An evaluation method for uncertainties in regional climate projections

Regional climate projections inevitably inherit uncertainties from general circulation models (GCMs). We therefore propose a new approach for identifying the dominant uncertainties. This approach employs the downscaling procedure by Adachi et al. to the uncertainty problem using multiple GCM projections. The mean state of the large‐scale atmospheric states and the deviation from this mean state are the two uncertainty factors considered here, which are provided by a GCM. These two factors are referred to as climatology and perturbation components, respectively. To demonstrate the effectiveness in identifying these uncertainty factors using the proposed approach, a regional projection of summertime climate in western Japan is conducted using four different future climate data that are calculated using an atmospheric GCM with different sea surface temperatures. Results show that the variability in surface air temperature projections is reasonably derived from the climatology uncertainty, whereas the variability in precipitation projections is equally influenced by the climatology and perturbation uncertainties. Both the climatology and perturbation uncertainties should therefore be considered when analysing regional climate projections. A better understanding of the sources that produce variability in regional climate projections will lead to improve the reliability of these projections. Here, we propose a new method for evaluating the uncertainty sources derived from a boundary condition in regional climate projections by combining four types of numerical simulations. Our results demonstrate that improvements both of the projected changes in tropical and midlatitude cyclones as well as the projected changes in the mean climate are particularly important for precipitation projections.