Search

Climate change is typically modeled using sophisticated mathematical models (climate models) of physical processes that range in temporal and spatial scales. Multi-model ensemble means of climate models show better correlation with the observations than any of the models separately. Currently, an open research question is how climate models can be combined to create an ensemble mean in an optimal way. We present a novel stochastic approach based on Markov chains to estimate model weights in order to obtain ensemble means. The method was compared to existing alternatives by measuring its performance on training and validation data, as well as model-as-truth experiments. The Markov chain method showed improved performance over those methods when measured by the root mean squared error in validation and comparable performance in model-as-truth experiments. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods that address the issues of finding optimal model weight for constructing ensemble means.

Violent fire-driven convection can manifest as towering pyrocumulus (pyroCu) or pyrocumulonimbus (pyroCb) clouds, which can have devastating impacts on the environment and society. Their associated fire spread is erratic, unpredictable and not generally suppressible. Research into large pyroconvective events has mainly focused on the atmospheric processes involved in normal atmospheric convection, or on surface fire weather and associated fuel conditions. There has been comparatively less attention paid to the role of the fire itself in these coupled fire–atmosphere events. This paper draws on recent insights into dynamic fire propagation and extreme wildfire development to investigate how the fire influences the occurrence of violent pyroconvective events. A static heat source of variable dimension and intensity is used. This is accompanied by a companion paper that extends the analysis by including the effect of fire geometry on the pyroconvective plume. The analyses indicate that the spatial expanse and intensity of large fires are critical factors driving the development of pyroconvective plumes and can override the influence of the stability of the atmosphere. These findings provide motivation for further investigation into the effect of the fire’s attributes on the immediate atmosphere and have the potential to improve forecasting of blow-up fire events.

Fire spread associated with violent pyrogenic convection is highly unpredictable and difficult to suppress. Wildfire-driven convection may generate cumulonimbus (storm) clouds, also known as pyrocumulonimbus (pyroCb). Research into such phenomena has tended to treat the fire on the surface and convection in the atmosphere above as separate processes. We used a numerical model to examine the effect of fire geometry on the height of a pyroconvective plume, using idealised model runs in a neutral atmosphere. The role of geometry was investigated because large areal fires have been associated with the development of pyroCb. Complementary results (detailed in Part I) are extended by considering the effect that fire shape can have on plume height by comparing circular, square, and rectangular fires of varying length and width, representing the difference between firelines and areal fires. Results reveal that the perimeter/area ratio influenced the amount of entrainment that the plume experiences and therefore the height to which the plume rises before it loses buoyancy. These results will aid in the prediction of blow-up fires (whereby a fire exhibits a rapid increase in rate of spread or rate of spread) and may therefore be useful in determining where fire agencies deploy their limited resources.

The relative importance of atmospheric advection and local land–atmosphere coupling to Australian precipitation is uncertain. Identifying the evaporative source regions and level of precipitation recycling can help quantify the importance of local and remote marine and terrestrial moisture to precipitation within the different hydroclimates across Australia. Using a three-dimensional Lagrangian back-trajectory approach, moisture from precipitation events across Australia during 1979–2013 was tracked to determine the source of moisture (the evaporative origin) and level of precipitation recycling. We show that source regions vary markedly for precipitation falling in different regions. Advected marine moisture was relatively more important than terrestrial contributions for precipitation in all regions and seasons. For Australia as a whole, contributions from precipitation recycling varied from ~11% in winter up to ~21% in summer. The strongest land–atmosphere coupling was in the northwest and southeast where recycled local land evapotranspiration accounted for an average of 9% of warm-season precipitation. Marine contributions to precipitation in the northwest of Australia increased in spring and, coupled with positive evaporation trends in the key source regions, suggest that the observed precipitation increase is the result of intensified evaporation in the Maritime Continent and Indian and Pacific Oceans. Less clear were the processes behind an observed shift in moisture contribution from winter to summer in southeastern Australia. Establishing the climatological source regions and the magnitude of moisture recycling enables future investigation of anomalous precipitation during extreme periods and provides further insight into the processes driving Australia’s variable precipitation.

A new regional climate projection ensemble has been created for the Australasia region as part of the World Climate Research Programs Coordinated Regional Downscaling Experiment (CORDEX). The CORDEX-Australasia ensemble is the largest regional climate projection ensemble ever created for the region. It is a 20-member ensemble made by 6 regional climate models downscaling 11 global climate models. Overall the ensemble produces a good representation of recent climate. Consistent biases within the ensemble include an underestimation of the diurnal temperature range and an underestimation of precipitation across much of southern Australia. Under a high emissions scenario projected temperature changes by the end of the twenty-first century reach ~ 5 K in the interior of Australia with smaller increases found toward the coast. Projected precipitation changes are towards drying, particularly in the most populated areas of the southwest and southeast of the continent. The projected precipitation change is very seasonal with summer projected to see little change leaning toward an increase. These results provide a foundation enabling future studies of regional climate changes, climate change impacts, and adaptation options for Australia.

An accurate description of changes in extreme rainfall events requires high resolution simulations. Regional climate models (RCMs), where GCM data are used to provide input boundary conditions, are widely used as a way to resolve finer spatial scale phenomena. A problem with this, however, is that the inherent systematic biases within the GCM simulation are transferred to the RCM through the model boundaries. In this work we focus on the impact of bias correction of lateral and lower boundary conditions on simulated extreme rainfall events. Here three bias correction approaches are investigated. In increasing order of complexity, these are corrections for the mean, mean and variance, and the nested bias correction (NBC) approach that also corrects for lag-1 autocorrelations at nested timescales. These corrections are implemented on six-hourly GCM data taken from the GCM simulations which are used to drive the RCM along the RCM lateral boundaries. To evaluate the performance of bias correction on simulation of extreme rainfall events, daily precipitation extremes indices from the World Meteorological Organization (WMO) Expert Team on Climate Risk and Sectoral Climate Indicators (ET-CRSCI) are used. The results show that bias correction on the boundary conditions produce the results in significant improvement in extremes indices. It is clear that sea surface temperature (SST) plays an important role in driving the simulation. The results indicate that within the domain (far from boundaries) the errors in precipitation extremes are strongly dependent on the RCM, with a smaller effect coming from changes in the lateral boundary conditions.

We examine the relative impact of population increases and climate change in affecting future water demand for Sydney, Australia. We use the Weather and Research Forecasting model, a water demand model and a stochastic weather generator to downscale four different global climate models for the present (1990–2010), near (2020–2040) and far (2060–2080) future. Projected climate change would increase median metered consumption, at 2019/2020 population levels, from around 484 GL under present climate to 484–494 GL under near future climate and 495–505 GL under far future climate. Population changes from 2014/2015 to 2024/2025 have a far larger impact, increasing median metered consumption from 457 to 508 GL under the present climate, 463 to 515 GL under near future climate and from 471 to 524 GL under far future climate. The projected changes in consumption are sensitive to the climate model used. Overall, while population growth is a far stronger driver of increasing water demand than climate change for Sydney, both act in parallel to reduce the time it would take for all storage to be exhausted. Failing to account for climate change would therefore lead to overconfidence in the reliability of Sydney's water supply. HIGHLIGHTS This paper combines an urban water consumption model, regional climate models and a stochastic weather generator to generate probabilistic consumption forecasts.; This paper analyses the effect of climate change on urban water consumption.; This paper compares the relative effect of climate change and population on urban water consumption.; This paper analyses the effect of dwelling type on urban water consumption.; This paper compares the statistical properties of weather variables from different regional climate models.

Regional climate models (RCM) are an important tool for simulating atmospheric information at finer resolutions often of greater relevance to local scale climate change impact assessment studies. The lateral and lower boundary conditions, which form the inputs to the RCM downscaling application, are outputs from the global climate model (GCM). These boundary variables are known to be biased in GCMs, providing the potential to use a statistical approach that corrects these biases before use in downscaling. An array of bias correction techniques have been developed to remove these biases before being used to drive the RCM, but questions remain on their efficacy in terms of the final downscaled output. This study assesses the impact of these bias correction strategies by focussing on how these corrections are translated as one proceeds from the lateral boundaries into the model interior. Of specific interest is the change in the correction from generation of the lateral boundary conditions as well as how correction information moves through the relaxation zone and into the interior of the model. Here we show that bias correction information passing into the regional climate model is limited by interpolations required to generate lateral boundary conditions and dominant outflow wind conditions in the boundaries. This work suggests that these limitations should be addressed in order for bias correction of lateral boundary conditions to robustly influence RCM simulations of climate in the interior of the model domain.

The vertical temperature profile in the atmosphere reflects a balance between radiative and convective processes and interactions with the oceanic and land surfaces. Changes in vertical temperature profiles can affect atmospheric stability, which in turn can impact various aspects of weather systems. In this study, we analyzed recent-past trends of temperature over the Australian region using a homogenized monthly upper-air temperature dataset and four reanalysis datasets (NCEP, ERA-Interim, JRA-55 and MERRA). We also used outputs of 12 historical and future regional climate model (RCM) simulations from the NSW/ACT (New South Wales/Australian Capital Territory) Regional Climate Modelling (NARCliM) project and 6 RCM simulations from the CORDEX (Coordinated Regional Downscaling Experiment) Australasian project to investigate projected changes in vertical temperature profiles. The results show that the currently observed positive trend in the troposphere and negative trend in the lower stratosphere will continue in the future with significant warming over the whole troposphere and largest over the middle to upper troposphere. The increasing temperatures are found to be latitude-dependent with clear seasonal variations, and a strong diurnal variation for the near surface layers and upper levels in tropical regions. Changes in the diurnal variability indicate that near surface layers will be less stable in the afternoon leading to conditions favoring convective systems and more stable in the early morning which is favorable for temperature inversions. The largest differences of future changes in temperature between the simulations are associated with the driving GCMs, suggesting that large-scale circulation plays a dominant role in regional atmospheric temperature change.

Coarse resolution global climate models (GCMs) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. However, evidence of where and when downscaling provides new information about both the current climate (added value, AV) and projected climate change signals, relative to driving data, is lacking. Seasons and locations where CORDEX-Australasia ERA-Interim and GCM-driven RCMs show AV for mean and extreme precipitation and temperature are identified. A new concept is introduced, ‘realised added value’, that identifies where and when RCMs simultaneously add value in the present climate and project a different climate change signal, thus suggesting plausible improvements in future climate projections by RCMs. ERA-Interim-driven RCMs add value to the simulation of summer-time mean precipitation, especially over northern and eastern Australia. GCM-driven RCMs show AV for precipitation over complex orography in south-eastern Australia during winter and widespread AV for mean and extreme minimum temperature during both seasons, especially over coastal and high-altitude areas. RCM projections of decreased winter rainfall over the Australian Alps and decreased summer rainfall over northern Australia are collocated with notable realised added value. Realised added value averaged across models, variables, seasons and statistics is evident across the majority of Australia and shows where plausible improvements in future climate projections are conferred by RCMs. This assessment of varying RCM capabilities to provide realised added value to GCM projections can be applied globally to inform climate adaptation and model development.

Accurate estimates of terrestrial water and energy cycle components are needed to better understand climate processes and improve models’ ability to simulate future change. Various observational estimates are available for the individual budget terms; however, these typically show inconsistencies when combined in a budget. In this work, a Conserving Land–Atmosphere Synthesis Suite (CLASS) of estimates of simultaneously balanced surface water and energy budget components is developed. Individual CLASS variable datasets, where possible, 1) combine a range of existing variable product estimates, and hence overcome the limitations of estimates from a single source; 2) are observationally constrained with in situ measurements; 3) have uncertainty estimates that are consistent with their agreement with in situ observations; and 4) are consistent with each other by being able to solve the water and energy budgets simultaneously. First, available datasets of a budget variable are merged by implementing a weighting method that accounts both for the ability of datasets to match in situ measurements and the error covariance between datasets. Then, the budget terms are adjusted by applying an objective variational data assimilation technique (DAT) that enforces the simultaneous closure of the surface water and energy budgets linked through the equivalence of evapotranspiration and latent heat. Comparing component estimates before and after applying the DAT against in situ measurements of energy fluxes and streamflow showed that modified estimates agree better with in situ observations across various metrics, but also revealed some inconsistencies between water budget terms in June over the higher latitudes. CLASS variable estimates are freely available viahttps://doi.org/10.25914/5c872258dc183.

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.

Cold drylands make up 20% of the world's water-limited regions. This paper presents a modification to the Time Series Segmented-RESidual TRENDs (TSS-RESTRENDs) method that allows for the use of temperature as an additional explanatory variable along with precipitation. TSS-RESTREND was performed over Mongolia, both with and without temperature. The addition of temperature reduced the number of pixels that fail the significance tests built into the TSS-RESTREND method from 17% to below 5%. It also improved the statistical confidence in almost all areas. Furthermore, the direction of change is consistent with previous findings that looked at vegetation trends over the same study region. When applied to all of the world's drylands, the inclusion of temperature improved the fit of the vegetation–climate relationship that underpins TSS-RESTREND in 98.8% of areas. The largest improvements to the fit were observed in both the cold drylands of central Asia and North America and the hot drylands of southern Australia. Including temperature also reduced the fraction of global vegetation change that could be attributed to neither climate nor land use from 25.5% to 15.5%.

Intense cyclones often result in severe impacts on mid-latitude coastal regions of southeastern Australia, including those due to associated natural hazards such as extreme winds, ocean waves, storm surges, precipitation, flooding, erosion, lightning and tornadoes in some cases. These low-pressure systems, known as east coast lows (ECLs), have been examined in a wide range of different studies, with considerable variations between such studies in what they consider to be an ECL, and their findings on the characteristics of these storm systems. Here we present reviews of literature and other information such as operational forecasting approaches, which are then used to produce a comprehensive synthesis of knowledge on ECLs and associated weather and ocean extremes. This includes aspects such as their definition, formation, meteorology, climatology and drivers of variability from short-term weather time scales up to long-term historical climate trends and future projections. Australian ECLs are also considered here in relation to similar phenomena from other regions of the world. A definition based on this synthesis of knowledge is as follows: ECLs are cyclones near southeastern Australia that can be caused by both mid-latitude and tropical influences over a range of levels in the atmosphere; Intense ECLs have at least one major hazard associated with their occurrence, including extreme winds, waves, rain or flooding. Knowledge gaps are examined and used to provide recommendations for future research priorities. This study is intended to lead to improved guidance and preparedness in relation to the impacts of these storms.

The use of regional climate models (RCMs) to localise results from coarse-resolution global climate models has recently attracted more interest in agricultural impact studies. Theoretically, it has advantages over global climate models in terms of realisation of future climate projections. However, there are few studies that have used dynamical downscaling results to assess climate change impacts on Australian cropping systems. In this study, we used post-processing bias-corrected climate data from the NSW/ACT Regional Climate Modelling (NARCliM) project to drive the Agricultural Production Systems SIMulator (APSIM) for modelling and evaluating the response of three major crops (wheat, canola and lupin) to projected climate conditions under various farm management practices in south-eastern Australia. Our results showed that historical crop yields from APSIM simulations forced by RCM output tended to underestimate yields from simulations forced by observations due to large biases in the NARCliM simulations. Therefore, bias correction was used to correct the APSIM outputs before conducting future impact analysis. The bias-corrected results showed that ensemble-mean yields based on 12 RCMs were projected to increase over the study area under the A2 emission scenario. However, the magnitude of yield increase depended on the time periods, crop type and location. Multiple linear regression models showed that the changes of radiation, rainfall and temperature and elevated CO2 concentration could explain 60–78% of crop yield changes. It is interesting to note that residue incorporation and nitrogen application had a large effect on percentage yield increases for wheat due to future climate change, but had limited effects on the response of lupin and canola yields. Our study suggests that sufficient bias-correction method is needed when using NARCliM outputs in crop models. Although uncertainties (e.g. the choice of emission scenarios and RCMs) still exist in our study, the results are of central importance in the development of high-yield adaptive strategies for local farmers and policy makers in south-eastern Australia.

Change to precipitation in a warming climate holds many implications for water management into the future, and an enhancement of a precipitation decrease or increase on or around mountains would have numerous impacts. Here, an intermediate resolution regional climate model (RCM) ensemble projects enhanced precipitation decrease on the windward slopes of over many mid-latitude mountains in winter, consistent with theory and model studies of idealised mountain ranges. This ensemble projects that an increase in convective rainfall determines the sign of total rainfall change in many regions in summer, only some of which are on or near mountains such as the European Alps. These same projected changes are present in inland slopes of the Australian Alps compared to surrounding regions as simulated by three RCM ensembles (the intermediate resolution and two high resolution ensembles), which agree on an enhanced precipitation decrease on the windward slopes in winter and spring, as well as an enhanced precipitation increase in summer driven by an increase in convective rainfall. The ensembles disagree on an enhanced precipitation decrease in autumn. The results represent regional-scale added value in the climate change signal of projections from high resolution models in cooler seasons, but suggest that the specific model components such as convection schemes strongly influence projections of summer rainfall change. Confidence in the simulation of change in convective rainfall, or convection-permitting modelling may be needed to raise confidence in summer rainfall projections over mountains.

A fundamental issue when evaluating the simulation of precipitation is the difficulty of quantifying specific sources of errors and recognizing compensation of errors. We assess how well a large ensemble of high‐resolution simulations represents the precipitation associated with strong cyclones. We propose a framework to breakdown precipitation errors according to different dynamical (vertical velocity) and thermodynamical (vertically integrated water vapor) regimes and the frequency and intensity of precipitation. This approach approximates the error in the total precipitation of each regime as the sum of three terms describing errors in the large‐scale environmental conditions, the frequency of precipitation and its intensity. We show that simulations produce precipitation too often, that its intensity is too weak, that errors are larger for weak than for strong dynamical forcing and that biases in the vertically integrated water vapor can be large. Using the error breakdown presented above, we define four new error metrics differing on the degree to which they include the compensation of errors. We show that convection‐permitting simulations consistently improve the simulation of precipitation compared to coarser‐resolution simulations using parameterized convection, and that these improvements are revealed by our new approach but not by traditional metrics which can be affected by compensating errors. These results suggest that convection‐permitting models are more likely to produce better results for the right reasons. We conclude that the novel decomposition and error metrics presented in this study give a useful framework that provides physical insights about the sources of errors and a reliable quantification of errors.

A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius–Clapeyron (CC) rate (6–7% K −1 ), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.

Global warming is likely to cause a progressive drought increase in some regions, but how population and natural resources will be affected is still underexplored. This study focuses on global population, forests, croplands and pastures exposure to meteorological drought hazard in the 21st century, expressed as frequency and severity of drought events. As input, we use a large ensemble of climate simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), population projections from the NASA-SEDAC dataset and land-use projections from the Land-Use Harmonization 2 project for 1981–2100. The exposure to drought hazard is presented for five Shared Socioeconomic Pathways (SSP1-SSP5) at four Global Warming Levels (GWLs: 1.5°C to 4°C). Results show that considering only Standardized Precipitation Index (SPI; based on precipitation), the SSP3 at GWL4 projects the largest fraction of the global population (14%) to experience an increase in drought frequency and severity (versus 1981–2010), with this value increasing to 60% if temperature is considered (indirectly included in the Standardized Precipitation-Evapotranspiration Index, SPEI). With SPEI, considering the highest GWL for each SSP, 8 (for SSP2, SSP4, SSP5) and 11 (SSP3) billion people, that is, more than 90%, will be affected by at least one unprecedented drought. For SSP5 at GWL4, approximately 2 × 10$^{6}$ km$^{2}$ of forests and croplands (respectively, 6% and 11%) and 1.5 × 10$^{6}$ km$^{2}$ of pastures (19%) will be exposed to increased drought frequency and severity according to SPI, but for SPEI this extent will rise to 17 × 10$^{6}$ km$^{2}$ of forests (49%), 6 × 10$^{6}$ km$^{2}$ of pastures (78%) and 12 × 10$^{6}$ km$^{2}$ of croplands (67%), being mid-latitudes the most affected. The projected likely increase of drought frequency and severity significantly increases population and land-use exposure to drought, even at low GWLs, thus extensive mitigation and adaptation efforts are needed to avoid the most severe impacts of climate change.

Compound events (CEs) are weather and climate events that result from multiple hazards or drivers with the potential to cause severe socio-economic impacts. Compared with isolated hazards, the multiple hazards/drivers associated with CEs can lead to higher economic losses and death tolls. Here, we provide the first analysis of multiple multivariate CEs potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate CEs including many socio-economically important regions such as North America, Russia and western Europe. We analyse the relative importance of different multivariate CEs in six continental regions to highlight CEs posing the highest risk. Our results provide initial guidance to assess the regional risk of CE events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate CEs., Compound climate events such as floods and droughts together can cause severe socio-economic impacts. Here, the authors analyse global hazard pairs from 1980–2014 and find global hotspots for the occurrence of compound events.

Accurate quantification of land degradation is a global need, particularly in the world’s dryland areas. However, there is a well-documented lack of field data and long-term observational studies for most of these regions. Remotely sensed data offers the only long-term vegetation record that can be used for land degradation assessment at a national, continental or global scale. Both the rainfall and vegetation datasets used for land degradation assessment contain errors and uncertainties, but little work has been done to understand how this may impact results. This study uses the recently developed Time Series Segmented RESidual TREND (TSS-RESTREND) method applied to six rainfall and two vegetation datasets to assess the impact of dataset selection on the estimates of dryland degradation over Australia. Large differences in the data and methods used to produce the precipitation datasets did not significantly impact results with the estimate of average change varying by 95% of regions. On the other hand, the vegetation dataset selection had a much greater impact. Calibration errors in the Global Inventory Monitoring and Modeling System Version 3 NDVI (GIMMSv3.0g) dataset caused significant errors in the trends over some of Australia’s dryland regions. Though identified over Australia, the problematic calibration in the GIMMSv3.0g dataset may have effected dryland NDVI values globally. These errors have been addressed in the updated GIMMSv3.1g which is strongly recommended for use in future studies. Our analysis suggests that using an ensemble composed of multiple runs performed using different datasets allows for the identification of errors that cannot be detected using only a single run or with the data quality flags of the input datasets. A multi-run ensemble made using different input datasets provides more comprehensive quantification of uncertainty and errors in space and time.

Wildfire can become a catastrophic natural hazard, especially during dry summer seasons in Australia. Severity is influenced by various meteorological, geographical, and fuel characteristics. Modified Mark 4 McArthur's Grassland Fire Danger Index (GFDI) is a commonly used approach to determine the fire danger level in grassland ecosystems. The degree of curing (DOC, i.e. proportion of dead material) of the grass is one key ingredient in determining the fire danger. It is difficult to collect accurate DOC information in the field, and therefore ground-observed measurements are rather limited. In this study, we explore the possibility of whether adding satellite-observed data responding to vegetation water content (vegetation optical depth, VOD) will improve DOC prediction when compared with the existing satellite-observed data responding to DOC prediction models based on vegetation greenness (normalised difference vegetation index, NDVI). First, statistically significant relationships are established between selected ground-observed DOC and satellite-observed vegetation datasets (NDVI and VOD) with an r2 up to 0.67. DOC levels estimated using satellite observations were then evaluated using field measurements with an r2 of 0.44 to 0.55. Results suggest that VOD-based DOC estimation can reasonably reproduce ground-based observations in space and time and is comparable to the existing NDVI-based DOC estimation models.

The negative impacts of climate extremes on socioeconomic sectors in Australia makes understanding their behaviour under future climate change necessary for regional planning. Providing robust and actionable climate information at regional scales relies on the downscaling of global climate model data and its translation into impact-relevant information. The New South Wales/Australian Capital Territory Regional Climate Modelling (NARCliM) project contains downscaled climate data over all of Australia at a 50 km resolution, with ensembles of simulations for the recent past (1990–2009), near future (2020–2039) and far future (2060–2079). Here we calculate and examine sector-relevant indices of climate extremes recommended by the Expert Team on Sector-specific Climate Indices (ET-SCI). We demonstrate the utility of NARCliM and the ET-SCI indices in understanding how future changes in climate extremes could impact aspects of the health and agricultural sectors in Australia. Consistent with previous climate projections, our results indicate that increases in heat and drought related extremes throughout the 21st century will occur. In the far future, maximum day time temperatures are projected to increase by up to 3.5 °C depending on season and location. The number of heatwaves and the duration of the most intense heatwaves will increase significantly in the near and far future, with greater increases in the north than south. All capital cities are projected to experience at least a tripling of heatwave days each year by the far future, compared to the recent past. Applying published heat-health relationships to projected changes in temperature shows that increases in mortality due to high temperatures for all cities examined would occur if projected future climates occurred today. Drought and the number of days above 30 °C are also projected to increase over the major wheat-growing regions of the country, particularly during spring when sensitivity of wheat to heat stress is greatest. Assuming no adaptation or acclimatisation, published statistical relationships between drought and national wheat yield suggest that national yields will have a less than one quarter chance of exceeding the annual historical average under far future precipitation change (excluding impacts of future temperature change and CO2 fertilization). The NARCliM data examined here, along with the ET-SCI indices calculated, provide a powerful and publicly available dataset for regional planning against future changes in climate extremes.

Global climate models play an important role in quantifying past and projecting future changes in drought. Previous studies have pointed to shortcomings in these models for simulating droughts, but systematic evaluation of their level of agreement has been limited. Here, historical simulations (1950–2004) for 20 models from the latest Coupled Model Intercomparison Project (CMIP5) were analyzed for a variety of drought metrics and thresholds using a standardized drought index. Model agreement was investigated for different types of drought (precipitation, runoff, and soil moisture) and how this varied with drought severity and duration. At the global scale, climate models were shown to agree well on most precipitation drought metrics, but systematically underestimated precipitation drought intensity compared to observations. Conversely, simulated runoff and soil moisture droughts varied significantly across models, particularly for intensity. Differences in precipitation simulations were found to explain model differences in runoff and soil moisture drought metrics over some regions, but predominantly with respect to drought intensity. This suggests it is insufficient to evaluate models for precipitation droughts to increase confidence in model performance for other types of drought. This study shows large but metric-dependent discrepancies in CMIP5 for modeling different types of droughts that relate strongly to the component models (i.e., atmospheric or land surface scheme) used in the coupled modeling systems. Our results point to a need to consider multiple models in drought impact studies to account for high model uncertainties.

High-resolution projections of climate change impacts on fire weather conditions in southeast Australia out to 2080 are presented. Fire weather is represented by the McArthur Forest Fire Danger Index (FFDI), calculated from an objectively designed regional climate model ensemble. Changes in annual cumulative FFDI vary widely, from − 337 (− 21%) to + 657 (+ 24%) in coastal areas and − 237 (− 12%) to + 1143 (+ 26%) in inland areas. A similar spread is projected in extreme FFDI values. In coastal regions, the number of prescribed burning days is projected to change from − 11 to + 10 in autumn and − 10 to + 3 in spring. Across the ensemble, the most significant increases in fire weather and decreases in prescribed burn windows are projected to take place in spring. Partial bias correction of FFDI leads to similar projections but with a greater spread, particularly in extreme values. The partially bias-corrected FFDI performs similarly to uncorrected FFDI compared to the observed annual cumulative FFDI (ensemble root mean square error spans 540 to 1583 for uncorrected output and 695 to 1398 for corrected) but is generally worse for FFDI values above 50. This emphasizes the need to consider inter-variable relationships when bias-correcting for complex phenomena such as fire weather. There is considerable uncertainty in the future trajectory of fire weather in southeast Australia, including the potential for less prescribed burning days and substantially greater fire danger in spring. Selecting climate models on the basis of multiple criteria can lead to more informative projections and allow an explicit exploration of uncertainty.

Global warming, in combination with the urban heat island effect, is increasing the temperature in cities. These changes increase the risk of heat stress for millions of city dwellers. Given the large populations at risk, a variety of mitigation strategies have been proposed to cool cities—including strategies that aim to reduce the ambient air temperature. This paper uses common heat stress metrics to evaluate the performance of several urban heat island mitigation strategies. The authors found that cooling via reducing net radiation or increasing irrigated vegetation in parks or on green roofs did reduce ambient air temperature. However, a lower air temperature did not necessarily lead to less heat stress because both temperature and humidity are important factors in determining human thermal comfort. Specifically, cooling the surface via evaporation through the use of irrigation increased humidity—consequently, the net impact on human comfort of any cooling was negligible. This result suggests that urban cooling strategies must aim to reduce ambient air temperatures without increasing humidity, for example via the deployment of solar panels over roofs or via cool roofs utilizing high albedos, in order to combat human heat stress in the urban environment.

Summary Addressing systematic biases in regional climate model simulations of extreme rainfall is a necessary first step before assessing changes in future rainfall extremes. Commonly used bias correction methods are designed to match statistics of the overall simulated rainfall with observations. This assumes that change in the mix of different types of extreme rainfall events (i.e. convective and non-convective) in a warmer climate is of little relevance in the estimation of overall change, an assumption that is not supported by empirical or physical evidence. This study proposes an alternative approach to account for the potential change of alternate rainfall types, characterized here by synoptic weather patterns (SPs) using self-organizing maps classification. The objective of this study is to evaluate the added influence of SPs on the bias correction, which is achieved by comparing the corrected distribution of future extreme rainfall with that using conventional quantile mapping. A comprehensive synthetic experiment is first defined to investigate the conditions under which the additional information of SPs makes a significant difference to the bias correction. Using over 600,000 synthetic cases, statistically significant differences are found to be present in 46% cases. This is followed by a case study over the Sydney region using a high-resolution run of the Weather Research and Forecasting (WRF) regional climate model, which indicates a small change in the proportions of the SPs and a statistically significant change in the extreme rainfall over the region, although the differences between the changes obtained from the two bias correction methods are not statistically significant.

We investigated the use of a recently developed satellite-based vegetation optical depth (VOD) data set to estimate fire severity and carbon emission over Australian tropical savannahs. VOD is sensitive to the dynamics of all aboveground vegetation and available nearly every two days. For areas burned during 2003–2010, we calculated the VOD change (ΔVOD) pre- and post-fire and the associated loss in the above ground biomass carbon. ΔVOD agreed well with the Normalized Burn Ratio change (ΔNBR) which is the metric used to estimate fire severity and carbon loss compared well with modelled emissions from the Global Fire Emissions Database (GFED). We found that the ΔVOD and ΔNBR are generally linearly related. The Pearson correlation coefficients (r) between VOD- and GFED-based fire carbon emissions for monthly and annual total estimates are very high, 0.92 and 0.96, respectively. A key feature of fire carbon emissions is the strong inter-annual variation, ranging from 21.1 Mt in 2010 to 84.3 Mt in 2004. This study demonstrates that a reasonable estimate of fire severity and carbon emissions can be achieved in a timely manner based on multiple satellite observations over Australian tropical savannahs, which can be complementary to the currently used approaches.

Global climate model simulations inherently contain multiple biases that, when used as boundary conditions for regional climate models, have the potential to produce poor downscaled simulations. Removing these biases before downscaling can potentially improve regional climate change impact assessment. In particular, reducing the low-frequency variability biases in atmospheric variables as well as modeled rainfall is important for hydrological impact assessment, predominantly for the improved simulation of floods and droughts. The impact of this bias in the lateral boundary conditions driving the dynamical downscaling has not been explored before. Here the use of three approaches for correcting the lateral boundary biases including mean, variance, and modification of sample moments through the use of a nested bias correction (NBC) method that corrects for low-frequency variability bias is investigated. These corrections are implemented at the 6-hourly time scale on the global climate model simulations to drive a regional climate model over the Australian Coordinated Regional Climate Downscaling Experiment (CORDEX) domain. The results show that the most substantial improvement in low-frequency variability after bias correction is obtained from modifying the mean field, with smaller changes attributed to the variance. Explicitly modifying monthly and annual lag-1 autocorrelations through NBC does not substantially improve low-frequency variability attributes of simulated precipitation in the regional model over a simpler mean bias correction. These results raise questions about the nature of bias correction techniques that are required to successfully gain improvement in regional climate model simulations and show that more complicated techniques do not necessarily lead to more skillful simulation.

Warmer temperatures are expected to increase extreme short-duration rainfall due to the increased moisture-holding capacity of the atmosphere. While attention has been paid to the impacts of climate change on future design rainfalls at daily or longer time scales, the potential changes in short duration design rainfalls have been often overlooked due to the limited availability of sub-daily projections and observations. This study uses a high-resolution regional climate model (RCM) to predict the changes in sub-daily design rainfalls for the Greater Sydney region in Australia. Sixteen methods for predicting changes to sub-daily future extremes are assessed based on different options for bias correction, disaggregation and frequency analysis. A Monte Carlo cross-validation procedure is employed to evaluate the skill of each method in estimating the design rainfall for the current climate. It is found that bias correction significantly improves the accuracy of the design rainfall estimated for the current climate. For 1 h events, bias correcting the hourly annual maximum rainfall simulated by the RCM produces design rainfall closest to observations, whereas for multi-hour events, disaggregating the daily rainfall total is recommended. This suggests that the RCM fails to simulate the observed multi-duration rainfall persistence, which is a common issue for most climate models. Despite the significant differences in the estimated design rainfalls between different methods, all methods lead to an increase in design rainfalls across the majority of the study region.

The east coast of Australia has a relatively high frequency of midlatitude cyclones, locally known as East Coast Lows (ECLs), which can cause severe weather including widespread flooding and coastal erosion. The elevated topography close to the east coast has been hypothesized to play a role in both the genesis and impacts of cyclones in this region, but existing studies have been limited to case studies of individual events. In this paper we present the results from two 20-year WRF simulations over the Australian region, and assess the results from removing all topography in the region on both mean atmospheric circulation and ECL frequency. Removing topography results in an increase in sea level pressure to the south of Australia and an increase in moisture flux convergence and rainfall near the east coast, as well as a decrease in potential vorticity to the north of the ECL region. This results in a change in the spatial distribution of cyclones, with a 37% decrease in the frequency of cyclones that develop to the south of the ECL region but a 20% increase in cyclones near the east coast. This results in little overall change in the frequency of ECLs, and suggests coarse topography is unlikely to be responsible for the difficulties in simulating coastal cyclones in global climate models.

High-resolution reanalyses offer the potential to improve our understanding of midlatitude cyclones, particularly smaller-scale systems and those with complex structures. However, previous studies have demonstrated large variations in the frequency and characteristics of Australian midlatitude cyclones between reanalyses when using their native resolution. In this paper we use satellite observations of winds and rainfall in order to evaluate the ability of the ERA-Interim, JRA55, MERRA and CFSR reanalyses to reproduce Australian east coast cyclones. The MERRA reanalysis produces a large number of erroneous small-scale lows without cyclonic wind patterns using a simple pressure-difference-based cyclone identification and tracking method. Consequently, we recommend the ERA-Interim reanalysis when using such methods, or applying more complex tracking methods that are able to compensate for these issues.

Dryland degradation is an issue of international significance as dryland regions play a substantial role in global food production. Remotely sensed data provide the only long term, large scale record of changes within dryland ecosystems. The Residual Trend, or RESTREND, method is applied to satellite observations to detect dryland degradation. Whilst effective in most cases, it has been shown that the RESTREND method can fail to identify degraded pixels if the relationship between vegetation and precipitation has broken-down as a result of severe or rapid degradation. This paper presents an extended version of the RESTREND methodology that incorporates the Breaks For Additive Seasonal and Trend method to identify step changes in the time series that are related to significant structural changes in the ecosystem, e.g. land use changes. When applied to Australia, this new methodology, termed Time Series Segmentation and Residual Trend analysis (TSS-RESTREND), was able to detect degradation in 5.25% of pixels compared to only 2.0% for RESTREND alone. This modified methodology was then assessed in two regions with known histories of degradation where it was found to accurately capture both the timing and directionality of ecosystem change.

This study is conducted over the data-poor Limpopo basin centered over southern Africa using reanalysis downscaled to useful resolution. Reanalysis products are of limited value in hydrological applications due to the coarse spatial scales they are available at. Dynamical downscaling of these products over a domain of interest offers a means to convert them to finer spatial scales in a dynamically consistent manner. Additionally, this downscaling also offers a way to resolve dominantatmospheric processes, leading to improved accuracy in the atmospheric variables derived. This study thus evaluates high-resolution downscaling of an objectively chosen reanalysis (ERA-I) over the Limpopo basin using Weather Research and Forecasting (WRF) as a regional climate model. The model generally under-estimates temperature and over-estimates precipitation over the basin, although reasonably consistent with observations. The model does well in simulating observed sustained hydrological extremes as assessed using the Standardized Precipitation Index (SPI) although it consistently under-estimates the severity ofmoisture deficit for the wettest part of the year during the dry years. The basin's aridity index (I) is above the severe drought threshold during summer and is more severe in autumn. This practically restricts rain-fed agriculture to around 3 months in a year over the basin. This study presents possible beneficial use of the downscaled simulations foroptimal hydrologic design and water resources planning in data scarce parts of the world.

Lateral and lower boundary conditions derived from a suitable global reanalysis dataset forms the basis for deriving a dynamically consistent finer resolution downscaled product for climate and hydrological assessment studies. A problem with this, however, is that systematic biases have been noted to be present in the global reanalysis datasets that form these boundaries, biases which can be carried into the downscaled simulations thereby reducing their accuracy or efficacy. In this work, three Weather Research and Forecasting (WRF) model downscaling experiments are undertaken to investigate the impact of bias correcting European Centre for Medium range Weather Forecasting Reanalysis ERA-Interim (ERA-I) atmospheric temperature and relative humidity using Atmospheric Infrared Sounder (AIRS) satellite data. The downscaling is performed over a domain centered over southern Africa between the years 2003 and 2012. The sample mean and the mean as well as standard deviation at each grid cell for each variable are used for bias correction. The resultant WRF simulations of near-surface temperature and precipitation are evaluated seasonally and annually against global gridded observational datasets and compared with ERA-I reanalysis driving field. The study reveals inconsistencies between the impact of the bias correction prior to downscaling and the resultant model simulations after downscaling. Mean and standard deviation bias corrected WRF simulations are, however, found to be marginally better than mean only bias corrected WRF simulations and raw ERA-I reanalysis driven WRF simulations. Performances, however, differ when assessing different attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted.

We use the Weather Research and Forecast model and estimate anthropogenic heat (AH) fluxes based on fine-scale energy consumption data for Sydney, Australia, to investigate the effects of urbanization on temperature. We examine both the impact of urban canopy effects (UCE) and AH which in combination causes the urban heat island effect. Sydney's urban heat island (UHI) varies from −1 to >3.4 °C between day and night and between seasons. UHI intensity is highest at night and an urban cool island is often experienced during the day. UCE contributes 80% of the UHI during summer nights because of the release of stored heat from urban infrastructure that has been absorbed during the day. During the day for UCE, the reduced net radiation and greater heat storage by urban infrastructure combine to slightly cool. In contrast, AH contributes 90% of the UHI during winter nights because it does not dissipate into the higher levels of the boundary layer efficiently. The opposite applies during summer nights and during daytime in both summer and winter where heat mixes effectively into the atmosphere. Our results show contrasting impacts of UCE and AH by time of day and time of year and point to major simulation biases if only one of these phenomena is represented, or if their seasonal contributions are not accounted for separately.

Two questions motivated this study: 1) Will meteorological droughts become more frequent and severe during the twenty-first century? 2) Given the projected global temperature rise, to what extent does the inclusion of temperature (in addition to precipitation) in drought indicators play a role in future meteorological droughts? To answer, we analyzed the changes in drought frequency, severity, and historically undocumented extreme droughts over 1981–2100, using the standardized precipitation index (SPI; including precipitation only) and standardized precipitation-evapotranspiration index (SPEI; indirectly including temperature), and under two representative concentration pathways (RCP4.5 and RCP8.5). As input data, we employed 103 high-resolution (0.448) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), based on a combination of 16 global circulation models (GCMs) and 20 regional circulation models (RCMs). This is the first study on global drought projections including RCMs based on such a large ensemble of RCMs. Based on precipitation only,;15% of the global land is likely to experience more frequent and severe droughts during 2071–2100 versus 1981–2010 for both scenarios. This increase is larger (;47% under RCP4.5,;49% under RCP8.5) when precipitation and temperature are used. Both SPI and SPEI project more frequent and severe droughts, especially under RCP8.5, over southern South America, the Mediterranean region, southern Africa, southeastern China, Japan, and southern Australia. A decrease in drought is projected for high latitudes in Northern Hemisphere and Southeast Asia. If temperature is included, drought characteristics are projected to increase over North America, Amazonia, central Europe and Asia, the Horn of Africa, India, and central Australia; if only precipitation is considered, they are found to decrease over those areas. Fil: Spinoni, Jonathan. Joint Research Centre; Italia Fil: Barbosa, Paulo. Joint Research Centre; Italia Fil: Bucchignani, Edoardo. Centro Euro Mediterraneo Sui Cambiamenti Climatici.; Italia Fil: Cassano, John. State University of Colorado at Boulder; Estados Unidos Fil: Cavazos, Tereza. Centro de Investigacion Cientifica y de Educacion Super; México Fil: Christensen, Jens H.. Danish Meteorological Institute; Dinamarca Fil: Christensen, Ole B.. Danish Meteorological Institute; Dinamarca Fil: Coppola, Erika. The Abdus Salam. International Centre for Theoretical Physics; Italia Fil: Evans, Jason. University of New South Wales. Faculty of Science; Australia Fil: Geyer, Beate. Institute of Coastal Research; Alemania Fil: Giorgi, Filippo. The Abdus Salam. International Centre for Theoretical Physics; Italia Fil: Hadjinicolaou, Panos. Environment And Water Research Center; Chipre Fil: Jacob, Daniela. Climate Service Center; Alemania Fil: Katzfey, Jack. Commonwealth Scientific And Industrial Research Organis; Australia Fil: Koenigk, Torben. Swedish Meteorological And Hydrological; Suecia Fil: Laprise, René. Université du Québec a Montreal; Canadá Fil: Lennard, Christopher J.. University of Cape Town; Sudáfrica Fil: Kurnaz, M. Levent. Bogazici University; Turquía Fil: Delei, L.I.. Chinese Academy of Sciences; República de China Fil: Llopart, Marta. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil Fil: McCormick, Niall. European Commission Joint Research Centre; España Fil: Naumann, Gustavo. European Commission Joint Research Centre; España Fil: Nikulin, Grigory. No especifíca; Fil: Ozturk, Tugba. No especifíca; Fil: Panitz, Hans Juergen. No especifíca; Fil: da Rocha, Rosmeri Porfirio. No especifíca; Fil: Rockel, Burkhardt. No especifíca; Fil: Solman, Silvina Alicia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Syktus, Jozef. The University of Queensland; Australia. University of Queensland; Australia Fil: Tangang, Fredolin. No especifíca; Fil: Vautard, Robert. No especifíca; Fil: Vautard, Robert. Centre National de la Recherche Scientifique; Francia Fil: Vogt, Jürgen V.. European Commission Joint Research Centre; España Fil: Winger, Katja. Université du Québec a Montreal; Canadá Fil: Zittis, George. No especifíca; Fil: Dosio, Alessandro. No especifíca

Air pollution has significant impacts on human health. Temperature inversions, especially near surface temperature inversions, can amplify air pollution by preventing convective movements and trapping pollutants close to the ground, thus decreasing air quality and increasing health issues. This effect of temperature inversions implies that trends in their frequency, strength and duration can have important implications for air quality. In this study, we evaluate the ability of three reanalysis-driven high-resolution regional climate model (RCM) simulations to represent near surface inversions at 9 sounding sites in southeast Australia. Then we use outputs of 12 historical and future RCM simulations (each with three time periods: 1990?2009, 2020?2039, and 2060?2079) from the NSW/ACT (New South Wales/Australian Capital Territory) Regional Climate Modelling (NARCliM) project to investigate changes in near surface temperature inversions. The results show that there is a substantial increase in the strength of near surface temperature inversions over southeast Australia which suggests that future inversions may intensify poor air quality events. Near surface inversions and their future changes have clear seasonal and diurnal variations. The largest differences between simulations are associated with the driving GCMs, suggesting that the large-scale circulation plays a dominant role in near surface inversion strengths. Fil: Ji, Fei. New South Wales. Office of Environment and Heritage; Australia Fil: Evans, Jason Peter. University of New South Wales; Australia Fil: Di Luca, Alejandro. University of New South Wales; Australia Fil: Jiang, Ningbo. New South Wales. Office of Environment and Heritage; Australia Fil: Olson, Roman. Yonsei University; Corea del Sur Fil: Fita Borrell, Lluís. Centre National de la Recherche Scientifique; Francia. Universite Pierre et Marie Curie; Francia. Laboratoire de Meteorologie Dynamique; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Argüeso, Daniel. Universidad de Las Islas Baleares. Departamento de Fisica; España Fil: Chang, Lisa T. C.. New South Wales. Office of Environment and Heritage; Australia Fil: Scorgie, Yvonne. New South Wales. Office of Environment and Heritage; Australia Fil: Riley, Matt. New South Wales. Office of Environment and Heritage; Australia

Ground observation absence in many parts of the world highlights the importance of merged satellite precipitation products. In this study, we aim to evaluate the effect of different sources of data in the uncertainties of a merged satellite product, by comparing the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final product (V06B) with a ground-based radar product, Multi-Radar Multi-Sensor (MRMS), using both pixel-based and object-based approaches. This study is focused on the eastern United States (land-only) during the hurricane days that occurred in 2016–2018. The results showed that IMERG had better agreement in terms of the average precipitation intensity and area with a bias reduction of 75% and 65%, respectively, when the passive microwave (PMW) sensor overpass is matched instantaneously with MRMS compared to temporally averaged MRMS data (MRMS-Averaged). PMW observations tend to show storms with smaller areas in the IMERG Final product in comparison with MRMS, possibly due to the effect of light precipitation not detected properly by PMW sensors. However, by removing the light precipitation (less than 1 mm/h) in the object-based approach, hurricane objects in the IMERG Final product tend to be larger during the PMW observations, which might be related to different viewing angles of sensors contributing to MRMS and IMERG products. Precipitation estimates have smaller areas with higher average intensity during the PMW observations in the IMERG Final product compared to data estimated by Morphed or IR (morph/IR) observations, which is probably related to the effect of morphing technique, leading to homogenization of the varying rainstorm characteristics. In addition, with the longer absence of PMW observations, the quality of morph/IR estimates in IMERG Final product deteriorates with a decreasing correlation coefficient, a growth in precipitation area and a downward trend in average precipitation intensity. Finally, the inter-comparison of PMW sensors showed the priority of imagers over sounders with GMI as the best among imagers and MHS as the best among sounders in terms of correlation and average intensity compared to MRMS; however, SSMIS was the best in capturing the precipitation area.

Heatwaves have implications for human health and ecosystem function. Over cities, the impacts of a heatwave event may be compounded by urban heat, where temperatures over the urban area are higher than their rural surroundings. Coastal cities often rely upon sea breezes to provide temporary relief. However, topographic features contributing to the development of Foehn-like conditions can offset the cooling influence of sea breezes. Using convection-permitting simulations (⩽4 km) we examine the potential for both mechanisms to influence heatwave conditions over the large coastal city of Sydney, Australia that is bordered by mountains. Heatwave onset in the hot period of January–February 2017 often coincides with a hot continental flow over the mountains into the city. The temperature difference between the coast and the urban–rural interface can reach 15.79 °C. Further, the urban heat island contributes on average an additional 1 °C in the lowest 1 km of the atmosphere and this often extends beyond the city limits. The cumulative heat induced by the urban environment reaches 10 °C over the city and 3 °C over adjacent inland areas. Strong sea breezes are important for heat dispersion with city temperature gradients reducing to within 1 °C. The resolution permits a comparison between urban types and reveals that the diurnal cycle of temperature, moisture content and wind are sensitive to the urban type. Here we show that convection permitting simulations can resolve the interaction between local breezes and the urban environment that are not currently resolved in coarser resolution models.

Extreme rainfall is predicted to increase with warming; however observations show differing rates of change. This study shows rainfall-associated cooling reduces the observed scaling rate. Projections show increased scaling rates in the future particularly for the strongest extremes. Models and physical reasoning predict that extreme precipitation will increase in a warmer climate due to increased atmospheric humidity1,2,3. Observational tests using regression analysis have reported a puzzling variety of apparent scaling rates including strong rates in midlatitude locations but weak or negative rates in the tropics4,5. Here we analyse daily extreme precipitation events in several Australian cities to show that temporary local cooling associated with extreme events and associated synoptic conditions reduces these apparent scaling rates, especially in warmer climatic conditions. A regional climate projection ensemble6 for Australia, which implicitly includes these effects, accurately and robustly reproduces the observed apparent scaling throughout the continent for daily precipitation extremes. Projections from the same model show future daily extremes increasing at rates faster than those inferred from observed scaling. The strongest extremes (99.9th percentile events) scale significantly faster than near-surface water vapour, between 5.7–15% °C−1 depending on model details. This scaling rate is highly correlated with the change in water vapour, implying a trade-off between a more arid future climate or one with strong increases in extreme precipitation. These conclusions are likely to generalize to other regions.

Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

Bushfires are one of the most frequent natural hazards experienced in Australia. Fires play an important role in shaping the landscape and its ecological dynamics, but may also have devastating effects that cause human injuries and fatalities, as well as broad-scale environmental damage. While there has been considerable effort to quantify changes in the occurrence of bushfire in Australia, a comprehensive assessment of the most extreme bushfire cases, which exact the greatest economic and environmental impacts, is lacking. In this paper we reflect upon recently developed understanding of bushfire dynamics to consider (i) historical changes in the occurrence of extreme bushfires, and (ii) the potential for increasing frequency in the future under climate change projections. The science of extreme bushfires is still a developing area, thus our conclusions about emerging patterns in their occurrence should be considered tentative. Nonetheless, historical information on noteworthy bushfire events suggests an increased occurrence in recent decades. Based on our best current understanding of how extreme bushfires develop, there is strong potential for them to increase in frequency in the future. As such there is a pressing need for a greater understanding of these powerful and often destructive phenomena.

Multiple reanalysis datasets currently exist that can provide boundary conditions for dynamic downscaling and simulating local hydro-climatic processes at finer spatial and temporal resolutions. Previous work has suggested that there are two reanalyses alternatives that provide the best lateral boundary conditions for downscaling over southern Africa. This study dynamically downscales these reanalyses (ERA-I and MERRA) over southern Africa to a high resolution (10 km) grid using the WRF model. Simulations cover the period 1981–2010. Multiple observation datasets were used for both surface temperature and precipitation to account for observational uncertainty when assessing results. Generally, temperature is simulated quite well, except over the Namibian coastal plain where the simulations show anomalous warm temperature related to the failure to propagate the influence of the cold Benguela current inland. Precipitation tends to be overestimated in high altitude areas, and most of southern Mozambique. This could be attributed to challenges in handling complex topography and capturing large-scale circulation patterns. While MERRA driven WRF exhibits slightly less bias in temperature especially for La Nina years, ERA-I driven simulations are on average superior in terms of RMSE. When considering multiple variables and metrics, ERA-I is found to produce the best simulation of the climate over the domain. The influence of the regional model appears to be large enough to overcome the small difference in relative errors present in the lateral boundary conditions derived from these two reanalyses.

This study presents future changes in extreme precipitation as projected within the New South Wales and Australian Capital Territory Regional Climate Modelling (NARCliM) project’s regional climate ensemble for south-east Australia. Model performance, independence and projected future changes were considered when designing the ensemble. We applied a quantile mapping bias correction to the climate model outputs based on theoretical distribution functions, and the implications of this for the projected precipitation extremes is investigated. Precipitation extremes are quantified using several indices from the Expert Team on Climate Change Detection and Indices set of indices. The bias correction was successful in removing most of the magnitude bias in extreme precipitation but does not correct biases in the length of maximum wet and dry spells. The bias correction also had a relatively small effect on the projected future changes. Across a range of metrics, robust increases in the magnitude of precipitation extreme indices are found. While these increases are often in-line with a continuation of the trends present over the last century, they are not found to be statistically significant within the ensemble as a whole. The length of the maximum consecutive wet spell is projected to remain at present-day levels, while the length of the maximum dry spell is projected to increase into the future. The combination of longer dry spells and increases in extreme precipitation magnitude indicate an important change in the character of the precipitation time series. This could have considerable hydrological implications since changes in the sequencing of events can be just as important as changes in event magnitude for hydrological impacts.

We present an assessment of the impact of future climate change on two key drivers of fire risk in Australia, fire weather and fuel load. Fire weather conditions are represented by the McArthur Forest Fire Danger Index (FFDI), calculated from a 12-member regional climate model ensemble. Fuel load is predicted from net primary production, simulated using a land surface model forced by the same regional climate model ensemble. Mean annual fine litter is projected to increase across all ensemble members, by 1.2 to 1.7 t ha−1 in temperate areas, 0.3 to 0.5 t ha−1 in grassland areas and 0.7 to 1.1 t ha−1 in subtropical areas. Ensemble changes in annual cumulative FFDI vary widely, from 57 to 550 in temperate areas, −186 to 1372 in grassland areas and −231 to 907 in subtropical areas. These results suggest that uncertainty in FFDI projections will be underestimated if only a single driving model is used. The largest increases in fuel load and fire weather are projected to occur in spring. Deriving fuel load from a land surface model may be possible in other regions, when this information is not directly available from climate model outputs.