecast system 3 and its prediction of sea surface temperature
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.
KeywordsIndian Ocean Dipole Forecast Skill Anomaly Correlation Coefficient Ensemble Spread Ocean Initial Condition
The improvements in the atmospheric model used in S3 are due to dedicated work by many individuals at ECMWF.
- 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 503Google Scholar
- Balmaseda MA, Vidard A, Anderson D (2008) The ECMWF ORA-S3 ocean analysis system. Mon Wea Rev 136:3018–3034Google Scholar
- 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 85:853–872CrossRefGoogle Scholar
- Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363Google Scholar
- 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
- 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, 65Google Scholar
- 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
- 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 CrossRefGoogle Scholar