Correlation between Inter-Model Similarities in Spatial Pattern for Present and Projected Future Mean Climate

When an averaging method is used for model future projection with weights determined according to the model performance in the present climate, generally, the stationarity of the model performance between present and future is implicitly assumed. Here we investigate this assumption using multi-model data. We consider the correlation between inter-model similarities in the spatial pattern for the present-day climate and future climate change for surface air temperature, precipitation and sea level pressure on global and zonal domains in the seasonal time scale. We further extend previous work by devising a bootstrap method to estimate the statistical significance of all correlations, which have previously not been estimated. Most of the correlation coefficients for precipitation were significant, but moderate or low in the absolute value. Many of those for the other variables were not significant. Also, we discuss the magnitude of the inter-model similarity used in this work.