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This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes. Plain Language Summary: This paper presents a data‐driven method to improve rainfall‐runoff models used to generate future water resources scenario in climate change studies. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to monthly streamflow simulations over a large data set of catchments in South‐East Australia. Our results show that DAISI improves model performance for a wide range of metrics and increases the sensitivity of the model to climate inputs, which is critical in climate change scenarios. Finally, the improvements identified by DAISI take a simple mathematical form with distinct regional patterns in three sub‐regions of the study domain (Western Victoria, central region, and Northern New South Wales). Several improvements of DAISI are discussed including the inclusion of additional observed variables such as evapotranspiration to better constrain model simulations. Key Points: Data Assimilation Informed model Structure Improvement method diagnoses hydrological model structures by combining data assimilation with a polynomial update of state equationsThe method was applied to the GR2M rainfall‐runoff model with significantly improved streamflow simulations in 201 Australian catchmentsThe method identified updates to state equations with marked regional characteristics that could guide future improvement of GR2M [ABSTRACT FROM AUTHOR]

Summary: We synthesise the findings from 10 years of ecological restoration in the Midlands of Tasmania, Australia, captured in the series of 14 papers in this special issue of Ecological Management and Restoration. The papers illustrate how expertise from disciplines as diverse as law, economics, social sciences, the arts, education, zoology, botany, genetics, climate modelling, agriculture, spatial sciences and fire ecology are necessary to address the complex social, ecological and financial questions that underpin restoration ecology. We highlight the complexity of the task, the multi‐disciplinary and collaborative approach needed, the importance of science to inform restoration practice and the problem of achieving functional connectivity. We also outline steps that need to be taken in the next 10 years. Together, the outcomes and recommendations from these studies provide a template for restoration in similar highly cleared and degraded agricultural landscapes affected by climate change in Australia and internationally. [ABSTRACT FROM AUTHOR]

We assess the impact of three bias correction approaches on present day means and extremes, and climate change signal, for six climate variables (precipitation, minimum and maximum temperature, radiation, vapour pressure and mean sea level pressure) from dynamically downscaled climate simulations over Queensland, Australia. Results show that all bias‐correction methods are effective at removing systematic model biases, however the results are variable and season‐dependent. Importantly, our results are based on fully independent cross‐validation, an advantage over similar studies. Linear scaling preserves the climate change signals for temperature, while quantile mapping and the distribution‐based transfer function modify the climate change signal and patterns of change. The Perkins score for all the values above the 95th percentile and below the 5th percentile was used to evaluate how well the climate model matches the observational data. Bias correction improved Perkins score for extremes for some variables and seasons. We rank the bias‐correction methods based on the Kling–Gupta efficiency (KGE) score calculated during the validation period. We find that linear scaling and empirical quantile mapping are the best approaches for Queensland for mean climatology. On average, bias correction led to an improvement in the KGE score of 23% annually. However, in terms of extreme values, quantile mapping and statistical distribution‐based transfer function approaches perform best, and linear scaling tends to perform worst. Our results show that, except linear scaling, all approaches impact the climate change signal. [ABSTRACT FROM AUTHOR]

High‐resolution climate change projections are increasingly necessary to inform climate policy and adaptation planning. Downscaling of global climate models (GCMs) is required to simulate the climate at the spatial scale relevant for local impacts. Here, we dynamically downscaled 15 CMIP6 GCMs to a 10 km resolution over Australia using the Conformal Cubic Atmospheric model (CCAM), creating the largest ensemble of high‐resolution downscaled CMIP6 projections for Australia. We compared the host CMIP6 models and downscaled simulations to the Australian Gridded Climate Data (AGCD) observational data and evaluated performance using the Kling‐Gupta efficiency and Perkins skill score. Downscaling improved performance over host GCMs for seasonal temperature and precipitation (10% and 43% respectively), and for annual cycles of temperature and precipitation (6% and 13% respectively). Downscaling also improved the fraction of dry days, reducing the bias for too many low‐rain days. The largest improvements were found in climate extremes, with enhancements to extreme minimum temperatures in all seasons varying from 142% to 201%, and to extreme precipitation of 52% in Austral winter and 47% in summer. The ensemble average integrated skill score improved by 16%. Temperature and precipitation biases were reduced in mountainous and coastal areas. CCAM downscaling outperformed host CMIP6 GCMs at multiple spatial scales and regions—continental Australia, Australian IPCC regions and Queensland's regions—with integrated added value ranging from 9% to 150% and higher over densely populated regions more exposed to climate impacts. This data set will be a valuable resource for understanding future climate changes in Australia. Plain Language Summary: High‐resolution climate models are used for producing climate change projections for assessing regional and local climate change impacts and for adaptation and policy formulation. We completed 15 high‐resolution climate simulations using the Conformal Cubic Atmospheric Model at a 10 km spatial resolution over the Australian continent and surrounds based on global climate models (GCMs) used in the Intergovernmental Panel on Climate Change Assessment Report 6. We evaluated the new high‐resolution data set by comparing it to observations and to their host global model across several regions within Australia. The high‐resolution simulations improved the representation of topography, seasonal temperature and annual cycles of temperature, precipitation, as well as the number of dry days, extreme precipitation and extreme minimum temperatures. The high‐resolution projections show improvement compared to GCMs consistently across spatial scales and regions, being particularly high (150%) over urban areas with high exposure to impacts. This new 10 km data set is the highest resolution, and largest ensemble of the latest climate projections for the Australian continent and surrounds and will be a valuable tool for evaluating future climate change impacts. Key Points: We dynamically downscaled 15 CMIP6 global climate model simulations over Australia to a 10 km spatial resolution using the Conformal Cubic Atmospheric modelTwo new assessment metrics are proposed to assess model performance and added value of downscalingThe integrated added value of downscaling can be as high as 150% over highly populated areas [ABSTRACT FROM AUTHOR]

Current methods for climate change assessment ignore the significant differences in uncertainty in model projections of the two key constituents of drought, precipitation, and evapotranspiration. We present here a new basis for assessing future drought using climate model simulations that addresses this limitation. The new method estimates the Standardized Precipitation Evapotranspiration Index (SPEI) in a two‐stage process. The first stage of our proposed approach is to derive the Standardized Precipitation Index (SPI) using reliable atmospheric variables, which are filtered with a wavelet‐based spectral transformation. This derived SPI is then converted to an equivalent SPEI by combining it with climate model evapotranspiration simulations. We assess the performance of our proposed approach across Australia. The consistency of general circulation model (GCM) drought projections, in terms of both frequency and severity, is improved using the derived SPI. Incorporating evapotranspiration further improves the consistency of the multiple GCMs and drought time scales. The proposed framework can also be generalized to other water resources applications, where the differences in GCM uncertainty for precipitation and evapotranspiration affect climate change impact assessments. Plain Language Summary: Drought is affected by both rainfall and evapotranspiration. Drought indices represent drought severity compared to normal conditions as a function of time. Some drought indices are based on rainfall alone and some use both rainfall and evapotranspiration in the calculations. To understand future drought risk, simulations from climate models are needed. Unfortunately, different climate models often disagree on amounts and patterns of rainfall in the future, the disagreement being considerably more on rainfall than for evapotranspiration. In this study, we attempt to reduce the impact of these differences by developing a new method to estimate future drought. We used a mathematical method known as wavelets to estimate drought indices based on rainfall. Evapotranspiration is then used directly from the climate model and combined with the rainfall based drought index to create one overall drought index. We used projections from multiple climate models to understand if our new method led to a greater agreement in how often and severe future droughts may be. Our results confirm that the new method offers greater consistency in drought projections for the future. Key Points: Current methods for projecting drought ignore differences in uncertainty between P and ET simulationsA new basis for projecting drought is proposed that explicitly accounts for relative uncertainty between P and ETOur results show that the new method offers greater consistency in drought projections for the future [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) are essential for investigating climate change, but their coarse scale limits their efficacy for climate adaptation planning at the regional scales where climate impacts manifest. Dynamical downscaling of GCM outputs better resolves regional climate and thus provides improved guidance for climate policy at regional scales. Being expensive to run, downscaling uses a subset of GCMs, necessitating careful GCM selection. This evaluation identifies a suitable subset of CMIP6 GCMs for downscaling over Australia by assessing individual GCMs against three criteria: (a) performance simulating daily climate variable distributions, climate means, extremes, and modes; (b) model independence; and (c) climate change signal diversity. Over Australia, GCMs are generally biased cold (warm) for maximum (minimum) temperature, with larger biases for minimum temperature. GCMs are generally wet biased, especially over the monsoonal north, but dry biased over eastern regions. Most GCMs show larger biases for temperature and precipitation over geographically complex, heavily populated eastern regions, relative to other regions. Evaluations identify a distinct group of 11 GCMs that perform consistently poorly across climate variables, statistics, and timescales with widespread, statistically significant biases, versus 13 GCMs that show consistent adequate‐to‐good performance with substantially reduced errors. Assessment of model independence highlights the lack of independence between several high‐performing GCMs, particularly from allied modeling groups, demonstrating the importance of careful ensemble selection when making selective samples of climate space. Once GCM climate signal diversity is considered, 6–8 mid‐to‐high‐performing, independent GCMs occupy the full range of the future climate space and, thus, are suitable for dynamical downscaling over CORDEX‐Australasia. Key Points: Per‐global climate model (GCM) performances varied markedly, though the best‐performing GCMs simulated consistently well across temperature variables and statisticsAssessment of model independence highlights the lack of independence between several high‐performing GCMsSix to eight mid‐to‐high‐performing, independent GCMs capturing a broad range of the future climate space are identified as suitable for downscaling [ABSTRACT FROM AUTHOR]

Aim: Many species are adapted to a particular fire regime and major deviations from that regime may lead to localized extinction. Here, we quantify immaturity risks to an obligate‐seeder forest tree using an objectively designed climate model ensemble and a probabilistic fire regime simulator to predict future fire regimes. Location: Alpine ash (Eucalyptus delegatensis) distribution, Victoria, south‐eastern Australia. Methods: We used a fire regime model (FROST) with six climate projections from a climate model ensemble across 3.7 million hectares of native forest and non‐native vegetation to examine immaturity risks to obligate‐seeder forests dominated by alpine ash (Eucalyptus delegatensis), which has a primary juvenile period of approximately 20 years. Our models incorporated current and future projected climate including fuel feedbacks to simulate fire regimes over 100 years. We then used Random Forest modelling to evaluate which spatial characteristics of the landscape were associated with high immaturity risks to alpine ash forest patches. Results: Significant shifts to the fire regime were predicted under all six future climate projections. Increases in both wildfire extent (total area burnt, area burnt at high intensity) and frequency were predicted with an average increase of up to 110 hectares burnt annually by short‐interval fires (i.e., within the expected minimum time to reproductive maturity). The immaturity risk posed by short‐interval fires to alpine ash forest patches was well explained by Random Forest models and varied with both location and environmental variables. Main conclusions: Alpine ash forests are predicted to be burned at greater intensities and shorter intervals under future fire regimes. About 67% of the current alpine ash distribution was predicted to be at some level of immaturity risk over the 100‐year modelling period, with the greatest risks to those patches located on the periphery of the current distribution, closer to roads or surrounded by a drier landscape at lower elevations. [ABSTRACT FROM AUTHOR]

During March 2021, large regions of Eastern Australia experienced prolonged heavy rainfall and extensive flooding. The maximum daily mean column integrated water vapor transport (IVT) over Sydney during this event was within the top 0.3% of all days since 1980, and the 10‐day mean IVT was in the top 0.2%. In this study, we have examined the change in frequency of extreme IVT events over Sydney in 16 climate models from the Coupled Model Intercomparison Project 6 under two Shared Socioeconomic Pathways (SSP245 and SSP585). Generalized Extreme Value modeling was used to further analyze the change in frequency of extreme IVT events. We found the probability of long duration high IVT events is projected to increase by 80% at the end of the century, but the future change in IVT is correlated to the rate of global and regional warming in each model. Plain Language Summary: During March 2021, large regions of Eastern Australia experienced prolonged heavy rainfall and extensive flooding. This was associated with strong horizontal water vapor transport over this region that persisted for approximately 10 days. The amount of water vapor transported over Sydney during this event was extreme and within the top 0.3% of all days since 1980. In this study, we used climate models to project how much more often events such as these may occur by the end of the twenty‐first century under two greenhouse gas emission scenarios. We found that the probability of long duration high water vapor transport over Sydney, as in March 2021, may increase by 80%. Key Points: Sydney's March 2021 floods were associated with persistent high water vapor transportThe probability of long‐duration high integrated water vapor transport events should increase by the end of the twenty‐first centuryDifferences in climate sensitivity within the CMIP6 ensemble may increase the spread of moisture flux projections [ABSTRACT FROM AUTHOR]

The NARCliM project contributes to the CORDEX initiative for Australasia. The first generation of NARCliM (N1.0) used CMIP3 global climate models (GCMs) and provided near and far future estimates of climate change across Australasia at 50‐km and southeast Australia at 10‐km resolution under a business‐as‐usual climate scenario. However, multiple sets of 20‐year periods in N1.0 did not permit analysis of long‐term, inter‐annual to decadal trends across the 21st century. Feedback on user needs for regional climate information revealed the desire for multiple emission scenarios and use of newer CMIP5 GCMs for dynamical downscaling. These limitations led to development of the second iteration of NARCliM, namely NARCliM1.5 (N1.5). The N1.5 downscaling exercise uses CMIP5 GCMs and is temporally expanded to cover 150 years (1950–2100) for two future Representative Concentration Pathways (RCP4.5 and RCP8.5). N1.5 simulations remain at the 50‐km and 10 km resolutions over the same domains as N1.0, thus producing an expanded and complementary data set for regional climate change. N1.5 simulations substantially improve over N1.0 in capturing the seasonal patterns and magnitudes of precipitation, including improvements in overall bias. Conversely, N1.5 shows similar results to N1.0 for maximum and minimum temperature, with no substantial improvement in overall bias. N1.5 projections project a hotter and drier future relative to N1.0. The combined N1.0 and N1.5 ensemble provides a wider spread of future climates more representative of that found in the full CMIP5 ensemble. Together, N1.0 and N1.5 ensembles provide an improved, more comprehensive data set for studying climate change. Plain Language Summary: The second iteration of New South Wales and Australian Capital Territory Regional Climate Modeling (NARCliM) simulations (N1.5) are described, complementing original NARCliM (N1.0) simulations and expanding the ensemble to 24 members. N1.5 simulations substantially improve over N1.0 simulations in capturing the seasonal patterns and magnitudes of precipitation. N1.5 projections add information to the NARCliM ensemble including the potential for hotter and drier futures than those projected in N1.0. Together, the N1.0 and N1.5 ensembles provide an improved, more comprehensive data set for studying climate change. The varying model performances identified here will help the development of the next generation of regional climate simulations over Australia. Key Points: The second iteration of NARCliM simulations (N1.5) are described complementing original NARCliM simulations (N1.0)N1.5 simulations substantially improve over N1.0 simulations in capturing the seasonal patterns and magnitudes of precipitationN1.5 projections add information to the NARCliM ensemble including the potential for hotter and drier futures than those projected in N1.0 [ABSTRACT FROM AUTHOR]

Climate change projections for the Australian monsoon have been highly uncertain in previous generations of coupled climate models. The new Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble provides an opportunity to address the uncertainty in future projections for northern Australia. We find that the range in Australian monsoon projections from the available CMIP6 ensemble is substantially reduced compared to CMIP5, although models continue to disagree on the magnitude and direction of change. While previous CMIP5 studies identified warming in the western equatorial Pacific as important for Australian monsoon projections, here we show that the western Pacific is not strongly connected to northern Australian precipitation changes in the CMIP6 models. By comparing groups of models based on their future projections, we note that the model‐to‐model differences in Australian monsoon projections are congruent with the zonally averaged precipitation response in the Southern Hemisphere tropics within each model. Key Points: Australian monsoon changes are smaller in the available CMIP6 ensemble than the CMIP5 ensembleCMIP6 models still disagree on changes to Australian monsoon precipitation in a warmer worldAustralian monsoon changes are congruent with the zonal mean precipitation change, more evident in CMIP6 than CMIP5 [ABSTRACT FROM AUTHOR]

Outputs from new state‐of‐the‐art climate models under the Coupled Model Inter‐comparison Project phase 6 (CMIP6) promise improvement and enhancement of climate change projections information for Australia. Here we focus on three key aspects of CMIP6: what is new in these models, how the available CMIP6 models evaluate compared to CMIP5, and their projections of the future Australian climate compared to CMIP5 focussing on the highest emissions scenario. The CMIP6 ensemble has several new features of relevance to policymakers and others, for example, the integrated matrix of socioeconomic and concentration pathways. The CMIP6 models show incremental improvements in the simulation of the climate in the Australian region, including a reduced equatorial Pacific cold tongue bias, slightly improved rainfall teleconnections with large‐scale climate drivers, improved representation of atmosphere and ocean extreme heat events, as well as dynamic sea level. However, important regional biases remain, evident in the excessive rainfall over the Maritime Continent and rainfall pattern biases in the nearby tropical convergence zones. Projections of Australian temperature and rainfall from the available CMIP6 ensemble broadly agree with those from CMIP5, except for a group of CMIP6 models with higher climate sensitivity and greater warming and increase in some extremes after 2050. CMIP6 rainfall projections are similar to CMIP5, but the ensemble examined has a narrower range of rainfall change in austral summer in Northern Australia and austral winter in Southern Australia. Overall, future national projections are likely to be similar to previous versions but perhaps with some areas of improved confidence and clarity. Key Points: CMIP6 shows incremental improvement of the simulation of Australian climate compared to CMIP5CMIP6 climate projections are similar to CMIP5 except for a group of higher sensitivity models with warmer projected change [ABSTRACT FROM AUTHOR]

Many general circulation models fail to capture the observed frequency of atmospheric blocking events in the Northern Hemisphere; however, few studies have examined models in the Southern Hemisphere and those studies that have, have often been based on only a few models. To provide a comprehensive view of how the current generation of coupled general circulation models performs in the Southern Hemisphere and how blocking frequency changes under enhanced greenhouse gas forcing, we examine the output of 23 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We find that models have differing biases during winter, when blocking occurrence is highest, though models underestimate blocking frequency south of Australia during summer. We show that models generally have a reduction in blocking frequency with future anthropogenic forcing, particularly in the Australia‐New Zealand sector with the number of winter blocked days reduced by about one third by the end of the 21st century. Plain Language Summary: Atmospheric blocking is a process in which an atmospheric wave breaks, diverting the jet stream and any incoming storms to the north or south. A blocking event is characterized by high pressure on the poleward side and low pressure on the equatorward side and persists for upward of 4 days. A key feature of blocks is their persistence, which can lead to events, which have significant impacts on society including heat waves in summer and periods of extreme cold in winter. Climate models often do not simulate enough blocking events, though most research on this problem has focused on the Northern Hemisphere and less is known about the Southern Hemisphere. We survey 23 climate models and show that during winter some models simulate too many blocking events, while others simulate too few, whereas during summer, almost all models simulate too few events to the south of Australia. We also show that with higher concentrations of greenhouse gases we expect there to be less blocking, particularly to the south of Australia and over New Zealand during winter. Key Points: We provide a comprehensive survey of Southern Hemisphere blocking using a large number of coupled modelsFuture projections generally suggest an overall decrease in Southern Hemisphere blocking frequencyThere is strong model agreement on blocking frequency decreases to the south of Australia during austral winter [ABSTRACT FROM AUTHOR]