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Previous research has suggested that the frequency and intensity of surface hazards associated with thunderstorms and convection, such as severe convective winds (SCWs), could potentially change in a future climate due to global warming. However, because of the small spatial scales associated with SCWs, they are unresolved in global climate models, and future climate projections are uncertain. Here, we evaluate the representation of SCW events in a convection-permitting climate model (Bureau of Meteorology Regional Projections for Australia, BARPAC-M), run over southeastern Australia for December–February months. We also assess changes in SCW event frequency in a projected future climate for the year 2050, and compare this with an approach based on identifying large-scale environments favourable for SCWs from a regional parent model (BARPA-R). This is done for three different types of SCW events that have been identified in this region, based on clustering of the large-scale environment. Results show that BARPAC-M representation of the extreme daily maximum wind gust distribution is improved relative to the gust distribution simulated by the regional parent model. This is due to the high spatial resolution of BARPAC-M output, as well as partly resolving strong and short-lived gusts associated with convection. However, BARPAC-M significant overestimates the frequency of simulated SCW events, particularly in environments having steep low-level temperature lapse rates. A future decrease in SCW frequency under steep lapse rate conditions is projected by BARPAC-M, along with less frequently favourable large-scale environments. In contrast, an increase in SCW frequency is projected under high surface moisture conditions, with more frequently favourable large-scale environments. Therefore, overall changes in SCWs for this region remain uncertain, due to different responses between event types, combined with historical model biases. [ABSTRACT FROM AUTHOR]

Regional understanding of severe surface winds produced by convective processes [severe convective winds (SCWs)] is important for decision-making in several areas of society, including weather forecasting and engineering design. Meteorological studies have demonstrated that SCWs can occur due to a number of different mesoscale and microscale processes, in a range of large-scale atmospheric environments. However, long-term observational studies of SCW characteristics often have not considered this diversity in physical processes, particularly in Australia. Here, a statistical clustering method is used to separate a large dataset of SCW events, measured by automatic weather stations around Australia, into three types, associated with strong background wind, steep lapse rate, and high moisture environments. These different types of SCWs are shown to have different seasonal and spatial variations in their occurrence, as well as different measured wind gust, lightning, and parent-storm characteristics. In addition, various convective diagnostics are tested in their ability to discriminate between measured SCW events and nonsevere events, with significant variations in skill between event types. Differences in environmental conditions and wind gust characteristics between event types suggests potentially different physical processes for SCW production. These findings are intended to improve regional understanding of severe wind characteristics, as well as environmental prediction of SCWs in weather and climate applications, by considering different event types. Significance Statement: The purpose of this study is to improve regional understanding of different types of severe wind events in Australia, specifically those associated with atmospheric convection. We did this by constructing a dataset of 413 severe convective wind events, using weather station and radar data within 20 regions around Australia. We then split those events into three different types, based on the environmental conditions that they occur within. We found that each event type tends to occur at different times of the year and in different regions, while also having different wind gust and lightning characteristics. In addition, the atmospheric conditions that are helpful for prediction of severe wind events differs between each type. These results are intended to be useful for prediction of severe wind events associated with convection and assessing their variability, characteristics, and impacts, in both weather forecasting and climate analysis. [ABSTRACT FROM AUTHOR]

Extreme wind gusts cause major socioeconomic damage, and the rarity and localised nature of those events make their analysis challenging by either modelling or empirical approaches. A 23-year long data record from 29 automatic weather stations located in New South Wales (eastern Australia) is used to study the distribution, frequency and average recurrence intervals (ARIs) of extreme gusts via a peaks-over-threshold approach. We distinguish between gust events generated by synoptic phenomena (e.g. cyclones and frontal systems), hereafter called "synoptic events", and convective phenomena (i.e. thunderstorms), hereafter called "convective events", using the wind time series. For synoptic events the frequency of gusts > 25 m/s decreases systematically inland from the coast, in contrast to convective gusts which are more uniformly distributed geographically and occur more often than synoptic gusts at nearly all inland locations. At inland locations the most extreme wind gusts are likewise dominated by convective events, whereas at coastal stations both gust types have similar intensities at low ARIs but convective events again dominate at the highest ARIs. Extreme gust directions were found to be predominantly westerly at inland locations and southerly at coastal ones, with more variable direction for convective than synoptic events. This study confirms the dominant role of thunderstorms in producing the most extreme gusts in the region, and shows that wind risk varies strongly with distance from the coast. [ABSTRACT FROM AUTHOR]

Understanding bushfire–atmosphere interactions is essential for accurate prediction of fire behaviour, and for the safe and effective strategic management of fires to mitigate risk to people and property. Bushfires with feedbacks to thunderstorms represent the most extreme form of fire–atmosphere interaction, with potential to initiate tornadoes, lightning and hazardous winds causing dangerous fire behaviour and new ignitions many kilometres from the fire front. However, there is very little evidence that links quantitative fire behaviour with observed thunderstorm dynamics. Here we combine stochastic modelling of fire behaviour with satellite and mobile weather radar data of a bushfire thunderstorm in Queensland, Australia. The results show the coupling between fire behaviour and thunderstorm development in a conditionally unstable atmosphere. The process by which the coupling occurs raises questions as to the cause and effect relationship of the bushfire-initiated thunderstorms and associated fire behaviour. Recommendations for future research are made, highlighting the need for understanding links between modelled and observed fire behaviour dynamics and atmospheric thermodynamics. Bushfires can support the evolution of thunderstorms. Here, we combine a variety of observations of a bushfire thunderstorm with ensemble fire spread modelling and fire severity mapping. Results show the coupling between the intensity of the fire and its burning through forest canopy with thunderstorm updrafts. [ABSTRACT FROM AUTHOR]

Severe winds associated with thunderstorms and convection are a hazard affecting key aspects of society, including emergency management and infrastructure design. Several studies around the world have shown that severe convective winds (SCWs) can occur due to several different processes, in a range of atmospheric environments, with significant regional and temporal variations. However, in eastern Australia, the types of SCWs and their variability have not been assessed outside of individual case studies. Here, a combination of reanalysis, lightning, radar, and station data are used to characterize a set of 36 SCW events in four locations in eastern Australia. These events are objectively chosen based on the strongest measured wind gusts from station data (greater than 25 m s−1) over a 14-yr period, with 6-hourly lightning data and a 30-dBZ radar reflectivity threshold used to infer moist convective processes. Radar data analysis suggests that these SCW events are produced by several different types of parent thunderstorms, with station observations suggesting a range of temporal characteristics for these different event types. A clustering algorithm applied to environmental data is used to suggest three dominant types of events, based on low-level moisture, low-level temperature lapse rate, and deep-layer mean wind speed and vertical shear. Based on the distribution of synoptic conditions and thunderstorm properties for each environmental cluster, it is suggested that these three event types correspond to the following: 1) shallow vertical transport of strong synoptic-scale winds to the surface, 2) downbursts driven by subcloud evaporation, and 3) intense thunderstorms including supercells. Significance Statement: The purpose of this study is to better understand the different types of severe wind events in eastern Australia that are produced by convective storms. We looked at 36 historical cases in four locations and find that severe winds can be produced by very different classes of convective storms. We also suggest that there are three key types of atmospheric environment that are associated with events in this region. These environments vary in terms of the vertical structure of temperature, moisture, and wind speed above the surface. Understanding the different types of environments that lead to severe convective winds can help to reduce uncertainties in future climate projections for this region based on environmental changes. [ABSTRACT FROM AUTHOR]

High‐resolution regional reanalysis datasets have the potential to provide valuable guidance to emergency management agencies, highlighting areas at risk of severe weather, including estimates of return periods of various hazardous weather phenomena. The BARRA regional reanalysis for Australia comprises a reanalysis for a broad region around Australia at moderately high spatial and temporal resolution (12 km/hourly), together with four subdomains at high resolution (1.5 km/1 h). Here, we document four applications of BARRA developed for emergency management: optimal placement of portable automatic weather stations for fire weather monitoring; climatology of low‐level wind shear conducive to cool‐season tornadogenesis; development of rainfall intensity–frequency–duration curves based on the gridded reanalysis data; and development of a climatology across Australia of parameters associated with severe thunderstorm occurrence. [ABSTRACT FROM AUTHOR]

Epidemic asthma events represent a significant risk to emergency services as well as the wider community. In southeastern Australia, these events occur in conjunction with relatively high amounts of grass pollen during the late spring and early summer, which may become concentrated in populated areas through atmospheric convergence caused by a number of physical mechanisms including thunderstorm outflow. Thunderstorm forecasts are therefore important for identifying epidemic asthma risk factors. However, the representation of thunderstorm environments using regional numerical weather prediction models, which are a key aspect of the construction of these forecasts, have not yet been systematically evaluated in the context of epidemic asthma events. Here, we evaluate diagnostics of thunderstorm environments from historical simulations of weather conditions in the vicinity of Melbourne, Australia, in relation to the identification of epidemic asthma cases based on hospital data from a set of controls. Skillful identification of epidemic asthma cases is achieved using a thunderstorm diagnostic that describes near-surface water vapor mixing ratio. This diagnostic is then used to gain insights on the variability of meteorological environments related to epidemic asthma in this region, including diurnal variations, long-term trends, and the relationship with large-scale climate drivers. Results suggest that there has been a long-term increase in days with high water vapor mixing ratio during the grass pollen season, with large-scale climate drivers having a limited influence on these conditions. Significance Statement: We investigate the atmospheric conditions associated with epidemic thunderstorm asthma events in Melbourne, Australia, using historical model simulations of the weather. Conditions appear to be associated with high atmospheric moisture content, which relates to environments favorable for severe thunderstorms, but also potentially pollen rupturing as suggested by previous studies. These conditions are shown to be just as important as the concentration of grass pollen for a set of epidemic thunderstorm asthma events in this region. This means that weather model simulations of thunderstorm conditions can be incorporated into the forecasting process for epidemic asthma in Melbourne, Australia. We also investigate long-term variability in atmospheric conditions associated with severe thunderstorms, including relationships with the large-scale climate and long-term trends. [ABSTRACT FROM AUTHOR]

Fire activity in Australia is strongly affected by high inter-annual climate variability and extremes. Through changes in the climate, anthropogenic climate change has the potential to alter fire dynamics. Here we compile satellite (19 and 32 years) and ground-based (90 years) burned area datasets, climate and weather observations, and simulated fuel loads for Australian forests. Burned area in Australia's forests shows a linear positive annual trend but an exponential increase during autumn and winter. The mean number of years since the last fire has decreased consecutively in each of the past four decades, while the frequency of forest megafire years (>1 Mha burned) has markedly increased since 2000. The increase in forest burned area is consistent with increasingly more dangerous fire weather conditions, increased risk factors associated with pyroconvection, including fire-generated thunderstorms, and increased ignitions from dry lightning, all associated to varying degrees with anthropogenic climate change. The degree to which wildfire activity in Australia is affected by climate change is not well quantified. Here, the authors show that the frequency of forest fires and the area burned have increased significantly over recent decades, mainly due to an increase in dangerous fire weather conditions through warmer temperature and circulation changes. [ABSTRACT FROM AUTHOR]

Thunderstorms can produce severe convective winds (SCWs) that damage buildings and other infrastructure such as electricity transmission towers. Understanding the climatology of SCWs is therefore important for planning and risk management. An archive of observed SCWs is used to examine a diverse set of diagnostics for indicating SCW environments based on reanalysis data. These diagnostics are then applied to climate model data to examine projections of future climate change for Australia. A diagnostic based on logistic regression is found to provide a better representation of observed SCW occurrences than other diagnostics. Projections for the future based on that diagnostic indicate increases and decreases between −16% and 34% in the occurrence frequency of regionally averaged SCW environments, based on the 10th and 90th percentile estimates of annual mean changes from a 12‐member ensemble of global climate models. Projections based on other severe weather diagnostics indicate a wider range of future changes, including increases and decreases of up to 50% in magnitude, with regional and seasonal variations through Australia. Changes in the frequency of SCW environments appears to be largely driven by increased low level moisture concentrations which can lead to increased convective available potential energy, countered in some cases by a stabilization of the mid‐troposphere temperature lapse rate. These results represent the most comprehensive estimate to date for constraining the range of uncertainty in projected future changes in convective environments for Australia, including severe thunderstorms and associated SCWs, noting that this has significant implications for risk management and climate adaptation purposes. Key Points: Environmental diagnostics are applied to reanalysis data and assessed in their ability to identify severe convective wind gusts in AustraliaSuitable diagnostics are used to investigate long‐term historical trends and projected future changes from climate model dataProjections are sensitive to the choice of diagnostic, with changes driven by changes in atmospheric moisture content and lapse rates [ABSTRACT FROM AUTHOR]

The world's most severe thunderstorm asthma event occurred in Melbourne, Australia on 21 November 2016, coinciding with the peak of the grass pollen season. The aetiological role of thunderstorms in these events is thought to cause pollen to rupture in high humidity conditions, releasing large numbers of sub-pollen particles (SPPs) with sizes very easily inhaled deep into the lungs. The humidity hypothesis was implemented into a three-dimensional atmospheric model and driven by inputs from three meteorological models. However, the mechanism could not explain how the Melbourne event occurred as relative humidity was very low throughout the atmosphere, and most available grass pollen remained within 40 m of the surface. Our tests showed humidity induced rupturing occurred frequently at other times and would likely lead to recurrent false alarms if used in a predictive capacity. We used the model to investigate a range of other possible pollen rupturing mechanisms which could have produced high concentrations of SPPs in the atmosphere during the storm. The mechanisms studied involve mechanical friction from wind gusts, electrical build up and discharge incurred during conditions of low relative humidity, and lightning strikes. Our results suggest that these mechanisms likely operated in tandem with one another, but the lightning method was the only mechanism to generate a pattern in SPPs following the path of the storm. If humidity induced rupturing cannot explain the 2016 Melbourne event, then new targeted laboratory studies of alternative pollen rupture mechanisms would be of considerable value to help constrain the parameterisation of the pollen rupturing process. [ABSTRACT FROM AUTHOR]

Most of the rainfall in southern Australia is associated with cyclones, cold fronts, and thunderstorms, and cases when these weather systems co-occur are particularly likely to cause extreme rainfall. Rainfall declines in some parts of southern Australia during the cool half of the year in recent decades have previously been attributed to decreases in the rainfall from fronts and/or cyclones, while thunderstorm-related rainfall has been observed to increase, particularly in the warm half of the year. However, the co-occurrence of these systems, particularly the co-occurrence of cyclones or fronts with thunderstorms, can be very important for rainfall in some areas, particularly heavy rainfall, and changes in the frequency of these combined weather systems have not been previously assessed. In this paper we show that the majority of the observed cool season rainfall decline between 1979–1996 and 1997–2015 in southeast Australia is associated with a decrease in the frequency of fronts and cyclones that produce rainfall, while there has simultaneously been an increase in the frequency of cold fronts and thunderstorms that do not produce rainfall in some regions. Thunderstorm rainfall has increased in much of southern Australia, particularly during the warm half of the year, including an increase in rainfall where a thunderstorm environment occurs at the same time as a cyclone or front. [ABSTRACT FROM AUTHOR]

There is a growing need to better understand and quantify risks associated with extreme weather, including severe thunderstorm‐related hazards such as hail and lightning. Hail occurrence based on a long‐term archive of radar observations is presented for the first time in many temperate and subtropical regions of Australia, together with lightning observations from a ground‐based network of sensors. Mean monthly and hourly occurrence frequencies are examined for hail and lightning. Environmental conditions obtained from hourly reanalysis data indicate stronger wind shear on average for hail than lightning. The environmental conditions also indicate higher freezing levels on average for lightning than hail. These environmental differences provide plausible physical reasons for observed differences between hail and lightning climatology through the year. The study results are intended to help inform future planning and preparedness for thunderstorm‐related risks, including for severe weather forecasting and climate risk applications. Plain Language Summary: Natural hazards caused by thunderstorms, such as hail and lightning, can have severe impacts on society. Radar data are used to examine hail events at 10 Australian locations, and comparisons are made with lightning observations. Results are averaged over several years to calculate average annual values, including for the hourly and the monthly number of the hail and the lightning events. We find differences between hail and lightning events in the environmental conditions that they occur in. These environmental differences are found to help explain differences through the year in the average risk of occurrence of hail or lightning. The results are intended to enhance resilience in relation to thunderstorm‐related risks and provide guidance for extreme weather modeling and climate adaptation purposes. Key Points: Occurrence frequencies of hail and lightning are presented based on observations for a wide range of regions around AustraliaEnvironmental conditions are found to provide insight on spatiotemporal variations in hail and lightning activityEnvironments associated with individual hazard types are recommended for use in improved preparedness for thunderstorm‐related damages [ABSTRACT FROM AUTHOR]

A systematic analysis of the main weather types influencing southern Australian rainfall is presented for the period 1979–2015. This incorporates two multi-method datasets of cold fronts and low pressure systems, which indicate the more robust fronts and lows as distinguished from the weaker and less impactful events that are often indicated only by a single method. The front and low pressure system datasets are then combined with a dataset of environmental conditions associated with thunderstorms, as well as datasets of warm fronts and high pressure systems. The results demonstrate that these weather types collectively account for about 86% of days and more than 98% of rainfall in Australia south of 25° S. We also show how the key rain-bearing weather systems vary throughout the year and for different regions, with the co-occurrence of simultaneous lows, fronts and thunderstorm conditions particularly important during the spring and summer months in southeast Australia. [ABSTRACT FROM AUTHOR]

Understanding wildfire‐atmosphere interactions is key to improved accuracy of predictions of wildfire behavior. This is needed for improved preparedness to mitigate loss of life and property during wildfire events, particularly for situations with strong fire‐atmosphere coupling. Here we present observations from the passage of a cold front over the Sir Ivan Dougherty wildfire on February 2017 in eastern Australia. We demonstrate that an increase in near‐surface atmosphere moisture associated with the cold front, when combined with changes in fire behavior at that time, led to reduced thermodynamic stability that helped to trigger a thunderstorm. This fire‐trigged supercell thunderstorm produced lightning, while radar observations identified a mesocyclonic circulation within the pyrocumulonimbus, similar to a supercell thunderstorm. Results highlight the need to monitor the thermodynamic properties of air masses approaching wildfires and the rapid evolution of pyrocumulonimbus, which may develop mesocyclone characteristics. Weather radar offers the most effective capability to achieve such insights. Plain Language Summary: Here we present observations of the behavior of a wildfire and associated meteorology over a large wildfire in eastern Australia. By analyzing weather radar data and meteorological data from close proximity to the fire, we show that an increase in atmospheric moisture due to the passage of a cold front over the fire ground, combined with changes in fire behavior at that time, was sufficient to trigger development of a wildfire‐induced thunderstorm. This fire‐generated storm produced lightning and began to rotate similar to a supercell thunderstorm. We conclude by highlighting the capability of weather radar in combination with standard meteorological observations to provide valuable insight to fire‐atmosphere coupling. These observations are essential to build understanding of extreme wildfire events for enabling improved prediction of their behavior to reduce loss of life and property in firestorms. Key Points: Genesis, growth, and decay of a pyroconvective thunderstorm are studied combining ground weather radar, atmospheric profiling, and micrometeorological observationsObservations of fire‐atmosphere interactions enable quantification of pyroconvective thunderstorm turbulence and kilometer‐scale vorticesAn "optimal" window of conditions is identified for a fire to trigger thunderstorm genesis and generate lightning that can ignite new fires [ABSTRACT FROM AUTHOR]

Lightning is a natural hazard that can lead to the ignition of wildfires, disruption and damage to power and telecommunication infrastructures, human and livestock injuries and fatalities, and disruption to airport activities. This paper examines the ability of six statistical and machine-learning classification techniques to distinguish between nonlightning and lightning days at the coarse spatial and temporal scales of current general circulation models and reanalyses. The classification techniques considered were 1) a combination of principal component analysis and logistic regression, 2) classification and regression trees, 3) random forests, 4) linear discriminant analysis, 5) quadratic discriminant analysis, and 6) logistic regression. Lightning-flash counts at six locations across Australia for 2004-13 were used, together with atmospheric variables from the ERA-Interim dataset. Tenfold cross validation was used to evaluate classification performance. It was found that logistic regression was superior to the other classifiers considered and that its prediction skill is much better than using climatological values. The sets of atmospheric variables included in the final logistic-regression models were primarily composed of spatial mean measures of instability and lifting potential, along with atmospheric water content. The memberships of these sets varied among climatic zones. [ABSTRACT FROM AUTHOR]

Lightning accompanied by inconsequential rainfall (i.e., 'dry' lightning) is the primary natural ignition source for wildfires globally. This paper presents a machine-learning and statistical-classification analysis of dry and 'wet' thunderstorm days in relation to associated atmospheric conditions. The study is based on daily data for lightning-flash count and precipitation from ground-based sensors and gauges and a comprehensive set of atmospheric variables that are based on ERA-Interim for the period from 2004 to 2013 at six locations in Australia. These locations represent a wide range of climatic zones (temperate, subtropical, and tropical). Quadratic surface representations and low-dimensional summary statistics were used to characterize the main features of the atmospheric fields. Four prediction skill scores were considered, and 10-fold cross validation was used to evaluate the performance of each classifier. The results were compared with those obtained by adopting the approach used in an earlier study for the U.S. Pacific Northwest. It was found that both approaches have prediction skill when tested against independent data, that mean atmospheric field quantities proved to be the most influential variables in determining dry-lightning activity, and that no single classifier or set of atmospheric variables proved to be consistently superior to its counterpart for the six sites examined here. [ABSTRACT FROM AUTHOR]

In the original published version of the paper, the figures reported in Sect. 4 relating the proportion of rainfall in southern Australia that is due to each of the weather types, were incorrectly stated for the entire Australian landmass, with the same error also affecting Figs. 6 and 8. [ABSTRACT FROM AUTHOR]