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
Observed Precipitation
Normal based on

3. Jul-Aug-Sep
2010

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

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:
Normally during El Nino

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

15. Lead time and forecast skill
Temperature and precipitation forecasts
Weather forecasts (from initial conditions)
Forecast Skill
(correlation)
good
fair
poor
zero
Seasonal forecasts (from boundary conditions like SST) 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% % %
Below| Near |
Below| Near | Above 
Data:
| || ||| ||||.| || | | || | | | . | | | | | | | | | |
Rainfall Amount (tenths of inch)
(30 years of historical data for a particular location & season)

26. Historical distribution Forecast distribution: tilt of the odds
A typical shift of the odds in a seasonal precipitation forecast
Historical distribution
Forecast distribution:
tilt of the odds
toward “above” P R O B A I L T Y
Below
Normal
Above
Normal
Near
Normal
Historically, the probabilities of “above” and “below” are 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 seasonEl
Nino
was in
prog-
ress.

39. 2004 (El Nino)
From

40. 2005 (neutral)

41. 2006 (El Nino)

42. 2007 (La Nina)

43. 2008 (neutral)

44. 2009 (El Nino)

45. 2010 (La Nina)
[incomplete]

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

53. From NASA/GISS (Goddard Institute for Space Studies)
Where has it been warming at the highest rate?

57. Phoenix, Arizona
urban
growth ↔
Yuma, Arizona