Tornado Outbreak Research Articles

From recovery to preparedness: an examination of resources, individual disaster communication, and anticipatory resilience capacity during the 2023 U.S. tornado season

ABSTRACT Multi-phase research is needed to understand how disaster-impacted residents maintain and adapt individual disaster communication (IDC; Spialek & Houston, 2018; Spialek et al., 2021). Residents engage in IDC when they search for, create, or share interpersonal or social media messages intended to help themselves and other individuals cope across disaster phases. This study examined how recovery-focused IDC and anticipatory resilience capacity explain the relationship between resources and preparedness focused IDC. We surveyed 417 residents who lived in 13 counties impacted by a spring 2023 tornado outbreak. Results revealed that individuals who have (a) more physical and social resources and (b) engage in more recovery-focused IDC following a disaster report higher levels of anticipatory resilience capacity in those recovery-focused messages, which in turn, leads individuals to participate in more preparedness-focused communication about potential future disasters. The findings support the development of interventions and campaigns to increase preparedness behaviors.

Quantifying Tornado Outbreak Intensity and Frequency Relationships with Interannual and Monthly Variability

Tornado outbreaks (TOs) are highly dangerous meteorological phenomena common in the United States and have limited known relationships with climate variability. Many of the challenges in understanding TOs result from a lack of formal TO quantification (both in definition and impact). Here, we present a TO definition based on a spatially cohesive and distinct region of tornado activity and present a TO intensity index using tornado characteristics within the TO region. In developing this index, we present a support vector regression-based detrending methodology to remove the secular trends within tornado reporting. The resulting TO definition suggests a decline in TO activity of roughly 1 TO per 4–5 years, with a similar decline in TO intensity. In addition, the relationship between this new quantification of TOs and common North American interannual and monthly climate variability indices is explored, namely the El Niño Southern Oscillation, the North Atlantic Oscillation, the Arctic Oscillation, and the Pacific North American Oscillation. In general, the links between these teleconnections and TO frequency and intensity were minimal (and sometimes in opposition when comparing TO frequency and intensity), but interesting patterns emerged that may offer an initial pathway to exploring longer-term TO predictability.

The regionality and seasonality of tornado trends in the United States

Continued efforts to build human resilience to the impacts of tornadoes require updated knowledge of tornado occurrences as well as how their occurrence characteristics may be changing in time and varying by region. We have temporally and geospatially disaggregated annual tornado reports in the United States and revealed that significant, long-term decreases in tornado days from 1960 to 2022 have occurred over the months of June through August, primarily within the Southern Great Plains. In contrast, long-term increases in days of tornado outbreaks have occurred over this period, particularly within the Southeast U.S. and during warm- as well as cool-season months. There are indications that these dichotomous linear trends in tornado days and tornado outbreaks have relaxed over the most recent decade. Our study highlights the need to better understand the role of internal climate variability and anthropogenic forcing in modulating tornado activity.

Assessing the behavior of historic masonry buildings to tornadic events: A comparative study of code-based analysis and point cloud based simulation

During the Midwest Tornado outbreak in December 2021, the historic downtown of Mayfield, Kentucky was heavily impacted. Employing digital documentation, numerical analysis, and on-site wind speed estimations, this study evaluates the behavior of the impacted historic masonry buildings. It explores the structural damages and stress distributions observed in historic masonry buildings impacted by tornadoes, comparing them to predictions made by the ASCE 7–22 standard for tornado loads. Through this investigation, it becomes evident that the stress distribution and damages produced by the on-site tornado wind speeds were significantly different than those derived from the ASCE 7–22 standard. The current standard does not account for most historic structures, typically classified as Risk Category.II. Based on the results of this research, even when these structures are treated as Risk Category III, the stresses estimated by the standard are much lower than the ones experienced on-site. This disparity raises critical questions for preservationists evaluating similar masonry structures in tornado-prone regions. Historic preservation emphasizes minimal intervention, so understanding and addressing the specific vulnerabilities of historic masonry structures to tornadoes is crucial. The analysis in this study identified uplift forces on the roof and high stresses on windward walls as primary causes of damage in these structures, which aligned with the observed collapse mechanisms. Preservationists can use these insights to develop targeted retrofitting strategies that address these vulnerabilities while minimizing impact on the historic fabric. Recognizing these nuances and their impact on structural behavior is crucial for safeguarding the resilience and significance of historic civil structures. Thus, this study lays the groundwork for developing evidence-based, preservation-sensitive guidelines for mitigating tornado damage to irreplaceable historic masonry structures.

An unusually prolonged Pacific-North American pattern promoted the 2021 winter Quad-State Tornado Outbreaks

This study examines the large-scale atmosphere-ocean environments that led to the winter tornado outbreak across the Ohio Valley on 10–11 December 2021, also known as the Quad-States Tornado Outbreaks. Here, we show that the Quad-States Tornado Outbreaks occurred under an exceptionally strong and prolonged negative Pacific-North American pattern (PNA), which developed around December 1 and persisted for a month. This unusual PNA produced a strong atmospheric ridge along the south and eastern US seaboard, which in turn helped warm the Gulf of Mexico and produced large-scale environments conducive for tornadogenesis across the Ohio Valley. Further analysis shows that a broad region across the Ohio Valley is particularly vulnerable to extensive winter tornado outbreaks during long-lived negative PNA, whereas a limited region in the central US is exposed to winter tornado activity during short-lived negative PNA. Finally, although the PNA is a mode of internal variability that occurs with or without El Niño—Southern Oscillation, the occurrence of prolonged negative PNA is more frequent during La Niña than during El Niño.

A Conceptual Model for the Development of Tornadoes in the Complex Orography of the Po Valley

Abstract The Po Valley in northern Italy is a hotspot for tornadoes in Europe in spite of being surrounded by two mountain ridges: the Alps in the north and the Apennines in the southwest. The research focuses on the case study of 19 September 2021, when seven tornadoes (four of them rated as F2) developed in the Po Valley in a few hours. The event was analyzed using observations and numerical simulations with the convection-permitting Modello Locale in Hybrid Coordinates (MOLOCH) model. Observations show that during the event in the Po Valley, there were two surface boundaries that created a triple point: an outflow boundary generated by convection triggered in the Alpine foothills and a dryline generated by downslope winds from the Apennines, while warm and moist air advected westward from the Adriatic Sea east (ahead) of the boundaries. Tornadoes developed about 20 km northeast of the triple point. Numerical simulations with 500-m grid spacing suggest that the development of supercells and drylines in the Po Valley was sensitive to the elevation of the Apennines. Simulated vertical profiles show that the best combination of instability and wind shear for the development of tornadoes was attained within a narrow area located ahead of the dryline. A conceptual model for the development of tornadoes in the Po Valley is proposed, and the differences between tornado environments over a flat terrain and over a region with complex terrain are discussed. Significance Statement The Po Valley is a highly populated area where some of the most violent tornadoes in Europe have developed. We investigated a tornado outbreak that occurred on 19 September 2021 in this region, in order to identify its main environmental characteristics. High-resolution numerical simulations revealed that values of instability and wind shear were compatible with the development of several tornadoes only in a narrow area close to the intersection of two surface boundaries (a triple point). Moreover, the atmospheric environment during the tornado outbreak was strongly influenced by the presence of mountain ridges surrounding the plain. We have summarized our results in a conceptual model that can potentially be used for forecasting applications.

Mid-century climate change impacts on tornado-producing tropical cyclones

Tornadoes are a co-occurring extreme that can be produced by landfalling tropical cyclones (TCs). These tornadoes can exacerbate the loss of life and property damage caused by the TC from which they were spawned. It is uncertain how the severe weather environments of landfalling TCs may change in a future climate and how this could impact tornado activity from TCs. In this study, we investigated four TCs that made landfall in the U.S. and produced large tornado outbreaks. We performed four-member ensembles of convective-allowing (4-km resolution) regional climate model simulations representing each TC in the historical climate and a mid-twenty-first century future climate. To identify potentially tornadic storms, or TC-tornado (TCT) surrogates, we used thresholds for three-hourly maximum updraft helicity and radar reflectivity, as tornadoes are not resolved in the model. We found that the ensemble-mean number of TCT-surrogates increased substantially (56–299%) in the future, supported by increases in most-unstable convective available potential energy, surface-to-700-hPa bulk wind shear, and 0–1-km storm-relative helicity in the tornado-producing region of the TCs. On the other hand, future changes in most-unstable convective inhibition had minimal influence on future TCT-surrogates. This provides robust evidence that tornado activity from TCs may increase in the future. Furthermore, TCT-surrogate frequency between 00Z and 09Z increased for three of the four cases, suggesting enhanced tornado activity at night, when people are asleep and more likely to miss warnings. All of these factors indicate that TC-tornadoes may become more frequent and a greater hazard in the future, compounding impacts from future increases in TC winds and precipitation.

A Stochastic Statistical Model for U.S. Outbreak-Level Tornado Occurrence Based on the Large-Scale Environment

Abstract Tornado outbreaks—when multiple tornadoes occur within a short period of time—are rare yet impactful events. Here we developed a two-part stochastic tornado outbreak index for the contiguous United States (CONUS). The first component produces a probability map for outbreak tornado occurrence based on spatially resolved values of convective precipitation, storm relative helicity (SRH), and convective available potential energy. The second part of the index provides a probability distribution for the total number of tornadoes given the outbreak tornado probability map. Together these two components allow stochastic simulation of location and number of tornadoes that is consistent with environmental conditions. Storm report data from the Storm Prediction Center for the 1979–2021 period are used to train the model and evaluate its performance. In the first component, the probability of an outbreak-level tornado is most sensitive to SRH changes. In the second component, the total number of CONUS tornadoes depends on the sum and gridpoint maximum of the probability map. Overall, the tornado outbreak index represents the climatology, seasonal cycle, and interannual variability of tornado outbreak activity well, particularly over regions and seasons when tornado outbreaks occur most often. We found that El Niño–Southern Oscillation (ENSO) modulates the tornado outbreak index such that La Niña is associated with enhanced U.S. tornado outbreak activity over the Ohio River Valley and Tennessee River Valley regions during January–March, similar to the behavior seen in storm report data. We also found an upward trend in U.S. tornado outbreak activity during winter and spring for the 1979–2021 period using both observations and the index. Significance Statement Tornado outbreaks are when multiple tornadoes happen in a short time span. Because of the rare, sporadic nature of tornadoes, it can be challenging to use observational tornado reports directly to assess how climate affects tornado and tornado outbreak activity. Here, we developed a statistical model that produces a U.S. map of the likelihood that an outbreak-level tornado would occur based on environmental conditions. In addition, using that likelihood map, the model predicts a range of how many tornadoes could occur in these events. We found that “storm relative helicity” (a proxy for potential rotation in a storm’s updraft) is especially important for predicting outbreak tornado likelihood, and the sum and maximum value of the likelihood map is important for predicting total numbers for an event. Overall, this model can represent the typical behavior and fluctuations in tornado outbreak activity well. Both the tornado outbreak model and observations agree that the state of sea surface temperature in the tropical Pacific (El Niño–Southern Oscillation) is linked to tornado outbreak activity over the Ohio River Valley and Tennessee River Valley in winter through early spring and that there are upward trends in tornado outbreak activity.

Systemic Vulnerabilities in Hispanic and Latinx Immigrant Communities Led to the Reliance on an Informal Warning System in the December 10–11, 2021, Tornado Outbreak

Systemic Vulnerabilities in Hispanic and Latinx Immigrant Communities Led to the Reliance on an Informal Warning System in the December 10–11, 2021, Tornado Outbreak

Revisiting tornado watch #211 - a spatial analysis of the May 31, 1985 tornadoes using present-day data for the state of Pennsylvania

On the evening of 31 May 1985, a devastating and deadly tornado outbreak impacted Ohio, Pennsylvania, New York, and Ontario, Canada. A total of 41 tornadoes occurred resulting in 88 fatalities: the deadliest tornado outbreak of the 1980s. Pennsylvania experienced the greatest number of tornadoes with 21 affecting the Commonwealth, causing 65 fatalities. Despite its severity, Pennsylvania’s deadliest tornado outbreak has not been spatially analyzed in academic literature. With the outbreak occurring nearly 40 years ago, one must consider the potential impacts today in comparison to the 1985 time-period. Has the natural and built landscape changed to the extent that the population would be further impacted both directly and indirectly? To assist in answering this question, this study performs a Geographic Information System (GIS) analysis of Pennsylvania’s historical and present-day land cover, census, and building data in locations impacted by the 1985 tornadoes. Data between the two time periods is compared and analyzed to quantify changes since 1985 to estimate if the impacts would be lesser or greater in relation to the expanding bull’s eye effect. This includes estimating the number of fatalities that would occur in the present-day.

A Comparison between the Only Two Documented Tornado Outbreak Events in China: Tropical Cyclone versus Extratropical Cyclone Environments

Abstract This study documents the features of tornadoes, their parent storms, and the environments of the only two documented tornado outbreak events in China. The two events were associated with Tropical Cyclone (TC) Yagi on 12 August 2018 with 11 tornadoes and with an extratropical cyclone (EC) on 11 July 2021 (EC 711) with 13 tornadoes. Most tornadoes in TC Yagi were spawned from discrete minisupercells, while a majority of tornadoes in EC 711 were produced from supercells imbedded in QLCSs or cloud clusters. In both events, the high-tornado-density area was better collocated with the K index rather than MLCAPE, and with entraining rather than non-entraining parameters possibly due to their sensitivity to midlevel moisture. EC 711 had a larger displacement between maximum entraining CAPE and vertical wind shear than TC Yagi, with the maximum entraining CAPE better collocated with the high-tornado-density area than vertical wind shear. Relative to TC Yagi, EC 711 had stronger entraining CAPE, 0–1-km storm relative helicity, 0–6-km vertical wind shear, and composite parameters such as an entraining significant tornado parameter, which caused its generally stronger tornado vortex signatures (TVSs) and mesocyclones with a larger diameter and longer life span. No significant differences were found in the composite parameter of these two events from U.S. statistics. Although obvious dry air intrusions were observed in both events, no apparent impact was observed on the potential of tornado outbreak in EC 711. In TC Yagi, however, the dry air intrusion may have helped tornado outbreak due to cloudiness erosion and thus the increase in surface temperature and low-level lapse rate.

Weighing structural damage and social susceptibility: A decision-making tool to perform longitudinal studies of geographically large hazard events.

Geographically large climatic hazard events are occurring more frequently, and with this increase, more research emphasis is being placed on their impact. However, a metrology for selecting which communities to survey following an event is not frequently discussed and as a result does not effectively incorporate all relevant disciplines in disaster research. This article provides a method for selecting communities for inclusion in field studies not only based on anticipated damage but also community-level social factors that show predictive power in long-term analyses. Due to the perishable nature of disaster data, this social metric and the field study decision tool were developed with the intent of being as rapidly implementable as possible while still providing insight regarding long-term post-event community trends. The community-level social metric was developed using a hindcasting technique for hazard events in the years 2011, 2012, and 2013. Thresholds for stability and decline were established for both the predicted and the actual community outcomes. Of the communities that the model predicted would recover, 73% recovered using the definition provided. Meanwhile 74% of the communities predicted to decline did indeed decline, again using the definition provided. To enhance operability of this social metric, a decision-making tool for community selection was also formalized and implemented in a field study conducted following the December 2021 quad-state tornado outbreak in the United States. The lessons learned from this first implementation were used to inform a refined version of the decision-making tool that is presented herein.

Testing Stochastic and Perturbed Parameter Methods in an Experimental 1-km Warn-on-Forecast System Using NSSL’s Phased-Array Radar Observations

Abstract The success of the National Severe Storms Laboratory’s (NSSL) experimental Warn-on-Forecast System (WoFS) to provide useful probabilistic guidance of severe and hazardous weather is mostly due to the frequent assimilation of observations, especially radar observations. Phased-array radar (PAR) technology, which is a potential candidate to replace the current U.S. operational radar network, would allow for even more rapid assimilation of radar observations by providing full-volumetric scans of the atmosphere every ∼1 min. Based on previous studies, more frequent PAR data assimilation can lead to improved forecasts, but it can also lead to ensemble underdispersion and suboptimal observation assimilation. The use of stochastic and perturbed parameter methods to increase ensemble spread is a potential solution to this problem. In this study, four stochastic and perturbed parameter methods are assessed using a 1-km-scale version of the WoFS and include the stochastic kinetic energy backscatter (SKEB) scheme, the physically based stochastic perturbation (PSP) scheme, a fixed perturbed parameters (FPP) method, and a novel surface-model scheme blending (SMSB) method. Using NSSL PAR observations from the 9 May 2016 tornado outbreak, experiments are conducted to assess the impact of the methods individually, in different combinations, and with different cycling intervals. The results from these experiments reveal the potential benefits of stochastic and perturbed parameter methods for future versions of the WoFS. Stochastic and perturbed parameter methods can lead to more skillful forecasts during periods of storm development. Moreover, a combination of multiple methods can result in more skillful forecasts than using a single method. Significance Statement Phased-array radar technology allows for more frequent assimilation of radar observations into ensemble forecast systems like the experimental Warn-on-Forecast System. However, more frequent radar data assimilation can eventually cause issues for prediction systems due to the lack of ensemble spread. Thus, the purpose of this study is to explore the use of four stochastic and perturbed parameter methods in a next-generation Warn-on-Forecast System to generate ensemble spread and help prevent the issues from frequent radar data assimilation. Results from this study indicate the stochastic and perturbed parameter methods can improve forecasts of storms, especially during storm development.

Role of single and compound Pacific natural variability in extratropical cyclone activity over North America

AbstractThe role of large‐scale natural variability in influencing the distribution of midlatitude storm tracks and extratropical cyclones is well known. However, less understood is how compound natural variability—the concurrence of two modes of variability—influences the geospatial characteristics of extratropical cyclones. Here, we evaluate compound modes of Pacific‐North American variability—ENSO and PNA with the slow varying PDO—on extratropical cyclones over the North American region during 1950–2013, using an automated tracking algorithm applied to the Twentieth Century Reanalysis version 3 data. Seasonal analysis is conducted on spatial statistics including track, genesis and lysis density and some mean fields in varying compound phases. Spatial correlations of track density with seasonal mean of individual variability indices show that the PNA, ENSO and PDO enhance cyclone activity over Pacific storm track significantly while shifting the North American and Atlantic storm tracks in seasons, that is, equatorward in the positive phase. But the decadal variability of PDO moves the Atlantic storm track poleward, particularly in boreal winter. By comparing to the single Pacific variability impacts, compound Pacific variability results demonstrate the dominance of the higher‐frequency modes of ENSO and PNA on extratropical cyclone activity, while the slow varying PDO can modulate the ENSO‐ and PNA‐related cyclone variability. For example, during a negative PNA, cyclones over the North Atlantic become more (less) frequent during a positive (negative) PDO. The in‐phase modes of the PDO and ENSO influence Pacific storm track significantly, increasing (decreasing) cyclone frequency when both are positive (negative) in boreal winter. Results are linked to and used to explain the previous findings on how natural variability influences the occurrence of tornado outbreaks and high precipitation over the southeastern United States, Great Lakes, Ohio Valley and other US regions. Understanding compound variability modulations may be useful to predict extratropical cyclone frequency, development and decay regions seasonally and to study the physical mechanisms of extreme weather events.

Discrete Extremes

Our contribution is to widen the scope of extreme value analysis applied to discrete-valued data. Extreme values of a random variable are commonly modeled using the generalized Pareto distribution, a peak-over-threshold method that often gives good results in practice. When data is discrete, we propose two other methods using a discrete generalized Pareto and a generalized Zipf distribution respectively. Both are theoretically motivated and we show that they perform well in estimating rare events in several simulated and real data cases such as word frequency, tornado outbreaks and multiple births.

Environmental ingredients that lead to tornado outbreak and tornado failure: A comparison between two similar recurving tropical cyclones

AbstractRecurving tropical cyclones (TCs) can sometimes produce tornado outbreaks, while some TCs with similar tracks and intensities may produce none of tornado, which makes it challenging to assess tornado risk within recurving TCs. This study investigates two recurving TCs, Typhoon Yagi (2018) and Typhoon In‐Fa (2021), that made landfall in eastern China. Despite the similar recurving tracks and intensities, Yagi produced 11 tornadoes while In‐Fa produced none. Results show that both TCs were characterized by similar large‐scale conditions that were dynamically favourable for tornadoes during the recurvature process. The non‐tornadic In‐Fa even featured a higher shear and helicity environment in its northeast sector than did the tornado‐productive Yagi. The greatest difference between Yagi and In‐Fa is the thermodynamic instability owing to the different lower–middle‐tropospheric lapse rates that are attributable to the differences in air trajectories at low levels. In‐Fa featured marginal instability due to the cooler air at low levels because almost all of the air parcels came from the Pacific Ocean while most air parcels for Yagi came from the warm land. The cooler low‐level air tends to create higher relative humidity in In‐Fa's interior and thus leads to widespread precipitation which in turn also contributes to the low‐level cooling. The different air trajectories are demonstrated related to the TC's translation speed, size and synoptic characteristics days before TC's landfall. Numerical simulations suggest that the upward motions within the widespread precipitation regions of In‐Fa are overall weaker than those of Yagi due to the limited instability in the former. These findings suggest that even though two TCs were characterized by similar tracks, intensities and large‐scale forcings, their different low‐level air pathways may have significant influence on priming the mesoscale environment for supercell or tornado formation.

Risk and Protective Factors Associated with Post-Tornado Distress among Persons with Disabilities

This study examined the direct effects of tornado exposure and protective resources on post-disaster distress among (N = 209) persons with disabilities following a tornado outbreak in the Midwest and Southeast on December 10–11, 2021 using structural equation modeling. Results found the degree of tornado exposure (e.g., injury, loss of loved ones, property damage, etc.) increased the likelihood of experiencing higher levels of PTSD and depression. Results also found that higher levels of protective resources (physical, social, and emotional) were found to protect against post-tornado PTSD and depression. Findings can inform future post-disaster mental health services for persons with disabilities.

The New Tornado Alley: Tornado outbreaks are migrating eastward from Texas and Oklahoma toward Tennessee and Kentucky, where people may not be prepared.

The New Tornado Alley: Tornado outbreaks are migrating eastward from Texas and Oklahoma toward Tennessee and Kentucky, where people may not be prepared.

Analyzing Tornado Warning Performance during Individual Storm Life Cycles

Abstract The National Weather Service plays a critical role in alerting the public when dangerous weather occurs. Tornado warnings are one of the most publicly visible products the NWS issues given the large societal impacts tornadoes can have. Understanding the performance of these warnings is crucial for providing adequate warning during tornadic events and improving overall warning performance. This study aims to understand warning performance during the lifetimes of individual storms (specifically in terms of probability of detection and lead time). For example, does probability of detection vary based on if the tornado was the first produced by the storm, or the last? We use tornado outbreak data from 2008 to 2014, archived NEXRAD radar data, and the NWS verification database to associate each tornado report with a storm object. This approach allows for an analysis of warning performance based on the chronological order of tornado occurrence within each storm. Results show that the probability of detection and lead time increase with later tornadoes in the storm; the first tornadoes of each storm are less likely to be warned and on average have less lead time. Probability of detection also decreases overnight, especially for first tornadoes and storms that only produce one tornado. These results are important for understanding how tornado warning performance varies during individual storm life cycles and how upstream forecast products (e.g., Storm Prediction Center tornado watches, mesoscale discussions, etc.) may increase warning confidence for the first tornado produced by each storm. Significance Statement In this study, we focus on better understanding real-time tornado warning performance on a storm-by-storm basis. This approach allows us to examine how warning performance can change based on the order of each tornado within its parent storm. Using tornado reports, warning products, and radar data during tornado outbreaks from 2008 to 2014, we find that probability of detection and lead time increase with later tornadoes produced by the same storm. In other words, for storms that produce multiple tornadoes, the first tornado is generally the least likely to be warned in advance; when it is warned in advance, it generally contains less lead time than subsequent tornadoes. These findings provide important new analyses of tornado warning performance, particularly for the first tornado of each storm, and will help inform strategies for improving warning performance.

Remote linkage of record-breaking U.S. Tornado outbreaks to the tropical cyclone in western North Pacific in December 2021

The frequency of tornadoes usually peaks during spring to summer rather than winter in climatology. However, the United States (U.S.) experienced more than 200 tornadoes in December 2021, which broke the historical record and caused 87 fatalities. Historically, the frequency of tornadoes in December tends to increase under El Niño conditions. Our results show that the monthly large-scale weather regime conducive to these record-breaking tornado outbreaks under a La Niña condition is closely associated with Typhoon Nyatoh in the western North Pacific. As the tropical cyclone (TC) recurved into the mid-latitudes, its interaction with the extratropical flows has caused distortions in the Asian jet stream and the dramatic development of anomalous anticyclone west of the dateline, which in turn strongly regulated the response of the monthly atmospheric teleconnection to La Niña forcing. Accurate forecasts of the monthly mean circulation for December 2021 first appeared in the European Center for Medium-Range Weather Forecasts sub-seasonal to seasonal forecasts from 29 November, with a forecast skill closely related to that of Typhoon Nyatoh. Given most studies on the warm seasons with frequent tornadoes, the present results advance our understanding of the TC effect on the monthly atmospheric response to El Niño-Southern Oscillation forcing and its linkage to the tornado occurrence during boreal winter.