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Attributing Variation in Regional Climate Change Model Experiments

This presentation by Chris Ferro from the Climate Analysis Group at the University of Reading explores methods for attributing variations in regional climate change derived from model experiments. It highlights tools for diagnosing changes in probability distributions and statistical techniques for analyzing extreme value events. Key findings include the impact of different global and regional climate models on temperature responses, as well as strategies for decomposing the effects of multiple influences on climate variability. Conclusions emphasize the importance of careful experiment design for effective climate analysis.

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Attributing Variation in Regional Climate Change Model Experiments

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  1. Attributing Variation in Regional Climate Change Model Experiments Chris Ferro Climate Analysis Group Department of Meteorology University of Reading, UK PRUDENCE Project Meeting, Toledo, 9 September 2004

  2. PRUDENCE Work • Tools for diagnosing changes in probability distributions • Beniston et al. (2004, in preparation); Ferro, Hannachi & Stephenson (2004, in revision); McGregor, Ferro & Stephenson (2004, submitted) • Statistical methods for analysing extreme values • Ferro & Pezzulli (2004, in preparation); Ferro & Segers (2004, in press); presentations at 9IMSC, Royal Met. Soc. and UK Extremes • Attributing variation in climate model experiments • Ferro (2004, PRUDENCE note); Ferro & Sanchez (2004?)

  3. Land-averaged annual mean 2m air temperature interpolated to CRU grid 30-year A2 scenario  Temperature (°C)  30-year control ECHAM4 HIRHAM ECHAM4 RCAO ECHAM4 HIRHAM ECHAM4 RCAO HadAM3H HIRHAM HadAM3H RCAO HadAM3H HIRHAM HadAM3H RCAO

  4. Land-averaged annual mean 2m air temperature interpolated to CRU grid HadAM3H HIRHAM HadAM3H RCAO ECHAM4 HIRHAM ECHAM4 RCAO

  5. Normal Linear Model • Linear models for temperature Ti j k on equivalent CO2 xk • Ti j k = i j + i j (xk – x0) + Zi j k

  6. i j =  + iG + jR + ijGR  overall mean iGeffect of GCM i jReffect of RCM j ijGReffect of combining GCM i with RCM j Decomposition Ti j k = i j + i j (xk – x0) + Zi j k i j =  + iG + jR + ijGR  overall CO2 response iGeffect of GCM i jReffect of RCM j ijGReffect of combining GCM i with RCM j

  7. Parameter Estimates Mean effects (°C): standard errors 0.03 CO2 responses (°C / ppkv): standard errors 0.09

  8. Diagnostic Plots control scenario residuals

  9. Variance Decomposition If R and GR are omitted then CO2 response is independent of RCM and the RCM difference, for each GCM, is independent of CO2.

  10. Contrasts • GCM CO2 responses: ECH – HAD = 1.60°C / ppkv • RCM effects: RCA – HIR = 0.48°C (HAD)0.91°C (ECH) • GCM effects: ECH – HAD for each RCM and year (°C) ● RCAO ○ HIRHAM

  11. Grid-point Analysis • Fit model separately at each grid point and plot maps: • Proportion of variation explained by each model term • Evolution of differences between GCMs for each RCM • Evolution of differences between RCMs for each GCM • Differences between GCM CO2 responses for each RCM • Differences between RCM CO2 responses for each GCM

  12. Variation Explained (%) model  Z G R GR G R GR

  13. GCM Contrasts: ECHAM4 – HadAM3H 1961 1975 1990 2071 2085 2100 HIRHAM RCAO °C

  14. RCM Contrasts: RCAO – HIRHAM 1961 1975 1990 2071 2085 2100 HadAM3H ECHAM4 °C

  15. Response Contrasts HIRHAM RCAO ECHAM4 – HadAM3H HadAM3H ECHAM4 RCAO – HIRHAM °C / ppkv

  16. Conclusions • Summary: quantify variability from different model components, assess their relative importance, synthesise output, infer climate changes and model differences. • Extensions: more models, scenarios, ensemble members and variables; non-linearity, serial dependence, multiple comparisons, random effects, multivariate responses. • Design set of experiments carefully with view to analysis! • c.a.t.ferro@reading.ac.uk

  17. 5% Significant Effects: α + αG + αR + β + ... GR + G + R + GR GR + G + R GR + R GR + G G + R GR R G

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