The Effect of Global Warming on Severe Thunderstorms in the

Preparing to load PDF file. please wait...

0 of 0
The Effect of Global Warming on Severe Thunderstorms in the

Transcript Of The Effect of Global Warming on Severe Thunderstorms in the

15 MARCH 2015



The Effect of Global Warming on Severe Thunderstorms in the United States
Department of Earth and Planetary Science, University of California, Berkeley, and the Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California
(Manuscript received 29 May 2014, in final form 1 December 2014)
How will warming temperatures influence thunderstorm severity? This question can be explored by using climate models to diagnose changes in large-scale convective instability (CAPE) and wind shear, conditions that are known to be conducive to the formation of severe thunderstorms. First, an ensemble of climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) is evaluated on its ability to reproduce a radiosonde climatology of such storm-favorable conditions in the current climate’s spring and summer seasons, focusing on the contiguous United States (CONUS). Of the 11 climate models evaluated, a high-performing subset of four (GFDL CM3, GFDL-ESM2M, MRI-CGCM3, and NorESM1-M) is identified. Second, the twenty-first-century changes in the frequency of environments favorable to severe thunderstorms are calculated in these high-performing models as they are forced by the RCP4.5 and RCP8.5 emissions pathways. For the RCP8.5 scenario, the models predict consistent CONUS-mean fractional springtime increases in the range of 50%–180% by the end of the twenty-first century; for the summer, three of the four models predict increases in the range of 40%–120% and one model predicts a small decrease. This disagreement between the models is traced to divergent projections for future CAPE and boundary layer humidity in the Great Plains. This paper also explores the sensitivity of the results to the relative weight given to wind shear in determining how ‘‘favorable’’ a large-scale environment is for the development of severe thunderstorms, and it is found that this weighting is not the dominant source of uncertainty in projections of future thunderstorm severity.

1. Introduction
In the United States, a thunderstorm is classified as ‘‘severe’’ if it produces wind speeds above a damaging threshold, hail exceeding a certain diameter, or a tornado (National Weather Service 2014). These storms down trees, loft roofs, flood roads, ignite fires with their lightning, and damage cars and crops with large hailstones. They are a significant cause of property damage, and are often deadly—in 2011 alone, over 500 people were killed by tornadoes in the United States (NOAA Storm Prediction Center 2012a). In spite of the catastrophic damage caused by severe thunderstorms in the current climate, their response to enhanced greenhouse
Denotes Open Access content.
Corresponding author address: Jacob Seeley, Department of Earth and Planetary Science, University of California, Berkeley, 449 McCone Hall, Haviland Road, Berkeley, CA 94720. E-mail: [email protected]

forcing remains a poorly understood regional climate change impact (Field 2012; Kunkel et al. 2013).
There are several reasons for this ongoing uncertainty. Most importantly, inconsistent reporting practices have obscured any storm trends that may have accompanied twentieth-century anthropogenic global warming (Brooks and Doswell 2001; Doswell et al. 2005; Verbout et al. 2006; Brooks and Dotzek 2008; Diffenbaugh et al. 2008). As a consequence, research has instead focused on identifying the large-scale ‘‘ingredients’’ of severe convective storms and evaluating how these ingredients will respond to increasing atmospheric greenhouse gas concentrations.
It has been recognized for quite some time that convective available potential energy (CAPE) and deep-layer wind shear—as well as other measures of wind shear, such as helicity—have skill in predicting the severity of thunderstorms in the case that such storms develop at all (Brooks et al. 1994; Rasmussen and Blanchard 1998; Rasmussen 2003). CAPE is a common measure of convective instability and sets an upper bound on the speed of updrafts, while ambient wind shear prolongs and intensifies storms by physically displacing deep-convective

DOI: 10.1175/JCLI-D-14-00382.1

Ó 2015 American Meteorological Society




updrafts from rain shafts and promoting storm-scale rotation. It is not surprising, therefore, that operational weather forecasters use combinations of CAPE and wind shear (along with other information) to issue ‘‘watches’’ for severe thunderstorms, where a watch indicates that meteorological conditions are favorable for the development of severe weather within a few hours (Johns and Doswell 1992). In particular, Brooks et al. (2003) showed that a weighted product of CAPE and 0–6-km wind shear in reanalysis is well correlated with the intensity of nearby observed storms.
The challenge is to determine how CAPE and wind shear—and specifically their regional and subdaily covariation—will change with warming temperatures. Increases in CAPE with global warming have been documented in both climate models (e.g., Sobel and Camargo 2011) and cloud-system-resolving models (Romps 2011), and these increases were recently given theoretical support by Singh and O’Gorman (2013). On the other hand, a first-order prediction for future wind shear calls for a reduced thermal wind gradient, and hence mean shear, as a result of polar amplification of warming (e.g., Trapp et al. 2007a, hereafter T07). These qualitative predictions for how global warming should affect CAPE and wind shear have opposing implications for the severity of future thunderstorms.
In light of this opposition, several recent climate model studies have attempted to quantitatively settle the competition between increasing CAPE and decreasing shear. T07 performed the first multimodel comparison of future severe thunderstorms in the United States and found significant divergence between a regional climate model and three general circulation models (e.g., their Fig. 3). Trapp et al. (2009) used NCAR’s CCSM3 to predict increases in CAPE that outpaced decreases in wind shear, resulting in an increase in environments favorable for severe thunderstorms; however, results from a single GCM should not be given too much weight, considering the disagreement between models shown in T07. Most recently, Diffenbaugh et al. (2013, hereafter D13) expanded on the results of T07 with an enlarged ensemble of 10 GCMs from the archive of phase 5 of the Coupled Model Intercomparison Project (CMIP5) (Taylor et al. 2012). D13 found robust increases in the frequency of severe-thunderstorm environments in the spring and fall across most of the United States, again as a result of increases in CAPE that were large enough to overcome decreases in wind shear. However, the ensemble of models used in D13 diverged significantly in its predictions for the summer months of June–August (JJA), which constitute half of the peak severe-thunderstorm season in the current climate (Kelly et al. 1985). The lingering uncertainty

regarding these important months merits additional study.
Furthermore, in the context of these studies, it is clear that the relative weight given to CAPE and shear in defining storm-favorable conditions is of central importance; depending on this weighting, the same fractional changes in CAPE and shear derived from a climate model’s global warming response could lead to quite different conclusions about changes in the frequency of severe thunderstorms. In fact, multiple studies have argued that the value of ambient wind shear is more strongly tied to a given thunderstorm’s severity than the background CAPE (Brooks et al. 2003; Allen et al. 2011; Brooks 2013). However, previous climate model studies of the effect of global warming on severe thunderstorms in the United States have used a threshold for the unweighted product of CAPE and shear to define when a GCM grid point is favorable for storms. This discrepancy between observational severe-thunderstorm proxies and the proxies that have been used in previous modeling studies is an unnecessary source of uncertainty in our current understanding of the future of thunderstorms.
The present study puts this line of research on more solid ground in two major ways. First, since the ensemble of climate models in D13 was not selected based on demonstrated skill at replicating the contemporary climatology of severe-thunderstorm conditions, it is plausible that some of the divergence in their ensemble’s predictions for the future, especially in the summer, can be traced to differences between the models in their base state of simulated severe-thunderstorm conditions. To test this hypothesis, in section 2, we derive an observational climatology of United States severe-thunderstorm environments from a decade of radiosonde observations and evaluate an ensemble of 11 CMIP5 climate models on its ability to capture the spatial pattern of these observations throughout the principal severe-thunderstorm season of March–August. Second, in section 3, we focus on the changes in severe-thunderstorm conditions predicted by the high-performing subset of models identified in section 2 as they respond to the range of greenhouse forcing spanned by the RCP4.5 and RCP8.5 emissions scenarios (van Vuuren et al. 2011). We explore the sensitivity of our results to the relative weight given to CAPE and shear in the definition of a severe-thunderstorm environment by repeating our analysis of future changes for a plausible range of shear weightings. Some conclusions and directions for future work are presented in section 4.
2. Evaluating the GCMs
The predictions of a global climate model (GCM) about the future of severe thunderstorms are more trustworthy

15 MARCH 2015



TABLE 1. The GCMs included in this study. Resolution is indicated in terms of (lon points) 3 (lat points) 3 (levels in the vertical). Expansion of GCM acronyms is available online at


Beijing Climate Center Beijing Climate Center Canadian Centre for Climate Modelling and Analysis National Center for Atmospheric Research Centre National de Recherches Météorologiques Institute of Atmospheric Physics, Tsinghua University Geophysical Fluid Dynamics Laboratory Geophysical Fluid Dynamics Laboratory Japan Agency for Marine-Earth Science and Technology Meteorological Research Institute Norwegian Climate Centre

128 3 64 3 26 320 3 160 3 26 128 3 64 3 35 288 3 192 3 26 256 3 128 3 31 128 3 60 3 26 144 3 90 3 48 144 3 90 3 24 256 3 128 3 40 320 3 160 3 48 144 3 96 3 26

if the model demonstrates skill at simulating where and how frequently these storms occur in the current climate. Unfortunately, since the typical size of thunderstorms (;25 km in diameter) remains below the threshold of resolution for current-climate models, evaluating models requires identifying severe-thunderstorm-favorable environments (STEnvs) when the large-scale conditions of CAPE and wind shear are simultaneously abundant at the scale of a GCM grid cell. A loose analogy can be drawn between STEnvs and the severe-thunderstorm watches issued for the United States by the National Weather Service’s Storm Prediction Center, although the latter typically cover an area larger than a GCM grid cell and are issued by meteorologists with access to more detailed characterizations of the atmosphere (Johns and Doswell 1992). Clearly the application of such individual expertise is not feasible for the systematic analysis of large quantities of GCM data. Nevertheless, the framework of severe-thunderstorm watches is instructive in the context of GCMs that do not resolve thunderstorms because a ‘‘watch’’ indicates only that atmospheric conditions are primed for the development of a storm, not that one has yet been observed. (This is in contrast to ‘‘warnings,’’ which are issued once a storm has been confirmed.) Identifying storm-favorable environments based on the ambient levels of CAPE and wind shear in the weather of a climate model results in a picture of where and when the simulated atmosphere could have supported severe thunderstorms.
To benchmark GCMs against observations of severethunderstorm conditions, we have derived maps of CAPE and 0–6-km wind shear at 18 resolution over the continental United States (CONUS) at 0000 UTC—from mid- to late afternoon local time, the peak hours of severe-thunderstorm formation (Kelly et al. 1985)— from a decade of radiosonde data as well as CMIP5 output for each of 11 GCMs. The radiosonde observations are provided by the Stratosphere–Troposphere

Processes and their Role in Climate (SPARC; World Climate Research Programme 2014) high-verticalresolution radiosonde data (HVRRD); each 0000 UTC sounding is filtered to detect instrument malfunction and interpolated to a uniform 100-m vertical resolution. The 11 CMIP5 GCMs we evaluate, listed in Table 1, have a range of spatial resolutions and are drawn from modeling agencies from around the world (Taylor et al. 2012). CAPE was calculated assuming the adiabatic, undiluted ascent of a near-surface parcel of air; parcel densities were computed using a root solver and an exact expression for equivalent potential temperature derived by Romps and Kuang (2010), which includes the effects of latent heat of fusion and the different heat capacities of the water phases. Wind shear was calculated as the magnitude of the vector difference between the nearsurface winds and the winds at a pressure level with a mean altitude of 6 km above the ground. For more details about the radiosonde network, the ensemble of GCMs, and the calculation of CAPE and wind shear, see the appendix.
Throughout this work, we identify 18 cells in the continental United States as STEnvs whenever the weighted product of CAPE (J kg21) and shear (m s21) in that cell at 0000 UTC exceeds a threshold. The criterion for STEnvs can be generally expressed as

(CAPE)(shear)g $ b ,


where g is the relative weight given to shear and b is a threshold value [(m s21)21g]. There are numerous
precedents for using such a discriminator line in CAPE–
shear phase space to identify large-scale environments
that are conducive to the formation of severe thunderstorms. Brooks et al. (2003) found that Eq. (1) with g 5 1.6 and b 5 46 800 (m s21)3.6 was most effective at detecting reanalysis ‘‘pseudo soundings’’ associated with
significant severe thunderstorms in the United States,




FIG. 1. (left) Mean annual reports [(8)22] of hail greater than 1 in. in diameter or winds in excess of 50 knots [kt (1 kt 5 0.51 m s21)], from 1955 to 2012 (NOAA Storm Prediction Center 2012b). Reports are binned in 18 cells
based on the latitude and longitude coordinates recorded for the report by the Storm Prediction Center. (right) Mean annual STEnvs [days per year with (CAPE)(shear) $ 36 300 (m s21)3 at 0000 UTC] derived from the SPARC
radiosonde network for the years 1999–2008.

while Allen et al. (2011) found that g 5 1.67 and b 5 115 000 (m s21)3.67 could do the same for short-term forecasts from a numerical weather prediction model for Australia. Both of these studies used databases of observed thunderstorms and arrived at g . 1, reflecting that the value of environmental shear is apparently of greater importance than the local CAPE in determining the severity of a given thunderstorm. Building on these insights, Allen et al. (2014a,b) used a discriminator line with g 5 1.67 in a detailed study of current and future severe-thunderstorm environments in Australia. On the other hand, climate model studies of severethunderstorm environments in the United States have almost exclusively used g 5 1 and b 5 10 000 (m s21)3 (Marsh et al. 2007; T07; Trapp et al. 2009; D13), with one study using b 5 20 000 (m s21)3 (Gensini et al. 2014). One purpose of the present study is to quantify the extent to which previous work may have reported inflated increases in United States severe-thunderstorm environments as a result of underweighting the effect of future decreases in shear.
However, for the purpose of evaluating climate models on their simulation of current-climate severethunderstorm conditions, we take g 5 1 and b 5 36 300 (m s21)3. The choice of g 5 1 in this section was made for simplicity and in order to have the most contact with previous multimodel studies of United States severe thunderstorms; in any case, the value of g is much more important when considering trends in STEnvs than it is when seeking a general picture of GCM performance in the current climate, and g will be allowed to vary substantially in section 3 when we analyze trends in STEnvs. The chosen value of b selects the upper 3% of

(CAPE)(shear) in the radiosonde data, and was found to result in a mean annual number of STEnvs that compares well with what was found in reanalysis by D13 and others, building confidence that we are considering a similarly extreme population of CAPE and shear combinations despite potential differences in the calculation of CAPE.
NOAA climatologies of past severe-thunderstorm watches indicate that the region of peak storm activity in today’s climate is the central United States, beginning east of the Rocky Mountains, extending from the middle of Texas north to the Dakotas, and tailing off toward the East Coast (NOAA Storm Prediction Center 2012b). This region of significant severe-thunderstorm activity in the central United States is readily apparent in historical reports of large hail and severe convective winds (Fig. 1, left), and is the most salient feature of the current climate’s pattern of severe-thunderstorm activity. Overall, Fig. 1 shows that the climatology of STEnvs derived from radiosondes is well correlated with the region of observed severe-thunderstorm damage in the central United States.
An exception is the region of southern Texas, where a large number of STEnvs occur but there have been few reports of severe-thunderstorm damage. This feature has been noted previously in United States reanalysis by Gensini and Ashley (2011) and others, and highlights an important point about what information can be gleaned from STEnvs. STEnvs do not account for factors that are known to be closely tied to storm initiation—from smallscale outflow boundaries to large-scale inversions—and are therefore agnostic about whether thunderstorms actually occurred. It is well known that southern Texas is frequently capped by an elevated mixed layer that is

15 MARCH 2015



advected eastward from the high desert terrain of the Mexican Plateau (Carlson and Ludlam 1968). In the absence of mechanisms to erode the inversion, this ‘‘lid’’ has such a strong inhibiting effect on thunderstorm formation in southern Texas that, even though CAPE and shear are abundant, severe thunderstorms are rare.
Clearly, this uncertainty regarding storm initiation limits our ability to translate trends in STEnvs into projections for future severe thunderstorms. Changes in the processes that inhibit and promote storm initiation, which cannot at present be resolved by GCMs, may have an attenuating or amplifying effect on the way STEnv trends will influence future thunderstorms. Van Klooster and Roebber (2009) derived an index of convective initiation potential from the large-scale variables resolved by climate models and found no change in this initiation potential over the twenty-first century, but that study only considered a single GCM. Another promising avenue for studying convective initiation is dynamically downscaling GCM output with a high-resolution regional climate model that can explicitly resolve convective storms, although such results are still model dependent and generating long integrations with this technique is computationally intensive (Trapp et al. 2007b, 2011; Robinson et al. 2013). A self-contained multimodel analysis of future changes in convective initiation may become a tractable problem only once GCM resolutions have substantially improved. Therefore, for the moment the best one can do is assume that the fraction of STEnvs that develop severe storms will be the same in the future as in the present, but there is not much to justify this assumption besides necessity.
With these limitations in mind, it is encouraging that the observational climatology of STEnvs does highlight the region of maximum severe-thunderstorm damage in the central United States. It is also worth noting the similarity between the radiosonde observations in Fig. 1 (right) and the distribution of severe-thunderstorm environments found previously in reanalysis by, for example, Brooks et al. (2003) and D13. Given the widely recognized deficiencies in the ability of reanalysis fields to represent sharp vertical gradients of thermodynamic quantities (Gensini et al. 2014), it was not a foregone conclusion that severe-thunderstorm conditions estimated from high-vertical-resolution radiosonde data would not appear substantially different from those derived from reanalysis. The similarity of the radiosonde climatology of STEnvs presented here with reanalysis data confirms that reanalysis is a suitable tool for the study of large-scale environments associated with severe thunderstorms.
But how well can CMIP5 GCMs reproduce the observed pattern of storm activity? About 93% of STEnvs

in the radiosonde data occur between the months of March and August, and it has been previously noted by Kelly et al. (1985) that more than 80% of thunderstorms producing damaging winds and large hail occur during these months. Therefore, we focus our analysis on the spring [March–May (MAM)] and summer (JJA) seasons. Figures 2a–l and 3a–l show the climatologies of STEnvs derived from the radiosonde data and 11 CMIP5 climate models for the current climate’s spring and summer seasons, respectively. The differences in model skill are most apparent during summer, when there is significant spread in how well the climate models capture the radiosonde observations’ concentration of STEnvs in the central United States. A majority of the GCMs depicted in Fig. 3 predict that much of the East Coast of the United States should be at least as frequently favorable for the development of summertime severe thunderstorms as the Great Plains, in stark contrast to the radiosonde observations, and some models actually have local STEnv minima in the Great Plains [e.g., BCC_CSM1.1(m) and CanESM2]. These differences between the models are not nearly as apparent for the spring months shown in Fig. 2, when most models qualitatively capture the concentration of STEnvs creeping up from Texas into the southern Great Plains. A likely explanation for the better performance of the models in the spring is the predominance of synoptic forcing, which is on a scale better resolved by GCMs, as compared to the mesoscale-system-dominated summer (Fritsch et al. 1986).
The GCM ensemble’s performance is summarized in Figs. 2m and 3m, where we show pattern correlations between the climatologies of STEnvs for the radiosonde data and the GCMs. We also quantify the overall seasonal bias in the number of STEnvs that occur in the GCMs. The pattern correlations confirm that many of the GCMs in our ensemble have very little predictive power in the summer. In this work, we stipulate that a GCM must have a pattern correlation of 0.5 for both MAM and JJA current-climate STEnvs (R2 in Figs. 2m and 3m) in order to be considered skillful at simulating severe-thunderstorm conditions. According to this criterion, the four high-performing models are GFDL CM3, GFDL-ESM2M, MRI-CGCM3, and NorESM1-M. The principal difference between high- and low-performing GCMs is the zonal distribution of STEnvs in the summer: the high-performing group has its summertime peak of STEnvs in the central United States, collocated with the defining feature of the radiosonde observations. On the other hand, the low-performing GCMs display a much broader swath of STEnvs and/or significant peaks in activity on the East Coast in the summer. When we consider the effect of global warming on STEnvs in




FIG. 2. Mean STEnvs per MAM for (a)–(k) the years 1996–2005 in 11 CMIP5 GCMs and (l) the years 1999–2008 in SPARC radiosonde observations. (m) A summary of the ability of the 11 GCMs in our ensemble to simulate the radiosonde observations. The ordinate of (m) is the spatial coefficient of determination R2 and is a measure of how well a GCM’s geographical distribution of STEnvs matches the distribution in the radiosonde data. The abscissa (‘‘Bias’’) is the ratio of a GCM’s CONUS-mean STEnvs (land grid points only) to that of the radiosonde climatology, and thus is a measure of how well a GCM predicts the observed number of STEnvs per season per year.

section 3, we will focus our attention on this subset of high-performing models to see if they display a more consistent summertime response than was found for the larger ensemble of D13. However, given the inherent subjectivity in evaluating climate models to determine which are ‘‘high performing’’ (Tebaldi and Knutti 2007), we will also present a summary of results for all 11 GCMs in our ensemble.

3. Severe thunderstorms in a warm future United States
The high-performing models identified in section 2 are used here to predict changes in thunderstorm severity approximately 75 years in the future. We use CMIP5 data from the decade 2079–88 of the RCP4.5 and RCP8.5 experiments to represent the future climate under medium

15 MARCH 2015



FIG. 3. As in Fig. 2, but for JJA.

and high levels of greenhouse forcing, respectively (van Vuuren et al. 2011), and identify STEnvs in the simulated weather of the GCMs for this decade using the same method presented in section 1. Under the assumption that the same fraction of STEnvs will be actualized into storms in the future as at present, changes in STEnvs tell us about how GCMs predict the frequency of severe thunderstorms will change. In section 3a, we again use g 5 1 and b 5 36 300 (m s21)3 as a (CAPE)(shear)g threshold, allowing us to diagnose how often in each GCM’s simulated future the subdaily product of CAPE and shear at local mid- to late afternoon would cause the environment to be classified as

a STEnv. The sensitivity of changes in STEnvs to the relative weight given to shear is explored in section 3b.
a. g 5 1 (CAPE and shear equally weighted)
The changes in annual-mean spring and summer STEnvs due to global warming are shown for the high-performing GCMs in Figs. 4 and 5, respectively. To probe the models’ response to a range of radiative forcing, we show results for both the RCP4.5 and RCP8.5 greenhouse emissions scenarios, which respectively represent medium-mitigation and high-carbon business-as-usual pathways (van Vuuren et al. 2011). Figure 4 shows that




FIG. 4. Changes due to global warming in annual-mean STEnvs during MAM in the high-performing GFDL CM3, GFDL-ESM2M, MRI-CGCM3, and NorESM1-M. Results for both the RCP4.5 and RCP8.5 greenhouse gas forcing scenarios are presented. Changes are calculated as the mean of the period 1996–2005 of the CMIP5 historical experiment subtracted from the mean of the period 2079–88 of the RCP experiment. A summary of the fractional CONUS-mean changes is given for each of the four models in the boxes at left.

in the spring, the ensemble of high-performing models predict a consistent response of increased STEnvs extending from Texas into the southern and central Great Plains. This region of increase coincides with the current climate’s spatial pattern of STEnvs—evident in both the radiosonde and GCM data shown in Fig. 2—suggesting a ‘‘stormy gets stormier’’ response for springtime severe thunderstorms. These results agree with those of D13, who found consistent increases in severe-thunderstorm environments during the spring for a 10-member ensemble of

CMIP5 models. The trends for this season are robust to the range of greenhouse forcing spanned by the RCP4.5 and RCP8.5 scenarios, with the magnitude of predicted CONUS-mean increases ranging from 30% to 150% for the RCP4.5 scenario, and from 50% to 180% for the RCP8.5 scenario. The fact that the increases for the RCP4.5 scenario are smaller than the RCP8.5 increases by 20%–50% suggests that the climate policies adopted in the coming decades will affect the severity of the spring thunderstorm season in the United States.

15 MARCH 2015



FIG. 5. As in Fig. 4, but for JJA.

The summertime response of the high-performing ensemble of models is considerably more diverse (Fig. 5). For the RCP8.5 scenario, three of the four highperforming models predict increases in the range of 40%–120%, while one model (NorESM1-M) predicts an approximately 10% decrease. In all cases, these changes are concentrated in the central and northern Great Plains, around the climatological maximum of STEnvs for the current-climate radiosonde data and four highperforming GCMs shown in Fig. 3. In contrast to the spring season, during the summer the RCP4.5 response is qualitatively different from the RCP8.5 response for two of the models, changing sign locally in the central

Great Plains for GFDL-ESM2M and in the CONUS mean for NorESM1-M.
One source of motivation for the present study was the hypothesis that a restricted ensemble of CMIP5 climate models, selected for its demonstrated skill at matching a radiosonde climatology of STEnvs, might display a more consistent response to greenhouse forcing than the larger ensemble used by D13, particularly in the summer. The results shown in Fig. 5 partially discredit this hypothesis, because the four highestperforming models identified in section 2 do not agree on even the sign of CONUS-mean changes in the frequency of summer STEnvs under the strong radiative




FIG. 6. Changes in MAM and JJA CONUS-mean STEnvs in the 11 GCMs listed in Table 1, as a function of their R2 ‘‘score’’ on their ability to match the spatial pattern of observed current-climate STEnvs. The letters a–k correspond to the same models as in Figs. 2 and 3; the ‘‘high performing’’ models are those that have an R2 above 0.5 for
both MAM and JJA.

forcing of the RCP8.5 scenario, and there is no clear distinction between the response of the ‘‘high performing’’ and ‘‘low performing’’ models in CONUS-mean percent increases in STEnvs (Fig. 6).
However, there is a clear outlier among the highperforming models: NorESM1-M predicts decreases in summer STEnvs throughout the Great Plains—unlike GFDL CM3, GFDL-ESM2M, and MRI-CGCM3, which together show a consistent increase in this region when forced by RCP8.5-level emissions. Variations in simulated future shear are not the source of the difference between NorESM1-M and the other three models, as all four of these models predict decreasing

CONUS-wide wind shear in the range from 25% to 214% for this season under RCP8.5 forcing. However, NorESM1-M is a significant outlier in this small ensemble of high-performing models for its predicted changes in CAPE and boundary layer humidity (Fig. 7). While the GFDL models and MRI-CGCM3 predict increases in CAPE on the order of 1 kJ kg21 throughout the Great Plains, NorESM1-M predicts that mean summertime CAPE will decrease by roughly 500 J kg21 in this region. The increases in CAPE in the first three models appear to be driven by increases in boundary layer specific humidity qy that roughly follow Clausius– Clapeyron scaling, while NorESM1-M’s decreases in