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IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security – Session 5 – Item 1 M_Widmann


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IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security – Session 5.1 Martin Widmann

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IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security – Session 5 – Item 1 M_Widmann

  1. 1. The challenge of providing defensible downscaled and bias-corrected climate simulations Martin Widmann School of Geography, Earth and Environmental Sciences University of Birmingham coworkers include D. Hannah, S. Krause, A. van Loon (Univ. Birmingham) D. Maraun (Univ. Graz), J. Gutierrez (Univ. Santander) IUKWC workshop, Pune, 30. Nov. 2016
  2. 2. Multi-model mean precipitation projections from CMIP5 and CMIP3 ensembles (relative to 1986-2005) (Knutti and Sedlacek, NCC 2013) Stippling: high model agreement Hatching: no significant change - biased - lack of spatial detail - India: problems with monsoon representation -> need for bias correction and downscaling
  3. 3. Statistical downscaling (Perfect Prog(nosis)) (courtesy D. Maraun) derive statistical link between large and small scales apply to GCM output requires realistically simulated predictors (perfect prognosis)
  4. 4. RCMs often need bias correction mean precipitation in ERA40-driven RCMs (from ENSEMBLES) Kotlarski et al., 2014
  5. 5. Perfect Prog(nosis) Downscaling vs. Model Output Statistics (bias correction, quantile mapping) Perfect Prog Model Output Statistics
  6. 6. There are many different downscaling methods Perfect Prog - deterministic (linear, non-linear, analog) - probabilistic/stochastic (linear, non-linear, resampling) - weather generators Challenging predictor requirements Bypasses complex synoptic- and mesoscale processes that may be successfully simulated and describes them with simple statistical models Model Output Statistics - deterministic (linear, non-linear (e.g. quantile mapping)) - probabilistic/stochastic (e.g. Maraun et al., JGR 2010)
  7. 7. Systematic validation (method agnostic)
  8. 8. Validation indices and performance measures Full list of indices available at (Maraun, Widmann et al., Earth Futures 2015)
  9. 9. VALUE validation: setup and implementation Experiment 1: - perfect predictors from ERA-I - Validation at 85 European stations - implemented as web portal - cross-validation - approx. 40 methods evaluated - upcoming special issue in IJC Experiment 2 (yet to be done): - use pseudo-reality to validate low-frequency variability
  10. 10. VALUE validation: bias in mean wet spell length (DJF) (Maraun et al., IJC submitted)
  11. 11. VALUE validation: correlation vs distance (Widmann, Bedia et al., IJC in preparation)
  12. 12. VALUE validation: bias in correlation length (Widmann, Bedia et al., IJC in preparation)
  13. 13. Bias correction: nonsense mapping (SH T onto NH precip) T precip obs precip after quantile mapping (SH) (Germany) (independent validation period) (Maraun, Shepherd, Widmann et al., Nat. Clim. Change revised) Bias correction can map completely unrelated variables and the lack of a link will not be detected by distribution-based cross-validation. Timeseries-based validation would detect the problem. mean T 95th precentile
  14. 14. Bias correction: temporal structure (precipitation over Peru) (Maraun, Shepherd, Widmann et al., NCC revised)
  15. 15. Bias correction: climate change signal (MAM temperatures in Sierra Nevada) GCM (GFDL-CM3) GCM corrected RCM (WRF) (Maraun, Shepherd, Widmann et al., NCC revised) 1981-2000 (2081-2100) – (1981-2000) Analogous problem holds for RCM bias correction: If the relevant processes are not simulated BC will not help.
  16. 16. N What do we need to redraw this for specific climate information? - precisely define target variable (aspects of distribution, temporal and spatial variability) - understand which processes are relevant for target variable - validate GCM-RCM-BC chain for the target to the extent possible (low-frequency is the problem) - evaluate representation of the relevant processes, in particular for low-frequency variability in the GCM-RCM-BC chain Can only be done in collaboration between global climate modelling, downscaling and impact communities ! No general advice on the ideal regional climate product possible.
  17. 17. END