Presentation on theme: "D. W. Shin, S. Cocke, Y.-K. Lim, T. E. LaRow, G. A. Baigorria, and J. J. OBrien Center for Ocean-Atmospheric Prediction Studies Florida State University,"— Presentation transcript:
D. W. Shin, S. Cocke, Y.-K. Lim, T. E. LaRow, G. A. Baigorria, and J. J. OBrien Center for Ocean-Atmospheric Prediction Studies Florida State University, Tallahassee, FL, USA Agricultural&Biological Engineering Department, Univ. of Florida March 6, 2008 at CPASW Interannual Crop Yield Simulations over the Southeast US using Global and Regional Climate Model Products
Outline 1.Background 2.The FSU/COAPS Climate Modeling System and The DSSAT Crop Model 3.Ensemble Runs 4.The FSU/COAPS GCM results 5.The FSU/COAPS RCM results 6.Station Level results 7.Crop Model results 8.Future Directions
FSUNRSM (20km) OASIS Coupler FSU/COAPS Climate Modeling System Regional Biosphere FSUGSM T63 (200km) Global Biosphere OCEAN HOPE-OM1, HOPE-G, HYCOM, MICOM Crop Model
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.
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
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). Ensemble runs
Observed weather Raw ensemble member 1 …. Raw ensemble member 20 Raw daily seasonal-climate Hindcast Bias-corrected ensemble member 1 …. Bias-corrected ensemble member 20 Bias-corrected daily seasonal-climate Hindcast Bias-correction Raw crop-yield ensem. member 1 …. Raw crop-yield ensem. member 20 Crop yield Hindcast CERES-Maize Bias-corrected crop-yield ens. member 1 …. Bias-corrected crop-yield ens. member 20 Crop yield Hindcast CERES-Maize Crop yield using observed weather Experimental Design
PEANUT YIELDS (1994-2003) Site specific soil profiles (U.S. Soil Conservation Service data) Rainfed conditions Identical planting date for each year: April 25
Maize Yield No bias-correction!
Peanut Yield No bias-correction!
Baigorria et al. (2007) see member 2&6
Tifton, GA Maize Yield global (green) vs. regional (red) model
Future Directions 1.More sites and other crops 2.A posteriori bias correction: precipitation 3.How can we use a climate ensemble forecast to issue an ACCEPTABLE probabilistic crop yield forecasts? 4.Dynamical vs. Statistical approaches Schoof et al. (2007); Lim et al. (2007) 5.CFS Statistical downscaling 6.A coupled version of atmospheric and crop models - nonlinear seasonal weather-yield interactions