IRI Multi-model Probability Forecasts for Precipitation and Temperature:

1. IRI Multi-model Probability Forecasts for Precipitation and Temperature: Oct-Nov-Dec 2010 and Jan-Feb-Mar 2011 Tony Barnston International Research Institute for Climate and Society

2. Observed Precipitation Jun-Jul-Aug 2010 Normal based on 1971-2000

3. Jul-Aug-Sep 2010

6. Jun-Jul-Aug 2010 Observed Temperature Normal based on 1971-2000

7. 95F 86F 77F 68F Jul-Aug-Sep 2010 104F 95F 86F 77F 68F

9. Departure from normal La Nina

10. Departure from normal La Nina

11. A strong key to seasonal climate forecasting: Departures from normal of Sea Surface Temperature (SST), especially in the tropics, influence seasonal climate.

12. Stronger El Niño El Nino Aug La Nina StrongerLa Niña Nino3.4

13. Tropical Pacific oceanic and atmospheric conditions: Normallyduring El Nino

14. Circulation pattern over North America for El Nino and La Nina El NinoLa Nina

15. Lead time and forecast skill Temperature and precipitation forecasts Weather forecasts (from initial conditions) Forecast Skill (correlation) Seasonal forecasts (from boundary conditions like SST) good fair poor zero 10 20 30 60 80 90 Forecast lead time (days)

16. El Nino La Nina El Nino La Nina El Nino/ La Nina situation over the last 12 months El Nino La Nina El Nino La Nina ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

17. El Nino

18. La Nina

19. La Nina

20. Nino3.4 SST anomaly predictions from September

21. from September

22. La Nina Forecast for late autumn 2010

23. How do we make a seasonal climate forecast?

24. IRI DYNAMICAL CLIMATE FORECAST SYSTEM 2-tiered 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 TROP. PACIFIC: THREE (multi-models, dynamical and statistical) TROP. ATL, INDIAN (ONE statistical) EXTRATROPICAL (damped persistence) 12 Forecast SST Ensembles 3/6 Mo. lead 24 24 30 12 30 30 GFDL has 10 to PSST

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

26. A typical shift of the odds in a seasonal precipitation forecast Forecast distribution: tilt of the odds toward “above” Historical distribution P R O B A B I L I T Y Below Normal Above Normal Near Normal Historically, the probabilities of “above” and “below” are 0.33. Shifting the mean by half a standard-deviation (to 69%ile) and reducing the variance by 20% changes the probability of below to 0.15 and of above to 0.53.

35. El Nino and Rainfall and Temperature

37. Correlation Skill for NINO3 forecasts Predictability of El Nino and La Nina – and consequently, predictability of the climate deviations from normal over parts of the world Skill bonus Northern Spring barrier useless low fair good Correlation between forecast and obs

38. Atlantic Hurricane Tracks for the 1982 season An El Nino was in prog- ress.

39. 2004 (El Nino) From http://weather.unisys.com

40. 2005 (neutral)

41. 2006 (El Nino)

42. 2007 (La Nina)

43. 2008 (neutral)

44. 2009 (El Nino)

45. 2010 (La Nina) [incomplete]

46. What should be defined as the NORMAL climate? Traditionally we have used a recent 30-year period. (Currently it is 1971-2000.) But if the “normal” is changing, using 30 past years gives us an old climate. How can we define the average current climate?