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Downscaling GCMs to local and regional levels

Institute of Food and Agricultural Sciences. Downscaling GCMs to local and regional levels. Guillermo A. Baigorria e-mail: *gbaigorr@ifas.ufl.edu http://plaza.ufl.edu/gbaigorr/GB/. SECC-WMO joint Meeting on

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Downscaling GCMs to local and regional levels

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  1. Institute of Food and Agricultural Sciences Downscaling GCMs to local and regional levels Guillermo A. Baigorria e-mail:*gbaigorr@ifas.ufl.edu http://plaza.ufl.edu/gbaigorr/GB/ SECC-WMO joint Meeting on Climate Change Impacts and Adaptations to Agriculture, Forestry and Fisheries at the National and Regional Levels Orlando, Florida, USA, 18-21 November 2008

  2. James W. Jones University of Florida Muthuvel Chelliah NOAA – Climate Prediction Center James J. O’Brien Center for Ocean-Atmospheric Prediction Studies – Florida State University Dong W. Shin Center for Ocean-Atmospheric Prediction Studies – Florida State University James W. Hansen International Research Institute for Climate and Society SECC-WMO joint Meeting on Climate Change Impacts and Adaptations to Agriculture, Forestry and Fisheries at the National and Regional Levels Orlando, Florida, USA, 18-21 November 2008

  3. GB GB GB High High High High High Low Low Low Low Low Spatial resolution 1 m 0.5 km 10 km Weather stationdata Radardata 20 km 100 km 400 km Regional NumericalClimate Model Global NumericalClimate Model GB

  4. Operational levelfor crop andenvironmental modeling GB GB High Low Past Future Seasonalclimate Climate Climate year month day Historical Record 1x1 m2 Weather stationnetwork Radar Space RCM G/RCM’s forecasts G/RCM’s hindcasts G/RCM Reanalysis ~400x400 km2 GCM GB

  5. Objective To present the different statistical downscalingmethods developed, extended and used bythe Southeast Climate Consortium (SECC) GB

  6. Observed mean surfacetemperature anomalies (July) W m-2 °C Observed latent heat flux anomalies (July) 8 6 4 2 0 -2 -4 -6 -8 -10 -12 0 -0.2 -0.4 -0.6 -0.8 -1.0 Weather station network

  7. Observed mean surfacetemperature anomalies (July) W m-2 °C Observed latent heat flux anomalies (July) 8 6 4 2 0 -2 -4 -6 -8 -10 -12 0 -0.2 -0.4 -0.6 -0.8 -1.0 Weather station network

  8. County: DeKalb Beta distribution Gaussian distribution Gamma distribution (Incoming solar radiation) (Max. and Min. Temperatures) (rainfall)  p x q s  Forecasted parameter (5th, 25th, 75th, 95th percentiles from 20 ensemble members) Observed parameter GB

  9. Bias correction based on cumulative probability distributions (a) Frequency correction F(x) F(x) Observed climatology Freq. corrected Observed climatology Raw hindcast x x (b) Amount correction F(x) F(x) Observed climatology Freq. corrected Observed climatology Freq. & amount corrected x x Frequency corrected Hindcast value Frequency & amount corrected Hindcast value Baigorria, GA, Jones, JW, Shin, DW, Mishra, A, O’Brien, JJ. 2007. Assessing uncertainties in crop model simulations using daily bias-corrected regional circulation model outputs. Climate Res. 34(3): 211-222 GB

  10. Time series NDJ’s Rainfall 0 10 20 30 40 50 mm GCM’s Nov-Dec-Jan rainfall data To extract the historical record Baigorria, GA, Hansen, JW, Ward, N, Jones, JW, O’Brien, JJ. 2008. Assessing predictability of cotton yields in the Southeastern USA based on regional atmospheric circulation and surface temperatures. J. Applied Meteorol. Climatol. 47(1): 76-91 GB

  11. 0 10 20 30 40 50 mm GCM’s Nov-Dec-Jan rainfall data Geospatial aggregation Baigorria, GA, Hansen, JW, Ward, N, Jones, JW, O’Brien, JJ. 2008. Assessing predictability of cotton yields in the Southeastern USA based on regional atmospheric circulation and surface temperatures. J. Applied Meteorol. Climatol. 47(1): 76-91 GB

  12. Rainfall • Max temp • Min temp • Rainfall • Max temp • Min temp Weather station network Historical recordof daily values Geospatial aggregation Temporal aggregation Historical recordof Monthly values GB

  13. Weather station network GCM’s NDJ rainfall data Cross-validated monthly forecasts GB

  14. Leave-one-out Cross Validation 2005 2003 2001 1999 1997 1995 Years 1993 1991 1989 1987 1985 1983 1981 Retroactive Validation 2005 2003 2001 1999 1997 1995 Years 1993 1991 GCM’s Hindcast Weather station Forecasted period 1989 1987 1985 1983 1981 GB

  15. 0 1 • Rainfall • Max temp • Min temp Weather Generator parameters • Rainfall • Max temp • Min temp • Rainfall • Max temp • Min temp • Rainfall • Max temp • Min temp • Rainfall • Max temp • Min temp • Rainfall • Max temp • Min temp • Rainfall • Max temp • Min temp • Rainfall • Max temp • Min temp Weather station network Temperature, Incoming solar radiation,Rainfall amount: Historical recordof daily values Parameter estimation Rainfall events:Two-state First-order Markov Chain Weather Generator Temporal downscaling Ensemble of daily values based on the climatology for each weather station GB

  16. Rainfall • Max temp • Min temp Weather Generator parameters • Rainfall • Max temp • Min temp • Rainfall • Max temp • Min temp • Rainfall • Max temp • Min temp • Rainfall • Max temp • Min temp • Rainfall • Max temp • Min temp • Rainfall • Max temp • Min temp • Rainfall • Max temp • Min temp Weather station network Cross-validated monthly forecasts Historical recordof daily values Parameter estimation Geospatial disaggregation Cross-validated monthly forecast for each weather station Parameter perturbation Weather Generator Temporal downscaling Ensemble of daily downscaled forecast for each weather station GB

  17. Pearson’s correlation -0.50 – -0.25 -0.25 – 0.00 0.00 – 0.25 0.25 – 0.50 0.50 – 0.75 0.75 – 1.00 Weather station Lake Geospatial correlations of observed rainfall events and amounts Daily Monthly January Baigorria, GA, Jones, JW, O’Brien, JJ. 2007. Understanding rainfall spatial variability in the southeast USA.Int. J. Climatol. 27(6): 749-760 GB

  18. 50th 50th 50th 50th Statistical distribution of downscaled data Weather station Region 25th percentile 50th percentile 75th percentile Spatial Aggregation of Downscaled Data Using Point Weather Generators Overestimation of worst and best scenarios GB

  19. 50th 25th 50th 25th 50th 50th 25th 25th Statistical distribution of downscaled data Weather station Region 25th percentile 50th percentile 75th percentile Spatial Aggregation of Downscaled Data Using Point Weather Generators Overestimation of worst and best scenarios GB

  20. 50th 75th 25th 50th 75th 25th 50th 75th 50th 25th 25th 75th Statistical distribution of downscaled data Weather station Region 25th percentile 50th percentile 75th percentile Spatial Aggregation of Downscaled Data Using Point Weather Generators Overestimation of worst and best scenarios GB

  21. December-January-February March-April-May June-July-August September-October-November Generated rainfall for seven weather stations for a thousand years Rainfall events Rainfall amounts WGEN 1:1 1:1 WGEN Generated r 1:1 1:1 GiST GiST Observed Pearson’s correlations (r) GB

  22. December-January-February March-April-May June-July-August September-October-November Generated temperatures for seven weather stations for a thousand years Maximum temperature Minimum temperature WGEN 1:1 1:1 WGEN Generated r 1:1 1:1 GiST GiST Observed Pearson’s correlations (r) GB

  23. Point weather generator Geospatial weather generator Generated rainfall for seven weather stations for 31 days Accumulated rainfall (mm) Weather stations with rainfall Days Days GB

  24. Weather station Region Interpolation 25th percentile 50th percentile 75th percentile Spatial Aggregation of Downscaled Data Using a Geospatial Weather Generator GB

  25. Conclusions • There is no best statistical downscaling method. • The best method depends on the Geospatio-temporalresolution of the input data (GCM, RCM or Reanalysis),and the Geospatio-temporal resolution of the outputdata needed. GB

  26. Conclusions • But always, it is necessary to perform the downscalingincorporating the uncertainty produced by the method.This can be achieved by generating several equally probable realizations and to assign probability levelsto the results. • For regional assessment, it is important to incorporatethe geospatial correlations among places to avoidoverestimating the worst and the best scenarios. GB

  27. Institute of Food and Agricultural Sciences Downscaling GCMs to local and regional levels Guillermo A. Baigorria e-mail:*gbaigorr@ifas.ufl.edu http://plaza.ufl.edu/gbaigorr/GB/ SECC-WMO joint Meeting on Climate Change Impacts and Adaptations to Agriculture, Forestry and Fisheries at the National and Regional Levels Orlando, Florida, USA, 18-21 November 2008

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