A Markov chain method for weighting climate model ensembles

Abstract. Climate change is typically modelled using sophisticated mathematical models (Climate Models) of physical processes taking place over long periods of time. Multi-model ensembles of climate models show better correlation with the observations than any of the models separately. Currently, an open research question is how climate models can be combined to create an ensemble in an optimal way. We present a novel approach based on Markov chains to estimate model weights in order to obtain ensemble means. The method was compared to existing alternatives by measuring its performance on training and validation data. The Markov chain method showed improved performance over those methods when measured by the root mean squared error and the R-squared metrics. The results of this comparative analysis should serve to motivate further studies in Markov chain and other nonlinear methods application, that address the issues of finding optimal model weight for constructing ensemble means.