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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.

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.

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

PURPOSE: As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19-free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS: This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19-free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS: Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19-free surgical pathways. Patients who underwent surgery within COVID-19-free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19-free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score-matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19-free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION: Within available resources, dedicated COVID-19-free surgical pathways should be establis

Drylands cover 41% of the earth’s land surface and include 45% of the world’s agricultural land. These regions are among the most vulnerable ecosystems to anthropogenic climate and land use change and are under threat of desertification. Understanding the roles of anthropogenic climate change, which includes the CO2 fertilization effect, and land use in driving desertification is essential for effective policy responses but remains poorly quantified with methodological differences resulting in large variations in attribution. Here, we perform the first observation-based attribution study of desertification that accounts for climate change, climate variability, CO2 fertilization as well as both the gradual and rapid ecosystem changes caused by land use. We found that, between 1982 and 2015, 6% of the world’s drylands underwent desertification driven by unsustainable land use practices compounded by anthropogenic climate change. Despite an average global greening, anthropogenic climate change has degraded 12.6% (5.43 million km2) of drylands, contributing to desertification and affecting 213 million people, 93% of who live in developing economies.

Inferring local land-atmosphere coupling through correlation of colocated soil moisture and future rainfall inherently assumes a one-dimensional (1-D) framing of the coupling mechanism. For the first time we demonstrate the importance of upholding this assumption by examining the statistical relationship between daily soil moisture and rainfall depths over Australia, specifying spatial scales (0.05°, 0.5°, 1°, and 2.5°) to constrain the relationship to local-only physical processes. At small scales, without consideration of the 1-D assumption, strong land-atmosphere coupling is suggested across much of the country. However, when adhering to a 1-D framework, small sample sizes make correlation unsuitable for assessing local coupling at these small scales. When adhering to a 1-D framework, at scales of 0.5° and above, we find positive correlations in northern Australia in the wet and transition seasons and negative correlations in southern Australia in austral winter. The correlation is scale dependent, suggesting that as spatial resolutions increase in the future and land-atmosphere coupling heterogeneity is resolved, spatial distributions of local coupling may differ from larger-scale estimates characteristic of current coarse resolution climate models.

We present a novel quasi-Bayesian method to weight multiple dynamical models by their skill at capturing both potentially non-linear trends and first-order autocorrelated variability of the underlying process, and to make weighted probabilistic projections. We validate the method using a suite of one-at-a-time cross-validation experiments involving Atlantic meridional overturning circulation (AMOC), its temperature-based index, as well as Korean summer mean maximum temperature. In these experiments the method tends to exhibit superior skill over a trend-only Bayesian model averaging weighting method in terms of weight assignment and probabilistic forecasts. Specifically, mean credible interval width, and mean absolute error of the projections tend to improve. We apply the method to a problem of projecting summer mean maximum temperature change over Korea by the end of the 21 st century using a multi-model ensemble. Compared to the trend-only method, the new method appreciably sharpens the probability distribution function (pdf) and increases future most likely, median, and mean warming in Korea. The method is flexible, with a potential to improve forecasts in geosciences and other fields.

A major conundrum in climate science is how to account for dependence between climate models. This complicates interpretation of probabilistic projections derived from such models. Here we show that this problem can be addressed using a novel method to test multiple non-exclusive hypotheses, and to make predictions under such hypotheses. We apply the method to probabilistically estimate the level of global warming needed for a September ice-free Arctic, using an ensemble of historical and representative concentration pathway 8.5 emissions scenario climate model runs. We show that not accounting for model dependence can lead to biased projections. Incorporating more constraints on models may minimize the impact of neglecting model non-exclusivity. Most likely, September Arctic sea ice will effectively disappear at between approximately 2 and 2.5 K of global warming. Yet, limiting the warming to 1.5 K under the Paris agreement may not be sufficient to prevent the ice-free Arctic.

Extreme wildfires have recently caused disastrous impacts in Australia and other regions of the world, including events with strong convective processes in their plumes (i.e., strong pyroconvection). Dangerous wildfire events such as these could potentially be influenced by anthropogenic climate change, however, there are large knowledge gaps on how these events might change in the future. The McArthur Forest Fire Danger Index (FFDI) is used to represent near-surface weather conditions and the Continuous Haines index (CH) is used here to represent lower to mid-tropospheric vertical atmospheric stability and humidity measures relevant to dangerous wildfires and pyroconvective processes. Projected changes in extreme measures of CH and FFDI are examined using a multi-method approach, including an ensemble of global climate models together with two ensembles of regional climate models. The projections show a clear trend towards more dangerous near-surface fire weather conditions for Australia based on the FFDI, as well as increased pyroconvection risk factors for some regions of southern Australia based on the CH. These results have implications for fields such as disaster risk reduction, climate adaptation, ecology, policy and planning, noting that improved knowledge on how climate change can influence extreme wildfires can help reduce future impacts of these events.

© 2019, Springer-Verlag GmbH Austria, part of Springer Nature. 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 choic

The spatial extent and organization of extreme storm events has important practical implications for flood forecasting. Recently, conflicting evidence has been found on the observed changes of storm spatial extent with increasing temperatures. To further investigate this question, a regional climate model assessment is presented for the Greater Sydney region, in Australia. Two regional climate models were considered: the first a convection-resolving simulation at 2-km resolution, the second a resolution of 10 km with three different convection parameterizations. Both the 2- and the 10-km resolutions that used the Betts-Miller-Janjic convective scheme simulate decreasing storm spatial extent with increasing temperatures for 1-hr duration precipitation events, consistent with the observation-based study in Australia. However, other observed relationships of extreme rainfall with increasing temperature were not well represented by the models. Improved methods for considering storm organization are required to better understand potential future changes.

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.

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.

Recently, Barbero et al. (2018) examined temperature-extreme precipitation scaling and argue that the local cooling effect leading to negative apparent scaling in Darwin found by Bao et al. (2017a) was simply a statistical artefact. Barbero et al. (2018) also propose that dew point temperature drives extreme precipitation, and should be used as a scaling variable. Here we address some of their criticisms and further clarify some conclusions. We maintain that scaling analyses via “binning methods” cannot be reliably applied to climate changes due to fundamental problems of misinterpreted causality, and that using dew point temperature does not solve these problems.

© 2018 Unprecedented droughts hit southern Amazonia in 2005 and 2010, causing a sharp increase in tree mortality and carbon loss. To better predict the rainforest's response to future droughts, it is necessary to understand its behavior during past events. Satellite observations provide a practical source of continuous observations of Amazonian forest. Here we used a passive microwave-based vegetation water content record (i.e., vegetation optical depth, VOD), together with multiple hydrometeorological observations as well as conventional satellite vegetation measures, to investigate the rainforest canopy dynamics during the 2005 and 2010 droughts. During the onset of droughts in the wet-to-dry season (May–July) of both years, we found large-scale positive anomalies in VOD, leaf area index (LAI) and enhanced vegetation index (EVI) over the southern Amazonia. These observations are very likely caused by enhanced canopy growth. Concurrent below-average rainfall and above-average radiation during the wet-to-dry season can be interpreted as an early arrival of normal dry season conditions, leading to enhanced new leaf development and ecosystem photosynthesis, as supported by field observations. Our results suggest that further rainfall deficit into the subsequent dry season caused water and heat stress during the peak of 2005 and 2010 droughts (August–October) that exceeded the tolerance limits of the rainforest, leading to widespread negative VOD anomalies over the southern Amazonia. Significant VOD anomalies were observed mainly over the western part in 2005 and mainly over central and eastern parts in 2010. The total area with significant negative VOD anomalies was comparable between these two drought years, though the average magnitude of significant negative VOD anomalies was greater in 2005. This finding broadly agrees with the field observations indicating that the reduction in biomass carbon uptake was stronger in 2005 than 2010. The enhanced canopy growth prece

Increases in greenhouse gas concentrations are expected to impact the terrestrial hydrologic cycle through changes in radiative forcings and plant physiological and structural responses. Here, we investigate the nature and frequency of non-stationary hydrological response as evidenced through water balance studies over 166 anthropogenically unaffected catchments in Australia. Non-stationarity of hydrologic response is investigated through analysis of long-term trend in annual runoff ratio (1984-2005). Results indicate that a significant trend (<0.01) in runoff ratio is evident in 20 catchments located in three main ecoregions of the continent. Runoff ratio decreased across the catchments with non-stationary hydrologic response with the exception of one catchment in northern Australia. Annual runoff ratio sensitivity to annual fractional vegetation cover was similar to or greater than sensitivity to annual precipitation in most of the catchments with non-stationary hydrologic response indicating vegetation impacts on streamflow. We use precipitation-productivity relationships as the first-order control for ecohydrologic catchment classification. A total of 12 out of 20 catchments present a positive precipitation-productivity relationship possibly enhanced by CO2 fertilization effect. In the remaining catchments, biogeochemical and edaphic factors may be impacting productivity. Results suggest vegetation dynamics should be considered in exploring causes of non-stationary hydrologic response.

Violent fire-driven convection can manifest as towering cumulus or cumulonimbus clouds, the latter of which are known as firestorms (pyroCb). These extreme fires can have devastating impacts onenvironment and society, and appear to be a worsening problem. The major concerns surrounding these large pyroconvective events are that their associated fire spread is highly unpredictable and that they’re generally not suppressible. Indeed, current methods of fire spread prediction, including the use of empirical and semi-empirical modelling approaches, fall short when attempting to predict fire behaviour associated with these events. In particular, they commonly under-predict the rate of spread in such situations, when broader-scale fire-atmosphere interactions are dominant. To date, research into large pyroconvective events has eitherfocused on the processes involved in normal atmospheric convection, or on surface fire weather andassociated fuel conditions. While some investigations have combined the two approaches, a definitive studyinto both the effects of the surface conditions and the fire-atmosphere interactions is still needed to betterunderstand their respective role in the development of extreme fires.This paper incorporates recent insights into dynamic fire propagation into a coupled fire-atmosphere firemodelling framework to test the combined effects of fire behaviour and atmospheric structure on theoccurrence of violent pyroconvective events. In particular, we consider the role of factors such as the spatialexpanse and intensity of the fire, and the stability of the atmosphere (both in terms of temperature lapse andmoisture profile). The effects of these variables on extreme pyroconvective development are investigated in asystematic way, varying these parameters using the coupled fire-atmosphere WRF-Fire (Weather Researchand Forecasting Model (WRF)). In the initial work presented in this paper we focus on the case where the fireis represented by a static heat so

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 simulations over the Australian region using the Weather Research and Forecasting Model 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 that coarse topography is unlikely to be responsible for the difficulties in simulating coastal cyclones in global climate models.

Lateral and lower boundary conditions derived from a suitable global reanalysis data set form 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 data sets 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 data sets 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 present a novel Bayesian statistical approach to computing model weights in climate change projection ensembles in order to create probabilistic projections. The weight of each climate model is obtained by weighting the current day observed data under the posterior distribution admitted under competing climate models. We use a linear model to describe the model output and observations. The approach accounts for uncertainty in model bias, trend and internal variability, including error in the observations used. Our framework is general, requires very little problem-specific input, and works well with default priors. We carry out cross-validation checks that confirm that the method produces the correct coverage.

Models and physical reasoning predict that extreme precipitation will increase in a warmer climate due to increased atmospheric humidity. 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 tropics. 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 ensemble 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 â '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.

© 2017 The Author(s). In this study, we have examined the ability of a regional climate model (RCM) to simulate the extended drought that occurred throughout the period of 2002 through 2007 in south-east Australia. In particular, the ability to reproduce the two drought peaks in 2002 and 2006 was investigated. Overall, the RCM was found to reproduce both the temporal and the spatial structure of the drought-related precipitation anomalies quite well, despite using climatological seasonal surface characteristics such as vegetation fraction and albedo. This result concurs with previous studies that found that about two-thirds of the precipitation decline can be attributed to the El Ninõ–Southern Oscillation (ENSO). Simulation experiments that allowed the vegetation fraction and albedo to vary as observed illustrated that the intensity of the drought was underestimated by about 10ĝ% when using climatological surface characteristics. These results suggest that in terms of drought development, capturing the feedbacks related to vegetation and albedo changes may be as important as capturing the soil moisture–precipitation feedback. In order to improve our modelling of multi-year droughts, the challenge is to capture all these related surface changes simultaneously, and provide a comprehensive description of land surface–precipitation feedback during the droughts development.

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.

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.

Recent research in large-scale hydroclimatic variability is surveyed, focusing on five topics: (i) variability in general, (ii) droughts, (iii) floods, (iv) land-atmosphere coupling, and (v) hydroclimatic prediction. Each surveyed topic is supplemented by illustrative examples of recent research, as presented at a 2016 symposium honoring the career of Professor Eric Wood. Taken together, the recent literature and the illustrative examples clearly show that current research into hydroclimatic variability is strong, vibrant, and multifaceted.

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

Floods are caused by a number of interacting factors, making it remarkably difficult to explain changes in flood hazard. This paper reviews the current understanding of historical trends and variability in flood hazard across Australia. Links between flood and rainfall trends cannot be made due to the influence of climate processes over a number of spatial and temporal scales as well as landscape changes that affect the catchment response. There are also still considerable uncertainties in future rainfall projections, particularly for sub-daily extreme rainfall events. This is in addition to the inherent uncertainty in hydrological modelling such as antecedent conditions and feedback mechanisms. Research questions are posed based on the current state of knowledge. These include a need for high-resolution climate modelling studies and efforts in compiling and analysing databases of sub-daily rainfall and flood records. Finally there is a need to develop modelling frameworks that can deal with the interaction between climate processes at different spatio-temporal scales, so that historical flood trends can be better explained and future flood behaviour understood.

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

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

Recent studies using regional climate models to make probabilistic projections break important new ground. However, they typically lack cross validation, pull the projections toward agreeing models (which can agree due to shared biases), and ignore model skill at reproducing internal variability when weighing the models. Here we conduct the first, to our knowledge, application of Bayesian model averaging (BMA) to make probabilistic projections using regional climate models (RCMs). We weigh the RCMs from the NARCliM project based on their skill at representing temperature over 12 southeast Australian regions in terms of trend, bias, and internal variability. The weights do not depend on model agreement with other models. Using the weighted ensemble, we provide probabilistic seasonal temperature projections. We cross validate the method, and demonstrate that weighted projections are well calibrated and more precise than the unweighted ones. We find considerable differences between the weighted and the unweighted projections in some cases.

Current and potential future storm-related wind and hail hazard in Australia is reviewed. Confidence in the current incidence of wind hazard depends upon the type of storm producing the hazard. Current hail hazard is poorly quantified in most regions of Australia. Future projections of wind hazard indicate decreases in wind hazard in northern Australia, increases along the east coast and decreases in the south, although such projections are considerably uncertain and are more uncertain for small-scale storms than for larger storms. A number of research gaps are identified and recommendations made.