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Regional climate models (RCMs) are used to dynamically downscale global climate models (GCMs) to provide high-resolution projections of future climate change to better inform policy and decision making at the regional scale. However, biases from GCMs are transferred to RCMs and this can limit the usefulness of the regional climate projections. This paper investigates the influence of bias correcting 4 GCMs from the Coupled Model Intercomparison Project 3, for regional climate simulations over the CORDEX-Australasia domain using Weather Research and Forecasting. The GCM outputs are bias corrected against ERA-Interim reanalysis as a surrogate truth. Results show that over decadal time scales bias correction removes large systematic precipitation and temperature biases. However, bias correction also introduced biases where there were none, introduced biases of the opposite sign, or enhanced existing biases in other regions in some instances. The dynamical mechanisms driving the changes in the biases are explored.

Ecosystems in shallow, micro-tidal lagoons are particularly sensitive to hydrologic changes. Lagoons are also highly complex transitional ecosystems between land and sea, and the signals of direct human disturbance to the lagoon can be confounded by variability of the climate system, but from an effective estuary management perspective the effects of climate versus direct human engineering interventions need to be identified separately. Although many estuarine lagoons have undergone substantial human interventions, such as artificial channels, the effects from the interaction of climate change with engineering interventions have not been well evaluated. This study developed a 3D finite-volume hydrodynamic model to assess changes in hydrodynamics of the Peel-Harvey Estuary, a large chocked-type lagoon, considering how attributes such as water retention time, salinity and stratification have responded to a range of factors, focusing on the drying climate trend and the opening of a large artificial channel over the period from 1970 to 2016, and how they will evolve under current climate projections. The results show that the drying climate has fundamentally changed the hydrology by comparable magnitudes to that of the opening of the artificial channel, and also highlight the complexity of their interacting impacts. Firstly, the artificial channel successfully improved the estuary flushing by reducing average water ages by 20–110 days; while in contrast the reduced precipitation and catchment inflow had a gradual opposite effect on the water ages, and during the wet season this has almost counteracted the reduction brought about by the channel. Secondly, the drying climate caused an increase in the salinity of the lagoon by 10–30 PSU; whilst the artificial channel increased the salinity during the wet season, it has reduced the likelihood of hypersalinity (> 40 PSU) during the dry season in some areas. The impacts also varied spatially in this large lagoon. The southern estuary, which has the least connection with ocean through the natural channel, is the most sensitive to climate change and the opening of the artificial channel. The projected future drying climate is shown to slightly increase the retention time and salinity in the lagoon, and increase the hypersalinity risk in the rivers. The significance of these changes for nutrient retention and estuary ecology are discussed, highlighting the importance of these factors when setting up monitoring programs, environmental flow strategies and nutrient load reduction targets.

The Weather Research and Forecasting (WRF) Model is evaluated as a regional climate model for the simulation of climate indices that are relevant to viticulture in Western Australia’s wine regions at a 5-km resolution under current and future climate. WRF is driven with ERA-Interim reanalysis for the current climate and three global climate models (GCMs) for both current and future climate. The focus of the analysis is on a selection of climate indices that are commonly used in climate–viticulture research. Simulations of current climate are evaluated against an observational dataset to quantify model errors over the 1981–2010 period. Changes to the indices under future climate based on the SRES A2 emissions scenario are then assessed through an analysis of future (2030–59) minus present (1970–99) climate. Results show that when WRF is driven with ERA-Interim there is generally good agreement with observations for all of the indices although there is a noticeable negative bias for the simulation of precipitation. The results for the GCM-forced simulations were less consistent. Namely, while the GCM-forced simulations performed reasonably well for the temperature indices, all simulations performed inconsistently for the precipitation index. Climate projections showed significant warming for both of the temperature indices and indicated potential risks to Western Australia’s wine growing regions under future climate, particularly in the north. There was disagreement between simulations with regard to the projections of the precipitation indices and hence greater uncertainty as to how these will be characterized under future climate.

Projections of future climate change (1970–1999 compared to 2030–2059) for southwest Western Australia (SWWA) are analysed for a regional climate model (RCM) ensemble using the Weather Research and Forecasting Model with boundary conditions from three CMIP3 general circulation models (GCMs); CCSM3, CSIROmk3.5 and ECHAM5. We show that the RCM adds value to the GCM and we suggest that this is through improved representation of regional scale topography and enhanced land–atmosphere interactions. Our results show that the mean daytime temperature increase is larger than the nighttime increase, attributed to reduced soil moisture and hence increased surface sensible heat flux in the model, and there is statistically significant evidence that the variance of minimum temperatures will increase. Changes in summer rainfall are uncertain, with some models showing rainfall increases and others projecting reductions. All models show very large fluctuations in summer rainfall intensity which has important implications because of the increased risk of flash flooding and erosion of arable land. There is model consensus indicating a decline in winter rainfall and the spatial distribution of this rainfall decline is influenced by regional scale topography in two of the three simulations. Winter rainfall reduction is consistent with the historical trend of declining rainfall in SWWA, which has been attributed in previous research to a reduction in the number of fronts passing over the region. The continuation of this trend is evident in all models by an increase in winter mean sea level pressure in SWWA, and a reduced number of winter front days. Winter rainfall does not show any marked variations in daily intensity.

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.

A high resolution (5 km), single initialization, 30 year (1970–1999) Weather Research and Forecast regional climate model (RCM) ensemble for southwest Western Australia (SWWA) is evaluated. The article focuses on the ability of the RCM to simulate winter cold fronts, which are the main source of rainfall for the region, and assesses the spatial and temporal characteristics of climate extremes within the region's cereal crop growing season. To explore uncertainty, a four-member ensemble was run, using lateral boundary conditions from general circulation models (GCMs) of the Coupled Model Intercomparison Project Phase 3; ECHAM5, Model for Interdisciplinary Research on Climate 3.2 (MIROC 3.2), Community Climate System Model version 3 (CCSM3) and Commonwealth Scientific and Industrial Research Organisation (CSIRO) mk3.5. Simulations are evaluated against gridded observations of temperature and precipitation and atmospheric conditions are compared to a simulation using ERA-Interim reanalysis boundary conditions, which is used as a surrogate truth. Results show that generally, the RCM simulations were able to represent the climatology of SWWA well, however differences in the positioning of the subtropical high pressure belt were apparent which influenced the number of fronts traversing the region and hence winter precipitation biases. Systematic temperature biases were present in some ensemble members and the RCM was found to be colder than the driving GCM in all simulations. Biases impacted model skill in representing temperature extremes and this was particularly apparent in the MIROC-forced simulation, which was the worst performing RCM for both temperature and precipitation. The dynamical causes of the biases are explored and findings show that nonetheless, the RCM provides added value, particularly in the spatio-temporal representation of wet season rainfall.

The authors evaluate a 30-yr (1981–2010) Weather Research and Forecast (WRF) Model regional climate simulation over the southwest of Western Australia (SWWA), a region with a Mediterranean climate, using ERA-Interim boundary conditions. The analysis assesses the spatial and temporal characteristics of climate extremes, using a selection of climate indices, with an emphasis on metrics that are relevant for forestry and agricultural applications. Two nested domains at 10- and 5-km resolution are examined, with the higher-resolution simulation resolving convection explicitly. Simulation results are compared with a high-resolution, gridded observational dataset that provides daily rainfall, minimum temperatures, and maximum temperatures. Results show that, at both resolutions, the model is able to simulate the daily, seasonal, and annual variation of temperature and precipitation well, including extreme events. The higher-resolution domain displayed significant performance gains in simulating dry-season convective precipitation, rainfall around complex terrain, and the spatial distribution of frost conditions. The high-resolution domain was, however, influenced by grid-edge effects in the southwestern margin, which reduced the ability of the domain to represent frontal rainfall along the coastal region. On the basis of these results, the authors feel confident in using the WRF Model for regional climate simulations for the SWWA, including studies that focus on the spatial and temporal representation of climate extremes. This study provides a baseline climatological description at a high resolution that can be used for impact studies and will also provide a benchmark for climate simulations driven by general circulation models.

The Weather Research and Forecasting (WRF) model was used to simulate fire weather for the south-west of Western Australia (SWWA) over multiple decades at a 5-km resolution using lateral boundary conditions from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA)-Interim reanalysis. Simulations were compared with observations at Australian Bureau of Meteorology meteorological stations and the McArthur Forest Fire Danger Index (FFDI) was used to quantify fire weather. Results showed that, overall, the WRF reproduced the annual cumulative FFDI at most stations reasonably well, with most biases in the FFDI ranging between –600 and 600. Biases were highest at stations within the metropolitan region. The WRF simulated the geographical gradients in the FFDI across the domain well. The source of errors in the FFDI varied markedly between the different stations, with no one particular variable able to account for the errors at all stations. Overall, this study shows that the WRF is a useful model for simulating fire weather for SWWA, one of the most fire-prone regions in 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.

Antecedent land surface conditions play a role in the amplification of temperature anomalies experienced during heatwaves by modifying the local partitioning of available energy between sensible and latent heating. Most existing analyses of heatwave amplification from soil moisture anomalies have focused on exceptionally rare events and consider seasonal scale timescales. However, it is not known how much the daily evolution of land surface conditions, both before and during a heatwave, contributes to the intensity and frequency of these extremes. We examine how the daily evolution of land surface conditions preceding a heatwave event contributes to heatwave intensity. We also diagnose why the land surface contribution to Australian heatwaves is not homogeneous due to spatiotemporal variations in land‐atmosphere coupling. We identify two coupling regimes: a land‐driven regime where surface temperatures are sensitive to local variations in sensible heating and an atmosphere‐driven regime where this is not the case. Northern Australia is consistently strongly coupled, where antecedent soil moisture conditions can influence temperature anomalies up to day 4 of a heatwave. For southern Australia, heatwave temperature anomalies are not influenced by antecedent soil moisture conditions due to an atmosphere‐driven coupling regime. Therefore, antecedent land surface conditions have a role in increasing the temperature anomalies experienced during a heatwave only over regions with strong land‐driven coupling. The timescales over which antecedent land surface conditions contribute to Australian heatwaves also vary regionally. Overall, the spatiotemporal variations of land‐atmosphere interactions help determine where and when antecedent land surface conditions contribute to Australian heat extremes.