Potential for added value in temperature simulated by high-resolution nested RCMs in present climate and in the climate change signal

Regional Climate Models (RCMs) have been developed in the last two decades in order to produce high-resolution climate information by downscaling Atmosphere-Ocean General Circulation Models (AOGCMs) simulations or analyses of observed data. A crucial evaluation of RCMs worth is given by the assessment of the value added compared to the driving data. This evaluation is usually very complex due to the manifold circumstances that can preclude a fair assessment. In order to circumvent these issues, here we limit ourselves to estimating the potential of RCMs to add value over coarse-resolution data. We do this by quantifying the importance of fine-scale RCM-resolved features in the near-surface temperature, but disregarding their skill. The Reynolds decomposition technique is used to separate the variance of the time-varying RCM-simulated temperature field according to the contribution of large and small spatial scales and of stationary and transient processes. The temperature variance is then approximated by the contribution of four terms, two of them associated with coarse-scales (e.g., corresponding to the scales that can be simulated by AOGCMs) and two of them describing the original contribution of RCM simulations. Results show that the potential added value (PAV) emerges almost exclusively in regions characterised by important surface forcings either due to the presence of fine-scale topography or land-water contrasts. Moreover, some of the processes leading to small-scale variability appear to be related with relatively simple mechanisms such as the distinct physical properties of the Earth surface and the general variation of temperature with altitude in the Earth atmosphere. Finally, the article includes some results of the application of the PAV framework to the future temperature change signal due to anthropogenic greenhouse gasses. Here, contrary to previous studies centred on precipitation, findings suggest for surface temperature a relatively low potential of RCMs to add value over coarser resolution models, with the greatest potential located in coastline regions due to the differential warming occurring in land and water surfaces.