Calibration, Bridging, and Merging to Improve GCM Seasonal Temperature Forecasts in Australia

Abstract

There are a number of challenges that must be overcome if GCM forecasts are to be widely adopted in climate-sensitive industries such as agriculture and water management. GCM outputs are frequently biased relative to observations and their ensembles are unreliable in conveying uncertainty through appropriate spread. The calibration, bridging, and merging (CBaM) method has been shown to be an effective tool for postprocessing GCM rainfall forecasts to improve ensemble forecast attributes. In this study, CBaM is modified and extended to postprocess seasonal minimum and maximum temperature forecasts from the POAMA GCM in Australia. Calibration is postprocessing GCM forecasts using a statistical..