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Long-term trends in the historical frequency of environments supportive of atmospheric convection are unclear, and only partially follow the expectations of a warming climate. This uncertainty is driven by the lack of unequivocal changes in the ingredients for severe thunderstorms (i.e., conditional instability, sufficient low-level moisture, initiation mechanism, and vertical wind shear). ERA5 hybrid-sigma data allow for superior characterization of thermodynamic parameters including convective inhibition, which is very sensitive to the number of levels in the lower troposphere. Using hourly data we demonstrate that long-term decreases in instability and stronger convective inhibition cause a decline in the frequency of thunderstorm environments over the southern United States, particularly during summer. Conversely, increasingly favorable conditions for tornadoes are observed during winter across the Southeast. Over Europe, a pronounced multidecadal increase in low-level moisture has provided positive trends in thunderstorm environments over the south, central, and north, with decreases over the east due to strengthening convective inhibition. Modest increases in vertical wind shear and storm-relative helicity have been observed over northwestern Europe and the Great Plains. Both continents exhibit negative trends in the fraction of environments with likely convective initiation. This suggests that despite increasing instability, thunderstorms in a warming climate may be less likely to develop due to stronger convective inhibition and lower relative humidity. Decreases in convective initiation and resulting precipitation may have long-term implications for agriculture, water availability, and the frequency of severe weather such as large hail and tornadoes. Our results also indicate that trends observed over the United States cannot be assumed to be representative of other continents.

As lightning-detection records lengthen and the efficiency of severe weather reporting increases, more accurate climatologies of convective hazards can be constructed. In this study we aggregate flashes from the National Lightning Detection Network (NLDN) and Arrival Time Difference long-range lightning detection network (ATDnet) with severe weather reports from the European Severe Weather Database (ESWD) and Storm Prediction Center (SPC) Storm Data on a common grid of 0.25° and 1-h steps. Each year approximately 75–200 thunderstorm hours occur over the southwestern, central, and eastern United States, with a peak over Florida (200–250 h). The activity over the majority of Europe ranges from 15 to 100 h, with peaks over Italy and mountains (Pyrenees, Alps, Carpathians, Dinaric Alps; 100–150 h). The highest convective activity over continental Europe occurs during summer and over the Mediterranean during autumn. The United States peak for tornadoes and large hail reports is in spring, preceding the maximum of lightning and severe wind reports by 1–2 months. Convective hazards occur typically in the late afternoon, with the exception of the Midwest and Great Plains, where mesoscale convective systems shift the peak lightning threat to the night. The severe wind threat is delayed by 1–2 h compared to hail and tornadoes. The fraction of nocturnal lightning over land ranges from 15% to 30% with the lowest values observed over Florida and mountains (∼10%). Wintertime lightning shares the highest fraction of severe weather. Compared to Europe, extreme events are considerably more frequent over the United States, with maximum activity over the Great Plains. However, the threat over Europe should not be underestimated, as severe weather outbreaks with damaging winds, very large hail, and significant tornadoes occasionally occur over densely populated areas.

The El Niño/Southern Oscillation modulates global weather and climate. Analyses of large-scale environmental indices show that it also affects the frequency of tornado and hail events in the central United States, which may help with predictability. The El Niño/Southern Oscillation (ENSO) is characterized by changes in sea surface temperature (SST) and atmospheric convection in the tropical Pacific, and modulates global weather and climate 1 , 2 , 3 , 4 . The phase of ENSO influences United States (US) temperature and precipitation and has long been hypothesized to influence severe thunderstorm occurrence over the US 5 , 6 , 7 , 8 , 9 , 10 , 11 . However, limitations 12 of the severe thunderstorm observational record, combined with large year-to-year variability 12 , 13 , have made it difficult to demonstrate an ENSO influence during the peak spring season. Here we use environmental indices 14 , 15 , 16 that are correlated with tornado and hail activity, and show that ENSO modulates tornado and hail occurrence during the winter and spring by altering the large-scale environment. We show that fewer tornadoes and hail events occur over the central US during El Niño and conversely more occur during La Niña conditions. Moreover, winter ENSO conditions often persist into early spring, and consequently the winter ENSO state can be used to predict changes in tornado and hail frequency during the following spring. Combined with our current ability to predict ENSO several months in advance 17 , our findings provide a basis for long-range seasonal prediction of severe thunderstorm activity.

Globally, thunderstorms are responsible for a significant fraction of rainfall, and in the mid-latitudes often produce extreme weather, including large hail, tornadoes and damaging winds. Despite this importance, how the global frequency of thunderstorms and their accompanying hazards has changed over the past 4 decades remains unclear. Large-scale diagnostics applied to global climate models have suggested that the frequency of thunderstorms and their intensity is likely to increase in the future. Here, we show that according to ERA5 convective available potential energy (CAPE) and convective precipitation (CP) have decreased over the tropics and subtropics with simultaneous increases in 0–6 km wind shear (BS06). Conversely, rawinsonde observations paint a different picture across the mid-latitudes with increasing CAPE and significant decreases to BS06. Differing trends and disagreement between ERA5 and rawinsondes observed over some regions suggest that results should be interpreted with caution, especially for CAPE and CP across tropics where uncertainty is the highest and reliable long-term rawinsonde observations are missing.

Tornadic thunderstorms rely on the availability of sufficient low-level moisture, but the source regions of that moisture have not been explicitly demarcated. Using the NOAA Air Resources Laboratory HYSPLIT model and a Lagrangian-based diagnostic, moisture attribution was conducted to identify the moisture source regions of tornadic convection. This study reveals a seasonal cycle in the origins and advection patterns of water vapor contributing to winter and spring tornado-producing storms (1981–2017). The Gulf of Mexico is shown to be the predominant source of moisture during both winter and spring, making up more than 50% of all contributions. During winter, substantial moisture contributions for tornadic convection also emanate from the western Caribbean Sea (>19%) and North Atlantic Ocean (>12%). During late spring, land areas (e.g., soil and vegetation) of the contiguous United States (CONUS) play a more influential role (>24%). Moisture attribution was also conducted for nontornadic cases and tornado outbreaks. Findings show that moisture sources of nontornadic events are more proximal to the CONUS than moisture sources of tornado outbreaks. Oceanic influences on the water vapor content of air parcels were also explored to determine if they can increase the likelihood of an air mass attaining moisture that will eventually contribute to severe thunderstorms. Warmer sea surface temperatures were generally found to enhance evaporative fluxes of overlying air parcels. The influence of atmospheric features on synoptic-scale moisture advection was also analyzed; stronger extratropical cyclones and Great Plains low-level jet occurrences lead to increased meridional moisture flux.

The response of severe convective storms to a warming climate is poorly understood outside of a few well studied regions. Here, projections from seven global climate models from the CMIP6 archive, for both historical and future scenarios, are used to explore the global response in variables that describe favorability of conditions for the development of severe storms. The variables include convective available potential energy (CAPE), convection inhibition (CIN), 0–6 km vertical wind shear (S06), storm relative helicity (SRH), and covariate indices (i.e., severe weather proxies) that combine them. To better quantify uncertainty, understand variable sensitivity to increasing temperature, and present results independent from a specific scenario, we consider changes in convective variables as a function of global average temperature increase across each ensemble member. Increases to favorable convective environments show an overall frequency increases on the order of 5%–20% per °C of global temperature increase, but are not regionally uniform, with higher latitudes, particularly in the Northern Hemisphere, showing much larger relative changes. The driving mechanism of these changes is a strong increase in CAPE that is not offset by factors that either resist convection (CIN), or modify the likelihood of storm organization (S06, SRH). Severe weather proxies are not the same as severe weather events. Hence, their projected increases will not necessarily translate to severe weather occurrences, but they allow us to quantify how increases in global temperature will affect the occurrence of conditions favorable to severe weather. Plain Language Summary Severe weather can occur when some combination of atmospheric ingredients are present. These ingredients are called “convective environments” and refer to quantities that measure, for example, atmospheric instability and wind shear. By combining these convective environments into so‐called severe weather proxies, modelers can measure the favorability of occurrence of severe convective storms. Moreover, they can address a recurrent challenge in severe weather modeling: to find a way to robustly analyze phenomena (hail storms, tornadoes, straight‐line winds) that are highly intermittent and not resolved in coarse numerical models. CMIP6 models, for example, cannot resolve directly these phenomena because of both temporal and spatial resolution limitations. Therefore, we computed the convective environments for a subset of CMIP6 models and scenarios, and evaluated how severe weather proxies are projected to change as a function of global temperature increase. The results show increases of 5%–20% per °C of global temperature change. However, favorable severe weather proxies do not necessarily mean severe weather events occur, and thus we expect the overall increase to severe weather occurrences to be smaller. This analysis suggests increasing global temperature will affect the occurrence of conditions favorable to severe weather. Key Points We evaluate the global response of convective environments to a warming climate in CMIP6 models Increases in severe weather proxies frequency vary from 5% to 20% per °C of global temperature increase Atmospheric instability is the key driver, both globally and particularly in northern latitudes

Sufficient low-level storm-relative flow is a necessary ingredient for sustained supercell thunderstorms and is connected to supercell updraft width. Assuming a supercell exists, the role of low-level storm-relative flow in regulating supercells’ low-level mesocyclone intensity is less clear. One possibility considered in this article is that storm-relative flow controls mesocyclone and tornado width via its modulation of overall updraft extent. This hypothesis relies on a previously postulated positive correspondence between updraft width, mesocyclone width, and tornado width. An alternative hypothesis is that mesocyclone characteristics are primarily regulated by horizontal streamwise vorticity irrespective of storm-relative flow. A matrix of supercell simulations was analyzed to address the aforementioned hypotheses, wherein horizontal streamwise vorticity and storm-relative flow were independently varied. Among these simulations, mesocyclone width and intensity were strongly correlated with horizontal streamwise vorticity, and comparatively weakly correlated with storm-relative flow, supporting the second hypothesis. Accompanying theory and trajectory analysis offers the physical explanation that, when storm-relative flow is large and updrafts are wide, vertically tilted streamwise vorticity is projected over a wider area but with a lesser average magnitude than when these parameters are small. These factors partially offset one another, degrading the correspondence of storm-relative flow with updraft circulation and rotational velocity, which are the mesocyclone attributes most closely tied to tornadoes. These results refute the previously purported connections between updraft width, mesocyclone width, and tornado width, and emphasize horizontal streamwise vorticity as the primary control on low-level mesocyclones in sustained supercells.

The influence of a warming climate on the occurrence of severe thunderstorm environments in Australia was explored using two global climate models: Commonwealth Scientific and Industrial Research Organisation Mark, version 3.6 (CSIRO Mk3.6), and the Cubic-Conformal Atmospheric Model (CCAM). These models have previously been evaluated and found to be capable of reproducing a useful climatology for the twentieth-century period (1980–2000). Analyzing the changes between the historical period and high warming climate scenarios for the period 2079–99 has allowed estimation of the potential convective future for the continent. Based on these simulations, significant increases to the frequency of severe thunderstorm environments will likely occur for northern and eastern Australia in a warmed climate. This change is a response to increasing convective available potential energy from higher continental moisture, particularly in proximity to warm sea surface temperatures. Despite decreases to the frequency of environments with high vertical wind shear, it appears unlikely that this will offset increases to thermodynamic energy. The change ismost pronounced during the peak of the convective season, increasing its length and the frequency of severe thunderstorm environments therein, particularly over the eastern parts of the continent. The implications of this potential increase are significant, with the overall frequency of potential severe thunderstorm days per year likely to rise over the major population centers of the east coast by 14% for Brisbane, 22% for Melbourne, and 30% for Sydney. The limitations of this approach are then discussed in the context of ways to increase the confidence of predictions of future severe convection.

An empirical model relating monthly hail occurrence to the large‐scale environment has been developed and tested for the United States (U.S.). Monthly hail occurrence for each 1°×1° grid box is defined as the number of hail events that occur there during a month; a hail event consists of a 3 h period with at least one report of hail larger than 1 in. The model is derived using climatological annual cycle data only. Environmental variables are taken from the North American Regional Reanalysis (NARR; 1979–2012). The model includes four environmental variables convective precipitation, convective available potential energy, storm relative helicity, and mean surface to 90 hPa specific humidity. The model differs in its choice of variables and their relative weighting from existing severe weather indices. The model realistically matches the annual cycle of hail occurrence both regionally and for the contiguous U.S. (CONUS). The modeled spatial distribution is also consistent with the observed hail climatology. However, the westward shift of maximum hail frequency during the summer months is delayed in the model relative to observations, and the model has a lower frequency of hail just east of the Rocky Mountains compared to observations. Year‐to‐year variability provides an independent test of the model. On monthly and annual time scales, the model reproduces observed hail frequencies. Overall model trends are small compared to observed changes, suggesting that further analysis is necessary to differentiate between physical and nonphysical trends. The empirical hail model provides a new tool for exploration of connections between large‐scale climate and severe weather. Key Points: Develops an empirical model to describe hail occurrence over the U.S. Index is capable of capturing the interannual and seasonal variability of hail Discusses the limitations of existing hail climatology from observations

Large hail causes significant economic losses in the United States each year. Despite these impacts, hail is not typically included in building and infrastructure design standards, and assessments of hazards from extreme hail size remain limited. Here, we use a novel approach and multiple hail size datasets to develop a new Generalized Extreme Value model through a Bayesian framework to identify large hail-prone regions across the country at 0.25° × 0.25°. This model is smoothed using Gaussian process regression for nationwide estimation of return likelihood. To contextualize local risk, hazard returns intersecting high-population exposure centers are compared. Fitted extreme value models suggest earlier work likely underestimates the hail hazard. Especially for higher return periods, the Bayesian approach is found to better model very rare hail occurrences than traditional approaches. This provides a framework for appreciating underlying risk from hail and motivates mitigative approaches through improving design standards.