Potential Distinct Impacts of Global Warming on Mesoscale Convective Systems and Isolated Deep Convection in the Central-eastern United States

manuscript submitted to Journal of Geophysical Research: Atmospheres 1 Potential Distinct Impacts of Global Warming on Mesoscale Convective Systems and 2 Isolated Deep Convection in the Central-eastern United States 3 Jianfeng Li1, Yun Qian1, L. Ruby Leung1, Weiran Liu2, Kai Zhang1, Paul Ullrich2,3, 4 Lingcheng Li1, Ye Liu1, Huilin Huang1, Zeyu Xue1 5 1Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory; 6 Richland, Washington, USA 7 2Department of Land, Air, and Water Resources, University of California, Davis, Davis, CA, 8 USA 9 3Division of Physical and Life Sciences, Lawrence Livermore National Laboratory, Livermore, 10 CA, USA 11 Corresponding authors: Jianfeng Li ([email protected]) and Yun Qian ([email protected]) 12 Key Points: 13  A regionally-refined global cloud-resolving model is used to investigate how global 14 warming affects convective systems of different sizes 15  In a warmer summer atmosphere, mesoscale convective systems (MCSs) become more 16 frequent, while isolated deep convection (IDC) diminishes 17  Summertime MCSs and IDC tend to occur closer to the southern and eastern coastal areas 18 of the United States under warming 19 20 manuscript submitted to Journal of Geophysical Research: Atmospheres Abstract 21 Using the regionally refined Simple Cloud-Resolving E3SM (Energy Exascale Earth System 22 Model) Atmosphere Model and the pseudo-global warming experiment framework, we 23 investigate the potential impact of global warming on convective storms of different sizes and 24 lifetimes in the central-eastern United States from July 1 to August 19, 2020. We find an increase 25 in the number of large and long-lasting mesoscale convective systems (MCSs) but a decrease in 26 small and short-duration isolated deep convection (IDC) events in a warming scenario following 27 the Shared Socioeconomic Pathways (SSP5-8.5). Although the mean MCS lifetime becomes 28 shorter under warming, IDC events persist longer. Also, regions with the most frequent MCS and 29 IDC occurrences shift significantly: MCSs and IDC tend to occur closer to the southern and 30 eastern coasts, where the relative humidity of the environment increases under global warming. 31 This work unveils the complexity of the response of convective systems to climate change. 32 Plain Language Summary 33 Convective systems are vital to the Earth’s hydrological cycle, atmospheric circulation, and 34 radiation balance. This study uses a regionally refined global cloud-resolving model and the 35 pseudo-global warming approach to investigate the potential impact of global warming on 36 summertime convective storms of different sizes in the central-eastern United States. Results 37 show that warming has contrasting impacts on the large and long-lasting mesoscale convective 38 systems and small and short-duration isolated deep convection events when it comes to event 39 number and mean lifetime. In addition, both types of convective systems tend to occur closer to 40 the southern and eastern coasts under future warming. This study highlights the complexity of 41 how convective systems respond to global warming. 42 1 Introduction 43 Deep convection produces heavy precipitation, which has critical impacts on the water 44 and biogeochemical cycles (Chapman et al., 2021; Hu et al., 2020; Kanchebe Derbile & Abudu 45 Kasei, 2012; Motew et al., 2018). It can also affect large-scale environments and radiation 46 balance through the vertical redistribution of heat, mass, and momentum within the atmosphere 47 (Feng et al., 2011; Houze, 2004). Furthermore, deep convection is associated with many natural 48 hazards, such as tornadoes, hail, lightning, flooding, and damaging gusts, severely threatening 49 human security and property (Folger, 2013; Koehler, 2020; Taszarek et al., 2020). 50 Deep convection is generally projected to increase in frequency and intensity in a warmer 51 atmosphere due to enhanced atmospheric moisture and larger convective available potential 52 energy (CAPE) (Diffenbaugh et al., 2013; Prein et al., 2017; Trapp et al., 2007; Westra et al., 53 2014). However, warming also often leads to increased convective inhibition (CIN) and 54 decreased inland relative humidity (RH) (Byrne & O’Gorman, 2016; Chen et al., 2020), which 55 are believed to suppress summertime deep convection in some areas of the United States 56 (Grabowski & Prein, 2019; Hoogewind et al., 2017; Taszarek et al., 2021; Trapp et al., 2019). 57 The competition between these two groups of factors (increased moisture and CAPE versus 58 greater CIN and lower RH) complicates how convective systems respond to global warming. 59 CIN and CAPE affect deep convection in different ways. CIN quantifies the negative buoyancy 60 that must be overcome to initiate convection, while CAPE measures atmospheric instability and 61 potential convective intensity but takes effect only after CIN is overcome for convective 62 initiation (Diffenbaugh et al., 2013; Trapp et al., 2007). Rasmussen et al. (2020) demonstrated 63 manuscript submitted to Journal of Geophysical Research: Atmospheres that, under global warming, increased CIN would suppress weak and moderate convection, while 64 larger CAPE would result in more strong convection. However, they used grid-scale radar 65 reflectivity to estimate the changes in the convection populations of different scales, which might 66 not represent the actual sizes and intensities of deep convective systems. Small and short67 duration isolated deep convection (IDC) can produce radar reflectivity as large as that of large 68 and long-lasting mesoscale convective systems (MCSs) (Li et al., 2021b). 69 This study exploits the updated FLExible object TRacKeR (FLEXTRKR) algorithm to 70 distinguish small and short-duration IDC from large and long-lasting MCS events, enabling us to 71 investigate the potential impacts of future warming on these two types of convective systems 72 with distinct properties (Li et al., 2021a). Using a regionally refined global convection73 permitting model – the Simple Cloud-Resolving E3SM (Energy Exascale Earth System Model) 74 Atmosphere Model (SCREAM) (Caldwell et al., 2021), we simulate convective systems of 75 different sizes over the United States east of the Rocky Mountains, as they were observed in the 76 summer of 2020 and under future warming conditions. We apply the pseudo-global warming 77 (PGW) experiment framework to construct the future warming scenario for SCREAM (Schär et 78 al., 1996). The SCREAM model and configuration employed, the updated FLEXTRKR 79 algorithm, and an observational MCS-IDC dataset are described in detail in Section 2. Section 3 80 evaluates SCREAM’s performance in reproducing observed MCS and IDC characteristics and 81 analyzes the potential impacts of global warming on MCS and IDC by comparing the simulated 82 MCS and IDC events under observed and pseudo-warming conditions. The uncertainties and 83 limitations of the study are also discussed in Section 3. Finally, we summarize this study in 84 Section 4. 85 2 Materials and Methods 86 2.1 SCREAM and Regional Refined Mesh 87 SCREAM is a global convection-permitting atmospheric-land model developed by the 88 U.S. Department of Energy (DOE) designed to utilize DOE’s high-performance supercomputer 89 resources to resolve some of the long-standing problems attributed to coarse-resolution in E3SM 90 simulations (Caldwell et al., 2021). SCREAM uses the non-hydrostatic version of the High Order 91 Method Modeling Environment (HOMME-NH) as the fluid-dynamic solver (Taylor et al., 2020). 92 The model's physical parameterizations include the Simplified Higher Order Closure (SHOC) 93 boundary layer turbulence scheme (Bogenschutz & Krueger, 2013), the Predicted Particle 94 Properties (P3) microphysics scheme (Morrison & Milbrandt, 2015), and the combination of the 95 Radiative Transfer for Energetics (RTE) and the Rapid Radiative Transfer Model for General 96 circulation models – Parallel (RRTMGP) for longwave and shortwave radiation (Pincus et al., 97 2019). The model does not include a deep convective parameterization, and aerosol 98 concentrations are prescribed (Caldwell et al., 2021). 99 Although SCREAM is demonstrably superior to coarse-resolution E3SM in simulating 100 precipitation and convective systems, it is computationally expensive (Caldwell et al., 2021). 101 This study, thus, constructs a regional refined mesh to not only reduce the computational burden 102 but also exploit the advanced features of SCREAM (Liu et al., 2023; Tang et al., 2019). 103 Regionally refined model (RRM) supports higher resolution in the region of interest and lower 104 resolution in other areas of the model domain (Figure 1). Since the coarse resolution region does 105 not explicitly resolve deep convection, the lack of a deep convective parameterization in 106 manuscript submitted to Journal of Geophysical Research: Atmospheres SCREAM is a potential issue, particularly for long-running simulations. Consequently, we apply 107 nudging over the coarse-resolution region to constrain the atmospheric states by those of a global 108 reanalysis but keep the high-resolution region of interest free-running and able to respond to 109 external forcing (Figure 1). We expect nudging to generate realistic boundary conditions for the 110 region of interest, mimicking convection-permitting regional climate model simulations 111 constrained by boundary conditions (Li et al., 2023). However, nudging allows some large-scale 112 circulation feedback between the region of interest and the surrounding areas in SCREAM RRM, 113 which is entirely absent in regional climate models because of the imposed boundary conditions. 114 Using the regional refined mesh configuration in Figure 1, which has a dynamical 115 horizontal resolution of ~3.2 km for the central-eastern United States and ~25 km for other areas, 116 we conduct an AMIP-type (AMIP: Atmospheric Model Intercomparison Project) SCREAM 117 RRM control simulation (hereafter named the CTRL simulation) from June 28 to August 19, 118 2020, with the first three days as spin-up. Its component set comprises an active atmospheric 119 component, i.e., SCREAM, an active land component – E3SM Land Model (ELM) (Golaz et al., 120 2019; Golaz et al., 2022), a simplified active sea ice component, a data ocean model with 121 prescribed hourly sea surface temperature (SST) and sea ice fractions from the European Centre 122 for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) reanalysis dataset, and 123 the Model for Scale Adaptive River Transport (MOSART) for river routing (Li et al., 2013). 124 SCREAM has 128 vertical levels with a vertical resolution of ~50 m in the boundary layer and a 125 model top at 2.25 hPa (Caldwell et al., 2021). The model has a dynamics time step of 8.33 126 seconds and a physics time step of 100 seconds. The atmospheric initial condition is based on a 127 combination of the hourly High-Resolution Rapid Refresh (HRRR) and ERA5 reanalysis 128 datasets (Dowell et al., 2022; Hersbach et al., 2020). HRRR has a horizontal resolution of 3 km, 129 covering the contiguous United States, while ERA5 has a global horizontal resolution of 0.25. 130 We use HRRR wherever it is available and ERA5 for areas not covered by HRRR when 131 constructing the SCREAM atmospheric initial condition. The land initial condition is spun up 132 through a 3-year (June 28, 2017, to June 28, 2020) SCREAM RRM land-only simulation 133 constrained by atmospheric forcing in 2020 from ERA5 (Liu et al., 2023). The simulation uses 134 prescribed hourly SST from ERA5. Three-dimensional zonal (U) and meridional (V) winds, 135 temperature (T), and specific humidity (Q) above 850 hPa are nudged towards hourly ERA5 136 reanalysis with a relaxation timescale of 6 hours for grids outside the red box in Figure 1 137 (Barthel Sorensen et al., 2024). 138 139 manuscript submitted to Journal of Geophysical Research: Atmospheres 140 Figure 1. The SCREAM RRM domain in a cylindrical equidistant projection. Blue lines 141 represent the boundaries of spectral element grids, and each spectral element comprises 4 142 physical grid boxes. The red rectangle outlines the free-running region within, while the green 143 rectangle outlines the region with observational MCS-IDC data. 144 2.2 PGW setup 145 To investigate the potential impacts of global warming on MCSs and IDC, we conduct 146 another simulation using the PGW approach (Schär et al., 1996; Xue et al., 2023) (hereafter 147 named the PGW simulation), which is nearly identical to the CTRL simulation but with four 148 exceptions. For the PGW simulation, 1) monthly PGW deltas (Equation 1) are added to the state 149 variables in the atmospheric initial condition (U, V, T, Q, surface pressure, and skin temperature) 150 and the ERA5 nudging fields (U, V, T, and Q); 2) monthly PGW deltas are added to the 151 atmospheric forcing state variables (10-meter U and V, 2-meter T, 2-meter Q, and surface 152 pressure) and monthly PGW ratios (Equation 2) are multiplied with the flux variables from the 153 atmospheric forcing (precipitation and surface downwelling longwave and shortwave fluxes) 154 (Lawrence et al., 2018) as forcing for the land-only simulation to provide initial conditions for 155 the land component; 3) temporally-interpolated hourly PGW deltas are added to prescribed SST; 156 4) the Sixth Assessment Report of the United Nations Intergovernmental Panel on Climate 157 Change (IPCC AR6) projected greenhouse gas concentrations in 2100 are used instead of the 158 values in 2020 (Meinshausen et al., 2019). Similar to Li et al. (2023), we select 11 models from 159 the Coupled Model Intercomparison Project Phase 6 (CMIP6) archive (except for E3SM v1.1) to 160 compute the multi-model mean PGW deltas and ratios between the historical period (1981–2010) 161 and the end of this century (2071–2100) under the Shared Socioeconomic Pathways (SSP5-8.5) 162 scenario, featuring a radiative forcing of 8.5 W m−2 by 2100. Figure S1 summarizes the 163 workflow of the PGW simulation. 164 ∆𝑃𝐺𝑊= 𝑋2071−2100 − 𝑋1981−2010 (1) 165 manuscript submitted to Journal of Geophysical Research: Atmospheres 𝑅𝑃𝐺𝑊 =𝑋2071−2100 𝑋1981−2010 (2) 166 where X is any meteorological variable (e.g., U, V, T, and Q); X2071-2100 denotes the multi-year 167 averaged monthly value of X between 2071 and 2100, similar to X1981-2010;  PGW refers to the 168 PGW delta of X, and RPGW represents the PGW ratio of X for the given month. 169 2.3 The updated FLEXTRKR algorithm and the observational MCS-IDC dataset 170 The updated FLEXTRKR algorithm, combined with the Storm Labeling in Three 171 Dimensions (SL3D) algorithm (Starzec et al., 2017), can track MCS and IDC simultaneously 172 using infrared brightness temperature (Tb), radar reflectivity, precipitation, and melting level 173 height (Li et al., 2021a). The algorithm first identifies cold cloud shields (CCSs) with Tb < 241K 174 at each time slice and then establishes the spatial connections of CCSs between two consecutive 175 hours using a spatial overlap threshold of 50%. A track is generated by linking all the CCSs from 176 the same cloud system. FLEXTRKR classifies a track as an MCS if the areas of the tracked 177 CCSs are >60,000 km2 for >6 consecutive hours and the track contains precipitation features 178 (PFs) with a major axis length >100 km and an embedded intense convective cell area ≥16 km2 179 for ≥6 continuous hours. Here, a PF is a continuous updraft, convective, or precipitating 180 stratiform area with precipitation >1 mm h−1, and an intense convective cell is a continuous 181 updraft or convective area with composite reflectivity ≥45 dBZ. The SL3D algorithm identifies 182 updraft, convective, and precipitating stratiform pixels. A non-MCS track is considered IDC if it 183 contains any PFs and convective core features (CCFs) during its lifetime. A CCF is a continuous 184 updraft or convective area with precipitation >0 mm h−1. 185 This study uses the updated FLEXTRKR algorithm and various source datasets to 186 develop a high-resolution (4 km, hourly) observational MCS-IDC dataset, which provides 187 tracking and characteristics of MCS and IDC events over the United States east of the Rocky 188 Mountains from July 1 to August 19, 2020. The source datasets are the same as those used in Li 189 et al. (2021a), except that the 3-D Gridded NEXRAD (Next Generation Weather Radar) WSR190 88D Radar (GridRad) reflectivity data has been updated from Version 3.1 to Version 4.2 191 (Bowman & Homeyer, 2021). The updated FLEXTRKR is also used to track MCS and IDC 192 events from the above SCREAM RRM simulations within the observational MCS-IDC data 193 domain (Figure 1). Notably, we remove convective systems associated with two hurricanes 194 (Hurricanes Hanna and Isaias) occurring during the study period from the SCREAM simulations 195 and the observational MCS-IDC dataset. Moreover, this study focuses exclusively on MCS and 196 IDC events in the U.S. land areas within the MCS-IDC data domain, which are defined as 197 convective systems staying within the region for at least half of their lifetimes. 198 3 Results 199 3.1 Evaluation of the CTRL simulation 200 Table 1 evaluates the CTRL simulation against the observational MCS-IDC dataset at the 201 event scale. The CTRL simulation roughly produces as many MCS (66 vs. 80) and IDC (19,638 202 vs. 15,887) events as observations, far better than the underestimated counts (by >70%) of MCS 203 events in global climate models generated with refined horizontal resolution at 50 and 25 km 204 over North America (Feng et al., 2021). The CTRL simulation also well reproduces the observed 205 MCS and IDC respective mean lifetime, CCS area, CCS core (Tb < 225 K) area, PF stratiform 206 area, PF convective and stratiform precipitation rates, and CCS, PF, and CCF major axis lengths. 207 manuscript submitted to Journal of Geophysical Research: Atmospheres However, the CTRL underestimates the MCS max 40-dBZ echo top height but overestimates the 208 MCS and IDC PF convective areas. The latter may be attributed to overestimated model radar 209 reflectivities associated with convective systems (not shown), a limitation also identified in the 210 simulation of the June 2012 North American derecho using SCREAM RRM and the Weather 211 Research and Forecasting Model (Li et al., 2023; Liu et al., 2023). The CTRL also effectively 212 captures the observed MCS evolutional characteristics (Figure S2), further validating the 213 excellent capability of SCREAM RRM in simulating the populations of convective systems. 214 Figures S3a-S3d and 2a-2d compare the CTRL-simulated spatial distributions of MCS 215 and IDC occurrences and their accumulated precipitation with the MCS-IDC dataset. The CTRL 216 captures the observed MCS and IDC spatial distribution patterns well. The uncentered pattern 217 correlations between CTRL and observations are 0.68 and 0.72 for MCS and IDC precipitation, 218 respectively (Figures 2a-2d). These values reach up to 0.93 and 0.90 for the MCS and IDC 219 occurrence frequencies (Figures S3a-S3d). The CTRL correctly identifies hotspots with frequent 220 MCS and IDC occurrences, such as the Great Plains and Midwest for MCS and the southeastern 221 and eastern coastal areas for IDC. However, on average, the CTRL underestimates the MCS 222 numbers by 23% and precipitation by 16%. The underestimation is most apparent in the Great 223 Plains, with frequent MCS occurrences, which is a common bias widely found in regional and 224 global climate models (Feng et al., 2021; Li et al., 2022; Lin et al., 2022; Prein et al., 2020). In 225 addition, on average, the CTRL overestimates IDC occurrences by 23%, while the IDC 226 precipitation is overestimated by only 5% due to an underestimated IDC precipitation rate (Table 227 1). 228 Overall, despite the presence of certain common model biases, the CTRL simulation 229 effectively captures the event-scale and spatial-scale characteristics of observed MCS and IDC 230 events. This allows for a meaningful comparison between the PGW and CTRL simulations. 231 manuscript submitted to Journal of Geophysical Research: Atmospheres Table 1. Comparison of the SCREAM RRM simulated MCS and IDC statistics and mean properties with the observational MCS-IDC dataset 232 Statistics and mean properties MCS IDC Obs CTRL PGW Obs CTRL PGW Event number 80 66 85 15,887 19,638 16,014 Lifetime / h 18.6 18.41 17.1 1.8 1.5 1.6 CCS area / km2 127,560 116,552 113,209 2,970 2,927 3,546 CCS major axis length / km 524 515 508 67 75 77 CCS core area / km2 66,444 55,516 61,202 667 647 1,103 PF major axis length / km 297 323 317 47 59 57 PF convective area / km2 8,363 12,911 15,027 388 970 1,020 PF stratiform area / km2 22,405 27,325 25,159 558 777 651 PF convective precipitation rate / mm h-1 4.6 4.1 3.9 4.3 3.7 3.7 PF stratiform precipitation rate / mm h-1 2.8 2.6 2.6 2.9 2.2 2.3 Max 40-dBZ echo top height / km 9.2 6.9 8.1 5.4 5.0 6.2 CCF major axis length / km 122 114 129 26 42 44 1Red colored numbers indicate the difference between the PGW and CTRL simulations is statistically significant at the 5% level. 233 234 manuscript submitted to Journal of Geophysical Research: Atmospheres 235 Figure 2. Spatial distributions of accumulated precipitation produced by (left panel) MCS and 236 (right panel) IDC events from (a, b) the observational MCS-IDC dataset, (c, d) the CTRL 237 simulation, and (e, f) the PGW simulation. We only count hours with hourly precipitation larger 238 than 0.01 mm for each grid cell. 239 3.2 Impact of global warming on event-scale statistics 240 By comparing the PGW and CTRL simulations, we find consistent increases in the mean 241 PF convective areas of MCS and IDC events, which aligns with larger max 40-dBZ echo top 242 heights under warming (Figure 3 and Table 1). In contrast, MCS and IDC PF stratiform areas are 243 reduced in the PGW compared to the CTRL simulations (Figure 3 and Table 1). The warming244 induced increase in MCS convective areas and decrease in stratiform areas is consistent with 245 Feng et al. (2024), who investigated the potential impact of global warming on a cluster of 246 springtime MCSs in the southern Great Plains during May 2015. They attributed the wider 247 convective areas to larger CAPE, resulting in more intense convective updrafts, and the reduced 248 stratiform areas to elevated stratiform cloud bases, leading to stronger precipitation evaporation 249 in a warmer atmosphere (Feng et al., 2024). Additionally, Figure 3 and Table 1 show a stronger 250 manuscript submitted to Journal of Geophysical Research: Atmospheres Open Research 401 The SCREAM source code is available at https://github.com/E3SM-Project/scream (last access: September 26, 402 2022). The MCS-IDC dataset used in the study is available at 403 https://portal.nersc.gov/project/m3780/jli628/SCREAM_MCS_IDC/ (last access: January 26, 2023). The 404 GridRad v4.2 data is from https://rda.ucar.edu/datasets/ds841-1/ (last access: November 3, 2022) (Bowman & 405 Homeyer, 2021). We download the ERA5 nudging fields and SST from https://rda.ucar.edu/datasets/ds633-0/ 406 (last access: October 2, 2022) (ECMWF, 2019). The HRRR reanalysis data is downloaded from the Google 407 Cloud Platform (https://console.cloud.google.com/marketplace/product/noaa-public/hrrr?project=python408 232920&pli=1; last access, December 7, 2022). Surface variables in the ERA5 initial condition and the ERA5 409 forcing data used in the SCREAM land-only simulations are downloaded from 410 https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview (last access: 411 December 26, 2022) (Hersbach et al., 2023b). Pressure-level variables in the ERA5 initial condition are 412 downloaded from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure413 levels?tab=overview (last access: December 26, 2022) (Hersbach et al., 2023a). 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