Most examples of seasonal mismatches in phenology span multiple trophic levels, with timing of animal reproduction, hibernation, or migration becoming detached from peak food supply. The consequences of such mismatches are difficult to link to specific future climate change scenarios because the responses across trophic levels have complex underlying climate drivers often confounded by other stressors. In contrast, seasonal coat color polyphenism creating camouflage against snow is a direct and potentially severe type of seasonal mismatch if crypsis becomes compromised by the animal being white when snow is absent. It is unknown whether plasticity in the initiation or rate of coat color change will be able to reduce mismatch between the seasonal coat color and an increasingly snow-free background. We find that natural populations of snowshoe hares exposed to 3 y of widely varying snowpack have plasticity in the rate of the spring white-to-brown molt, but not in either the initiation dates of color change or the rate of the fall brown-to-white molt. Using an ensemble of locally downscaled climate projections, we also show that annual average duration of snowpack is forecast to decrease by 29–35 d by midcentury and 40–69 d by the end of the century. Without evolution in coat color phenology, the reduced snow duration will increase the number of days that white hares will be mismatched on a snowless background by four- to eightfold by the end of the century. This novel and visually compelling climate change-induced stressor likely applies to >9 widely distributed mammals with seasonal coat color.

Western United States wildfire increases have been generally attributed to warming temperatures, either through effects on winter snowpack or summer evaporation. However, near-surface air temperature and evaporative demand are strongly influenced by moisture availability and these interactions and their role in regulating fire activity have never been fully explored. Here we show that previously unnoted declines in summer precipitation from 1979 to 2016 across 31-45% of the forested areas in the western United States are strongly associated with burned area variations. The number of wetting rain days (WRD; days with precipitation ≥2.54 mm) during the fire season partially regulated the temperature and subsequent vapor pressure deficit (VPD) previously implicated as a primary driver of annual wildfire area burned. We use path analysis to decompose the relative influence of declining snowpack, rising temperatures, and declining precipitation on observed fire activity increases. After accounting for interactions, the net effect of WRD anomalies on wildfire area burned was more than 2.5 times greater than the net effect of VPD, and both the WRD and VPD effects were substantially greater than the influence of winter snowpack. These results suggest that precipitation during the fire season exerts the strongest control on burned area either directly through its wetting effects or indirectly through feedbacks to VPD. If these trends persist, decreases in summer precipitation and the associated summertime aridity increases would lead to more burned area across the western United States with far-reaching ecological and socioeconomic impacts.

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Gridded topoclimatic datasets are increasingly used to drive many ecological and hydrological models and assess climate change impacts. The use of such datasets is ubiquitous, but their inherent limitations are largely unknown or overlooked particularly in regard to spatial uncertainty and climate trends. To address these limitations, we present a statistical framework for producing a 30-arcsec (∼800-m) resolution gridded dataset of daily minimum and maximum temperature and related uncertainty from 1948 to 2012 for the conterminous United States. Like other datasets, we use weather station data and elevation-based predictors of temperature, but also implement a unique spatio-temporal interpolation that incorporates remotely sensed 1-km land skin temperature. The framework is able to capture several complex topoclimatic variations, including minimum temperature inversions, and represent spatial uncertainty in interpolated normal temperatures. Overall mean absolute errors for annual normal minimum and maximum temperature are 0.78 and 0.56 ∘ C, respectively. Homogenization of input station data also allows interpolated temperature trends to be more consistent with US Historical Climate Network trends compared to those of existing interpolated topoclimatic datasets. The framework and resulting temperature data can be an invaluable tool for spatially explicit ecological and hydrological modelling and for facilitating better end-user understanding and community-driven improvement of these widely used datasets.

Observations from the main mountain climate station network in the western United States (U.S.) suggest that higher elevations are warming faster than lower elevations. This has led to the assumption that elevation-dependent warming is prevalent throughout the region with impacts to water resources and ecosystem services. Here we critically evaluate this network's temperature observations and show that extreme warming observed at higher elevations is the result of systematic artifacts and not climatic conditions. With artifacts removed, the network's 1991-2012 minimum temperature trend decreases from +1.16°C decade À1 to +0.106°C decade À1 and is statistically indistinguishable from lower elevation trends. Moreover, longer-term widely used gridded climate products propagate the spurious temperature trend, thereby amplifying 1981-2012 western U.S. elevation-dependent warming by +217 to +562%. In the context of a warming climate, this artificial amplification of mountain climate trends has likely compromised our ability to accurately attribute climate change impacts across the mountainous western U.S.

Remotely sensed land skin temperature (LST) is increasingly being used to improve gridded interpolations of near-surface air temperature. The appeal of LST as a spatial predictor of air temperature rests in the fact that it is an observation available at spatial resolutions fine enough to capture topoclimatic and biophysical variations. However, it remains unclear if LST improves air temperature interpolations over what can already be obtained with simpler terrain-based predictor variables. Here, the relationship between LST and air temperature is evaluated across the conterminous United States (CONUS). It is found that there are significant differences in the ability of daytime and nighttime observations of LST to improve air temperature interpolations. Daytime LST mainly indicates finescale biophysical variation and is generally a poorer predictor of maximum air temperature than simple linear models based on elevation, longitude, and latitude. Moderate improvements to maximum air temperature interpolations are thus limited to specific mountainous areas in winter, to coastal areas, and to semiarid and arid regions where daytime LST likely captures variations in evaporative cooling and aridity. In contrast, nighttime LST represents important topoclimatic variation throughout the mountainous western CONUS and significantly improves nighttime minimum air temperature interpolations. In regions of more homogenous terrain, nighttime LST also captures biophysical patterns related to land cover. Both daytime and nighttime LST display large spatial and seasonal variability in their ability to improve air temperature interpolations beyond simpler approaches.

Terrestrial ecosystems have continued to provide the critical service of slowing the atmospheric CO2 growth rate. Terrestrial net primary productivity (NPP) is thought to be a major contributing factor to this trend. Yet our ability to estimate NPP at the regional scale remains limited due to large uncertainties in the response of NPP to multiple interacting climate factors and uncertainties in the driver data sets needed to estimate NPP. In this study, we introduced an improved NPP algorithm that used local driver data sets and parameters in China. We found that bias decreased by 30% for gross primary production (GPP) and 17% for NPP compared with the widely used global GPP and NPP products, respectively. From 2000 to 2012, a pixel‐level analysis of our improved NPP for the region of China showed an overall decreasing NPP trend of 4.65 Tg C a−1. Reductions in NPP were largest for the southern forests of China (−5.38 Tg C a−1), whereas minor increases in NPP were found for North China (0.65 Tg C a−1). Surprisingly, reductions in NPP were largely due to decreases in solar radiation (82%), rather than the more commonly expected effects of drought (18%). This was because for southern China, the interannual variability of NPP was more sensitive to solar radiation (R2 in 0.29–0.59) relative to precipitation (R2 < 0.13). These findings update our previous knowledge of carbon uptake responses to climate change in terrestrial ecosystems of China and highlight the importance of shortwave radiation in driving vegetation productivity for the region, especially for tropical forests.

Multireservoir systems require robust and adaptive control policies capable of managing hydroclimatic variability and human demands across a range of time scales. This is especially true for river basins with high intraannual and interannual variability, such as monsoonal systems that need to buffer against seasonal droughts while also managing extreme floods. Moreover, the timing, intensity, duration, and frequency of these hydrologic extremes may evolve with deeply uncertain changes in socioeconomic and climatic pressures. This study contributes an innovative method for exploring how possible changes in the timing and magnitude of the monsoonal cycle impact the robustness of reservoir operating policies designed assuming stationary hydrologic and socioeconomic conditions. We illustrate this analysis on the Red River basin in Vietnam, where reservoirs and dams serve as important sources of hydropower production, multisectoral water supply, and flood protection for the capital city of Hanoi. Applying our scenario discovery approach, we find that reservoir operations designed assuming stationarity provide robust hydropower performance in the Red River but that increased mean streamflow, amplification of the within‐year monsoonal cycle, and increased interannual variability all threaten their ability to manage flood risk. Additionally, increased agricultural water demands can only be tolerated if they are accompanied by greater mean flow, exacerbating food‐flood trade‐offs in the basin. These findings highlight the importance of exploring the impacts of a wide range of deeply uncertain socioeconomic and hydrologic factors when evaluating system robustness in monsoonal river basins, considering in particular both lower‐order moments of annual streamflow and intraannual monsoonal behavior.