Evaluating reanalysis-driven CORDEX regional climate models over Australia: model performance and errors.

The ability of regional climate models (RCMs) to accurately simulate current and future climate is increasingly important for impact assessment. This is the first evaluation of all reanalysis-driven RCMs within the CORDEX Australasia framework [four configurations of the Weather Forecasting and Research (WRF) model, and single configurations of COSMO-CLM (CCLM) and the Conformal-Cubic Atmospheric Model (CCAM)] to simulate the historical climate of Australia (1981–2010) at 50 km resolution. Simulations of near-surface maximum and minimum temperature and precipitation were compared with gridded observations at annual, seasonal, and daily time scales. The spatial extent, sign, and statistical significance of biases varied markedly between the RCMs. However, all RCMs showed widespread, statistically significant cold biases in maximum temperature which were the largest during winter. This bias exceeded − 5 K for some WRF configurations, and was the lowest for CCLM at ± 2 K. Most WRF configurations and CCAM simulated minimum temperatures more accurately than maximum temperatures, with biases in the range of ± 1.5 K. RCMs overestimated precipitation, especially over Australia’s populous eastern seaboard. Strong negative correlations between mean monthly biases in precipitation and maximum temperature suggest that the maximum temperature cold bias is linked to precipitation overestimation. This analysis shows that the CORDEX Australasia ensemble is a valuable dataset for future impact studies, but improving the representation of land surface processes, and subsequently of surface temperatures, will improve RCM performance. The varying RCM capabilities identified here serve as a foundation for the development of future regional climate projections and impact assessments for Australia.

Figure 12. Annual mean near-surface atmospheric maximum tempera- ture bias with respect to Australian Gridded Climate Data (AGCD) observations (a) for the RCMs (c–h). Stippled areas indicate locations where an RCM shows statistically significant bias (P < 0.05). b Sig- nificance stippling for the ensemble mean bias follows Tebaldi et al. (2011). Statistically insignificant areas are shown in colour, denoting that less than half of the models are significantly biased. In areas of significant agreement (stippled), at least half of RCMs are signifi- cantly biased, and at least 66% of the significant RCMs agree on the direction of the bias. Areas of significant disagreement are shown in white, which are where at least half of the models are significantly biased and less than 66% significant models agree on the bias direction—see main text for additional detail on the stippling regime.