Regional climate projections for the South West of Western Australia to simulate changes in mean and extreme rainfall and temperature

The southwest of Western Australia (SWWA) is an area of significant agricultural production and an internationally recognised biodiversity hotspot. The region has experienced marked rainfall reductions over the last four decades and there is uncertainty as to the extent of future changes to the hydrological regime. Hence, there is a need for regional climate information in SWWA to better inform climate adaptation strategies for several key sectors, including agriculture and forestry. The overarching aim of this project is to provide such information, with a focus on changes in rainfall and temperature extremes. The Weather Research and Forecasting (WRF) model was used as a regional climate model for SWWA. Given the known sensitivity of WRF to physics options and driving data, the most appropriate physical parameterisations were tested on a yearly time-scale. Based on these findings, a 30-year climatology was produced for SWWA (1981-2010) at a 5 km resolution by downscaling ERA-Interim reanalysis. Comparisons against observations showed that the model was able to simulate the daily, seasonal and annual variation of temperature and precipitation well, including extreme events. The model was then used to downscale an ensemble of 4 general circulation models (GCMs) for the historical period (1970- 1999) and compared against both observations and the GCMs. WRF was shown to add value to the GCM data for 3 out of the 4 GCMs evaluated, particularly in the spatio-temporal distribution of winter rainfall. Finally, the ensemble was run from 2030-2059 to examine projected climate change in SWWA. Results project that maximum temperature extremes will increase, consistent with mean changes however the variance of maximum temperatures is not projected to change significantly. While mean minimum temperatures are not projected to increase as much as maximum temperatures, there is strong evidence that the variability of minimum temperatures will increase. This has the potential to raise the likelihood of night time temperature extremes. Simulations project a reduction in rainfall, particularly during winter. This decline is related to fewer frontal systems traversing the SWWA and hence fewer rain days. The study found no evidence to suggest that the intensity of rain bearing winter storms is likely to change

Sensitivity of WRF to driving data and physics options on a seasonal time-scale for the southwest of Western Australia

Regional climate models are sensitive to the forcing data used, as well as different model physics options. Additionally, the behaviour of physics parameterisations may vary depending on the location of the domain due to different climatic regimes. In this study, we carry out a sensitivity analysis of the weather research and forecasting model to different driving data and model physics options over a 10-km resolution domain in the southwest of Western Australia, a region with Mediterranean climate. Simulations are carried out on a seasonal time-scale, in order to better inform future long-term regional climate simulations for this region. We show that the choice of radiation scheme had a strong influence on both temperature and precipitation; the choice of planetary boundary layer scheme has a particularly large influence on minimum temperatures; and, the choice of cumulus scheme or more complex micro-physics did not strongly influence precipitation simulations. More importantly, we show that the same radiation scheme, when used with different driving data, can lead to different results.27 page(s

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

[eng] 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

Design, evaluation and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble

NARCliM2.0 comprises two Weather Research and Forecasting (WRF) regional climate models (RCMs) downscaling five CMIP6 global climate models contributing to the Coordinated Regional Downscaling Experiment over Australasia at 20 km resolution, and south-east Australia at 4 km convection-permitting resolution. We first describe NARCliM2.0’s design, including selecting two, definitive RCMs via testing seventy-eight RCMs using different parameterisations for planetary boundary layer, microphysics, cumulus, radiation, and land surface model (LSM). We then assess NARCliM2.0's skill in simulating the historical climate versus CMIP3-forced NARCliM1.0 and CMIP5-forced NARCliM1.5 RCMs and compare differences in future climate projections. RCMs using the new Noah-MP LSM in WRF with default settings confer substantial improvements in simulating temperature variables versus RCMs using Noah-Unified. Noah-MP confers smaller improvements in simulating precipitation, except for large improvements over Australia’s southeast coast. Activating Noah-MP’s dynamic vegetation cover and/or runoff options primarily improve simulation of minimum temperature. NARCliM2.0 confers large reductions in maximum temperature bias versus NARCliM1.0 and 1.5 (1.x), with small absolute biases of ~0.5 K over many regions versus over ~2 K for NARCliM1.x. NARCliM2.0 reduces wet biases versus NARCliM1.x by as much as 50 %, but retains dry biases over Australia’s north. NARCliM2.0 is biased warmer for minimum temperature versus NARCliM1.5 which is partly inherited from stronger warm biases in CMIP6 versus CMIP5 GCMs. Under shared socioeconomic pathway (SSP)3-7.0, NARCliM2.0 projects ~3 K warming by 2060–79 over inland regions versus ~2.5 K over coastal regions. NARCliM2.0-SSP3-7.0 projects dry futures over most of Australia, except for wet futures over Australia’s north and parts of western Australia which are largest in summer. NARCliM2.0-SSP1-2.6 projects dry changes over Australia with only few exceptions. NARCliM2.0 is a valuable resource for assessing climate change impacts on societies and natural systems and informing resilience planning by reducing model biases versus earlier NARCliM generations and providing more up-to-date future climate projections utilising CMIP6