Comment on npg-2022-7

Abstract

Ensemble simulations of future climate can be described as consisting of a forced climate change response and noise, where the noise arises from internal variability and errors in the different models. In the ensemble mean the noise is reduced, making it easier to identify the mean of the forced response. The noise in the ensemble mean can potentially be reduced further by spatial smoothing, and this potential has been explored by previous authors. Depending on the variable, the resolution and the size of the ensemble it has been reported that the benefit of spatial smoothing of the ensemble mean may be small, and that spatial smoothing may have the unwanted side-effect that it modifies genuine features in the forced response. However, the spatial smoothing methods that have been tested previously used the same degree of smoothing at all locations, which limits their effectiveness. We derive a novel adaptive smoothing methodology for the ensemble mean that utilizes ensemble information with respect to signal, uncertainty and spatial correlations in order to vary the degree of smoothing in space. The methodology corresponds to simple intuitive concepts, such as the idea that locations with higher signal to noise ratio should be smoothed less. We apply the method to EURO-CORDEX simulations of future annual mean rainfall, and by using cross-validation within the ensemble are able to demonstrate a three times greater increase in potential predictive accuracy than from the non-adaptive smoothing methods we compare with. The adaptive smoothing method also preserves sharp features in the ensemble mean to a greater extent than the non-adaptive methods. We conclude that adaptive smoothing may be a useful post-processing tool for improving the potential accuracy of climate projections.