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

2. 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 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.

3. 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% % %
Below| Near |
Below| Near | Above 
Data:
| || ||| ||||.| || | | || | | | . | | | | | | | | | |
Rainfall Amount (mm)
(30 years of historical data for one station and season)

4. Example of a climate forecast with a strong probability shift
10% % %
Below| Near |
Below| Near | Above 
Rainfall Amount (mm)

5. Example of a climate forecast with a weak probability shift
25% % %
Below| Near |
Below| Near | Above 
Rainfall Amount (mm)

6. Example of a climate forecast with no probability shift
33% % %
Below| Near |
Below| Near | Above 
Rainfall Amount (mm)

7. 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

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

9. JJA SST forecast

10. JAS SST forecast

11. P(wet) during La Nina

12. P(wet) during El Nino

13. JJA precipitation tercile

14. JAS precipitation tercile

15. 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)

16. 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

17. Coupled multi-model JJA precipitation

18. Coupled multi-model JAS precipitation

19. 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%