1. 10 9 24 10 24 10 FORECAST SST TROP. PACIFIC (multi-models, dynamical and statistical) TROP. ATL, INDIAN (statistical) EXTRATROPICAL (damped persistence) GLOBAL ATMOSPHERIC MODELS ECPC(Scripps) ECHAM4.5(MPI) CCM3.x(NCAR) NCEP(MRF9) NSIPP(NASA) COLA2.x Forecast SST Ensembles 3/6 Mo. lead Persisted SST Ensembles 3 Mo. lead IRI DYNAMICAL CLIMATE FORECAST SYSTEM POST PROCESSING MULTIMODEL ENSEMBLING PERSISTED GLOBAL SST ANOMALY 2-tiered OCEAN ATMOSPHERE

2. Empirical tools also used Mason & Goddard, 2001, Bull.Amer.Meteor.Soc.

3. 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 | || ||| ||||.| || | | || | | |. | | | | | | | | | | Rainfall Amount (mm) Below| Near | Below| Near | Above  Below| Near | The tercile category system: Below, near, and above normal (30 years of historical data for a particular location & season) Forecasts of the climate Data: 33% 33% 33% Probability:

4. OCT | Nov-Dec-Jan* Dec-Jan-Feb Jan-Feb-Mar Feb-Mar-Apr Monthly issued probability forecasts of seasonal global precipitation and temperature *probabilities of extreme (low and high) 15% issued also Four lead times - example:

5. Atmospheric General Circulation Models Used in the IRI's Seasonal Forecasts, for Superensembles Name Where Model Was Developed Where Model Is Run NCEP MRF-9 NCEP, Washington, DC QDNR, Queensland, Australia ECHAM 4.5 MPI, Hamburg, Germany IRI, Palisades, New York NSIPP NASA/GSFC, Greenbelt, MD NASA/GSFC, Greenbelt, MD COLA COLA, Calverton, MD COLA, Calverton, MD ECPC SIO, La Jolla, CA SIO, La Jolla, CA CCM3.x NCAR, Boulder, CO NCAR, Boulder, CO (forthcoming) GFDL, Princeton, NJ GFDL or IRI Sources of the Global Sea Surface Temperature Forecasts Tropical PacificTropical AtlanticIndian OceanExtratropical Oceans NCEP CoupledCPTEC Statistical IRI StatisticalDamped Persistence LDEO Coupled Constr Analogue

6. Ranked Probability Skill Score (RPSS) 3 RPS fcst =  (Fcst icat – Obs icat ) 2 icat =1 icat ranges from 1 (below normal) to 3 (above normal) RPSS = 1 - (RPS fcst / RPS clim )

7. Skill of Model Hindcasts Using Observed SST

9. RPSS Skill of Individual Model Simulations: JAS 1950-97 Precipitation

10. Real-time Forecast Skill

11. Real-time Forecast Skill

12. 1998-2001 Reliability Diagram

13. Reliability Diagram longer “AMIP” period from Goddard et al. 2003 (EGS-AGU-EUG)

14. Next: Bayesian weighting method for multi-model ensembling IRI uses two multi-model ensembling methods: Bayesian method Canonical variate method Both methods analyze historical model performance in responding to observed SST.

15. JAS 2003: Six GCM Precip.Forecasts

16. Likelihood k * represents the category that was observed at time t "a multi-year product of the probabilities that were hindcast for the category that was observed..." Dirichlet distribution is appropriate for a multinomial process (i.e. terciles) –Combination of a and b is also Dirichlet with parameter a+b Rajagopalan et al., 2002:. Mon. Wea. Rev.

17. Ways to compute weights Effective sample sizes Individual GCM + Climatology: 1 Multiple GCMs + Climatology: 2

18. Combine the individual weights from several models using a Two-Stage scheme: 3 (a) For each model in turn: (b) For the pooled ensemble thus created: Final weights:

19. Model Weights – “Two-Stage” JAS

20. Model Weights – Two-Stage, Cross-Validated (XV) JAS

21. Climatological Weights JAS

22. Combination Forecasts of Jul-Sep Precipitation One-StageTwo-Stage Two-Stage, xv, Spatial SmoothingTwo-Stage Cross-Validated JAS 2003

23. Canonical variate method: A kind of discriminant analysis. Input: ensemble mean, ensemble spread, ensemble skewness Algorithm finds linear combinations of predictors leading to each catergorical (tercile) result. Differences among the sets of predictor weights are maximized.

24. Multi-model ensembling of dynamical predictions appears to be slightly superior to currently used statistical tools at NOAA/NCEP/CPC

25. temperature

26. precipitation

27. Most important current needs in IRI forecast system: Improvement of SST prediction in 2-tiered system 1) Tropical Pacific: better model consolidation, with development of multiple evolving scenarios 2) Outside tropical Pacific:“Smarter” persistence scenario, or multivariate statistical model (e.g. CCA) Use of 1-tiered climate model for some regions Slab ocean model in locations where 2-tiered system fails (Indian Ocean, far west Pacific and tropical Atlantic oceans)