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Reanalysis-driven regional climate simulations using the Weather Research and Forecasting (WRF) model in New South Wales (NSW) and Australian Regional Climate Modelling (NARCliM) Version 2.0 are assessed for capturing precipitation extreme indices. Seven configurations of the WRF model driven by ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) for Australia from 1979 to 2020 at 20 km resolution are evaluated. We assess the spatiotemporal patterns of six selected Expert Team on Sector-Specific Climate Indices (ET-SCI) precipitation extremes by comparing regional climate model (RCM) simulations against gridded observations. The RCMs evaluated have varying levels of accuracy in simulating precipitation extremes. While they capture climatology and coefficient of variation of precipitation extremes relatively well, temporal correlation and trend reproduction present challenges. Some RCMs perform more effectively for specific extreme indices, while others encounter challenges in accurately replicating them. No single RCM excels in all aspects, highlighting the need to consider specific strengths when selecting RCMs for global climate model (GCM) driven simulations.

Seasonal forecasts are increasingly important tools in agricultural crop management. Regions with Mediterranean-type climates typically adopt rain-fed agriculture with minimal irrigation, hence accurate seasonal forecasts of rainfall during the growing season are potentially useful in decision making. In this paper we examined the bias and skill of a seasonal forecast system (ACCESS-S1) in simulating growing season precipitation (GSP) for south-west Western Australian (SWWA), a region with a Mediterranean-type climate and significant cereal crop production. Focusing on July–September (3-month) and May–October (6-month) forecasts, with 0- and 1-month lead times, we showed that overall ACCESS-S1 had a dry bias for SWWA rainfall and a tendency to simulate close to average rainfall during both wetter and drier than average rainfall years. ACCESS-S1 showed particularly poor skill at these timeframes for very wet and very dry years. The limitations in ACCESS-S1 for SWWA GSP were associated with inaccuracies in the timing of heavy rainfall events. In addition, limitations of the ACCESS-S1 model in accurately capturing SST and wind anomaly patterns over the tropical Indian Ocean during extreme rainfall years also contributed to errors in SWWA GSP forecasts. Model improvements in these regions have the potential to improve seasonal rainfall forecasts for SWWA.

This study explores spatial changes in trends of 29 climate extremes indices between 1981 and 2020 in Bangladesh, a country that frequently experiences the impacts of extreme events. This is the first study to examine trends in extreme climate over Bangladesh using a high resolution (0.05° × 0.05°) gridded daily temperature and precipitation data rather than individual stations. Trends in mean daily maximum and minimum temperatures were +0.38 °C and +0.25 °C per decade, respectively, for the last 40 years. The maximum increases in both warm days and nights were 12.75 days per decade with the fastest rate of warming in the eastern part of the country, whereas the maximum decreases in both cold days and nights were 7 days per decade with the western part experiencing the greatest increase in extreme cold. Significant reductions in total precipitation (225 mm per decade) were observed in the major parts of the country. However, the number of very heavy precipitation days (R20mm) showed a statistically significant increase (2.25 days per decade) in the southern coastal areas, with the tendency of more extreme wet days (R30 mm). The maximum decrease in annual total precipitation was 225 mm per decade while the consecutive dry days increased by 12.5 days per decade. While previous studies reported a faster rise in minimum temperature than maximum temperature, this study shows a faster increase in maximum temperature instead of minimum temperature in the study area. Extreme temperature changes were statistically more significant than changes in extreme precipitation. This study also shows statistically significant and consistent spatial trends of extreme temperature and precipitation when using high-resolution gridded observational datasets instead of station data.

Recent studies using global climate models have suggested that irrigation, assuming unlimited water supply and that all crops can be irrigated, may be an option for regional cooling in southeast Australia. In this paper, using idealised simulations, we aim to quantify the maximum possible benefits of irrigation in southeast Australia during a record breaking summer. By limiting the amount of irrigation applied to the actual amount used, and only to areas which are equipped for it, our study provides much needed practical context on the maximum potential benefits of irrigation for southeast Australia. We conduct simulations with the Weather Research and Forecasting regional atmospheric model to investigate the sensitivity to both land surface models and irrigation parameters (start hour, duration and frequency). All simulations used the same total amount of irrigation water. Our results show that irrigation could potentially reduce the mean seasonal maximum temperature during the Angry summer of 2012/2013 by −1.44°C to −2.13°C over irrigated regions. Maximum possible cooling was achieved by applying irrigation during the middle of the day, over 4 h, with daily application. During early hours of the morning, increasing the number of hours that irrigation is on has little to no effect on maximum cooling, but longer duration of irrigation later during the day leads to larger cooling. Similar results were found when decreasing irrigation frequency. The amount of cooling was strongly dependent on the land surface model, with use of the CLM model resulting in 2.32 to 2.66 times more cooling as compared to the Noah and Noah-MP land surface models, highlighting the importance of using multiple land surface models. Our results also highlight that considering realistic irrigation volumes and equipped-areas leads to cooling benefits that are largely local, and studies using global models that do not take this into consideration are likely over-estimating the potential cooling benefits of irrigation.

Adapting to the impacts of future warming, and in particular the impacts of heatwaves, is an increasingly important challenge. One proposed strategy is land-surface radiation management via crop albedo enhancement. This has been argued to be an effective method of reducing daily hot temperature extremes regionally. However, the influence of crop albedo enhancement on heatwave events, which last three or more days, is yet to be explored and this remains an important knowledge gap. Using a fully coupled earth system model with 10 ensemble members, we show that crop albedo enhancement by up to ＋0.1 reduces the frequency of heatwave days over Europe and North America by 10 to 20 days; with a larger reduction over Europe under a future climate driven by SSP2-4.5. The average temperature anomaly during heatwaves (the magnitude of the event), is reduced by 0.8 °C to 1.2 °C where the albedo was enhanced, but reductions in mean heatwave duration are limited. There was a marked reduction in the mean annual cumulative heatwave intensity across most of Eurasia and North America, ranging from 32 °C to as high as 80 °C in parts of southern Europe. These changes were largely driven by a reduction in net radiation, decreasing the sensible heat flux, which reduces the maximum temperature, and therefore, heatwave frequency and intensity. These changes were largely localised to where the albedo enhancement was applied with no significant changes in atmospheric circulation or precipitation, which presents advantages for implementation. While our albedo perturbation of up to ＋0.1 is large and represents the likely upper limit of what is possible with more reflective crops, and we assume that more reflective crops are grown everywhere and instantly, these results provide useful guidance to policy makers and farmers on the maximum possible benefits of using more reflective crops in limiting the impacts of heatwaves under future climate.

Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionable mitigation and adaptation strategies. Regional climate models are commonly used to better understand the dynamics of these events. These models have very large input parameter sets, and the parameters within the physics schemes substantially influence the model’s performance. However, parameter sensitivity analysis (SA) of regional models for heat extremes is largely unexplored. Here, we focus on the southeast Australian region, one of the global hotspots of heat extremes. In southeast Australia Weather Research and Forecasting (WRF) model is the widely used regional model to simulate extreme weather events across the region. Hence in this study, we focus on the sensitivity of WRF model parameters to surface meteorological variables such as temperature, relative humidity, and wind speed during two extreme heat events over southeast Australia. Due to the presence of multiple parameters and their complex relationship with output variables, a machine learning (ML) surrogate-based global SA method is considered for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity of 24 adjustable parameters in seven different physics schemes of the WRF model. Results show that out of these 24, only three parameters, namely the scattering tuning parameter, multiplier of saturated soil water content, and profile shape exponent in the momentum diffusivity coefficient, are important for the considered meteorological variables. These SA results are consistent for the two different extreme heat events. Further, we investigated the physical significance of sensitive parameters. This study’s results will help in further optimising WRF parameters to improve model simulation.

Abstract 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.

Climate observations and projections for Australia show an increase in warm temperature extremes, including the frequency, duration and intensity of heatwaves. Recent global scale studies have suggested that agricultural land-use management options, such as increasing crop albedo, could reducing local warming. Australia has approximately 3,727,210 km2 of cropland agricultural land-use, the majority of which is in southwest Western Australia and southeast Australia. This presents a potential opportunity to reduce regional warming via crop albedo enhancement. We use a regional climate model at 10 km resolution, to show that crop albedo enhancement of up to 0.1 could reduce monthly mean daily maximum temperatures by −1.0 °C to −1.2 °C, and monthly highest maximum temperatures by up to −1.4 °C to −1.6 °C during the cropping season. This cooling is approximately 3 times higher over Australia than global climate models predict. We highlight stronger cooling over southwest Western Australia as compared to southeast Australia, the opposite to global model studies which poorly resolve southwestern agricultural regions. The regional cooling was driven by a reduction in surface net shortwave radiation leading to a decrease in both sensible and latent heat flux of up to 50 W m−2 and 20 W m−2 respectively, when albedo is increased by up to 0.1. There were no cloud feedbacks or effects on precipitation. Our results highlight the importance of using regional climate models at a sufficiently high spatial resolution when investigating agricultural land-use management to reduce regional warming.

Globally, combinations of drought and warming are driving widespread tree mortality and crown dieback. Yet thresholds triggering either tree mortality or crown dieback remain uncertain, particularly with respect to two issues: (i) the degree to which heat waves, as an acute stress, can trigger mortality, and (ii) the degree to which chronic historical drought can have legacy effects on these processes. Using forest study sites in southwestern Australia that experienced dieback associated with a short-term drought with a heatwave (heatwave-compounded drought) in 2011 and span a gradient in long-term precipitation (LTP) change, we examined the potential for chronic historical drought to amplify tree mortality or crown dieback during a heatwave-compounded drought event for the dominant overstory species Eucalyptus marginata and Corymbia calophylla . We show pronounced legacy effects associated with chronically reduced LTP (1951–1980 versus 1981–2010) at the tree level in both study species. When comparing areas experiencing 7.0% and 11.5% decline in LTP, the probability of tree mortality increased from low (0.55) in both species, and probability of crown dieback increased from high (0.74) to nearly complete (0.96) in E. marginata . Results from beta regression analysis at the stand-level confirmed tree-level results, illustrating a significant inverse relationship between LTP reduction and either tree mortality ( F = 10.39, P = 0.0073) or dieback ( F = 54.72, P