Search

A large literature on the sensitivity of streamflow response to climate variations has emerged over the past several decades, but the underlying mechanisms are not fully understood. The usual approaches to this problem have been simple and do not fully address its complexity, which involves the individual and joint effects of large‐scale climate modes and smaller‐scale weather features on streamflow volumes, the presence of persistence in these time series and their effects on antecedent conditions. Ongoing improvements in observational and reanalysis datasets and online access allow for better quantification of the connections between streamflow response, climate variability modes and regional weather features. The purpose of this paper is to determine whether Bayesian networks can be used to better identify key factors and their associated pathways leading to streamflow generation. A Markov blanket approach is described and illustrated using monthly streamflow series recorded at eight gauging stations within or immediately adjacent to the coastal region of eastern mainland Australia. The method is compared and contrasted with conventional multiple linear regression with discussion around this type of approach as part of a growing interest in machine learning applications. One example is used to illustrate the application of both approaches to a streamflow series exhibiting strong non‐stationarity. Results for the other streamflow series are included in a concise synthesis. Overall, the results suggest that Bayesian networks have several desirable features in terms of transparency, interpretability and explanatory insight. The findings from this study lend support to the use of Bayesian networks for modelling connections between streamflow volume and the variability of climate and regional weather. This improved understanding of the key controls of streamflow variability is intended to help address growing needs around informing social, cultural, economic and ecological aspects of water planning and management. [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]

Reconstructed weather datasets, such as reanalyses based on model output with data assimilation, often show systematic biases in magnitude when compared with observations. Postprocessing approaches can help adjust the distribution so that the reconstructed data resemble the observed data as closely as possible. In this study, we have compared various statistical bias-correction approaches based on quantile–quantile matching to correct the data from the Twentieth Century Reanalysis, version 2c (20CRv2c), with observation-based data. Methods included in the comparison utilize a suite of different approaches: a linear model, a median-based approach, a nonparametric linear method, a spline-based method, and approaches that are based on the lognormal and Weibull distributions. These methods were applied to daily data in the Australian region for rainfall, maximum temperature, relative humidity, and wind speed. Note that these are the variables required to compute the forest fire danger index (FFDI), widely used in Australia to examine dangerous fire weather conditions. We have compared the relative errors and performances of each method across various locations in Australia and applied the approach with the lowest mean-absolute error across multiple variables to produce a reliable long-term bias-corrected FFDI dataset across Australia. The spline-based data correction was found to have some benefits relative to the other methods in better representing the mean FFDI values and the extremes from the observed records for many of the cases examined here. It is intended that this statistical bias-correction approach applied to long-term reanalysis data will help enable new insight on climatological variations in hazardous phenomena, including dangerous wildfires in Australia extending over the past century. [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]

Understanding the relationships between large-scale, low-frequency climate variability modes, fire weather conditions and lighting-ignited wildfires has implications for fire-weather prediction, fire management and conservation. This article proposes a Bayesian network framework for quantifying the influence of climate modes on fire weather conditions and occurrence of lightning-ignited wildfires. The main objectives are to describe and demonstrate a probabilistic framework for identifying and quantifying the joint and individual relationships that comprise the climate-wildfire system; gain insight into potential causal mechanisms and pathways; gauge the influence of climate modes on fire weather and lightning-ignition relative to that of local-scale conditions alone; assess the predictive skill of the network; and motivate the use of techniques that are intuitive, flexible and for which user‐friendly software is freely available. A case study illustrates the application of the framework to a forested region in southwest Australia. Indices for six climate variability modes are considered along with two hazard variables (observed fire weather conditions and prescribed burn area), and a 41-year record of lightning-ignited wildfire counts. Using the case study data set, we demonstrate that the proposed framework: (1) is based on reasonable assumptions provided the joint density of the variables is converted to multivariate normal; (2) generates a parsimonious and interpretable network architecture; (3) identifies known or partially known relationships between the variables; (4) has potential to be used in a predictive setting for fire weather conditions; and (5) climate modes are more directly related to fire weather conditions than to lightning-ignition counts. [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]

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]

Anticyclones are one of the main synoptic features of the global midlatitudes, where they are associated with dry, settled conditions. Previous studies have identified southern Australia as a region where significant long-term changes have occurred in recent decades, including a decline in cool season rainfall and an increase in the frequency of anticyclones. This paper uses several reanalyses to assess the climatology, variability, trends and impacts of anticyclones in southern Australia and several subregions. A robust increase in the frequency of anticyclones in southern Australia is demonstrated between 1960–1979 and 1997–2014, particularly in southeast Australia and the Tasman Sea during the cool months of the year. At least 30% of the recent cool season rainfall decline in southeast Australia can be associated with an increased frequency of anticyclones in this region. However, there are no clear trends in anticyclone frequency near southwestern Western Australia, where long-term rainfall declines are due to changes in other weather systems. [ABSTRACT FROM AUTHOR]

ABSTRACT Extratropical cyclones that develop near the east coast of Australia often have severe consequences such as flash flooding and damaging winds and seas, as well as beneficial consequences such as being responsible for heavy rainfall events that contribute significantly to total rainfall and runoff. There is subjective evidence that the development of most major events, commonly known as East Coast Lows ( ECLs), is associated with the movement of a high amplitude upper-tropospheric trough system over eastern Australia. To test this hypothesis, this study examines a number of large-scale diagnostic quantities in the upper-troposphere associated with extratropical cyclogenesis. Climatologies of these diagnostic quantities, based on reanalyses, are examined and compared with a database of observed ECL events. Results are examined in relation to seasonal and geographic variations. A diagnostic quantity based on geostrophic vorticity is shown to provide a good indication of the likely occurrence of east-coast cyclogenesis. The potential application of these diagnostics to global climate model simulations of past and future climates is also discussed. [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]