Downscaling.intro.day2.andresen

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Downscaling.intro.day2.andresen

  1. 1. Principles of Climate Downscaling Rainfall Stations in East Africa over laid with the ERA40 grid # # # # # # # # # # # # # # # # ## # # ## # # # # # # # ## # # # # # ## # ## # ## # # # # # ## ## # # # # # # # ## ## ## # # ## # # # # # ## # # ## # # # ## ## # # # ## # # # # # ## # ## # ## # # # # # ### # ## # # # ## # ## # ## ## # # # # ## # # # # # # ### # ## # ## # # # #### # # # # # ## ## ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # ## ## # ## # # ## # ## # ## # # # # # # # ## # # # # ## # # # # ## # # # # ## # # # # # # # # # ## # # # # # ## # # # # # ## # # ## # # # # ## # # # # # # # # # # # # # ## # # # # ## # # # # # # KEY: # 54 stations initially identified for inclusion in downscaling % missing months # 0 - 10 # 10 - 20
  2. 2. Why Downscale?Original GCM projectionsare on the order ofHundreds of kilometers…
  3. 3. While the Climate-Dependent Process Domain of Interest is Local…
  4. 4. Two Major Downscaling Approaches• Statistical – Estimate of local series based on empirical /stochastic relationship• Dynamic – Estimate of local series based on process-based model output
  5. 5. Downscaling Types• Simple Step Change (‘Delta Method’)• Synthetic Weather Generator• Statistical Modeling• Dynamic Downscaling (RCM)• Combinations of all the above
  6. 6. ‘Delta Method’• Future projection is based on the historical series ‘adjusted’ by a GCM-based step change: TF = TH + Δe• Simplest of all approaches, can be carried out very rapidly.• Approach does not allow for any changes in variability, projections statistically constrained to historical climate
  7. 7. Synthetic Weather Generator Method• Perturbed series are based on weather generator output based on adjusted distributions from GCM output (usually monthly)• TF = f(rand. draw from pert. dist.), f(var. interrelations)• Provides more realistic series than step change, can allow for changes in variability. Possible to generate many years of projected series.• Assumes historical relationships between variables will hold in future. Can be time- consuming. Typically does not simulate frequency and severity of extremes well.
  8. 8. Statistical Modeling Approach• Perturbed series are developed as statistically based on historical series with technique such as multiple regression or canonical correlation, e.g. TF = f(free atmosphere variablesH,F)• Allows realistic, physically-based projections• Model output constrained by limits of the original input data.• Can be very time consuming, expensive.
  9. 9. NCAR ECHAM Canadian 4 GCMS, 32 daily 2 Emission temperature,Hadley Scenarios, precipitation 4 Downscaling scenarios forA2, B2 techniques 1990-2100multipledownscalingmethodologies
  10. 10. Model-Projected Mean Temperature Differences Pontiac, MI 1990-2099
  11. 11. Model-Projected Precipitation Ratios Pontiac, MI 1990-2099
  12. 12. Dynamical Downscaling• Perturbed series is based on the output of a process- based model (RCM) initiated with GCM projections. Mt. Kenya• Since it is process-based, method should work for almost any future scenarios. Theoretically, the best approach.• Models may not accurately simulate some variables, e.g. precipitation, clouds. Based on gridded output, series are still areal averages. Can be very time, labor, intensive and require special computational infrastructure.
  13. 13. Summary• The vast majority of impact assessment research requires downscaled climate series.• The choice of downscaling strategy depends on the type of application, ultimate research objectives, and available project resources.• Safe to assume that some type of downscaling will be needed for impact assessment well into the future.

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