An improved workflow for calibration and downscaling of GCM climate forecasts for agricultural applications – A case study on prediction of sugarcane yield in Australia

Abstract

Seasonal climate forecasts can improve the accuracy of early-season estimates of crop yield and influence seasonal crop management decisions. Climate forecasting centres around the globe now routinely run global climate models (GCMs) to provide ensemble forecasts. However, raw GCM forecasts require post-processing to improve their reliability and to enable systematic integration with crop models. Post-processing to meet crop model input requirements is highly challenging and simple bias-correction methods can perform poorly in this regard. As a result of the difficulties, GCM forecasts are often sidelined in favour of other inputs such as climate analogues. In this study, we evaluate two var..