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

2. ENSO
Quick
Look:
Done
Jointly
with
CPC

3. 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

4. IRI DYNAMICAL CLIMATE FORECAST SYSTEM
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

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