Circulation classification and statistical downscaling – the experience of the STARDEX project Clare Goodess* & the STARDEX team *Climatic Research.

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Presentation transcript:

Circulation classification and statistical downscaling – the experience of the STARDEX project Clare Goodess* & the STARDEX team *Climatic Research Unit, UEA, Norwich, UK http://www.cru.uea.ac.uk/projects/stardex/

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

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., 12-20 classes)

Precipitation/Weather Regimes French Alpes Maritimes Guy Plaut, CNRS-INLN Greenland Anticyclone Sole Cyclone 1971-1983 (left) & 1983-1995 (right)

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

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

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

Emilia Romagna, N Italy NCEP CDD (DJF), 1979-1993 ARPA-SMR AUTH

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?)

HadAM3P: predictor validation CNRS-INLN: Daily CPs (Z@700), 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

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

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 http://www.cru.uea.ac.uk/projects/stardex/