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Soil moisture (SM) influences near‐surface air temperature by partitioning downwelling radiation into latent and sensible heat fluxes, through which dry soils generally lead to higher temperatures. The strength of this coupled soil moisture‐temperature (SM‐T) relationship is not spatially uniform, and numerous methods have been developed to assess SM‐T coupling strength across the globe. These methods tend to involve either idealized climate‐model experiments or linear statistical methods which cannot fully capture nonlinear SM‐T coupling. In this study, we propose a nonlinear machine‐learning (ML)‐based approach for analyzing SM‐T coupling and apply this method to various mid‐latitude regions using historical reanalysis datasets. We first train convolutional neural networks (CNNs) to predict daily maximum near‐surface air temperature (TMAX) given daily SM and geopotential height fields. We then use partial dependence analysis to isolate the average sensitivity of each CNN's TMAX prediction to the SM input under daily atmospheric conditions. The resulting SM‐T relationships broadly agree with previous assessments of SM‐T coupling strength. Over many regions, we find nonlinear relationships between the CNN's TMAX prediction and the SM input map. These nonlinearities suggest that the coupled interactions governing SM‐T relationships vary under different SM conditions, but these variations are regionally dependent. We also applymore »this method to test the influence of SM memory on SM‐T coupling and find that our results are consistent with previous studies. Although our study focuses specifically on local SM‐T coupling, our ML‐based method can be extended to investigate other coupled interactions within the climate system using observed or model‐derived datasets.

The tropospheric response to Sudden Stratospheric Warmings (SSWs) is associated with an equatorward shift in the midlatitude jet and associated storm tracks, while Strong Polar Vortex (SPV) events elicit a contrasting response. Recent analyses of the North Atlantic jet using probability density functions of a jet latitude index have identified three preferred jet latitudes, raising the question of whether the tropospheric response to SSWs and SPVs results from a change in relative frequencies of these preferred jet regimes rather than a systematic jet shift. We explore this question using atmospheric reanalysis data from 1979 to 2018 (26 SSWs and 33 SPVs), and a 202‐years integration of the Whole Atmosphere Community Climate Model (92 SSWs and 68 SPVs). Following SSWs, the northern jet regime becomes less common and the central and southern regimes become more common. These changes occur almost immediately following “split” vortex events, but are more delayed following “displacement” events. In contrast, the northern regime becomes more frequent and the southern regime less frequent following SPV events. Following SSWs, composites of 500‐hPa geopotential heights, surface air temperatures, and precipitation most closely resemble composites of the southern jet regime, and are generally opposite in sign to the composites ofmore »the northern jet regime. These comparisons are reversed following SPVs. Thus, one possible interpretation is that the two southernmost regimes appear to be favored following SSWs, while the southernmost regime becomes less common following SPVs.

Atmospheric rivers (ARs) are essential features of the global water cycle. Although AR definitions are commonly based on integrated vapor transport (IVT), ARs of a given IVT can induce a wide range of surface precipitation and wind impacts. We develop an AR “flavor” metric that partitions AR IVT into moisture‐dominant and wind‐dominant components. We use this metric to create a climatological catalog of “wet” and “windy” ARs along the U.S. West Coast from 1980 to 2016. Windy ARs are generally associated with stronger surface winds than are wet ARs, with the largest differences at low IVT. Windy ARs are also associated with greater daily precipitation totals than are wet ARs, with the difference widening at higher IVT, notably over mountainous regions. Pacific Northwest ARs have become increasingly moisture dominated over 1980–2016, which has important implications for western U.S. water availability and flood risk.

Atmospheric rivers (ARs) often generate extreme precipitation, with AR temperature strongly influencing hydrologic impacts by altering the timing and magnitude of runoff. Long‐term changes in AR temperatures therefore have important implications for regional hydroclimate—especially in locations where a shift to more rain‐dominated AR precipitation could affect flood risk and/or water storage in snowpack. In this study, we provide the first climatology of AR temperature across five U.S. West Coast subregions. We then assess trends in landfalling AR temperatures for each subregion from 1980 to 2016 using three reanalysis products. We find AR warming at seasonal and monthly scales. Cool‐season warming ranges from 0.69 to 1.65 °C over the study period. We detect monthly scale warming of >2 °C, with the most widespread warming occurring in November and March. To understand the causes of AR warming, we quantify the density of AR tracks from genesis to landfall and analyze along‐track AR temperature for each month and landfall region. We investigate three possible influences on AR temperature trends at landfall: along‐track temperatures prior to landfall, background temperatures over the landfall region, and AR temperature over the coastal ocean adjacent to the region of landfall. Generally, AR temperatures at landfall more closelymore »match coastal and background temperature trends than along‐track AR temperature trends. The seasonal asymmetry of the AR warming and the heterogeneity of influences have important implications for regional water storage and flood risk—demonstrating that changes in AR characteristics are complex and may not be directly inferred from changes in the background climate.