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D. W. Shin , S. Cocke, Y.-K. Lim, T. E. LaRow, G. A. Baigorria , and J. J. O’Brien

Interannual Crop Yield Simulations over the Southeast US using Global and Regional Climate Model Products. D. W. Shin , S. Cocke, Y.-K. Lim, T. E. LaRow, G. A. Baigorria , and J. J. O’Brien. Center for Ocean-Atmospheric Prediction Studies Florida State University, Tallahassee, FL, USA

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D. W. Shin , S. Cocke, Y.-K. Lim, T. E. LaRow, G. A. Baigorria , and J. J. O’Brien

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  1. Interannual Crop Yield Simulations over the Southeast US using Global and Regional Climate Model Products D. W. Shin, S. Cocke, Y.-K. Lim, T. E. LaRow, G. A. Baigorria, and J. J. O’Brien Center for Ocean-Atmospheric Prediction Studies Florida State University, Tallahassee, FL, USA Agricultural&Biological Engineering Department, Univ. of Florida March 6, 2008 at CPASW

  2. Outline • Background • The FSU/COAPS Climate Modeling System and The DSSAT Crop Model • Ensemble Runs • The FSU/COAPS GCM results • The FSU/COAPS RCM results • Station Level results • Crop Model results • Future Directions

  3. Background RISA http://www.climate.noaa.gov/cpo_pa/risa/ Regional Integrated Sciences and Assessments http://secc.coaps.fsu.edu http://AgClimate.org

  4. FSU/COAPS Climate Modeling System FSUGSM T63 (200km) FSUNRSM (20km) OASIS Coupler Global Biosphere Regional Biosphere OCEAN HOPE-OM1, HOPE-G, HYCOM, MICOM Crop Model Crop Model

  5. DSSAT (Crop Model) • DSSAT: Decision Support System for Agrotechnology Transfer • DSSAT: a microcomputer software program combining crop soil and weather data bases and programs to manage them, with crop models and application programs, to simulate multi-year outcomes of crop management strategies. • DSSAT allows users to ask "what if" questions and simulate results by conducting, in minutes on a desktop computer, experiments which would consume a significant part of an agronomist's career.

  6. Linking Climate Models to Crop Models • Grand idea is to be able to make forecast before season regarding crop situations and perhaps suggest “best management” practices for that year • At present, we are looking into peanut or corn yields in some selected stations in southeast USA

  7. Ensemble runs The regional model was centered over the southeast U.S. and run at 20 km resolution, roughly resolving the county scale. Outputs from the model such as max/min surface temperature, precipitation and shortwave radiation at the surface is used as inputs into the crop model to determine crop yields. Using the FSU/COAPS GSM & RSM system, warm season (March-September, 7 month simulation) and cold season (October-march, 6 month simulation) ensemble simulations are performed for the period of 19 yrs (1987-2005) to characterized uncertainty in the forecast. Twenty member ensembles of the regional model are generated using different initial conditions and model configurations (i.e., the ensemble methods based on different convective schemes).

  8. GSM Results

  9. PRECIPITATION: Temporal correlation (1987-2005)

  10. MME Hindcast Skill: Temporal Correlation/ 1981-2001 (Lee et al. 2007) Precipitation APCC MMEP DEMETER MMEP JJA DJF

  11. 2m Temperature: Temporal correlation (1987-2005)

  12. MME Hindcast Skill: Temporal Correlation/ 1981-2001 (Lee et al. 2007) 2m Air Temperature DEMETER MMEP APCC MMEP JJA DJF

  13. PRECIPITATION: Temporal correlation (1987-2005) FSU/COAPS (1987-2005) CFS (1981-2003) Saha et al (2006)

  14. 2m Temperature: Temporal correlation (1987-2005) FSU/COAPS (1987-2005) CFS (1981-2003) Saha et al (2006)

  15. RSM Results

  16. FSU Regional Model Downscaling (Regional model) 20 km

  17. MAXIMUM TEMPERATURE 19 year (1987-2005) ave (oC) (model – obs) Ensemble Mean

  18. MINIMUM TEMPERATURE 19 year (1987-2005) ave (oC) (model – obs) Ensemble Mean

  19. PRECIPITATION 19 year (1987-2005) ave (mm/day) (model – obs) Ensemble Mean

  20. Tmax: Temporal correlation (1987-2005)

  21. Tmin:Temporal correlation (1987-2005)

  22. PRECIPITATION: Temporal correlation (1987-2005)

  23. Station Level Results

  24. Station Level (Tifton, GA, 1987) Tmax

  25. Station Level (Tifton, GA, 1987) Tmin

  26. Station Level (Tifton, GA, 1987) Prcp

  27. Crop Model Results

  28. Experimental Design Bias-corrected daily seasonal-climate Hindcast Raw daily seasonal-climate Hindcast Bias-correction Bias-corrected ensemble member 1 …. Bias-corrected ensemble member 20 Raw ensemble member 1 …. Raw ensemble member 20 CERES-Maize CERES-Maize Crop yield Hindcast Crop yield Hindcast Raw crop-yield ensem. member 1 …. Raw crop-yield ensem. member 20 Bias-corrected crop-yield ens. member 1 …. Bias-corrected crop-yield ens. member 20 Observed weather CERES-Maize Crop yield using observed weather

  29. PEANUT YIELDS (1994-2003) Site specific soil profiles (U.S. Soil Conservation Service data) Rainfed conditions Identical planting date for each year: April 25

  30. Maize Yield No bias-correction!

  31. Peanut Yield No bias-correction!

  32. Baigorria et al. (2007) see member 2&6

  33. Maize Yield global (green) vs. regional (red) model Tifton, GA

  34. Future Directions • More sites and other crops • A posteriori bias correction: precipitation • How can we use a climate ensemble forecast to issue an ACCEPTABLEprobabilistic crop yield forecasts? • Dynamical vs. Statistical approaches • Schoof et al. (2007); Lim et al. (2007) • CFS  Statistical downscaling • A coupled version of atmospheric and crop models • - nonlinear seasonal weather-yield interactions

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