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Projections of extreme indices over Europe from a pattern scaling approach

Projections of extreme indices over Europe from a pattern scaling approach. NCCR WP2 Meeting 05.10.2010. MOTIVATION. IAC ETH. Pattern scaling:. A2. A1B. ?. ?. ?. ?. ?. ?. TIME. No simulations are available Estimate information. ?. ?. ?. MOTIVATION. IAC ETH.

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Projections of extreme indices over Europe from a pattern scaling approach

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  1. Projections of extreme indices over Europe from a pattern scaling approach NCCR WP2 Meeting 05.10.2010

  2. MOTIVATION IACETH Pattern scaling: A2 A1B ? ? ? ? ? ? TIME • No simulations are available • Estimate information ? ? ? LUSTENBERGER ANDREAS

  3. MOTIVATION IACETH • GOAL OF THIS PROJECT • APPLICABILITY ON EXTREME INDICES • QUANTIFY AND UNDERSTAND THE LIMITATIONS LUSTENBERGER ANDREAS

  4. DATA IACETH • ENSEMBLES: 19 regional climate models (RCM) • Spatial resolution: 25km • Daily data (temperature, precipitation) • Transient experiments 1951-2050 or 1951-2100 • Scenarios: A1B LUSTENBERGER ANDREAS

  5. THEORY IACETH DMI-ARPEGE:1951-2100 SLOPE = SCALING FACTOR Correlation ρ= -0.8346 Annual mean TMEAN(K) Time-Slice Method[Mitchell et al. (2003)] SCALING FACTOR = ΔEXTREMEA1B / ΔXA1B = CONSTANT ΔEXTREME=SCALING FACTOR · ΔX Number of frost days per year (FD) LUSTENBERGER ANDREAS

  6. RESULTS IACETH 1 Relative error: SMHI HadCM3Q3 Numberoffrostdays (FD) 2021-2050 0.8 0.6 0.4 0.2 60°N 0 -0.2 45°N -0.4 -0.6 -0.8 30°N 30°W 15°W 0 15°E 30°E 45°E -1 LUSTENBERGER ANDREAS

  7. RESULTS IACETH Pearson correlation -0.82 1 Lillieforstest (H0) -0.84 -0.86 -0.88 Red: 1980-2009 Green: 2021-2050 Blue: 2070-2099 -0.90 -0.92 0 0 -6 -5 -4 -3 -2 -1 1 Skewness Noviolationofthelinearityassumption But themeanas an estimatorseemstobeproblematic LUSTENBERGER ANDREAS

  8. RESULTS IACETH 1 Relative error: SMHI HadCM3Q3 Numberoffrostdays (FD) 2021-2050 0.8 0.6 0.4 0.2 60°N 0 -0.2 45°N -0.4 -0.6 -0.8 30°N 30°W 15°W 0 15°E 30°E 45°E -1 LUSTENBERGER ANDREAS

  9. CONCLUSIONS IACETH • The skill of the projected extreme indices depends on the… • …Simulated trends • …Bias of the RCM • …Sample length • …Choice of the estimator • …Linearity • Pattern scaling works reasonably well for temperature related extreme indices. • Pattern scaling does not work for precipitation related extreme indices due to no significant/very weak trends in precipitation and/or low signal-to-noise ratio. LUSTENBERGER ANDREAS

  10. Future Challenges IACETH • Quantify and understand all sources of uncertainty • Take changes in the variance into account • „Multi-pattern approach“ • Nonlinear pattern scaling approach (e.g. neural networks) • Other explanatory variables (e.g. radiation) or combinations of different explanatory variables • How to combine the different RCMs (Multi-model approach) LUSTENBERGER ANDREAS

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