170 likes | 282 Views
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. PRUDENCE Work. Tools for diagnosing changes in probability distributions
E N D
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
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?)
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
Land-averaged annual mean 2m air temperature interpolated to CRU grid HadAM3H HIRHAM HadAM3H RCAO ECHAM4 HIRHAM ECHAM4 RCAO
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
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
Parameter Estimates Mean effects (°C): standard errors 0.03 CO2 responses (°C / ppkv): standard errors 0.09
Diagnostic Plots control scenario residuals
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.
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
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
Variation Explained (%) model Z G R GR G R GR
GCM Contrasts: ECHAM4 – HadAM3H 1961 1975 1990 2071 2085 2100 HIRHAM RCAO °C
RCM Contrasts: RCAO – HIRHAM 1961 1975 1990 2071 2085 2100 HadAM3H ECHAM4 °C
Response Contrasts HIRHAM RCAO ECHAM4 – HadAM3H HadAM3H ECHAM4 RCAO – HIRHAM °C / ppkv
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
5% Significant Effects: α + αG + αR + β + ... GR + G + R + GR GR + G + R GR + R GR + G G + R GR R G