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On average, modern numerical weather prediction forecasts for daily tornado frequency exhibit no skill beyond day 10. However, in this extended-range lead window, there are particular model cycles that have exceptionally high forecast skill for tornadoes because of their ability to correctly simulate the future synoptic pattern. Here, model initial conditions that produced a more skillful forecast for tornadoes over the United States were exploited while also highlighting potential causes for low-skill cycles within the Global Ensemble Forecasting System, version 12 (GEFSv12). There were 88 high-skill and 91 low-skill forecasts in which the verifying day-10 synoptic pattern for tornado conditions revealed a western U.S. thermal trough and an eastern U.S. thermal ridge, a favorable configuration for tornadic storm occurrence. Initial conditions for high skill forecasts tended to exhibit warmer sea surface temperatures throughout the tropical Pacific Ocean and Gulf of Mexico, an active Madden–Julian oscillation, and significant modulation of Earth-relative atmospheric angular momentum. Low-skill forecasts were often initialized during La Niña and negative Pacific decadal oscillation conditions. Significant atmospheric blocking over eastern Russia—in which the GEFSv12 overforecast the duration and characteristics of the downstream flow—was a common physical process associated with low-skill forecasts. This work helps to increase our understanding of the common causes of high- or low-skill extended-range tornado forecasts and could serve as a helpful tool for operational forecasters. 

Elevated mixed layers (EMLs) influence the severe convective storm climatology in the contiguous United States (CONUS), playing an important role in the initiation, sustenance, and suppression of storms. This study creates a high-resolution climatology of the EML to analyze variability and potential changes in EML frequency and characteristics for the first time. An objective algorithm is applied to ERA5 to detect EMLs, defined in part as layers of steep lapse rates (≥8.0°C km⁻¹) at least 200 hPa thick, in the CONUS and northern Mexico from 1979 to 2021. EMLs are most frequent over the Great Plains in spring and summer, with a standard deviation of 4–10 EML days per year highlighting sizable interannual variability. Mean convective inhibition associated with the EML’s capping inversion suggests many EMLs prohibit convection, although—like nearly all EML characteristics—there is considerable spread and notable seasonal variability. In the High Plains, statistically significant increases in EML days (4–5 more days per decade) coincide with warmer EML bases and steeper EML lapse rates, driven by warming and drying in the low levels of the western CONUS during the study period. Additionally, increases in EML base temperatures result in significantly more EML-related convective inhibition over the Great Plains, which may continue to have implications for convective storm frequency, intensity, severe perils, and precipitation if this trend persists.

Elevated mixed layers (EMLs) play a role in the spatiotemporal frequency of severe convective storms and precipitation across the contiguous United States and northern Mexico. This research creates a detailed EML climatology from a modern reanalysis dataset to uncover patterns and potential changes in EML frequency and associated meteorological characteristics. EMLs are most common over the Great Plains in spring and summer, but show significant variability year-to-year. Robust increases in the number of days with EMLs have occurred since 1979 across the High Plains. Lapse rates associated with EMLs have trended steeper, in part due to warmer EML base temperatures. This has resulted in increasing EML convective inhibition, which has important implications for regional climate.

 

A supercell is a distinct type of intense, long-lived thunderstorm that is defined by its quasi-steady, rotating updraft. Supercells are responsible for most damaging hail and deadly tornadoes, causing billions of dollars in losses and hundreds of casualties annually. This research uses high-resolution, convection-permitting climate simulations across 15-yr epochs that span the twenty-first century to assess how supercells may change across the United States. Specifically, the study explores how late-twentieth-century supercell populations compare with their late-twenty-first-century counterparts for two—intermediate and pessimistic—anthropogenic climate change trajectories. An algorithm identifies, segments, and tracks supercells in the simulation output using updraft helicity, which measures the magnitude of corkscrew flow through a storm’s updraft and is a common proxy for supercells. Results reveal that supercells will be more frequent and intense in future climates, with robust spatiotemporal shifts in their populations. Supercells are projected to become more numerous in regions of the eastern United States, while decreasing in frequency in portions of the Great Plains. Supercell risk is expected to escalate outside of the traditional severe storm season, with supercells and their perils likely to increase in late winter and early spring months under both emissions scenarios. Conversely, the latter part of the severe storm season may be curtailed, with supercells expected to decrease midsummer through early fall. These results suggest the potential for more significant tornadoes, hail, and extreme rainfall that, when combined with an increasingly vulnerable society, may produce disastrous consequences. 

Explicit representation of finer‐scale processes can affect the sign and magnitude of the precipitation response to climate change between convection‐permitting and convection‐parameterizing models. We compare precipitation across two 15‐year epochs, a historical (HIST) and an end‐of‐21st‐century (EoC85), between a set of dynamically downscaled regional climate simulations at 3.75 km grid spacing (WRF) and bias‐corrected Community Earth System Model (CESM) output used to initialize and force the lateral boundaries of the downscaled simulations. In the historical climate, the downscaled simulations demonstrate less overall error than CESM when compared to observations for most portions of the conterminous United States. Both sets of simulations overestimate the incidence of environments with moderate to high precipitable water while CESM generally simulates rainfall that is too frequent but less intense. Within both sets of simulations, EoC85 rainfall amounts decrease in low‐moisture environments due to reduced rainfall frequency and intensity while rainfall amounts increase in high‐moisture environments as they occur more often. Overall, reductions in rainfall are stronger in WRF than in CESM, particularly during the warm season. This reduced drying in CESM is attributed to relatively higher rainfall frequency in environments with high concentrations of precipitable water and weak vertical motion. As a result, an increase in the occurrence of high moisture environments in EoC85 naturally favors more rainfall in CESM than WRF. Our results present an in‐depth examination of the characteristics of changes in overall accumulated precipitation and highlight an extra dimension of uncertainty when comparing convection‐permitting models against convection‐parameterizing models.

 

This study presents a novel, high-resolution, dynamically downscaled dataset that will help inform regional and local stakeholders regarding potential impacts of climate change at the scales necessary to examine extreme mesoscale conditions. WRF-ARW version 4.1.2 was used in a convection-permitting configuration (horizontal grid spacing of 3.75 km; 51 vertical levels; data output interval of 15-min) as a regional climate model for a domain covering the contiguous US Initial and lateral boundary forcing for the regional climate model originates from a global climate model simulation by NCAR (Community Earth System Model) that participated in phase 5 of the Coupled Model Inter comparison Project. Herein, we use a version of these data that are regridded and bias corrected. Two 15-year downscaled simulation epochs were examined comprising of historical (HIST; 1990–2005) and potential future (FUTR; 2085–2100) climate using Representative Concentration Pathway (RCP) 8.5. HIST verification against independent observational data revealed that annual/seasonal/monthly temperature and precipitation (and their extremes) are replicated admirably in the downscaled HIST epoch, with the largest biases in temperature noted with daily maximum temperatures (too cold) and the largest biases in precipitation (too dry) across the southeast US during the boreal warm season. The simulations herein are improved compared to previous work, which is significant considering the differences in previous modeling approaches. Future projections of temperature under the RCP 8.5 scenario are consistent with previous works using various methods. Future precipitation projections suggest statistically significant decreases of precipitation across large segments of the southern Great Plains and Intermountain West, whereas significant increases were noted in the Tennessee/Ohio Valleys and across portions of the Pacific Northwest. Overall, these simulations serve as an additional datapoint/method to detect potential future changes in extreme meso-γ weather phenomena.

 

The Madden–Julian Oscillation (MJO) is the dominant mode of intraseasonal variability in the tropics and has a documented influence on extratropical extreme weather through modulation of synoptic atmospheric conditions. MJO phase has been correlated with anomalous tornado and severe hail frequency in the United States (US). However, the robustness of this relationship is unsettled, and the variability of physical pathways to modulation is poorly understood, despite the socioeconomic impacts that tornadoes and hail evoke. We approached this problem using pentad MJO indices and practically perfect severe weather hindcasts. MJO lifecycles were cataloged and clustered to document variability and potential pathways to enhanced subseasonal tornado and hail predictability. Statistically significant increases in US tornado and hail probabilities were documented 3–4 weeks following the period of the strongest upper-level divergence for the 53 active MJO events that propagated past the Maritime continent, contrasting with the 47 MJO events that experienced the barrier effect, during boreal spring 1979–2019. The 53 MJO events that propagated past the Maritime continent revealed three prevailing MJO evolutions—each containing unique pathways and modulation of US tornado and hail frequency—advancing our knowledge and capability to anticipate these hazards at extended lead times.

 

Abstract Extratropical cyclones are the primary driver of sensible weather conditions across the mid-latitudes of North America, often generating various types of precipitation, gusty non-convective winds, and severe convective storms throughout portions of the annual cycle. Given ongoing modifications of the zonal atmospheric thermal gradient due to anthropogenic forcing, analyzing the historical characteristics of these systems presents an important research question. Using the North American Regional Reanalysis, boreal cool-season (October–April) extratropical cyclones for the period 1979–2019 were identified, tracked, and classified based on their genesis location. Additionally, bomb cyclones—extratropical cyclones that recorded a latitude normalized pressure fall of 24 hPa in 24-hr—were identified and stratified for additional analysis. Cyclone lifespan across the domain exhibits a log-linear relationship, with 99% of all cyclones tracked lasting less than 8 days. On average, ≈ 270 cyclones were tracked across the analysis domain per year, with an average of ≈ 18 year −1 being classified as bomb cyclones. The average number of cyclones in the analysis domain has decreased in the last 20 years from 290 year −1 during the period 1979–1999 to 250 year −1 during the period 2000–2019. Spatially, decreasing trends in the frequency of cyclone track counts were noted across a majority of the analysis domain, with the most significant decreases found in Canada’s Northwest Territories, Colorado, and east of the Graah mountain range. No significant interannual or spatial trends were noted with bomb cyclone frequency. 

Abstract Previous studies have identified environmental characteristics that skillfully discriminate between severe and significant-severe weather events, but they have largely been limited by sample size and/or population of predictor variables. Given the heightened societal impacts of significant-severe weather, this topic was revisited using over 150 000 ERA5 reanalysis-derived vertical profiles extracted at the grid-point nearest—and just prior to—tornado and hail reports during the period 1996–2019. Profiles were quality-controlled and used to calculate 84 variables. Several machine learning classification algorithms were trained, tested, and cross-validated on these data to assess skill in predicting severe or significant-severe reports for tornadoes and hail. Random forest classification outperformed all tested methods as measured by cross-validated critical success index scores and area under the receiver operating characteristic curve values. In addition, random forest classification was found to be more reliable than other methods and exhibited negligible frequency bias. The top three most important random forest classification variables for tornadoes were wind speed at 500 hPa, wind speed at 850 hPa, and 0–500-m storm-relative helicity. For hail, storm-relative helicity in the 3–6 km and -10 to -30 °C layers, along with 0–6-km bulk wind shear, were found to be most important. A game theoretic approach was used to help explain the output of the random forest classifiers and establish critical feature thresholds for operational nowcasting and forecasting. A use case of spatial applicability of the random forest model is also presented, demonstrating the potential utility for operational forecasting. Overall, this research supports a growing number of weather and climate studies finding admirable skill in random forest classification applications. 

Assessing how increasing greenhouse gas concentrations may modify regional climates is an ongoing challenge. Relatively little work has examined how climate change may influence processes related to regional thunderstorm activity. This is important to consider in areas where thunderstorms are important for maintaining regional hydroclimates. This study focuses on three convection‐allowing climate simulations—namely, a retrospective simulation (1990–2005) and two possible climate change scenarios (2085–2100)—with domains encompassing the conterminous United States. Regional and seasonal variability is noted in the response of thunderstorm activity as measured by thresholds in simulated radar reflectivity factor in the two climate change scenarios. A decrease in thunderstorm activity is projected for the Southern Plains, whereas the Southeast and Midwest experience an increase in thunderstorm activity. An examination of environmental parameters related to thunderstorm activity reveals an overall increase in convective instability but spatially varying changes in convective inhibition.