Evaluation of the regional climate response in Australia to large-scale climate modes in the historical NARCliM simulations

Regional Climate Modelling (RCM) projects are designed to: improve the regional representation of climate obtained from Global Climate Models (GCM) and to provide addedvalue (Giorgi and Mearns 1991) at the regional scale to the climate change signal. Global models are run at coarse spatial resolutions and they do not properly represent regional/ local features such as: topography, coastlines and land cover changes. Using GCM outputs as driving conditions for RCMs, which are run at higher resolution over a specified smaller area, allows one to obtain consistent climate information, but with a better representation of some of the details of the area under study (Hong and Kanamitsu 2014). RCM outputs (‘high’ resolution climatological datasets) are commonly analyzed using surface observations (mostly precipitation and minimum/maximum temperatures) from station measurements (Fernández et al. 2007) or gridded versions of them (Corney et al. 2013; JiménezGuerrero et al. 2013; Ji et al. 2016). A common analysis is to present a comparison of the simulated fields with the observed fields, by means of statistical values such as bias, root mean square error (RMSE), pattern correlation (Evans and McCabe 2010; Argüeso et al. 2011) and quantile plots (García-Díez et al. 2012). Beyond the basic evaluation statistics a wide variety of analysis has been performed focused on other aspects of the simulations Abstract NARCliM (New South Wales (NSW)/Australian Capital Territory (ACT) Regional Climate Modelling project) is a regional climate modeling project for the Australian area. It is providing a comprehensive dynamically downscaled climate dataset for the CORDEX-AustralAsia region at 50-km resolution, and south-East Australia at a resolution of 10 km. The first phase of NARCliM produced 60-year long reanalysis driven regional simulations to allow evaluation of the regional model performance. This long control period (1950–2009) was used so that the model ability to capture the impact of large scale climate modes on Australian climate could be examined. Simulations are evaluated using a gridded observational dataset. Results show that using model independence as a criteria for choosing atmospheric model configuration from different possible sets of parameterizations may contribute to the regional climate models having different overall biases. The regional models generally capture the regional climate