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Recent studies point to a significant rise in the number of summer extreme weather events that correspond with the presence of amplified, quasistationary midtropospheric planetary waves, weakened atmospheric circulation in the Northern Hemisphere, and coincide with reduced summer Arctic sea ice cover. This study explores potential connections between 1979 and 2016 summer heat wave frequency across the USA and regional Arctic sea ice extent (SIE) in various Arctic basins. Most notable SIE interannual relationships exist across the southern Plains and southeastern US during low Hudson Bay summer SIE. Locally increased frequencies of summer heat waves coincide with unseasonably warm conditions developed and sustained by the presence of an omega blocking pattern situated over the southern US throughout summer. The block appears following anomalous atmospheric warming and reduced mean zonal winds observed throughout spring (March–May) over northeastern Canada, the northwestern Atlantic basin, and Greenland. Spring preconditioning of summer ice melt is favored by the presence of strong negative phase of the North Atlantic Oscillation and positive Greenland Blocking Index. Summer synoptic flow related to Hudson Bay ice melt over North America appears to be influenced by the background state of atmospheric variability, namely, the positive phase of the Atlantic Multidecadal Oscillation. Antecedent local humidity, soil moisture, and precipitation conditions are shown to influence the “flavor” of the heat waves, which are more likely to be oppressive in the southeastern US and extreme across the southern Plains during summers experiencing low Hudson SIE. Low summer sea ice in Hudson Bay is statistically linked to an increased frequency of summer U.S. heat wavesExtreme heat waves are more common across southern Plains; oppressive heat waves are more common in the SoutheastHeat wave flavor is shaped by local surface‐atmospheric conditions

Soil moisture is a key drought indicator; however, current in situ soil moisture infrastructure is inadequate for large‐scale drought monitoring. One initiative of the ongoing National Soil Moisture Network program is the development of a near real‐time drought monitoring product that integrates in situ, model, and satellite remote sensing data. Data integration from diverse sources requires large‐scale validation prior to integration. This study develops a framework for assessing the fidelity of in situ, model, and satellite soil moisture data sets. Here we evaluate data from over 100 in situ monitoring stations that are part of nine monitoring networks; North American Land Data Assimilation System Phase 2 and Climate Prediction Center land surface models; and Soil Moisture Active‐Passive, Soil Moisture and Ocean Salinity, and European Space Agency‐Climate Change Initiative (ESA‐CCI) satellite products. The results indicate the majority of in situ stations exhibit low error variance and are spatially representative; however, some networks and individual stations exhibit anomalously high error variance or are sited in a way that make them not spatially representative of a larger area. Overall, North American Land Data Assimilation System Phase 2 is the modeled product that consistently performed best, and Soil Moisture Active‐Passive L3 is the remotely sensed product that consistently performed the best. They were able to both capture in situ soil moisture variability and provide an accurate depiction of drought conditions. The methods and verification framework applied in this study can be used to evaluate any soil moisture data set in any region of the world. Soil moisture is an important indicator of drought; however, there are very few monitoring stations that directly measure soil moisture across the contiguous United States. High‐quality model and/or satellite remote sensing soil moisture estimates can help fill in the gaps of in‐ground soil moisture measurements. However, integration of soil moisture data from diverse sources requires assessment of data quality. This study applies a series of methods for evaluating the integrity of in‐ground, model, and satellite soil moisture data sets across the United States. The results indicate that the majority of in‐ground sensors tested exhibit high data quality, although some exhibit high measurement error and/or low spatial representativeness. The results also indicate that the land surface models that are part of the North American Land Data Assimilation System Phase 2 and the Soil Moisture Active‐Passive remotely sensed soil moisture products best match in‐ground soil moisture measurement variability. Additionally, North American Land Data Assimilation System Phase 2 and Soil Moisture Active‐Passive data sets accurately depict drought occurrence. The methods applied here can be used to evaluate the quality of soil moisture data sets in any region of the world. A framework is applied to validate in situ, model, and satellite soil moisture data for drought monitoringSCAN in situ stations exhibit high relative error variance, compared with other in situ networksNLDAS‐2 models and SMAP L3 remote sensing products outperform other products tested

Soil moisture feedbacks that initiate, enhance, or suppress convection initiation and precipitation are important components of regional hydroclimatology. However, soil moisture feedbacks and the processes through which they operate are notoriously challenging to observe and study outside of model environments. In this study, we combine a climatological assessment of event frequency‐based measurements of soil moisture‐precipitation coupling in the central United States with a process‐based analysis of the mechanisms by which wet‐ and dry‐soil feedbacks may operate in the region. We use the Thunderstorm Observation by Radar algorithm to identify the location of convection initiation, circumventing the issue of using precipitation accumulation as a proxy for convection initiation. Results show substantial spatial variability in the climatological sign and strength of soil moisture‐precipitation coupling in the central United States, including regions that exhibit signs of both wet‐ and dry‐soil feedbacks. Within the regions with the strongest feedback signals, we find consistently strong coupling between soil moisture and the partitioning of surface heat flux, and strong coupling between surface heat flux—particularly sensible heat flux—and diurnal change in planetary boundary layer height. In all three regions assessed, the process‐based metrics confirmed the potential of wet‐ and/or dry‐soil feedbacks leading to convection initiation. Soil moisture can affect the occurrence, location, and intensity of precipitation in many global regions. Observations of the drivers of so‐called soil moisture feedbacks are important for constraining models; however, feedbacks are challenging to observe and study outside of model environments. We use a data set of thousands of thunderstorm initiation locations with model‐based soil moisture to measure the sign, strength, and variability in soil moisture feedback to precipitation in the central United States. We denote three regions with strong evidence of both wet‐soil and dry‐soil feedbacks to precipitation. The sign and strength of soil moisture‐precipitation coupling was assessed using thousands of convection events points in the central USMultiple regions show signs of both wet and dry soil feedbacks, with strong coupling between soil moisture and surface heat fluxClimatological assessment and process‐based metrics confirmed wet‐ and/or dry‐soil feedbacks leading to convection in the central US The sign and strength of soil moisture‐precipitation coupling was assessed using thousands of convection events points in the central US Multiple regions show signs of both wet and dry soil feedbacks, with strong coupling between soil moisture and surface heat flux Climatological assessment and process‐based metrics confirmed wet‐ and/or dry‐soil feedbacks leading to convection in the central US

Heat waves are characteristic features of summertime climate in the Midwest United States and can have significant agricultural, hydrological, and societal impacts. Historically, heat waves in the Midwest state of Illinois have been either extreme (high temperature and low humidity) or oppressive (high temperature and high humidity) in nature, but our knowledge of the factors determining which heat wave type occurs is limited. We use self-organizing maps to classify synoptic-scale atmospheric circulation patterns associated with oppressive and extreme heat events and analysis of variance to evaluate the atmospheric and land surface features responsible for differences in humidity that characterize the two. We find that the majority of extreme and oppressive heat events are associated with similar synoptic-scale atmospheric conditions. Additionally, both locally evaporated moisture and advected moisture sources were important for determining which of the two heat wave types occurred. Specifically, oppressive heat waves were characterized by abundant antecedent precipitation, surplus soil moisture, and elevated evapotranspiration and related atmospheric humidity. Lower humidity levels during extreme heat wave events were driven by relative reductions in evapotranspiration due to limited soil water content. Overall, our results suggest that the onset of heat waves in Illinois is primarily driven by circulation features in the upper atmosphere; however, the distinction of extreme or oppressive heat wave is due to differences in boundary layer humidity, driven in part by land surface moisture availability for evapotranspiration. Extreme heat waves and oppressive heat waves in Illinois are associated with similar synoptic-scale atmospheric conditionsIncreased boundary layer humidity during oppressive heat events is attributed to enhanced moisture advection and local evapotranspirationSoil moisture availability for evapotranspiration has a large role in determining the nature of summer heat wave that occurs in Illinois