SECC – CCSP Meeting November 7, 2008 Downscaling GCMs to local and regional levels Institute of Food and Agricultural Sciences Guillermo A. Baigorria e-mail:

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Presentation transcript:

SECC – CCSP Meeting November 7, 2008 Downscaling GCMs to local and regional levels Institute of Food and Agricultural Sciences Guillermo A. Baigorria SECC-WMO joint Meeting on Climate Change Impacts and Adaptations to Agriculture, Forestry and Fisheries at the National and Regional Levels Orlando, Florida, USA, November 2008

SECC – CCSP Meeting November 7, 2008 James W. Jones University of Florida Muthuvel Chelliah NOAA – Climate Prediction Center SECC-WMO joint Meeting on Climate Change Impacts and Adaptations to Agriculture, Forestry and Fisheries at the National and Regional Levels Orlando, Florida, USA, November 2008 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 – CCSP Meeting November 7, 2008 GB 1 m 0.5 km 10 km 20 km 100 km 400 km Regional Numerical Climate Model Global Numerical Climate Model Radar data Spatial resolution Weather station data Low High Low High Low High Low High Low High GB

SECC – CCSP Meeting November 7, 2008 GB Space Past Weather station network Radar dayyear ~400x400 km 2 1x1 m 2 Historical Record Climatemonth Low HighFuture ClimateSeasonal climate RCM G/RCM GCM Operational level for crop and environmental modeling Reanalysis G/RCM’s hindcasts G/RCM’s forecasts GB

SECC – CCSP Meeting November 7, 2008 To present the different statistical downscaling methods developed, extended and used by the Southeast Climate Consortium (SECC) Objective GB

SECC – CCSP Meeting November 7, 2008 Observed latent heat flux anomalies (July) W m Observed mean surfacetemperature anomalies (July) Observed mean surface temperature anomalies (July) °C Weather station network

SECC – CCSP Meeting November 7, 2008 Observed latent heat flux anomalies (July) W m Observed mean surfacetemperature anomalies (July) Observed mean surface temperature anomalies (July) °C Weather station network

SECC – CCSP Meeting November 7, 2008 County: DeKalb   p qs Gamma distribution Beta distribution Gaussian distribution (rainfall) (Incoming solar radiation) (Max. and Min. Temperatures) GB Observed parameter Forecasted parameter (5 th, 25 th, 75 th, 95 th percentiles from 20 ensemble members) x

SECC – CCSP Meeting November 7, 2008 Bias correction based on cumulative probability distributions (a) Frequency correction (b) Amount correction Observed climatology Raw hindcast Observed climatology Freq. corrected Observed climatology Freq. corrected Frequency corrected Hindcast value Frequency & amount corrected Hindcast value x F(x) x x x Observed climatology Freq. & amount corrected GB Baigorria, GA, Jones, JW, Shin, DW, Mishra, A, O’Brien, JJ Assessing uncertainties in crop model simulations using daily bias-corrected regional circulation model outputs. Climate Res. 34(3):

SECC – CCSP Meeting November 7, 2008 GCM’s Nov-Dec-Jan rainfall data GB mm Baigorria, GA, Hansen, JW, Ward, N, Jones, JW, O’Brien, JJ Assessing predictability of cotton yields in the Southeastern USA based on regional atmospheric circulation and surface temperatures. J. Applied Meteorol. Climatol. 47(1): To extract the historical record Time series NDJ’s Rainfall

SECC – CCSP Meeting November 7, 2008 GCM’s Nov-Dec-Jan rainfall data Geospatialaggregation GB mm Baigorria, GA, Hansen, JW, Ward, N, Jones, JW, O’Brien, JJ 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

SECC – CCSP Meeting November 7, 2008 Historical record of daily values Weather station network Rainfall Max temp Min temp Historical record of Monthly values Rainfall Max temp Min temp Temporal aggregation Geospatial aggregation GB

SECC – CCSP Meeting November 7, 2008 GCM’s NDJ rainfall data Weather station network Cross-validated monthly forecasts GB

SECC – CCSP Meeting November 7, 2008 GB GCM’s Hindcast Weather station Forecasted period

SECC – CCSP Meeting November 7, 2008 Weather station network Historical record of daily values Rainfall Max temp Min temp Weather Generator WeatherGeneratorparameters Parameter estimation 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 Temporal downscaling Ensemble of daily values based on the climatology for each weather station GB 01 Temperature, Incoming solar radiation, Rainfall amount: Rainfall events: Two-state First-order Markov Chain

SECC – CCSP Meeting November 7, 2008 Weather station network Historical record of daily values Rainfall Max temp Min temp Weather Generator WeatherGeneratorparameters Parameter estimation Parameter perturbation 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 Temporal downscaling Ensemble of daily downscaled forecast for each weather station Cross-validated monthly forecasts Cross-validated monthly forecast for each weather station Geospatial disaggregation GB

SECC – CCSP Meeting November 7, – – – – – – 1.00 Weather station Lake Pearson’s correlation Geospatial correlations of observed rainfall events and amounts Daily Monthly January GB Baigorria, GA, Jones, JW, O’Brien, JJ Understanding rainfall spatial variability in the southeast USA. Int. J. Climatol. 27(6):

SECC – CCSP Meeting November 7, th 25 th percentile 50 th percentile 75 th percentile Statistical distribution of downscaled data Weather station Region Spatial Aggregation of Downscaled Data Using Point Weather Generators Overestimation of worst and best scenarios GB

SECC – CCSP Meeting November 7, th 25 th 50 th 25 th 50 th 25 th 25 th percentile 50 th percentile 75 th percentile Statistical distribution of downscaled data Weather station Region Spatial Aggregation of Downscaled Data Using Point Weather Generators Overestimation of worst and best scenarios GB

SECC – CCSP Meeting November 7, th 75 th 50 th 25 th 75 th 50 th 25 th 75 th 50 th 25 th 75 th 25 th 25 th percentile 50 th percentile 75 th percentile Statistical distribution of downscaled data Weather station Region Spatial Aggregation of Downscaled Data Using Point Weather Generators Overestimation of worst and best scenarios GB

SECC – CCSP Meeting November 7, 2008 Observed Pearson’s correlations (r) Generated r Generated rainfall for seven weather stations for a thousand years Rainfall amounts Rainfall events WGEN WGEN GiST GiST 1:1 December-January-February March-April-May June-July-August September-October-November GB

SECC – CCSP Meeting November 7, 2008 Observed Pearson’s correlations (r) Generated r Generated temperatures for seven weather stations for a thousand years Minimum temperature Maximum temperature WGEN WGEN GiST GiST 1:1 December-January-February March-April-May June-July-August September-October-November GB

SECC – CCSP Meeting November 7, 2008 Point weather generator Geospatial weather generator Days Generated rainfall for seven weather stations for 31 days Accumulated rainfall (mm) Weather stations with rainfall GB

SECC – CCSP Meeting November 7, 2008 Spatial Aggregation of Downscaled Data Using a Geospatial Weather Generator Weather station RegionInterpolation 75 th percentile 50 th percentile 25 th percentile GB

SECC – CCSP Meeting November 7, 2008 Conclusions GB 1.There is no best statistical downscaling method. 2.The best method depends on the Geospatio-temporal resolution of the input data (GCM, RCM or Reanalysis), and the Geospatio-temporal resolution of the output data needed.

SECC – CCSP Meeting November 7, 2008 Conclusions GB 3.But always, it is necessary to perform the downscaling incorporating the uncertainty produced by the method. This can be achieved by generating several equally probable realizations and to assign probability levels to the results. 4.For regional assessment, it is important to incorporate the geospatial correlations among places to avoid overestimating the worst and the best scenarios.

SECC – CCSP Meeting November 7, 2008 Downscaling GCMs to local and regional levels Institute of Food and Agricultural Sciences Guillermo A. Baigorria SECC-WMO joint Meeting on Climate Change Impacts and Adaptations to Agriculture, Forestry and Fisheries at the National and Regional Levels Orlando, Florida, USA, November 2008