A Markov chain method for weighting climate model ensembles

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