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Large hail events are typically infrequent, with significant time gaps between occurrences at specific locations. However, when these events do happen, they can cause rapid and substantial economic losses within a matter of minutes. Therefore, it is crucial to have the ability to accurately observe and understand hail phenomena to improve the mitigation of this impact. While in situ observations are accurate, they are limited in number for an individual storm. Weather radars, on the other hand, provide a larger observation footprint, but current radar-derived hail size estimates exhibit low accuracy due to horizontal advection of hailstones as they fall, the variability of hail size distributions (HSDs), complex scattering and attenuation, and mixed hydrometeor types. In this paper, we propose a new radar-derived hail product developed using a large dataset of hail damage insurance claims and radar observations. We use these datasets coupled with environmental information to calculate a hail damage estimate (HDE) using a deep neural network approach aiming to quantify hail impact, with a critical success index of 0.88 and a coefficient of determination against observed damage of 0.79. Furthermore, we compared HDE to a popular hail size product (MESH), allowing us to identify meteorological conditions that are associated with biases on MESH. Environments with relatively low specific humidity, high CAPE and CIN, low wind speeds aloft, and southerly winds at the ground are associated with a negative MESH bias, potentially due to differences in HSD, hail hardness, or mixed hydrometeors. In contrast, environments with low CAPE, high CIN, and relatively high specific humidity aloft are associated with a positive MESH bias.

Abstract Solar photovoltaic and wind power are central to Australia’s renewable energy future, implying an energy sector vulnerable to weather and climate variability. Alignment of weather systems and the influence of large-scale climate modes of variability risks widespread reductions in solar and wind resources, and could induce grid-wide impacts. We therefore systematically analyse the relationship between compound solar radiation and wind speed droughts with weather systems and climate modes of variability over multiple time scales. We find that compound solar and wind droughts occur most frequently in winter, affecting at least five significant energy-producing regions simultaneously on 10% of days. The associated weather systems vary by season and by drought type, although widespread cloud cover and anticyclonic circulation patterns are common features. Indices of major climate modes are not strong predictors of grid-wide droughts, and are typically within one standard deviation of the mean during seasons with the most widespread events. However, the spatial imprints of the teleconnections display strong regional variations, with drought frequencies varying by more than ten days per season between positive and negative phases of climate modes in some regions. The spatial variability of these teleconnection patterns suggests that droughts in one region may be offset by increased resource in another. Our work highlights the opportunity for minimising the impact of energy production variability by utilising weather and climate intelligence. Exploiting the spatial variability associated with daily weather systems and the seasonal influence of climate modes could help build a more climate-resilient renewables-dominated energy system.

Abstract While compound weather and climate events (CEs) can lead to significant socioeconomic consequences, their response to climate change is mostly unexplored. We report the first multi-model assessment of future changes in return periods for the co-occurrence of heatwaves and drought, and extreme winds and precipitation based on the Coupled Model Intercomparison Project (CMIP6) and three emission scenarios. Extreme winds and precipitation CEs occur more frequently in many regions, particularly under higher emissions. Heatwaves and drought occur more frequently everywhere under all emission scenarios examined. For each CMIP6 model, we derive a skill score for simulating CEs. Models with higher skill in simulating historical CEs project smaller increases in the number of heatwaves and drought in Eurasia, but larger numbers of strong winds and heavy precipitation CEs everywhere for all emission scenarios. This result is partly masked if the whole CMIP6 ensemble is used, pointing to the considerable value in further improvements in climate models.

Compound drought and heatwaves (CDHWs) can cause significant socio-economic and ecological impacts. A better understanding of historical spatiotemporal changes of Australian CDHWs and their underlying physical mechanisms can help improve their predictability. We analyse changes in Australian CDHW metrics in the extended summer season (November to March) during the period 1958–2020. Our results suggest that CDHWs increased significantly in terms of their frequency, duration, amplitude, and severity in the recent period (1989/90–2019/20) relative to a historical period (1958/59–1988/89), particularly in eastern Australia. We further analysed the influence of co-occurring modes of El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on the frequency and severity of Australian CDHWs using two Coupled Model Intercomparison Project phase 6 (CMIP6) single model initial-condition large ensembles. We found that frequency, duration, and severity of CDHWs are significantly increased during strong El Niño phases across northeast Australia compared to neutral ENSO and IOD conditions. This increase is widespread over northeast and southeast Australia during the concurrence of strong El Niños and moderate-strong positive IOD events. Our results show that an extreme CDHW season in terms of their frequency, duration, and severity occurs one out of every two seasons of strong El Niño over the northeast Australia. Moreover, the same applies for the co-occurring strong El Niño and positive IOD over the northeast and southeast Australia as well. Our results highlight the importance of ENSO and IOD combinations for Australian CDHW events. Our findings provide insights into the importance of climate variability in driving Australian CDHWs.

Many natural disasters in Australia occur as a result of meteorological drivers occurring in combination. These Compound Events (CEs) amplify risk to socio-economic and ecological systems relative to single meteorological events. We undertake a bivariate assessment for two hazard combinations important in Australia: heatwaves and drought, and strong winds and heavy precipitation. We use observations and reanalysis data to build a high-resolution climatology spanning the period from 1980 to 2014 and identify the north-western Australian coast as a region particularly prone to the joint occurrence of wind and precipitation extremes. Heatwaves and drought frequently co-occur multiple times per year over most of Australia with shorter return periods in the tropical north. We show that CMIP6 models capture the return periods of both CEs well. We then examine projected changes in both CEs under future conditions (2066–2 100) under a moderate and high emission scenario. All CMIP6 models project decreases in the return periods (i.e. more frequent events) of co-occurring heatwaves and droughts with the south of Australia particularly affected. Northern Australia is projected to become more at risk from wind and precipitation extremes while southern Australia experiences lower risk. The high level of skill in simulating the two CEs examined, combined with the coherent spatial patterns projected by the CMIP6 models suggest there is merit in using these models to examine the large-scale risk of CEs in the future, and that these can be used to support national scale planning for the changing risk of 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.

Atmospheric river (AR) systems play a significant role in the simultaneous occurrence of high coastal water levels and heavy precipitation in the Netherlands. Based on observed precipitation values (E-OBS) and the output of a numerical storm surge model (WAQUA/DSCMv5) forced with ERA-Interim sea level pressure and wind fields, we find that the majority of compound events (CEs) between 1979 and 2015 have been accompanied by the presence of an AR over the Netherlands. In detail, we show that CEs have a 3 to 4 times higher chance of occurrence on days with an AR over the Netherlands compared to any random day (i.e. days without knowledge on presence of an AR). In contrast, the occurrence of a CE on a day without AR is 3 times less likely than on any random day. Additionally, by isolating and assessing the prevailing sea level pressure (SLP) and sea surface temperature (SST) conditions with and without AR involvement up to 7 days before the events, we show that the presence of ARs constitutes a specific type of forcing conditions that (i) resemble the SLP anomaly patterns during the positive phase of the North Atlantic Oscillation (NAO+) with a north–south pressure dipole over the North Atlantic and (ii) cause a cooling of the North Atlantic subpolar gyre and eastern boundary upwelling zone while warming the western boundary of the North Atlantic. These conditions are clearly distinguishable from those during compound events without the influence of an AR which occur under SLP conditions resembling the East Atlantic (EA) pattern with a west–east pressure dipole over northern Europe and are accompanied by a cooling of the West Atlantic. Thus, this study shows that ARs are a useful tool for the early identification of possible harmful meteorological conditions over the Netherlands and supports an effort for the establishment of an early warning system.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20502-8.

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