Articles | Volume 6, issue 3
https://doi.org/10.5194/wcd-6-807-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-6-807-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Linking weather regimes to the variability of warm-season tornado activity over the United States
Matthew Graber
Department of Climate, Meteorology & Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61820, United States
Department of Climate, Meteorology & Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61820, United States
Robert J. Trapp
Department of Climate, Meteorology & Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61820, United States
Related authors
No articles found.
Yulan Hong, Stephen W. Nesbitt, Robert J. Trapp, and Larry Di Girolamo
Atmos. Meas. Tech., 16, 1391–1406, https://doi.org/10.5194/amt-16-1391-2023, https://doi.org/10.5194/amt-16-1391-2023, 2023
Short summary
Short summary
Deep convective updrafts form overshooting tops (OTs) when they extend into the upper troposphere and lower stratosphere. An OT often indicates hazardous weather conditions. The global distribution of OTs is useful for understanding global severe weather conditions. The Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Terra satellites provides 2 decades of records on the Earth–atmosphere system with stable orbits, which are used in this study to derive 20-year OT climatology.
Related subject area
Atmospheric predictability
Causal relationships and predictability of the summer East Atlantic teleconnection
Systematic evaluation of the predictability of different Mediterranean cyclone categories
Understanding winter windstorm predictability over Europe
Intrinsic predictability limits arising from Indian Ocean Madden–Julian oscillation (MJO) heating: effects on tropical and extratropical teleconnections
Predictable decadal forcing of the North Atlantic jet speed by sub-polar North Atlantic sea surface temperatures
Exploiting the signal-to-noise ratio in multi-system predictions of boreal summer precipitation and temperature
Uncertainty growth and forecast reliability during extratropical cyclogenesis
Convection-parameterized and convection-permitting modelling of heavy precipitation in decadal simulations of the greater Alpine region with COSMO-CLM
Improved extended-range prediction of persistent stratospheric perturbations using machine learning
Increased vertical resolution in the stratosphere reveals role of gravity waves after sudden stratospheric warmings
The impact of microphysical uncertainty conditional on initial and boundary condition uncertainty under varying synoptic control
Subseasonal precipitation forecasts of opportunity over central southwest Asia
Predictability of a tornado environment index from El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation
Differences in the sub-seasonal predictability of extreme stratospheric events
Impact of Eurasian autumn snow on the winter North Atlantic Oscillation in seasonal forecasts of the 20th century
Bimodality in ensemble forecasts of 2 m temperature: identification
Flow dependence of wintertime subseasonal prediction skill over Europe
Seasonal forecasts of the Saharan heat low characteristics: a multi-model assessment
Emergence of representative signals for sudden stratospheric warmings beyond current predictable lead times
The impact of GPS and high-resolution radiosonde nudging on the simulation of heavy precipitation during HyMeX IOP6
Seasonal climate influences on the timing of the Australian monsoon onset
Subseasonal prediction of springtime Pacific–North American transport using upper-level wind forecasts
A dynamic and thermodynamic analysis of the 11 December 2017 tornadic supercell in the Highveld of South Africa
How an uncertain short-wave perturbation on the North Atlantic wave guide affects the forecast of an intense Mediterranean cyclone (Medicane Zorbas)
Robust predictors for seasonal Atlantic hurricane activity identified with causal effect networks
Subseasonal midlatitude prediction skill following Quasi-Biennial Oscillation and Madden–Julian Oscillation activity
Large impact of tiny model domain shifts for the Pentecost 2014 mesoscale convective system over Germany
Julianna Carvalho-Oliveira, Giorgia Di Capua, Leonard F. Borchert, Reik V. Donner, and Johanna Baehr
Weather Clim. Dynam., 5, 1561–1578, https://doi.org/10.5194/wcd-5-1561-2024, https://doi.org/10.5194/wcd-5-1561-2024, 2024
Short summary
Short summary
We demonstrate with a causal analysis that an important recurrent summer atmospheric pattern, the so-called East Atlantic teleconnection, was influenced by the extratropical North Atlantic in spring during the second half of the 20th century. This causal link is, however, not well represented by our evaluated seasonal climate prediction system. We show that simulations able to reproduce this link show improved surface climate prediction credibility over those that do not.
Benjamin Doiteau, Florian Pantillon, Matthieu Plu, Laurent Descamps, and Thomas Rieutord
Weather Clim. Dynam., 5, 1409–1427, https://doi.org/10.5194/wcd-5-1409-2024, https://doi.org/10.5194/wcd-5-1409-2024, 2024
Short summary
Short summary
The predictability of Mediterranean cyclones is investigated through a large dataset of 1960 cyclones tracks, ensuring robust statistical results. The motion speed of the cyclone appears to determine the predictability of its location. In particular, the location of specific slow cyclones concentrated in the Gulf of Genoa is remarkably well predicted. It is also shown that the intensity of deep cyclones, occurring in winter, is particularly poorly predicted in the Mediterranean region.
Lisa Degenhardt, Gregor C. Leckebusch, and Adam A. Scaife
Weather Clim. Dynam., 5, 587–607, https://doi.org/10.5194/wcd-5-587-2024, https://doi.org/10.5194/wcd-5-587-2024, 2024
Short summary
Short summary
This study investigates how dynamical factors that are known to influence cyclone or windstorm development and strengthening also influence the seasonal forecast skill of severe winter windstorms. This study shows which factors are well represented in the seasonal forecast model, the Global Seasonal forecasting system version 5 (GloSea5), and which might need improvement to refine the forecast of severe winter windstorms.
David Martin Straus, Daniela I. V. Domeisen, Sarah-Jane Lock, Franco Molteni, and Priyanka Yadav
Weather Clim. Dynam., 4, 1001–1018, https://doi.org/10.5194/wcd-4-1001-2023, https://doi.org/10.5194/wcd-4-1001-2023, 2023
Short summary
Short summary
The global response to the Madden–Julian oscillation (MJO) is potentially predictable. Yet the diabatic heating is uncertain even within a particular episode of the MJO. Experiments with a global model probe the limitations imposed by this uncertainty. The large-scale tropical heating is predictable for 25 to 45 d, yet the associated Rossby wave source that links the heating to the midlatitude circulation is predictable for 15 to 20 d. This limitation has not been recognized in prior work.
Kristian Strommen, Tim Woollings, Paolo Davini, Paolo Ruggieri, and Isla R. Simpson
Weather Clim. Dynam., 4, 853–874, https://doi.org/10.5194/wcd-4-853-2023, https://doi.org/10.5194/wcd-4-853-2023, 2023
Short summary
Short summary
We present evidence which strongly suggests that decadal variations in the intensity of the North Atlantic winter jet stream can be predicted by current forecast models but that decadal variations in its position appear to be unpredictable. It is argued that this skill at predicting jet intensity originates from the slow, predictable variability in sea surface temperatures in the sub-polar North Atlantic.
Juan Camilo Acosta Navarro and Andrea Toreti
Weather Clim. Dynam., 4, 823–831, https://doi.org/10.5194/wcd-4-823-2023, https://doi.org/10.5194/wcd-4-823-2023, 2023
Short summary
Short summary
Droughts and heatwaves have become some of the clearest manifestations of a changing climate. Near-term adaptation strategies can benefit from seasonal predictions, but these predictions still have limitations. We found that an intrinsic property of multi-system forecasts can serve to better anticipate extreme high-temperature and low-precipitation events during boreal summer in several regions of the Northern Hemisphere with different levels of predictability.
Mark J. Rodwell and Heini Wernli
Weather Clim. Dynam., 4, 591–615, https://doi.org/10.5194/wcd-4-591-2023, https://doi.org/10.5194/wcd-4-591-2023, 2023
Short summary
Short summary
Midlatitude storms and their downstream impacts have a major impact on society, yet their prediction is especially prone to uncertainty. While this can never be fully eliminated, we find that the initial rate of growth of uncertainty varies for a range of forecast models. Examination of the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) suggests ways in which uncertainty growth could be reduced, leading to sharper and more reliable forecasts over the first few days.
Alberto Caldas-Alvarez, Hendrik Feldmann, Etor Lucio-Eceiza, and Joaquim G. Pinto
Weather Clim. Dynam., 4, 543–565, https://doi.org/10.5194/wcd-4-543-2023, https://doi.org/10.5194/wcd-4-543-2023, 2023
Short summary
Short summary
We evaluate convection-permitting modelling (CPM) simulations for the greater Alpine area to assess its added value compared to a 25 km resolution. A new method for severe precipitation detection is used, and the associated synoptic weather types are considered. Our results document the added value of CPM for precipitation representation with higher intensities, better rank correlation, better hit rates, and an improved amount and structure, but with an overestimation of the rates.
Raphaël de Fondeville, Zheng Wu, Enikő Székely, Guillaume Obozinski, and Daniela I. V. Domeisen
Weather Clim. Dynam., 4, 287–307, https://doi.org/10.5194/wcd-4-287-2023, https://doi.org/10.5194/wcd-4-287-2023, 2023
Short summary
Short summary
We propose a fully data-driven, interpretable, and computationally scalable framework to characterize sudden stratospheric warmings (SSWs), extract statistically significant precursors, and produce machine learning (ML) forecasts. By successfully leveraging the long-lasting impact of SSWs, the ML predictions outperform sub-seasonal numerical forecasts for lead times beyond 25 d. Post-processing numerical predictions using their ML counterparts yields a performance increase of up to 20 %.
Wolfgang Wicker, Inna Polichtchouk, and Daniela I. V. Domeisen
Weather Clim. Dynam., 4, 81–93, https://doi.org/10.5194/wcd-4-81-2023, https://doi.org/10.5194/wcd-4-81-2023, 2023
Short summary
Short summary
Sudden stratospheric warmings are extreme weather events where the winter polar stratosphere warms by about 25 K. An improved representation of small-scale gravity waves in sub-seasonal prediction models can reduce forecast errors since their impact on the large-scale circulation is predictable multiple weeks ahead. After a sudden stratospheric warming, vertically propagating gravity waves break at a lower altitude than usual, which strengthens the long-lasting positive temperature anomalies.
Takumi Matsunobu, Christian Keil, and Christian Barthlott
Weather Clim. Dynam., 3, 1273–1289, https://doi.org/10.5194/wcd-3-1273-2022, https://doi.org/10.5194/wcd-3-1273-2022, 2022
Short summary
Short summary
This study quantifies the impact of poorly constrained parameters used to represent aerosol–cloud–precipitation interactions on precipitation and cloud forecasts associated with uncertainties in input atmospheric states. Uncertainties in these parameters have a non-negligible impact on daily precipitation amount and largely change the amount of cloud. The comparison between different weather situations reveals that the impact becomes more important when convection is triggered by local effects.
Melissa L. Breeden, John R. Albers, and Andrew Hoell
Weather Clim. Dynam., 3, 1183–1197, https://doi.org/10.5194/wcd-3-1183-2022, https://doi.org/10.5194/wcd-3-1183-2022, 2022
Short summary
Short summary
We use a statistical dynamical model to generate precipitation forecasts for lead times of 2–6 weeks over southwest Asia, which are needed to support humanitarian food distribution. The model signal-to-noise ratio is used to identify a smaller subset of forecasts with particularly high skill, so-called subseasonal forecasts of opportunity (SFOs). Precipitation SFOs are often related to slowly evolving tropical phenomena, namely the El Niño–Southern Oscillation and Madden–Julian Oscillation.
Michael K. Tippett, Chiara Lepore, and Michelle L. L’Heureux
Weather Clim. Dynam., 3, 1063–1075, https://doi.org/10.5194/wcd-3-1063-2022, https://doi.org/10.5194/wcd-3-1063-2022, 2022
Short summary
Short summary
The El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO) are phenomena that affect the weather and climate of North America. Although ENSO hails from from the tropical Pacific and the AO high above the North Pole, the spatial patterns of their influence on a North American tornado environment index are remarkably similar in computer models. We find that when ENSO and the AO act in concert, their impact is large, and when they oppose each other, their impact is small.
Rachel Wai-Ying Wu, Zheng Wu, and Daniela I.V. Domeisen
Weather Clim. Dynam., 3, 755–776, https://doi.org/10.5194/wcd-3-755-2022, https://doi.org/10.5194/wcd-3-755-2022, 2022
Short summary
Short summary
Accurate predictions of the stratospheric polar vortex can enhance surface weather predictability. Stratospheric events themselves are less predictable, with strong inter-event differences. We assess the predictability of stratospheric acceleration and deceleration events in a sub-seasonal prediction system, finding that the predictability of events is largely dependent on event magnitude, while extreme drivers of deceleration events are not fully represented in the model.
Martin Wegmann, Yvan Orsolini, Antje Weisheimer, Bart van den Hurk, and Gerrit Lohmann
Weather Clim. Dynam., 2, 1245–1261, https://doi.org/10.5194/wcd-2-1245-2021, https://doi.org/10.5194/wcd-2-1245-2021, 2021
Short summary
Short summary
Northern Hemisphere winter weather is influenced by the strength of westerly winds 30 km above the surface, the so-called polar vortex. Eurasian autumn snow cover is thought to modulate the polar vortex. So far, however, the modeled influence of snow on the polar vortex did not fit the observed influence. By analyzing a model experiment for the time span of 110 years, we could show that the causality of this impact is indeed sound and snow cover can weaken the polar vortex.
Cameron Bertossa, Peter Hitchcock, Arthur DeGaetano, and Riwal Plougonven
Weather Clim. Dynam., 2, 1209–1224, https://doi.org/10.5194/wcd-2-1209-2021, https://doi.org/10.5194/wcd-2-1209-2021, 2021
Short summary
Short summary
While the assumption of Gaussianity leads to many simplifications, ensemble forecasts often exhibit non-Gaussian distributions. This work has systematically identified the presence of a specific case of
non-Gaussianity, bimodality. It has been found that bimodality occurs in a large portion of global 2 m temperature forecasts. This has drastic implications on forecast skill as the minimum probability in a bimodal distribution often lies at the maximum probability of a Gaussian distribution.
Constantin Ardilouze, Damien Specq, Lauriane Batté, and Christophe Cassou
Weather Clim. Dynam., 2, 1033–1049, https://doi.org/10.5194/wcd-2-1033-2021, https://doi.org/10.5194/wcd-2-1033-2021, 2021
Short summary
Short summary
Forecasting temperature patterns beyond 2 weeks is very challenging, although occasionally, forecasts show more skill over Europe. Our study indicates that the level of skill varies concurrently for two distinct forecast systems. It also shows that higher skill occurs when forecasts are issued during specific patterns of atmospheric circulation that tend to be particularly persistent.
These results could help forecasters estimate a priori how trustworthy extended-range forecasts will be.
Cedric G. Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, Philippe Peyrillé, and Cyrille Flamant
Weather Clim. Dynam., 2, 893–912, https://doi.org/10.5194/wcd-2-893-2021, https://doi.org/10.5194/wcd-2-893-2021, 2021
Short summary
Short summary
This work assesses the forecast of the temperature over the Sahara, a key driver of the West African Monsoon, at a seasonal timescale. The seasonal models are able to reproduce the climatological state and some characteristics of the temperature during the rainy season in the Sahel. But, because of errors in the timing, the forecast skill scores are significant only for the first 4 weeks.
Zheng Wu, Bernat Jiménez-Esteve, Raphaël de Fondeville, Enikő Székely, Guillaume Obozinski, William T. Ball, and Daniela I. V. Domeisen
Weather Clim. Dynam., 2, 841–865, https://doi.org/10.5194/wcd-2-841-2021, https://doi.org/10.5194/wcd-2-841-2021, 2021
Short summary
Short summary
We use an advanced statistical approach to investigate the dynamics of the development of sudden stratospheric warming (SSW) events in the winter Northern Hemisphere. We identify distinct signals that are representative of these events and their event type at lead times beyond currently predictable lead times. The results can be viewed as a promising step towards improving the predictability of SSWs in the future by using more advanced statistical methods in operational forecasting systems.
Alberto Caldas-Alvarez, Samiro Khodayar, and Peter Knippertz
Weather Clim. Dynam., 2, 561–580, https://doi.org/10.5194/wcd-2-561-2021, https://doi.org/10.5194/wcd-2-561-2021, 2021
Short summary
Short summary
The prediction capabilities of GPS, operational (low-resolution) and targeted (high-resolution) radiosondes for data assimilation in a Mediterranean heavy precipitation event at different model resolutions are investigated. The results show that even if GPS provides accurate observations, their lack of vertical information hampers the improvement, demonstrating the need for assimilating radiosondes, where the location and timing of release was more determinant than the vertical resolution.
Joel Lisonbee and Joachim Ribbe
Weather Clim. Dynam., 2, 489–506, https://doi.org/10.5194/wcd-2-489-2021, https://doi.org/10.5194/wcd-2-489-2021, 2021
Short summary
Short summary
Why do some monsoon seasons start early, while others start late? For the Australian monsoon, some previous research suggested the El Niño–Southern Oscillation in the months before the onset influenced the monsoon timing. This research tests if this is still correct and if other large-scale climate patterns also influenced onset timing. We found that a strong La Niña pattern usually coincided with an early onset but weak La Niña and El Niño patterns did not show a consistent pattern.
John R. Albers, Amy H. Butler, Melissa L. Breeden, Andrew O. Langford, and George N. Kiladis
Weather Clim. Dynam., 2, 433–452, https://doi.org/10.5194/wcd-2-433-2021, https://doi.org/10.5194/wcd-2-433-2021, 2021
Short summary
Short summary
Weather variability controls the transport of ozone from the stratosphere to the Earth’s surface and water vapor from oceanic source regions to continental land masses. Forecasting these types of transport has high societal value because of the negative impacts of ozone on human health and the role of water vapor in governing precipitation variability. We use upper-level wind forecasts to assess the potential for predicting ozone and water vapor transport 3–6 weeks ahead of time.
Lesetja E. Lekoloane, Mary-Jane M. Bopape, Tshifhiwa Gift Rambuwani, Thando Ndarana, Stephanie Landman, Puseletso Mofokeng, Morne Gijben, and Ngwako Mohale
Weather Clim. Dynam., 2, 373–393, https://doi.org/10.5194/wcd-2-373-2021, https://doi.org/10.5194/wcd-2-373-2021, 2021
Short summary
Short summary
We analysed a tornadic supercell that tracked through the northern Highveld region of South Africa for 7 h. We found that atmospheric conditions were conducive for tornado-associated severe storms over the region. A 4.4 km resolution model run by the South African Weather Service was able to predict this supercell, including its timing. However, it underestimated its severity due to underestimations of other important factors necessary for real-world development of these kinds of storms.
Raphael Portmann, Juan Jesús González-Alemán, Michael Sprenger, and Heini Wernli
Weather Clim. Dynam., 1, 597–615, https://doi.org/10.5194/wcd-1-597-2020, https://doi.org/10.5194/wcd-1-597-2020, 2020
Short summary
Short summary
In September 2018 an intense Mediterranean cyclone with structural similarities to a hurricane, a so-called medicane, caused severe damage in Greece. Its development was uncertain, even just a few days in advance. The reason for this was uncertainties in the jet stream over the North Atlantic 3 d prior to cyclogenesis that propagated into the Mediterranean. They led to an uncertain position of the upper-level disturbance and, as a result, of the position and thermal structure of the cyclone.
Peter Pfleiderer, Carl-Friedrich Schleussner, Tobias Geiger, and Marlene Kretschmer
Weather Clim. Dynam., 1, 313–324, https://doi.org/10.5194/wcd-1-313-2020, https://doi.org/10.5194/wcd-1-313-2020, 2020
Short summary
Short summary
Seasonal outlooks of Atlantic hurricane activity are required to enable risk reduction measures and disaster preparedness. Many seasonal forecasts are based on a selection of climate signals from which a statistical model is constructed. The crucial step in this approach is to select the most relevant predictors without overfitting. Here we show that causal effect networks can be used to identify the most robust predictors. Based on these predictors we construct a competitive forecast model.
Kirsten J. Mayer and Elizabeth A. Barnes
Weather Clim. Dynam., 1, 247–259, https://doi.org/10.5194/wcd-1-247-2020, https://doi.org/10.5194/wcd-1-247-2020, 2020
Short summary
Short summary
Tropical storms are key for harnessing midlatitude weather prediction skill 2–8 weeks into the future. Recently, stratospheric activity was shown to impact tropical storminess and thus may also be important for midlatitude prediction skill on these timescales. This work analyzes two forecast systems to assess whether they capture this additional skill. We find there is enhanced prediction out through week 4 when both the tropical and stratospheric phenomena are active.
Christian Barthlott and Andrew I. Barrett
Weather Clim. Dynam., 1, 207–224, https://doi.org/10.5194/wcd-1-207-2020, https://doi.org/10.5194/wcd-1-207-2020, 2020
Short summary
Short summary
The mesoscale convective system (MCS) that affected Germany at Pentecost 2014 was one of the most severe for decades. However, the predictability of this system was very low. By moving the model domain by just one grid point changed whether the MCS was successfully simulated or not. The decisive factor seems to be small differences in the initial track of the convection: cooler air near the coast inhibited development there, but tracks slightly more inland found more favorable conditions.
Cited articles
Allen, J. T., Tippett, M. K., and Sobel, A. H.: Influence of the El Nino/Southern Oscillation on tornado and hail frequency in the United States, Nat. Geosci., 8, 278–283, https://doi.org/10.1038/ngeo2385, 2015.
Ashley, W. S. and Strader, S. M.: Recipe for Disaster: How the Dynamic Ingredients of Risk and Exposure Are Changing the Tornado Disaster Landscape, B. Am. Meteorol. Soc., 97, 767–786, https://doi.org/10.1175/BAMS-D-15-00150.1, 2016.
Brooks, H. E., Lee, J. W., and Craven, J. P.: The Spatial Distribution of Severe Thunderstorm and Tornado Environments from Global Reanalysis Data, Atmos. Res., 67–68, 73–94, https://doi.org/10.1016/S0169-8095(03)00045-0, 2003.
Brooks, H. E., Carbin, G. W., and Marsh, P. T.: Increased Variability of Tornado Occurrence in the United States, Science, 346, 349–352, https://doi.org/10.1126/science.1257460, 2014.
Charney, J. G. and DeVore, J. G.: Multiple Flow Equilibria in the Atmosphere and Blocking, J. Atmos. Sci., 36, 1205–1216, https://doi.org/10.1175/1520-0469(1979)036<1205:MFEITA>2.0.CO;2, 1979.
Cook, A. R. and Schaefer, J. T.: The Relation of El Nino-Southern Oscillation (ENSO) to Winter Tornado Outbreaks, Mon. Weather Rev., 136, 3121–3137, https://doi.org/10.1175/2007MWR2171.1, 2008.
Corti, S., Molteni, F., and Palmer, T. N.: Signature of Recent Climate Change in Frequencies of Natural Atmospheric Circulation Regimes, Nature, 398, 799–802, https://doi.org/10.1038/19745, 1999.
Cwik, P., McPherson, R. A., Richman, M. B., and Mercer, A. E.: Climatology of 500-hPa Geopotential Height Anomalies Associated with May Tornado Outbreaks in the United States, Int. J. Climatol., 43, 893–913, https://doi.org/10.1002/joc.7841, 2022.
Davies, D. L. and Bouldin, D. W.: A Cluster Separation Measure, IEEE Trans. Pattern Anal. Mach. Intelligience, PAMI-1, 224–227, https://doi.org/10.1109/TPAMI.1979.4766909, 1979.
Del Genio, A. D., Yao, M.-S., and Jonas, J.: Will Moist Convection be Stronger in a Warmer Climate?, Geophys. Res. Lett., 34, L16703, https://doi.org/10.1029/2007GL030525, 2007.
Diffenbaugh, N. S., Scherer, M., and Trapp, R. J.: Robust Increases in Severe Thunderstorm Environments in Response to Greenhouse Forcing, P. Natl. Acad. Sci. USA, 110, 16361–16366, https://doi.org/10.1073/pnas.1307758110, 2013.
Elliott, M.: 2021 Preliminary Killer Tornadoes, NWS Storm Prediction Center [data set], https://www.spc.noaa.gov/climo/torn/STATIJ21.txt (last access: 28 April 2024), 2023a.
Elliott, M.: 2022 Preliminary Killer Tornadoes, NWS Storm Prediction Center [data set], https://www.spc.noaa.gov/climo/torn/STATIJ22.txt (last access: 28 April 2024), 2023b.
Elliott, M.: 2023 Preliminary Killer Tornadoes, NWS Storm Prediction Center [data set], https://www.spc.noaa.gov/climo/torn/STATIJ23.txt (last access: 28 April 2024), 2023c.
Elliott, M.: U.S. Killer Tornado Statistics, NWS Storm Prediction Center, https://www.spc.noaa.gov/climo/torn/fatalmap.php (last access: 28 April 2024), 2024.
Gensini, V. A. and Brooks, H. E.: Spatial Trends in United States Tornado Frequency, Nature, 1, 38, https://doi.org/10.1038/s41612-018-0048-2, 2018.
Graber, M.: Matt0604/Kmeans: Weather Regimes Graber et al. 2025 (Version 1), Zenodo [code], https://doi.org/10.5281/zenodo.16043868, 2025.
Graber, M., Trapp, R. J., and Wang, Z.: The Regionality and Seasonality of Tornado Trends in the United States, Npj Clim. Atmos. Sci., 7, 144, https://doi.org/10.1038/s41612-024-00698-y, 2024.
Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I., and Wernli, H.: Balancing Europe's Wind-power Output through spatial development informed by weather regimes, Nat. Clim. Change, 7, 557–562, https://doi.org/10.1038/nclimate3338, 2017.
Grams, C. M., Ferranti, L., and Magnusson, L.: How to make use of weather regimes in Extended-range Predictions for Europe, ECMWF Newsl., 165, 14–19, https://www.ecmwf.int/en/newsletter/165/meteorology/how-make-use-weather-regimes-extended-range-predictions-europe (last access: 16 July 2025), 2020.
Hannachi, A., Straus, D. M., Franzke, C. L. E., Corti, S., and Woollings, T.: Low-Frequency Nonlinearity and Regime Behavior in the Northern Hemisphere Extratropical Atmosphere, Rev. Geophys., 55, 199–234, https://doi.org/10.1002/2015RG000509, 2017.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023a.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023b.
Kodinariya, T. M. and Makwana, P. R.: Review on Determining Number of Clusters in K-Means Clustering, Int. J. Adv. Res. Comput. Sci. Manag. Stud., 1, 90–95, 2013.
Lee, S. H., Tippett, M. K., and Polvani, L. M.: A New Year-Round Weather Regime Classification for North America, J. Climate, 36, 7091–7108, https://doi.org/10.1175/JCLI-D-23-0214.1, 2023.
Marsh, P.: 2020 Preliminary Killer Tornadoes, NWS Storm Prediction Center [data set], https://www.spc.noaa.gov/climo/torn/STATIJ20.txt (last access: 28 April 2024), 2021.
Marsh, P. and Guyer, J.: 2018 Preliminary Killer Tornadoes, NWS Storm Prediction Center [data set], https://www.spc.noaa.gov/climo/torn/STATIJ18.txt (last access: 28 April 2024), 2019.
Marsh, P. and Guyer, J.: 2019 Preliminary Killer Tornadoes, NWS Storm Prediction Center [data set], https://www.spc.noaa.gov/climo/torn/STATIJ19.txt (last access: 28 April 2024), 2020.
Mercer, A. E. and Bates, A.: Meteorological Differences Characterizing Tornado Outbreak Forecasts of Varying Quality, Atmosphere, 1, 16, https://doi.org/10.3390/atmos10010016, 2019.
Mercer, A. E., Shafer, C. M., Doswell III, C. A., Leslie, L. M., and Richman, M. B.: Synoptic Composites of Tornadic and Nontornadic Outbreaks, Mon. Weather Rev., 140, 2590–2608, https://doi.org/10.1175/MWR-D-12-00029.1, 2012.
Michelangeli, P.-A., Vautard, R., and Legras, B.: Weather Regimes: Recurrence and Quasi Stationarity, J. Atmos. Sci., 52, 1237–1256, https://doi.org/10.1175/1520-0469(1995)052<1237:WRRAQS>2.0.CO;2, 1995.
Miller, D., Wang, Z., Trapp, R. J., and Harnos, D. S.: Hybrid Prediction of Weekly Tornado Activity Out to Week 3: Utilizing Weather Regimes, Geophys. Res. Lett., 47, e2020GL087253, https://doi.org/10.1029/2020GL087253, 2020.
Miller, D., Gensini, V. A., and Barrett, B. S.: Madden-Julian Oscillation Influences United States Springtime Tornado and Hail Frequency, Npj Clim. Atmos. Sci., 5, 37, https://doi.org/10.1038/s41612-022-00263-5, 2022.
Moore, T. W.: Annual and Seasonal Tornado Trends in the Contiguous United States and its Regions, Int. J. Climatol., 38, 1582–1594, https://doi.org/10.1002/joc.5285, 2018.
Moore, T. W. and DeBoer, T. A.: A Review and Analysis of Possible Changes to the Climatology of Tornadoes in the United States, Prog. Phys. Geogr. Earth Environ., 43, 365–390, https://doi.org/10.1177/0309133319829398, 2019.
NCEI: U.S. Billion-Dollar Weather and Climate Disasters, NOAA National Centers for Environmental Information (NCEI), https://doi.org/10.25921/stkw-7w73, 2024.
Niloufar, N., Devineni, N., Were, V., and Khanbilvardi, R.: Explaining the Trends and Variability in the United States Tornado Records using Climate Teleconnections and Shifts in Observational Practices, Sci. Rep., 11, 1741, https://doi.org/10.1038/s41598-021-81143-5, 2021.
Rasmussen, E. N. and Blanchard, D. O.: A Baseline Climatology of Sounding-Serived Supercell and Tornado Forecast Parameters, Weather Forecast., 13, 1148–1164, https://doi.org/10.1175/1520-0434(1998)013<1148:ABCOSD>2.0.CO;2, 1998.
Robertson, A. W. and Ghil, M.: Large-Scale Weather Regimes and Local Climate over the Western United States, J. Climate, 12, 1796–1813, https://doi.org/10.1175/1520-0442(1999)012<1796:LSWRAL>2.0.CO;2, 1999.
Schaller, N., Sillmann, J., Anstey, J., Fischer, E. M., Grams, C. M., and Russo, S.: Influence of Blocking on Northern European and Western Russian Heatwaves in Large Climate Model Ensembles, Environ. Res. Lett., 13, 054015, https://doi.org/10.1088/1748-9326/aaba55, 2018.
Storm Prediction Center: Severe Weather Database Files (1950–2024), 1950-2024_actual_tornadoes.csv, NOAA/National Weather Service, National Centers for Environmental Prediction, Storm Prediction Center [data set], https://www.spc.noaa.gov/wcm/#data, last access: 1 May 2025.
Strader, S. M., Ashley, W. S., Pingel, T. J., and Krmenec, A. J.: Projected 21st Century Changes in Tornado Exposure, Risk, and Disaster Potential, Clim. Change, 141, 301–313, https://doi.org/10.1007/s10584-017-1905-4, 2017.
Strader, S. M., Gensini, V. A., Ashley, W. S., and Wagner, A. N.: Changes in Tornado risk and Societal Vulnerability Leading to Greater Tornado Impact Potential, Npj Nat. Hazards, 1, 20, https://doi.org/10.1038/s44304-024-00019-6, 2024.
Straus, D. M., Corti, S., and Molteni, F.: Circulation Regimes: Chaotic Variability versus SST-Force Predictability, J. Climate, 20, 2251–2272, https://doi.org/10.1175/JCLI4070.1, 2007.
Thompson, D. B. and Roundy, P. E.: The Relationship between the Madden-Julian Oscillation and US Violent Tornado Outbreaks in the Spring, Mon. Weather Rev., 141, 2087–2095, https://doi.org/10.1175/MWR-D-12-00173.1, 1998.
Thompson, R. L., Smith, B. T., Grams, J. S., Dean, A. R., and Broyles, C.: Convective Modes for Significant Severe Thunderstorms in the Contiguous United States. Part II: Supercell and QLCS Tornado Environments, Weather Forecast., 27, 1136–1154, https://doi.org/10.1175/WAF-D-11-00116.1, 2012.
Tippett, M. K., Sobel, A. H., Camargo, S. J., and Allen, J. T.: An Empirical Relation between U.S. Tornado Activity and Monthly Environmental Parameters, J. Climate, 27, 2983–2999, https://doi.org/10.1175/JCLI-D-13-00345.1, 2014.
Tippett, M. K., Lepore, C., and L'Heureux, M. L.: Predictability of a Tornado Environment Index from El Nino Southern Oscillation (ENSO) and the Arctic Oscillation, Weather Clim. Dyn., 3, 1063–1075, https://doi.org/10.5194/wcd-3-1063-2022, 2022.
Tippett, M. K., Malloy, K., and Lee, S. H.: Modulation of U.S. Tornado Activity by year-round North American Weather Regimes, Mon. Weather Rev., 152, 2189–2202, https://doi.org/10.1175/MWR-D-24-0016.1, 2024.
Trapp, R. J.: Mesoscale-Convective Processes in the Atmosphere, Cambridge University Press, 377 pp., https://doi.org/10.1017/CBO9781139047241, 2013.
Trapp, R. J.: On the Significance of Multiple Consecutive Days of Tornado Activity, Mon. Weather Rev., 142, 1452–1459, https://doi.org/10.1175/MWR-D-13-00347.1, 2014.
Trapp, R. J., Diffenbaugh, N. S., Brooks, H. E., Baldwin, M. E., Robinson, E. D., and Pal, J. S.: Changes in Severe Thunderstorm Environment Frequency during the 21st Century caused by Anthropogenically Enhanced Global Radiative Forcing, P. Natl. Acad. Sci. USA, 104, 19719–19723, https://doi.org/10.1073/pnas.0705494104, 2007.
Vigaud, N., Robertson, A. W., and Tippett, M. K.: Predictability of Recurrent Weather Regimes over North America during Winter from Submonthly Reforecasts, Mon. Weather Rev., 146, 2559–2577, 2018a.
Vigaud, N., Tippett, M. K., and Robertson, A. W.: Probabilistic Skill of Subseasonal Precipitation Forecasts for the East Africa-West Asia Sector during September-May, Weather Forecast., 33, 1513–1532, https://doi.org/10.1175/WAF-D-18-0074.1, 2018b.
Wakimoto, R. M. and Wilson, J. W.: Non-supercell Tornadoes, Mon. Weather Rev., 117, 1113–1140, https://doi.org/10.1175/1520-0493(1989)117<1113:NST>2.0.CO;2, 1989.
Zhang, W., S-Y Wang, S., Chikamoto, Y., Gillies, R., LaPlante, M., and Hari, V.: A Weather Pattern Responsible for Increasing wildfires in the western United States, Environ. Res. Lett., 20, 014007, https://doi.org/10.1088/1748-9326/ad928f, 2024.
Short summary
This study identifies five warm-season weather regimes (WRs) to investigate the link between tornado activity and large-scale atmospheric circulations. Certain WRs strongly affect tornado activity due to their relationship with the environment. When a WR that positively affects tornado activity persists over multiple days, it further increases the tornado activity probability. The links found highlight the potential application of WRs for developing better seasonal prediction of tornadoes.
This study identifies five warm-season weather regimes (WRs) to investigate the link between...