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Biodiversity indicators are used to assess progress towards conservation and sustainability goals. However, the spatial scales, methods and assumptions of the underlying reporting metrics can affect the provided information. Using mountain ecosystems as an example, we compare biodiversity protection at subnational scale using the site-based approach of the 2030 Agenda for Sustainable Development (SDG indicator 15.4.1) with an area-based approach compatible with the targets of the Kunming–Montreal Global Biodiversity Framework.

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 apply 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 of 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.