A maximum entropy method for combining AOGCMs for regional intra-year climate change assessment

This paper deals with different responses from various Atmosphere-Ocean Global Climate Models (AOGCMs) at the regional scale. What can be the best use of AOGCMs for assessing the climate change in a particular region? The question is complicated by the consideration of intra-year month-to-month variability of a particular climate variable such as precipitation or temperature in a specific region. A maximum entropy method (MEM), which combines limited information with empirical perspectives, is applied to assessing the probability-weighted multimodel ensemble average of a climate variable at the region scale. The method is compared to and coupled with other two methods: the root mean square error minimization method and the simple multimodel ensemble average method. A mechanism is developed to handle a comprehensive range of model uncertainties and to identify the best combination of AOGCMs based on a balance of two rules: depending equally on all models versus giving higher priority to models more strongly verified by the historical observation. As a case study, the method is applied to a central US region to compute the probability-based average changes in monthly precipitation and temperature projected for 2055, based on outputs from a set of AOGCMs. Using the AOGCM data prepared by international climate change study groups and local climate observation data, one can apply the MEM to precipitation or temperature for a particular region to generate an annual cycle, which includes the effects from both global climate change and local intra-year climate variability.