Precipitation over urban areas in the western Maritime Continent using a convection-permitting model - Argüeso, Daniel; Di Luca, Alejandro; Evans, Jason P. (Climate Dynamics, 2016)

Seasonal mean temperature changes control future heat waves - Argüeso, Daniel; Di Luca, Alejandro; Perkins‐Kirkpatrick, Sarah E.; Evans, Jason P. (Geophysical Research Letters, 2016)
Abstract Increased temperature will result in longer, more frequent, and more intense heat waves. Changes in temperature variability have been deemed necessary to account for future heat wave characteristics. However, this has been quantified only in Europe and North America, while the rest of the globe remains unexplored. Using late century global climate projections, we show that annual mean temperature increases is the key factor defining heat wave changes in most regions. We find that commonly studied areas are an exception rather than the standard and the mean climate change signal generally outweighs any influence from variability changes. More importantly, differences in warming across seasons are responsible for most of the heat wave changes and their consideration relegates the contribution of variability to a marginal role. This reveals that accurately capturing mean seasonal changes is crucial to estimate future heat waves and reframes our interpretation of future temperature extremes. , Key Points The influence of projected temperature variability changes on future heat waves varies across the globe Future heat waves are primarily controlled by annual mean changes, except in Europe and North America Mean seasonal warming is responsible for over 95% of projected heat wave changes in most region

Evaluating the representation of Australian East Coast Lows in a regional climate model ensemble - Di Luca, Alejandro; Evans, Jason; Pepler, Acacia; Alexander, Lisa; Argüeso, Daniel (Journal of Southern Hemisphere Earth System Science, 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