d. w. shin, s. cocke, y.-k. lim, t. e. larow, g. a. baigorria, and j. j. obrien center for...
TRANSCRIPT
D. W. Shin, S. Cocke, Y.-K. Lim, T. E. LaRow, G. A. Baigorria, and J. J. O’Brien
Center for Ocean-Atmospheric Prediction StudiesFlorida State University, Tallahassee, FL, USA
Agricultural&Biological Engineering Department, Univ. of FloridaMarch 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
Background
RISA http://www.climate.noaa.gov/cpo_pa/risa/Regional Integrated Sciences and Assessments
http://secc.coaps.fsu.edu
http://AgClimate.org
FSUNRSM(20km)
OASIS Coupler
FSU/COAPS Climate Modeling System
Regional Biosphere
FSUGSMT63 (200km)
Global Biosphere
OCEANHOPE-OM1, HOPE-G,
HYCOM, MICOM
Crop Model
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
PrecipitationPrecipitation
DEMETER MMEPAPCC MMEP
JJA
DJF
MME Hindcast Skill: Temporal Correlation/ 1981-2001MME Hindcast Skill: Temporal Correlation/ 1981-2001(Lee et al. 2007)(Lee et al. 2007)
2m Air Temperature2m Air TemperatureDEMETER MMEPAPCC MMEP
JJA
DJF
MME Hindcast Skill: Temporal Correlation/ 1981-2001MME Hindcast Skill: Temporal Correlation/ 1981-2001(Lee et al. 2007)(Lee et al. 2007)
Saha et al (2006)
PRECIPITATION: Temporal correlation (1987-2005)FSU/COAPS (1987-2005) CFS (1981-2003)
FSU/COAPS (1987-2005) CFS (1981-2003)Saha et al (2006)
2m Temperature: Temporal correlation (1987-2005)
Station Level Results
Observed weather
Raw ensemble member 1….
Raw ensemble member 20
Raw daily seasonal-climateHindcast
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
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!
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