Using information content of coupled climate model simulations to generate multimodel projections of monsoon variability

Under the auspices of the IPCC fourth annual report, simulations of the 20th century and several scenarios for future climate change have been produced by a set of GCMs developed by many modeling groups. The ability of these models to reproduce the large-scale circulation of the last century has been extensively analyzed; however the ability of these climate models to simulate monsoon variability and to forecast future changes in monsoon variability has had less attention. Krishnamurti et al. (2000) found that use of multiple models in generation of seasonal climate forecasts increases the predictability relative to any individual model. Following this philosophy, we use a multimodel ensemble of simulations of the 20th century to assess the predictability of monsoon regional climate from global GCM runs. Using the conditional entropy of model-generated probability distributions to observational and reanalysis data probability distributions from several sources (VASClimO precipitation, Reynolds SST and ECMWF ERA40 reanalysis atmospheric circulation), we calculate the information content of GCM simulations of the climate variability of various monsoon region variables. We perform this analysis in a grid-to-grid comparison of model and data, as well as comparing regional principal component time series of observed and reanalysis data to time series of these principal components projected on model data. We consider the predictability of monsoon variability on several timescales. Metrics of the predictability of monsoon regions in the 20th century are used to create multimodel ensemble forecasts of future monsoon climate.