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Abstract Satellite‐based post‐tornado assessments have been widely used for the detection of tornado tracks, which heavily relies on the identification of vegetation changes through observations at visible and near‐infrared channels. During the deadly 10–11 December 2021 tornado outbreak, a series of violent tornadoes first touched down over northeastern Arkansas, an area dominated by cropland with rare vegetation coverage in winter. Through the examination of Moderate Resolution Imaging Spectroradiometer multi‐spectral observations, this study reveals significant scars on shortwave infrared channels over this region, but none are captured by visible and near‐infrared channels. The dominant soil type is aquert (one of vertisols), whose high clay content well preserves the severe changes in soil structure during the tornado passage, when the topmost soil layer was removed and underlying soil with higher moisture content was exposed to the air. This study suggests a quick post‐tornado assessment method over less vegetated area by using shortwave infrared channels. 

Historically, meteorological drought in the western United States (WUS) has been driven primarily by precipitation deficits. However, our observational analysis shows that, since around 2000, rising surface temperature and the resulting high evaporative demand have contributed more to drought severity (62%) and coverage (66%) over the WUS than precipitation deficit. This increase in evaporative demand during droughts, mostly attributable to anthropogenic warming according to analyses of both observations and climate model simulations, is the main cause of the increased drought severity and coverage. The unprecedented 2020–2022 WUS drought exemplifies this shift in drought drivers, with high evaporative demand accounting for 61% of its severity, compared to 39% from precipitation deficit. Climate model simulations corroborate this shift and project that, under the fossil-fueled development scenario (SSP5-8.5), droughts like the 2020–2022 event will transition from a one-in-more-than-a-thousand-year event in the pre-2022 period to a 1-in-60-year event by the mid-21st century and to a 1-in-6-year event by the late-21st century. 

Abstract. ​​​​​​​Land–atmosphere coupling (LAC) has long been studied, focusing on land surface and atmospheric boundary layer processes. However, the influence of humidity in the lower troposphere (LT), especially that above the planetary boundary layer (PBL), on LAC remains largely unexplored. In this study, we use radiosonde observations from the US Southern Great Plains (SGP) site and an entrained parcel buoyancy model to investigate the impact of LT humidity on LAC there during the warm season (May–September). We quantify the effect of LT humidity on convective buoyancy by measuring the difference between the 2–4 km vertically integrated buoyancy with the influence of background LT humidity and that without it. Our results show that, under dry soil conditions, anomalously high LT humidity is necessary to produce the buoyancy profiles required for afternoon precipitation events (APEs). These APEs under dry soil moisture cannot be explained by commonly used local LAC indices such as the convective triggering potential and low-level humidity index (CTP / HILow), which do not account for the influence of the LT humidity. On the other hand, consideration of LT humidity is unnecessary to explain APEs under wet soil moisture conditions, suggesting that the boundary layer moisture alone could be sufficient to generate the required buoyancy profiles. These findings highlight the need to consider the impact of LT humidity, which is often decoupled from the humidity near the surface and is largely controlled by moisture transport, in understanding land–atmospheric feedbacks under dry soil conditions, especially during droughts or dry spells over the SGP. 

Abstract. The Great Plains and southwest regions of the US are highly vulnerable to precipitation-related climate disasters such as droughts and floods. In this study, we propose a self-organizing map–analogue (SOMA) approach to empirically quantify the contribution of atmospheric moist circulation (mid-tropospheric geopotential and column moisture transport) to the regional precipitation anomalies, variability, and multi-decadal changes. Our results indicate that moist circulation contributes significantly to short-term precipitation variability, accounting for 54 %–61 % of the total variance in these regions, though these contributions vary significantly across seasons. As indicated in previous research, Pacific Decadal Oscillation (PDO) is one of the major climate modes influencing the long-term multi-decadal variation in precipitation. By contrasting three multi-decadal periods (1950–1976, 1977–1998, 1999–2021) with shifting PDO phases and linking the phase shift to self-organizing map (SOM) nodes, we found that circulation changes contribute considerably to the multi-decadal changes in precipitation anomaly in terms of the mean and days of dry and wet extremes, especially for the southern Great Plains (GP) and southwest. However, these circulation-induced changes are not totally related to the PDO phase shift (mostly less than half) since internal variability or anthropogenically induced changes in circulation can also be potential contributors. Our approach improves upon flow analogue and SOM-based methods and provides insights into the contribution of atmospheric circulation to regional precipitation anomalies and variability. 

In regions of the world where topography varies significantly with distance, most global climate models (GCMs) have spatial resolutions that are too coarse to accurately simulate key meteorological variables that are influenced by topography, such as clouds, precipitation, and surface temperatures. One approach to tackle this challenge is to run climate models of sufficiently high resolution in those topographically complex regions such as the North American Regionally Refined Model (NARRM) subset of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 (E3SM v2). Although high-resolution simulations are expected to provide unprecedented details of atmospheric processes, running models at such high resolutions remains computationally expensive compared to lower-resolution models such as the E3SM Low Resolution (LR). Moreover, because regionally refined and high-resolution GCMs are relatively new, there are a limited number of observational datasets and frameworks available for evaluating climate models with regionally varying spatial resolutions. As such, we developed a new framework to quantify the added value of high spatial resolution in simulating precipitation over the contiguous United States (CONUS). To determine its viability, we applied the framework to two model simulations and an observational dataset. We first remapped all the data into Hierarchical Equal-Area Iso-Latitude Pixelization (HEALPix) pixels. HEALPix offers several mathematical properties that enable seamless evaluation of climate models across different spatial resolutions including its equal-area and partitioning properties. The remapped HEALPix-based data are used to show how the spatial variability of both observed and simulated precipitation changes with resolution increases. This study provides valuable insights into the requirements for achieving accurate simulations of precipitation patterns over the CONUS. It highlights the importance of allocating sufficient computational resources to run climate models at higher temporal and spatial resolutions to capture spatial patterns effectively. Furthermore, the study demonstrates the effectiveness of the HEALPix framework in evaluating precipitation simulations across different spatial resolutions. This framework offers a viable approach for comparing observed and simulated data when dealing with datasets of varying spatial resolutions. By employing this framework, researchers can extend its usage to other climate variables, datasets, and disciplines that require comparing datasets with different spatial resolutions. 

Anthropogenically forced-warming and La Niña forced-precipitation deficits caused at least a sixfold risk increase for compound extreme low precipitation and high temperature in California–Nevada from October 2020 to September 2021.