Abstract
The general decrease in the quality of climate model output with decreasing scale suggests a need for spatial smoothing to suppress the most unreliable small-scale features. However, even if correctly simulated, a large-scale average retained by the smoothing may not be representative of the local conditions, which are of primary interest in many impact studies. Here, the authors study this trade-off using simulations of temperature and precipitation by 24 climate models within the Third Coupled Model Intercomparison Project, to find the scale of smoothing at which the mean-square difference between smoothed model output and gridbox-scale reality is minimized. This is done for present-day time mean climate, recent temperature trends, and projections of future climate change, using cross validation between the models for the latter. The optimal scale depends strongly on the number of models used, being much smaller for multimodel means than for individual model simulations. It also depends on the variable considered and, in the case of climate change projections, the time horizon. For multimodel-mean climate change projections for the late twenty-first century, only very slight smoothing appears to be beneficial, and the resulting potential improvement is negligible for practical purposes. The use of smoothing as a means to improve the sampling for probabilistic climate change projections is also briefly explored.
Dissemination of Climate Model Output to the Public and Commercial Sector
