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Objective / Approach

D13 Summary: Recommendations on the most reliable predictor variables and evaluation of inter-relationships. Objective / Approach. Qualify the accuracy of predictors –> Criterion for the „robust-ness“ in methods. (D16) Accuracy of inter-relationships –> Benchmark to assess stationarity

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Objective / Approach

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  1. D13 Summary:Recommendations on the most reliable predictor variables and evaluation of inter-relationships

  2. Objective / Approach • Qualify the accuracy of predictors –> Criterion for the „robust-ness“ in methods. (D16) • Accuracy of inter-relationships –> Benchmark to assess stationarity • Compare GCM against NCEP • Seasonal mean • Daily standard deviation • Inter-relationships

  3. http://www.iac.ethz.ch/staff/freich/download/STARDEX/D13_web/http://www.iac.ethz.ch/staff/freich/download/STARDEX/D13_web/

  4. Results: MSLP HadAM3P NCEP HadAM3P-NCEP DJF JJA

  5. Comparison to earlier Model Versions Icelandic Low: HadCM2: too shallow (10 hPa) HadAM3P: too deep (4 hPa) NW Europe Westerlies: HadCM2: too weak HadAM3P: too strong

  6. Results: Summer T850 HadAM3P NCEP HadAM3P-NCEP T850 St.dev(T850)

  7. Results: Summer Q850 (g/kg) HadAM3P NCEP HadAM3P-NCEP Q850 St.dev(Q850)

  8. Results for Specific Predictors • AUTH: • Good representation of frequency in cyclonic and anticyclonic CPs. in Greece. • Cyclones travel too far south. • Thickness errors in summer gives too large within-CP variability. • ADGB: • Geopotential and geostrophic wind pdfs are realistic • Potential problems with relative humidity in summer avoided by choice of northern grid point. • DMI: • Vorticity based on MSLP is noisy in NCEP. • Use grid-point MSLP as predictor instead.

  9. Results for Specific Predictors • ETH: • GCM captures coarse pattern of P intensity / frequency in Alps better than NCEP. • No obvious effects from GCM circulation errors. • U-STUTT: • Lower-tropospheric (westerly) moisture flux overestimated in winter and underestimated in summer. JJA DJF

  10. Results for Specific Predictors • 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?)

  11. Results for Specific Predictors • 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

  12. Conclusions • In winter HadAM3P: • represents continental-scale predictors better than earlier model versions. • has too strong westerlies and underestimates variance (cyclone activity). Error compensation in downscaling ? • In summer HadAM3P: • has large biases for lower tropospheric temperature, temperature variability and humidity • Concern with reliability over Southern Europe? Careful with single grid points? • For other seasons see Archive of Figures: • http://www.iac.ethz.ch/staff/freich/download/ STARDEX/D13_web/ • To bee transferred to UEA

  13. Finalisation of D13 • Amendments of synthesis report so far (Version 2): • New figure added illustrating problems in summer • Indicate potential problems with NCEP humidity • References on testing GCM inter-relationships • New partner report from CNRS-INLN included • Additional comments? • Summary table, qualifying reliability: high, medium, low • Inclusion of other ensemble members? • All potential predictors covered ? • Further partner reports / extensions (inter-relationships)?

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