Schepen, Andrew, Everingham, Yvette, and Wang, Quan (2020) An improved workflow for calibration and downscaling of GCM climate forecasts for agricultural applications – a case study on prediction of sugarcane yield in Australia. Agricultural and Forest Meteorology, 291. 107991.

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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 variants of a recently-developed post-processing method designed to systematically and reliably calibrate and downscale GCM forecasts for use in crop models. In one variant, local GCM forecasts of rainfall, temperature and solar radiation are post-processed directly. The second variant is a novel adaption in which the predictive input is instead the GCM's forecast of a large-scale climate pattern, in this case related to the El Nino-Southern Oscillation. The post-processed climate forecasts, which are in the form of ensemble time series, are used to drive an APSIM-sugar model to generate long-lead forecasts of biomass in north-eastern Australia from 1982 to 2016. A rigorous probabilistic assessment of forecast attributes suggests that local GCM forecast calibration provides the most skilful forecasts overall although the ENSO-related forecasts give more skilful biomass forecasts at certain times, implying model combination could be worthwhile to maximise skill. The generated biomass forecasts are unbiased and reliable for short to long lead times, suggesting that the downscaling methods will be of value to trial in a range of crop forecast applications, and support the quantitative, meaningful use of GCM forecasts in agriculture.

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