IRI forecast April 2010 SASCOF-1 Michael Tippett Tony Barnston Dave DeWitt Shua Li

What probabilistic forecasts represent Near-Normal Below Normal Above Normal Historical distribution (climatological distribution) (33.3%, 33.3%, 33.3%) FREQUENCY Forecast distribution (15%, 32%, 53%) The uncertainty can be expressed (quantitatively) in a number of ways: 1) Probabilities of discrete events Confidence level is varied. Interval length is fixed. 2) Error bars / confidence intervals Confidence level is fixed. Interval length is varied. 3) Probability distribution on a continuous scale Breakpoints of categories are determined by historical observations. The probabilities of this distribution are the climatological probabilities. Forecast distribution (say of the ensemble members at a point, or over a region) represent a shift in the range of possibilities. Now categorical probabilities are not equal – they differ from climatology. NORMALIZED RAINFALL Historically, the probabilities of above and below are 0.33. Shifting the mean by one half standard deviation and reducing the variance by 20% changes the probability of below to 0.15 and of above to 0.53.

Below| Near | Below| Near | Above  Abbreviating a predicted shift in the probability distribution: Terciles (Below normal,, near normal, above normal) Climatological probabilities = 1/3 33% 33% 33% Below| Near | Below| Near | Above  Data: | || ||| ||||.| || | | || | | | . | | | | | | | | | | 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 Rainfall Amount (mm) (30 years of historical data for one station and season)

Example of a climate forecast with a strong probability shift 10% 25% 65% Below| Near | Below| Near | Above  0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 Rainfall Amount (mm)

Example of a climate forecast with a weak probability shift 25% 35% 40% Below| Near | Below| Near | Above  0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 Rainfall Amount (mm)

Example of a climate forecast with no probability shift 33% 33% 33% Below| Near | Below| Near | Above  0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 Rainfall Amount (mm)

IRI DYNAMICAL CLIMATE FORECAST SYSTEM 2-tier OCEAN ATMOSPHERE GLOBAL ATMOSPHERIC MODELS ECPC(Scripps) ECHAM4.5(MPI) CCM3.6(NCAR) NCEP(MRF9) NSIPP(NASA) COLA2 GFDL PERSISTED GLOBAL SST ANOMALY Persisted SST Ensembles 3 Mo. lead 10 POST PROCESSING MULTIMODEL ENSEMBLING 24 24 10 FORECAST SST SCENARIOS Multimodel mean from CFS + LDEO + CA Positive and negative perturbations based on past performance. 12 Forecast SST Ensembles 3/6 Mo. lead 24 model weighting 24 30 12 30 30

SST forecast Multimodel mean from CFS + LDEO + CA Positive and negative perturbations based on past performance.

JJA SST forecast

JAS SST forecast

P(wet) during La Nina

P(wet) during El Nino

JJA precipitation tercile

JAS precipitation tercile

Coupled MME Forecast System Shuhua Li, David G. DeWitt, Donna Lee Four coupled GCMs MME with pooling approach Smoothing and damping as for 2-tier system Different schedule (after the 15th)

Summary of the 4 CGCMs Model Resolution Ensemble Remarks T42 12 T62 15 ECHAM-GML T42 12 Semi-coupled ECHAM-MOM3 Anomaly-coupled ECHAM-MOM3-DIR2 Fully-coupled NCEP-CFS T62 15

Coupled multi-model JJA precipitation

Coupled multi-model JAS precipitation

Summary SST forecast La Nina Historically associated with shifts in probability Not always 1997 (El Nino),1999 (La Nina) Very slight enhancement of the above normal tercile category probability ~40% instead of 33%