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Like many western boundary currents, the East Australian Current (EAC) extension is projected to get stronger and warmer in the future. The CMIP5 multimodel mean (MMM) projection suggests up to 5°C of warming under an RCP85 scenario by 2100. Previous studies employed Sverdrup balance to associate a trend in basin wide zonally integrated wind stress curl (resulting from the multidecadal poleward intensification in the westerly winds over the Southern Ocean) with enhanced transport in the EAC extension. Possible regional drivers are yet to be considered. Here we introduce the NEMO-OASIS-WRF coupled regional climate model as a framework to improve our understanding of CMIP5 projections. We analyze a hierarchy of simulations in which the regional atmosphere and ocean circulations are allowed to freely evolve subject to boundary conditions that represent present-day and CMIP5 RCP8.5 climate change anomalies. Evaluation of the historical simulation shows an EAC extension that is stronger than similar ocean-only models and observations. This bias is not explained by a linear response to differences in wind stress. The climate change simulations show that regional atmospheric CMIP5 MMM anomalies drive 73% of the projected 12 Sv increase in EAC extension transport whereas the remote ocean boundary conditions and regional radiative forcing (greenhouse gases within the domain) play a smaller role. The importance of regional changes in wind stress curl in driving the enhanced EAC extension is consistent with linear theory where the NEMO-OASIS-WRF response is closer to linear transport estimates compared to the CMIP5 MMM.

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.

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.

The Maritime Continent is a region of intense rainfall characterised by a strong diurnal cycle. This study investigates the sensitivity of rainfall characteristics to resolution in a coupled regional climate model configuration, in particular focusing on processes that modulate the diurnal cycle. Model biases are resolution dependent. Increasing resolution from 3/4° to 1/4° improves the mean state sea surface temperature and precipitation biases. However, at higher resolutions (1/12°) rainfall becomes too strong in most areas. Daily maximum rainfall is simulated about 2–4 h earlier than in observations over both the land and the ocean, with only small improvements over high topography at higher resolution. We develop a technique to examine cross-coastal processes associated with the rainfall diurnal cycle along all coastlines. This is used to investigate the sensitivity of the diurnal cycle to resolution and to the direction of the prevailing wind. During offshore prevailing winds, most land rainfall is confined near the coastline and associated with a shallow land-sea breeze circulation at all resolution (though rainfall partly develops directly inland at 1/12°). During onshore prevailing winds, rainfall propagates from the coastline to the island interior at 1/4° and 1/12°, whereas it appears directly over the island interior at 3/4°, and this is associated with a deep convective cell across the coastline for all resolutions. Oceanic rainfall propagates far offshore during offshore prevailing winds at all resolutions, whereas it tends to remain confined near the coastline under onshore prevailing winds condition, particularly at higher resolution.

NARCliM (New South Wales and Australian Capital Territory Regional Climate Modelling project) is a climate downscaling project for Australia and the surrounding regions. Present and future climate simulations are performed using a 1-way nested dynamical downscaling approach and span 2 domains. We focus on the inner 10 km domain that extends across south-east Australia. Three regional climate models (RCMs) based on the Weather Research and Forecasting System (WRF) version 3.3 dynamically downscale 4 global climate model (GCM) simulations to finer resolutions. This project complements and improves on already available GCM projections for the region. Our simulations cover 3 epochs: present (1990-2009), near future (2020-2039), and far future (2060-2079). Here, we focus on the mean surface air temperature and precipitation. The RCMs are better able to capture spatial patterns of temperature and precipitation and improve the temperature root mean square error (RMSE) compared to the GCMs, at least for the inner domain. The RCMs tend to be biased cold compared to observations and are wetter than the GCMs during warm seasons. The downscaled RCM projections exhibit a weaker warming over land compared to the GCMs. The RCMs project no significant precipitation changes in the far future over most areas. However, Victoria is expected to see significant springtime drying of 15 mm mo-1, which is considerably higher than previous GCM results. This drying is associated with a larger strengthening of the subtropical ridge than modelled previously by the GCMs. In addition, the RCMs project significant precipitation changes in contradicting directions for some inland areas during winter.

The New South Wales (NSW)/Australian Capital Territory (ACT) Regional Climate Modelling (NARCliM) project aims to deliver robust climate change projections for southeast Australia at a scale relevant for decision-making. In the first phase of the project, the Weather Research and Forecasting (WRF) model with 3 physics scheme combinations, driven by NCEP/NCAR reanalysis dataset as 'perfect' boundary conditions, was run for a 60 yr period from 1950-2009 to assess the model's ability to simulate regional climate for southeast Australia. In this study, model results for daily precipitation and maximum and minimum temperatures were compared to gridded observations from the Australian Water Availability Project (AWAP) to evaluate model performance at varying time scales using a number of statistical metrics. Results show that all simulations have good representation of daily, monthly, seasonal, annual, multiannual and decadal variation in precipitation and temperature. However, there is a bias in precipitation in the northwest part of the domain (25-100%) and along the Great Dividing Range (75-150%). The temperatures are systematically underestimated across the domain (2-3°C for maximum temperature and 1-2°C for minimum temperature), suggesting the need for bias correction. The evaluation results indicate that the cumulus scheme is critical to precipitation simulation, and planetary boundary layer and radiation schemes are more important in temperature simulations. The findings from this study give us confidence in the WRF model for long-term regional climate modelling for southeast Australia. They also provide guidance in the parameterisation of the WRF model in providing more reliable precipitation and/or temperature projections.

This study evaluates the added value in the representation of surface climate variables from an ensemble of regional climate model (RCM) simulations by comparing the relative skill of the RCM simulations and their driving data over a wide range of RCM experimental setups and climate statistics. The methodology is specifically designed to compare results across different variables and metrics, and it incorporates a rigorous approach to separate the added value occurring at different spatial scales. Results show that the RCMs’ added value strongly depends on the type of driving data, the climate variable, and the region of interest but depends rather weakly on the choice of the statistical measure, the season, and the RCM physical configuration. Decomposing climate statistics according to different spatial scales shows that improvements are coming from the small scales when considering the representation of spatial patterns, but from the large-scale contribution in the case of absolute values. Our results also show that a large part of the added value can be attained using some simple postprocessing methods.

As part of a special issue on natural hazards, this paper reviews the current state of scientific knowledge of Australian heatwaves. Over recent years, progress has been made in understanding both the causes of and changes to heatwaves. Relationships between atmospheric heatwaves and large-scale and synoptic variability have been identified, with increasing trends in heatwave intensity, frequency and duration projected to continue throughout the 21st century. However, more research is required to further our understanding of the dynamical interactions of atmospheric heatwaves, particularly with the land surface. Research into marine heatwaves is still in its infancy, with little known about driving mechanisms, and observed and future changes. In order to address these knowledge gaps, recommendations include: focusing on a comprehensive assessment of atmospheric heatwave dynamics; understanding links with droughts; working towards a unified measurement framework; and investigating observed and future trends in marine heatwaves. Such work requires comprehensive and long-term collaboration activities. However, benefits will extend to the international community, thus addressing global grand challenges surrounding these extreme events.

We examine the joint contribution of urban expansion and climate change on heat stress over the Sydney region. A Regional Climate Model was used to downscale present (1990-2009) and future (2040-2059) simulations from a Global Climate Model. The effects of urban surfaces on local temperature and vapor pressure were included. The role of urban expansion in modulating the climate change signal at local scales was investigated using a human heat-stress index combining temperature and vapor pressure. Urban expansion and climate change leads to increased risk of heat-stress conditions in the Sydney region, with substantially more frequent adverse conditions in urban areas. Impacts are particularly obvious in extreme values; daytime heat-stress impacts are more noticeable in the higher percentiles than in the mean values and the impact at night is more obvious in the lower percentiles than in the mean. Urban expansion enhances heat-stress increases due to climate change at night, but partly compensates its effects during the day. These differences are due to a stronger contribution from vapor pressure deficit during the day and from temperature increases during the night induced by urban surfaces. Our results highlight the inappropriateness of assessing human comfort determined using temperature changes alone and point to the likelihood that impacts of climate change assessed using models that lack urban surfaces probably underestimate future changes in terms of human comfort.

Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensemble members that can be simulated such that choices must be made concerning which global climate models (GCMs) to downscale from, and which regional climate models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCM and RCM ensembles, as well as spanning the range of future climate projections present in the GCM ensemble. The RCM selection process uses performance evaluation metrics to eliminate poor performing models from consideration, followed by explicit consideration of model independence in order to retain as much information as possible in a small model subset. In addition to these two steps the GCM selection process also considers the future change in temperature and precipitation projected by each GCM. The final GCM selection is based on a subjective consideration of the GCM independence and future change. The created ensemble provides a more robust view of future regional climate changes. Future research is required to determine objective criteria that could replace the subjective aspects of the selection process. © Author(s) 2014.

Regional climate models are prone to biases in precipitation that are problematic for use in impact models such as hydrology models. A large number of methods have already been proposed aimed at correcting various moments of the rainfall distribution. They all require that the model produce the same or a higher number of rain days than the observational data sets, which are usually gridded data sets. Models have traditionally met this condition because their spatial resolution was coarser than the observational grids. But recent climate simulations use higher resolution and the models are likely to systematically produce fewer rain days than the gridded observations. In this study, model outputs from a simulation at 2 km resolution are compared with gridded and in situ observational data sets to determine whether the new scenario calls for revised methodologies. The gridded observations are found to be inadequate to correct the high-resolution model at daily timescales, because they are subjected to too frequent low intensity precipitation due to spatial averaging. A histogram equalisation bias correction method was adapted to the use of station, alleviating the problems associated with relative low-resolution observational grids. The wet-day frequency condition might not be satisfied for extremely dry biases, but the proposed approach substantially increases the applicability of bias correction to high-resolution models. The method is efficient at bias correcting both seasonal and daily characteristic of precipitation, providing more accurate information that is crucial for impact assessment studies. © Author(s) 2013.