Tornado Risks Will Shift with a Changing Climate. Abraham L. Solomon 1 *, J. Lu 1, B. Cash 1, E. Palipane 1, and J. Kinter 1

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1 Tornado Risks Will Shift with a Changing Climate. Abraham L. Solomon *, J. Lu, B. Cash, E. Palipane, and J. Kinter Affiliations [] IGES / COLA 404 Powder Mill Rd. Suite 302 Calverton, MD

2 In this paper, the distribution of tornadoes within the continental U.S. is assessed from observational datasets and climate models using an index (N DSEV ) calculated from variables characterizing the large-scale environment in which severe weather develops. This reformulated index correlates better with observed tornado data than similar indices, investigated in previous studies. Moreover, when evaluated for a pair of high-resolution climate models, N DSEV predicts an increase of more than 45% in the annual number of severe weather days, consistent with findings by previous authors. More important, the spatial distribution of that increased risk is predicted to exhibit a northward shift, driven by a change in the mean winds. Such a change in the distribution of severe weather events should be a factor in planning concerning infrastructure improvements, insurance needs, and individual readiness. Introduction Record setting weather events during the past decade have occurred in conjunction with some of the hottest global mean temperatures in the instrumental record []. Additionally, some longer term trends in weather related losses have been observed over the past half century [2]. This fact has led many to consider 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 individual events lie outside the range of natural variability, but simulations of future climate can inform the question: Will similar extreme events be more likely to occur in a warmer world? The implications of global warming for certain aspects of the climate are relatively straightforward. For instance, as the global mean surface temperature increases, the frequency of high temperature extremes is expected to increase. More complex phenomena are more challenging to predict, like the mid-latitude storms which provide precipitation as well as spawn tornadoes, produce hail and lightning. A number of papers have been published confirming a link between climate change and increased risk from severe thunderstorms and tornadoes [7, 8, 9]. These investigations must rely on the relationships between severe weather events and the larger atmospheric environment that produces them, because the models used for studying global climate (GCMs) do not directly simulate convection at the scale of individual storms. It has been demonstrated that the spatial distribution and seasonal cycle of tornado frequency can be reasonably reproduced by defining indices for severe weather days based on environmental parameters from reanalysis data [0,, 2]. Such indices define a severe weather day based on some measure of convection and vertical shear exceeding an empirical threshold. This approach is limited both by the accuracy with which these simple relations can reproduce the actual tornado risk and the accuracy with which the environmental parameters themselves can be predicted. This paper will introduce a new index for severe weather that exhibits sig- 2

3 nificant skill at reproducing the observed record of tornadoes in the U.S. This new index is closely related to those employed in previous studies; however, we use an objectively determined characterization of tornadic environments from a reanalysis, instead of adopting threshold criteria estimated from observations and applying them to gridded data. This approach acknowledges the inability of the current generation of global models to reproduce the extreme environments in which severe weather develops. Climate models do not accurately reproduce the diurnal cycle or frequency of extreme CAPE values seen in observations [3]. Coarse model resolutions (typically greater than 00km) imply that model cannot simulate the large spatial heterogeneity seen in proximity soundings of severe weather [4], so thresholds based on those values may not be the most relevant for model assessment. This new index will be useful for more general model inter-comparison, allowing a more robust consensus on the projections of severe weather in a changing climate. To demonstrate the potential for this index, it is applied to a pair of state-of-the-art climate models to evaluate their ability to reproduce the observed tornado risk in the U.S. and to project that risk to change in a warming climate. In the next section the new index will be defined and the method for evaluating it from a gridded atmospheric dataset will be explained. The third section will describe the reanalysis and model data used. The fourth and fifth sections cover the results from the reanalysis and climate models. 2 NDSEV - The Number of Days with Severe Weather This study employs a power-law relationship between convective precipitation rate (P ) and lower tropospheric vertical wind shear (S) to define a threshold for tornadic environments. This general approach was first used by Brooks 2003b, who calculated a best discriminator from tornado observations, using local measurements of convective available potential energy (CAPE) and shear (S). This parameterization has been employed in several publications to assess severe weather environments from reanalysis [0, 3, 5], as well as from models of present and future climate [3, 8, 9, 6]. These studies defined indices to identify supercell type thunderstorms, which are responsible for the majority of damaging tornadoes [7]. These large convective systems are characterized by buoyant updrafts, rainy downdrafts and deep mesocyclonic vortices. High values of CAPE are associated with buoyant updrafts through observations and through a theoretical relation to maximum potential vertical velocities. This relation to the strength of updrafts has motivated the use of CAPE in the construction of severe weather indices (e.g. Trapp 2007), although some studies have used alternate measures of vertical velocity [7, 8]. The mesocyclones necessary to produce damaging tornadoes typically are less than 0km in scale and hence cannot be simulated by the coarse reso- 3

4 lutions of climate models. Lower tropospheric, vertical wind shear, represented here by the difference (S = V 500hpa V 0m ) between near surface 0m winds and mid-tropospheric 500hpa winds, promotes the rotation in thunderstorms leading to mesocyclone development. Since vertical shear of the large scale flow is well resolved by models, S has been used in the majority of studies of this type. Tippett 202 considered a wider range of environmental variables as predictors for tornado frequency and found that P was more skillful than CAPE. We compared the results of Brooks 2003b best discriminator, objective estimation of a new power-law using CAPE, as well as this alternative approach using P for multiple reanalyses and model simulations. The most skillful predictors based on our approach proved to be P and S, so we will only discuss results based on this parameterization. Since rainy downdrafts are also characteristic of supercell thunderstorms, this alternate parameterization is conceptually consistent with more conventional approaches to identify storms with a potential for tornadogenesis. The criterion for a tornadic environment in this study is based on a tornado potential power-law relation: T γ (λ, φ, t) = P S γ τ γ () where the exponent γ and threshold τ γ are determined so as to best reproduce the observed tornado seasonal cycle. At each grid point the timeseries of T can be used to count the number of days per year when a given threshold is exceeded. This is the definition of NDSEV: NDSEV γ (λ, φ) = (t f t 0 ) λ φ cos φ t f t=t 0 H(T γ (λ, φ, t) τ γ ), (2) where H is the Heaviside function, (λ, φ) is the longitude and latitude of the grid point. For each value of γ a threshold τ γ is found such that the annual mean number of tornados predicted by the model matches the observations from (when the observations are most reliable). Then the optimal value of γ is determined by minimizing a cost function, defined as the root-mean-square error between the observed monthly mean tornado counts and the inferred monthly frequencies from NDSEV γ. 3 Data and Model For this study, the power-law relationship is determined from the North American Regional Reanalysis (NARR) [9]. This high-resolution reanalysis provides data on a 32km Lambert grid at 6-hourly intervals. Data from was employed and all the 4-times daily data was retained, thereby making no assumptions 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 and time provides a better representation of the local environment in which sub-grid scale convective events occur than older reanalyses with grid spacings of more 4

5 than 00km. The NARR also assimilated observed precipitation, which provides some additional confidence in the P fields needed for the calculation of NDSEV. To determine the optimum power-law relation, N DSEV was calculated for 200 equally space values of γ in the range (0, 0). While this brute force optimization method is somewhat computationally intensive, it is simple and reliable. The observations of U.S. tornadoes were acquired from the Storm Prediction Center (SPC) of the National Weather Service and a monthly climatology was calculated for the period The cost function for this range of γ exhibited a clear minimum, resulting in γ = 0.25 and τ γ = 9[kg/m 2 (m/s).25 ] with a cost of 0.4% of the annual mean tornado counts. This study makes use of a set of high resolution climate experiments completed in 200 as an international collaborative effort between the Center for Ocean-Land-Atmosphere studies (COLA), the European Centre for Midrange Weather Forecasting (ECMWF) and the Japanese Agency for Marine-Earth Science and Technology (JAMSTEC). The Athena project [20] employed the ECMWF Integrated Forecast System (IFS) [2], used for numerical weather prediction and data-assimilation, to simulate global climate with greater precision than had been previously possible. This undertaking allows a comparison of climate simulations with minimal differences in model configuration aside from horizontal resolution. Studies of the Athena project have already shown that increasing the model resolution improved the representation of tropical atmosphere [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. Two simulations of the Climate of the 20th Century (AMIP type simulations) were run continuously for 47 years with best available estimates of sea-surface temperature (SST) (see Jung 20 for details), starting on January, 96 using initial conditions from ERA-40. The two simulations were run at differing horizontal resolutions, with spectral truncations of T59 and T279, corresponding to average grid spacings of 25km and 6km respectively. The other pair of simulations employed the same two IFS model configurations, but were performed as time-slice experiments of the 2st century (207-27), with the difference in the annual cycle of SST at each grid point taken from the IPCC AR4 integration of CCSM3.0. The higher resolution (T279) data was interpolated to a consistent grid with the lower resolution (T59) simulation using an areaweighted, conservative algorithm. Four times daily data from each experiment was used, just as in the evaluation of the NARR. The results from the Athena models provide both an opportunity to see if N DSEV provides reasonable climatologies of tornadoes from the AMIP simulations of 20th century climate and to see two realizations of the change in tornado distributions projected for the 2st century. 5

6 4 Climatology of N DSEV NARR NDSEV 32 o W 20 o W 08 o W 96 o W 84 o W 72 o W SEASONALITY 32 o W 20 o W 08 o W 96 o W 84 o W 72 o W Figure : The number of severe weather days (N DSEV ) per year, per 0,000km 2, as derived from the North American Regional Reanalysis for the period , is plotted on the left. The month of most frequent tornado occurrence at each grid point (for points with more than one tornado per year) is on the right. Fig. illustrates the overall quality of agreement between N DSEV and key aspects of the observed tornado risk in the continental U.S. The climatology of N DSEV (left panel), indicates a broad C-shaped region of elevated risk in the 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 from the Texas panhandle to the Great Lakes is coincident with the well documented Tornado Alley. A second region of comparable risk extending eastward from Texas to Missouri is sometimes referred to as Dixie Alley [26]. Additional local maxima in Florida and along the mid-atlantic bight are also reflected in observations. Some overestimation of tornado frequency along the Gulf coast is due to heavy precipitation from non-supercell storm systems such as tropical storms. Further confidence in this index may be inferred from the seasonal progression of tornado risk illustrated by plotting the month of most frequent tornado activity at each point (right panel). This figure indicates that early season (Feb-Mar-Apr) tornado risk is primarily confined to the southeast and then expands westward and northward as the season proceeds, this is consistent with previous findings [27, 2]. Table : 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 The inter-annual variability of tornado occurrence is pronounced, but even this simple index based only on the climatological seasonal cycle can capture much of the observed variance. Table. lists the correlations between monthly 6

7 OBS NARR T59 T J F M A M J J A S O N D Figure 2: Seasonal cycle of monthly mean tornado frequencies for the period from SPC observations (black), NARR (red). Also shown are values from two Athena models at resolutions T59 (blue) and T279 (green) for the period observed tornado counts from and those indicated by N DSEV from NARR, with bold values indicating significance at the 95% confidence level. We find a significant correlation in every month except February when tornado counts are very low. Prior to 990 N DSEV exhibits a consistent range of variability; however, the observations tend to have lower tornado counts (not shown). This apparent trend is probably due to inconsistencies in tornado reporting practices over time, which have been well documented [0, 28, 2], and not indicative of an abrupt increase in tornadic events during the past two decades. Based on this assumption, the mean monthly tornado counts in Fig. 2 are calculated only for this latter portion of the record ( ). Here we see general agreement between the observations and N DSEV, with maximum values in May and June, falling off rapidly to low numbers of tornadoes in the autumn and winter months. A large discrepancy is found in September, when N DSEV indicates larger numbers of tornadoes than have been observed. These events are concentrated along the Gulf and East coasts, reflecting heavy precipitation events associated with tropical storms. Also plotted in this figure are monthly means calculated from two of the Athena model AMIP simulations at horizontal resolutions of T59 and T279. These climatologies were calculated using the same value of γ derived for NARR and τ γ prescribed to match the annual mean number of tornado observations from Both models capture the overall structure of the observed seasonal cycle supporting the robustness of the power-law relationship determined from NARR. The seasonal march of tornado risk can be seen more clearly by looking at the monthly climatologies of NDSEV in Fig. 3 for the peak of the tornado season. These distributions may be compared with Fig. 7 of Brooks 2003a (hereafter B03), which illustrates the risk for individual days throughout the year based 7

8 APRIL 2.5 MAY 2.5 JUNE o W 20 o W 08 o W 96 o W 84 o W 72 o W 32 o W 20 o W 08 o W 96 o W 84 o W 72 o W 32 o W 20 o W 08 o W 96 o W 84 o W 72 o W Figure 3: Distribution of N DSEV 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 the southeast, with maxima in eastern Oklahoma and northern Louisiana. This distribution is very similar to the April st analysis of B03 (Fig. 7 (b)), although N DSEV shows a persistent overestimate of risk along the Gulf coast. May (center) shows a westward and northward expansion of the region of high risk, with localized maxima along the eastern seaboard that are also reflected in observations [B03 Fig. 7 (c)]. The large May N DSEV values are shifted somewhat eastward relative to the observations for May 20th in B03 and NDSEV is too high along the lower portion of the Mississippi river. June N DSEV (right) shows a pronounced Tornado Alley extending from Northern Texas to the Great Lakes and reduced frequencies from Louisiana to Alabama relative to April and May. When compared with B03 Fig.7 (d) the main discrepancy is in northeast Colorado, where observations show a large frequency of tornado occurrence not reflected in NDSEV. Considering the fact that the power-law relation for N DSEV was derived from spatially averaged, climatological tornado data, the skill with which N DSEV can reproduce the spatial distribution of tornado risk and its inter-annual variability is remarkable. Furthermore, the same power-law applied to the Athena climate models also produced reasonable spatio-temporal distributions of tornado risk, providing some confidence in the generality of this approach and its potential for making predictions from GCM simulations. 5 Climate Change The April-May-June (AMJ) N DSEV distribution from the Athena model AMIP simulations is plotted in the upper panels of Fig. 4. These three months have the largest tornado counts in both observations and NARR and also show the greatest changes in the predicted future climate (not shown), so we focus on the AMJ means. On the left are values from the lower resolution (T59) model, while the higher resolution (T279) model results are plotted on the right. The overall distribution of tornado risk, confined to the region east of the Rockies with maxima in the Great Plains, is reflected by NDSEV derived from both these models. Some biases relative to the climatology from the NARR should 8

9 T59 T o W 05 o W 90 o W 75 o W 60 o W 20 o W 05 o W 90 o W 75 o W 60 o W DIFF. 2st 20th DIFF. 2st 20th o W 05 o W 90 o W 75 o W 60 o W 20 o W 05 o W 90 o W 75 o W 60 o W.5 Figure 4: The distribution of NDSEV per season (upper panels) for April-May- June (AMJ) simulated by the Athena models at T59 (left) and T279 (right) horizontal resolutions. Lower panels show the predicted changes in the AMJ N DSEV distributions for each model. be noted. The T59 model has a distribution which is more broad and has lower peak values than NARR; however, it clearly depicts both a NNE oriented Tornado Alley and an East-West oriented maximum in the Gulf region. The T279 N DSEV is dominated by a pronounced maximum running NNE from Texas to Minnesota. The T279 model does indicate moderate tornado frequencies in Louisiana, Missouri and Mississippi, but the values are much lower than those of NARR or the T59 model. These measurable differences may be surprising given the similarity of the two models, which differ only in their horizontal resolution. This points to the sensitivity of regional predictions for such extreme events as those reflected by NDSEV. The threshold τ γ determined from the calibration period ( ) was applied to the time-slice simulations in order to evaluate the predicted changes in the future number of tornadoes based on this power-law relationship. First it should be noted that both models predict an overall increase in the number of U.S. tornadoes in a warmer world, consistent with findings of previous studies [8, 9]. The T59 model projects a 64% increases in the annual number of U.S. tornadoes and the T279 simulation shows a 45% increase. In both models, over 40% of the increase in annual tornado counts occurs during the months of 9

10 AMJ, at the height of the tornado season. The two models indicate similar changes in the distribution of AMJ tornadoes for this AB future climate scenario. The lower panels of Fig. 4 illustrate the fact that both Athena models predict a dipole pattern for the AMJ NDSEV changes. This pattern of decreased tornado frequencies throughout much of the Gulf region, south of 35N, and increased frequencies across the northeast U.S. has not been discussed in previous studies using CAPE based power-law relations, although similar changes in severe convective potential can be seen in Fig. 6 of Van Klooster Most predictions of future tornado distributions have shown increases across much of the U.S., which have been attributed to the thermodynamic changes anticipated in a warming world that result in a broad rise in mean CAPE [8, 9]. These previous studies have noted decreases in the magnitude of vertical shear (S) in their simulated future climates, consistent with reduced meridional temperature gradients. For the months of AMJ, both Athena models predict a poleward shift of the maximum S values due to a change in the distribution of zonal winds at the 500mb level (Fig. 5 lower panels). This poleward shift is not accompanied by any overall decrease in S during this AMJ season, in contrast to the assessments in previous studies. This discrepancy may be due to their analysis of annual trends instead of the seasonal changes discussed here. Both models predict small increases in mean AMJ convective precipitation across broad portions of the U.S. (not shown), but these changes do not explain the dipole pattern seen in the N DSEV changes. The vertical shear S is dominated by the contribution from the 500mb zonal winds, since its magnitude is much larger than that of the surface or meridional wind components. In Fig. 5 the upper panels show the climatological AMJ distributions of 500mb zonal winds over the U.S., which are everywhere westerly and have a localized maximum near 40N. The seasonal march of tornado risk generally follows the northward progression of these westerly winds throughout the year, because the equatorward flank of the tropospheric jet is a critical environment where warm, moist, tropical air can encounter significant vertical shear and growing extra-tropical instabilities. The change in mean 500mb AMJ zonal winds between the 2st century (207-27) and the 20th ( ) are plotted in the lower panels of Fig. 5. Both models project a poleward shift of the maximum westerly winds during the AMJ season revealed as a dipole pattern of zonal wind changes with strengthened westerlies north of 40N and weakened winds to the south. This pattern of changes corresponds closely with the changes seen in NDSEV (Fig. 4). A number of climate change investigations have focused on predictions of a poleward shift of the atmospheric jet streams [29, 30, 3]. Although there remains no consensus as to the primary mechanism responsible for this phenomenon, it has been observed in many GCM studies of climate. This poleward shift of the mean tropospheric winds should be considered a first order prediction of climate change with major implications for the future of mid-latitude weather. 0

11 T59 T o W 05 o W 90 o W 75 o W 60 o W 20 o W 05 o W 90 o W 75 o W 60 o W 5 DIFF. 2st 20th DIFF. 2st 20th o W 05 o W 90 o W 75 o W 60 o W 20 o W 05 o W 90 o W 75 o W 60 o W 2.5 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 relationships used in defining threshold based indices of severe weather. These indices, such as N DSEV investigated in this study, provide a means for assessing severe weather phenomena with global models that do not explicitly resolve the relevant convective motions. Previous studies have used definitions based on the best discriminator introduced by Brooks 2003b, which relates observations of CAPE and vertical shear (S) from proximity soundings to the severity of concurrent convection. This empirical relation was compared to an objectively derived power-law relation based on convective precipitation (P ) and S, revealing that the new objective definition provided a more skillful predictor of severe weather, based on observations of tornadoes in the U.S. This finding is consistent with recent work by Tippett et al. 202, which found P to be a better predictor of tornado frequencies than CAPE. The method for deriving N DSEV is very simple, reducing the power-law relation to a function with a single free parameter (γ), which can be determined by minimizing a cost function constructed from the climatological monthly U.S. tornado observations. This approach is similar to that employed by Tippett et al 202, although they used monthly means of their predictor variables and employed storm-relative helicity instead of S. The method was applied to the North American Regional Reanalysis (NARR)

12 resulting in an estimate of tornado frequency, NDSEV, that captures the seasonal progression of the tornado distribution and exhibits skill at reproducing the inter-annual variability of monthly tornado observations. Severe weather indices based on these larger scale environmental variables allow predictions to be made from the coarse resolution simulations of climate made with GCMs. These GCMs can be run for hundreds of years relatively inexpensively compared with the numerical weather prediction models that are typically run for only hours to days. This permits our first access to the tantalizing question of what the consequences of anthropogenic climate change are in regard to severe convective weather such as tornadoes. Several studies have already presented evidence that a warmer world will be more conducive to severe weather [7, 8, 9]. These increases in the strength of convection, number of severe thunderstorms and tornadoes have been attributed to increased CAPE in a warmer, wetter atmosphere. This study also finds an overall increase in NDSEV, corresponding to a general increase in both mean P and its extreme values. More important than the overall trend in occurrences of severe weather is any change in the distribution of that weather related risk. Ultimately, we would like to provide regional forecasts for the coming century that could help inform decisions about infrastructure improvements, insurance needs and individual readiness. Using a pair of state of the art high-resolution climate simulations, a consistent pattern of shifting tornado risk at the height of the tornado season was observed. This northward shift of the maximum tornado frequency is consistent with a change in the mean zonal circulation in the mid-troposphere. This trend in atmospheric circulation has been observed in many simulations of climate change 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. References [] Huber, Daniel G. and Gulledge, Jay. Extreme Weather and Climate Change: Understanding the Link and Managing the Risk Science and Impacts Program. (Center for Climate and Energy Solutions: Arlington, VA (20). [2] Bouwer, L.M. Have Disaster Losses Increased Due to Anthropogenic Climate Change? Bull. Amer. Meteor. Soc., 92, (20). [3] Diffenbaugh, N. S., R. J. Trapp, and H. Brooks. Does Global Warming Influence Tornado Activity?, Eos. Trans. AGU, 89(53), 553 (2008). [4] Coumou, Dim, Stefan Rahmstorf, A decade of weather extremes, Nature Clim. Change, 2, (202). [5] Doswell III, C. A., Carbin, G. W. and Brooks, H. E. The tornadoes of spring 20 in the USA: an historical perspective. Weather, 67: 8894 (202). 2

13 [6] Trenberth, Kevin. Framing the way to relate climate extremes to climate change. Climatic Change (202), doi: 0.007/s [7] Del Genio, A. D., M.-S. Yao, and J. Jonas. Will moist convection be stronger in a warmer climate? Geophys. Res. Lett., 34, L6703 (2007). [8] Trapp, Robert J., Noah S. Diffenbaugh, Harold E. Brooks, Michael E. Baldwin, Eric D. Robinson, and Jeremy S. Pal Changes in severe thunderstorm environment frequency during the 2st century caused by anthropogenically enhanced global radiative forcing Proc. Nat. Acad. Sci., 04 (50) (2007). [9] Trapp, R. J., N. S. Diffenbaugh, and A. Gluhovsky. Transient response of severe thunderstorm forcing to elevated greenhouse gas concentrations, Geophys. Res. Lett., 36, L0703 (2009). [0] Brooks, H.E., Lee, J.W., Craven, J.P. The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data. Atmos. Res. 6768, 7394 (2003b). [] Timbal, B., R. Kounkou, G. A. Mills. Changes in the risk of cool-season tornadoes over southern australia due to model projections of anthropogenic warming. J. Climate, 23, (200). [2] Tippett, M. K., A. H. Sobel, and S. J. Camargo. Association of U.S. tornado occurrence with monthly environmental parameters, Geophys. Res. Lett., 39, L0280 (202). [3] Marsh, P.T., Brooks, H.E., Karoly, D.J. Assessment of the severe weather environment in North America simulated by a global climate model. Atmos. Sci. Lett., 8 (2007). [4] Brooks, Harold E., Charles A. Doswell, Jeremy Cooper. On the environments of tornadic and nontornadic mesocyclones. Weather Forecast, 9, (994). [5] Gensini, V. A., and W. S. Ashley. Climatology of potentially severe convective environments from North American regional reanalysis. Electronic J. Severe Storms Meteor. 6 (8), 40 (20). [6] Van Klooster, S.L., Roebber, P.J. Surface-based convective potential in the contiguous United States in a business-as-usual future climate. J. Clim., 22, (2009). [7] Davies-Jones, Robert. Streamwise Vorticity: The Origin of Updraft Rotation in Supercell Storms. J. Atmos. Sci., 4, (984). [8] Brooks, H.E., Severe thunderstorms and climate change. Atmos. Res., (202). Available online 9 April 202, ISSN , 0.06/j.atmosres

14 [9] Mesinger, Fedor, and Coauthors. North American Regional Reanalysis. Bull. Am. Meteor. Soc., 87, doi: (2006). [20] Kinter III JL, Cash B, Achuthavarier D, Adams J, Altshuler E, Dirmeyer PA, Huang B, Jin E, Marx L, Manganello J, Stan C, Wakefield T, Palmer T, Hamrud M, Jung T, Miller M, Towers P, Wedi N, Satoh M, Tomita H, Kodama C, Yamada Y, Andrews P, Baer T, Ezell M, Halloy C, John D, Loftis B, Wong K. Revolutionizing climate modelingproject Athena: a multi- institutional, international collaboration. Bull. Am. Meteor. Soc. (20) (submitted). [2] Bechtold, P., M. Khler, T. Jung, F. Doblas-Reyes, M. Leutbecher, M. Rodwell, F. Vitart, and G. Balsamo. Advances in Simulating Atmospheric Variability with the ECMWF Model: From Synoptic to Decadal Time-Scales. Quart. J. Roy. Meteor. Soc., 34, (2008). [22] Satoh, M., Oouchi, K., Nasuno, T., Taniguchi, H., Yamada, Y., Tomita, H., Kodama, C., Kinter III, J., Achuthavarier, D. Manganello, J, Cash, B., Jung, T., Palmer, T. and Wedi, N. Intra-Seasonal Oscillation and its control of tropical cyclones simulated by high-resolution global atmospheric models.climate Dyn., (20). doi0.007/s [23] Manganello, J. V., K. I. Hodges, J. L. Kinter III, B. A. Cash, L. Marx, T. Jung, D. Achuthavarier, J. M. Adams, E. L. Altshuler, B. Huang, E. K. Jin, C. Stan, P. Towers and N. Wedi. Tropical Cyclone Climatology in a 0-km Global Atmospheric GCM: Toward Weather-Resolving Climate Modeling. J. Climate, 25, (202). [24] Jung, T., M. J. Miller, T. N. Palmer, P. Towers, N. Wedi, D. Achuthavarier, J. M. Adams, E. L. Altshuler, B. A. Cash, J. L. Kinter III, L. Marx, C. Stan, K. I. Hodges. High-Resolution Global Climate Simulations with the ECMWF Model in the Athena Project: Experimental Design, Model Climate and Seasonal Forecast Skill. J. Climate (20) (online). [25] Dirmeyer, P. A. B. A. Cash, J. L. Kinter III, T. Jung, L. Marx, M. Satoh, C. Stan, H. Tomita, P. Towers, N. Wedi, D. Achuthavarier, J. M. Adams, E. L. Altshuler, B. Huang, E. K. Jin, and J. Manganello. Simulating the diurnal cycle of rainfall in global climate models: Resolution versus parameterization.climate Dyn. (20). (online), DOI 0.007/s [26] Dixon, P.G., Mercer, A.E., Choi, J., & Allen, J.S. Bull. Am. Meteor. Soc., 92, 433 (20). [27] Brooks, H.E., Doswell III, C.A., Kay, M.P. Climatological estimates of local daily tornado probability. Weather Forecast. 8, (2003a). 4

15 [28] Doswell III, C.A., Brooks, H.E., Dotzek, N. On the implementation of the Enhanced Fujita Scale in the USA. Atmos. Res. 93, (2009). [29] Kushner, P. J., I. M. Held, and T. L. Delworth. Southern Hemisphere atmospheric circulation response to global warming. J. Climate, 4, (200). [30] Lorenz, D. J., and E. T. DeWeaver. The tropopause height and the zonal wind response to global warming in the IPCC scenario integrations. J. Geophys. Res., 2 (2007).D09, doi:0.029/2006jd [3] Lu, Jian, Gang Chen, Dargan M. W. Frierson. Response of the Zonal Mean Atmospheric Circulation to El Nio versus Global Warming. J. Climate, 2, (2008). doi: 7 Author Contributions A. Solomon is the corresponding author, developed the new method for calculating 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 the science and content of the paper. B. Cash was an integral part of the Athena project, which provided the global climate simulations and consulted on the framework of the paper. E. Palipane prepared much of the data for analysis. J. Kinter is the principal investigator on the Athena project and provided guidance on the layout of the paper. 8 Competing Financial Interests The authors declare no competing financial interests. 9 Figure Legends Fig. The spatial distribution of U.S. tornado risk. The number of severe weather days (NDSEV ) per year, per 0,000km 2, as derived from the North American Regional Reanalysis for the period , is plotted on the left. The month of most frequent tornado occurrence at each grid 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 from SPC observations (black), NARR (red). Also shown are values from two Athena models at resolutions T59 (blue) and T279 (green) for the period

16 Fig. 3 Tornado risk for selected months. Distribution of N DSEV 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 climate model. The distribution of N DSEV per season (upper panels) for April-May-June (AMJ) simulated by the Athena models at T59 (left) and T279 (right) horizontal resolutions. Lower panels show the predicted changes in the AMJ N DSEV distributions for each model. Fig. 5 Mean winds and their future changes. Same as Fig. 4 for 500mb zonal winds [m/s]. 0 Tables Table : 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