Tornado Risks Will Shift with a Changing Climate.Abraham L. Solomon1*,

J. Lu1, B. Cash1, E. Palipane1, and J. Kinter 1

Affiliations[1] IGES / COLA

4041 Powder Mill Rd. Suite 302Calverton, MD 20705-3106

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In this paper, the distribution of tornadoes within the continental U.S. is as-sessed from observational datasets and climate models using an index (NDSEV )calculated from variables characterizing the large-scale environment in which se-vere weather develops. This reformulated index correlates better with observedtornado data than similar indices, investigated in previous studies. Moreover,when evaluated for a pair of high-resolution climate models, NDSEV predictsan increase of more than 45% in the annual number of severe weather days, con-sistent with findings by previous authors. More important, the spatial distribu-tion of that increased risk is predicted to exhibit a northward shift, driven by achange in the mean winds. Such a change in the distribution of severe weatherevents should be a factor in planning concerning infrastructure improvements,insurance needs, and individual readiness.

1 Introduction

Record setting weather events during the past decade have occurred in conjunc-tion with some of the hottest global mean temperatures in the instrumentalrecord [1]. Additionally, some longer term trends in weather related losses havebeen observed over the past half century [2]. This fact has led many to con-sider whether climate change is driving a trend in extreme weather [3, 4, 5, 6].There may not be sufficient historical data at this point to determine if in-dividual events lie outside the range of natural variability, but simulations offuture climate can inform the question: “Will similar extreme events be morelikely to occur in a warmer world?” The implications of global warming for cer-tain aspects of the climate are relatively straightforward. For instance, as theglobal mean surface temperature increases, the frequency of high temperatureextremes is expected to increase. More complex phenomena are more challeng-ing to predict, like the mid-latitude storms which provide precipitation as wellas spawn tornadoes, produce hail and lightning.

A number of papers have been published confirming a link between climatechange and increased risk from severe thunderstorms and tornadoes [7, 8, 9].These investigations must rely on the relationships between severe weatherevents and the larger atmospheric environment that produces them, becausethe models used for studying global climate (GCMs) do not directly simulateconvection at the scale of individual storms. It has been demonstrated that thespatial distribution and seasonal cycle of tornado frequency can be reasonablyreproduced by defining indices for severe weather days based on environmen-tal parameters from reanalysis data [10, 11, 12]. Such indices define a severeweather day based on some measure of convection and vertical shear exceedingan empirical threshold. This approach is limited both by the accuracy withwhich these simple relations can reproduce the actual tornado risk and the ac-curacy with which the environmental parameters themselves can be predicted.

This paper will introduce a new index for severe weather that exhibits sig-

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nificant skill at reproducing the observed record of tornadoes in the U.S. Thisnew index is closely related to those employed in previous studies; however, weuse an objectively determined characterization of tornadic environments froma reanalysis, instead of adopting threshold criteria estimated from observationsand applying them to gridded data. This approach acknowledges the inability ofthe current generation of global models to reproduce the extreme environmentsin which severe weather develops. Climate models do not accurately reproducethe diurnal cycle or frequency of extreme CAPE values seen in observations[13]. Coarse model resolutions (typically greater than 100km) imply that modelcannot simulate the large spatial heterogeneity seen in proximity soundings ofsevere weather [14], so thresholds based on those values may not be the mostrelevant for model assessment. This new index will be useful for more generalmodel inter-comparison, allowing a more robust consensus on the projectionsof severe weather in a changing climate. To demonstrate the potential for thisindex, it is applied to a pair of state-of-the-art climate models to evaluate theirability to reproduce the observed tornado risk in the U.S. and to project thatrisk to change in a warming climate. In the next section the new index will bedefined and the method for evaluating it from a gridded atmospheric datasetwill be explained. The third section will describe the reanalysis and model dataused. The fourth and fifth sections cover the results from the reanalysis andclimate models.

2 NDSEV - The Number of Days with SevereWeather

This study employs a power-law relationship between convective precipitationrate (P ) and lower tropospheric vertical wind shear (S) to define a threshold fortornadic environments. This general approach was first used by Brooks 2003b,who calculated a ”best discriminator” from tornado observations, using localmeasurements of convective available potential energy (CAPE) and shear (S).This parameterization has been employed in several publications to assess se-vere weather environments from reanalysis [10, 13, 15], as well as from modelsof present and future climate [13, 8, 9, 16].

These studies defined indices to identify supercell type thunderstorms, whichare responsible for the majority of damaging tornadoes [17]. These large con-vective systems are characterized by buoyant updrafts, rainy downdrafts anddeep mesocyclonic vortices. High values of CAPE are associated with buoyantupdrafts through observations and through a theoretical relation to maximumpotential vertical velocities. This relation to the strength of updrafts has moti-vated the use of CAPE in the construction of severe weather indices (e.g. Trapp2007), although some studies have used alternate measures of vertical velocity[7, 18]. The mesocyclones necessary to produce damaging tornadoes typicallyare less than 10km in scale and hence cannot be simulated by the coarse reso-

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lutions of climate models. Lower tropospheric, vertical wind shear, representedhere by the difference (S = |~V500hpa − ~V10m|) between near surface 10m windsand mid-tropospheric 500hpa winds, promotes the rotation in thunderstormsleading to mesocyclone development. Since vertical shear of the large scale flowis well resolved by models, S has been used in the majority of studies of this type.

Tippett 2012 considered a wider range of environmental variables as predic-tors for tornado frequency and found that P was more skillful than CAPE. Wecompared the results of Brooks 2003b ”best discriminator”, objective estima-tion of a new power-law using CAPE, as well as this alternative approach usingP for multiple reanalyses and model simulations. The most skillful predictorsbased on our approach proved to be P and S, so we will only discuss resultsbased on this parameterization. Since rainy downdrafts are also characteristicof supercell thunderstorms, this alternate parameterization is conceptually con-sistent with more conventional approaches to identify storms with a potentialfor tornadogenesis. The criterion for a tornadic environment in this study isbased on a ”tornado potential” power-law relation:

Tγ(λ, φ, t) = P × Sγ ≥ τγ (1)

where the exponent γ and threshold τγ are determined so as to best reproducethe observed tornado seasonal cycle. At each grid point the timeseries of Tcan be used to count the number of days per year when a given threshold isexceeded. This is the definition of NDSEV:

NDSEVγ(λ, φ) =1

(tf − t0)∆λ∆φ cosφ

tf∑t=t0

H(Tγ(λ, φ, t)− τγ), (2)

where H is the Heaviside function, (λ, φ) is the longitude and latitude of the gridpoint. For each value of γ a threshold τγ is found such that the annual meannumber of tornados predicted by the model matches the observations from 1990-2010 (when the observations are most reliable). Then the optimal value of γ isdetermined by minimizing a cost function, defined as the root-mean-square errorbetween the observed monthly mean tornado counts and the inferred monthlyfrequencies from NDSEVγ .

3 Data and Model

For this study, the power-law relationship is determined from the North Ameri-can Regional Reanalysis (NARR) [19]. This high-resolution reanalysis providesdata on a 32km Lambert grid at 6-hourly intervals. Data from 1979-2010 wasemployed and all the 4-times daily data was retained, thereby making no as-sumptions about the timing of tornado occurrence as some authors have done,based on the diurnal cycle of CAPE [8]. The high resolution in both space andtime provides a better representation of the local environment in which sub-gridscale convective events occur than older reanalyses with grid spacings of more

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than 100km. The NARR also assimilated observed precipitation, which providessome additional confidence in the P fields needed for the calculation ofNDSEV .To determine the optimum power-law relation, NDSEV was calculated for 200equally space values of γ in the range (0, 10). While this brute force optimiza-tion method is somewhat computationally intensive, it is simple and reliable.The observations of U.S. tornadoes were acquired from the Storm PredictionCenter (SPC) of the National Weather Service and a monthly climatology wascalculated for the period 1990-2010. The cost function for this range of γ ex-hibited a clear minimum, resulting in γ = 0.25 and τγ = 91[kg/m2 · (m/s).25]with a cost of 10.4% of the annual mean tornado counts.

This study makes use of a set of high resolution climate experiments com-pleted in 2010 as an international collaborative effort between the Center forOcean-Land-Atmosphere studies (COLA), the European Centre for MidrangeWeather Forecasting (ECMWF) and the Japanese Agency for Marine-EarthScience and Technology (JAMSTEC). The Athena project [20] employed theECMWF Integrated Forecast System (IFS) [21], used for numerical weatherprediction and data-assimilation, to simulate global climate with greater preci-sion than had been previously possible. This undertaking allows a comparisonof climate simulations with minimal differences in model configuration asidefrom horizontal resolution. Studies of the Athena project have already shownthat increasing the model resolution improved the representation of tropical at-mosphere [22], tropical cyclones [23], extra-tropical cyclones and blocking [24],and the diurnal cycle of precipitation [25].

For this study four of the Athena IFS simulations will be discussed. Twosimulations of the Climate of the 20th Century (AMIP type simulations) wererun continuously for 47 years with best available estimates of sea-surface tem-perature (SST) (see Jung 2011 for details), starting on January, 1 1961 usinginitial conditions from ERA-40. The two simulations were run at differing hori-zontal resolutions, with spectral truncations of T159 and T1279, correspondingto average grid spacings of 125km and 16km respectively. The other pair of sim-ulations employed the same two IFS model configurations, but were performedas ”time-slice experiments” of the 21st century (2071-2117), with the differencein the annual cycle of SST at each grid point taken from the IPCC AR4 in-tegration of CCSM3.0. The higher resolution (T1279) data was interpolatedto a consistent grid with the lower resolution (T159) simulation using an area-weighted, conservative algorithm. Four times daily data from each experimentwas used, just as in the evaluation of the NARR. The results from the Athenamodels provide both an opportunity to see if NDSEV provides reasonable cli-matologies of tornadoes from the AMIP simulations of 20th century climate andto see two realizations of the change in tornado distributions projected for the21st century.

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4 Climatology of NDSEV

NARR NDSEV

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Figure 1: The number of severe weather days (NDSEV ) per year, per10,000km2, as derived from the North American Regional Reanalysis for theperiod 1979-2010, is plotted on the left. The month of most frequent tornadooccurrence at each grid point (for points with more than one tornado per year)is on the right.

Fig. 1 illustrates the overall quality of agreement between NDSEV and keyaspects of the observed tornado risk in the continental U.S. The climatology ofNDSEV (left panel), indicates a broad ”C-shaped” region of elevated risk inthe middle of the country comparable to the climatology of Brooks 2003 (Fig. 4)based on observations of tornadoes. A region of high risk stretching NNE fromthe Texas panhandle to the Great Lakes is coincident with the well documented”Tornado Alley.” A second region of comparable risk extending eastward fromTexas to Missouri is sometimes referred to as ”Dixie Alley” [26]. Additionallocal maxima in Florida and along the mid-Atlantic bight are also reflected inobservations. Some overestimation of tornado frequency along the Gulf coastis due to heavy precipitation from non-supercell storm systems such as tropi-cal storms. Further confidence in this index may be inferred from the seasonalprogression of tornado risk illustrated by plotting the month of most frequenttornado activity at each point (right panel). This figure indicates that earlyseason (Feb-Mar-Apr) tornado risk is primarily confined to the southeast andthen expands westward and northward as the season proceeds, this is consistentwith previous findings [27, 12].

Table 1: Correlations of monthly tornado observations with NARR NDSEV.Bold entries indicate significant correlations at the 95% confidence level.

J F M A M J J A S O N D.75 .17 .53 .37 .57 .36 .45 .45 .65 .63 .50 .36

The inter-annual variability of tornado occurrence is pronounced, but eventhis simple index based only on the climatological seasonal cycle can capturemuch of the observed variance. Table. 1 lists the correlations between monthly

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J F M A M J J A S O N D0

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Figure 2: Seasonal cycle of monthly mean tornado frequencies for the period1990-2010 from SPC observations (black), NARR (red). Also shown are valuesfrom two Athena models at resolutions T159 (blue) and T1279 (green) for theperiod 1990-2007.

observed tornado counts from 1979-2010 and those indicated by NDSEV fromNARR, with bold values indicating significance at the 95% confidence level.We find a significant correlation in every month except February when tornadocounts are very low. Prior to 1990 NDSEV exhibits a consistent range ofvariability; however, the observations tend to have lower tornado counts (notshown). This apparent trend is probably due to inconsistencies in tornado re-porting practices over time, which have been well documented [10, 28, 12], andnot indicative of an abrupt increase in tornadic events during the past twodecades. Based on this assumption, the mean monthly tornado counts in Fig.2 are calculated only for this latter portion of the record (1990-2010). Herewe see general agreement between the observations and NDSEV , with maxi-mum values in May and June, falling off rapidly to low numbers of tornadoesin the autumn and winter months. A large discrepancy is found in September,when NDSEV indicates larger numbers of tornadoes than have been observed.These events are concentrated along the Gulf and East coasts, reflecting heavyprecipitation events associated with tropical storms. Also plotted in this figureare monthly means calculated from two of the Athena model AMIP simulationsat horizontal resolutions of T159 and T1279. These climatologies were calcu-lated using the same value of γ derived for NARR and τγ prescribed to matchthe annual mean number of tornado observations from 1990-2007. Both mod-els capture the overall structure of the observed seasonal cycle supporting therobustness of the power-law relationship determined from NARR.

The seasonal march of tornado risk can be seen more clearly by looking at themonthly climatologies of NDSEV in Fig. 3 for the peak of the tornado season.These distributions may be compared with Fig. 7 of Brooks 2003a (hereafterB03), which illustrates the risk for individual days throughout the year based

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APRIL

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Figure 3: Distribution of NDSEV per month for April (left), May (middle)and June (right) as derived from NARR.

on observations. In April (left), large tornado frequencies are confined to thesoutheast, with maxima in eastern Oklahoma and northern Louisiana. Thisdistribution is very similar to the April 1st analysis of B03 (Fig. 7 (b)), al-though NDSEV shows a persistent overestimate of risk along the Gulf coast.May (center) shows a westward and northward expansion of the region of highrisk, with localized maxima along the eastern seaboard that are also reflected inobservations [B03 Fig. 7 (c)]. The large May NDSEV values are shifted some-what eastward relative to the observations for May 20th in B03 and NDSEV istoo high along the lower portion of the Mississippi river. June NDSEV (right)shows a pronounced Tornado Alley extending from Northern Texas to the GreatLakes and reduced frequencies from Louisiana to Alabama relative to April andMay. When compared with B03 Fig.7 (d) the main discrepancy is in north-east Colorado, where observations show a large frequency of tornado occurrencenot reflected in NDSEV . Considering the fact that the power-law relation forNDSEV was derived from spatially averaged, climatological tornado data, theskill with which NDSEV can reproduce the spatial distribution of tornado riskand its inter-annual variability is remarkable. Furthermore, the same power-lawapplied to the Athena climate models also produced reasonable spatio-temporaldistributions of tornado risk, providing some confidence in the generality of thisapproach and its potential for making predictions from GCM simulations.

5 Climate Change

The April-May-June (AMJ)NDSEV distribution from the Athena model AMIPsimulations is plotted in the upper panels of Fig. 4. These three months havethe largest tornado counts in both observations and NARR and also show thegreatest changes in the predicted future climate (not shown), so we focus onthe AMJ means. On the left are values from the lower resolution (T159) model,while the higher resolution (T1279) model results are plotted on the right. Theoverall distribution of tornado risk, confined to the region east of the Rockieswith maxima in the Great Plains, is reflected by NDSEV derived from boththese models. Some biases relative to the climatology from the NARR should

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T159

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Figure 4: The distribution of NDSEV per season (upper panels) for April-May-June (AMJ) simulated by the Athena models at T159 (left) and T1279 (right)horizontal resolutions. Lower panels show the predicted changes in the AMJNDSEV distributions for each model.

be noted. The T159 model has a distribution which is more broad and has lowerpeak values than NARR; however, it clearly depicts both a NNE oriented Tor-nado Alley and an East-West oriented maximum in the Gulf region. The T1279NDSEV is dominated by a pronounced maximum running NNE from Texasto Minnesota. The T1279 model does indicate moderate tornado frequencies inLouisiana, Missouri and Mississippi, but the values are much lower than thoseof NARR or the T159 model. These measurable differences may be surprisinggiven the similarity of the two models, which differ only in their horizontal res-olution. This points to the sensitivity of regional predictions for such extremeevents as those reflected by NDSEV .

The threshold τγ determined from the calibration period (1990-2007) wasapplied to the time-slice simulations in order to evaluate the predicted changesin the future number of tornadoes based on this power-law relationship. First itshould be noted that both models predict an overall increase in the number ofU.S. tornadoes in a warmer world, consistent with findings of previous studies[8, 9]. The T159 model projects a 64% increases in the annual number of U.S.tornadoes and the T1279 simulation shows a 45% increase. In both models,over 40% of the increase in annual tornado counts occurs during the months of

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AMJ, at the height of the tornado season.The two models indicate similar changes in the distribution of AMJ torna-

does for this A1B future climate scenario. The lower panels of Fig. 4 illustratethe fact that both Athena models predict a dipole pattern for the AMJ NDSEVchanges. This pattern of decreased tornado frequencies throughout much of theGulf region, south of 35N, and increased frequencies across the northeast U.S.has not been discussed in previous studies using CAPE based power-law rela-tions, although similar changes in severe convective potential can be seen inFig. 6 of Van Klooster 2009. Most predictions of future tornado distributionshave shown increases across much of the U.S., which have been attributed tothe thermodynamic changes anticipated in a warming world that result in abroad rise in mean CAPE [8, 9]. These previous studies have noted decreases inthe magnitude of vertical shear (S) in their simulated future climates, consis-tent with reduced meridional temperature gradients. For the months of AMJ,both Athena models predict a poleward shift of the maximum S values due toa change in the distribution of zonal winds at the 500mb level (Fig. 5 lowerpanels). This poleward shift is not accompanied by any overall decrease in Sduring this AMJ season, in contrast to the assessments in previous studies. Thisdiscrepancy may be due to their analysis of annual trends instead of the seasonalchanges discussed here. Both models predict small increases in mean AMJ con-vective precipitation across broad portions of the U.S. (not shown), but thesechanges do not explain the dipole pattern seen in the NDSEV changes.

The vertical shear S is dominated by the contribution from the 500mb zonalwinds, since its magnitude is much larger than that of the surface or meridionalwind components. In Fig. 5 the upper panels show the climatological AMJ dis-tributions of 500mb zonal winds over the U.S., which are everywhere westerlyand have a localized maximum near 40N. The seasonal march of tornado riskgenerally follows the northward progression of these westerly winds through-out the year, because the equatorward flank of the tropospheric jet is a criticalenvironment where warm, moist, tropical air can encounter significant verticalshear and growing extra-tropical instabilities. The change in mean 500mb AMJzonal winds between the 21st century (2071-2117) and the 20th (1961-2007) areplotted in the lower panels of Fig. 5. Both models project a poleward shiftof the maximum westerly winds during the AMJ season revealed as a dipolepattern of zonal wind changes with strengthened westerlies north of 40N andweakened winds to the south. This pattern of changes corresponds closely withthe changes seen in NDSEV (Fig. 4). A number of climate change investigationshave focused on predictions of a poleward shift of the atmospheric jet streams[29, 30, 31]. Although there remains no consensus as to the primary mechanismresponsible for this phenomenon, it has been observed in many GCM studies ofclimate. This poleward shift of the mean tropospheric winds should be consid-ered a first order prediction of climate change with major implications for thefuture of mid-latitude weather.

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Figure 5: Same as Fig. 4 for 500mb zonal winds [m/s].

6 Conclusions

This paper has introduced a new method for determining the power-law relation-ships used in defining threshold based indices of severe weather. These indices,such as NDSEV investigated in this study, provide a means for assessing severeweather phenomena with global models that do not explicitly resolve the rel-evant convective motions. Previous studies have used definitions based on the”best discriminator” introduced by Brooks 2003b, which relates observations ofCAPE and vertical shear (S) from proximity soundings to the severity of concur-rent convection. This empirical relation was compared to an objectively derivedpower-law relation based on convective precipitation (P ) and S, revealing thatthe new objective definition provided a more skillful predictor of severe weather,based on observations of tornadoes in the U.S. This finding is consistent withrecent work by Tippett et al. 2012, which found P to be a better predictor oftornado frequencies than CAPE. The method for deriving NDSEV is very sim-ple, reducing the power-law relation to a function with a single free parameter(γ), which can be determined by minimizing a cost function constructed fromthe climatological monthly U.S. tornado observations. This approach is similarto that employed by Tippett et al 2012, although they used monthly meansof their predictor variables and employed storm-relative helicity instead of S.The method was applied to the North American Regional Reanalysis (NARR)

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resulting in an estimate of tornado frequency, NDSEV , that captures the sea-sonal progression of the tornado distribution and exhibits skill at reproducingthe inter-annual variability of monthly tornado observations.

Severe weather indices based on these larger scale environmental variablesallow predictions to be made from the coarse resolution simulations of climatemade with GCMs. These GCMs can be run for hundreds of years relativelyinexpensively compared with the numerical weather prediction models that aretypically run for only hours to days. This permits our first access to the tan-talizing question of what the consequences of anthropogenic climate change arein regard to severe convective weather such as tornadoes. Several studies havealready presented evidence that a warmer world will be more conducive to se-vere weather [7, 8, 9]. These increases in the strength of convection, number ofsevere thunderstorms and tornadoes have been attributed to increased CAPEin a warmer, wetter atmosphere. This study also finds an overall increase inNDSEV , corresponding to a general increase in both mean P and its extremevalues.

More important than the overall trend in occurrences of severe weather isany change in the distribution of that weather related risk. Ultimately, we wouldlike to provide regional forecasts for the coming century that could help informdecisions about infrastructure improvements, insurance needs and individualreadiness. Using a pair of state of the art high-resolution climate simulations, aconsistent pattern of shifting tornado risk at the height of the tornado season wasobserved. This northward shift of the maximum tornado frequency is consistentwith a change in the mean zonal circulation in the mid-troposphere. This trendin atmospheric circulation has been observed in many simulations of climatechange and should be considered the first order response of the mean winds.Such a change in the circulation will have consequences for future precipitation,wind resources as well as tornadoes and severe weather.

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7 Author Contributions

A. Solomon is the corresponding author, developed the new method for calculat-ing NDSEV , evaluated the data, generated the figures and wrote the paper. J.Lu was a principal advisor on the analysis and consulted frequently on both thescience and content of the paper. B. Cash was an integral part of the Athenaproject, which provided the global climate simulations and consulted on theframework of the paper. E. Palipane prepared much of the data for analysis. J.Kinter is the principal investigator on the Athena project and provided guidanceon the layout of the paper.

8 Competing Financial Interests

The authors declare no competing financial interests.

9 Figure Legends

Fig. 1 The spatial distribution of U.S. tornado risk.The number of severe weather days (NDSEV ) per year, per 10,000km2, as de-rived from the North American Regional Reanalysis for the period 1979-2010,is plotted on the left. The month of most frequent tornado occurrence at eachgrid point (for points with more than one tornado per year) is on the right.

Fig. 2 Monthly mean tornado occurrences within the U.S.Seasonal cycle of monthly mean tornado frequencies for the period 1990-2010from SPC observations (black), NARR (red). Also shown are values from twoAthena models at resolutions T159 (blue) and T1279 (green) for the period1990-2007.

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Fig. 3 Tornado risk for selected months.Distribution of NDSEV per month for April (left), May (middle) and June(right) as derived from NARR.

Fig. 4 Current U.S. tornado risk and future changes simulated by a global cli-mate model.The distribution of NDSEV per season (upper panels) for April-May-June(AMJ) simulated by the Athena models at T159 (left) and T1279 (right) hor-izontal resolutions. Lower panels show the predicted changes in the AMJNDSEV distributions for each model.

Fig. 5 Mean winds and their future changes.Same as Fig. 4 for 500mb zonal winds [m/s].

10 Tables

Table 1: Correlations of monthly tornado observations with NARR NDSEV.Bold entries indicate significant correlations at the 95% confidence level.

J F M A M J J A S O N D.75 .17 .53 .37 .57 .36 .45 .45 .65 .63 .50 .36

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