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Attributing Variation in a Regional Climate-change Modelling Experiment

Attributing Variation in a Regional Climate-change Modelling Experiment. Chris Ferro Centre for Global Atmospheric Modelling Department of Meteorology University of Reading, UK. CRU Seminar, Norwich, 16 December 2004. PRUDENCE Statistical model Illustrative example Grid-point analysis

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Attributing Variation in a Regional Climate-change Modelling Experiment

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  1. Attributing Variation in a Regional Climate-change Modelling Experiment Chris Ferro Centre for Global Atmospheric Modelling Department of Meteorology University of Reading, UK CRU Seminar, Norwich, 16 December 2004

  2. PRUDENCE Statistical model Illustrative example Grid-point analysis Discussion

  3. The PRUDENCE Project European climate simulations 9 limited-area RCMs, driven by various global GCMs Control 1961–1990 Scenarios 2071–2100 http://prudence.dmi.dk Orography at 50km resolution

  4. Experimental Design Control Scenario Observed Emissions SRES A2 Emissions Observed SST/SICE AOGCM AOGCM AGCM AGCM RCM RCM RCM RCM

  5. Aims Characterise projected climate change effects of model differences relative importance of different model components: emissions, GCM, RCM and initial conditions

  6. Selected Experiments HADCM3 / OPYC3 / HADAM3H ECHAM4 RCM C A C A Group CHRM 1 1 ETHZ CLM 1 1 GKSS HADRM 1 1 HC HIRHAM 1 1 1 1 DMI PROMES 1 1 UCM RACMO 1 1 KNMI RCAO 1 1 1 1 SMHI REGCM 1 1 ICTP REMO 1 1 MPI

  7. Land-averaged annual mean 2m air temperature interpolated to CRU grid ECHAM HADAM RCAO RCAO HIRHAM HIRHAM Temperature (°C) Temperature (°C) Year Year

  8. General Linear Model Ti j k l is temperature for GCM i, RCM j, Period k, Year l Ti j k l = i j k + i j k tl + i j k tl2 + Zi j k l Residuals Zi j k l∼ N(0, 2i j k), cor(Zi j k l, Zi j k l) = i k (j, j)

  9. Model Components Ti j k l = i j k + i j k tl + i j k tl2 + Zi j k l i j k =  + iG +jR +kP +i jGR + i kGP + j kRP +i j kGRP  overall mean iG effect of GCM i jR effect of RCM j kP effect of Period k i jGR effect of combining GCM i with RCM j i kGP effect of GCM i in Period k j kRP effect of RCM j in Period k i j kGRP effect of combining GCM i with RCM j in Period k

  10. Simplified Model Ti j k l =  + iG + jR + kP + i jGR + i kGP + ( + iG + jR + kP) tl + ( + jR ) tl2 + Zi j k l

  11. Diagnostic Plots ECHAM HADAM PP QQ RCAO HIRHAM

  12. Variance Partition ECHAM HADAM RCAO HIRHAM

  13. Temperature Contrasts (C) HIRHAM HIRHAM RCAO RCAO ECHAM HADAM warmer than ECHAM at 1961 becomes progressively cooler – warm bias greater for HIRHAM RCAO warmer than HIRHAM – warm bias greater for ECHAM HADAM

  14. Response Contrasts HADAM – ECHAM response -0.19C/decade (-0.37, -0.02) HADAM response lower than ECHAM independently of RCM, Period, Year RCAO response lower than HIRHAM at start of integration, becomes progressively higher independently of Period, GCM C / decade Year

  15. Grid-point Analysis Fit model separately at each grid point and plot maps: Proportion of variation explained by each model term Evolution of GCM temperature contrasts for each RCM Evolution of RCM temperature contrasts for each GCM Evolution of GCM response contrasts for each RCM Evolution of RCM response contrasts for each GCM

  16. Variation Explained (%) model Z T G R GR GT RT GRT

  17. Changes in Mean Temperature (°C) ECHAM HADAM HIRHAM RCAO

  18. Temperature Contrasts: HADAM – ECHAM 1961 1990 2071 2100 HIRHAM RCAO

  19. Response Contrasts: HADAM – ECHAM 1961 1990 2071 2100 HIRHAM RCAO

  20. Temperature Contrasts: RCAO – HIRHAM 1961 1990 2071 2100 ECHAM HADAM

  21. Response Contrasts: RCAO - HIRHAM 1961 1990 2071 2100 ECHAM HADAM

  22. Selected Experiments HADCM3 / OPYC3 / HADAM3H ECHAM4 RCM C A C A Group CHRM 1 1 ETHZ CLM 1 1 GKSS HADRM 1 1 HC HIRHAM 1 1 1 1 DMI PROMES 1 1 UCM RACMO 1 1 KNMI RCAO 1 1 1 1 SMHI REGCM 1 1 ICTP REMO 1 1 MPI

  23. Residual Standard Deviation (⁰C)

  24. Temperature Comparisons (⁰C)

  25. Response Comparisons (⁰C/century)

  26. Review Method: statistical model synthesises output and enables inference on climate changes, model differences, and the relative importance of model components. Results: warming signal dominates, choice of RCM affects temperature in mountainous regions, choice of GCM affects temperature and response in Atlantic and response over land, inter-annual variation is greater than GCM and RCM effects over land, effects of model differences change over space and time, other RCMs have different behaviour Extensions: RCMs, annual cycle, model resolution, EOFs, multivariate, spatial, ensemble generation

  27. Discussion Danger of over-simplified summaries of model output Features can be hidden by poor experimental design If you were a politician… http://www.met.rdg.ac.uk/~sws02caf c.a.t.ferro@reading.ac.uk

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