1. Synthesis of Seasonal Prediction Skill
– In the Context of Climate Change
T.N.Palmer
ECMWF
WCRP/CLIVAR

2. The overarching objectives of WCRP are:
To determine the predictability of climate
To determine the effect of human activities on climate

3. Sir David King
“There is no bigger problem than climate change. The threat is quite simple, it’s a threat to our civilization.”

4. The Importance of Seasonal Prediction – In the Context of Climate Change
Climate-change is expected to lead to more extreme climate variability.
The provision to society of reliable forecasts of specific occurrences of extreme climate fluctuations on seasonal timescales should be an essential element in any strategy on climate adaptation.
Seasonal forecasts can play a key role in validating probabilistic projections of climate change.

5.  Development of a European Multi-Model Ensemble System for
Seasonal to Interannual Prediction

6. Transition to Operations
 


7. Ensemble Reliability: 2m-Temp.>0
0.049
0.902
0.147
0.058
0.904
0.151
0.099
0.923
0.176
-0.007
0.886
0.107
-0.055
0.838
0.068
0.903
0.164
0.222
0.994
0.227
0.075
0.921
0.153
Demeter multi-model

8. DEMETER End-to-end Forecast System
Seasonal
forecast
………… 1 2 3 4 62 63
…………
Downscaling 1 2 3 4 62 63
Application
model
………… 1 2 3 4 62 63
non-linear transformation
Probability of Precip & Temp…
Probability of Malaria Incidence/Crop yield

9. Applications Where DEMETER Predictions Have Shown Skill
Malaria incidence
Crop yield prediction
Hydrology
Energy demand
Hurricane forecasts

10. Study reliability of precipitation based on Giorgi Regions
Regions used in the analysis presented in this work:
NAU North Australia
SAU South Australia
AMZ Amazons
SSA Southern South America
CAM Central America
WNA West North America
CAN Central North America
ENA East North America
ALA Alaska
GRL Greenland
MED Mediterranean
NEU North Europe
WAF West Africa
EAF East Africa
SAF South Africa
SAH Sahara
SEA South East Asia
EAS East Asia
SAS South Asia
CAS Central Asia
TIB Tibet
NAS North Asia
Study reliability of precipitation based on Giorgi Regions

11. Southern South America
Region
2m Temperature
Precipitation
JJA
DJF
ET-(x)
ET+(x)
Ep-(x)
Ep+(x)
Australia
10.7
10.1
1.3
-0.4
-1.3
-2.5
-3.1
-3.6
Amazon Basin
14.4
9.1
23.4
25.7
2.2
2.1
9.5
8.9
Southern South America
8.5
8.2
-1.2
1.8
7.8
5.0
-0.7
-2.8
Central America
12.1
9.9
14.8
6.3
2.6
8.7
Western North America
6.5
7.7
3.9
2.3
3.2
5.5
-0.6
0.0
Central North America
-4.1
-7.5
0.3
-1.8
-7.0
3.7
5.3
Eastern North America
0.6
5.7
4.1
-4.5
-8.3
9.2
6.0
Alaska
3.0
-0.1
2.4
4.9
Greenland
3.6
4.2
8.0
5.8
-1.4
-0.5
-2.1
-2.0
Mediterranean Basin
7.6
0.1
1.6
-0.9
Northern Europe
-4.4
-4.2
4.8
2.9
-1.0
1.9
-1.1
Western Africa
10.4
11.8
18.1
17.2
-1.6
-4.9
-3.5
Eastern Africa
12.6
13.3
10.3
-0.3
1.2
Southern Africa
5.6
15.9
15.7
0.7
5.4
Sahara
7.4
6.9
-2.6
-4.8
-2.7
Southeast Asia
5.9
14.7
3.4
2.5
East Asia
4.7
7.9
10.8
10.0
South Asia
13.1
8.6
-3.0
2.0
0.5
Central Asia
0.8
3.8
Tibet
North Asia
Brier Skill Score for Lower/Upper tercile ( )
Temperature and Precipitation

12. DEMETER Reliability of Seasonal Precip for selected Giorgi regions
Eastern North America dry DJF
South Asia wet JJA
DEMETER Reliability of Seasonal Precip for selected Giorgi regions
Amazon Basin wet DJF
Central North America wet JJA
Northern Europe dry DJF
Southeast Asia dry JJA

13. Blocking frequency in climate models
Northern Hemisphere blocking frequency for DEMETER hindcasts
November start, , 9-member ensembles
January (third month)
ERA40 Single models
CNRM
ECMWF
Met Office

14. Potential-Vorticity Perspective of Blocking

15. T255
T95

16. DEMETER provides a baseline for future seasonal forecast systems (TFSP, Stochastic Physics, Perturbed Parameters) to beat! What is our strategy for developing future forecast systems with minimal deficiencies in blocking, monsoons etc? How are we to execute such a strategy?

17. Change in Probability of Lower/Upper Tercile Precip Seasons Under Climate Change
Change in probability of dry (lower tercile) DJF
Change in probability of wet (upper tercile) JJA
From IPCC AR4 multi-model ensembles

18. How Trustworthy Are These Precipitation Projections?
In the light of known systematic deficiencies in climate models

19. Does the UK need a national water grid. Cost £16billion?

20. Blocking 2005/6
Climatological blocking
European droughts occur because of anomalous Euro/Atlantic blocking activity

21. “The WCRP strategic framework points to the need for seamless predictions spanning timescales from weeks to decades.”

22. The chain is only as strong as its weakest link.

23. Use Seasonal Forecasts to quantify the strength of links 1-3

24. The Calibration Technique
-use calibration to discount AR4 raw probabilities (Palmer Doblas-Reyes Weisheimer Rodwell, submitted to BAMS)

25. Eastern North America dry DJF
DEMETER Reliability
AR4 Uncalibrated
AR4 Calibrated

26. Amazon Basin wet DJF DEMETER Reliability AR4 Calibrated
AR4 Uncalibrated

27. Northern Europe dry DJF
DEMETER Reliability
AR4 Uncalibrated
AR4 Calibrated

28. South Asia wet JJA
DEMETER Reliability
AR4 Uncalibrated
AR4 Calibrated

29. Central North America wet JJA
DEMETER Reliability
AR4 Uncalibrated
AR4 Calibrated

30. Southeast Asia dry JJA AR4 Calibrated AR4 Uncalibrated
DEMETER Reliability

31. Cost of Water Grid = C
Economic and Social Losses from water shortages = L
Invest in water grid if probability of drought >C/L=Pcrit
If Puncalibrated(drought)<Pcrit and Pcalibrated(drought)<Pcrit
then seasonal forecasts will impact quantitatively on the decision whether or not to invest in the water grid.

32. Conclusions
DEMETER provides baseline of skill for future systems (TFSP, Stochastic Physics, Perturbed Parameters) to beat.
Seasonal forecasts are important in their own right as part of a climate adaptation strategy
In a seamless system seasonal forecasts can be used to discount probabilistic projections of climate change in regions of forecast unreliability. Important for decisions on infrastructure investment for climate adaptation.
We as a community need to articulate a strategy to reduce existing model deficiencies and corresponding forecast unreliability.