Comparison between high-resolution climate simulations using single- and double-nesting approaches within the Big-Brother experimental protocol - Matte, Dominic; Laprise, René; Thériault, Julie Mireille (Climate Dynamics, 2016)

How does large-scale nudging in a regional climate model contribute to improving the simulation of weather regimes and seasonal extremes over North America? - Lucas-Picher, Philippe; Cattiaux, Julien; Bougie, Alexandre; Laprise, René (Climate Dynamics, 2016)

Quantifying the overall added value of dynamical downscaling and the contribution from different spatial scales - Di Luca, Alejandro; Argüeso, Daniel; Evans, Jason P.; De Elía, Ramón; Laprise, René (Journal of Geophysical Research: Atmospheres, 2016)
Abstract This study evaluates the added value in the representation of surface climate variables from an ensemble of regional climate model (RCM) simulations by comparing the relative skill of the RCM simulations and their driving data over a wide range of RCM experimental setups and climate statistics. The methodology is specifically designed to compare results across different variables and metrics, and it incorporates a rigorous approach to separate the added value occurring at different spatial scales. Results show that the RCMs' added value strongly depends on the type of driving data, the climate variable, and the region of interest but depends rather weakly on the choice of the statistical measure, the season, and the RCM physical configuration. Decomposing climate statistics according to different spatial scales shows that improvements are coming from the small scales when considering the representation of spatial patterns, but from the large‐scale contribution in the case of absolute values. Our results also show that a large part of the added value can be attained using some simple postprocessing methods. , Key Points A rigorous methodology that allows evaluating the overall benefits of high‐resolution simulations The most reliable source of added value is the better representation of the spatial variability Substantial added value can also be attained using simple postprocessing methods

Objective Calibration of Regional Climate Models: Application over Europe and North America - Bellprat, Omar; Kotlarski, Sven; Lüthi, Daniel; De Elía, Ramón; Frigon, Anne; Laprise, René; Schär, Christoph (Journal of Climate, 2016)
Abstract An important source of model uncertainty in climate models arises from unconfined model parameters in physical parameterizations. These parameters are commonly estimated on the basis of manual adjustments (expert tuning), which carries the risk of overtuning the parameters for a specific climate region or time period. This issue is particularly germane in the case of regional climate models (RCMs), which are often developed and used in one or a few geographical regions only. This study addresses the role of objective parameter calibration in this context. Using a previously developed objective calibration methodology, an RCM is calibrated over two regions (Europe and North America) and is used to investigate the transferability of the results. A total of eight different model parameters are calibrated, using a metamodel to account for parameter interactions. The study demonstrates that the calibration is effective in reducing model biases in both domains. For Europe, this concerns in particular a pronounced reduction of the summer warm bias and the associated overestimation of interannual temperature variability that have persisted through previous expert tuning efforts and are common in many global and regional climate models. The key process responsible for this improvement is an increased hydraulic conductivity. Higher hydraulic conductivity increases the water availability at the land surface and leads to increased evaporative cooling, stronger low cloud formation, and associated reduced incoming shortwave radiation. The calibrated parameter values are found to be almost identical for both domains; that is, the parameter calibration is transferable between the two regions. This is a promising result and indicates that models may be more universal than previously considered.