Julia Andrys

Regional climate simulations provide essential high-resolution information for climate services. This study evaluates future changes in mean climate and 10 extremes using three generations of the NARCliM (NSW and Australian Regional Climate Modelling) project, which downscale CMIP3, CMIP5, and CMIP6 models. Projections show statistically significant increases in maximum and minimum temperatures across all NARCliM generations, with consistent spatial patterns. The magnitude of warming is primarily influenced by driving GCMs and emissions scenarios. In contrast, precipitation projections exhibit greater variability between generations, reflecting model and scenario differences and underscoring the challenge of projecting future precipitation. Extreme heat indices are projected to increase across Australia, with consistent spatial patterns and stronger changes under higher emissions, indicating more frequent and severe extreme heat events. Precipitation extremes display more variability across regions, model generations, and scenarios, although certain trends are robust. The intensity of very extreme rainfall (above the 99th percentile) is projected to increase, as is the maximum length of dry spells. Conversely, the maximum length of wet spells and the number of heavy rain days are expected to decrease. NARCliM2.0 specifically suggests shorter wet periods and fewer heavy rain days, but more intense extreme rainfall. These findings demonstrate the relative robustness of temperature and its extremes compared to precipitation and emphasize the value of broader GCM ensembles in future downscaling efforts to improve confidence in regional projections.

Understanding regional climate model (RCM) capabilities to simulate current climate informs model development and climate change assessments. This is the first evaluation of the NARCliM2.0 ensemble of seven Weather Forecasting and Research RCMs driven by ECMWF Reanalysis v5 (ERA5) over Australia at 20 km resolution contributing to CORDEX-CMIP6 Australasia and southeastern Australia at convection-permitting resolution (4 km). The performances of these seven ERA5 RCMs (R1–R7) in simulating mean and extreme maximum and minimum temperatures and precipitation are evaluated against observations at annual, seasonal, and daily timescales and compared to corresponding performances of previous-generation CORDEX-CMIP5 Australasia ERA-Interim-driven RCMs. ERA5 RCMs substantially reduce cold biases for mean and extreme maximum temperature versus ERA-Interim RCMs, with the best-performing ERA5 RCMs showing small mean absolute biases (ERA5-R5: 0.54 K; ERA5-R1: 0.81 K, respectively) but produce no improvements for minimum temperature. At 20 km resolution, improvements in mean and extreme precipitation for ERA5 RCMs versus ERA-Interim RCMs are principally evident over southeastern Australia, whereas strong biases remain over northern Australia. At convection-permitting scale over southeastern Australia, mean absolute biases for mean precipitation for the ERA5 RCM ensemble are around 79 % smaller versus the ERA-Interim RCMs that simulate for this region. Although ERA5 reanalysis data confer improvements over ERA-Interim, only improvements in precipitation simulation by ERA5 RCMs are attributable to the ERA5 driving data, with RCM improvements for maximum temperature being more attributable to model design choices, suggesting improved driving data do not guarantee all RCM performance improvements, with potential implications for CMIP6-forced dynamical downscaling. This evaluation shows that NARCliM2.0 ERA5 RCMs provide valuable reference simulations for upcoming CMIP6-forced downscaling over CORDEX-Australasia and are informative datasets for climate impact studies. Using a subset of these RCMs for simulating CMIP6-forced climate projections over CORDEX-Australasia and/or at convection-permitting scales could yield tangible benefits in simulating regional climate.

NARCliM2.0 (New South Wales and Australian Regional Climate Modelling) comprises two Weather Research and Forecasting (WRF) regional climate models (RCMs) which downscale five Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models contributing to the Coordinated Regional Downscaling Experiment (CORDEX) over Australasia at 20 km resolution and southeast Australia at 4 km convection-permitting resolution. We first describe NARCliM2.0's design, including selecting two definitive RCMs via testing 78 RCMs using different parameterisations for the 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 multi-parameterisation (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 improves the 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 2060–2079 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 the 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.

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.

Ecosystems in shallow micro-tidal lagoons are particularly sensitive to hydrologic changes. Lagoons are complex transitional ecosystems between land and sea, and the signals of direct human disturbance 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. This study developed a 3D finite-volume hydrodynamic model to assess changes in hydrodynamics of the Peel–Harvey Estuary, a large shallow lagoon with restricted connection with ocean; this was done by 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 introduction of the artificial channel has fundamentally modified the flushing and mixing within the lagoon, and the drying climate has changed the hydrology by comparable magnitudes to that of the opening of the artificial channel. The results 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 d, while in contrast the reduced precipitation and catchment inflow had a gradual opposite effect on the water ages; 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 (Practical Salinity Unit); 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 opening of the artificial channel was also shown to increase the seawater fluxes and salinity stratification, while the drying climate acted to reduce the salinity stratification in the main body of the estuary. The impacts also varied spatially in this large lagoon. The southern estuary, which has the least connection with the 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 programmes, environmental flow strategies and nutrient load reduction targets.

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 (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.

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 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.