The changing hail threat over North America in response to anthropogenic climate change

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1 In the format provided by the authors and unedited. SUPPLEMENTARY INFORMATION DOI: /NCLIMATE3321 The changing hail threat over North America in response to anthropogenic climate change Julian C. Brimelow 1 *, William R. Burrows 2 and John M. Hanesiak 3 1 Meteorological Service of Canada, Environment and Climate Change Canada, Edmonton T6B 1K5, Canada. 2 Atmospheric Science and Technology Directorate, Environment and Climate Change Canada, Edmonton T6B 1K5, Canada. 3 Centre for Earth Observation Science, Department of Environment and Geography University of Manitoba, Winnipeg R3T 2N2, Canada. * julian.brimelow@canada.ca NATURE CLIMATE CHANGE Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

2 S1. Study Area The 15 ecoregions are based primarily on the Level I zones specified in the EPA ecological regions of North America (CEC 1997; Level II data was sometimes used for areas with more complex topography or spatial climate variability. For clarity, or to obtain a sufficiently large area, similar ecoregions were merged. The Great Plains was sub-divided into three areas (at 41º N and 49º N) to capture the northward expansion of thunderstorm (and severe thunderstorm) activity during the spring and summer. The eastern boundary for the Northern Rockies (southern Alberta only) and Colorado Rockies and High Plains (see Fig. S1) was determined using the 1100 m height contour. The Great Lakes region was split into western and eastern portions to reflect the contrasting thunderstorm environments and lightning activity. 1

3 a b Fig. S1: (a) Study area and 15 ecoregions discussed in the text. Also shown in (b) are the provinces, states and territories. Magenta polygon represents location of hail alley over western Canada. Table S1: Each of the 15 ecoregions and their associated acronym and colour shown in Fig. S1. Ecoregion Acronym Colour Atlantic ATL Appalachians APP Colorado Rockies and High Plains CHP Intermountain Desert DES Great Lakes West GLW Great Lakes East GLE Northwest Boreal Forest NWF Northeast Boreal Forest NEF Northern Rockies ROC Southeast Coastal Plains SEC Southern Great Plains SGP Southern Temperate Forest STF Taiga TGA Canadian Prairies CAP U.S. Prairies USP 2

4 S2. Model Selection NARCCAP data comprise dynamically downscaled data from four Global Climate Models (GCMs) using six regional climate models (RCMs) each GCM was downscaled using three RCMs, for a total of 12 pairings (Mearns et al. 2012). At the time we initiated the research, only 10 pairings were available. Computational limitations meant that we could not run HAILCAST for all 10 pairings initially available to us. Additionally, because hail is a relatively rare phenomenon, ideally we would target model pairings that accurately replicated the current hail climatology and more-orless identified activity in areas known to be hot spots for hail. Identifying the model pairings was a two-step process. First, we used the results of Elguindi and Grundstein (2013) to exclude those model parings that ranked lowest for reproducing the current climate over N. America (see their Table 3). Based on their findings we excluded: ECP2-GFDL and RegCM3-CGCM3. HadCM3- HRM3 ranked highest of all the model parings. Second, for the remaining models, we ran HAILCAST for a 5-y period at the end of the 20 th century. This process identified other issues (discussed below) with the input data used to run HAILCAST. Figure S2 shows the mean number of severe hail days (D s 2 cm) for 1 March through 30 September for 1996 through 2000 (1994 to 1998 for the CCSM3-MM5I pairing) for the three model pairings eventually used in our study (HadCM3-MM5I, CCSM3- MM5I and HadCM3-HRM3) and the other pairings that were excluded (CGCM3-WRFG, CCSM3-CRCM3 and CGCM3-CRCM3). The contrast between the output using the CRCM3 RCM and the other pairings is clear, with the CRCM3 runs producing almost a factor of 10 more hail days than the HadCM3-MM5 and 3

5 CCSM3-MM5. Investigations revealed that the hyperactivity in the HAILCAST runs driven using CRCM3 was due to a persistent and unrealistic spike in surface moisture in this RCM. This led to unrealistically high values of CAPE (especially over the mountainous terrain), updrafts that were too strong and long-lived and ultimately hail that was too frequent and too large. Bukovsky et al. (2013) also noted the moisture spike in the CRCM3 data when studying the ability of NARCCAP models to simulate the North American monsoon. Specifically, the high near surface moisture in the CRCM3 data was at odds with the other pairings and observations (see their Fig. 6). We were unable to find a satisfactory workaround (in part, because of missing data at 1000 hpa in the CRCM3 output) and decided to exclude the CRCM3 pairings. 4

6 CCSM3-MM5I CGCM3-WRFG HadCM3-MM5I CCSM3-CRCM3 HadCM3-HRM3 CGCM3-CRCM3 5 Fig. S2: Mean number of severe hail days (D s 2.0 cm) from 1 March through 30 September between 1996 and 2000 for six GCM-RCM pairings ( for CCSM3-MM5). Model pairings on the right-hand side were not used.

7 Also evident in Fig. S2 is that the CCSM3-WRFG pairing produced virtually no hail over the southern Great Plains (a region known to be a hotspot for hail activity); instead it produced a broad area of enhanced severe hail activity over the mid-western U.S. (a region not typically associated with frequent hail activity). A detailed investigation found that the likely cause of this disparity is the significant warm and dry bias that arises when the Grell-Devenyi (Grell and Devenyi 2002) convective parameterization scheme is applied to the version of WRF used for NARCCAP. It appears that this particular configuration biases precipitation towards lower intensities, especially in areas dominated by convective precipitation (e.g., Mukhopadhyay et al. 2010; Kim et al. 2013; Wang and Kotamarthi 2014; Li et al. 2015). This in turn could trigger a positive land-atmosphere feedback, which would exacerbate the anomalously warm and dry conditions. Consequently, the CAPE values are too low over the Great Plains during the warm season leading to a gross underestimation of hail occurrence there. The problem was present regardless of which GCM the WRFG model was used to downscale. For these reasons, we reluctantly decided to not use the data from CCSM3-WRFG. This issue underscores the finding from previous research (e.g., Marsh et al. 2009, Allen et al. 2014) regarding the importance of selecting the appropriate convective parameterization scheme when using regional climate models to investigate the response of severe thunderstorms to ACC. The three remaining pairings constitute two GCMs and two RCMs (HadCM3-MM5; HadCM3-HRM3 and CCSM3-MM5). Technical details for these three parings are provided in Tables S2 and S3. Table S2: Characteristics of the two Global Climate Models used. Acronym Modelling Reference Grid Spacing; Model ECS Group Vertical Layers Top CCSM3 NCAR Collins et al. (2006) T47 (1.9 x 1.9 ); hpa 2.7 ºC HadCM3 Hadley Centre Pope et al. (2000) 2.5 x 3.75 ; hpa 3.3 ºC 6

8 Table S3: Characteristics of the two Regional Climate Models used. HRM3 MM5I Modelling Group Hadley Centre Pennsylvania State University Dynamics Hydrostatic, Compressible Non-hydrostatic, Compressible Lateral Boundary Treatment 4 points 4 points (linear relaxation) Land Surface Scheme MOSES NOAH Vegetation Types 53 classes 16 classes from SiB model Boundary Layer First order turbulent mixing Hong-Pan Explicit Moist Physics Prognostic cloud liquid and ice; liquid Dudhia simple ice potential temperature Cumulus Parameterization Mass Flux, including downdraft Kain-Fritsch mass flux Vertical Levels Vertical Coordinate Hybrid terrain following & pressure Sigma Grid Number 171 x x 129 Sponge Zone Depth 8 grid points 15 grid points Time Step 300 s 120 s Spectral Nudging No No Longwave Radiation Scheme PRECIS RRTM Shortwave Radiation scheme PRECIS MM5 cloud scheme Projection Rotated Pole Lambert Conformal HAILCAST output for severe hail days (Fig. S2) is very similar when MM5 is used to downscale data from HadCM3 and CCSM3. HAILCAST shows more severe hail activity over the SGP, STF and APP for CCSM3-MM5 compared to runs made using HadCM3-MM5. However, HAILCAST output using HadCM3-MM5 predicts more severe hail over CHP and over the foothills of the Canadian Rockies. When run using output from HadCM3-HRM3, the severe hail activity is more widespread and more frequent, although the underlying pattern is similar to that found using HadCM3-MM5 and CCSM3-MM5. All three pairings tend to result in the maximum severe hail activity being displaced too far to the west over the higher terrain (Fig. S2 and Fig. S3). Gilleland et al. (2016) speculated that the NARCCAP RCMs were pushing moisture too far into the mountains because of an anomalously strong southeasterly flow. 7

9 S3. Environmental and model output parameters Future changes in environmental and model parameters were used to gain insight into the processes driving changes in the occurrence and size of hail (see Table S4). Table S4: Environmental and HAILCAST output variables. Negative (positive) changes in the melting factor reflect an increase (decrease) in the amount of melting. Name Details Units T Surface temperature ºC T d Surface dewpoint temperature ºC MLCAPE Mixed layer convective available J kg -1 potential energy MLCIN Mixed-layer convective inhibition J kg -1 BWS Bulk wind shear between 850 hpa and s hpa ESI Energy Shear Index m 2 s -3 (MLCAPE x BWS) W x Maximum updraft velocity m s -1 LWC x Maximum cloud liquid water content g m -3 D x Maximum hail diameter aloft cm D s Maximum hail diameter at surface cm Δ(D s /D x ) Melting factor % AKE Accumulated kinetic energy J The Accumulated Kinetic Energy (AKE) That the hail energy per unit area (a.k.a kinetic energy or impact energy) associated with a hail event would be a better measure of hail intensity than the number of hail days dates back to circa 1960 (e.g., Schleusener and Jennings 1960). Historically, kinetic energy from hailfalls has also been frequently utilized in the analysis of hailpad data (e.g., Changnon 1969; Morgan and Towery 1975; Strong and Lozowski 1977) and to estimate the expected amount of hail damage (e.g., Laurie 1960; Changnon 1971; Morgan and Towery 1975; Strong and Lozowski 1977). Additionally, the integrated kinetic energy from individual events over a year (or several years) 8

10 has been used to quantify the overall hail threat (e.g., Dessens et al. 2015). Here we apply the concept of kinetic energy to the predicted hail diameter from HAILCAST (see Methods for details). 9

11 S4. Multi-model means for key variables for a MAM JJA M S b c 10

12 d e Fig. S3: Mean multi-model values between 1971 and 2000 (except for CCSM3-MM5) for (a) Hail days (D s > 0.5 cm), (b) Severe hail days (D s 2 cm), (c) Very large hail days (D s 4 cm), (d) Days with hail aloft only (HALO), and (e) Accumulated Kinetic Energy (AKE). Left panel is for spring (March through May; MAM), centre panel is for summer June through August; JJA), and the right panel is for March through September (M S). 11

13 S5. Multi-model means for environmental and HAILCAST output values for Table S5: Mean multi-model environmental and HAILCAST output statistics for for all hail days (surface-based and elevated) by ecoregion for the spring (MAM). Horizontal lines indicate transition between adjacent columns of ecoregions arranged from north to south. MAM T (ºC) Td (ºC) MLCAPE (J kg -1 ) MLCIN (J kg -1 ) BWS (s -1 ) ESI (m 2 s -3 ) W x (m s -1 ) LWC x (g m -3 ) D s (cm) ROC DES TGA NWF CAP USP CHP SGP NEF GLW STF GLE APP SEC ATL

14 Table S6: Same as Table S5, except for the summer (JJA). JJA T (ºC) Td (ºC) MLCAPE (J kg -1 ) MLCIN (J kg -1 ) BWS (s -1 ) ESI (m 2 s -3 ) W x (m s -1 ) LWC x (g m -3 ) D s (cm) ROC DES TGA NWF CAP USP CHP SGP NEF GLW STF GLE APP SEC ATL Table S7: Same as Table S5, except for March through September (M S). M S T (ºC) Td (ºC) MLCAPE (J kg -1 ) MLCIN (J kg -1 ) BWS (s -1 ) 13 ESI (m 2 s -3 ) W x (m s -1 ) LWC x (g m -3 ) D s (cm) ROC DES TGA NWF CAP USP CHP SGP NEF GLW STF GLE APP SEC ATL

15 S6. Multi-model mean future changes for M S a d a b e c f Fig. S4: Mean multi-model changes for M S for future ( ) minus present ( ) (a) Hail days (GE1; D s 1.0 cm) per season, (b) Severe hail days 14 (GE2; D s 2 cm) per season, (c) Very large hail days (GE4; D s 4 cm) per season, (d) Accumulated Kinetic Energy (AKE) in Joules per season, (e) Days with hail aloft only (HALO) per season and (f) Maximum diameter at the surface (D s ) in cm. Coloured cells indicate mean changes for all model pairings that agree on the direction of change, cells with coloured circles indicate mean changes for at least two model pairings that are statistically significant (90% significance).

16 S7. Future changes in HAILCAST parameters Table S8: Changes (in %) in multi-model aggregated counts for spring. Changes are for future minus present. Mean differences (in percent) are shown for pairings with differences in the same direction and which are significant at 95% significance level using a one-sided Mann-Whitney U test. Bolded values indicate agreement between all three model pairings, whereas symbols are used to indicate which two model parings were used. Negative (positive) changes in the melting factor reflect an increase (decrease) in the amount of melting. Here * represents HadCM3-MM5 and CCSM3-MM5, # represents HadCM3-MM5 and HadCM3-HRM3, and + represents HadCM3-HRM3 and CCSM3-MM5. Blank cells indicate there was either no agreement in the sign of the changes, or changes for at least two pairings were the same but not statistically significant. MAM ΔGE1 ΔGE2 ΔGE4 ΔAKE ΔD x ΔD s Δ(D s /D x ) ΔHALO ΔPersev ROC 40.9 # 42.8 # 76.8 # 67.2 # -8.5 * -6.0 * 89.4 # -8.3 * DES 8.9 # 27.1 # 39.5 # # TGA 36.8 # 7.6 # -1.9 # # NWF # 81.8 # 12.8 # # CAP # 8.8 # USP CHP 14.7 * 27.1 * # 5.8 SGP # # * NEF # # GLW -8.2 # 34.6 # # # STF # * GLE 8.4 # APP * * SEC * * ATL * *

17 Table S9: Same as for Table S8, except for JJA. JJA ΔGE1 ΔGE2 ΔGE4 ΔAKE ΔD x ΔD s Δ(D s /D x ) ΔHALO ΔPersev ROC # DES # TGA 45.4 * NWF CAP -6.4 # -6.0 # # # USP -9.7 * # -3.2 # CHP # # SGP * * * # 2.0 NEF * GLW * * # STF * * * * # # GLE * * * # 3.5 # APP * * * SEC * * * * ATL * * * * # # Table S10: Same as for Table S8, except for M S. M S ΔGE1 ΔGE2 ΔGE4 ΔAKE ΔD x ΔD s Δ(D s /D x ) ΔHALO ΔPersev ROC 14.2 # # 27.6 # DES # TGA 29.5 * 46.4 * 21.0 # NWF CAP # USP # CHP -7.3 # # SGP * * # 1.9 NEF -9.3 * -9.4 * GLW * * # STF * * # GLE * * # # 2.4 # APP * * * * * SEC * * * * * * ATL * * * *

18 Changes in maximum hail diameter Fig. S5 shows histograms of modelled hail diameters over the study area for each model pairing, by season and for the present and future climate. The frequency of occurrence of extremely large hail ( 7.5 cm) is very rare. Specifically, fewer than 1% of all modelled hail events (over the study area over a 30-year period) were larger than 7.5 cm in diameter for the HadCM3-MM5 and CCSM3-MM5 runs. The relative frequency of such events is higher for the HadCM3-HRM3 runs at ~3.5%. Fig. S5: Histograms of grid counts over the entire 17 study area by season for present and future windows, and for each of the three model pairings.

19 These modelled sizes compare very well with an analysis of hail events in the U.S. Storm Data archive by Jewell and Brimelow (2009) they found that around 2% of reports annually were greater than ~7.0 cm. Similarly, the fraction of hailstones with diameters larger than 5 cm (i.e., ~7%) for two for the model pairings agrees very well with the finding by Schaefer et al. (2004) that hail larger than ~ 5 cm accounts for about 8% of all U.S. Storm Data reports. Also of note, is that the modelled hail-size distributions follow an exponential (or gamma) distribution and that this agrees with the shape of hail-size distributions determined from hailpad data (e.g., Fraile et al. 1999). 18

20 S8. Changes in month of peak AKE Table S11: Future changes in timing of peak AKE. Results are only show when two or more pairings passed both a Mann-Whitney U test and a Kolmogorov Smirnov test (both at the 95% significance level). Percentages show the mean changes (for the relevant pairings) in the month of peak AKE for grid cells within each ecoregion. The symbol * represents HadCM3-MM5 and CCSM3-MM5, # represents HadCM3-MM5 and HadCM3-HRM3, and + represents HadCM3- HRM3 and CCSM3-MM5. Ecoregion Present Maximum Shift %Earlier %Same %Later APP June Later DES June Earlier DS May Earlier * ROC July Earlier CHP July Earlier (June) TGA July Earlier & Longer NEF July Earlier #

21 S9. Future changes in environmental and HAILCAST parameters Table S12: Same as Table S8, except for mean multi-model changes (in %) in environmental and HAILCAST parameters for each model pairing and for spring (MAM). MAM ΔT ΔTd ΔCAPE ΔCIN ΔShear ΔESI ΔWx ΔLWCx ROC -5.9 * -7.8 * * -3.3 # * # DES * TGA 6.7 # 11.4 # 6.0 # # # NWF 7.4 # 11.8 # 9.9 # -9.7 # -3.1 # 5.7 # CAP 7.5 # 10.7 # 8.4 # # 8.4 # 6.9 # 5.2 # USP CHP # * SGP NEF * * GLW * STF GLE 5.7 # 7.9 # 11.1 # # 8.0 # 3.3 # APP SEC # ATL

22 Table S13: Same as Table S11, except for summer (JJA). JJA ΔT ΔTd ΔCAPE ΔCIN ΔShear ΔESI ΔWx ΔLWCx ROC # DES TGA # # NWF CAP * USP CHP SGP NEF GLW STF GLE APP SEC * ATL Table S14: Same as Table S11, except for M S. M S ΔT ΔTd ΔCAPE ΔCIN ΔShear ΔESI ΔWx ΔLWCx ROC DES # TGA # NWF CAP USP # CHP SGP * NEF GLW * STF # GLE APP * SEC ATL

23 S10. Factors affecting changes in AKE The accumulated kinetic energy (AKE) is governed primarily by the number of hail days and the mean hail diameter on those days. For simplicity, we consider the modulation of changes in AKE (ΔAKE) only in terms of changes in the number of severe hail days (ΔGE2) and in the mean D s (ΔD s ). Data for individual model pairings in Figs. S6a,b support this choice for spring and summer combined, ΔGE2 and ΔD s explain 59% ( p < 0.001) and 50% (p < 0.001) of the variance in ΔAKE, respectively. In contrast, ΔGE1 explains 49% (p < 0.001) of the variance in ΔAKE (not shown). D s is modulated primarily by T d (Fig. S6c; r = 0.69; p < 0.001); T d is in turn highly correlated with CAPE (r = 0.60; p < 0.001), W x (r = 0.71; p < 0.001) and LWC x (r = 0.91; p < 0.001). Fig. S6d shows that for spring and summer combined, ΔMLCIN explains almost 20% of ΔGE2 (p = 0.001), with increases in ΔMLCIN associated with a proportionate decrease in ΔGE2. Considering ecoregions with the highest climatological MLCIN (i.e., SGP, USP, CHP, DES, STF and GLW), the variance of ΔGE2 explained by MLCIN increases to 29% (Fig. 6d; p = 0.02) for spring and summer combined. The variance explained increases to 47% (p < 0.001) in the summer when MLCIN is higher (see Tables S5 and S6). 22

24 a b c d Fig S6: Scatter plots of changes (in %) for (a) ΔAKE versus ΔGE2, (b) ΔAKE versus ΔD s, (c) ΔDs versus ΔT d, and (d) ΔGE2 versus ΔMLCIN. Data are for individual model pairings for spring and summer combined and for data aggregated by ecoregion. Only model pairings having statistically significant changes at the 95% confidence level (using a two-sided Mann-Whitney U test) are shown. In (d) purple and blue dots are for summer only and spring and summer combined, respectively. Purple trend line is for summer only, and black trend line is for spring and summer combined. It is important to keep in mind that the complex interplay between several factors may not always act in the same direction as far as D s and GE2 are concerned. For this reason, storms cannot necessarily capitalize on favourable severe hailstorm environments and increase AKE. 23

25 For example, increases in T and T d, are almost always accompanied by an increase in MLCAPE and stronger MLCIN (Tables S11 and S12). As shown above, greater buoyancy (or MLCAPE) increases the potential for stronger updrafts, higher in-cloud liquid water content and ultimately larger hail. In contrast, greater MLCIN potentially results in fewer hail days, particularly in seasons and regions where the mean MLCIN on hail days approaches -20 J kg -1. The reason for this threshold is because about 95% of the hail days in HAILCAST occur when MLCIN is greater than (i.e., more negative) -20 J kg -1. Consequently, the updraft of 3 m s -1 at cloud base is increasingly unlikely to overcome the MLCIN as it approaches (or exceeds) -20 J kg -1. Although the sensitivity of storm formation to MLCIN values near -20 J kg -1 is somewhat arbitrary, in that it is related to the choice of the velocity at cloud base, the important message is that in the future it could be more difficult to trigger storms in regions where MLCIN becomes stronger (and assuming a constant velocity at cloud base). Another important consideration for explaining changes in AKE is that higher temperatures cause more melting (see Fig. S7), which may (depending on the amount of warming and size of hail) mitigate (or even reverse) increases in the diameter of hail aloft due to stronger updrafts see, for example, data for APP and SEC in spring (Table S8). 24

26 a b Fig. S7: Changes (future-present) in occurrences of hard hail (coloured) and HALO events (solid contours for increases and dotted contours for decreases) in JJA as a function of ESI and the temperature of the lifted parcel for (a) CHP and (b) STF. Data are for runs using HadCM3-HRM3. 25

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