This study integrates historical crop condition ratings with high-resolution regional climate simulations to quantify how U.S. crop conditions may evolve under intermediate and pessimistic anthropogenic emissions scenarios. State-, month-, and crop-specific gradient-boosted tree models were trained using climate predictors related to temperature, precipitation, water balance, and soil moisture. These models were used to compare late-twentieth-century crop conditions (1990–2005) with projected mid-century (2040–2055) and late-century (2085–2100) conditions. Because non-climate factors such as technology, management, genetics, and adaptation are held constant for modeling purposes, the results represent a baseline estimate of climate-driven crop condition risk. Projections indicate widespread declines in crop condition ratings, with declines generally intensifying and becoming more spatially coherent from mid- to late-century under higher emissions. The most robust declines occur from July onward, coinciding with reproductive crop development and yield formation. These changes are consistent with projected warming, reduced mean precipitation totals, and greater precipitation variability across key agricultural regions. Nationally, crop conditions are also projected to become more variable from year to year for most crops. Variability increases are largest during the late-vegetative to reproductive transition, thereby increasing uncertainty during the most yield-sensitive period of the summer growing season. Regionally, production risk depends on both changes in mean conditions and changes in variability. Northern U.S. small grains and portions of Midwest row crops are projected to experience declining mean conditions and increasing interannual variability, while many southern U.S. cropping systems exhibit more persistent condition declines. These findings provide a climate-driven basis for agricultural policy and adaptation planning by identifying where crop monitoring, risk management programs, water resource planning, and regional resilience strategies may be most needed to sustain agricultural production through the twenty-first century.

Output from dynamically downscaled, convection-permitting regional climate simulations is used to examine projected changes in the frequency, timing and spatial distribution of synthetic severe convective storm (SCS) detections based on peril proxies across three 15-year epochs-1990-2005, 2040-2055 and 2085-2100-under intermediate and pessimistic emissions scenarios, including the first mid-century projections of SCS activity, providing a rare assessment of how SCS climatology may evolve throughout the 21st century. An explicit multi-hazard approach is employed to define severe convective perils using permutations of simulated upward vertical velocity and lowest model level reflectivity thresholds, allowing SCS activity to be assessed collectively for tornado, wind and hail perils. In response to enhanced greenhouse forcing and related changes in fundamental severe storm ingredients, projected synthetic SCS activity exhibits spatiotemporal changes relative to the historical baseline, including lengthening of the severe season, increases in the overall frequency of SCS days and an eastward shift of event frequency maxima. In addition to changes in mean frequency, results illustrate enhanced variability and increases in relatively high-activity days across all future epochs, driven by more frequent spring and early summer occurrences. Regionally, increases in SCS days are projected for south-central and Gulf Coast states, the southern Great Plains and the Midwest in the spring and the Midwest, Northeast and Southeast during the summer, while decreases are projected for the Great Plains during the summer. These changes are accompanied by a broadening geographic footprint of SCS activity, indicating a redistribution of severe storm risk under warming. Results provide physically grounded insight into the potential evolving severe storm landscape, which stakeholders, policymakers and the public may use to mitigate and build resilience against future events.

Abstract The U.S. agricultural landscape is ever-evolving, with uncertainty in future losses driven by cropland exposure interacting with a changing weather and climate risk landscape. Among these weather hazards is hail—currently the third-costliest peril affecting crop production in the United States. This research employs a combination of historical and future projection cropland layers, observed severe hail reports, crop-hail insurance data, and output from convection-permitting climate simulations to demonstrate the interrelationship between changing cropland exposure and hail risk during the twenty-first century. Impacts of weather hazards on buildings, infrastructure, and other societal assets are driven by an expanding exposure footprint, and agricultural systems may follow similar trends in some U.S. regions. Results from a Monte Carlo simulation reveal that, while Midwest cropland is most exposed to hail, the Great Plains contain the highest vulnerability based on the collocation of an extensive hail footprint during the summer growing season and the expansion of cropland. Despite projected decreases in severe hail days in the Great Plains, the increase in regional cropland exposure illustrates the “expanding bull’s-eye effect” concept within the context of agriculture, highlighting that disaster potential (i.e., major crop yield loss) is not solely determined by the total area of cropland; rather, it is how croplands are distributed across the landscape that defines how risk and vulnerability are realized in a major crop loss scenario. Results from this research may inform data-driven decision-making efforts and risk communication across the agricultural community, including stakeholders in crop insurance, sustainability, and other pursuits in regional climatology. Significance Statement The purpose of this study is to understand how the crop-hail risk landscape may change through the twenty-first century. The agricultural landscape continuously evolves, with increasing uncertainty in future losses driven by cropland exposure interacting with a changing weather and climate risk landscape. Results indicate regional variation, as the Great Plains face the greatest exposure-driven risk, reflecting the “expanding bull’s-eye effect” in an agricultural context. Meanwhile, the Midwest and South may undergo hazard-driven loss increases despite negligible change in cropland extent. These findings highlight the need for proactive, regionally tailored adaptation strategies, offering a novel basis for understanding future crop-hail risk.

Changes in the frequency and intensity of mesoscale convective systems (MCSs) are assessed using convection- permitting regional climate model simulations. We present a novel classification method that relates MCSs to the magnitude of their large-scale forcing environments to better understand the changing nature of MCSs in a possible future climate scenario. Overall, the annual frequency, intensity, and amount of precipitation associated with MCSs are projected to increase for broad portions of the eastern conterminous United States (CONUS). Furthermore, changes in the characteristics of MCSs show larger, longer-lived, and faster MCSs particularly over the Midwest and Southeast. Seasonal examination of this response reveals a robust intensification of March-May (MAM) MCSs. The higher frequency of MAM MCSs is found to occur in weakly forced synoptic environments. Increased rainfall amounts are explained by an intensifica- tion of MCSs due to changing thermodynamics and enhanced lower-tropospheric moisture transport into the central CONUS by the North Atlantic subtropical high. Differences in midlatitude storm tracks are favorable toward the enhanced development of MCSs associated with strong baroclinic forcing within the Midwest and northern plains but are only realized in the presence of strong changes in the lower-tropospheric moisture transport. Overall, these results suggest a shift in the behavior of MAM MCSs that more closely corresponds to the behavior of June-August (JJA) MCSs in the historical climate. The increased occurrence of MCSs in weakly forced environments, particularly in MAM, deserves further investigation.

The importance of mesoscale convective systems (MCSs) and their precipitation is well-established, and any future spatiotemporal shifts in their frequency or intensity could have far-reaching societal impacts. This work describes how MCS activity in the conterminous United States east of the continental divide (ECONUS) is modified by two future climate change scenarios. For this study, MCSs are identified in output from a convection-permitting regional climate model (CP-RCM) for three 15-year periods—namely, a retrospective baseline (1990–2005) and two end-of-century (2085–2100) climate change scenarios based on RCP 4.5 (EoC 4.5) and RCP 8.5 (EoC 8.5). The data reveal an eastward shift in regional MCS activity. Annually, days with MCSs largely remain the same or decrease west of the Mississippi River, whereas areas east of the Mississippi River experience more MCS days and MCS precipitation. The largest seasonal increases in MCS days and precipitation occur during the spring in parts of the Midwest and Northeast, whereas the largest decreases occur in parts of the Southern Plains during the summer. Overall, EoC 8.5 produced larger regional changes compared to EoC 4.5, suggesting that future CP-RCM experiments could benefit from considering multiple climate change scenarios.

This research seeks to understand simulated supercell precipitation characteristics across the conterminous United States (CONUS) using high-resolution, convection-permitting, dynamically downscaled simulations for three 15-year epochs. Epochs include a historical end-of-20th-century period (1990-2005) and two end-of-21st-century (2085-2100) scenarios for intermediate and pessimistic greenhouse gas concentration trajectories. Simulated updraft helicity, which measures the corkscrew flow within a storm's updraft, is used as a proxy for supercells. An algorithm tracks and catalogues updraft helicity swaths that, when buffered, are used to acquire simulated precipitation from supercells. The historical epoch provides a baseline climatology of supercell precipitation for a contemporary climate, which is then compared against the two future epochs to assess how supercell precipitation may change during the 21st century. Despite their relatively small size, supercells provide critical precipitation to the Wheat and Corn Belts, large expanses of CONUS pasture and rangeland, regional aquifers and several large river basins. Many areas in the central CONUS receive upwards of 3%-6% of their annual and 5%-8% of their warm-season precipitation from these storms. Results suggest that precipitation contribution from supercells will decrease in the future across most of the High Plains and Central and Northern Great Plains with robust increases likely across the south-central and Southeast regions. Supercell precipitation rates are expected to increase for large portions of the CONUS by the end-of-the-21st-century, suggesting a growing threat for flash floods from these storms as they become more efficient precipitation producers. This research provides an initial perspective on the magnitude of supercell precipitation and potential changes to this important hydrologic input to assist water-sensitive industries, private and public insurance markets, agriculture entities, as well as inform plans to mitigate and build resilience to rapid environmental and societal change. This research illustrates the precipitation contributions to the United States hydroclimate from rotating thunderstorms known as supercells and how those contributions will change in the 21st century. Results will assist water-sensitive industries, insurance markets and agriculture entities to mitigate and build resilience to rapid environmental and societal changes. image

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 degrees 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. SIGNIFICANCE STATEMENT: 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.

Short-duration, high-intensity rainfall can initiate deadly and destructive debris flows after wildfire. Methods to estimate the conditions that can trigger debris flows exist and guidance to determine how often those thresholds will be exceeded under the present climate are available. However, the limited spatiotemporal resolution of climate models has hampered efforts to characterize how rainfall intensification driven by global warming may affect debris-flow hazards. We use novel, dynamically downscaled (3.75-km), convection-permitting simulations of short-duration (15-min) rainfall to evaluate threshold exceedance for late 21st-century climate scenarios in the American Southwest. We observe significant increases in the frequency and magnitude of exceedances for regions dominated by cool- and warm-season rainfall. We also observe an increased frequency of exceedance in regions where postfire debris flows have not been documented, and communities are unaccustomed to the hazard. Our findings can inform planning efforts to increase resiliency to debris flows under a changing climate.

Hailstorms are analyzed across the United States using explicit hailstone size calculations from convection-permitting regional climate simulations for historical, mid-century, and end of twenty-first-century epochs. Near-surface hailstones <4 cm are found to decrease in frequency by an average of 25%, whereas the largest stones are found to increase by 15–75% depending on the greenhouse gas emissions pathway. Decreases in the frequency of near-surface severe hail days are expected across the U.S. High Plains, with 2–4 fewer days projected—primarily in summer. Column-maximum severe hail days are projected to increase robustly in most locations outside of the southern Plains, a distribution that closely mimics projections of thunderstorm days. Primary mechanisms for the changes in hailstone size are linked to future environments supportive of greater instability opposed by thicker melting layers. This results in a future hailstone size dichotomy, whereby stronger updrafts promote more of the largest hailstones, but significant decreases occur for a majority of smaller diameters due to increased melting.

The potential for changes in extreme precipitation events due to anthropogenic climate change may have significant societal impacts (e.g., agricultural productivity, property loss, and mortality). This project uses a dynamically downscaled, convection-permitting regional climate model to investigate extreme daily precipitation in the CONUS, defi ned explicitly as the 99th percentile 24-h accumulated value. The simulation output includes a historical (HIST) baseline (1990-2005) and two epochs at the end of the twenty-first century (EOC; 2085-2100) under intermediate and pessimistic emissions scenarios. Independent observations illustrate that HIST admirably represents extreme precipitation climatology for most locations in the domain. Comparisons between HIST and the two EOC scenarios for the 99th percentile of daily precipitation show statistically significant increases during December-May across the Midwest and Ohio Valley and statistically significant decreases for the southern Great Plains during December-February. Extreme value analysis further reveals increasing variability in precipitation extremes for eight climatologically unique cities across the CONUS by the end of the twenty-first century and significant increases in return period precipitation amounts for most cities examined. These results provide additional guidance for stakeholders to reduce societal impacts and economic loss from daily precipitation extremes and create a more climate-resilient society.

Mesoscale convective systems (MCSs) are a substantial source of precipitation in the eastern U.S. and may be sensitive to regional climatic change. We use a suite of convection-permitting climate simulations to examine possible changes in MCS precipitation. Specifically, annual and regional totals of MCS and non-MCS precipitation generated during a retrospective simulation are compared to end-of-21st-century simulations based on intermediate and extreme climate change scenarios. Both scenarios produce more MCS precipitation and less non-MCS precipitation, thus significantly increasing the proportion of precipitation associated with MCSs across the U.S.

Abstract 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.

Abstract An increasingly volatile hydroclimate increases California's reliance on precipitation from atmospheric rivers (ARs) for water resources. Here, we simulate the AR that contributed to the Oroville Dam crisis in early February 2017 under global climate conditions representing preindustrial, present‐day, mid‐, and late‐21st century environments. This event consisted of two distinct AR pulses: the first snowy, westerly, and cool followed by a southwesterly and warm pulse resulting in copious rain‐on‐snow. We estimate that climate change to date results in ∼11% and ∼15% increase in precipitation over the Feather River Basin in Northern California for the first and second pulses, respectively, with late‐21st century enhancements upwards of ∼21% and ∼59%, respectively. Although both pulses were enhanced by the imposed climate changes, the thermodynamic response and subsequent precipitation increases were most substantial during the second pulse. The disparate changes demonstrated here highlight that not all ARs will respond similarly in a warmer world.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Corresponding author: Allison Michaelis, amichaelis@niu.edu

Successive atmospheric river (AR) events-known as AR families-can result in prolonged and elevated hydrological impacts relative to single ARs due to the lack of recovery time between periods of precipitation. Despite the outsized societal impacts that often stem from AR families, the large-scale environments and mechanisms associated with these compound events remain poorly understood. In this work, a new reanalysis-based 39-yr catalog of 248 AR family events affecting California between 1981 and 2019 is introduced. Nearly all (94%) of the interannual variability in AR frequency is driven by AR family versus single events. Using k-means clustering on the 500-hPa geopotential height field, six distinct clusters of large-scale patterns associated with AR families are identified. Two clusters are of particular interest due to their strong relationship with phases of El Nino-Southern Oscillation (ENSO). One of these clusters is characterized by a strong ridge in the Bering Sea and Rossby wave propagation, most frequently occurs during La Nina and neutral ENSO years, and is associated with the highest cluster-average precipitation across California. The other cluster, characterized by a zonal elongation of lower geopotential heights across the Pacific basin and an extended North Pacific jet, most frequently occurs during El Nino years and is associated with lower cluster-average precipitation across California but with a longer duration. In contrast, single AR events do not show obvious clustering of spatial patterns. This difference suggests that the potential predictability of AR families may be enhanced relative to single AR events, especially on subseasonal to seasonal time scales.

Extreme heat is investigated in a series of high‐resolution time‐slice global simulations comparing the current and late‐21st century climates. An increase in climate‐relative extreme heat is found in the region surrounding the Black Sea. Similarities between the synoptic‐scale flows in current and future heat events combined with a decrease in future summer precipitation suggests that the increased future severity stems from strengthened land‐atmosphere feedbacks driven primarily by the changes in precipitation. The resulting intensification of heat events beyond the mean warming driven by climate change could generate significant future heat hazards in vulnerable regions. Given the continental cool bias in the present‐day simulations, the resulting estimates of future extreme heat are likely to be conservative.

Tropical cyclones (TCs) propagating into baroclinic midlatitude environments can transform into extratropical cyclones, in some cases resulting in high-impact weather conditions far from the tropics. This study extends analysis of extratropical transition (ET) changes in multiseasonal global simulations using the Model for Prediction Across Scales-Atmosphere (MPAS-A) under present-day and projected future conditions. High resolution (15 km) covers the Northern Hemisphere; TCs and ET events are tracked based on sea level pressure minima accompanied by a warm core and use of a cyclone phase space method. Previous analysis of these simulations showed large changes in ET over the North Atlantic (NATL) basin, with ET events exhibiting a 4 degrees-5 degrees northward latitudinal shift and similar to 6-hPa strengthening of the post-transition extratropical cyclone. Storm-relative composites, primarily representing post-transformation cold-core events, indicate that this increase in post-transition storm intensity is associated with an intensification of the neighboring upper-level trough and downstream ridge, and a poleward shift in the storm center, conducive to enhanced trough-TC interactions after ET completion. Additionally, the future composite ET event is located in the right-jet entrance of an outflow jet that is strengthened relative to its present-day counterpart. Localized impacts associated with ET events, such as heavy precipitation and strong near-surface winds, are significantly enhanced in the future-climate simulations; 6-hourly precipitation for NATL events increases at a super-Clausius-Clapeyron rate with area-average precipitation increasing over 30%. Furthermore, intensified precipitation contributes to enhanced lower-tropospheric potential vorticity and stronger upper-tropospheric outflow, implying the potential for more extreme downstream impacts under the future climate scenario.

A complex and underexplored relationship exists between atmospheric rivers (ARs) and mesoscale frontal waves (MFWs). The present study further explores and quantifies the importance of diabatic processes to MFW development and the AR–MFW interaction by simulating two ARs impacting Northern California’s flood-vulnerable Russian River watershed using the Model for Prediction Across Scales-Atmosphere (MPAS-A) with and without the effects of latent heating. Despite the storms’ contrasting characteristics, diabatic processes within the system were critical to the development of MFWs, the timing and magnitude of integrated vapor transport (IVT), and precipitation impacts over the Russian River watershed in both cases. Low-altitude circulations and lower-tropospheric moisture content in and around the MFWs are considerably reduced without latent heating, contributing to a decrease in moisture transport, moisture convergence, and IVT. Differences in IVT are not consistently dynamic (i.e., wind-driven) or thermodynamic (i.e., moisture-driven), but instead vary by case and by time throughout each event. For one event, AR conditions over the watershed persisted for 6 h less and the peak IVT occurred 6 h earlier and was reduced by ~17%; weaker orographic and dynamic precipitation forcings reduced precipitation totals by ~64%. Similarly, turning off latent heating shortened the second event by 24 h and reduced precipitation totals by ~49%; the maximum IVT over the watershed was weakened by ~42% and delayed by 18 h. Thus, sufficient representation of diabatic processes, and by inference, water vapor initial conditions, is critical for resolving MFWs, their feedbacks on AR evolution, and associated precipitation forecasts on watershed scales.

Persistent anomalies (PAs) are associated with a variety of impactful weather extremes, prompting research into how their characteristics will respond to climate change. Previous studies, however, have not provided conclusive results, owing to the complexity of the phenomenon and to difficulties in general circulation model (GCM) representations of PAs. Here, we diagnose PA activity in ten years of current and projected future output from global, high-resolution (15-km mesh) time-slice simulations performed with the Model for Prediction Across Scales-Atmosphere (MPAS-A). These time slices span a range of ENSO states. They include high-resolution representations of sea-surface temperatures and GCM-based sea ice for present and future climates. Future projections, based on the RCP8.5 scenario, exhibit strong Arctic amplification and tropical upper warming, providing a valuable experiment with which to assess the impact of climate change on PA frequency. The MPAS-A present-climate simulations reproduce the main centers of observed PA activity, but with an eastward shift in the North Pacific and reduced amplitude in the North Atlantic. The overall frequency of positive PAs in the future simulations is similar to that in the present-day simulations, while negative PAs become less frequent. Although some regional changes emerge, the small, generally negative changes in PA frequency and meridional circulation index indicate that climate change does not lead to increased persistence of midlatitude flow anomalies or increased waviness in these simulations.

Analysis of a strong landfalling atmospheric river is presented that compares the evolution of a control simulation with that of an adjoint-derived perturbed simulation using the Coupled Ocean–Atmosphere Mesoscale Prediction System. The initial-condition sensitivities are optimized for all state variables to maximize the accumulated precipitation within the majority of California. The water vapor transport is found to be substantially enhanced at the California coast in the perturbed simulation during the time of peak precipitation, demonstrating a strengthened role of the orographic precipitation forcing. Similarly, moisture convergence and vertical velocities derived from the transverse circulation are found to be substantially enhanced during the time of peak precipitation, also demonstrating a strengthened role of the dynamic component of the precipitation. Importantly, both components of precipitation are associated with enhanced latent heating by which (i) a stronger diabatically driven low-level potential vorticity anomaly strengthens the low-level wind (and thereby the orographic precipitation forcing), and (ii) greater moist diabatic forcing enhances the Sawyer–Eliassen transverse circulation and thereby increases ascent and dynamic precipitation. A Lagrangian parcel trajectory analysis demonstrates that a positive moisture perturbation within the atmospheric river increases the moisture transport into the warm conveyor belt offshore, which enhances latent heating in the perturbed simulation. These results suggest that the precipitation forecast in this case is particularly sensitive to the initial moisture content within the atmospheric river due to its role in enhancing both the orographic precipitation forcing and the dynamic component of precipitation.