Ensemble forecasting of monthly and seasonal reference crop evapotranspiration based on global climate model outputs

Abstract

Long-range forecasts of climatic variables are generated by climate centres around the world using global climate models (GCMs). This paper investigates ensemble forecasting of reference crop evapotranspiration (ETo) based on GCM outputs. The Penman-Monteith formula is used to calculate raw forecasts of ETo from GCM forecasts of solar radiation, temperature, wind speed, and vapor pressure. The Bayesian joint probability (BJP) modelling approach is applied to post-process raw monthly forecasts, separately for different lead times (month 1, 2 and 3 ahead). The Schaake shuffle is then employed to link the ensemble members of post-processed forecasts for all lead times to give a temporal structure. Forecasts of seasonal ETo total are obtained by aggregating the monthly forecasts. For comparison purposes, seasonal forecasts are also derived directly by post-processing raw seasonal forecasts without going through the monthly steps. Three case studies are presented for post-processing forecasts from the Australian Community Climate and Earth System Simulator-Seasonal (ACCESS-S1). Both raw forecasts and observations of monthly and seasonal ETo are found to be reasonably normally distributed. The post-processed forecasts of monthly and seasonal ETo are skilful in reference to climatology forecasts and statistically reliable in ensemble spread. The indirect and direct ways of generating forecasts of seasonal ETo total show similar skill and reliability, demonstrating the effectiveness of the Schaake shuffle. In this paper, the proposed post-processing method is evaluated through leave-one-out cross validation. The method can be easily adapted for post-processing raw GCM forecasts in real-time to produce ensemble forecasts of monthly and seasonal ETo.