CR 33:27-41 (2006)  -  doi:10.3354/cr033027

Translating climate forecasts into agricultural terms: advances and challenges

James W. Hansen1,*, Andrew Challinor2, Amor Ines3, Tim Wheeler4, Vincent Moron1

1International Research Institute for Climate and Society, 61 Route 9W, Palisades, New York 10964-8000, USA
2Department of Meteorology, University of Reading, Reading RG6 6BB, UK
3Department of Biological and Agricultural Engineering, Texas A&M University, College Station, Texas 77843, USA
4Department of Agriculture, University of Reading, PO Box 236, Reading RG6 6AR, UK

ABSTRACT: Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop–climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of ‘weather within climate.’


KEY WORDS: Yield forecasting · General circulation model · GCM · Crop simulation model · Stochastic weather generator · Calibration · Probabilistic forecasting


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