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The Community Atmosphere Biosphere Land Exchange (CABLE) model is a third‐generation land surface model (LSM). CABLE is commonly used as a stand‐alone LSM, coupled to the Australian Community Climate and Earth Systems Simulator global climate model and coupled to the Weather Research and Forecasting (WRF) model for regional applications. Here, we evaluate an updated version of CABLE within a WRF physics ensemble over the COordinated Regional Downscaling EXperiment (CORDEX) AustralAsia domain. The ensemble consists of different cumulus, radiation and planetary boundary layer (PBL) schemes. Simulations are carried out within the NASA Unified WRF modeling framework, NU‐WRF. Our analysis did not identify one configuration that consistently performed the best for all diagnostics and regions. Of the cumulus parameterizations the Grell‐Freitas cumulus scheme consistently overpredicted precipitation, while the new Tiedtke scheme was the best in simulating the timing of precipitation events. For the radiation schemes, the RRTMG radiation scheme had a general warm bias. For the PBL schemes, the YSU scheme had a warm bias, and the MYJ PBL scheme a cool bias. Results are strongly dependent on the region of interest, with the northern tropics and southwest Western Australia being more sensitive to the choice of physics options compared to southeastern Australia which showed less overall variation and overall better performance across the ensemble. Comparisons with simulations using the Unified Noah LSM showed that CABLE in NU‐WRF has a more realistic simulation of evapotranspiration when compared to GLEAM estimates. Key Points: We identify WRF physics configurations that perform well with CABLESimulation of latent heat fluxes over Australia are more skillful with WRF‐LIS‐CABLE than WRF‐Noah [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

ABSTRACT 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. [ABSTRACT FROM AUTHOR]

The dynamics and large-scale drivers of heat wave (HW) events in Australia are well documented. However, the influence of soil moisture in modulating HWs is largely unexplored. We focus here on a recent significant HW event in southeast Australia that preceded the Black Saturday bushfires (3-7 February 2009). During this period, the southeast of Australia experienced unprecedented warm conditions, which, in conjunction with high fuel load and mesoscale weather conditions, led to devastating bushfires. We examine how different initial soil moisture conditions with lead times of 5, 10, and 15 days prior to the event would have altered its overall dynamics at the continental scale. We show that at short lead times (5 days), the influence of perturbing soil moisture is mostly linear. Decreasing (increasing) soil moisture increases (decreases) maximum temperatures, associated with an intensification of the upper-level anticyclone. The effect of increasing soil moisture is more nonlinear than decreasing soil moisture with increasing lead time; namely, increasing soil moisture can also lead to an increase in maximum temperature over some parts of the domain, rather than a decrease everywhere. At lead times of up to 15 days, the imposed perturbation in soil moisture, mostly confined to the Tropics, is essentially lost such that the impact on maximum temperatures on the day of the event cannot be related to the sign of the imposed perturbation in soil moisture. Our results highlight the importance of accurate soil moisture estimates in capturing the intensity and spatial extent of HW events in southeast Australia, but only at relatively short lead times. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]