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Severe thunderstorms and climate change
Abstract and Figures
As the planet warms, it is important to consider possible impacts of climate change on severe thunderstorms and tornadoes. To further that discussion, the current distribution of severe thunderstorms as a function of large-scale environmental conditions is presented. Severe thunderstorms are much more likely to form in environments with large values of convective available potential energy (CAPE) and deep-tropospheric wind shear. Tornadoes and large hail are preferred in high-shear environments and non-tornadic wind events in low shear. Further, the intensity of tornadoes and hail, given that they occur, tends to be almost entirely a function of the shear and only weakly depends on the thermodynamics.Climate model simulations suggest that CAPE will increase in the future and the wind shear will decrease. Detailed analysis has suggested that the CAPE change will lead to more frequent environments favorable for severe thunderstorms, but the strong dependence on shear for tornadoes, particularly the strongest ones, and hail means that the interpretation of how individual hazards will change is open to question. The recent development of techniques to use higher-resolution models to estimate the occurrence of storms of various kinds is discussed. Given the large interannual variability in environments and occurrence of events, caution is urged in interpreting the observational record as evidence of climate change.
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... Although the increased frequency of tornadoes is not significant, the widespread spatial increase in localized up-trending areas throughout the eastern half of the US suggests an increase in the extent of this hazard coincident with the changing climate over the past twenty years. Climate history extensions of tornado frequency and intensity, as well as spatial hot spot analyses and trend assessments, support earlier conceptual linking of severe thunderstorm and air circulation patterns resulting in tornadoes [14]. The analyses reported here similarly support utilizing synoptic climatological methods to assess the impacts of climate change on future tornado-favorable environments [16]. ...
... In December 2021, multiple tornadoes (at least 50) occurred in a 12 h period in the central Mississippi basin (i.e., along the Arkansas, Illinois, Indiana, Kentucky, Mississippi, Ohio, and Tennessee corridor). Some Climate history extensions of tornado frequency and intensity, as well as spatial hot spot analyses and trend assessments, support earlier conceptual linking of severe thunderstorm and air circulation patterns resulting in tornadoes [14]. The analyses reported here similarly support utilizing synoptic climatological methods to assess the impacts of climate change on future tornado-favorable environments [16]. ...
... Sustainability 2022, 14, 4158 Acknowledgments: The authors acknowledge to the funding of applied and Agency-relevant research by the U.S. Environmental Protection Agency as part of the Office of Research and Development's Strategic Research Action Plan 3, Sustainable and Healthy Communities Output 9.3. Additionally, the authors thank Ted Angradi for his timely and helpful review of the manuscript. ...
There is increasing evidence from across the globe that climate change results in changes in the frequency, location, and impact of natural hazards. Much of this evidence is conceptual, inferential, or simply assumed. To provide objective support to confirm these hypotheses, we constructed county-level time-series datasets (2000–2019) for nine natural hazards for the entire United States. Hazards considered for this study included hurricanes, tropical storms, landslides, wildfires, earthquakes, drought, inland flooding, coastal flooding, and tornadoes. Geospatial analysis techniques were used to calculate the percentage (range: 0–100) of land area in each county exposed to each natural hazard for all the years that hazard data were available. The best available data were acquired from publicly accessible sources. Cumulative distribution functions were calculated for each hazard in five-year intervals to test for statistically significant changes in distribution patterns across the five-year time periods using the Kolmogorov–Smirnov test. There were significant changes in hurricanes, tropical storms, and drought over the two decades; changes in tornadoes, landslides, and wildfires were not significant in terms of frequency, likely due to the site-specific nature of their occurrences. The intensity and spatial distribution and an emerging hot spot and spatial trend analyses and an emerging hot spot and spatial trend analyses were also completed (except for flooding events and earthquakes due to insufficient data). All datasets provide empirical support for earlier inferences concerning the connections between the hazards and climate change. Analyses showed apparent changes in the frequency and intensity of hurricanes, tropical storms, and drought-related to climate change factors. Internal and coastal flooding also demonstrated these connections, although the length of the dataset did not permit significant testing but shows significant hot spots and trending locations. Tornadoes, landslides, and wildfires showed significant hot spots and trending locations, but the specific locational nature of the data did not show significant changes in frequency. Earthquakes showed no significant changes over the time period.
... The K index indicates the static stability of the layer from 850 hPa to 500 hPa, and lightning usually occurs in the high value area of the K index [4]. Strong thunderstorms usually occur in the high value area of convective available potential energy (CAPE) [5] and the strong shear area of deep convection [6]. Since the sounding data are observations of a single station at a single moment, they cannot represent the weather state and the change pattern over time on a large scale, and the nowcasting results have some limitations. ...
... Since the number of variables of the lightning occurrence frequency and the WRF model products are different and cannot be merged to form a spatiotemporal sequence in the time dimension, a two-dimensional convolutional network called CNN Net is first used to convolve the WRF model products to perform feature extraction and compress the number of variables to 1. Since the CNN Net performs feature extraction for WRF model products separately, the model products do not contain a temporal dimension, and the north-south and east-west dimension is also 256 × 256, so the dimensions of WRF model products are [256,256,13], namely [rows, columns, variables]. The convolved results are merged with the lightning occurrence frequency in the time dimension, which contains the starting forecast moment from 3 h in the past to 3 h in the future, forming a spatiotemporal sequence of dimensions [6,256,256,1]. Subsequently, ConvLSTM Net is used to extract features in time and space and then perform spatiotemporal sequence prediction to achieve hourly lightning occurrence area nowcasting for the next 0 to 3 h. ...
The evolution of lightning generation and extinction is a nonlinear and complex process, and the nowcasting results based on extrapolation and numerical models largely differ from the real situation. In this study, a multiple-input and multiple-output lightning nowcasting model, namely Convolutional Long- and Short-Term Memory Lightning Forecast Net (CLSTM-LFN), is constructed to improve the lightning nowcasting results from 0 to 3 h based on video prediction methods in deep learning. The input variables to CLSTM-LFN include historical lightning occurrence frequency and physical variables significantly related to lightning occurrence from numerical model products, which are merged with each other to provide effective information for lightning nowcasting in time and space. The results of batch forecasting tests show that CLSTM-LFN can achieve effective forecasts of 0 to 3 h lightning occurrence areas, and the nowcasting results are better than those of the traditional lightning parameterization scheme and only inputting a single data source. After analyzing the importance of input variables, the results show that the role of numerical model products increases significantly with increasing forecast time, and the relative importance of convective available potential energy is significantly larger than that of other physical variables.
... National and regional authorities could organize irrigation methods, locations and times based on the results obtained from streamflow trend studies (Rogger et al., 2017). The authorities could promote a best management practices of water in territories affected by a positive trend in the hydrological drought (Brooks, 2013;Forzieri et al., 2014;Samaniego et al., 2019). Drought episodes frequently have a local character, so studies with a large quantity and quality of information from the gauging stations are useful for operational decision-making. ...
... Common garden experiments investigating the consequences of imposed water deficits on seedlings/saplings of woody plant species usually apply a combination of water withholding followed by rewatering treatments for one or more time periods [10,[54][55][56][57][58][59], or continuous water stress (limited watering) for prolonged drought periods, or, also sometimes, water suspension until seedling desiccation [59][60][61][62][63]. However, it is generally accepted that the most striking consequence of climate change is the increased intensity and frequency of extreme weather events [64]. These events are related not necessarily to decreased precipitation, but to shifts in temporal precipitation patterns (e.g., periods of extended drought followed by heavy rainfall events). ...
Seasonality, rather than annual precipitation levels, is expected to affect the adaptive responses of plant populations under future climate change. To estimate adaptive traits’ variation, we conducted a common garden experiment with two beech populations from contrasting climatic origins (Evros with longer drought intervals during summer and higher precipitation seasonality, and Drama representing a more temperate ecosystem). We simulated two different watering treatments (frequent vs. non-frequent) on beech seedlings, according to predicted monthly precipitation levels expected to prevail in 2050 by the CSIRO MK3.6 SRESA1B model, considering as reference area a natural beech stand in Mt. Rodopi, Greece. A series of morphological and stem anatomical traits were measured. Seedling survival was greater for the Evros population compared to that of Drama under non-frequent watering, while no difference in survival was detected under frequent watering. Leaf morphological traits were not generally affected by watering frequency except for leaf circularity, which was found to be lower under non-frequent watering for both populations. Stomata density in leaves was found to be higher in the Evros population and lower in the Drama population under non-frequent watering than frequent. Stem anatomical traits were higher under non-frequent watering for Evros but lower for the Drama population. Multivariate analyses clearly discriminated populations under non-frequent rather than frequent watering, indicating genetic adaptation to the population’s environment of origin.
... The long-term studies of CAPE parameter furnish important information in predicting the extreme weather events like thunderstorms and tornadoes. This parameter is one of the reliable measures for climate change (Riemann-Campe et al. 2009;Murugavel et al. 2012;Brooks 2013). A study by Dhaka et al. (2010) indicated that the temperature of upper tropospheric layers at 100 hpa pressure level is influenced by the changes in CAPE parameter. ...
The current work was attempted to analyze various atmospheric parameters that influence the convective potential energy available (CAPE) development over Puducherry region, India during pre-monsoon season. k-index (KI), total totals index (TTI), improved K-index, improved total totals index, total precipitable water (TPW), deep convective index (DCI), S Index (SI), Dew point depression (DPD) and humidity index (HI) are the atmospheric related parameters that are utilized for this study. Fifth generation ECMWF atmospheric reanalysis (ERA5) daily data for the PRMS of 2021 were used to measure all rainfall-related variables. High CAPE values were seen during May month and low values are seen during March month. CAPE values have increased dramatically in the last 5 years (2016–2020) compared to the previous 15 years, indicating the severity of thunderstorms and convective activity over Puducherry. In all pre-monsoon seasons, even on moderate CAPE days, high daily mean rainfall was observed. The improved KI and TTI values were more helpful in detecting the severity of instability on some high CAPE days than the regular KI and TTI parameters that indicate fewer chances for instability. During the month of April, high TPW values were observed, indicating a high availability of moisture in the atmosphere that contributes to convective instability. With an increase in temperature and potential temperature parameters, the relative humidity parameter rises, contributing to severe atmospheric instability.
... До НКЯ відносяться такі метеорологічні явища, як шквал, град, грози, зливи та смерч. За останні роки у зв'язку зі значними змінами клімату частота виникнення цих явищ збільшилася [1][2][3][4][5][6]. В окремих випадках вони мають катастрофічний характер та завдають значних збитків економіці, інфраструктурі та населенню. ...
Tornadoes and strong squalls are dangerous for almost all spheres of human life and the economy of the region. The degree of negative impact depends on their type, quantity, intensity, area of formation and geographical features of the territory. The article defines the dynamics of the number of tornadoes and strong squalls in the North-Western Black Sea region (Odessa, Nikolayev and Kherson regions of Ukraine) from 2006 to 2020.Geographical position of the south-west of Ukraine, synoptic processes and a variety of climatic conditions contribute to the frequent occurrence of severe convective phenomena and creating the extraordinary complexity of their space-time distribution. The study revealed current trends in the formation of dangerous convective phenomena in the south-west of Ukraine. One of the most squall-prone regions of Ukraine is the territory of the North-Western Black Sea region. During 2006-2020 there was an increase in the number of squalls and tornadoes in the North-Western Black Sea region in comparison with previous years.
... The available thunderstorm data record is compromised by issues such as observational bias that complicate its use for modelling (Verbout et al., 2006;Edwards et al., 2018), so it is worthwhile to consider meteorological environments that are conducive to severe thunderstorms. Such storms, especially supercell storms, are more probable in the presence of elevated values of convective available potential energy (CAPE) and of certain measures of vertical wind shear (e.g., Brooks et al., 2003;Brooks, 2013) such as storm relative helicity (SRH), which have been used by weather forecasters and climatologists for more than two decades. High values of the combined variable PROD = √ CAPE × SRH are favorable to severe thunderstorms, and PROD has been used as a proxy of severe thunderstorm activity (e.g., by Tippett et al., 2016;Koch et al., 2021); see for example Brooks et al. (2003, Equation (1)) and Koch et al. (2021, Section 1) for justification for this. ...
Severe thunderstorms cause substantial economic and human losses in the United States (US). Simultaneous high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are favorable to severe weather, and both they and the composite variable $\mathrm{PROD}=\sqrt{\mathrm{CAPE}} \times \mathrm{SRH}$ can be used as indicators of severe thunderstorm activity. Their extremal spatial dependence exhibits temporal non-stationarity due to seasonality and large-scale atmospheric signals such as El Ni\~no-Southern Oscillation (ENSO). In order to investigate this, we introduce a space-time model based on a max-stable, Brown--Resnick, field whose range depends on ENSO and on time through a tensor product spline. We also propose a max-stability test based on empirical likelihood and the bootstrap. The marginal and dependence parameters must be estimated separately owing to the complexity of the model, and we develop a bootstrap-based model selection criterion that accounts for the marginal uncertainty when choosing the dependence model. In the case study, the out-sample performance of our model is good. We find that extremes of PROD, CAPE and SRH are more localized in summer and less localized during El Ni\~no events, and give meteorological interpretations of these phenomena.
... This suggests that a robust vertical shear environment is necessary for large hail occurrences. As Brooks [45] mentions, given their occurrence, the intensity of tornadoes and hail is entirely a function of shear and only weakly depends on thermodynamic features. The increased VWS can elongate a storm's updraft, so hail process volumes and hailstone residence times increase and create a vast embryo source region [114]. ...
The magnitude of damage caused by hail depends on its size; however, direct observation or indirect estimation of hail size remains a significant challenge. One primary reason for estimations by proxy, such as through remote sensing methods, is that empirical relationships or statistical models established in one region may not apply to other areas. This study employs a machine learning method to build a hail size estimation model without assuming relations in advance. It uses FY-4A AGRI data to provide cloud-top information and ERA5 data to add vertical environment information. Before training the model, we conducted a principal component analysis (PCA) to analyze the highly influential factors on hail sizes. A total of 18 features, composed of four groups, namely brightness temperature (BT), the difference in BT (BTD), thermodynamics, and dynamics groups, were chosen from 29 original features. Dynamic and BTD features show superior performance in identifying large hail. Although the selected features are more closely correlated to hail sizes than unselected ones, the relationships are complicated and nonlinear. As a result, a two-layer regression back propagation neural network (BPNN) model with powerful fitting ability is trained with selected features to predict maximum hail diameter (MHD). The linear fitting R2 between predicted and observed MHDs is 0.52 on the test set, which signifies that our model performs well compared with other hail size estimation models. We also examine the model concerning all three hail cases in Shanghai, China, between 2019 and 2021. The model attained more satisfactory results than the radar-based maximum estimated hail size (MEHS) method, which overestimates the MHDs, thus further supporting the operational applications of our model.
... This difference in the population density (i.e., different regions of Europe, coastal area versus inland area) introduces a bias in the reporting of tornadoes. This is because tornadoes (and also other types of severe weather events like hail or extreme winds) are "targets of opportunity" [3]. Thus, an observer needs to witness the event and then to report it and systems need to exist for collecting and verifying the reports (i.e., tornado database). ...
Tornadoes are associated with damages, injuries, and even fatalities in Europe. Knowing the spatial distribution of tornadoes is essential for developing disaster risk reduction strategies. Unfortunately, there is a population bias on tornado reporting in Europe. To account for this bias, a Bayesian modeling approach was used based on tornado observations and population density for relatively small regions of Europe. The results indicated that the number of tornadoes could be 53% higher that are currently reported. The largest adjustments produced by the model are for Northern Europe and parts of the Mediterranean regions.
Soil is negatively affected by many degradation factors, of which soil erosion is the most serious, affecting soil quality, crop production, and environmental components. Soil quality is an issue dealt with in the New European Green Deal. In order to meet the set goals, it will be necessary to address soil degradation and water erosion in the agricultural landscape, and increase the area of green infrastructure within the landscape (e.g., fragments of woodland, windbreaks, and grassland). In this context, climate change is also expected to affect the frequency and intensity of torrential rainfall, leading to increased runoff, reduced infiltration, and greater soil loss. Therefore, in this study, we have elaborated the issue of agricultural landscape and erosion, looking at erosion control measures necessary in dealing with existing erosion processes in an intensively farmed area with chernozem soils, and compared these with scenarios assumed for 2050. In these future scenarios, the commonly applied agrotechnical measures will not suffice to keep soil loss at a tolerable level. In the future, it will be necessary to discuss a further reduction in the size of land blocks, with the inclusion of green infrastructure in the landscape. In addition to solving problems of erosion, this would increase diversity in the area and enable sustainable agricultural management.
Severe thunderstorms can present a significant threat to life and property in Australia.
A unique and broad database of severe thunderstorm reports has been
constructed for the Australian region for 2003–2010 from observer reports of hailstones,
winds in excess of 90 km h–1 and, less frequently, tornadoes. Based on this
database, a climatology of atmospheric environments associated with the occurrence
of severe thunderstorms in Australia was developed using pseudo-proximity
soundings from the MesoLAPS numerical weather prediction model simulations.
Observed soundings have been used to verify derived soundings from MesoLAPS
simulations, with a reasonable performance over much of the continent. Proximity
rawinsonde soundings from the MesoLAPS simulations were identified for each
of the severe thunderstorm reports to develop the climatology of environments.
This climatology was then used to derive discriminants between environments
with an increased likelihood of severe thunderstorm occurrence and other thunderstorm
environments. This appears to be the best way to produce a long-term
climatology of severe thunderstorm environment occurrence in a sparsely populated
continent without considering the complex problem of initiation.
An 8-yr climatology of storms producing large hail is estimated from satellite measurements using Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). This allows a unique, consistent comparison between regions that cannot be consistently compared using ground-based records because of varying data collection standards. Severe hailstorms are indicated most often in a broad region of northern Argentina and southern Paraguay and a smaller region in Bangladesh and eastern India. Numerous hailstorms are also estimated in the central and southeastern United States, northern Pakistan and northwestern India, central and western Africa, and southeastern Africa (and adjacent waters). Fewer hailstorms are estimated for other regions over land and scattered across subtropical oceans. Very few are estimated in the deep tropics other than in Africa. Most continental regions show seasonality with hailstorms peaking in late spring or summer. The South Asian monsoon alters the hailstorm climatology around the Indian subcontinent. About 75% of the hailstorms on the eastern side (around Bangladesh) occur from April through June, generally before monsoon onset. Activity shifts northwest to northern India in late June and July. An arc along the foothills in northern Pakistan becomes particularly active from mid-June through mid-August. The AMSR-E measurements are limited to early afternoon and late night. Tropical Rainfall Measuring Mission (TRMM) measurements are used to investigate diurnal variability in the tropics and subtropics. All of the prominent regions have hailstorm peaks in late afternoon and early evening. The United States and central Africa have the fewest overnight and early morning storms, while subtropical South America and Bangladesh have the most.
The temperature of the surface atmosphere over land has been rising
during recent decades. But surface temperature, or, more accurately,
enthalpy which can be calculated from temperature, is only one component
of the energy content of the surface atmosphere. The other parts include
kinetic energy and latent heat. It has been advocated in certain
quarters that ignoring additional terms somehow calls into question
global surface temperature analyses. Examination of all three of these
components of atmospheric energetics reveals a significant increase in
global surface atmospheric energy since the 1970s. Kinetic energy has
decreased but by over two orders of magnitude less than the increases in
both enthalpy and latent heat which provide approximately equal
contributions to the global increases in heat content. Regionally, the
enthalpy or the latent heat component can dominate the change in heat
content. Although generally changes in latent heat and enthalpy act in
concert, in some regions they can have the opposite signs.
Over the last 50 yr, the number of tornadoes reported in the United States has doubled from about 600 per year in the 1950s to around 1200 in the 2000s. This doubling is likely not related to meteorological causes alone. To account for this increase a simple least squares linear regression was fitted to the annual number of tornado reports. A "big tornado day" is a single day when numerous tornadoes and/ or many tornadoes exceeding a specified intensity threshold were reported anywhere in the country. By defining a big tornado day without considering the spatial distribution of the tornadoes, a big tornado day differs from previous definitions of outbreaks. To address the increase in the number of reports, the number of reports is compared to the expected number of reports in a year based on linear regression. In addition, the F1 and greater Fujita-scale record was used in determining a big tornado day because the F1 and greater series was more stationary over time as opposed to the F2 and greater series. Thresholds were applied to the data to determine the number and intensities of the tornadoes needed to be considered a big tornado day. Possible threshold values included fractions of the annual expected value associated with the linear regression and fixed numbers for the intensity criterion. Threshold values of 1.5% of the expected annual total number of tornadoes and/or at least 8 F1 and greater tornadoes identified about 18.1 big tornado days per year. Higher thresholds such as 2.5% and/or at least 15 F1 and greater tornadoes showed similar characteristics, yet identified approximately 6.2 big tornado days per year. Finally, probability distribution curves generated using kernel density estimation revealed that big tornado days were more likely to occur slightly earlier in the year and have a narrower distribution than any given tornado day.
The Summary for Policy Makers of the IPCC Special Report on Managing the
Risks of Extreme Events and Disasters to Advance Climate Change
Adaptation will be approved by the world governments in November 2011.
The focus of the Special Report is on climate change and its role in
altering the frequency, severity, and impact of extreme events or
disasters, and on the costs of both impacts and the actions taken to
prepare for, respond to, and recover from extreme events and disasters.
The emphasis is on understanding the factors that make people and
infrastructure vulnerable to extreme events, on recent and future
changes in the relationship between climate change and extremes, and on
managing the risks of disasters over a wide range of spatial and
temporal scales. The assessment considers a broad suite of adaptations
and explores the limits to adaptation. The assessment was designed to
build durable links and foundations for partnerships between the
stakeholder communities focused on climate change and those focused on
disaster risk reduction. The Special Report begins with material that
frames the issues, followed by an assessment of the reasons that
communities are vulnerable. Two chapters assess the role of past and
future climate change in altering extremes and the impact of these on
the physical environment and human systems. Three chapters assess
available knowledge on impacts and adaptation, with separate chapters
considering the literature, stakeholder relationships, and potential
policy tools relevant to the local, national, and international scales.
Longer-term components of adaptation to weather and climate extremes and
disasters are assessed in the context of moving toward sustainability.
The final chapter provides case studies that integrate themes across
several chapters or are so unique that they need to be considered
separately.
This study assesses the impact of imperfect sampling in the presatellite era (between 1878 and 1965) on North Atlantic hurricane activity measures and on the long-term trends in those measures. The results indicate that a substantial upward adjustment of hurricane counts may be needed prior to 1965 to account for likely ‘‘missed’ ’ hurricanes due to sparse density of reporting ship traffic. After adjusting for the estimate of missed hurricanes in the basin, the long-term (1878–2008) trend in hurricane counts changes from significantly positive to no significant change (with a nominally negative trend). The adjusted hurricane count record is more strongly connected to the difference between main development region (MDR) sea surface temperature (SST) and tropical-mean SST than with MDR SST. These results do not support the hypothesis that the warming of the tropical North Atlantic due to anthropogenic greenhouse gas emissions has caused Atlantic hurricane frequency to increase. 1.
Atlantic hurricane variability on decadal and interannual time scales is reconsidered in a framework based on a leading mode of coupled ocean-atmosphere variability known as the Atlantic meridional mode (AMM). It is shown that a large part of the variability of overall "hurricane activity," which depends on the number of storms in a season, their duration, and their intensity, can be explained by systematic shifts in the cyclogenesis regions. These shifts are strongly correlated with the AMM on interannual as well as multidecadal time scales. It is suggested that the AMM serves to unify a number previously documented relationships between hurricanes and Atlantic regional climate variability.