Jatin Kala

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.

The Indian Ocean Dipole (IOD) and the El Niño Southern Oscillation (ENSO) are key modes of natural climate variability which co-occur and influence regional rainfall in Australia. This study evaluates the skill of 60 CMIP6 global climate models, including 16 pre-selected models for dynamical downscaling, in simulating the characteristics of IOD & ENSO, their inter-relationship, and their combined and independent influences on Australian rainfall. Focusing on the austral winter-spring season (JJASON) during 1950–2014, we use partial correlation & regression techniques to disentangle the influence of ENSO from the IOD-rainfall relationship and vice versa. Compared with observations, most CMIP6 models overestimate IOD & ENSO variability, frequencies of IOD & co-occurring IOD-ENSO events, and the influence of ENSO on IOD. More models reasonably capture the observed independent IOD-rainfall correlation in Southern (SA) and Southeastern (SEA) Australia than observed combined correlation, while in Eastern (EA) and Northern (NA) Australia, more models reasonably reproduce observed combined ENSO-rainfall correlation than observed independent one. A similar result emerges in regression analyses, but it is more pronounced for ENSO in EA and NA than for IOD in SA and SEA. The ranking of CMIP6 models based on four statistical skill metrics shows that each model demonstrates distinct skill variations, highlighting the importance of skill-specific analyses when evaluating relationships between IOD/ENSO and Australian rainfall. Our results emphasise the need to consider both combined and independent effects of IOD/ENSO on Australian rainfall, and our ranking of the pre-selected CMIP6 models for dynamical downscaling will be helpful to better understand biases in these regional projections for Australia.

Southwest Western Australia (SWWA), with its Mediterranean climate, has undergone a persistent drying trend since the 1970s. As such, it is often referred to as the “canary in the coal mine” of climate change. This review examines drivers of SWWA rainfall and rainfall decline, focusing on the cool season (April to October) when most rainfall occurs, and includes the influence of weather systems, modes of natural climate variability, and land‐use change. While paleo‐climate evidence shows that similar declines have occurred in the past, the current trend is at the upper end of natural climate variability, indicating an increasing influence from anthropogenic climate change. The reasons for this drying trend are complex, with research linking decreasing SWWA rainfall to a strengthening subtropical ridge and more frequent positive phases of the Southern Annular Mode resulting in fewer winter fronts reaching the region. Decreasing baroclinity has been associated with a decrease in cold front rainfall amounts and a possible shift toward increased post‐frontal showery activity. While the El Niño Southern Oscillation and Indian Ocean Dipole (IOD) have shown weak historical links to SWWA rainfall, recent trends toward more frequent positive IOD may change this association. Finally, the influence of 20th–21st century land use changes has also been identified as a contributing factor to the rainfall decline. Given the complex interplay of these drivers and the increasing influence of anthropogenic climate change on the region's rainfall, a holistic approach is becoming crucial. We provide multiple avenues for further research.

This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change

This study applies a benchmarking framework to assess a 34-member ensemble of regional climate models that have dynamically downscaled Coordinated Model Intercomparison Project (CMIP6) models over the Australasian region. Four modelling centres contributed regional climate models to this ensemble using three regional climate models (RCMs) and a total of five model configurations. The RCMs compared are the Conformal Cubic Atmospheric Model (CCAM), the Weather Research and Forecast (WRF) model and the Bureau Atmospheric Regional Projections for Australia (BARPA-R). Assessment is conducted over the Australian continent using a separation into four major climate zones over a 30-year historical climatological period (1985–2014). Rainfall and near-surface temperatures are compared against six benchmarks measuring mean state patterns, spatial and temporal variance, seasonal cycles, long-term trends and selected extreme indices. Benchmark thresholds are derived either from previous studies or comparison with the driving model ensemble. Major model biases vary between ensemble members and include dry biases in northern and southern Australia, winter wet biases and a persistent low bias in the winter diurnal temperature range across all the modelling centres. Daily variability at large length scales is comparable in the driving global climate model and downscaled regional climate model length scales, and long-term trends are largely determined by the driving global climate model. Overall, the ensemble was deemed to be fit for purpose for impact studies. Strengths and weaknesses of the systematic benchmarking framework used here are discussed.

Understanding, quantifying and visualising projected ranges of future regional climate change is important for informing robust climate change impact assessments. Here, we examine projections of Australian sub-continental regionally averaged surface air temperature and precipitation in the Sixth Coupled Model Intercomparison Project (CMIP6) global and Coordinated Regional climate Downscaling Experiment (CORDEX)-Australasia regional model ensembles and illustrate the relative sources of uncertainty from emissions scenarios, models and internal climate variability. As expected, the uncertainty in temperature change for all regions by the end of the century is predominantly determined by the emissions scenario. Here, we examine a low and high emissions scenario, bookending a range of plausible cases. In contrast, the uncertainty in precipitation changes towards the end of the 21st Century is largely related to model-to-model differences, in particular owing to the differences between global models, with regional models contributing a smaller, but still significant, source of uncertainty. Regional models can significantly alter precipitation projections; however, we find few cases of consistency across the regional models. Decadal variability is an important contributing factor for precipitation uncertainty for the entire 21st Century. Large changes in interannual precipitation variability are projected by some climate models by the end of the 21st Century, and these changes tend to be well correlated to mean precipitation changes. Robust responses to climate change must account for all of these dimensions in a structured way.

We synthesise advances in the understanding of the physical processes that play a role in developing, intensifying, and terminating meteorological droughts. We focus on Australia, where new understanding of drought drivers across different climate regimes provides insights into drought processes elsewhere in the world. Drawing on observational, climate model and machine learning-based research, we conclude that meteorological drought develops and intensifies largely through an absence of synoptic processes responsible for strong moisture transport and heavy precipitation. The subsequent presence of these synoptic processes is key to drought termination. Large-scale modes of climate variability modulate drought through teleconnections, which alter drought-determining synoptic behaviour. On local scales, land surface processes play an important role in intensifying dry conditions and propagating meteorological drought through the hydrological cycle. In the future, Australia may experience longer and more intense droughts than have been observed in the instrumental record, although confidence in drought projections remains low. We propose a research agenda to address key knowledge gaps to improve the understanding, simulation and projection of drought in Australia and around the world.

Australia experiences meteorological droughts due to insufficient moisture transport and heavy precipitation, which are influenced by climate variability and land processes, and are expected to become longer and more frequent, according to a review of observational and model-based studies.

Some of the most important considerations when undertaking dynamical downscaling of global climate models (GCMs) using regional climate models are the choice of model physical parameterisations, the use of spectral nudging, and whether to bias correct the driving GCMs prior to downscaling. While each of these factors have been extensively examined, very few studies have compared the effect of all 3 on model biases against independent observations during the historical period, as well as the change in future climate. We carry out this analysis and focus on the CORDEX-Australasia domain with all simulations driven using a common GCM. We found that the choice of model parameterisaton schemes had by far the largest influence on model biases and the change in climate, especially for precipitation during summer. While bias correction reduced large systematic biases for some variables in some regions, it also increased biases elsewhere, and results were not consistent for all variables. Our results show that it is important to first assess the performance of non-corrected GCM-driven simulations against the reference re-analysis driven simulations, as bias correction may not be necessary if the GCM-driven simulation already performs well compared to the reference simulation. Spectral nudging had a limited influence on both model biases and the change in climate, except for summer precipitation in the tropics. While we only use a single RCM and a single GCM, our key finding is that given limited computational and data resources, regional climate modelling groups should prioritize a multi-physics ensemble of the RCM to better account for internal physics-driven variability, over the use of bias-correction or spectral nudging.

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.

South-west Western Australia (SWWA) is home to a world class grains industry that is significantly affected by periods of drought. Previous research has shown a link between the Southern Annular Mode (SAM) and rainfall in SWWA, especially during winter months. Hence, the predictability of the SAM and its relationship to SWWA rainfall can potentially improve forecasts of SWWA drought, which would provide valuable information for farmers. In this paper, focusing on the 0-month lead time forecast, we assess the bias and skill of ACCESS-S2, the Australian Bureau of Meteorology’s current operational sub-seasonal to seasonal forecasting system, in simulating seasonal rainfall for SWWA during the growing season (May–October). We then analyse the relationship between the SAM and SWWA precipitation and how well this is captured in ACCESS-S2 as well as how well ACCESS-S2 forecasts the monthly SAM index. Finally, ACCESS-S2 rainfall forecasts and the simulation of SAM are assessed for a case study of extreme drought in 2010. Our results show that forecasts tend to have greater skill in the earlier part of the season (May–July). ACCESS-S2 captures the significant inverse SAM–rainfall relationship but underestimates its strength. The model also shows overall skill in forecasting the monthly SAM index and simulating the MSLP and 850-hPa wind anomaly patterns associated with positive and negative SAM phases. However, for the 2010 drought case study, ACCESS-S2 does not indicate strong likelihoods of the upcoming dry conditions, particularly for later in the growing season, despite predicting a positive (although weaker than observed) SAM index. Although ACCESS-S2 is shown to skillfully depict the SAM–SWWA rainfall relationship and generally forecast the SAM index well, the seasonal rainfall forecasts still show limited skill. Hence it is likely that model errors unrelated to the SAM are contributing to limited skill in seasonal rainfall forecasts for SWWA, as well as the generally low seasonal-timescale predictability for the region.