IRI’s ENSO and Climate Forecasts Tony Barnston, Shuhua Li,

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Presentation transcript:

IRI’s ENSO and Climate Forecasts Tony Barnston, Shuhua Li, and Mike Tippett

ENSO Quick Look: Done Jointly with CPC

IRI’s climate forecast system SST predicted first, climate predicted using the SST prediction. We mainly use other peoples’ SST forecasts. 2 AGCMs run at IRI, 4 at other institutions Predictions of each AGCM statistically corrected for spatial biases, based on hindcasts Corrected forecasts are pooled to form 1 forecast Probability forecasts based on standard error of the system in hindcasts

IRI DYNAMICAL CLIMATE FORECAST SYSTEM - - - - - 2-tiered - - - - - - - - OCEAN ATMOSPHERE (tier 1) (tier 2) PERSISTED GLOBAL SST ANOMALY GLOBAL ATMOSPHERIC MODELS ECPC(Scripps) ECHAM4.5(MPI) CCM3.6(NCAR) GFDL NCEP(CFSv2) COLA2.6 Persisted SST Ensembles 3 Mo. lead 10 POST PROCESSING (CCA) for each model individually MULTIMODEL ENSEMBLING (equal weights) 24 24 10 FORECAST SST (3 scenarios) TROP. PACIFIC: (multi-models, dynamical and statistical) TROP. ATL, INDIAN: ( multi-models, dynamical and statistical) EXTRATROPICAL: (damped persistence) 12 Forecast SST Ensembles 0-3 Mo. lead 24 24 30 24 36 Forecast PDF→ tercile probab

Temperature Feb-Mar-Apr 2013 x Probability of exceeding climatol average . in northern Brazil in northern Brazil .