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SNAPSHOT USA is a multicontributor, long‐term camera trap survey designed to survey mammals across the United States. Participants are recruited through community networks and directly through a website application (https://www.snapshot-usa.org/). The growing Snapshot dataset is useful, for example, for tracking wildlife population responses to land use, land cover, and climate changes across spatial and temporal scales. Here we present the SNAPSHOT USA 2021 dataset, the third national camera trap survey across the US. Data were collected across 109 camera trap arrays and included 1711 camera sites. The total effort equaled 71,519 camera trap nights and resulted in 172,507 sequences of animal observations. Sampling effort varied among camera trap arrays, with a minimum of 126 camera trap nights, a maximum of 3355 nights, a median 546 nights, and a mean 656 ± 431 nights. This third dataset comprises 51 camera trap arrays that were surveyed during 2019, 2020, and 2021, along with 71 camera trap arrays that were surveyed in 2020 and 2021. All raw data and accompanying metadata are stored on Wildlife Insights (https://www.wildlifeinsights.org/), and are publicly available upon acceptance of the data papers. SNAPSHOT USA aims to sample multiple ecoregions in the United States with adequate representation of each ecoregion according to its relative size. Currently, the relative density of camera trap arrays varies by an order of magnitude for the various ecoregions (0.22–5.9 arrays per 100,000 km2), emphasizing the need to increase sampling effort by further recruiting and retaining contributors. There are no copyright restrictions on these data. We request that authors cite this paper when using these data, or a subset of these data, for publication. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic‐scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long‐wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations. Plain Language Summary: Atmospheric rivers (ARs) are a weather pattern that brings high amounts of atmospheric water and winds in a relatively narrow region. ARs are typically considered a "west coast" phenomenon, largely because the majority of the scientific research on ARs has focused on ARs in western coastal regions: particularly the western United States (US). ARs occur in continental interiors, but there has been some debate about whether these ARs represent the same type of weather as those in western coastal regions. This paper uses two objective methods for identifying ARs and finds times when ARs are present in three locations in the eastern half of the US: Norman, OK, Bloomington, IN, and Washington, DC. Examination of weather conditions during these AR times shows remarkable similarity to conditions associated with west coast ARs. This gives strong evidence that ARs do occur in the eastern half of the US. This result is important because it suggests that ARs may be important for water resources and extreme weather in the eastern half of the US, just as they are in the western US. This result also suggests that ARs may be important for water resources and extremes in other continental interiors. Key Points: Atmospheric rivers (ARs) east of the Rockies are associated with baroclinic wavesWestern coastal ARs and eastern/midwest ARs are dynamically similarSynoptic‐scale uplift, combined with convective instability, provide efficient mechanisms for generating precipitation [ABSTRACT FROM AUTHOR]

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While the majority of adults across the United States report that they believe that global warming is happening, far fewer report discussing global warming (Howe et al., 2015, https://doi.org/10.1038/nclimate2583; Marlon et al., 2022, https://climatecommunication.yale.edu/visualizations‐data/ycom‐us/). One way to inspire further climate action and engagement is to increase individuals' capacity to confidently and effectively discuss climate change. Climate communication science highlights that such communication is most effective when it is anecdotal, narrative, tailored to the audience, and place‐based. To generate climate conversations and inspire action in a variety of communities, partners at the University of Minnesota Extension piloted a program to train community members from across the state of Minnesota in effective climate communication through a series of instructional workshops, coaching, and participant‐led communication activities. Following the training portion of the program, participants identified and hosted their own climate‐related communication activities in their communities. These "climate conversations" took place across Minnesota and included community events, dialogue with elected leaders, and conversations stimulated through literature, among other activities. In their communities, program participants sparked conversations, initiated long‐term climate action efforts, and improved their sense of efficacy in response to climate change. Participants also reported that they improved their climate conversation skills, increased their local climate knowledge, established a support network with fellow participants, had reduced anxiety around communicating, and increased their confidence in being able to communicate about climate change in their communities. This pilot program provides a framework for future cohort‐ and community‐based climate communication programs in the state and beyond. Plain Language Summary: More frequent, effective climate conversations initiated by a diversity of trusted voices can help to increase climate concern and desire for action at the community level. However, in the United States, there is a disconnect between the level of concern individuals have about climate change and the extent to which individuals talk about the issue. To help bridge this gap, the University of Minnesota Climate Adaptation Partnership developed a training program aimed at inspiring and equipping local community members across Greater Minnesota with the skills and confidence to have effective conversations about climate change in their communities. This paper summarizes the programmatic activities we used to support our goals, and some reflections on the program's results. This pilot program provides a framework for future efforts that can be facilitated by Extension programs, community‐based organizations, universities, and others to inspire and accelerate similar community‐centered climate conversations. Key Points: We piloted a training program to teach Minnesotans to effectively communicate about climate change and its impacts on their communitiesParticipants improved behavioral and conversation skills to communicate about climate change more effectivelyThis program provides a framework for future programs aimed at increasing the diversity of people engaging in local climate conversations [ABSTRACT FROM AUTHOR]

A mass‐conserving method to downscale precipitation from global climate models (GCMs) using sub‐grid‐scale topography and modeled 700‐mb wind direction is presented. Runoff is simulated using a stand‐alone hydrological model, with this and several other methods as inputs, and compared to runoff simulated using historical observations over the western contiguous United States. Results suggest the mitigation of grid‐scale biases is more critical than downscaling for some regions with large wet biases (e.g., the Great Basin and Upper Colorado). In other regions (e.g., the Pacific Northwest) the new method produces more realistic sub‐grid‐scale variability in runoff compared to unadjusted GCM output and a simpler downscaling method. The presented method also brings the runoff centroid timing closer to that simulated with observations for all subregions examined. Plain Language Summary: Due to limitations in computing power which necessitates coarse spatial resolution, climate models do not include many details on mountains and their impact on precipitation. For this reason, it is difficult to estimate the impact of climate change on the availability of water for human consumption in places like the western United States, where mountain snowpack is an important source of water. This paper presents a way to adjust precipitation estimates from climate models by using some simple statistics about nearby mountains and valleys. The adjusted precipitation is then used in a hydrologic model to calculate the runoff that is simulated with different precipitation inputs. Results show that in most cases, the precipitation adjustment improves estimates of the resultant runoff relative to simulations with observed precipitation. The main exceptions are in very dry areas where the climate model produces far too much precipitation. With further work, the proposed adjustment may be integrated into the climate model itself to make it easier for those managing water resources (e.g., controlling reservoir levels) to use the model output to plan and adapt to climate change. Key Points: A mass‐conserving method for downscaling orographic precipitation improves modeled runoff from the CESM2Considering upwind topography further improves modeled runoff compared to simpler adjustmentsNot tuning to individual model grid points makes this method more generalizable than many existing statistical downscaling methods [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are often built upon the "niche conservatism" assumption, such that they ignore the possibility of "evolutionary rescue" and may underestimate species' future range limits under climate change. We select aphids and ladybirds as model species and develop an eco‐evolutionary model to explore evolutionary rescue in a predator–prey system under climate change. We model the adaptive change of species' thermal performances, accounting for biotic interactions. Our study suggests that, without considering evolutionary adaptation, the warming climate will result in a reduction in aphid populations and the extinction of ladybirds in large parts of the United States. However, when incorporating evolutionary adaptation into the model, aphids can adapt to climate change, whereas ladybirds demonstrate geographic variation in their evolutionary rescue potential. Specifically, ladybirds in southern regions are more likely to be rescued than those in the north. In certain northern regions, ladybirds do not avoid extinction due to severe warming trends and seasonality of the climate. While higher warming trends do prompt stronger evolutionary changes in phenotype, they also lead to reduced aphid population abundance such that ecology constrains ladybird population growth. Higher seasonality induces an ecological effect by limiting the length of reproductive season, thereby reducing the capacity for evolutionary rescue. Together, these findings reveal the complex interplay between ecological and evolutionary dynamics in the context of evolutionary adaptation to climate change. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Cultural use berries are prized foods and medicines across the United States and Canada, with almost 200 different species used by Indigenous Peoples. Berries are increasingly being impacted by environmental and land‐use change. Berry habitats, how and when berry plants reproduce, and the volume of berries available for harvest each year are shifting widely. These changes are impacting access to, availability of, and consumption of berries. Biocultural stewardship practices, like low‐intensity fire, transplanting, and thinning, can be used in response to these stressors to support berry plant health and productivity as well as a sustained relationship with this important food. Summary: Almost 200 different species of berries are used for food and medicine by Indigenous Peoples, with unparalleled nutritional and cultural significance among plant foods. Environmental and land‐use change is increasingly compromising access to, availability of, and consumption of berries. In this review, I consider (a) how climate and land‐use change are impacting cultural use berries across species and places, as documented by Indigenous Peoples and in the scientific literature, and (b) how stewardship practices are being applied to promote resilience and sustainability in berrying landscapes experiencing stress. Climate impacts on Arctic and subarctic berry species include earlier ripening, changes in taste, or increased variability in abundance. These same regions are experiencing a proliferation of shrubs, while forests throughout the lower 48 and Canada are suffering from suffocating fuel loads and stand densities that are not conducive to berry habitat for many species. In the Pacific West, berries are influenced by prolonged droughts and increasing spring and summer temperatures. Climate change impacts are amplified by shifts in land use for forestry and agriculture. Biocultural stewardship practices, like low‐intensity fire, thinning, transplanting, and cultural care, can be used to mitigate these impacts and promote berry microclimate habitats. There is opportunity for intertribal networking and knowledge sharing around berry stewardship practices that will support local and regional climate change responses. [ABSTRACT FROM AUTHOR]

Existing estimates of the climate mitigation potential from cropland carbon sequestration (C‐sequestration) are limited because they tend to assume constant rates of soil organic carbon change over all available cropland area, use relatively coarse land delineations, and often fail to adequately consider the agronomic and socioeconomic dimensions of agricultural land use. This results in an inflated estimate of the C‐sequestration potential. We address this gap by defining a more appropriate land base for cover cropping in the United States for C‐sequestration purposes: stable croplands in annual production systems that can integrate cover cropping without irrigation. Our baseline estimate of this suitable stable cropland area is 32% of current U.S. cropland extent. Even an alternative, less restrictive definition of stability results in a large reduction in area (44% of current U.S. croplands). Focusing cover crop implementation to this constrained land base would increase durability of associated C‐sequestration and limit soil carbon loss from land conversion to qualify for carbon‐specific incentives. Applying spatially‐variable C‐sequestration rates from the literature to our baseline area yields a technical potential of 19.4 Tg CO2e yr−1 annually, about one‐fifth of previous estimates. We also find the cost of realizing about half (10 Tg CO2e yr−1) of this potential could exceed 100 USD Mg CO2e−1, an order of magnitude higher than previously thought. While our economic analyses suggest that financial incentives are necessary for large‐scale adoption of cover cropping in the U.S., they also imply any C‐sequestration realized under such incentives is likely to be additional. Plain Language Summary: As a "Nature‐based Climate Solution" (NCS), the practice of cover cropping has received widespread attention and investment recently. Through cover cropping, croplands may be able to absorb and hold more carbon in soil, drawing down CO2 in the atmosphere (i.e., sequester carbon). However, the sequestration that is feasible, which reflects socioeconomic and land use considerations, remains unquantified. We estimate this feasible potential for carbon sequestration in the U.S. croplands from cover cropping and find it to be a fraction of previous estimates. A large portion of the reduction is due to our use of a more realistic and rigorous definition of the land area suitable for cover cropping for climate mitigation purposes. This land area comprises "croplands remaining croplands" (or stable croplands) that are planted to annual crops, like corn and soybeans, because they can more easily integrate the practice. We exclude irrigated croplands to conserve water resources. At just under 44 million ha, our baseline area estimate is 32% of the current U.S. cropland extent. The rest of the reduction is associated with our use of more conservative rates of increase in soil carbon from cover cropping than earlier studies that are more realistic for assessing soil carbon sequestration potential over large areas. Our economic analyses suggests that without financial incentives, implementing cover cropping could be expensive for most U.S. farmers. This high cost hurdle needs to be evaluated against the cost‐effectiveness of alternatives that compete for the same limited resources for climate mitigation. Key Points: For U.S. croplands, the feasible potential of carbon sequestration from cover cropping is a fraction of previous estimatesDefining the land base suitable for mitigation practices more realistically and rigorously is critical to estimating the feasible potentialCover cropping, though beneficial for soil health, may not be a low‐cost pathway for climate mitigation at scale in the U.S. [ABSTRACT FROM AUTHOR]

Drought has affected much of the western United States since about the year 2000, including the Upper Colorado River Basin (UCRB). Using a time series of UCRB streamflow derived from a tree‐ring based reconstruction of UCRB streamflow for the years 1 CE through 1905 CE, together with naturalized UCRB streamflow values for 1906 CE through 2021 CE, we identify 51 drought events, including the 2000–2021 drought. Although the recent 2000–2021 drought has been relatively severe, it is not the most severe of the UCRB drought events we identified. Results also indicate that natural variability combined with projected climate warming can result in UCRB drought events that are more severe than any drought since 1 CE. Plain Language Summary: A long and severe drought has affected much of the western United States since about the year 2000 CE, including the Upper Colorado River Basin (UCRB). Comparing this drought to past UCRB droughts (during 1 CE through 2021 CE), we find that the 2000–2021 drought is not the most severe UCRB drought. The results also suggest that natural variability combined with projected climate warming could result in UCRB drought events that are more severe than any drought since 1 CE. Key Points: We identify 51 drought events for the years 1 CE through 2021 CE for the Upper Colorado River Basin (UCRB)Twelve of the 51 UCRB droughts we identify were more severe than the 2000–2021 droughtNatural variability and/or a 1°C temperature increase can trigger UCRB droughts that are more severe than the 2000–2021 drought [ABSTRACT FROM AUTHOR]

To better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time‐evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station‐based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science. Plain Language Summary: While temperatures around the world continue to warm due to human‐caused climate change, some areas have observed smaller temperature trends than others. Understanding this regional variability in the rate of warming is important when assessing future projections. One location that has observed less warming is across the United States during their summer season. To evaluate temperature variability in this region using real‐world observations and climate model simulations, we use a statistical method from artificial intelligence called neural networks. The goal of the neural network setup is to learn temperature patterns across the United States and then identify whether climate change effects have exceeded the range of natural variability that has occurred in the past. This is called the timing of emergence (ToE), which is the first year that the effect has clearly appeared. We find that the average United States minimum temperature increase has already emerged in historical records. However, we find no ToE for the average maximum temperature, other than in the Western United States. Another important finding of this study is that by using higher resolution climate model data (i.e., more latitude and longitude points), we find better accuracy in the neural network predictions. Key Points: Forced temperature changes have emerged in observations during summer in the United States as detected by an artificial neural networkIncreasing spatial resolution improves neural network skill for predicting the year of a given summer temperature mapWestern United States land surface climate properties contribute to earlier timing of emergence predictions for the SPEAR climate model [ABSTRACT FROM AUTHOR]

There is growing urgency for improved public and commercial services to support a resilient, secure, and thriving United States (US) in the face of mounting decision‐support needs for environmental stewardship and hazard response, as well as for climate change adaptation and mitigation. Sustained space‐based Earth observations are critical infrastructure to support the delivery of science and decision‐support information with local, national, and global utility. This is reflected in part through the United States' sustained support of a suite of weather and land‐imaging satellites. However, outside of these two areas, the US lacks an overarching, systematic plan or framework to identify, prioritize, fund, and implement sustained space‐based Earth observations to meet the Nation's full range of needs for science, government policy, and societal support. To aid and accelerate the discussion on our nation's needs, challenges and opportunities associated with sustained critical space‐based Earth observations, the Keck Institute for Space Studies (KISS) sponsored a multi‐week think‐tank study to offer ways forward. Based on this study, the KISS study team suggests the establishment of a robust coordination framework to help address US needs for sustained Earth observations. This coordination framework could account for: (a) approaches to identify and prioritize satellite observations needed to meet US needs for science and services, (b) the rapidly evolving landscape of space‐based Earth viewing architecture options and technology improvements with increasing opportunities and lower cost access to space, and (c) the technical and programmatic underpinnings required for proper and comprehensive data stewardship to support a wide range of research and public services. Plain Language Summary: The Keck Institute of Space Studies has carried out a think tank study to codify best practices, articulate successes, and identify challenges and opportunities in the prioritization, acquisition, curation, and stewardship of sustained space‐based Earth observations. The goal of the study is to accelerate discussion and plans for a greater and more impactful US contribution to the global satellite observing system that will support decision‐making regarding climate change, environmental hazards, and national security. Based on this study, the KISS study team suggests the establishment of a nimble and responsive coordination framework to help guide and shepherd US concerns regarding sustained Earth observations. This coordination framework should account for: (a) approaches to identify and prioritize satellite observations needed to meet US needs for science and services, (b) the rapidly evolving landscape of space‐based Earth viewing architecture options and technology improvements with increasing opportunities and lower cost access to space and (c) the technical and programmatic underpinnings required for proper and comprehensive data stewardship with a broad science and services user base in mind. Key Points: There is growing urgency for improved public and commercial services to support a resilient, secure, and thriving USSpace‐based Earth observations represent an essential component of the infrastructure needed to support the delivery of needed informationThe US would benefit from an overarching plan for sustained Earth observations to support our science, policy, and resilience goals [ABSTRACT FROM AUTHOR]

Electricity and water systems in the Western US (WUS) are closely connected, with hydropower comprising 20% of total annual WUS generation, and electricity related to water comprising about 7% of total WUS electricity use. Because of these interdependencies, the threat of climate change to WUS resources will likely have compounding electricity impacts on the Western Interconnect grid. This study describes a WUS‐wide water system model with a particular emphasis on estimating climate impacts on hydropower generation and water‐related electricity use, which can be linked with a grid expansion model to support climate‐resilient electricity planning. The water system model combines climatically‐driven physical hydrology and management of both water supply and demand allocation, and is applied to an ensemble of 15 climate scenarios out to 2050. Model results show decreasing streamflow in key basins of the WUS under most scenarios. Annual water‐related electricity use increases up to 4%, and by up to 6% during the summer months, driven by growing agricultural demands met increasingly through a shift toward energy‐intensive groundwater to replace declining surface water. Total annual hydropower generation changes by +5% to −20% by mid‐century but declines in most scenarios, with decreases in summer generation by up to nearly −30%. Water‐related electricity use increases tend to coincide with hydropower generation declines, annually and seasonally, demonstrating the importance of concurrently evaluating the climate signal on both water‐for‐energy and energy‐for‐water to inform planning for grid reliability and decarbonization goals. Plain Language Summary: Electricity and water systems in the Western United States (WUS) have a strong dependency. Water fuels hydropower generation and electricity is used to pump, transport, treat, heat, and dispose of water. Climate change poses a serious threat to water availability in the WUS and is likely to also affect hydropower generation and electricity use related to water. This study develops a WUS water system model to evaluate the impact of a set of climate change scenarios on the dynamic interplay between water availability and demand and hydropower generation and electricity use by the water sector by 2050. We find that in many key basins, streamflow decreases under the climate scenarios. At the same time, reliance on groundwater increases to meet growing agricultural water demand. Hydropower generation shows decreases in most cases while energy use related to water increases. Because these hydropower generation declines tend to occur during periods when electricity demand for water grows, electricity grid planners in the Western Interconnect will benefit from this type of study by informing future grid buildouts that maintain reliability and decarbonization goals. Key Points: Interactions among hydro‐climate, hydropower, and water's energy use are critical for evaluating Western US (WUS) water‐energy climate risksUnder climate change, our WUS water model finds declining streamflows, growing agricultural demands, and increased groundwater useWith warming and drying, water‐related energy use grows while hydropower generation decreases under most climate scenarios by mid‐century [ABSTRACT FROM AUTHOR]

Accurate assessment of changes in water availability with changing climate is vital for effective mitigation and adaptation. In this research, we employ a parsimonious Budyko curve method to evaluate changes in water availability under low‐ (SSP126) and high‐emission (SSP585) scenarios for 331 river basins in the contiguous United States. We also assess the relative role of changes in precipitation (∆P) and potential evapotranspiration (∆PET) with changing climate on the increase in water availability vulnerability. Results highlight that around 43% (28%) of basins are projected to experience increased vulnerability to changing climate in high‐emission (low‐emission) scenarios. Sub‐humid basins are most often impacted, while arid and semi‐arid basins exhibit lower sensitivity to changes. Intriguingly, ∆PET emerges as the dominant control on vulnerability, surpassing ∆P, particularly under SSP585 scenario. The analysis prompts water managers to focus on long‐term mitigation planning and scientists to further constraint climate and water budget forecasts in affected basins. Plain Language Summary: Assessment of changes in water availability because of the changing climate is useful for mitigation and adaptation planning. In this study, water availability change in river basins in the contiguous United States is assessed. Which basins will become more vulnerable in terms of reduced water availability in the future climate and how the vulnerability changes within different aridity zones is studied. The study presents a novel methodology to assess and quantify whether the changes in potential evapotranspiration or precipitation are the main driver of future water vulnerability. We find that vulnerability increase due to an increase in potential evapotranspiration to be large. From the different aridity zones, sub‐humid basins are projected to most often experience an increase in vulnerability. Key Points: Changes in water availability under climate change scenarios are evaluatedSub‐humid basins are most often impactedVulnerability increase is large due to increase in Potential Evapotranspiration [ABSTRACT FROM AUTHOR]

This study empirically estimates the effects of climate on land‐use change across the conterminous United States and uses the empirical model to simulate the effects of a range of future climate change scenarios on the allocation of land to forestry, agriculture, and development. Ricardian estimation linking climate with the net returns to land production is integrated with a discrete‐choice estimation of plot‐level land‐use change. Comparing projected land‐use changes across scenarios, we find that drier and warmer climate scenarios favor forest land, wetter and cooler climate scenarios favor developed land, and wetter and warmer climate scenarios favor crop lands. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Botanical careers are more important than ever, given that environmental challenges such as climate change and deforestation threaten plants daily and because plants contribute to solutions to these problems. Plants act as our sources of food, medicine, textiles, and oxygen, which means finding ways to mitigate these environmental challenges is crucial. Despite this, little is known about what career opportunities exist for botanists outside of academia and how well academia is training graduate students for these careers. This study centers on the current state of academic botanical careers and how well students completing post‐baccalaureate degrees (herein referred to as graduate students) are being prepared to fill careers within the botanical workforce. Summary: Plant science plays a crucial role in our society and in ongoing efforts to address many global challenges, including food insecurity and climate change. Despite a predicted increase in botanical career opportunities, little is known about how well academia is training graduate students for careers outside of academia.To further our understanding of the current state of academic training for botanical careers, we surveyed 85 faculty and 40 graduate students working in academia in the plant sciences in the United States.We found that the top challenges to university professors in academia are lack of support staff and funding, whereas students completing their post‐baccalaureate degrees cited finances and lack of supportive mentoring as their top challenges. Despite the fact that most graduate students surveyed wanted a career at a research‐intensive university, many botanists in academia are retiring without being replaced by more botanists. Faculty expertise is also misaligned with needs from industry and government employers, causing challenges to training graduate students for these careers outside of academia. Although our data point to a lack of career opportunities within academia, we also note that current graduate student education still emphasizes such careers and is not properly preparing graduate students for the careers they are more likely to obtain within the private and government sectors.We discuss the implications of these findings and present several recommendations for preparing future generations of plant scientists for more realistic career trajectories. [ABSTRACT FROM AUTHOR]

Mortality due to extreme temperatures is one of the most worrying impacts of climate change. In this analysis, we use historic mortality and temperature data from 106 cities in the United States to develop a model that predicts deaths attributable to temperature. With this model and projections of future temperature from climate models, we estimate temperature‐related deaths in the United States due to climate change, changing demographics, and adaptation. We find that temperature‐related deaths increase rapidly as the climate warms, but this is mainly due to an expanding and aging population. For global average warming below 3°C above pre‐industrial levels, we find that climate change slightly reduces temperature‐related mortality in the U.S. because the reduction of cold‐related mortality exceeds the increase in heat‐related deaths. Above 3°C warming, whether the increase in heat‐related deaths exceeds the decrease in cold‐related deaths depends on the level of adaptation. Southern U.S. cities are already well adapted to hot temperatures and the reduction of cold‐related mortality drives overall lower mortality. Cities in the Northern U.S. are not well adapted to high temperatures, so the increase in heat‐related mortality exceeds the reduction in cold‐related mortality. Thus, while the total number of climate‐related mortality may not change much, climate change will shift mortality in the U.S. to higher latitudes. Plain Language Summary: Deaths due to extreme temperatures is one of the most important impacts of climate change. Here we estimate future temperature related deaths in the US using climate projection, population projection, and adaptation assumptions. We find that in 3°C of global warming level, temperature‐related deaths will increase by a factor of five, mostly due to aging and increasing population. Below 3°C warming, impact of climate change slightly decreases temperature‐related deaths, because the decrease in cold‐related deaths exceeds the increase in heat‐related deaths. Above 3°C warming, impact of climate change depends on the level of adaptation. Furthermore, we find that temperature‐related mortality shifts Northward. This is due to increase of heat‐related deaths in Northern cities, which are currently poorly adapted to heat. Key Points: Temperature‐related deaths in the U.S. will increase by a factor of 5 with 3°C of warming, mostly due to aging and increasing populationImpact of climate change is not significant until 3°C of global warming, and it depends on the level of adaptation beyond that pointTemperature‐related deaths will shift Northward, due to increasing heat‐related deaths in poorly heat‐adapted Northern cities [ABSTRACT FROM AUTHOR]

Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency. Plain Language Summary: Winter storms can have devastating socioeconomic impacts across the United States. Climate models are used to understand and predict these winter storms and how they might change in the future, therefore helping to inform climate policy and emergency services. It is essential that we evaluate these climate models so that their performance and accuracy can be optimized. One way of evaluating climate models is to assess their ability in reproducing the large‐scale atmospheric conditions which occur during extreme events. Here we apply a cyclone tracking algorithm and self‐organizing maps, a machine‐learning approach, to automate the process of identifying key patterns associated with historical winter storm activity. We then use this approach to assess if CMIP6 climate models are able to reproduce the same patterns including the winter storm frequency and intensity. Our results show that, regardless of resolution, most CMIP6 models struggle to simulate the more extreme patterns and tend to favor weaker patterns that are not as conducive to cyclone formation. As a result, most models simulate too few winter storms, however, the error in storm intensity is more varied, with some models producing stronger storms and others producing weaker storms on average. Key Points: Despite resolution, CMIP6 models struggle to reproduce amplified synoptic patterns over the US, with a robust bias toward zonal patternsMost CMIP6 models produce too few cyclones over the northeastern US, while model errors are more varied when considering cyclone intensityIncreased resolution increases the number and intensity of simulated extratropical cyclones but does not improve model representation of synoptic patterns [ABSTRACT FROM AUTHOR]

Summer streamflow predictions are critical for managing water resources; however, warming‐induced shifts from snow to rain regimes impact low‐flow predictive models. Additionally, reductions in snowpack drive earlier peak flows and lower summer flows across the western United States increasing reliance on groundwater for maintaining summer streamflow. However, it remains poorly understood how groundwater contributions vary interannually. We quantify recession limb groundwater (RLGW), defined as the proportional groundwater contribution to the stream during the period between peak stream flow and low flow, to predict summer low flows across three diverse western US watersheds. We ask (a) how do snow and rain dynamics influence interannual variations of RLGW contributions and summer low flows?; (b) which watershed attributes impact the effectiveness of RLGW as a predictor of summer low flows? Linear models reveal that RLGW is a strong predictor of low flows across all sites and drastically improves low‐flow prediction compared to snow metrics at a rain‐dominated site. Results suggest that strength of RLGW control on summer low flows may be mediated by subsurface storage. Subsurface storage can be divided into dynamic (i.e., variability saturated) and deep (i.e., permanently saturated) components, and we hypothesize that interannual variability in dynamic storage contribution to streamflow drives RLGW variability. In systems with a higher proportion of dynamic storage, RLGW is a better predictor of summer low flow because the stream is more responsive to dynamic storage contributions compared to deep‐storage‐dominated systems. Overall, including RLGW improved low‐flow prediction across diverse watersheds. Plain Language Summary: Water managers across the western United States depend on accurate streamflow prediction models for water planning and allocation during summer months. Historically, these models use snow metrics to predict summer flows, but increasing temperatures across the western US are decreasing snow input and accumulation and increasing early snow melt rates leading to changes in streamflow generation mechanisms. Here, we seek to understand how stream flows will respond under warmer climates in three watersheds with distinct climate and underlying bedrock in the western US. We expanded upon commonly used low‐flow model snow metrics to include snow and streamflow metrics from the previous year as well as groundwater dynamics during the annual recession curve. We found that including variables outside of commonly used snow parameters in the models produced more accurate predictions of summer low flows. We provide a framework for future analysis of streamflow response to warming across the western US. Key Points: Recession limb groundwater proportion is a strong predictor of summer low flowsRecession limb dynamics are especially important for improving low‐flow prediction at rain‐dominated sitesRecession limb groundwater may be a better predictor of low flow at sites where dynamic storage exceeds deep storage [ABSTRACT FROM AUTHOR]

Forests are critical to the planetary operational system and evolved without human management for millions of years in North America. Actively managing forests to help them adapt to a changing climate and disturbance regime has become a major focus in the United States. Aside from a subset of forests wherein wood production, human safety, and experimental research are primary goals, we argue that expensive management interventions are often unnecessary, have uncertain benefits, or are detrimental to many forest attributes such as resilience, carbon accumulation, structural complexity, and genetic and biological diversity. Natural forests (i.e., those protected and largely free from human management) tend to develop greater complexity, carbon storage, and tree diversity over time than forests that are actively managed; and natural forests often become less susceptible to future insect attacks and fire following these disturbances. Natural forest stewardship is therefore a critical and cost effective strategy in forest climate adaptation. [ABSTRACT FROM AUTHOR]

As the future climate becomes hotter or drier, forests may be exposed to more frequent or severe droughts. To inform efforts to ensure resilient forests, it is critical to know which forests may be most exposed to future drought and where. Longer duration droughts lasting 2–3 years or more are especially important to quantify because forests are likely to experience impacts. We summarized exposure to 36‐month drought for forests across the conterminous United States using the Standardized Precipitation‐Evapotranspiration Index (SPEI) overlaid on forest inventory plot locations. Exposure was quantified under 10 scenarios that combined five modeled climates and two Representative Concentration Pathways (RCPs, 4.5 and 8.5) through 2070. Future projections indicate a tripling of the monthly spatial extent of forests exposed to severe or extreme drought—38% of forests were exposed on average by mid‐century as opposed to 11% during 1991–2020 (2041–2070). Increases in drought exposure were greatest under hotter (HadGEM2‐ES), drier (IPSL‐CM5A‐MR), and middle (NorESM1‐M) climate models, under either RCP. Projections agreed that forests in portions of the western United States, especially the southwestern United States, could face high levels of exposure. Forest types including pinyon/juniper, woodland hardwoods, and ponderosa pine were projected to be exposed to drought more than 50% of the time on average across all scenarios by mid‐century, when no forest type was exposed more than 25% of the time under any scenario during the recent period. Projections agreed less for the eastern United States, but in some scenarios, particularly under RCP 8.5, large portions of the East could be exposed to drought nearly as often as parts of the West. Moreover, a substantial portion of oak/hickory forests occur in eastern regions, where projections agree on increased drought exposure. This study provides novel insights about the changing conditions forests face in both the eastern and western United States. Our results can be combined with information about the sensitivities and adaptive capacities of forest ecosystems to prioritize drought adaptation efforts. [ABSTRACT FROM AUTHOR]

Policy PointsSince its founding, the Supreme Court has played a major role in defining the parameters of governments' public health powers and the scope of individual health‐related rights. Although conservative courts have been less favorable to public health objectives, federal courts have, for the most part, advanced public health interests through consensus and adherence to the rule of law.In establishing the current six–three conservative supermajority, the Trump administration and the Senate shifted the Supreme Court dramatically. A majority of Justices, led by Chief Justice Roberts, did shift the Court in a decidedly conservative direction. It did so incrementally, guided by the Chief's intuition that the Institution itself should be preserved, mindful of maintaining public trust and appearing outside the political fray. That has all changed because Roberts' voice no longer holds sway. Five members of the Court have displayed a willingness to overturn even long‐held precedent and dismantle public health policy in favor of the Justices' core ideological tenants—notably the extensive reach of the First and Second Amendments and a parsimonious view of executive and administrative action.Public health is vulnerable to judicial rulings in this new conservative era. This includes classic public health powers in infectious disease control as well as reproductive rights; lesbian, gay, bisexual, trans, queer or questioning, and others (LGBTQ+) rights; firearm safety; immigration; and climate change.Congress has the power to curb the most extreme actions of the Court while still adhering to the vital ideal of a nonpolitical branch. That does not require Congress itself to overreach (such as by "packing" the Supreme Court, as Franklin Delaeno Roosevelt once proposed). Congress could, however, 1) disempower lower federal judges from issuing injunctions that apply nationwide, 2) limit the Supreme Court's so‐called shadow docket, 3) alter the way that presidents appoint federal judges, and 4) set reasonable term limits for federal judges and Supreme Court Justices. [ABSTRACT FROM AUTHOR]

Flooding is one of the most devastating natural disasters causing significant economic losses. One of the dominant drivers of flood losses is heavy precipitation, with other contributing factors such as built environments and socio‐economic conditions superimposed to it. To better understand the risk profile associated with this hazard, we develop probabilistic models to quantify the future likelihood of fluvial flood‐related property damage exceeding a critical threshold (i.e., high property damage) at the state level across the conterminous United States. The model is conditioned on indicators representing heavy precipitation amount and frequency derived from observed and downscaled precipitation. The likelihood of high property damage is estimated from the conditional probability distribution of annual total property damage, which is derived from the joint probability of the property damage and heavy precipitation indicators. Our results indicate an increase in the probability of high property damage (i.e., exceedance of 70th percentile of observed annual property damage for each state) in the future. Higher probability of high property damage is projected to be clustered in the states across the western and south‐western United States, and parts of the U.S. Northwest and the northern Rockies and Plains. Depending on the state, the mean annual probability of high property damage in these regions could range from 38% to 80% and from 46% to 95% at the end of the century (2090s) under RCP4.5 and RCP8.5 scenarios, respectively. This is equivalent to 20%–40% increase in the probability compared to the historical period 1996–2005. Results show that uncertainty in the projected probability of high property damage ranges from 14% to 35% across the states. The spatio‐temporal variability of the uncertainty across the states and three future decades (i.e., 2050s, 2070s, and 2090s) exhibits nonstationarity, which is driven by the uncertainty associated with the probabilistic prediction models and climate change scenarios. Plain Language Summary: Floods create significant economic losses in the United States and many other places across the world. Floods and flood‐related losses are expected to change due to changes in heavy precipitation in a warmer climate. Inferring how (including when and where) flood‐related losses could change in the future is crucial because of significant implications for flood risk management, insurance, and infrastructure resilience. We develop probabilistic models to project the likelihood of (fluvial) flood‐related high property damage (annual total property damage exceeding a critical threshold) conditioning on precipitation indicators under two greenhouse gas emission scenarios. We estimate relatively higher probability of high property damage for the states across the western and south‐western U.S. and parts of the U.S. Northwest and the northern Rockies and Plains, where projected changes range from 46% to 95% for a high‐emission scenario. In these regions, future changes in the probability of high property damage compared to the historical period vary from 20% to 40%. Overall, our results identify regions with higher likelihood of high property damage in the future, and they are useful for developing long‐term planning and resource mobilization, adaptation, and insurance instruments. Key Points: Future likelihood of flood‐related property damage is quantified using probabilistic models conditioned on precipitation indicatorsIncrease in the probability of flood‐related property damage is projected toward the mid and end of the century across the USNonstationary uncertainties in the projected flood‐related property damage originate from the probabilistic models and climate scenarios [ABSTRACT FROM AUTHOR]

Continued climate warming is reducing seasonal snowpacks in the western United States, where >50% of historical water supplies were snowmelt‐derived. In the Upper Colorado River Basin, declining snow water equivalent (SWE) and altered surface water input (SWI, rainfall and snowmelt available to enter the soil) timing and magnitude affect streamflow generation and water availability. To adapt effectively to future conditions, we need to understand current spatiotemporal distributions of SWE and SWI and how they may change in future decades. We developed 100‐m SnowModel simulations for water years 2001–2013 and two scenarios: control (CTL) and pseudo‐global‐warming (PGW). The PGW fraction of precipitation falling as snow was lower relative to CTL, except for November–April at high elevations. PGW peak SWE was lower for low (−45%) and mid elevations (−14%), while the date of peak SWE was uniformly earlier in the year for all elevations (17–23 days). Currently unmonitored high elevation snow represented a greater fraction of total PGW SWE. PGW peak daily SWI was higher for all elevations (30%–42%), while the dates of SWI peaks and centroids were earlier in the year for all elevations under PGW. PGW displayed elevated winter SWI, lower summer SWI, and changes in spring SWI timing were elevation‐dependent. Although PGW peak SWI was elevated and earlier compared to CTL, SWI was more evenly distributed throughout the year for PGW. These simulated shifts in the timing and magnitude of SWE and SWI have broad implications for water management in dry, snow‐dominated regions. Plain Language Summary: Snowpack water storage has historically functioned as a reliable extension of manmade reservoir storage. Loss of this storage has consequences for water resource management, ecological communities, and natural hazards including wildfire. We modeled snow accumulation and melt at high spatial resolution in the Upper Colorado River Basin to assess patterns in the timing and magnitude of snow storage and snowmelt for historical and future scenarios. We analyze these patterns in relation to existing snow monitoring station coverage, and ask how this coverage may need to change in future decades to better represent water availability. Our results indicate widespread future snow storage losses at lower elevations, but limited change at higher elevations that will likely remain conducive to seasonal snow accumulation and melt for decades to come. Peak snow storage and peak snowmelt occurred earlier for all elevations in future years, with increased peak surface water input noted at all elevations. A greater fraction of future snow storage will be in currently unmonitored high elevations. Projected elevation dependent changes from this study have implications for other dry, snow dominated regions, and additional work is needed to evaluate combined effects of widespread snow loss and earlier, flashier input on coordinated water management. Key Points: Projections show lower peak snow water equivalent (SWE) below 3,000 m and earlier peak SWE, peak surface water input (SWI) at all elevationsGreater future peak SWI and reduced annual snow‐derived SWI for all elevations, with a more even SWI distribution throughout the yearA greater fraction of future SWE will be in high elevations that are currently unmonitored [ABSTRACT FROM AUTHOR]

Trends in flood magnitudes vary across the conterminous USA (CONUS). There have been attempts to identify what controls these regionally varying trends, but these attempts were limited to certain—for example, climatic—variables or to smaller regions, using different methods and datasets each time. Here we attribute the trends in annual maximum streamflow for 4,390 gauging stations across the CONUS in the period 1960–2010, while using a novel combination of methods and an unprecedented variety of potential controlling variables to allow large‐scale comparisons and minimize biases. Using process‐based flood classification and complex networks, we find 10 distinct clusters of catchments with similar flood behavior. We compile a set of 31 hydro‐climatological and land use variables as predictors for 10 separate Random Forest models, allowing us to find the main controls the flood magnitude trends for each cluster. By using Accumulated Local Effect plots, we can understand how these controls influence the trends in the flood magnitude. We show that hydro‐climatologic changes and land use are of similar importance for flood magnitude trends across the CONUS. Static land use variables are more important than their trends, suggesting that land use is able to attenuate (forested areas) or amplify (urbanized areas) the effects of climatic changes on flood magnitudes. For some variables, we find opposing effects in different regions, showing that flood trend controls are highly dependent on regional characteristics and that our novel approach is necessary to attribute flood magnitude trends reliably at the continental scale while maintaining sensitivity to regional controls. Key Points: A wide variety of controls are necessary to explain flood magnitude trends across the United States between 1960 and 2010Climatic changes and land cover conditions are of similar importance for flood magnitude trends at the regional scaleControls on flood trends can have highly nonlinear effects and can have opposing effects in different hydro‐climatological subregions [ABSTRACT FROM AUTHOR]

Over the past decade, a consensus has emerged among academics and policy makers that climate change could threaten the stability of banks, insurers, and the broader financial system. In response, regulators from around the world have begun implementing policies to mitigate emerging climate risks in the financial sector. The United States, however, lags significantly behind other countries in addressing such risks. This article argues that the United States' sluggishness in responding to climate‐related financial risk is problematic because the U.S. banking system is uniquely susceptible to climate change. The United States' vulnerability stems, in part, from a little‐known statutory provision that prohibits U.S. regulators from relying on external credit ratings in bank capital requirements. Because of this deviation from internationally accepted capital standards, when a credit rating agency downgrades a "dirty" company, U.S. banks that lend to that company need not compensate by maintaining a bigger capital cushion. Over time, this dynamic will likely incentivize "dirty" companies to borrow more from U.S. banks, intensifying the U.S. banking system's exposure to climate risks. This article contends that the United States must overcome this unusual weakness by taking bold steps to safeguard the domestic financial system from the climate crisis. [ABSTRACT FROM AUTHOR]

Maintaining regional‐scale freshwater connectivity is challenging due to the dendritic, easily fragmented structure of freshwater networks, but is essential for promoting ecological resilience under climate change. Although the importance of stream network connectivity has been recognized, lake‐stream network connectivity has largely been ignored. Furthermore, protected areas are generally not designed to maintain or encompass entire freshwater networks. We applied a coarse‐filter approach to identify potential freshwater corridors for diverse taxa by calculating connectivity scores for 385 lake‐stream networks across the conterminous United States based on network size, structure, resistance to fragmentation, and dam prevalence. We also identified 2080 disproportionately important lakes for maintaining intact networks (i.e., hubs; 2% of all network lakes) and analyzed the protection status of hubs and potential freshwater corridors. Just 3% of networks received high connectivity scores based on their large size and structure (medians of 1303 lakes, 498.6 km north–south stream distance), but these also contained a median of 454 dams. In contrast, undammed networks (17% of networks) were considerably smaller (medians of six lakes, 7.2 km north–south stream distance), indicating that the functional connectivity of the largest potential freshwater corridors in the conterminous United States currently may be diminished compared with smaller, undammed networks. Network lakes and hubs were protected at similar rates nationally across different levels of protection (8%–18% and 6%–20%, respectively), but were generally more protected in the western United States. Our results indicate that conterminous United States protection of major freshwater corridors and the hubs that maintain them generally fell short of the international conservation goal of protecting an ecologically representative, well‐connected set of fresh waters (≥17%) by 2020 (Aichi Target 11). Conservation planning efforts might consider focusing on restoring natural hydrologic connectivity at or near hubs, particularly in larger networks, less protected, or biodiverse regions, to support freshwater biodiversity conservation under climate change. [ABSTRACT FROM AUTHOR]

Aim: Anticipating when and where changes in species' demographic rates will lead to range shifts in response to changing climate remains a major challenge. Despite evidence of increasing mortality in dry forests across the globe in response to drought and warming temperatures, the overall impacts on the distribution of dry forests are largely unknown because we lack comparable large‐scale data on tree recruitment rates. Here, our aim was to develop range‐wide population models for dry forest tree species (pinyon pine and juniper), quantifying both mortality and recruitment, to better understand where and under what conditions species range contractions are occurring. Location: Western United States. Major taxa studied: Two pinyon pine (Pinus spp.) and three juniper (Juniperus spp.) species. Methods: We developed range‐wide demographic models for five species using forest inventory data from across the western United States and estimated population trends and climate vulnerability. Results: We find that four of the five species are declining in parts of their range, with Pinus edulis having the largest proportion of populations declining (24%). Population vulnerability increases with aridity and temperature, with up to ~50% of populations declining in the warmest and driest conditions. Mortality and recruitment were both essential to explaining where populations are declining. Main conclusions Our results suggest that dry forest species are undergoing an active range shift driven by both changing recruitment and mortality, and that increasing temperatures and drought threaten the long‐term viability of many of these species in their current range. While four of the five species examined were experiencing some declines, P. edulis is currently most vulnerable. Management actions such as reducing tree density may be able to mitigate some of these impacts. The framework we present to estimate range‐wide demographic rates can be applied to other species to determine where range contractions are most likely. [ABSTRACT FROM AUTHOR]

The increasingly urgent need to develop knowledge and practices to manage flood risks drives innovative information design. However, experts often disagree about design practices. As a result, flood‐risk estimates can diverge, leading to different conclusions for decision‐making. Using examples of household‐scale fluvial (riverine) flood‐risk information in the United States, we assess design features and risk communication approaches that may lead to more actionable information for decision‐making. We argue that increased attention to uncertainty characterization and model diagnostics is a critical intermediate step for developing simpler approaches for designing flood‐risk information. Simpler frameworks are desirable because flood risks change over time, and simpler frameworks are less costly to update. Developing frameworks for large spatial domains require collaboration grounded in principles of open science. Finally, systematically evaluating how decision‐makers access and use information can provide new insights to guide risk communication and information design. Plain Language Summary: Climate change can cause temperatures to rise and precipitation to become more extreme. The impacts of these changes on flooding vary in space and time and are uncertain. For example, changes in future flooding differ between urban and rural areas. We discuss the diversity of available approaches for creating flood‐risk information. We suggest avenues for improvement, including (a) refining model diagnostics and uncertainty characterization to identify simpler model frameworks; (b) increasing information transparency and accessibility; and (c) improving understanding of links between decision‐making and risk communication. Key Points: Designing flood‐risk information for decisions about property‐scale flood adaptation poses nontrivial conceptual and operational challengesMany national‐scale flood‐risk estimates fail to account for regional flood‐risk dynamics, especially information that integrates projected future changesPromising avenues toward more actionable flood‐risk information include improving uncertainty characterization, design transparency, and risk communication [ABSTRACT FROM AUTHOR]

Hurricanes have caused major healthcare system disruptions. No systematic assessment of hurricane risk to United States hospital‐based healthcare delivery has been performed. Here, we show that 25 of 78 metropolitan statistical areas (MSAs) on the United States Atlantic and Gulf Coasts have half or more of their hospitals at risk of flooding from relatively weak hurricanes. 0.82 m of sea level rise expected within this century from climate change increases the odds of hospital flooding 22%. Furthermore, in 18 MSAs at least half of the roads within 1.6 km of hospitals were at risk of flooding from a category 2 storm. These findings identify previously undescribed risks to hospital‐based care delivery in Atlantic and Gulf Coast communities. They suggest that lower intensity hurricanes can have outsized impacts on healthcare access, particularly in places where per capita bed availability is low. Plain Language Summary: Hurricanes often incapacitate hospitals, a critical component of healthcare delivery. Climate change is contributing to more intense hurricanes and sea level rise that increase flood risk. Flooding can curtail operations of and limit access to hospitals. We analyzed how hurricane landfalls affect flooding risks to hospitals that serve highly populated cities on the Atlantic and Gulf Coasts of the United States. We find that even relatively weak hurricanes can flood most of the hospitals in urban coastal areas and that sea level rise expected within this century due to climate change significantly increases flooding risk. Key Points: A total 25 of 78 metropolitan statistical areas (MSAs) on the U.S. Atlantic and Gulf Coasts have half or more of their hospitals at risk of flooding from relatively weak hurricanes0.82 m of sea level rise expected within this century from climate change increases the odds of hospital flooding 22%In 18 MSAs, at least half of the roads within 1.6 km of hospitals were at risk of flooding from a category 2 cyclone [ABSTRACT FROM AUTHOR]

Understanding the effects of climate change on the phenological structure of plant communities will require measuring variation in sensitivity among thousands of co‐occurring species across regions. Herbarium collections provide vast resources with which to do this, but may also exhibit biases as sources of phenological data. Despite general recognition of these caveats, validation of herbarium‐based estimates of phenological sensitivity against estimates obtained using field observations remains rare and limited in scope. Here, we leveraged extensive datasets of herbarium specimens and of field observations from the USA National Phenology Network for 21 species in the United States and, for each species, compared herbarium‐ and field‐based estimates of peak flowering dates expected under standardized temperature conditions, and of sensitivity of peak flowering time to geographic and interannual variation in mean minimum temperatures (TMIN). We found strong agreement between herbarium‐ and field‐based estimates for standardized peak flowering time (r = 0.91, p < 0.001) and for the direction and magnitude of sensitivity to both geographic TMIN variation (r = 0.88, p < 0.001) and interannual TMIN variation (r = 0.82, p < 0.001). This agreement was robust to substantial differences between datasets in 1) the long‐term TMIN conditions observed among collection and phenological monitoring sites and 2) the interannual TMIN conditions observed in the time periods encompassed by both datasets for most species. Our results show that herbarium‐based sensitivity estimates are reliable among species spanning a wide diversity of life histories and biomes, demonstrating their utility in a broad range of ecological contexts, and underscoring the potential of herbarium collections to enable phenoclimatic analysis at taxonomic and spatiotemporal scales not yet captured by observational data. [ABSTRACT FROM AUTHOR]

Aim: Climate change is an increasingly important driver of biodiversity loss. The ectothermic nature of amphibians may make them particularly sensitive to changes in temperature and precipitation regimes, adding to declines from other threats. While active season environmental conditions can influence growth and survival, effects of variation in winter conditions on population dynamics are less well‐studied. Given that extreme winter temperatures can influence amphibian survival and fitness, we expected that increased winter severity—as measured by variability in winter temperatures and snow cover—would be associated with decreased occupancy, and that populations that experience more severe winters would have the largest sensitivities and show the greatest declines. Location: Eastern United States. Time period: 2001–2015. Major taxa studied: Anurans. Methods: We used large‐scale citizen science data from the eastern half of the United States, a diverse biogeographic and climatic region, to assess how variation in winter severity influenced occupancy dynamics (i.e. presence or absence of species across sites and years) of 11 spring and summer breeding anuran species. Results: Most species had increased occupancy in years with greater than average snow cover and warmer than average mean winter temperatures. Surprisingly, climatic conditions in winter affected occupancy dynamics of species with varying life history characteristics, including both spring and summer breeding species, those that overwinter under the soil, and those that overwinter in ponds and stream beds. For two wide‐ranging species (Lithobates catesbeianus and Lithobates clamitans), colder winter temperatures reduced occupancy more at northern latitudes, while the association between days of snow cover and latitude was equivocal. Main conclusions As the climate continues to change, expected reductions in snowpack may reduce occupancy of already declining anuran populations, while milder winters may improve overwinter survival for some species. The contradictory impacts of temperature and snow cover illustrate the importance of considering multi‐dimensional impacts of climate change on anuran populations. [ABSTRACT FROM AUTHOR]

Identifying relatively intact areas within ecosystems and determining the conditions favoring their existence is necessary for effective management in the context of widespread environmental degradation. In this study, we used 3766 surveys of randomly selected sites in the United States and U.S. Territories to identify the correlates of sites categorized as "oases" (defined as sites with relatively high total coral cover). We used occupancy models to evaluate the influence of 10 environmental predictors on the probability that an area (21.2‐km2 cell) would harbor coral oases defined at four spatial extents: cross‐basin, basin, region, and subregion. Across all four spatial extents, oases were more likely to occur in habitats with high light attenuation. The influence of the other environmental predictors on the probability of oasis occurrence were less consistent and varied with the scale of observation. Oases were most likely in areas of low human population density, but this effect was evident only at the cross‐basin and subregional extents. At the regional and subregional extents oases were more likely where sea‐surface temperature was more variable, whereas at the larger spatial extents the opposite was true. By identifying the correlates of oasis occurrence, the model can inform the prioritization of reef areas for management. Areas with biophysical conditions that confer corals with physiological resilience, as well as limited human impacts, likely support coral reef oases across spatial extents. Our approach is widely applicable to the development of conservation strategies to protect biodiversity and ecosystems in an era of magnified human disturbance. [ABSTRACT FROM AUTHOR]

Stochastic precipitation generators (SPGs) are often used to produce synthetic precipitation series for water resource management. Typically, an SPG assumes a stationary climate. We present an hourly precipitation generation algorithm for nonstationary conditions informed by the global climate model (GCM) forecasted average monthly temperature (AMT). The physical basis for precipitation formation is considered explicitly in the design of the algorithm using hourly pressure change events (PCE) to define the relationship between hourly precipitation and AMT. The algorithm consists of a multivariable Markov Chain and a moving window driven by time, temperature, and pressure change. We demonstrate the methodology by generating a 100‐year, continuous, synthetic hourly precipitation time series using GCM AMT projections for the Northeast United States. When compared with historical observations, the synthetic results suggest that future precipitation in this region will be more variable, with more frequent mild events and fewer but intensified extremes, especially in warm seasons. The synthetic time series suggests that there will be less precipitation in the summers, while winters will be wetter, consistent with other research on climate change projections for the Northeast United States. This SPG provides physically plausible weather ensembles for water resource studies involving climate change. Key Points: This article introduces a nonparametric stochastic process in generating synthetic precipitations for nonstationary climateThe basis of this algorithm is highly related to the physical mechanism of precipitation formation defined by pressure change events [ABSTRACT FROM AUTHOR]

In this study, we investigate both the changes of tropical cyclone (TC) rainfall hazard in the United States under climate change and the relative importance of the factors that cause the changes. We find that under the SSP5 8.5 scenario, the 100‐year TC rainfall level can increase by up to 320% along the U.S. coastline by the end of this century. The influence of TC rainfall‐producing ability increase is more significant than the influence of TC frequency increase on the increase of the 100‐year TC rainfall level (up to 180% vs. 60% increase). Among the different physical drivers for the increase in storm rainfall‐producing ability, the increase of TC intensity is the leading factor, followed by changes in TC duration and atmospheric temperature. The projected increase of TC rainfall hazard is robust against the uncertainty in the TC frequency projection. Plain Language Summary: Rainfall associated with landfalling tropical cyclones (TC) can cause extreme flooding and landslides, which are hazardous for coastal and inland areas. We project that by the end of the 21st century, TC rainfall hazard in the United States will increase by as much as 320% compared to its current value. The increase in TC rainfall hazard can be caused by two factors: more TCs making impact and TCs becoming more capable of producing rainfall in the future. The increased ability for TCs to produce rainfall is linked to increased TC intensity and duration as well as a warmer and more humid atmosphere in the future. Though uncertainties exist in the projected trend of future TC landfall frequency, TC rainfall hazard is very likely to increase due to the significant increase in TC rainfall‐producing ability. Key Points: U.S. landfalling tropical cyclone (TC) rainfall hazard will increase in the future due to increased TC landfalling frequency and TC rainfall‐producing abilityStorm intensity change is the leading factor for TC rainfall‐producing ability to increaseThe projected increase of TC rainfall hazard is robust against the uncertainty in TC frequency projection [ABSTRACT FROM AUTHOR]

As the need to monitor and evaluate progress on climate change adaptation is increasingly recognized, practitioners may benefit from applying lessons about effective monitoring from the conservation field. This study focuses on monitoring conservation interventions that aim to foster climate change adaptation by assessing: what ways practitioners are adopting best practices from monitoring and evaluation (M&E) in conservation; what practitioners are monitoring in relation to reported outcomes; how monitoring comprehensiveness varies in practice and what factors enable more comprehensive monitoring; and practitioner views on what could improve M&E of adaptation actions. We conducted this study using a portfolio of 76 adaptation projects implemented across the United States and employed a mixed‐methods design that included document analysis, an online survey, and semi‐structured interviews. The majority (84%) of projects reported social outcomes at project completion in addition to ecological outcomes (100%), but monitoring plans focused primarily on ecological and biophysical changes. Only 21% of projects connected monitoring metrics to a theory of change linking actions to expected outcomes. Involvement of an external research partner was identified as a key factor in supporting more comprehensive monitoring efforts. Results provide applied insights for enhancing delivery of social and ecological outcomes from adaptation projects, and suggest research pathways to improve monitoring and effectiveness of climate‐informed conservation. [ABSTRACT FROM AUTHOR]

On a warming planet, a key challenge natural resource managers face is protecting wildlife while mitigating climate change—as through forest carbon storage—to the greatest extent possible. But in some ecosystems, habitat restoration for imperiled species may be incompatible with maximizing carbon storage. For example, promoting early successional forest conditions does not maximize stand‐level carbon storage, whereas uniformly promoting high stocking or mature forest conditions in the name of carbon storage excludes species that require open or young stands. Here, we briefly review the literature regarding carbon and wildlife trade‐offs and then explore four case studies from the Northern Forest region of the United States. In each case, human activities have largely dampened the influence of natural disturbances; restoring or emulating these disturbances is typically required for habitat restoration even when doing so equates to less carbon storage at the stand level. We propose that applying a climate adaptation lens can help managers and planners navigate these trade‐offs and steer away from maladaptive practices that may ultimately reduce adaptive capacity. Instead, critically evaluating the consequences of stand‐level management actions on both carbon and wildlife can then facilitate landscape‐scale climate adaptation planning that supports a diversity of habitats alongside opportunities to invest in maximizing forest carbon. [ABSTRACT FROM AUTHOR]

The article explores the role of rheumatologists in advocating for patients' health and well-being amidst the climate crisis which represents an emerging threat to the health of humanity and the planet. Topics covered include how the climate emergency can affect the health of rheumatic disease patients, and the ways in how rheumatologists and the American College of Rheumatology (ACR) can help in developing strategies to mitigate the effects of the climate emergency.

Water use in the United States reached its lowest level in 2015 in at least four decades. National trends, however, mask local challenges throughout the U.S. In some places, decreases in surface water use were more than offset by increases in groundwater use, leading to net increases in total withdrawals. Other places have seen increasing rates of water shortages caused by mixes of socioeconomic and climate forces. This study examines recent trends in freshwater use and makes projections in future freshwater use over the next 50 years. Projections are based on socioeconomic and climate scenarios from the Intergovernmental Panel on Climate Change Fifth Assessment Report. Scenarios are paired with five climate models from the downscaled Multivariate Adaptive Constructed Analogs (MACA). We find total consumptive water use will decrease by as much as 8% under the best‐case scenario but increase by as much as 235% under the worst‐case scenario. Results depend on both climate and socioeconomic changes, but because agriculture is the dominant use of water in most regions, climate change impacts overwhelm all other factors under hot and dry futures. For the wetter climate models, water use decreases even under the highest emissions levels and highest population growth rates. Plain Language Summary: This study examines recent trends in water use and makes projections for future water use over the next 50 years. Projections are based on scenarios consistent with international climate assessments and provided for a wide range of climate models. Water use could decrease by as much as 8% under the best‐case scenario but increase by as much as 235% under the worst‐case scenario. Because agriculture is the largest user of water in most regions, impacts of climate change on water use are larger than impacts from non‐climate factors like population growth. Key Points: Dry regions see largest increases in freshwater use, with 8% decreases under best‐case scenarios but 235% increases under worst‐case scenariosDespite efficiency gains, population growth is expected to increase domestic water uses in fast growing regionsBecause agriculture is the dominant use of water in most regions, results vary more by climate model more than by socioeconomic scenario [ABSTRACT FROM AUTHOR]

The subseasonal features of the annual trends of runoff and other associated hydroclimatic variables in the upper Colorado River basin (UCRB) are examined using multiple data sets from in situ observations, reanalysis, and modeling for early spring (February, March, and April), given that about 58% of annual mean runoff decline from 1980 to 2018 stem from its decreases in the three months. Our analysis suggests that the strong annual trends of hydroclimatic variables in March are more statistically significant than other two months. While recent observational studies attribute the decline of runoff for either annual total or cool and warm seasons to regional warming and precipitation decrease, we suggested, for the first time, that a larger decreasing trend of the runoff in March is caused by the declining cloud optical depth which induces further decrease in precipitation and additional increase in temperature on top of climatic warming. The extra warming can reduce available water resource in the basin likely by enhancing evaporation in March. The recent cloud suppression likely results from stronger subsidence and larger moisture flux divergence over southwestern United States because of abnormal circulation patterns in varying climate, in turn leading to drier atmosphere which is unfavorable for cloud formation/development over the UCRB region. The cloud influence on the runoff in March in the UCRB revealed in this study implies the importance of understanding subseasonal variations of hydroclimate in the changing climate, as well as a need of future studies on the response of circulation patterns to climate change at subseasonal level and its implication on local hydroclimate. Key Points: Hydroclimatic variables in the Upper Colorado River Basin show stronger annual trends in March than other months in early springThinner clouds spur extra warming and thereby additional runoff decline in MarchIntensified large‐scale subsidence leads to dryness and cloud suppression in March [ABSTRACT FROM AUTHOR]