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Statistical Downscaling and Circulation Classification in the STARDEX Project

Learn about the experience of the STARDEX project in statistical downscaling and circulation classification. Explore robustness criteria, choices to be made, precipitation/weather regimes, and predictor validation. Discover the potential degradation of performance when using predictors from GCMs and the comparison of downscaled changes with RCM changes.

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Statistical Downscaling and Circulation Classification in the STARDEX Project

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

  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., 12-20 classes)

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

  5. Fuzzy rule optimisation technique 12 CPs defined from SLP (Andras Bardossy) CP02 CP09

  6. 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), 1979-1993 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 (Z@700), clusters, transition probabilities • Inter-relationships: Good correspondence for CPs conditional to heavy precipitation. Frequency errors (Sampling?). 30% 35% 35% HadAM3P 34% 29% 37% 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 http://www.cru.uea.ac.uk/projects/stardex/

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