1. Circulation classification and statistical downscaling – the experience of the STARDEX project Clare Goodess* & the STARDEX team *Climatic Research Unit, UEA, Norwich, UK

2. Robustness criteria for statistical downscaling
Appropriate spatial scale (physics/GCM)
Data widely/freely available (obs/GCM)

3. Choices to be made Surface and/or upper air
Continuous vs discrete (CTs) predictors
Circulation only or include atmospheric humidity/stability etc
Spatial domain
Lags – temporal and spatial
Number of predictors
Few PC/sEOFs or clusters (e.g., 3-5) vs CT classifications (e.g., classes)

4. Precipitation/Weather Regimes French Alpes Maritimes
Guy Plaut, CNRS-INLN
Greenland Anticyclone Sole Cyclone
(left) & (right)

5. 12 CPs defined from SLP (Andras Bardossy)
Fuzzy rule optimisation technique
12 CPs defined from SLP (Andras Bardossy)
CP02
CP09
Winter
Spring
Summer
Autumn
CP02
CP09
CP11
Cp11
Wet days (%)
Mean prec.(mm/day)
Above prec90 (%)
0.281
2.13
0.452
0.144
0.976
0.180
0.125
0.610
0.089
0.238
1.456
0.263
0.142
0.896
0.176
0.130
0.791
0.140
0.191
1.349
0.188
0.112
0.858
0.132
1.149
0.177
0.268
2.016
0.348
0.146
1.067
0.187
0.967
0.159
Winter
Spring
Summer
Autumn
CP02
CP09
CP11
Cp11
Positive trend
Positive significant
Negative trend
Negative significant
566
129 45
309 4
302 11
100 1
511 48
221 6
390 25
464 37
147 3
406 13
205
304 7
307
212
399 26
308 9
303
171
440
369 2
242 81
530 73

6. conditioned to wet and dry CPs
Probability of precipitation at station 75103
conditioned to wet and dry CPs
Andras Bardossy, USTUTT-IWS

7. Heavy winter rainfall and links with North Atlantic Oscillation/SLP
CC1: Heavy rainfall (R90N) CC1: mean sea level pressure
Malcolm Haylock, UEA/STARDEX

8. Emilia Romagna, N Italy
NCEP CDD (DJF),
ARPA-SMR
AUTH

9. HadAM3P: predictor validation
UEA and ARPA-SMR:
Principal Components of MSLP, Z500, T850
Good correspondence in # of significant components and explained variance (seasonal variation).
Differences in patterns larger in summer. (Sampling uncertainty?)

10. HadAM3P: predictor validation
CNRS-INLN:
Daily CPs clusters, transition probabilities
Inter-relationships: Good correspondence for CPs conditional to heavy precipitation. Frequency errors (Sampling?).
35%
30%
35%
HadAM3P
37%
34%
29%
NCEP/OBS

11. HadAM3P: predictor validation
U-STUTT:
Lower-tropospheric (westerly) moisture flux overestimated in winter and underestimated in summer.
DJF
JJA

12. Will performance be degraded when predictors are taken from GCMs?
How do the statistically-downscaled changes in extremes compare with RCM changes?
Are the observed predictor/ predictand relationships reproduced by RCMs - & are they stationary?
Iberia (16 stations): Spearman correlations for each of 6 models & season averaged across 7 rainfall indices – NCEP predictors