Future humidity trends over the western United States in the CMIP5 global climate models and variable infiltration capacity hydrological modeling system

Global climate models predict relative humidity (RH) in the western US will decrease at a rate of about 0.1–0.6 percentage points per decade, albeit with seasonal differences (most drying in spring and summer), geographical variability (greater declines in the interior), stronger reductions for greater anthropogenic radiative forcing, and notable spread among the models. Although atmospheric moisture content increases, this is more than compensated for by higher air temperatures, leading to declining RH. Fine-scale hydrological simulations driven by the global model results should reproduce these trends. It is shown that the MT-CLIM meteorological algorithms used by the Variable Infiltration Capacity (VIC) hydrological model, when driven by daily Tmin, Tmax, and precipitation (a configuration used in numerous published studies), do not preserve the original global model\u27s humidity trends. Trends are biased positive in the interior western US, so that strong RH decreases are changed to weak decreases, and weak decreases are changed to increases. This happens because the MT-CLIM algorithms VIC incorporates infer an overly large positive trend in atmospheric moisture content in this region, likely due to an underestimate of the effect of increasing aridity on RH. The result could downplay the effects of decreasing RH on plants and wildfire. RH trends along the coast have a weak negative bias due to neglect of the ocean\u27s moderating influence. A numerical experiment where the values of Tdew are altered to compensate for the RH error suggests that eliminating the atmospheric moisture bias could, in and of itself, decrease runoff up to 14% in high-altitude regions east of the Sierra Nevada and Cascades, and reduce estimated Colorado River runoff at Lees Ferry up to 4% by the end of the century. It could also increase the probability of large fires in the northern and central US Rocky Mountains by 13 to 60%

Artificial amplification of warming trends across the mountains of the western United States

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\u27s 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\u27s 1991–2012 minimum temperature trend decreases from +1.16°C decade−1 to +0.106°C decade−1 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

Decreasing net primary production due to drought and slight decreases in solar radiation in China from 2000 to 2012

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 \u3c 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

Spatiotemporal Variability of Topoclimatic Air Temperature across the Conterminous United States

The main objective of this dissertation was to improve gridded spatiotempora

Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature

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

Camouflage mismatch in seasonal coat color due to decreased snow duration

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 \u3e9 widely distributed mammals with seasonal coat color

Global evaluation of MTCLIM and related algorithms for forcing of ecological and hydrological models

We assessed the performance of the MTCLIM scheme for estimating downward shortwave (SWdown) radiation and surface humidity from daily temperature range (DTR), as well as several schemes for estimating downward longwave radiation (LWdown), at 50 Baseline Solar Radiation Network stations globally. All of the algorithms performed reasonably well under most climate conditions, with biases and mean absolute errors generally less than 3% and 20%, respectively, over more than 70% of the global land surface. However, estimated SWdown had a bias of −26% at coastal sites, due to the ocean\u27s moderating influence on DTR, and in continental interiors, SWdown had an average bias of −15% in the presence of snow, which was reduced by MTCLIM 4.3\u27s snow correction if local topography was taken into account. Vapor pressure (VP) and relative humidity (RH) had large negative biases (up to −50%) under the most arid conditions. At coastal sites, LWdown had positive biases of up to 10%, while biases at interior sites exhibited a weak dependence on DTR. The largest biases in both RH (negative) and LWdown (positive) were concentrated over the world\u27s deserts, while smaller positive humidity biases were found over tropical and boreal forests. Evaluation of the diurnal cycle showed negative morning, and positive afternoon biases in vapor pressure deficit and LWdown related to errors in the interpolation of the diurnal air temperature