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Intensifying weather events are key characteristics of climate change that are fundamentally changing ecological disturbance regimes. Intensifying drought is a particular threat to species, ecosystems, and ecosystem services worldwide. Proactive drought adaptation measures are acutely needed, but without a better understanding of drought vulnerability at the appropriate scale and geography, such measures may not be effective, or even anticipated as potential options. A recent conceptual framework for ecological drought aligns a holistic suite of potential drivers with the key components of climate change vulnerability (exposure, sensitivity, and adaptive capacity). We leverage the ecological drought framework and components of vulnerability to introduce a six‐step process for developing a drought vulnerability assessment (DVA) that (1) is place‐based and avoids mismatches between assessment geography and management action, (2) uses existing empirical datasets and leverages machine learning techniques and remotely sensed data from a recent drought, (3) emphasizes the inclusion of stakeholders and the importance of data visualization and science communication, and (4) is flexible and adaptable to a wide range of planning contexts. We illustrate the DVA process with a case study for forested watersheds in the Missouri Headwaters (MH), Montana, USA, that is focused on the impact of an early 2000s drought event on forest health. We show how the DVA provides insights on drought vulnerability that are helpful starting points for co‐developing region‐specific management actions to prepare for the next drought, including strategies to enhance ecologically available water, reduce competition for water, promote ecosystem persistence under drought conditions, and prioritize sites for forest restoration, transition, or protection. The work described here provides a model for developing a DVA in other places that, when used in a participatory adaptation planning process, supports the implementation of effective adaptation strategies. [ABSTRACT FROM AUTHOR]

Under climate change, ecosystems are experiencing novel drought regimes, often in combination with stressors that reduce resilience and amplify drought's impacts. Consequently, drought appears increasingly likely to push systems beyond important physiological and ecological thresholds, resulting in substantial changes in ecosystem characteristics persisting long after drought ends (i.e. ecological transformation). In the present article, we clarify how drought can lead to transformation across a wide variety of ecosystems including forests, woodlands, and grasslands. Specifically, we describe how climate change alters drought regimes and how this translates to impacts on plant population growth, either directly or through drought's interactions with factors such as land management, biotic interactions, and other disturbances. We emphasize how interactions among mechanisms can inhibit postdrought recovery and can shift trajectories toward alternate states. Providing a holistic picture of how drought initiates long-term change supports the development of risk assessments, predictive models, and management strategies, enhancing preparedness for a complex and growing challenge. [ABSTRACT FROM AUTHOR]

INTRODUCTION. Drought conditions developed rapidly during the spring and summer of 2017 over the northern Great Plains states of South Dakota, North Dakota, and Montana (Fig. la). On 2 May [...]

The acute decline in global biodiversity includes not only the loss of rare species, but also the rapid collapse of common species across many different taxa. The loss of pollinating insects is of particular concern because of the ecological and economic values these species provide. The western bumble bee (Bombus occidentalis) was once common in western North America, but this species has become increasingly rare through much of its range. To understand potential mechanisms driving these declines, we used Bayesian occupancy models to investigate the effects of climate and land cover from 1998 to 2020, pesticide use from 2008 to 2014, and projected expected occupancy under three future scenarios. Using 14,457 surveys across 2.8 million km2 in the western United States, we found strong negative relationships between increasing temperature and drought on occupancy and identified neonicotinoids as the pesticides of greatest negative influence across our study region. The mean predicted occupancy declined by 57% from 1998 to 2020, ranging from 15 to 83% declines across 16 ecoregions. Even under the most optimistic scenario, we found continued declines in nearly half of the ecoregions by the 2050s and mean declines of 93% under the most severe scenario across all ecoregions. This assessment underscores the tenuous future of B. occidentalis and demonstrates the scale of stressors likely contributing to rapid loss of related pollinator species throughout the globe. Scaled-up, international species-monitoring schemes and improved integration of data from formal surveys and community science will substantively improve the understanding of stressors and bumble bee population trends. [ABSTRACT FROM AUTHOR]

Climate change is expected to disproportionately affect species occupying ecosystems with relatively hard boundaries, such as alpine ecosystems. Wildlife managers must identify actions to conserve and manage alpine species into the future, while considering other issues and uncertainties. Climate change and respiratory pathogens associated with widespread pneumonia epidemics in bighorn sheep (Ovis canadensis) may negatively affect mountain goat (Oreamnos americanus) populations. Mountain goat demographic and population data are challenging to collect and sparsely available, making population management decisions difficult. We developed predictive models incorporating these uncertainties and analyzed results within a structured decision making framework to make management recommendations and identify priority information needs in Montana, USA. We built resource selection models to forecast occupied mountain goat habitat and account for uncertainty in effects of climate change, and a Leslie matrix projection model to predict population trends while accounting for uncertainty in population demographics and dynamics. We predicted disease risks while accounting for uncertainty about presence of pneumonia pathogens and risk tolerance for mixing populations during translocations. Our analysis predicted that new introductions would produce more area occupied by mountain goats at mid‐century, regardless of the effects of climate change. Population augmentations, carnivore management, and harvest management may improve population trends, although this was associated with considerable uncertainty. Tolerance for risk of disease transmission affected optimal management choices because translocations are expected to increase disease risks for mountain goats and sympatric bighorn sheep. Expected value of information analyses revealed that reducing uncertainty related to population dynamics would affect the optimal choice among management strategies to improve mountain goat trends. Reducing uncertainty related to the presence of pneumonia‐associated pathogens and consequences of mixing microbial communities should reduce disease risks if translocations are included in future management strategies. We recommend managers determine tolerance for disease risks associated with translocations that they and constituents are willing to accept. From this, an adaptive management program can be constructed wherein a portfolio of management actions are chosen based on risk tolerance in each population range, combined with the amount that uncertainty is reduced when paired with monitoring, to ultimately improve achievement of fundamental objectives. [ABSTRACT FROM AUTHOR]

Soil moisture predictability on seasonal to decadal (S2D) continuum timescales over North America is examined from the Community Earth System Modeling (CESM) experiments. The effects of ocean and land initializations are disentangled using two large ensemble datasets—initialized and uninitialized experiments from the CESM. We find that soil moisture has significant predictability on S2D timescales despite limited predictability in precipitation. On sub-seasonal to seasonal timescales, precipitation variability is an order of magnitude greater than soil moisture, suggesting land surface processes, including soil moisture memory, reemergence, land–atmosphere interactions, transform a less predictable precipitation signal into a more predictable soil moisture signal. [ABSTRACT FROM AUTHOR]

Future reduction in mountain snowpack due to anthropogenic climate change poses a threat to many snow‐adapted species worldwide. Mountain topography exerts a strong control on snowpack not only due to elevation but also through the effect of slope and aspect on the surface energy balance. We develop high‐resolution projections of snowpack in order to provide improved, physically based estimates of the spatial distribution of future snowpack to inform species conservation efforts for the wolverine (Gulo gulo) in two study areas in the Rocky Mountains: one in Montana with known den sites and one in Colorado with recent wolverine activity and potential for reintroduction. Here we assess springtime snowpack loss in actual and potential denning areas under five future climate scenarios for the mid‐21st century. Snowpack in April and May is likely to persist into the mid‐21st century in the upper half of current denning elevations in all but the warmest future climate scenario, while large declines are projected for the lower half of the denning elevations. We gain new insight into the influence of topographical aspect on future snowpack and quantify the potential for enhanced snow persistence on north and east facing slopes under future scenarios that is only revealed in simulations where terrain slopes are resolved. Plain Language Summary: Climate change and its effect on snow are a threat to high mountain ecosystems and species worldwide. The future of mountain snowpack is complex, with multiple drivers, and with a strong elevation dependence. What has received much less attention is the dependence on topographical aspect—how will the snowpack on north facing versus south facing slopes respond differently under climate change. In this paper we develop snow projections motivated by a conservation issue for the wolverine: the future of the springtime snowpack at elevations of observed and potential wolverine denning for two study areas in the Rocky Mountains by the mid‐21st century. While there is significant snowpack loss in the lower half of denning elevations, the upper denning elevations retain springtime snowpack, supporting conservation actions through midcentury in these regions. Key Points: Early spring snowpack declines sharply in the lower half of wolverine den elevations studied but persists through midcentury higher upSpringtime snowpack is less affected by climate change on north and east facing slopes leading to potential for climate refugiaHigh‐resolution modeling that resolves topographical aspect may provide useful information to support conservation decisions [ABSTRACT FROM AUTHOR]

Water balance influences the distribution, abundance, and diversity of plant species across Earth's terrestrial system. In this study, we examine changes in the water balance and, consequently, the dryland extent across eight ecoregions of the north-central United States by quantifying changes in the growing season (May–September) moisture index (MI) by 2071–99, relative to 1980–2005, under three high-resolution (~4 km) downscaled climate projections (CNRM-CM5, CCSM4, and IPSL-CM5A-MR) of high-emission scenarios (RCP8.5). We find that all ecoregions are projected to become drier as based on significant decreases in MI, except four ecoregions under CNRM-CM5, which projects relatively more moderate warming and much greater increases in precipitation relative to the other two projections. The mean projected MI across the entire study area changes by from +4% to −33%. The proportion of dryland (MI < 0.65) is projected to increase under all projections, but more significantly under the warmer and drier projections represented by CCSM4 and IPSL-CM5A-MR; these two projections also show the largest spatial increases in the arid (33%–53%) and hyperarid (135%–180%) dryland classes and the greatest decrease in the dry subhumid (from −56% to −88%) dryland class. Among the ecoregions, those in the semiarid class have the highest increase in potential evapotranspiration, those in the nondryland and dry subhumid class have the largest decrease in MI, and those in the dry subhumid class have the greatest increase in dryland extent. These changes are expected to have important implications for agriculture, ecological function, biodiversity, vegetation dynamics, and hydrological budget. [ABSTRACT FROM AUTHOR]

Several studies have projected increases in drought severity, extent and duration in many parts of the world under climate change. We examine sources of uncertainty arising from the methodological choices for the assessment of future drought risk in the continental US (CONUS). One such uncertainty is in the climate models’ expression of evaporative demand (E0), which is not a direct climate model output but has been traditionally estimated using several different formulations. Here we analyze daily output from two CMIP5 GCMs to evaluate how differences in E0 formulation, treatment of meteorological driving data, choice of GCM, and standardization of time series influence the estimation of E0. These methodological choices yield different assessments of spatio-temporal variability in E0 and different trends in 21st century drought risk. First, we estimate E0 using three widely used E0 formulations: Penman-Monteith; Hargreaves-Samani; and Priestley-Taylor. Our analysis, which primarily focuses on the May-September warm-season period, shows that E0 climatology and its spatial pattern differ substantially between these three formulations. Overall, we find higher magnitudes of E0 and its interannual variability using Penman-Monteith, in particular for regions like the Great Plains and southwestern US where E0 is strongly influenced by variations in wind and relative humidity. When examining projected changes in E0 during the 21st century, there are also large differences among the three formulations, particularly the Penman-Monteith relative to the other two formulations. The 21st century E0 trends, particularly in percent change and standardized anomalies of E0, are found to be sensitive to the long-term mean value and the amplitude of interannual variability, i.e. if the magnitude of E0 and its interannual variability are relatively low for a particular E0 formulation, then the normalized or standardized 21st century trend based on that formulation is amplified relative to other formulations. This is the case for the use of Hargreaves-Samani and Priestley-Taylor, where future E0 trends are comparatively much larger than for Penman-Monteith. When comparing Penman-Monteith E0 responses between different choices of input variables related to wind speed, surface roughness, and net radiation, we found differences in E0 trends, although these choices had a much smaller influence on E0 trends than did the E0 formulation choices. These methodological choices and specific climate model selection, also have a large influence on the estimation of trends in standardized drought indices used for drought assessment operationally. We find that standardization tends to amplify divergences between the E0 trends calculated using different E0 formulations, because standardization is sensitive to both the climatology and amplitude of interannual variability of E0. For different methodological choices and GCM output considered in estimating E0, we examine potential sources of uncertainty in 21st century trends in the Standardized Precipitation Evapotranspiration Index (SPEI) and Evaporative Demand Drought Index (EDDI) over selected regions of the CONUS to demonstrate the practical implications of these methodological choices for the quantification of drought risk under climate change. [ABSTRACT FROM AUTHOR]

The future rate of climate change in mountains has many potential human impacts, including those related to water resources, ecosystem services, and recreation. Analysis of the ensemble mean response of CMIP5 global climate models (GCMs) shows amplified warming in high elevation regions during the cold season in boreal midlatitudes. We examine how the twenty-first century elevation-dependent response in the daily minimum surface air temperature [d(ΔTmin)/dz] varies among 27 different GCMs during winter for the RCP 8.5 emissions scenario. The focus is on regions within the northern hemisphere mid-latitude band between 27.5°N and 40°N, which includes both the Rocky Mountains and the Tibetan Plateau/Himalayas. We find significant variability in d(ΔTmin)/dz among the individual models ranging from 0.16 °C/km (10th percentile) to 0.97 °C/km (90th percentile), although nearly all of the GCMs (24 out of 27) show a significant positive value for d(ΔTmin)/dz. To identify some of the important drivers associated with the variability in d(ΔTmin)/dz during winter, we evaluate the co-variance between d(ΔTmin)/dz and the differential response of elevation-based anomalies in different climate variables as well as the GCMs' spatial resolution, their global climate sensitivity, and their elevation-dependent free air temperature response. We find that d(ΔTmin)/dz has the strongest correlation with elevation-dependent increases in surface water vapor, followed by elevation-dependent decreases in surface albedo, and a weak positive correlation with the GCMs' free air temperature response. [ABSTRACT FROM AUTHOR]

During the last five decades, the Tibetan Plateau has experienced a warming trend of 0.4 °C/decade in winter, which is at least twice that of any other season. Some studies have suggested that this anomalous winter warming is caused, in part, by the observed increases in near-surface water vapour and its amplifying effect on the surface longwave downward radiation (LDR). This study uses observations of surface-specific humidity ( q) and temperature as input to a one-dimensional radiative transfer model to assess the influence of lower atmospheric increases in water vapour on surface LDR, and the sensitivity of this process to different elevations and seasons on the Tibetan Plateau. The results from three idealized experiments are examined based on realistic atmospheric column profiles of temperature and moisture. They show that when an equal mass of water vapour is added into the atmospheric boundary layer during winter, a substantially greater increase (8×) in LDR is found at the high-elevation site relative to the low-elevation site. During summer, the LDR increases are much smaller as are the differences between the two sites. Experiments, where both q and temperature are increased, suggest that the influence of temperature changes on LDR is much greater than those caused by changes in q in all cases, except for the high-elevation-winter case when the opposite is true. These results provide further evidence for the possibility of a strong modulation of surface LDR caused by increases in atmospheric water vapour in high altitude regions (>3000 m) during the cold season. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This chapter describes the climatic setting of the Aral Sea region, investigates how the climate might change during the 21st century, and discusses potential impacts on water resources. Temperature and precipitation fields are analyzed to describe the mean climate for the Aral Sea region. Composite analysis has been employed on the precipitation field from the Global Precipitation Climatology Project (GPCP v2.2) to assess the spatial pattern of changes in precipitation during the last several decades. Furthermore, temperature and precipitation projections available from the 2007 Intergovernmental Panel on Climate Change report are synthesized to examine the nature of climate change during this century. Cold season precipitation has increased during recent decades, particularly over the mountainous terrain east of the Aral Sea. Climate models also project increases (5-20%) in winter precipitation during the 21st century; however, several models suggest decreases (0 to -15%) in precipitation during summer. Despite the increases in cold season precipitation, the large increases in temperature (4ºC) during the 21st century are likely to cause increased evaporation which could exacerbate the regional water budget deficit. This may constrain the water supply in the region, particularly during summer and autumn when water demand is highest. To fully understand the impacts of future climate change on regional water resources, hydrologic models that include anthropogenic management of water will be required. [ABSTRACT FROM AUTHOR]

How robust is our assessment of impacts to ecosystems and species from a rapidly changing climate during the 21st century? We examine the challenges of uncertainty, complexity and constraints associated with applying climate projections to understanding future biological responses. This includes an evaluation of how to incorporate the uncertainty associated with different greenhouse gas emissions scenarios and climate models, and constraints of spatiotemporal scales and resolution of climate data into impact assessments. We describe the challenges of identifying relevant climate metrics for biological impact assessments and evaluate the usefulness and limitations of different methodologies of applying climate change to both quantitative and qualitative assessments. We discuss the importance of incorporating extreme climate events and their stochastic tendencies in assessing ecological impacts and transformation, and provide recommendations for better integration of complex climate–ecological interactions at relevant spatiotemporal scales. We further recognize the compounding nature of uncertainty when accounting for our limited understanding of the interactions between climate and biological processes. Given the inherent complexity in ecological processes and their interactions with climate, we recommend integrating quantitative modeling with expert elicitation from diverse disciplines and experiential understanding of recent climate-driven ecological processes to develop a more robust understanding of ecological responses under different scenarios of future climate change. Inherently complex interactions between climate and biological systems also provide an opportunity to develop wide-ranging strategies that resource managers can employ to prepare for the future. [ABSTRACT FROM AUTHOR]

• The growth and distribution of plant species across water limited environment is inherently limited by PET. • Penman-Monteith and Thornthwaite are widely used measures of PET. • Methodical choice of important variables (here PET) can be substantially different in projecting habitat suitability of water limited species. • The conservation management decisions of water limited species can be markedly different depending on choice of PET used for SDM. The growth and distribution of plant species in water limited environments is often limited by the atmospheric evaporative demands which is measured in terms of potential evapotranspiration (PET). While PET estimated by different methods have been widely used to assess vegetation response to climate change, species distribution models offer unique opportunity to compare their efficiency in predicting habitat suitability of plant species. In this study, we perform the first multi-species comparison of two widely used metrics of PET i.e., Penman-Monteith and Thornthwaite, and show how they result in similar or different on projected distribution of water limited species and potential consequences on their conservation strategies across North Central U.S. To build species distribution models of eight species, we used two sets of environmental predictors which were identical except for the metric of PET (Penman-Monteith vs Thornthwaite) and projected habitat suitability for historical (2005) and future (2099) periods. We found an excellent model performance with no difference under two sets of predictors (AUC = ∼0.93). The relative influence of Thornthwaite PET on habitat prediction was higher than Penman PET for most of the species. We observed that the area of the projected suitable habitat was always higher under Thornthwaite set of predictors than Penman set of predictors (ranges from 25 % to 941 %), with the exception of Pinus contorta for which the reverse was true. In most cases, these differences were non-trivial, indicating that the choice of the PET metric, although both of them are commonly used, can have dramatic consequences on the conservation management decisions. Therefore, the conservation management decisions can be markedly different depending on the choice of the PET metric used for species distribution modeling of water limited species. [ABSTRACT FROM AUTHOR]

Managing resources under climate change is a high-stakes and daunting task, especially because climate change and associated complex biophysical responses engender sustained directional changes as well as abrupt transformations. This environmental non-stationarity challenges assumptions and expectations among scientists, managers, rights holders, and stakeholders. These challenges are anything but straightforward – a high degree of uncertainty impedes our ability to predict the environmental trajectory with confidence, and affected resources often span multiple governance jurisdictions or are subject to competing management objectives. Fortunately, tools exist to help grapple with such challenges. Two commonly used tools are scenario planning (SP) and structured decision making (SDM). SP is a well-established approach for assessing system response and facilitating decision making under a wide range of conditions that are uncertain and uncontrollable, such as those associated with adapting to climate change. However, SP lacks a defined structure for establishing objectives, quantifying tradeoffs, and evaluating the performance of candidate decisions to meet those objectives. SDM, on the other hand, is rooted in decision theory and focuses on explicit (often quantitative) assessment of the expected outcomes of choosing among a set of decision alternatives. SDM has been criticized for an inability to account for surprises and for imposing an overly narrow framing of problems to increase tractability. We discuss the strengths and limitations of SDM and SP as experienced through their application in various resource-management contexts, and then propose a new generalized framework – Scenario-Based Decision Analysis (SBDA) – that integrates these complementary approaches. SBDA structures resource management problems and solutions while considering uncertainties and surprises to inform resource management decision making. • Adapting to climate change is complicated by uncertainty and competing values. • Scenario planning and structured decision making confront aspects of this challenge. • We review their strengths and limitations through resource management applications. • Scenario-Based Decision Analysis (SBDA) integrates these complementary tools. • It leverages each tool's strengths to assist managers facing non-stationary futures. [ABSTRACT FROM AUTHOR]