An Analysis of Anomalous Winter and Spring Tornado Frequency by Phase of the El Niño/Southern Oscillation, the Global Wind Oscillation, and the Madden-Julian Oscillation

Todd W. Moore; Jennifer M. St. Clair; Tiffany A. DeBoer

doi:10.1155/2018/3612567

An Analysis of Anomalous Winter and Spring Tornado Frequency by Phase of the El Niño/Southern Oscillation, the Global Wind Oscillation, and the Madden-Julian Oscillation

Moore, Todd W.;St. Clair, Jennifer M.;DeBoer, Tiffany A. 2018-07-16 00:00:00 Hindawi Advances in Meteorology Volume 2018, Article ID 3612567, 14 pages https://doi.org/10.1155/2018/3612567 Research Article An Analysis of Anomalous Winter and Spring Tornado Frequency by Phase of the El Niño/Southern Oscillation, the Global Wind Oscillation, and the Madden-Julian Oscillation Todd W. Moore , Jennifer M. St. Clair , and Tiﬀany A. DeBoer Department of Geography and Environmental Planning, Towson University, Towson, MD 21252, USA Correspondence should be addressed to Todd W. Moore; tmoore@towson.edu Received 25 February 2018; Revised 29 May 2018; Accepted 7 June 2018; Published 16 July 2018 Academic Editor: Anthony R. Lupo Copyright © 2018 Todd W. Moore et al. +is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Winter and spring tornado activity tends to be heightened during the La Niña phase of the El Niño/Southern Oscillation and suppressed during the El Niño phase. Despite these tendencies, some La Niña seasons have fewer tornadoes than expected and some El Niño seasons have more than expected. To gain insight into such anomalous seasons, the two La Niña winters and springs with the fewest tornadoes and the two El Niño winters and springs with the most tornadoes between 1979 and 2016 are identiﬁed and analyzed in this study. +e relationships between daily tornado count and the Global Wind Oscillation and Madden-Julian Oscillation in these anomalous seasons are also explored. Lastly, seasonal and daily composites of upper-level ﬂow, low-level ﬂow and humidity, and atmospheric instability are generated to describe the environmental conditions in the anomalous seasons. +e results of this study highlight the potential for large numbers of tornadoes to occur in a season if favorable conditions emerge in association with individual synoptic-scale events, even during phases of the El Niño/Southern Oscillation, Global Wind Os- cillation, and Madden-Julian Oscillation that seem to be unfavorable for tornadoes. +ey also highlight the potential for anomalously few tornadoes in a season even when the oscillations are in favorable phases. during the neutral phase (N) of ENSO, followed by the LN 1. Introduction then EN phases. +e most recent studies report that tornado More tornadoes occur in the United States (US) per year activity is heightened during the LN phase [12–14, 17–19]. than in any other country [1], and these tornadoes are ca- Recent studies also illustrate that the seasons with the most pable of producing incredible economic and human loss tornadoes tend to be classiﬁed as LN. Lee et al. [17], for [2–5]. Despite their relatively common occurrence, there is example, analyzed the number of tornadoes rated 3 or higher notable intra- and interannual variability in the number of on the Fujita or Enhanced Fujita damage scales (hereafter US tornadoes [6–10]. Due to the danger and variability of referred to as E(F)) and reported that the ﬁve most extreme tornado outbreak years were characterized as persistent LN tornado activity, there is a need to improve seasonal outlook capabilities. One approach is to identify relationships be- events or LN events that were transitioning to a diﬀerent tween seasonal tornado count and climate oscillations, such phase. Allen et al. [12] similarly reported that most of the as the El Niño/Southern Oscillation (ENSO) (e.g., [11–19]). seasons with a tornado count >100% of climatology oc- Numerous studies over the past couple of decades have curred during the LN phase, whereas most that were <75% analyzed the relationship between tornadoes and ENSO. An of climatology occurred during the EN phase. early study by Monfredo [15] reported that strong and vi- +e relatively consistent reporting of heightened tornado olent tornadoes were more common during the La Niña activity with the LN phase of ENSO elicits the possibility of (LN) phase of ENSO and less common during the El Niño predicting the likelihood of below- or above-normal tornado (EN) phase. Cook and Schaefer [16] later reported that activity based on ENSO conditions. Elsner et al. [18] developed winter tornado outbreaks were stronger and more frequent a statistical model to evaluate variability in tornado activity in 2 Advances in Meteorology which ENSO was the most important predictor. +eir model previous studies illustrate that tornado counts and ENSO are also indicated that tornado activity was heightened in the related in these seasons [12–14, 17–19]. Multiple indices represent ENSO conditions. Some Midwest and Southeast regions of the US during LN and in the Great Plains region during EN. Allen et al. [12] and Lepore represent mostly the oceanic component of ENSO (e.g., the et al. [13] developed extended logistic regression models using Niño 1.2, 3, 3.4, and 4 regions and the Oceanic Niño Index ENSO to predict the likelihood of below-normal, normal, or (ONI)), some represent mostly the atmospheric component above-normal tornado activity in spring. +eir models simi- (e.g., the Southern Oscillation Index (SOI)), and some larly showed that tornado activity is increased during the LN combine both (e.g., the Multivariate ENSO Index (MEI) and phase, particularly in the south central US. Bivariate ENSO Index (BEST)). +e classiﬁcation of seasons Anomalous seasons occur. Some LN seasons have fewer as LN or EN will likely change in some cases, depending on than expected tornadoes and some EN seasons have more than the index. In this study, ONI data were obtained from the expected. Lee et al. [19], for example, noted that an anoma- Climate Prediction Center [28] to represent ENSO condi- lously large number of tornadoes occurred in the 2015-2016 EN tions, which is consistent with recent eﬀorts to predict the likelihood of an active or inactive season based on the state of winter. Elsner et al. [18] also noted the occurrence of anom- alous seasons and attributed them to additional unknown ENSO [12, 13]. ONI is also one of the commonly used factors. While previous studies have noted these anomalous indices by the Climate Prediction Center to diagnose ENSO seasons, none have provided detailed descriptions and analyses conditions and to place current events into historical per- of them. More research, including work focusing on the spective. It is given as anomalies of 3-month running means ° ° anomalous seasons, is needed to improve our understanding of of sea surface temperature in the Niño 3.4 region (5 N–5 S, ° ° the tornado-ENSO link and to reﬁne statistical models. 120 W–170 W). Series of ONI for DJF and MAM were A case study approach was taken here to describe and extracted and merged with the seasonal tornado counts. analyze the two EN winters and springs with the most Once merged, seasons were classiﬁed as LN if ONI ≤−0.5 tornadoes, and the two LN winters and springs with the and EN if ONI ≥0.5. Seasons with −0.5< ONI< 0.5 were fewest tornadoes. +e states of the Madden-Julian Oscilla- classiﬁed as neutral (N). +e two most active EN DJFs and tion (MJO) and Global Wind Oscillation (GWO) are of MAMs (i.e., with the most tornadoes) and two least active particular interest here. Both of these oscillations vary over LN DJFs and MAMs (i.e., with the fewest tornadoes) were subseasonal timescales (i.e., shorter than the timescale over extracted, yielding eight seasons as the focus of this study. which ENSO varies) and have been linked to variations in Climate oscillations other than ENSO have been linked tornado activity [20–23]. Seasonal and daily composites are to tornado activity. +ose that vary over subseasonal also generated to document the synoptic patterns that ac- timescales may provide insight into the intraseasonal dis- companied the anomalous seasons. tribution of tornadoes in the anomalous seasons. Two such +e speciﬁc objectives of this study are to oscillations are the MJO and GWO. +e MJO is a tropical wave of enhanced convection that propagates the globe from (1) identify and describe the two EN winters and springs −1 west to east at approximately 5 m·s [29]. It is linked to with the most tornadoes and the two LN winters and variability in tornado activity in the midlatitudes through springs with the fewest tornadoes; changes in global relative atmospheric angular momentum (2) analyze GWO and MJO activity during these (AAM), which arise from tropical convective forcing and anomalous seasons; teleconnected Rossby wave propagation in the midlatitudes (3) document the synoptic patterns that accompany [20, 23, 30–32]. Other factors, such as friction and mountain torque, aﬀect AAM in addition to tropical convection these anomalous seasons. [30–32]. +e GWO combines these factors and, therefore, provides a comprehensive representation of AAM and 2. Data and Methods midlatitude circulation patterns [31, 32]. Daily GWO data Tornado data were taken from the Storm Prediction were obtained from the Earth System Research Laboratory’s Center’s Severe Weather Database (speciﬁcally, the GWO dataset [33]. +e attributes of the GWO used in this 1950–2016_actual_tornadoes.csv ﬁle) [24]. +is dataset was study are AAM anomalies, AAM time tendency, and GWO a subset to the 18,251 E(F)1+ tornadoes that occurred over phase. +e relationship between AAM anomalies and AAM the period December 1978–November 2016 in the con- time tendency determines the phase of GWO, which run tiguous US. +e time series of E(F)1+ tornadoes is less from 1 to 8. Phases 2 and 3 represent anomalously low AAM, inﬂuenced by detection and reporting changes over time whereas phases 6 and 7 represent anomalously high AAM. Phases 1, 4, 5, and 8 represent transition states. Daily MJO than is the E(F)0+ series, which includes a notable upward trend [25, 26]. +e beginning of the period of record is data were obtained from the Australian Bureau of Mete- consistent with recent studies [12, 13] and corresponds to the orology’s Real-Time Multivariate (RMM) MJO dataset ﬁrst year of the North American Regional Reanalysis (NARR) [34, 35]. +e attributes used here are the ﬁrst two principal product [27], which is used to generate seasonal and daily components of empirical orthogonal functions that consist atmospheric composites. Seasonal tornado counts were of a meridional average of the 200 mb zonal wind, the generated for winter (December of previous year, January, 850 mb zonal wind, and outgoing longwave radiation be- ° ° and February (DJF)) and spring (March, April, and May tween 15 N and 15 S (referred to as RMM1 and RMM2) and (MAM)). DJF and MAM are the focus of this study because the MJO phase. Similar to GWO, the MJO index has eight Advances in Meteorology 3 ρ = –0.20 ρ = –0.19 150 p = 0.23 p = 0.25 200 4 0 0 –3 –2 –1 0 1 2 3 –3 –2 –1 0 1 2 3 Oceanic Niño Index (ONI) Oceanic Niño Index (ONI) Active ENs Inactive LNs Active ENs Inactive LNs 3. MAM 1983, 251 3. MAM 2000, 178 1. DJF 1983, 133 1. DJF 1985, 9 tornadoes tornadoes tornadoes tornadoes 4. MAM 1982, 340 4. MAM 1985, 179 2. DJF 2016, 133 2. DJF 1986, 21 tornadoes tornadoes tornadoes tornadoes (a) (b) Figure 1: Number of E(F)1+ tornadoes in (a) DJF and (b) MAM and the concurrent Oceanic Niño Index (ONI). Vertical dashed blue and red lines are placed at ONI −0.5 and 0.5 and demarcate LN and EN, respectively. Še blue and red diamonds represent the two most inactive and active LNs and ENs, respectively. Spearman’s ρ rank correlation coe¦cients and p values are provided in the top-right corners of the graphs. phases, but they represent the location of enhanced tropical Table 1: Minimum, mean, and maximum values of global at- 2 −2 mospheric angular momentum (AAM; kg·m ·s ) anomalies convection. Phase 1 of the MJO indicates that the enhanced during the active EN and inactive LN seasons. convection is located near eastern Africa and the subsequent phases represent its eastward progression. Seasons Minimum Mean Maximum AAM anomalies, AAM time tendency, RMM1, and Active EN seasons RMM2 were used to construct GWO and MJO phase space DJF 1983 1.3 2.6 4.4 diagrams for each of the anomalous seasons. Daily tornado DJF 2016 0.4 1.9 4.0 counts were generated (where a day is de�ned as 0000–2359 MAM 1982 −1.3 0.1 1.4 UTC) and plotted in the phase space diagrams along with the MAM 1983 0.1 1.8 3.5 progression of GWO and MJO. Daily tornado counts were Inactive LN seasons also assessed across the phases of MJO and GWO, and DJF 1985 −2.0 −0.9 0.1 Kruskal–Wallis tests were used to determine if the mean rank DJF 1986 −1.1 0.1 1.3 MAM 1985 −1.0 0.1 1.1 of the tornado counts signi�cantly varied across the phases of MAM 2000 −2.7 −1.5 −0.4 the oscillations. Šis nonparametric test was chosen because of the skewed nature of daily tornado counts. Še synoptic patterns associated with the anomalous in DJF 2016). Še seasonal anomaly composites represent the seasons were characterized using gridded composites from dižerence between the mean composites and the 1981–2010 NARR [27]. Še following variables were chosen to be com- climatology. Daily composites are averages of the 3 hr NARR parable to composites in previous research (e.g., [12, 20, 21]): data. All composites were generated with NOAA’s Earth upper-level •ow was characterized using 300 mb geopotential System Research Laboratory’s online plotting tool [37, 38]. heights; low-level •ow was characterized using 850 mb geo- Once generated, the Network Common Data Form (NetCDF) potential heights; low-level moisture was characterized using of the composites was imported into ArcMAP [39] for plotting 850 mb speci�c humidity; and atmospheric stability was and display. characterized using surface-based convective available poten- tial energy (CAPE). Surface-based CAPE was used because 3. Results and Discussion Gensini et al. [36] illustrate that surface-based parcels are more accurate than 100 mb mixed layer parcels, largely due to errors 3.1. Identication and Description of the Anomalous Seasons. in low-level moisture �elds. Še seasonal mean composites are Še relationships between the number of E(F)1+ tornadoes averages of the variables for the dates under consideration and ONI in DJF and MAM are depicted in Figure 1. Še (e.g., mean 300 mb geopotential height in DJF 2016 is gen- negative relationship reported by others, whereby tornado erated by averaging the 300 mb geopotential height for the days frequency tends to be greater during the LN phase and lesser Tornado count Tornado count 4 Advances in Meteorology 35 35 Active EN: DJF 2016, 133 tornadoes Active EN: DJF 1983, 133 tornadoes 30 30 25 25 15 15 10 10 5 5 0 0 1 112131415161718191 1 112131415161718191 Day of season Day of season (a) (b) 35 35 Active EN: MAM 1982, 340 tornadoes Active EN: MAM 1983, 251 tornadoes 30 30 25 25 20 20 15 15 10 10 5 5 1 112131415161718191 1 112131415161718191 Day of season Day of season (c) (d) 35 35 Inactive LN: DJF 1986, 21 tornadoes Inactive LN: DJF 1985, 9 tornadoes 30 30 1 11 21 31 41 51 61 71 81 91 1 112131415161718191 Day of season Day of season (e) (f) 35 35 Inactive LN: MAM 2000, 178 tornadoes Inactive LN: MAM 1985, 179 tornadoes 30 30 25 25 20 20 15 15 10 10 5 5 0 0 1 112131415161718191 1 112131415161718191 Day of season Day of season (g) (h) Figure 2: Daily tornado count of the two EN DJFs (a, b) and MAMs (c, d) with the most tornadoes and of the two LN DJFs (e, f) and MAMs (g, h) with the fewest tornadoes. during the EN phase [12–14, 17–19], is visually apparent, but tornadoes in EN DJFs and 359% greater than the median. +e the correlation is not statistically signiﬁcant in this subset. two most active EN MAM seasons were 1982 and 1983, when 340 and 251 E(F)1+ tornadoes occurred, respectively. MAM +e central tendencies in Table 1 also illustrate the negative association by showing that the mean and median tornado 1982 had approximately 79% more E(F)1+ tornadoes than the counts of the LN seasons exceed those of the EN seasons. mean EN MAMs (76% greater than the median). MAM 1983 +e two most inactive LN DJF and MAM seasons are exceeded the mean and median counts of EN MAM seasons marked with blue diamonds in Figure 1, and the two most by approximately 30%. active ENs are marked with red diamonds. +e two most active +e two DJF seasons with the fewest E(F)1+ tornadoes EN DJFs, which occurred in 2016 and 1983, are especially were 1985 and 1986, with 9 and 21 tornadoes, respectively. noticeable. +ere were 133 E(F)1+ tornadoes in both seasons. DJF 1985 had approximately 80% fewer tornadoes than the +ese counts are 183% greater than the mean number of mean and median LN DJF, and DJF 1986 had 64% and 54% Tornado count Tornado count Tornado count Tornado count Tornado count Tornado count Tornado count Tornado count Advances in Meteorology 5 Active EN Day of Daily Active EN Day of Daily season tornado season tornado GWO MJO count count 4 0 4 0 76 76 75 75 5 5 2 2 10 10 8 5 8 5 15 15 50 50 0 0 20 20 1 4 1 4 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 02 4 dAAM/dt RMM1 (a) (b) Day of Daily Day of Daily season tornado season tornado count count 4 4 0 0 7 76 75 75 5 5 2 2 10 10 8 5 8 5 15 15 50 50 0 0 20 20 1 4 1 4 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 02 4 dAAM/dt RMM1 (c) (d) Day of Daily Day of Daily season tornado season tornado count count 4 0 4 0 76 76 75 75 10 10 15 15 8 5 8 5 20 20 50 50 0 0 1 4 1 4 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 02 4 dAAM/dt RMM1 (e) (f) Day of Daily Day of Daily season tornado season tornado count count 4 0 4 0 76 76 75 75 5 5 2 2 10 10 8 5 8 5 15 15 50 50 0 0 20 20 1 4 1 4 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 024 dAAM/dt RMM1 (g) (h) Figure 3: Phase space diagrams for the GWO (a, c, e, and g) and MJO (b, d, f, and h) during the two EN DJFs and MAMs with the most tornadoes. MAM 1983 MAM 1982 DJF 2016 DJF 1983 AAM AAM AAM AAM MAM 1983 MAM 1982 DJF 2016 DJF 1983 RMM2 RMM2 RMM2 RMM2 6 Advances in Meteorology Table 2: Mean (mean rank) of daily tornado count by GWO and MJO phase. GWO phase 1 2 3 4 5 6 7 8 Active EN DJF 0.0 (70.0) — — — — 1.5 (83.3) 1.3 (92.8) 2.3 (96.3) Active EN MAM 3.8 (100.5) 2.9 (86.8) 7.5 (106.9) 3.2 (81.1) 3.6 (83.9) 3.3 (89.5) 2.7 (98.3) 2.8 (88.2) Inactive LN DJF 0.1 (89.4) 0.1 (90.5) 0.1 (86.4) 0.1 (87.2) 0.0 (84.0) 0.5 (100.5) 0.8 (106.5) 0.1 (90.2) Inactive LN MAM 2.3 (115.4) 3.6 (100.0) 1.4 (88.8) 1.0 (79.8) 2.1 (93.6) 1.8 (111.0) 2.6 (79.8) 1.8 (84.8) MJO phase Active EN DJF 3.5 (113.2) 0.4 (90.0) 1.9 (101.4) 0.7 (81.2) 1.1 (80.6) 4.0 (119.9) 1.3 (80.5) 0.3 (80.6) Active EN MAM 1.1 (58.0) 2.5 (85.4) 2.6 (87.3) 5.9 (116.1) 1.2 (72.5) 1.5 (83.3) 4.5 (101.9) 3.1 (97.8) Inactive LN DJF 0.4 (100.3) 0.0 (84.0) 0.1 (88.0) <0.1 (87.3) 0.1 (91.4) 0.2 (91.7) 0.5 (92.7) 0.1 (87.9) Inactive LN MAM 2.0 (96.6) 2.2 (87.3) 2.7 (92.3) 0.5 (70.1) 2.3 (103.0) 2.6 (96.7) 2.0 (95.6) 1.7 (94.5) a 2 A Kruskal–Wallis test indicates that the mean rank of tornado count varies across the phases of MJO (X � 23.9; df � 7; p � 0.001). Post hoc comparisons show that the mean rank of tornado count is greater in phase 6 than in phases 4, 7, and 8. A Kruskal–Wallis test indicates that the mean rank of tornado count varies across the phases of MJO (X �17.9; df � 7; p � 0.012). Post hoc comparisons show that the mean rank of tornado count is greater in phase 4 than in phase 1. fewer than the mean and median counts, respectively. +e AAM anomalies were positive throughout both of the least active LN MAM seasons were 1985 when 179 E(F)1+ anomalously active EN DJFs (Figures 3(a) and 3(c)). +e 2 −2 mean AAM anomaly was 2.6 kg·m tornadoes occurred and 2000 when 178 E(F)1+ occurred. ·s in DJF 1983 and 2 −2 +ese seasons had approximately 43% fewer tornadoes than 1.9 kg·m ·s in DJF 2016 (Table 1). AAM anomalies were the mean LN MAM tornado count and approximately 24% also positive throughout the active EN MAM 1983 season, 2 −2 fewer than the median count. when the mean was 1.8 kg·m ·s (Figure 3(g), Table 1). As with most seasons, the tornadoes in the anomalous Tornadoes in these seasons, therefore, occurred on GWO seasons were not uniformly distributed (Figure 2). In DJF phase 5–8 days when AAM was anomalously high and the 2016, for example, 50% of the tornadoes occurred on 4 days, amplitude was most often >1. Positive anomalies were each with 10+ tornadoes. +ere were 6 days in DJF 1983 expected in these seasons because AAM tends to be with 10+ tornadoes that account for 68% of the tornadoes heightened during the EN phase of ENSO [40], but it is in that season. +e tornadoes in the anomalous MAM unexpected, based on previous studies linking enhanced seasons were spread over a larger number of days, but there tornado activity to anomalously low AAM [21, 22], that all were still clusters of activity. Twelve days in MAM 1982 and of the days with tornadoes in DJF 1983, DJF 2016, and eight in MAM 1983 had 10+ tornadoes. +e tornadoes on MAM 1983 had anomalously high AAM. Despite the these days account for 60% and 52% of the seasonal count, positive AAM anomalies throughout these seasons, the respectively. tendency of AAM was volatile, and tornadoes occurred on days when AAM tendency was increasing and decreasing. In MAM 1982, there were periods when AAM was 3.2. Role of GWO and MJO in the Anomalous Seasons. anomalously low, which resulted in a lower seasonal mean of 2 −2 Previous studies illustrate that tornado activity is heightened 0.1 kg·m ·s (Figure 3(e), Table 1). In this season, numerous during certain phases of the GWO and MJO. Gensini and tornadoes occurred on GWO phase 1–4 in addition to 5–8 Marinaro [21] reported that daily tornado anomalies in days. However, there is not a signiﬁcant diﬀerence in daily spring (March–June) are greatest on GWO phase 1 and 2 tornado counts across the phases of GWO in this season or days when AAM is negative. Moore [22] similarly reported any of the others (Table 2). a tendency for tornado frequency to be greater in MAM AAM is often relatively low during the LN phase of ENSO seasons when GWO phase 2, 3, and 4 days are more [40]. It is, therefore, not surprising that AAM was anomalously common. Moore [22] also showed this to be true in DJF. low throughout most of the inactive LN seasons (Figure 4). 2 −2 Barrett and Gensini [23] reported that tornado days in April +e mean AAM anomaly was −1.5·kg·m ·s in MAM 2000 2 −2 are most common on phase 6 and 8 days of the MJO and less and −0.9 kg·m ·s in DJF 1985 (Table 1). AAM ﬂuctuated common on phase 3, 4, and 7 days. +ey also reported that between negative and positive anomalies in MAM 1985 and 2 −2 tornado days are most common with phases 5 and 8 and less DJF 1986, which led to higher mean values of 0.1 kg·m ·s in common with phases 2 and 3 in May. +ompson and each season (Figures 4(c) and 4(e); Table 2). Similar to the Roundy [20] reported that violent tornado outbreaks in active EN seasons, there is not a signiﬁcant diﬀerence in daily MAM are most common on MJO phase 2 days and least tornado counts across the GWO phases in any of the inactive common on phase 8 days. GWO and MJO vary on sub- LN seasons. Also, similar to the tornadoes in the active EN seasonal timescales. +ey are, therefore, capable of modu- seasons, those in the inactive LN seasons occurred during lating tornado activity within a given season and may periods of increasing and decreasing AAM (Figure 4). provide insight into some of the subseasonal periods of MJO varied more than GWO throughout the anomalous suppressed and heightened tornado activity during these seasons (Figures 3 and 4). +e progression from phase anomalous seasons (as seen in Figure 2). 1 through 8 is apparent, with multiple oscillations in most Advances in Meteorology 7 Day of Daily Daily Day of Inactive LN Inactive LN season tornado tornado season GWO MJO count count 0 0 4 4 76 76 1 1 75 75 2 2 2 2 8 5 8 5 3 3 50 50 0 0 1 4 1 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 02 4 dAAM/dt RMM1 (a) (b) Day of Daily Day of Daily season tornado season tornado count count 0.0 0.0 4 4 76 76 2.5 2.5 75 75 5.0 5.0 2 2 8 5 8 5 7.5 7.5 50 50 0 0 1 4 1 4 −2 −2 25 25 23 2 −4 −4 −4 −2 02 4 −4 −2 02 4 dAAM/dt RMM1 (c) (d) Day of Daily Day of Daily season tornado season tornado count count 0 0 4 4 76 76 5 5 75 75 10 10 2 2 8 5 8 5 15 15 20 20 50 50 0 0 1 4 1 4 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 02 4 dAAM/dt RMM1 (e) (f) Day of Daily Day of Daily season tornado season tornado count count 0 0 4 4 76 76 5 5 75 75 10 10 2 2 8 5 8 5 15 15 50 20 50 20 0 0 1 4 1 4 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 024 dAAM/dt RMM1 (g) (h) Figure 4: Phase space diagrams for the GWO (a, c, e, and g) and MJO (b, d, f, and h) during the two LN DJFs and MAMs with the fewest tornadoes. MAM 2000 MAM 1985 DJF 1986 DJF 1983 AAM AAM AAM AAM MAM 2000 MAM 1985 DJF 1986 DJF 1983 RMM2 RMM2 RMM2 RMM2 1540 8 Advances in Meteorology EN DJF 1983 EN DJF 2016 300 mb geopotential height and anomaly 300 mb geopotential height and anomaly 200 200 –200 –200 Tornado touchdowns (a) (b) EN DJF 1983 EN DJF 2016 850 mb heights and speciﬁc humidity anomaly 850 mb heights and speciﬁc humidity anomaly –2 –2 (c) (d) EN DJF 1983 EN DJF 2016 Convective available potential energy anomaly Convective available potential energy anomaly –800 –800 (e) (f) Figure 5: (a, b) 300 mb geopotential heights (contours (m)) and anomalies (color (m)), (c, d) 850 mb geopotential heights (contours (m)) −1 −1 and speci�c humidity anomalies (color (g·kg )), and (e, f) CAPE anomalies (J·kg ) for the two anomalously active EN DJFs. seasons. Tornadoes concentrate on certain MJO phase days phase 6—tornadoes occurred on 10 of the 19 (53%) phase 6 more so than with GWO phases, which led to signi�cant days. In the two EN MAMs, the mean and mean ranks of the tornado counts are greatest with phase 4 (Table 2). Še dižerences in the mean number of tornadoes per day across the phases. In the two active EN DJFs, for example, the mean statistical tests indicated that the mean rank of phase 4 is and mean ranks of the tornado counts were greatest with signi�cantly greater than that of phase 1 (see the subscript phases 1 and 6 of the MJO (Table 2). A Kruskal–Wallis test below Table 2). Še percentage of days with tornadoes was and subsequent post hoc comparisons indicate that the mean also greatest with phase 4 (23 of 31 (74%) phase 4 days had rank of phase 6 is signi�cantly greater than the mean ranks tornadoes). Še percentage of days with tornadoes was also of phases 4, 7, and 8; remaining comparisons yielded in- high with phases 7 and 8 (69% and 68%, resp.). Šere were signi�cant dižerences (see the subscript below Table 2). Še not any signi�cant dižerences in daily tornado counts across percentage of days with tornadoes was also greatest with the phases of MJO in the inactive LN seasons (Table 2). 1560 1500 Advances in Meteorology 9 EN MAM 1982 EN MAM 1983 300 mb geopotential height and anomaly 300 mb geopotential height and anomaly 200 200 –200 –200 Tornado touchdown (a) (b) EN MAM 1982 EN MAM 1983 850 mb heights and speciﬁc humidity anomaly 850 mb heights and speciﬁc humidity anomaly 2 2 –2 –2 (c) (d) EN MAM 1982 EN MAM 1983 Convective available potential energy anomaly Convective available potential energy anomaly 800 800 –800 –800 (e) (f) Figure 6: (a, b) 300 mb geopotential heights (contours (m)) and anomalies (color (m)), (c, d) 850 mb geopotential heights (contours (m)) −1 −1 and speci�c humidity anomalies (color (g·kg )), and (e, f) CAPE anomalies (J·kg ) for the two anomalously active EN MAMs. 3.3. Atmospheric Composites of the Anomalous Seasons. over the western and central US, respectively, in both of Še seasonal composites of 300 mb geopotential height, the active DJFs. Anomalously high heights were also pres- 850 mb geopotential height and speci�c humidity, and ent during the MAM 1982 seasons, but only over the CAPE are shown in Figures 5–8. Anomalously high geo- southern and eastern US (Figure 6(a)). Low height anom- potential heights at 300 mb were present across most of the alies were present over the contiguous US during MAM 1983 contiguous US in DJF 2016 and across the north central and (Figure 6(b)), which is consistent with the EN composite northeast US in DJF 1983 (Figures 5(a) and 5(b)). Še reported by Allen et al. [12]. Še patterns seen in the presence of higher than normal heights is more similar to the composites of low-level moisture are inconsistent across the LN composite reported by Allen et al. [12] rather than their active seasons. For example, anomalously high speci�c EN composite. Upper-level ridges and troughs were present humidity was present in the southeastern US during MAM 1520 1540 10 Advances in Meteorology LN DJF 1985 LN DJF 1986 300 mb geopotential height and anomaly 300 mb geopotential height and anomaly 200 200 –200 –200 Tornado touchdowns (a) (b) LN DJF 1985 LN DJF 1986 850 mb heights and speciﬁc humidity anomaly 850 mb heights and speciﬁc humidity anomaly 2 2 –2 –2 (c) (d) LN DJF 1985 LN DJF 1986 Convective available potential energy anomaly Convective available potential energy anomaly 800 800 –800 –800 (e) (f) Figure 7: (a, b) 300 mb geopotential heights (contours (m)) and anomalies (color (m)), (c, d) 850 mb geopotential heights (contours (m)) −1 −1 and speciﬁc humidity anomalies (color (g·kg )), and (e, f) CAPE anomalies (J·kg ) for the two anomalously inactive LN DJFs. 1982, but the humidity in this region was anomalously low +e patterns of 300 mb geopotential height varied be- during MAM 1983 (Figures 6(c) and 6(d)). +e humidity tween the two LN DJFs (Figures 7(a) and 7(b)). A dipole was also anomalously low in southeast Texas and Louisiana pattern was present in DJF 1985, with anomalously low heights spanning the northern Great Plains southwestward during both DJF seasons, but was higher to the north and east where most of the tornadoes occurred (Figures 5(c) and to the Southwest and high heights across the Paciﬁc 5(d)). CAPE was near normal or anomalously high over Northwest and Southeast US. +e US was split in DJF 1986, most of the eastern US during the DJF 1983, DJF 2016, and with anomalously high heights across the western region and MAM 1982 active EN seasons (Figures 5(e), 5(f) , and 6(e)). low heights across the eastern. +e low-level humidity Similar to some of the patterns of 300 mb geopotential anomaly patterns also varied between the DJFs (Figures 7(c) height, these patterns of elevated CAPE are more similar to and 7(d)). +e upper-level height and low-level humidity the CAPE composites shown by Allen et al. [12] in asso- patterns were more similar in the two inactive LN ciation with the LN phase rather than the EN phase. MAMs—near normal or anomalously high heights most of 1500 1540 Advances in Meteorology 11 LN MAM 1985 LN MAM 2000 300 mb geopotential height and anomaly 300 mb geopotential height and anomaly 200 200 –200 –200 Tornado touchdown (a) (b) LN MAM 1985 LN MAM 2000 850 mb heights and speciﬁc humidity anomaly 850 mb heights and speciﬁc humidity anomaly 2 2 –2 –2 (c) (d) LN MAM 1985 LN MAM 2000 Convective available potential energy anomaly Convective available potential energy anomaly 800 800 –800 –800 (e) (f) Figure 8: (a, b) 300 mb geopotential heights (contours (m)) and anomalies (color (m)), (c, d) 850 mb geopotential heights (contours (m)) −1 −1 and speci�c humidity anomalies (color (g·kg )), and (e, f) CAPE anomalies (J·kg ) for the two anomalously active LN MAMs. the US and anomalously humid conditions across most of its unexpected and do not capture the environment on the days eastern half (Figures 8(a)–8(d)). Anomalously low CAPE was when many tornadoes occurred. For example, the negative CAPE anomalies across the eastern US in MAM 1983 are present across the Southeast US in DJF 1985 when the fewest tornadoes occurred (Figure 7(e)). Anomalously high CAPE was unexpected, given that anomalously many tornadoes oc- present across the portions of the US with the most tornadoes in curred in this season (Figure 6(f)). Examination of CAPE on the remaining seasons (Figures 7(f), 8(e), and 8(f)). 1-2 May 1983 and 18–20 May 1983, when 39 and 51 tornadoes Some of the seasonal composites illustrate reasonable occurred, respectively, illustrates that elevated CAPE spread patterns. Anomalously high CAPE is shown in both of the from the Gulf of Mexico northward into the eastern US active EN DJFs, for example, in Figures 5(e) and 5(f). Most of (Figure 9). Še negative anomalies of low-level humidity the eastern US was also anomalously humid in these seasons across the south central US in DJF 2016 also did not represent (Figures 5(c) and 5(d)). Other seasonal composites are well the environments that were present when many of the 1540 1560 12 Advances in Meteorology EN 1-2 May 1983 EN 18–20 May 1983 Convective available potential energy Convective available potential energy 2400 2400 0 0 Tornado touchdown (a) (b) −1 Figure 9: CAPE (J·kg ) on (a) 1-2 May 1983 and (b) 18–20 May 1983, when 39 and 51 tornadoes occurred, respectively. EN 23 December 2015 EN 23-24 February 2016 850 mb heights and speciﬁc humidity 850 mb heights and speciﬁc humidity 11.5 11.5 0 0 Tornado touchdown (a) (b) −1 Figure 10: 850 mb geopotential heights (contours (m)) and speci�c humidity (color (g·kg )) on (a) 23 December 2015 and (b) 23-24 February 2016, when 18 and 36 tornadoes occurred, respectively. tornadoes occurred (Figure 5(d)). Closed shortwave troughs illustrate that large numbers of tornadoes are possible even during EN seasons when such large numbers might be un- with upstream southerly moisture advection into the US were present on 23 December 2015 and 23-24 February 2016, when expected. Še other seasons were far less anomalous. 18 and 36 tornadoes occurred, respectively (Figure 10). Šese GWO does not explain the anomalous nature of the cases illustrate that anomalously active seasons can have seasons. Climatological studies show that tornado activity in seemingly unfavorable seasonal composites. DJF and MAM tends to be heightened during GWO phases 1–4 when AAM is anomalously low [21, 22], but nearly all of the tornadoes in the active EN seasons occurred on GWO 4. Conclusions phase 5–8 days when AAM was anomalously high. Fur- Previous studies have established a relationship between thermore, daily tornado count did not signi�cantly vary across the phases of GWO in any of the seasons. Še tornado and ENSO in DJF and MAM, generally with more tornadoes during the LN phase and fewer during the EN concentration of tornadoes on high AAM days (GWO phase 5–8 days) during EN seasons, as suggested here, would phase [12–14, 17–19]. Šis study was focused on the seasons that do not �t this relationship—EN seasons with many undoubtedly weaken the statistical relationship between tornadoes and LN seasons with few tornadoes. Speci�cally, tornadoes and GWO that was reported by others, whereby the two EN DJFs and MAMs with the most tornadoes and the tornadoes are most common on GWO phase 1–4 days two LN DJFs and MAMs with the fewest tornadoes were when AAM is anomalously low [21, 22]. Analyzing the described and analyzed. Še most anomalous seasons were tornado-GWO relationship by ENSO phase might amend DJF 1983 and 2016, both of which were active EN seasons this relationship and provide additional insight into the with 133 E(F)1+ tornadoes. Šey were, therefore, 183% interactions between tornado activity, ENSO, and GWO. (359%) above the mean (median) EN DJF. Šese seasons Another consideration is that the GWO data used in this 1360 Advances in Meteorology 13 [3] K. M. Simmons and D. Sutter, Economic and Societal Impacts study are based on globally integrated AAM. As noted by of Tornadoes, American Meteorological Society, Boston, MA, Gensini and Allen [41], this may confound the results. USA, 2011. +erefore, it would also be worthwhile to reassess the re- [4] K. R. Knupp, T. A. Murphy, T. A. Coleman et al., “Meteo- lationship between tornado activity and GWO with AAM rological overview of the devastating 27 April 2011 tornado calculated on a hemispheric or latitudinal basis. outbreak,” Bulletin of the American Meteorological Society, Unlike with GWO, daily tornado count did signiﬁcantly vol. 95, no. 7, pp. 1041–1062, 2014. vary across the phases of MJO, but only in the active EN [5] National Weather Service, “Natural hazards statistics,” Jan- seasons. +e mean and mean rank of the tornado counts was uary 2018, http://www.nws.noaa.gov/om/hazstats.shtml. greatest with phase 4 in MAM. Clustering of tornadoes on [6] H. E. Brooks, G. W. Carbin, and P. T. Marsh, “Increased MJO phase 4 days in MAM is unique because climatological variability of tornado occurrence in the United States,” Sci- studies [20, 23] that aggregated data over a larger number of ence, vol. 346, no. 6207, pp. 349–352, 2014. seasons reported heightened tornado activity with phase 2, 5, [7] M. K. 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Publisher: Hindawi Publishing Corporation
Copyright: Copyright © 2018 Todd W. Moore et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ISSN: 1687-9309
eISSN: 1687-9317
DOI: 10.1155/2018/3612567
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Abstract

Hindawi Advances in Meteorology Volume 2018, Article ID 3612567, 14 pages https://doi.org/10.1155/2018/3612567 Research Article An Analysis of Anomalous Winter and Spring Tornado Frequency by Phase of the El Niño/Southern Oscillation, the Global Wind Oscillation, and the Madden-Julian Oscillation Todd W. Moore , Jennifer M. St. Clair , and Tiﬀany A. DeBoer Department of Geography and Environmental Planning, Towson University, Towson, MD 21252, USA Correspondence should be addressed to Todd W. Moore; tmoore@towson.edu Received 25 February 2018; Revised 29 May 2018; Accepted 7 June 2018; Published 16 July 2018 Academic Editor: Anthony R. Lupo Copyright © 2018 Todd W. Moore et al. +is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Winter and spring tornado activity tends to be heightened during the La Niña phase of the El Niño/Southern Oscillation and suppressed during the El Niño phase. Despite these tendencies, some La Niña seasons have fewer tornadoes than expected and some El Niño seasons have more than expected. To gain insight into such anomalous seasons, the two La Niña winters and springs with the fewest tornadoes and the two El Niño winters and springs with the most tornadoes between 1979 and 2016 are identiﬁed and analyzed in this study. +e relationships between daily tornado count and the Global Wind Oscillation and Madden-Julian Oscillation in these anomalous seasons are also explored. Lastly, seasonal and daily composites of upper-level ﬂow, low-level ﬂow and humidity, and atmospheric instability are generated to describe the environmental conditions in the anomalous seasons. +e results of this study highlight the potential for large numbers of tornadoes to occur in a season if favorable conditions emerge in association with individual synoptic-scale events, even during phases of the El Niño/Southern Oscillation, Global Wind Os- cillation, and Madden-Julian Oscillation that seem to be unfavorable for tornadoes. +ey also highlight the potential for anomalously few tornadoes in a season even when the oscillations are in favorable phases. during the neutral phase (N) of ENSO, followed by the LN 1. Introduction then EN phases. +e most recent studies report that tornado More tornadoes occur in the United States (US) per year activity is heightened during the LN phase [12–14, 17–19]. than in any other country [1], and these tornadoes are ca- Recent studies also illustrate that the seasons with the most pable of producing incredible economic and human loss tornadoes tend to be classiﬁed as LN. Lee et al. [17], for [2–5]. Despite their relatively common occurrence, there is example, analyzed the number of tornadoes rated 3 or higher notable intra- and interannual variability in the number of on the Fujita or Enhanced Fujita damage scales (hereafter US tornadoes [6–10]. Due to the danger and variability of referred to as E(F)) and reported that the ﬁve most extreme tornado outbreak years were characterized as persistent LN tornado activity, there is a need to improve seasonal outlook capabilities. One approach is to identify relationships be- events or LN events that were transitioning to a diﬀerent tween seasonal tornado count and climate oscillations, such phase. Allen et al. [12] similarly reported that most of the as the El Niño/Southern Oscillation (ENSO) (e.g., [11–19]). seasons with a tornado count >100% of climatology oc- Numerous studies over the past couple of decades have curred during the LN phase, whereas most that were <75% analyzed the relationship between tornadoes and ENSO. An of climatology occurred during the EN phase. early study by Monfredo [15] reported that strong and vi- +e relatively consistent reporting of heightened tornado olent tornadoes were more common during the La Niña activity with the LN phase of ENSO elicits the possibility of (LN) phase of ENSO and less common during the El Niño predicting the likelihood of below- or above-normal tornado (EN) phase. Cook and Schaefer [16] later reported that activity based on ENSO conditions. Elsner et al. [18] developed winter tornado outbreaks were stronger and more frequent a statistical model to evaluate variability in tornado activity in 2 Advances in Meteorology which ENSO was the most important predictor. +eir model previous studies illustrate that tornado counts and ENSO are also indicated that tornado activity was heightened in the related in these seasons [12–14, 17–19]. Multiple indices represent ENSO conditions. Some Midwest and Southeast regions of the US during LN and in the Great Plains region during EN. Allen et al. [12] and Lepore represent mostly the oceanic component of ENSO (e.g., the et al. [13] developed extended logistic regression models using Niño 1.2, 3, 3.4, and 4 regions and the Oceanic Niño Index ENSO to predict the likelihood of below-normal, normal, or (ONI)), some represent mostly the atmospheric component above-normal tornado activity in spring. +eir models simi- (e.g., the Southern Oscillation Index (SOI)), and some larly showed that tornado activity is increased during the LN combine both (e.g., the Multivariate ENSO Index (MEI) and phase, particularly in the south central US. Bivariate ENSO Index (BEST)). +e classiﬁcation of seasons Anomalous seasons occur. Some LN seasons have fewer as LN or EN will likely change in some cases, depending on than expected tornadoes and some EN seasons have more than the index. In this study, ONI data were obtained from the expected. Lee et al. [19], for example, noted that an anoma- Climate Prediction Center [28] to represent ENSO condi- lously large number of tornadoes occurred in the 2015-2016 EN tions, which is consistent with recent eﬀorts to predict the likelihood of an active or inactive season based on the state of winter. Elsner et al. [18] also noted the occurrence of anom- alous seasons and attributed them to additional unknown ENSO [12, 13]. ONI is also one of the commonly used factors. While previous studies have noted these anomalous indices by the Climate Prediction Center to diagnose ENSO seasons, none have provided detailed descriptions and analyses conditions and to place current events into historical per- of them. More research, including work focusing on the spective. It is given as anomalies of 3-month running means ° ° anomalous seasons, is needed to improve our understanding of of sea surface temperature in the Niño 3.4 region (5 N–5 S, ° ° the tornado-ENSO link and to reﬁne statistical models. 120 W–170 W). Series of ONI for DJF and MAM were A case study approach was taken here to describe and extracted and merged with the seasonal tornado counts. analyze the two EN winters and springs with the most Once merged, seasons were classiﬁed as LN if ONI ≤−0.5 tornadoes, and the two LN winters and springs with the and EN if ONI ≥0.5. Seasons with −0.5< ONI< 0.5 were fewest tornadoes. +e states of the Madden-Julian Oscilla- classiﬁed as neutral (N). +e two most active EN DJFs and tion (MJO) and Global Wind Oscillation (GWO) are of MAMs (i.e., with the most tornadoes) and two least active particular interest here. Both of these oscillations vary over LN DJFs and MAMs (i.e., with the fewest tornadoes) were subseasonal timescales (i.e., shorter than the timescale over extracted, yielding eight seasons as the focus of this study. which ENSO varies) and have been linked to variations in Climate oscillations other than ENSO have been linked tornado activity [20–23]. Seasonal and daily composites are to tornado activity. +ose that vary over subseasonal also generated to document the synoptic patterns that ac- timescales may provide insight into the intraseasonal dis- companied the anomalous seasons. tribution of tornadoes in the anomalous seasons. Two such +e speciﬁc objectives of this study are to oscillations are the MJO and GWO. +e MJO is a tropical wave of enhanced convection that propagates the globe from (1) identify and describe the two EN winters and springs −1 west to east at approximately 5 m·s [29]. It is linked to with the most tornadoes and the two LN winters and variability in tornado activity in the midlatitudes through springs with the fewest tornadoes; changes in global relative atmospheric angular momentum (2) analyze GWO and MJO activity during these (AAM), which arise from tropical convective forcing and anomalous seasons; teleconnected Rossby wave propagation in the midlatitudes (3) document the synoptic patterns that accompany [20, 23, 30–32]. Other factors, such as friction and mountain torque, aﬀect AAM in addition to tropical convection these anomalous seasons. [30–32]. +e GWO combines these factors and, therefore, provides a comprehensive representation of AAM and 2. Data and Methods midlatitude circulation patterns [31, 32]. Daily GWO data Tornado data were taken from the Storm Prediction were obtained from the Earth System Research Laboratory’s Center’s Severe Weather Database (speciﬁcally, the GWO dataset [33]. +e attributes of the GWO used in this 1950–2016_actual_tornadoes.csv ﬁle) [24]. +is dataset was study are AAM anomalies, AAM time tendency, and GWO a subset to the 18,251 E(F)1+ tornadoes that occurred over phase. +e relationship between AAM anomalies and AAM the period December 1978–November 2016 in the con- time tendency determines the phase of GWO, which run tiguous US. +e time series of E(F)1+ tornadoes is less from 1 to 8. Phases 2 and 3 represent anomalously low AAM, inﬂuenced by detection and reporting changes over time whereas phases 6 and 7 represent anomalously high AAM. Phases 1, 4, 5, and 8 represent transition states. Daily MJO than is the E(F)0+ series, which includes a notable upward trend [25, 26]. +e beginning of the period of record is data were obtained from the Australian Bureau of Mete- consistent with recent studies [12, 13] and corresponds to the orology’s Real-Time Multivariate (RMM) MJO dataset ﬁrst year of the North American Regional Reanalysis (NARR) [34, 35]. +e attributes used here are the ﬁrst two principal product [27], which is used to generate seasonal and daily components of empirical orthogonal functions that consist atmospheric composites. Seasonal tornado counts were of a meridional average of the 200 mb zonal wind, the generated for winter (December of previous year, January, 850 mb zonal wind, and outgoing longwave radiation be- ° ° and February (DJF)) and spring (March, April, and May tween 15 N and 15 S (referred to as RMM1 and RMM2) and (MAM)). DJF and MAM are the focus of this study because the MJO phase. Similar to GWO, the MJO index has eight Advances in Meteorology 3 ρ = –0.20 ρ = –0.19 150 p = 0.23 p = 0.25 200 4 0 0 –3 –2 –1 0 1 2 3 –3 –2 –1 0 1 2 3 Oceanic Niño Index (ONI) Oceanic Niño Index (ONI) Active ENs Inactive LNs Active ENs Inactive LNs 3. MAM 1983, 251 3. MAM 2000, 178 1. DJF 1983, 133 1. DJF 1985, 9 tornadoes tornadoes tornadoes tornadoes 4. MAM 1982, 340 4. MAM 1985, 179 2. DJF 2016, 133 2. DJF 1986, 21 tornadoes tornadoes tornadoes tornadoes (a) (b) Figure 1: Number of E(F)1+ tornadoes in (a) DJF and (b) MAM and the concurrent Oceanic Niño Index (ONI). Vertical dashed blue and red lines are placed at ONI −0.5 and 0.5 and demarcate LN and EN, respectively. Še blue and red diamonds represent the two most inactive and active LNs and ENs, respectively. Spearman’s ρ rank correlation coe¦cients and p values are provided in the top-right corners of the graphs. phases, but they represent the location of enhanced tropical Table 1: Minimum, mean, and maximum values of global at- 2 −2 mospheric angular momentum (AAM; kg·m ·s ) anomalies convection. Phase 1 of the MJO indicates that the enhanced during the active EN and inactive LN seasons. convection is located near eastern Africa and the subsequent phases represent its eastward progression. Seasons Minimum Mean Maximum AAM anomalies, AAM time tendency, RMM1, and Active EN seasons RMM2 were used to construct GWO and MJO phase space DJF 1983 1.3 2.6 4.4 diagrams for each of the anomalous seasons. Daily tornado DJF 2016 0.4 1.9 4.0 counts were generated (where a day is de�ned as 0000–2359 MAM 1982 −1.3 0.1 1.4 UTC) and plotted in the phase space diagrams along with the MAM 1983 0.1 1.8 3.5 progression of GWO and MJO. Daily tornado counts were Inactive LN seasons also assessed across the phases of MJO and GWO, and DJF 1985 −2.0 −0.9 0.1 Kruskal–Wallis tests were used to determine if the mean rank DJF 1986 −1.1 0.1 1.3 MAM 1985 −1.0 0.1 1.1 of the tornado counts signi�cantly varied across the phases of MAM 2000 −2.7 −1.5 −0.4 the oscillations. Šis nonparametric test was chosen because of the skewed nature of daily tornado counts. Še synoptic patterns associated with the anomalous in DJF 2016). Še seasonal anomaly composites represent the seasons were characterized using gridded composites from dižerence between the mean composites and the 1981–2010 NARR [27]. Še following variables were chosen to be com- climatology. Daily composites are averages of the 3 hr NARR parable to composites in previous research (e.g., [12, 20, 21]): data. All composites were generated with NOAA’s Earth upper-level •ow was characterized using 300 mb geopotential System Research Laboratory’s online plotting tool [37, 38]. heights; low-level •ow was characterized using 850 mb geo- Once generated, the Network Common Data Form (NetCDF) potential heights; low-level moisture was characterized using of the composites was imported into ArcMAP [39] for plotting 850 mb speci�c humidity; and atmospheric stability was and display. characterized using surface-based convective available poten- tial energy (CAPE). Surface-based CAPE was used because 3. Results and Discussion Gensini et al. [36] illustrate that surface-based parcels are more accurate than 100 mb mixed layer parcels, largely due to errors 3.1. Identication and Description of the Anomalous Seasons. in low-level moisture �elds. Še seasonal mean composites are Še relationships between the number of E(F)1+ tornadoes averages of the variables for the dates under consideration and ONI in DJF and MAM are depicted in Figure 1. Še (e.g., mean 300 mb geopotential height in DJF 2016 is gen- negative relationship reported by others, whereby tornado erated by averaging the 300 mb geopotential height for the days frequency tends to be greater during the LN phase and lesser Tornado count Tornado count 4 Advances in Meteorology 35 35 Active EN: DJF 2016, 133 tornadoes Active EN: DJF 1983, 133 tornadoes 30 30 25 25 15 15 10 10 5 5 0 0 1 112131415161718191 1 112131415161718191 Day of season Day of season (a) (b) 35 35 Active EN: MAM 1982, 340 tornadoes Active EN: MAM 1983, 251 tornadoes 30 30 25 25 20 20 15 15 10 10 5 5 1 112131415161718191 1 112131415161718191 Day of season Day of season (c) (d) 35 35 Inactive LN: DJF 1986, 21 tornadoes Inactive LN: DJF 1985, 9 tornadoes 30 30 1 11 21 31 41 51 61 71 81 91 1 112131415161718191 Day of season Day of season (e) (f) 35 35 Inactive LN: MAM 2000, 178 tornadoes Inactive LN: MAM 1985, 179 tornadoes 30 30 25 25 20 20 15 15 10 10 5 5 0 0 1 112131415161718191 1 112131415161718191 Day of season Day of season (g) (h) Figure 2: Daily tornado count of the two EN DJFs (a, b) and MAMs (c, d) with the most tornadoes and of the two LN DJFs (e, f) and MAMs (g, h) with the fewest tornadoes. during the EN phase [12–14, 17–19], is visually apparent, but tornadoes in EN DJFs and 359% greater than the median. +e the correlation is not statistically signiﬁcant in this subset. two most active EN MAM seasons were 1982 and 1983, when 340 and 251 E(F)1+ tornadoes occurred, respectively. MAM +e central tendencies in Table 1 also illustrate the negative association by showing that the mean and median tornado 1982 had approximately 79% more E(F)1+ tornadoes than the counts of the LN seasons exceed those of the EN seasons. mean EN MAMs (76% greater than the median). MAM 1983 +e two most inactive LN DJF and MAM seasons are exceeded the mean and median counts of EN MAM seasons marked with blue diamonds in Figure 1, and the two most by approximately 30%. active ENs are marked with red diamonds. +e two most active +e two DJF seasons with the fewest E(F)1+ tornadoes EN DJFs, which occurred in 2016 and 1983, are especially were 1985 and 1986, with 9 and 21 tornadoes, respectively. noticeable. +ere were 133 E(F)1+ tornadoes in both seasons. DJF 1985 had approximately 80% fewer tornadoes than the +ese counts are 183% greater than the mean number of mean and median LN DJF, and DJF 1986 had 64% and 54% Tornado count Tornado count Tornado count Tornado count Tornado count Tornado count Tornado count Tornado count Advances in Meteorology 5 Active EN Day of Daily Active EN Day of Daily season tornado season tornado GWO MJO count count 4 0 4 0 76 76 75 75 5 5 2 2 10 10 8 5 8 5 15 15 50 50 0 0 20 20 1 4 1 4 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 02 4 dAAM/dt RMM1 (a) (b) Day of Daily Day of Daily season tornado season tornado count count 4 4 0 0 7 76 75 75 5 5 2 2 10 10 8 5 8 5 15 15 50 50 0 0 20 20 1 4 1 4 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 02 4 dAAM/dt RMM1 (c) (d) Day of Daily Day of Daily season tornado season tornado count count 4 0 4 0 76 76 75 75 10 10 15 15 8 5 8 5 20 20 50 50 0 0 1 4 1 4 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 02 4 dAAM/dt RMM1 (e) (f) Day of Daily Day of Daily season tornado season tornado count count 4 0 4 0 76 76 75 75 5 5 2 2 10 10 8 5 8 5 15 15 50 50 0 0 20 20 1 4 1 4 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 024 dAAM/dt RMM1 (g) (h) Figure 3: Phase space diagrams for the GWO (a, c, e, and g) and MJO (b, d, f, and h) during the two EN DJFs and MAMs with the most tornadoes. MAM 1983 MAM 1982 DJF 2016 DJF 1983 AAM AAM AAM AAM MAM 1983 MAM 1982 DJF 2016 DJF 1983 RMM2 RMM2 RMM2 RMM2 6 Advances in Meteorology Table 2: Mean (mean rank) of daily tornado count by GWO and MJO phase. GWO phase 1 2 3 4 5 6 7 8 Active EN DJF 0.0 (70.0) — — — — 1.5 (83.3) 1.3 (92.8) 2.3 (96.3) Active EN MAM 3.8 (100.5) 2.9 (86.8) 7.5 (106.9) 3.2 (81.1) 3.6 (83.9) 3.3 (89.5) 2.7 (98.3) 2.8 (88.2) Inactive LN DJF 0.1 (89.4) 0.1 (90.5) 0.1 (86.4) 0.1 (87.2) 0.0 (84.0) 0.5 (100.5) 0.8 (106.5) 0.1 (90.2) Inactive LN MAM 2.3 (115.4) 3.6 (100.0) 1.4 (88.8) 1.0 (79.8) 2.1 (93.6) 1.8 (111.0) 2.6 (79.8) 1.8 (84.8) MJO phase Active EN DJF 3.5 (113.2) 0.4 (90.0) 1.9 (101.4) 0.7 (81.2) 1.1 (80.6) 4.0 (119.9) 1.3 (80.5) 0.3 (80.6) Active EN MAM 1.1 (58.0) 2.5 (85.4) 2.6 (87.3) 5.9 (116.1) 1.2 (72.5) 1.5 (83.3) 4.5 (101.9) 3.1 (97.8) Inactive LN DJF 0.4 (100.3) 0.0 (84.0) 0.1 (88.0) <0.1 (87.3) 0.1 (91.4) 0.2 (91.7) 0.5 (92.7) 0.1 (87.9) Inactive LN MAM 2.0 (96.6) 2.2 (87.3) 2.7 (92.3) 0.5 (70.1) 2.3 (103.0) 2.6 (96.7) 2.0 (95.6) 1.7 (94.5) a 2 A Kruskal–Wallis test indicates that the mean rank of tornado count varies across the phases of MJO (X � 23.9; df � 7; p � 0.001). Post hoc comparisons show that the mean rank of tornado count is greater in phase 6 than in phases 4, 7, and 8. A Kruskal–Wallis test indicates that the mean rank of tornado count varies across the phases of MJO (X �17.9; df � 7; p � 0.012). Post hoc comparisons show that the mean rank of tornado count is greater in phase 4 than in phase 1. fewer than the mean and median counts, respectively. +e AAM anomalies were positive throughout both of the least active LN MAM seasons were 1985 when 179 E(F)1+ anomalously active EN DJFs (Figures 3(a) and 3(c)). +e 2 −2 mean AAM anomaly was 2.6 kg·m tornadoes occurred and 2000 when 178 E(F)1+ occurred. ·s in DJF 1983 and 2 −2 +ese seasons had approximately 43% fewer tornadoes than 1.9 kg·m ·s in DJF 2016 (Table 1). AAM anomalies were the mean LN MAM tornado count and approximately 24% also positive throughout the active EN MAM 1983 season, 2 −2 fewer than the median count. when the mean was 1.8 kg·m ·s (Figure 3(g), Table 1). As with most seasons, the tornadoes in the anomalous Tornadoes in these seasons, therefore, occurred on GWO seasons were not uniformly distributed (Figure 2). In DJF phase 5–8 days when AAM was anomalously high and the 2016, for example, 50% of the tornadoes occurred on 4 days, amplitude was most often >1. Positive anomalies were each with 10+ tornadoes. +ere were 6 days in DJF 1983 expected in these seasons because AAM tends to be with 10+ tornadoes that account for 68% of the tornadoes heightened during the EN phase of ENSO [40], but it is in that season. +e tornadoes in the anomalous MAM unexpected, based on previous studies linking enhanced seasons were spread over a larger number of days, but there tornado activity to anomalously low AAM [21, 22], that all were still clusters of activity. Twelve days in MAM 1982 and of the days with tornadoes in DJF 1983, DJF 2016, and eight in MAM 1983 had 10+ tornadoes. +e tornadoes on MAM 1983 had anomalously high AAM. Despite the these days account for 60% and 52% of the seasonal count, positive AAM anomalies throughout these seasons, the respectively. tendency of AAM was volatile, and tornadoes occurred on days when AAM tendency was increasing and decreasing. In MAM 1982, there were periods when AAM was 3.2. Role of GWO and MJO in the Anomalous Seasons. anomalously low, which resulted in a lower seasonal mean of 2 −2 Previous studies illustrate that tornado activity is heightened 0.1 kg·m ·s (Figure 3(e), Table 1). In this season, numerous during certain phases of the GWO and MJO. Gensini and tornadoes occurred on GWO phase 1–4 in addition to 5–8 Marinaro [21] reported that daily tornado anomalies in days. However, there is not a signiﬁcant diﬀerence in daily spring (March–June) are greatest on GWO phase 1 and 2 tornado counts across the phases of GWO in this season or days when AAM is negative. Moore [22] similarly reported any of the others (Table 2). a tendency for tornado frequency to be greater in MAM AAM is often relatively low during the LN phase of ENSO seasons when GWO phase 2, 3, and 4 days are more [40]. It is, therefore, not surprising that AAM was anomalously common. Moore [22] also showed this to be true in DJF. low throughout most of the inactive LN seasons (Figure 4). 2 −2 Barrett and Gensini [23] reported that tornado days in April +e mean AAM anomaly was −1.5·kg·m ·s in MAM 2000 2 −2 are most common on phase 6 and 8 days of the MJO and less and −0.9 kg·m ·s in DJF 1985 (Table 1). AAM ﬂuctuated common on phase 3, 4, and 7 days. +ey also reported that between negative and positive anomalies in MAM 1985 and 2 −2 tornado days are most common with phases 5 and 8 and less DJF 1986, which led to higher mean values of 0.1 kg·m ·s in common with phases 2 and 3 in May. +ompson and each season (Figures 4(c) and 4(e); Table 2). Similar to the Roundy [20] reported that violent tornado outbreaks in active EN seasons, there is not a signiﬁcant diﬀerence in daily MAM are most common on MJO phase 2 days and least tornado counts across the GWO phases in any of the inactive common on phase 8 days. GWO and MJO vary on sub- LN seasons. Also, similar to the tornadoes in the active EN seasonal timescales. +ey are, therefore, capable of modu- seasons, those in the inactive LN seasons occurred during lating tornado activity within a given season and may periods of increasing and decreasing AAM (Figure 4). provide insight into some of the subseasonal periods of MJO varied more than GWO throughout the anomalous suppressed and heightened tornado activity during these seasons (Figures 3 and 4). +e progression from phase anomalous seasons (as seen in Figure 2). 1 through 8 is apparent, with multiple oscillations in most Advances in Meteorology 7 Day of Daily Daily Day of Inactive LN Inactive LN season tornado tornado season GWO MJO count count 0 0 4 4 76 76 1 1 75 75 2 2 2 2 8 5 8 5 3 3 50 50 0 0 1 4 1 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 02 4 dAAM/dt RMM1 (a) (b) Day of Daily Day of Daily season tornado season tornado count count 0.0 0.0 4 4 76 76 2.5 2.5 75 75 5.0 5.0 2 2 8 5 8 5 7.5 7.5 50 50 0 0 1 4 1 4 −2 −2 25 25 23 2 −4 −4 −4 −2 02 4 −4 −2 02 4 dAAM/dt RMM1 (c) (d) Day of Daily Day of Daily season tornado season tornado count count 0 0 4 4 76 76 5 5 75 75 10 10 2 2 8 5 8 5 15 15 20 20 50 50 0 0 1 4 1 4 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 02 4 dAAM/dt RMM1 (e) (f) Day of Daily Day of Daily season tornado season tornado count count 0 0 4 4 76 76 5 5 75 75 10 10 2 2 8 5 8 5 15 15 50 20 50 20 0 0 1 4 1 4 −2 −2 25 25 23 23 −4 −4 −4 −2 02 4 −4 −2 024 dAAM/dt RMM1 (g) (h) Figure 4: Phase space diagrams for the GWO (a, c, e, and g) and MJO (b, d, f, and h) during the two LN DJFs and MAMs with the fewest tornadoes. MAM 2000 MAM 1985 DJF 1986 DJF 1983 AAM AAM AAM AAM MAM 2000 MAM 1985 DJF 1986 DJF 1983 RMM2 RMM2 RMM2 RMM2 1540 8 Advances in Meteorology EN DJF 1983 EN DJF 2016 300 mb geopotential height and anomaly 300 mb geopotential height and anomaly 200 200 –200 –200 Tornado touchdowns (a) (b) EN DJF 1983 EN DJF 2016 850 mb heights and speciﬁc humidity anomaly 850 mb heights and speciﬁc humidity anomaly –2 –2 (c) (d) EN DJF 1983 EN DJF 2016 Convective available potential energy anomaly Convective available potential energy anomaly –800 –800 (e) (f) Figure 5: (a, b) 300 mb geopotential heights (contours (m)) and anomalies (color (m)), (c, d) 850 mb geopotential heights (contours (m)) −1 −1 and speci�c humidity anomalies (color (g·kg )), and (e, f) CAPE anomalies (J·kg ) for the two anomalously active EN DJFs. seasons. Tornadoes concentrate on certain MJO phase days phase 6—tornadoes occurred on 10 of the 19 (53%) phase 6 more so than with GWO phases, which led to signi�cant days. In the two EN MAMs, the mean and mean ranks of the tornado counts are greatest with phase 4 (Table 2). Še dižerences in the mean number of tornadoes per day across the phases. In the two active EN DJFs, for example, the mean statistical tests indicated that the mean rank of phase 4 is and mean ranks of the tornado counts were greatest with signi�cantly greater than that of phase 1 (see the subscript phases 1 and 6 of the MJO (Table 2). A Kruskal–Wallis test below Table 2). Še percentage of days with tornadoes was and subsequent post hoc comparisons indicate that the mean also greatest with phase 4 (23 of 31 (74%) phase 4 days had rank of phase 6 is signi�cantly greater than the mean ranks tornadoes). Še percentage of days with tornadoes was also of phases 4, 7, and 8; remaining comparisons yielded in- high with phases 7 and 8 (69% and 68%, resp.). Šere were signi�cant dižerences (see the subscript below Table 2). Še not any signi�cant dižerences in daily tornado counts across percentage of days with tornadoes was also greatest with the phases of MJO in the inactive LN seasons (Table 2). 1560 1500 Advances in Meteorology 9 EN MAM 1982 EN MAM 1983 300 mb geopotential height and anomaly 300 mb geopotential height and anomaly 200 200 –200 –200 Tornado touchdown (a) (b) EN MAM 1982 EN MAM 1983 850 mb heights and speciﬁc humidity anomaly 850 mb heights and speciﬁc humidity anomaly 2 2 –2 –2 (c) (d) EN MAM 1982 EN MAM 1983 Convective available potential energy anomaly Convective available potential energy anomaly 800 800 –800 –800 (e) (f) Figure 6: (a, b) 300 mb geopotential heights (contours (m)) and anomalies (color (m)), (c, d) 850 mb geopotential heights (contours (m)) −1 −1 and speci�c humidity anomalies (color (g·kg )), and (e, f) CAPE anomalies (J·kg ) for the two anomalously active EN MAMs. 3.3. Atmospheric Composites of the Anomalous Seasons. over the western and central US, respectively, in both of Še seasonal composites of 300 mb geopotential height, the active DJFs. Anomalously high heights were also pres- 850 mb geopotential height and speci�c humidity, and ent during the MAM 1982 seasons, but only over the CAPE are shown in Figures 5–8. Anomalously high geo- southern and eastern US (Figure 6(a)). Low height anom- potential heights at 300 mb were present across most of the alies were present over the contiguous US during MAM 1983 contiguous US in DJF 2016 and across the north central and (Figure 6(b)), which is consistent with the EN composite northeast US in DJF 1983 (Figures 5(a) and 5(b)). Še reported by Allen et al. [12]. Še patterns seen in the presence of higher than normal heights is more similar to the composites of low-level moisture are inconsistent across the LN composite reported by Allen et al. [12] rather than their active seasons. For example, anomalously high speci�c EN composite. Upper-level ridges and troughs were present humidity was present in the southeastern US during MAM 1520 1540 10 Advances in Meteorology LN DJF 1985 LN DJF 1986 300 mb geopotential height and anomaly 300 mb geopotential height and anomaly 200 200 –200 –200 Tornado touchdowns (a) (b) LN DJF 1985 LN DJF 1986 850 mb heights and speciﬁc humidity anomaly 850 mb heights and speciﬁc humidity anomaly 2 2 –2 –2 (c) (d) LN DJF 1985 LN DJF 1986 Convective available potential energy anomaly Convective available potential energy anomaly 800 800 –800 –800 (e) (f) Figure 7: (a, b) 300 mb geopotential heights (contours (m)) and anomalies (color (m)), (c, d) 850 mb geopotential heights (contours (m)) −1 −1 and speciﬁc humidity anomalies (color (g·kg )), and (e, f) CAPE anomalies (J·kg ) for the two anomalously inactive LN DJFs. 1982, but the humidity in this region was anomalously low +e patterns of 300 mb geopotential height varied be- during MAM 1983 (Figures 6(c) and 6(d)). +e humidity tween the two LN DJFs (Figures 7(a) and 7(b)). A dipole was also anomalously low in southeast Texas and Louisiana pattern was present in DJF 1985, with anomalously low heights spanning the northern Great Plains southwestward during both DJF seasons, but was higher to the north and east where most of the tornadoes occurred (Figures 5(c) and to the Southwest and high heights across the Paciﬁc 5(d)). CAPE was near normal or anomalously high over Northwest and Southeast US. +e US was split in DJF 1986, most of the eastern US during the DJF 1983, DJF 2016, and with anomalously high heights across the western region and MAM 1982 active EN seasons (Figures 5(e), 5(f) , and 6(e)). low heights across the eastern. +e low-level humidity Similar to some of the patterns of 300 mb geopotential anomaly patterns also varied between the DJFs (Figures 7(c) height, these patterns of elevated CAPE are more similar to and 7(d)). +e upper-level height and low-level humidity the CAPE composites shown by Allen et al. [12] in asso- patterns were more similar in the two inactive LN ciation with the LN phase rather than the EN phase. MAMs—near normal or anomalously high heights most of 1500 1540 Advances in Meteorology 11 LN MAM 1985 LN MAM 2000 300 mb geopotential height and anomaly 300 mb geopotential height and anomaly 200 200 –200 –200 Tornado touchdown (a) (b) LN MAM 1985 LN MAM 2000 850 mb heights and speciﬁc humidity anomaly 850 mb heights and speciﬁc humidity anomaly 2 2 –2 –2 (c) (d) LN MAM 1985 LN MAM 2000 Convective available potential energy anomaly Convective available potential energy anomaly 800 800 –800 –800 (e) (f) Figure 8: (a, b) 300 mb geopotential heights (contours (m)) and anomalies (color (m)), (c, d) 850 mb geopotential heights (contours (m)) −1 −1 and speci�c humidity anomalies (color (g·kg )), and (e, f) CAPE anomalies (J·kg ) for the two anomalously active LN MAMs. the US and anomalously humid conditions across most of its unexpected and do not capture the environment on the days eastern half (Figures 8(a)–8(d)). Anomalously low CAPE was when many tornadoes occurred. For example, the negative CAPE anomalies across the eastern US in MAM 1983 are present across the Southeast US in DJF 1985 when the fewest tornadoes occurred (Figure 7(e)). Anomalously high CAPE was unexpected, given that anomalously many tornadoes oc- present across the portions of the US with the most tornadoes in curred in this season (Figure 6(f)). Examination of CAPE on the remaining seasons (Figures 7(f), 8(e), and 8(f)). 1-2 May 1983 and 18–20 May 1983, when 39 and 51 tornadoes Some of the seasonal composites illustrate reasonable occurred, respectively, illustrates that elevated CAPE spread patterns. Anomalously high CAPE is shown in both of the from the Gulf of Mexico northward into the eastern US active EN DJFs, for example, in Figures 5(e) and 5(f). Most of (Figure 9). Še negative anomalies of low-level humidity the eastern US was also anomalously humid in these seasons across the south central US in DJF 2016 also did not represent (Figures 5(c) and 5(d)). Other seasonal composites are well the environments that were present when many of the 1540 1560 12 Advances in Meteorology EN 1-2 May 1983 EN 18–20 May 1983 Convective available potential energy Convective available potential energy 2400 2400 0 0 Tornado touchdown (a) (b) −1 Figure 9: CAPE (J·kg ) on (a) 1-2 May 1983 and (b) 18–20 May 1983, when 39 and 51 tornadoes occurred, respectively. EN 23 December 2015 EN 23-24 February 2016 850 mb heights and speciﬁc humidity 850 mb heights and speciﬁc humidity 11.5 11.5 0 0 Tornado touchdown (a) (b) −1 Figure 10: 850 mb geopotential heights (contours (m)) and speci�c humidity (color (g·kg )) on (a) 23 December 2015 and (b) 23-24 February 2016, when 18 and 36 tornadoes occurred, respectively. tornadoes occurred (Figure 5(d)). Closed shortwave troughs illustrate that large numbers of tornadoes are possible even during EN seasons when such large numbers might be un- with upstream southerly moisture advection into the US were present on 23 December 2015 and 23-24 February 2016, when expected. Še other seasons were far less anomalous. 18 and 36 tornadoes occurred, respectively (Figure 10). Šese GWO does not explain the anomalous nature of the cases illustrate that anomalously active seasons can have seasons. Climatological studies show that tornado activity in seemingly unfavorable seasonal composites. DJF and MAM tends to be heightened during GWO phases 1–4 when AAM is anomalously low [21, 22], but nearly all of the tornadoes in the active EN seasons occurred on GWO 4. Conclusions phase 5–8 days when AAM was anomalously high. Fur- Previous studies have established a relationship between thermore, daily tornado count did not signi�cantly vary across the phases of GWO in any of the seasons. Še tornado and ENSO in DJF and MAM, generally with more tornadoes during the LN phase and fewer during the EN concentration of tornadoes on high AAM days (GWO phase 5–8 days) during EN seasons, as suggested here, would phase [12–14, 17–19]. Šis study was focused on the seasons that do not �t this relationship—EN seasons with many undoubtedly weaken the statistical relationship between tornadoes and LN seasons with few tornadoes. Speci�cally, tornadoes and GWO that was reported by others, whereby the two EN DJFs and MAMs with the most tornadoes and the tornadoes are most common on GWO phase 1–4 days two LN DJFs and MAMs with the fewest tornadoes were when AAM is anomalously low [21, 22]. Analyzing the described and analyzed. Še most anomalous seasons were tornado-GWO relationship by ENSO phase might amend DJF 1983 and 2016, both of which were active EN seasons this relationship and provide additional insight into the with 133 E(F)1+ tornadoes. Šey were, therefore, 183% interactions between tornado activity, ENSO, and GWO. (359%) above the mean (median) EN DJF. Šese seasons Another consideration is that the GWO data used in this 1360 Advances in Meteorology 13 [3] K. M. Simmons and D. Sutter, Economic and Societal Impacts study are based on globally integrated AAM. As noted by of Tornadoes, American Meteorological Society, Boston, MA, Gensini and Allen [41], this may confound the results. USA, 2011. +erefore, it would also be worthwhile to reassess the re- [4] K. R. Knupp, T. A. Murphy, T. A. Coleman et al., “Meteo- lationship between tornado activity and GWO with AAM rological overview of the devastating 27 April 2011 tornado calculated on a hemispheric or latitudinal basis. outbreak,” Bulletin of the American Meteorological Society, Unlike with GWO, daily tornado count did signiﬁcantly vol. 95, no. 7, pp. 1041–1062, 2014. vary across the phases of MJO, but only in the active EN [5] National Weather Service, “Natural hazards statistics,” Jan- seasons. +e mean and mean rank of the tornado counts was uary 2018, http://www.nws.noaa.gov/om/hazstats.shtml. greatest with phase 4 in MAM. Clustering of tornadoes on [6] H. E. Brooks, G. W. Carbin, and P. T. Marsh, “Increased MJO phase 4 days in MAM is unique because climatological variability of tornado occurrence in the United States,” Sci- studies [20, 23] that aggregated data over a larger number of ence, vol. 346, no. 6207, pp. 349–352, 2014. seasons reported heightened tornado activity with phase 2, 5, [7] M. K. Tippett, “Changing volatility of U.S. annual tornado and 7 of the MJO in boreal spring months. While only reports,” Geophysical Research Letters, vol. 41, no. 19, representative of MAM 1982 and 1983, the clustering of pp. 6956–6961, 2014. tornadoes on MJO phase 4 days in these EN seasons [8] L. Guo, K. Wang, and H. B. Bluestein, “Variability of tornado highlights the possibility for a large number of tornadoes occurrence over the continental United States since 1950,” Journal of Geophysical Research: Atmospheres, vol. 121, no. 12, when subseasonal climate oscillations are in unfavorable pp. 6943–6953, 2016. phases. +e mean and mean rank of the tornado counts was [9] T. W. Moore, “On the temporal and spatial characteristics of also greatest with phase 6 of the MJO in the anomalously tornado days in the United States,” Atmospheric Research, active EN DJFs. 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Schaefer, “+e relation of El Niño- and MJO interact across seasons to inﬂuence tornado Southern Oscillation (ENSO) to winter tornado activity,” Monthly Weather Review, vol. 136, no. 8, pp. 3121–3137, 2008. activity will likely improve seasonal and subseasonal [17] S.-K. Lee, R. Atlas, D. B. Enﬁeld, C. Wang, and L. Hailong, “Is outlooks. there an optimal ENSO pattern that enhances large-scale atmospheric processes conducive to major tornado out- Conflicts of Interest breaks in the US?,” Journal of Climate, vol. 26, no. 5, pp. 1626–1642, 2013. +e authors declare that there are no conﬂicts of interest [18] J. B. Elsner, T. H. Jagger, and T. Fricker, “Statistical models for regarding the publication of this paper. tornado climatology: long and short-term views,” PLoS One, vol. 11, no. 11, Article ID e0166895, 2016. [19] S.-K. Lee, A. T. Wittenberg, D. B. Enﬁeld, S. J. Weaver, References C. Wang, and R. 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Allen, “U.S. hail frequency and the global wind oscillation,” Geophysical Research Letters, vol. 45, United States related to global relative angular momentum,” Monthly Weather Review, vol. 144, no. 2, pp. 801–810, 2016. no. 3, pp. 1611–1620, 2018. [22] T. W. Moore, “Annual and seasonal tornado activity in the United States and the global wind oscillation,” Climate Dy- namics, vol. 50, no. 11-12, pp. 4323–4334, 2017. [23] B. S. Barrett and V. A. Gensini, “Variability of central United States April–May tornado day likelihood by phase of the Madden-Julian Oscillation,” Geophysical Research Letters, vol. 40, no. 11, pp. 2790–2795, 2013. [24] Storm Prediction Center, “Severe weather database ﬁles (1950–2016),” January 2018, http://www.spc.noaa.gov/wcm/. [25] S. M. Verbout, H. E. Brooks, L. M. Leslie, and S. M. Schultz, “Evolution of the U.S. tornado database: 1954–2003,” Weather and Forecasting, vol. 21, no. 1, pp. 86–93, 2006. [26] K. E. Kunkel, T. R. Karl, H. Brooks et al., “Monitoring and understanding trends in extreme storms,” Bulletin of the American Meteorological Society, vol. 94, no. 4, pp. 499–514, [27] F. Mesinger, G. DiMego, E. Kalnay et al., “North American Regional Reanalysis,” Bulletin of the American Meteorological Society, vol. 87, no. 3, pp. 343–360, 2006. [28] Climate Prediction Center, “Cold & warm episodes by sea- son,” January 2018, http://www.cpc.ncep.noaa.gov/products/ analysis_monitoring/ensostuﬀ/ONI_v5.php. [29] R. A. Madden and P. R. Julian, “Description of global-scale circulation cells in the tropics with a 40–50 day period,” Journal of Atmospheric Sciences, vol. 29, no. 6, pp. 1109–1123, [30] K. M. Weickmann, S. Khalsa, and J. Eischeid, “+e atmo- spheric angular momentum cycle during the tropical Madden-Julian Oscillation,” Monthly Weather Review, vol. 120, no. 10, pp. 2252–2263, 1992. [31] K. M. Weickmann and E. Berry, “A synoptic-dynamic model of sub-seasonal atmospheric variability,” Monthly Weather Review, vol. 135, no. 2, pp. 449–474, 2007. [32] K. M. Weickmann and E. Berry, “+e tropical Madden-Julian oscillation and the global wind oscillation,” Monthly Weather Review, vol. 137, no. 5, pp. 1601–1614, 2009. [33] Earth System Research Laboratory, “GWO dataset,” February 2018, https://www.esrl.noaa.gov/psd/map/clim/gwo.data.txt. [34] M. Wheeler and H. H. Hendon, “An all-season real-time multivariate MJO index: development of an index for mon- itoring and prediction,” Monthly Weather Review, vol. 132, no. 8, pp. 1917–1932, 2004. [35] Australian Bureau of Meteorology, “Madden-julian oscilla- tion (MJO),” February 2018, http://www.bom.gov.au/climate/ mjo/. [36] V. A. Gensini, T. L. Mote, and H. E. Brooks, “Severe- thunderstorm reanalysis environments and collocated ra- diosonde observations,” Journal of Applied Meteorology and Climatology, vol. 53, no. 3, pp. 742–751, 2014. [37] National Oceanic and Atmospheric Administration, “Daily average NCEP NARR composites,” February 2018, https:// www.esrl.noaa.gov/psd/cgi-bin/data/narr/plotday.pl/. [38] National Oceanic and Atmospheric Administration, “NARR monthly/seasonal climate composites,” February 2018, https:// www.esrl.noaa.gov/psd/cgi-bin/data/narr/plotmonth.pl. [39] ESRI, ArcMAP Desktop, Release 10.5.1, Environmental Sys- tems Research Institute, Redlands, CA, USA, 2017. [40] J. O. Dickey, S. L. Marcus, and T. M. Chin, “+ermal wind forcing and atmospheric angular momentum: origin of the International Journal of The Scientific Advances in Advances in Geophysics Chemistry Scientica World Journal Public Health Hindawi Hindawi Hindawi Hindawi Publishing Corporation Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 http://www www.hindawi.com .hindawi.com V Volume 2018 olume 2013 www.hindawi.com Volume 2018 Journal of Environmental and Public Health Advances in Meteorology Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 Submit your manuscripts at www.hindawi.com Applied & Environmental Journal of Soil Science Geological Research Hindawi Volume 2018 Hindawi www.hindawi.com www.hindawi.com Volume 2018 International Journal of International Journal of Agronomy Ecology International Journal of Advances in International Journal of Forestry Research Microbiology Agriculture Hindawi Hindawi Hindawi Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 International Journal of Journal of Journal of International Journal of Biodiversity Archaea Analytical Chemistry Chemistry Marine Biology Hindawi Hindawi Hindawi Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018

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References

U.S. Tornado climatology,

National Centers for Environmental Information
Tornado losses in the United States,

S. Changnon
Meteorological overview of the devastating 27 April 2011 tornado outbreak,

K. R. Knupp, T. A. Murphy, T. A. Coleman et al.
Natural hazards statistics,

National Weather Service
Increased variability of tornado occurrence in the United States,

H. E. Brooks, G. W. Carbin, and P. T. Marsh
Changing volatility of U.S. annual tornado reports,

M. K. Tippett
Variability of tornado occurrence over the continental United States since 1950,

L. Guo, K. Wang, and H. B. Bluestein
On the temporal and spatial characteristics of tornado days in the United States,

T. W. Moore
Annual and season tornado trends in the contiguous United States and its regions,

T. W. Moore
Association of U.S. tornado occurrence with monthly environmental parameters,

M. K. Tippett, A. H. Sobel, and S. J. Camargo
Influence of the El Niño/Southern Oscillation on tornado and hail frequency in the United States,

J. T. Allen, M. K. Tippett, and A. H. Sobel
ENSO-based probabilistic forecasts of March–May U.S. tornado and hail activity,

C. Lepore, M. K. Tippett, and J. T. Allen
The impact of El Niño-Southern Oscillation (ENSO) on winter and early spring U.S. tornado outbreaks,

A. R. Cook, L. M. Leslie, D. B. Parsons et al.
Relationships between phases of the El Niño-Southern Oscillation and character of the tornado season in the south-central United States,

W. Monfredo
The relation of El Niño-Southern Oscillation (ENSO) to winter tornado activity,

A. R. Cook and J. T. Schaefer
Is there an optimal ENSO pattern that enhances large-scale atmospheric processes conducive to major tornado outbreaks in the US?

S.-K. Lee, R. Atlas, D. B. Enfield, C. Wang, and L. Hailong
Statistical models for tornado climatology: long and short-term views,

J. B. Elsner, T. H. Jagger, and T. Fricker
US regional tornado outbreaks and their links to spring ENSO phases and North Atlantic SST variability,

S.-K. Lee, A. T. Wittenberg, D. B. Enfield, S. J. Weaver, C. Wang, and R. Atlas
The relationship between the Madden-Julian Oscillation and US violent tornado outbreaks in the spring,

D. B. Thompson and P. E. Roundy
Tornado frequency in the United States related to global relative angular momentum,

V. A. Gensini and A. Marinaro
Annual and seasonal tornado activity in the United States and the global wind oscillation,

T. W. Moore
Variability of central United States April–May tornado day likelihood by phase of the Madden-Julian Oscillation,

B. S. Barrett and V. A. Gensini
Severe weather database files (1950–2016),

Storm Prediction Center
Evolution of the U.S. tornado database: 1954–2003,

S. M. Verbout, H. E. Brooks, L. M. Leslie, and S. M. Schultz
Monitoring and understanding trends in extreme storms,

K. E. Kunkel, T. R. Karl, H. Brooks et al.
North American Regional Reanalysis,

F. Mesinger, G. DiMego, E. Kalnay et al.
Cold & warm episodes by season,

Climate Prediction Center
Description of global-scale circulation cells in the tropics with a 40–50 day period,

R. A. Madden and P. R. Julian
The atmospheric angular momentum cycle during the tropical Madden-Julian Oscillation,

K. M. Weickmann, S. Khalsa, and J. Eischeid
A synoptic-dynamic model of sub-seasonal atmospheric variability,

K. M. Weickmann and E. Berry
The tropical Madden-Julian oscillation and the global wind oscillation,

K. M. Weickmann and E. Berry
GWO dataset,

Earth System Research Laboratory
An all-season real-time multivariate MJO index: development of an index for monitoring and prediction,

M. Wheeler and H. H. Hendon
Madden-julian oscillation (MJO),

Australian Bureau of Meteorology
Severe-thunderstorm reanalysis environments and collocated radiosonde observations,

V. A. Gensini, T. L. Mote, and H. E. Brooks
Daily average NCEP NARR composites,

National Oceanic and Atmospheric Administration
NARR monthly/seasonal climate composites,

National Oceanic and Atmospheric Administration
Thermal wind forcing and atmospheric angular momentum: origin of the Earth’s delayed response to ENSO,

J. O. Dickey, S. L. Marcus, and T. M. Chin
U.S. hail frequency and the global wind oscillation,

V. A. Gensini and J. T. Allen

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An Analysis of Anomalous Winter and Spring Tornado Frequency by Phase of the El Niño/Southern Oscillation, the Global Wind Oscillation, and the Madden-Julian Oscillation

An Analysis of Anomalous Winter and Spring Tornado Frequency by Phase of the El Niño/Southern Oscillation, the Global Wind Oscillation, and the Madden-Julian Oscillation

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An Analysis of Anomalous Winter and Spring Tornado Frequency by Phase of the El Niño/Southern Oscillation, the Global Wind Oscillation, and the Madden-Julian Oscillation

An Analysis of Anomalous Winter and Spring Tornado Frequency by Phase of the El Niño/Southern Oscillation, the Global Wind Oscillation, and the Madden-Julian Oscillation

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