LETTERSPUBLISHED ONLINE: 16 MARCH 2015 | DOI: 10.1038/NGEO2385

Influence of the El Niño/Southern Oscillation ontornado and hail frequency in the United StatesJohn T. Allen1*, Michael K. Tippett2,3 and Adam H. Sobel2,4

The El Niño-Southern Oscillation (ENSO) is characterizedby changes in sea surface temperature (SST) and atmo-spheric convection in the tropical Pacific, and modulatesglobal weather and climate1–4. The phase of ENSO influencesUnited States (US) temperature and precipitation and haslong been hypothesized to influence severe thunderstormoccurrence over the US5–11. However, limitations12 of thesevere thunderstorm observational record, combined withlarge year-to-year variability12,13, have made it di�cult todemonstrate an ENSO influence during the peak spring season.Here we use environmental indices14–16 that are correlatedwith tornado and hail activity, and show that ENSO modulatestornado and hail occurrence during the winter and spring byaltering the large-scale environment. We show that fewertornadoes and hail events occur over the central US duringEl Niño and conversely more occur during La Niña conditions.Moreover, winter ENSO conditions often persist into earlyspring, and consequently the winter ENSO state can be usedto predict changes in tornado and hail frequency duringthe following spring. Combined with our current ability topredict ENSO several months in advance17, our findingsprovide a basis for long-range seasonal prediction of severethunderstorm activity.

ENSO’s modification of the upstream mid-latitude flow overthe Pacific contributes significantly to year-to-year variability ofUS extratropical cyclones, precipitation, surface temperatures andlow-level moisture advection from the Gulf of Mexico1–4. Theseperturbations suggest that ENSO should influence the large-scaleenvironmental features that produce severe thunderstorms6–11, ina similar manner to the Madden–Julian Oscillation18. On average,severe thunderstorms producing tornadoes, hail and damagingwinds cause US$ 1.6 billion of damage per year across the US,and billion dollar events are not uncommon19. Tornado occurrencehas previously been related to ENSO phase during the wintermonths and over small geographical regions6–11. There has beenless indication of a relation during the spring or summer months,when the frequency of severe thunderstorms peaks8,9. A recentstudy related Pacific SSTs and tornadoes using the Trans-NinoIndex (TNI; ref. 11), showing a significant correlation to detrendedtornado frequency, which had only a weak relationship to otherENSO indices. However, the strength of the relationship to intensetornadoes was not statistically robust, with results weakened bychoices of sample stratification, detrending or the influence ofoutliers. No attempt has thus far been made to show a relationshipbetweenENSOandhail occurrence.Herewe address this issue usingdata that are less problematic than the tornado and hail reportsthemselves, namely proxies derived using environmental data20.

Environmental indices are used to relate favourable large-scaleatmospheric conditions (such as strong vertical wind shear and ther-modynamic potential energy) to severe thunderstorm occurrencefor both short-range prediction and climate projection20–24. Here weemploy environmental indices for tornado and hail occurrence thathave been shown to skillfully represent climatological and interan-nual variability14–16 (seeMethods). For comparison to the indices, weuse the number of hail events in excess of one inch in diameter andthe total number of observed tornadoes in a 1◦× 1◦ grid box overa season. Seasonal values of the indices, observations and environ-mental conditions were derived for two seasons: December, January,February (DJF) and March, April, May (MAM). Correlations withobservations for National Oceanic and Atmospheric Administra-tion (NOAA) climate regions25, together withmean spatial maps forDJF and MAM (Supplementary Table 1 and Figs 1–3) demonstratethe suitability of the indices to describe the climatology, seasonalcycle and interannual variability of tornado and hail occurrence.

Seasonal composites were created for the La Niña and El Niñophases of ENSO, selecting years of moderate to strong signal26where values of seasonal mean Oceanic Nino Index (ONI) havemagnitude greater than 1 for DJF and 0.5 for MAM. CompositeDJF anomalies show an asymmetric ENSO signal, with increases ofsevere weather over the central and southeast US during La Niñaand over Florida during El Niño (Fig. 1). For hail, the influencecorresponds to a significant increase of 0.5 hail events per seasonper grid cell over Florida and the Gulf coast during El Niño years,and a small decrease over the southeast US (Fig. 1a,e). This changeis large relative to the climatological frequency of 0.25 hail eventsor less per season. In La Niña years, significant hail increases areidentified for the plains to southeast where frequency is low, whereasfrequencies over Florida are smaller. For tornadoes, the pattern issimilar (Fig. 1b,f), with significant seasonal increases of 0.5 eventsper grid cell along the Gulf Coast and Florida during El Niño anda northward-displaced decrease in frequency over the southeastand lower Midwest. There is an effective doubling of frequencyin this region during El Niño years. During La Niña conditions,significant increases to tornado events are found from the plainseastward (Fig. 1d,h). Significant decreases are also found in tornadofrequency for Florida, where ENSO-induced frequency reductionis close to the climatological frequency, implying a total frequencyclose to zero.

The climatological frequencies of both hail and tornadoes arehigher in MAM and are reflected in MAM ENSO composites withlarge amplitudes, despite weakening of the ENSO signal duringthis period7,8. The largest ENSO composite values are seen overthe southern Plains states and the southeast, again with asymmetrybetween the two phases (Fig. 2). The ENSO signal shifts west relative

1International Research Institute for Climate and Society, The Earth Institute of Columbia University, Palisades, New York 10964, USA. 2Department ofApplied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA. 3Center of Excellence for Climate Change Research,Department of Meteorology, King Abdulaziz University, Jeddah 21589, Saudi Arabia. 4Lamont-Doherty Earth Observatory, Columbia University, Palisades,New York 10964, USA. *e-mail: JohnTerrAllen@gmail.com

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Figure 1 | Composite mean anomalies of winter (December, January, February) hail and tornadoes conditioned on the winter ENSO state. a–h, DJFanomalies are shown for the El Niño and La Niña states determined using simultaneous ONI for the hail index (a,c), tornado index (b,d), hail events (e,g)and tornadoes (f,h). Statistical significance (stippled) is indicated where a grid point passes the two-tailed 95th percentile. The results are from a10,000-sample Monte Carlo simulation permutation test from the 34 years, with events chosen to produce a distribution of di�erences between the meanof ENSO phase composite seasons and the mean climatology of all remaining seasons.

to the winter one following the climatological frequency, with astronger signal remaining over Florida in the tornado index. El Niñoresults in a large significant seasonal decrease of up to two hailevents per grid cell (>30%) over the south, southeast and centralplains states (Fig. 2a). La Niña conditions result in an eastwardshift and significant increase in hail occurrence (Fig. 2c). Thesepatterns are also found for the hail event composites, althoughwith decreased amplitude in the El Niño case and weaker statisticalsignificance (Fig. 2e,g).

An established physicalmechanism for the statistical relationshipbetween ENSO phase and severe thunderstorm occurrence is themodulation of the jet stream position over the continent by ENSO-related tropical forcing1,3,8. El Niño composite anomalies of 300 hPawind speed are positive over the Gulf of Mexico and negativeover the continent (Fig. 3a). The vector anomalies (anomalies inwind direction) of the monthly wind field are consistent withmore frequent upper level cyclonic flow (low pressure) patternsduring this phase. Hence, fewer low-pressure systems occur over

the Plains, whereas more occur over the southeast and Florida.This interpretation is further supported by negative geopotentialheight anomalies over southeast US (Supplementary Fig. 4). DuringLa Niña, the change to the jet stream flow compared to climatologyis smaller (Fig. 3b). Stronger flow over the continent and reducedflow further south result in a greater frequency of anticyclonic (highpressure) systems over Texas and the southwest, resulting in positivecomposite geopotential height anomalies.

The surface (10m) winds also show the impact of the ENSO-modulated jet stream in the form of convergence associatedwith the development of surface low-pressure systems (Fig. 3c,d).In El Niño years, the surface winds that typically bring warmmoist air over the plains from the Gulf of Mexico are weakened,reflecting the southward shift of cold air from the continent behindfronts responding to the shifted position of cyclogenesis. The flowanomaly contributes to the significant moisture reduction over thesouthern plains, consequently decreasing mixed-layer convectiveavailable potential energy (MLCAPE), a measure of atmospheric

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Figure 2 | Composite mean anomalies of spring (March, April, May) hail and tornadoes conditioned on the spring ENSO state. a–h, MAM anomaliesare shown for the El Niño and La Niña states determined using simultaneous ONI for the hail index (a,c), tornado index (b,d), hail events (e,g) andtornadoes (f,h). Statistical significance (stippled) is indicated as for Fig. 1.

thermodynamic energy (Fig. 3e and Supplementary Fig. 4). La Niñapreferentially favours greater northward advection of warm moistair over eastern Texas and the southern US, thereby increasingMLCAPE over the continent (Fig. 3f). During La Niña years,increasing southeasterly surface flow results in larger 0–3 km StormRelative Helicity (SRH), increasing the likelihood of both tornadoesand large hail (Supplementary Fig. 4). During El Niño years, thiseffect is reversed,with reduced SRHover large areas of the continent.

La Niña composite surface temperature anomalies are greaterthan 1 ◦C, particularly over western Texas and the high plains(Supplementary Fig. 5). This surface temperature increase enhancesthe climatological north-to-south temperature gradient, favouringmore frequent extratropical cyclogenesis. Further, this surface tem-perature anomaly results in a stronger environmental mixed layerof steep lapse rates (Supplementary Fig. 5), thereby enhancingMLCAPE, and potentially increasing the resistance to convection.In contrast, El Niño surface temperature anomalies are cooler than−1 ◦C over large parts of the southern US, and are positive over thenorth. This anomaly pattern opposes the climatological tempera-

ture gradient, thereby reducing the frequency of cyclogenesis eastof the Rockies. Weaker vertical mixing over the high terrain alsoresults (Supplementary Fig. 5), reducing inhibition to convection,and diminishing vertical instability. Correspondingly in El Niño,convective precipitation (cPrcp) is reduced over the area clima-tologically favoured by both hail and tornadoes, with enhancedprecipitation over the southwesternUS. In LaNiña, cPrcp is reducedover the Gulf coast, south and southwest.

The relationship of ENSO to the frequency of tornado and hailevents as well as environmental indices as a percentage of theirclimatological frequency is computed over the geographical regionincludingOklahoma, Arkansas and northern Texas (SupplementaryFigs 3 and 6). Although there is a high degree of variability thatcannot be explained by ENSO phase, nearly all La Niña seasonsare associated with frequencies greater than 100% of climatology,and provide the few cases above 175%. Seasons with frequencybelow 75% of climatology are nearly exclusively associated withmoderate to strong El Niño conditions. This association is reflectedby significant correlations for both the cumulative area frequency of

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Figure 3 | Mean spring environmental composite anomalies for upper level and surface winds, and for convective available potential energy by ENSOstate. a–f, MAM anomalies are shown for 300 hPa wind (a,b), 10 m wind (c,d) and 180 mb MLCAPE (e,f) for El Niño and La Niña anomaly compositesbased on simultaneous ONI. Significance (stippling) is shown as for Fig. 1. Arrows show mean vector anomalies of 300 hPa and 10 m winds respectively.

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the hail (Pearson−0.51, Rank−0.45) and tornado (Pearson−0.40,Rank −0.33) indices with simultaneous ONI. Questions remainas to the best index to characterize Pacific SSTs for applicationto this problem. Both detrended frequency and TNI (ref. 11) andNino 3.4 with environment indices as shown here have significant

correlations to tornado occurrence, and thus further evaluationis necessary to determine the optimal index choice. However, forintense tornadoes, there remains uncertainty as to whether thereis a robust relationship, and this search is complicated by thevariability of observed intense tornadoes (mean of 20 per year, range

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NATURE GEOSCIENCE DOI: 10.1038/NGEO2385 LETTERSfrom 2 to 86)11. It is also difficult to interpret the weaker signalin the modelled environmental patterns in prior work11 comparedto results presented here. Despite the differences in study design,both results consistently indicate enhanced US tornado activityassociated with anomalously cool Pacific SSTs.

Because winter ENSO conditions may persist into early spring,the DJF ENSO phase has the potential to provide predictiveinformation regarding MAM severe thunderstorms. To assess thisrelationship, we relate the DJF ONI to the MAM frequency of hailand tornadoes (Fig. 4a). There is a clear statistically significantcorrelation to MAM hail and tornadoes for moderate to strongDJF ENSO events (Pearson: Hail−0.55, Tornado−0.45, Rank: Hail−0.56, Tornado−0.43). This pattern is also found in the compositeanomalies of both hail and tornadoes, which show a similar patternanomaly with reduced magnitude (Supplementary Fig. 7). Thisinformation is then used to construct a probabilistic forecast fortornado and hail index-based activity based on the ENSO state ofthe previous winter using extended logistic regression (ELR; ref. 27).Index-based activity is classified as above, near and below normalbased on the upper quartile, middle two quartiles and lower quartileof the data. The probability of each of the respective categories can bedetermined for a given value of ONI (Fig. 4b). The coefficients of theELR are significant, indicating skill, and the Ranked Probability SkillScore (RPSS) of the forecasts is 0.23 for the hail index and 0.14 forthe tornado index. This suggests that for moderate to strong ENSOphase, the probabilities of above or below normal frequency activityare predictable over the US. This predictive skill can be explained bythe persistence of the ENSO pattern into the following spring, anddemonstrates that seasonal predictions of hail and tornado activityare feasible in years with strong ENSO conditions by 1 March atworst, and possible based on ENSO forecasts the previous fall.

MethodsReports data to produce the climatology of observed hail events and tornadoes issourced from the National Climatic Data Center (NCDC) Storm Data28 for theperiod January 1979 to December 2012 (freely available athttp://www.ncdc.noaa.gov/stormevents). These data are used to produce seasonaltotal number of hail and tornado events for 33 DJF and 34 MAM periods. Thenumber of hail events in a 1◦×1◦ grid box over a season are defined as thenumber of three-hourly periods with one or more occurrences of hail in excess ofone inch in diameter. For tornadoes, the total number of observed tornadoes fora season is also placed on the aforementioned grid.

Observed occurrences of severe weather and environmental variables fromthe North American Regional Reanalysis (NARR; ref. 29) for the period1979–2012 are related using previously derived indices14–16 (variables used toderive the indices are available athttp://nomads.ncdc.noaa.gov/data.php?name=access#narr_datasets). The tornadoindex depends on convective precipitation (cPrcP) and 0–3 km storm relativehelicity (SRH). For the hail index, these same parameters are weighted differently,and used along with two additional parameters; 180 hPa mixed-layer convectiveavailable potential energy (MLCAPE) and the mean specific humidity betweenthe ground (2m) and 90 hPa above the ground (Qmean). These regression modelsare defined in prior work14–16, and here are applied to estimate seasonal frequencyof occurrence for DJF and MAM analogously to observed events.

Significant positive correlations between the MAM values of the indices andthe events were found over regions east of the Rocky Mountains, where bothtornadoes and hail are prevalent (Supplementary Table 1). Both indices have theleast skill at simulating occurrence over the southeast, where differing stormenvironments occur. For the highly variable and low-frequency DJF period,correlations are lower, particularly for the hail index, but remain significant overregions with non-zero climatological frequency. To test whether individual yearsinfluence the performance of the indices, rank correlation using Spearman’s Rhowas also computed. To compensate for the limitations of the observations in theearly parts of the climatology, we also show the correlations for the most recent17 years, which increases correlations markedly in all cases, further strengtheningour confidence in index performance, especially when combined with spatialdistributions (Supplementary Figs 1 and 2). In the observed climatology, anumber of biases arise that are not related to the meteorological occurrence ofeither hail or tornadoes12,13,16. Conversely, the indices produce a smootheddistribution consistent with the gradual falloff in the frequency of favourableenvironmental conditions.

ENSO composite anomalies are produced using the difference of the mean ofthe ENSO phase years from the mean climatology of all years. Environmentalcomposite anomalies were constructed using mean environmental conditionsduring the MAM period as a difference from the mean climatological state.Significance stippling shown on composite anomalies was generated for each gridpoint using a two-tailed 95% significance test based on a 10,000 member MonteCarlo simulation permutation test from the 34 years sampled to produce adistribution of differences between the randomly selected mean of the number ofyears in the ENSO phase composite and the mean of the remaining sampleof years.

The Climate Prediction Center Oceanic Niño Index (ONI; ref. 26) values forDJF and MAM are used to describe the ENSO state. ONI describes the runningthree-month mean anomaly of mid-Pacific SSTs corresponding to the Niño 3.4region, relative to the prior 30-year mean to remove warming signals. Moderateto Strong events are defined by |ONI| greater than 1 for DJF and |ONI| greaterthan 0.5 for MAM. This distinction is necessary to reflect the smaller variabilityduring the spring8,10. Selected MAM seasons for El Niño were 1983, 1987, 1992,1993, 1998, 2005, 2010, and La Niña 1985, 1989, 1999, 2000, 2008, 2011. For DJF,only the El Niño composite changed, and 1993 was replaced by 1995 and 2005was replaced by 2003.

An extended logistic regression27 for forecasting frequency categories overthe box described in Supplementary Fig. 3 was developed based on observedvalues of DJF ONI. The optimal regression includes a constant term, ONI and thesquare root of the percentile values as predictors (Hail coefficients: Constant−10.59±2.690, ONI 1.42±0.448,

√%tile 0.62±0.158, Tornado coefficients:

Constant −9.87±2.508, ONI 0.82±0.307,√%tile 0.89±0.224). Data for

occurrence were segmented into above the 75th percentile, near normal spanningthe 25th to 75th percentiles inclusive, and below the 25th percentile before thefitting procedure. Forecast skill of the extended logistic regression (ELR) wasanalysed using the Ranked Probability Skill Score (RPSS; ref. 30) metric withclimatological frequencies (that is, 25%, 50% and 25% for the below, normal andabove categories) as the baseline forecast.

Received 14 July 2014; accepted 6 February 2015;published online 16 March 2015

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AcknowledgementsThe authors are supported by grants from the National Oceanic and AtmosphericAdministration (NA05OAR4311004 and NA14OAR4310185), the Office of NavalResearch (N00014-12-1-0911) and a Columbia University Research Initiatives for Scienceand Engineering (RISE) award. The views expressed herein are those of the authors anddo not necessarily reflect the views of NOAA or any of its sub-agencies.

Author contributionsJ.T.A. carried out production of all results and led writing of the paper and researchdesign. M.K.T. contributed to the research design and assisted with statistical analysis.All authors participated in the interpretation of results and the writing andediting process.

Additional informationSupplementary information is available in the online version of the paper. Reprints andpermissions information is available online at www.nature.com/reprints.Correspondence and requests for materials should be addressed to J.T.A.

Competing financial interestsThe authors declare no competing financial interests.

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