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High‐resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR‐ESDM) is a computationally‐efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi‐Gaussian time series, gridded or point‐based, into multi‐decadal long‐term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long‐term predictor trends are bias‐corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high‐resolution bias‐corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR‐ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high‐resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1‐in‐1000 hottest and coldest temperatures are <0.5°C and biases in the 1‐in‐1000 wet day precipitation amounts are <5 mm/day. As climate impacts intensify, STAR‐ESDM represents a significant advance in generating consistent high‐resolution projections to comprehensively assess climate risk and optimize resilience globally. Plain Language Summary: The STAR‐ESDM tool is able to quickly and accurately generate future climate projections for weather stations and high‐resolution grids anywhere in the world. It does this by breaking down global or regional climate model output into different components, from the long‐term trend to the day‐to‐day variability, then merging projected changes with observations. When tested against projections generated by a complex and computationally expensive dynamical global model, STAR‐ESDM produced almost the same output, even for extreme temperature and precipitation values, at a fraction of the computational cost. Moreover, unlike most statistical downscaling models, this method isn't tied to any specific geographic area or predictand and/or predictor data set. It can be applied to any regional or global data set, whether generated by a climate or reanalysis model, derived from satellite observations, recorded at weather stations, and more. As climate impacts escalate, STAR‐ESDM offers a flexible and effective way to generate the high‐resolution climate projections needed to better gauge climate risk and enhance resilience anywhere in the world where reliable observational or quasi‐observational data, including reanalysis or satellites, are available. This is particularly relevant to under‐resourced regions, which are often most vulnerable to climate impacts as well as most lacking in future projections. Key Points: STAR‐ESDM is a rapid, flexible and generalizable approach to bias‐correct and downscale climate model output to any finer‐resolution data setPredictors/predictands can be derived from global or regional models, satellites, reanalysis, gridded observations and weather stationsProjected changes in temperature and precipitation mirror those of a high‐resolution global model at a fraction of the computational cost [ABSTRACT FROM AUTHOR]

A cross-site analysis was conducted on seven diverse, forested watersheds in the northeastern United States to evaluate hydrological responses (evapotranspiration, soil moisture, seasonal and annual streamflow, and water stress) to projections of future climate. We used output from four atmosphere-ocean general circulation models ( AOGCMs; CCSM4, Had GEM2- CC, MIROC5, and MRI- CGCM3) included in Phase 5 of the Coupled Model Intercomparison Project, coupled with two Representative Concentration Pathways ( RCP 8.5 and 4.5). The coarse resolution AOGCMs outputs were statistically downscaled using an asynchronous regional regression model to provide finer resolution future climate projections as inputs to the deterministic dynamic ecosystem model Pn ET- BGC. Simulation results indicated that projected warmer temperatures and longer growing seasons in the northeastern United States are anticipated to increase evapotranspiration across all sites, although invoking CO2 effects on vegetation (growth enhancement and increases in water use efficiency ( WUE)) diminish this response. The model showed enhanced evapotranspiration resulted in drier growing season conditions across all sites and all scenarios in the future. Spruce-fir conifer forests have a lower optimum temperature for photosynthesis, making them more susceptible to temperature stress than more tolerant hardwood species, potentially giving hardwoods a competitive advantage in the future. However, some hardwood forests are projected to experience seasonal water stress, despite anticipated increases in precipitation, due to the higher temperatures, earlier loss of snow packs, longer growing seasons, and associated water deficits. Considering future CO2 effects on WUE in the model alleviated water stress across all sites. Modeled streamflow responses were highly variable, with some sites showing significant increases in annual water yield, while others showed decreases. This variability in streamflow responses poses a challenge to water resource management in the northeastern United States. Our analyses suggest that dominant vegetation type and soil type are important attributes in determining future hydrological responses to climate change. [ABSTRACT FROM AUTHOR]

Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training observations used at the montane landscape of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three downscaling methods: the delta method (or the change factor method), monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD), and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models ( AOGCMs) ( CCSM3, Had CM3, PCM, and GFDL- CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8º resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model Pn ET- BGC (eight ensembles of six runs).The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model impacted modeled soil moisture and streamflow, which in turn affected forest growth, net N mineralization, net soil nitrification, and stream chemistry. All three downscaling methods were highly sensitive to the observations used, resulting in projections that were significantly different between station-based and grid-based observations. The choice of downscaling method also slightly affected the results, however not as much as the choice of observations. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce biased results in model applications run at greater temporal and/or spatial resolutions. These results underscore the importance of carefully considering field observations used for training, as well as the downscaling method used to generate climate change projections, for smaller-scale modeling studies. Different sources of variability including selection of AOGCM, emissions scenario, downscaling technique, and data used for training downscaling models, result in a wide range of projected forest ecosystem responses to future climate change. [ABSTRACT FROM AUTHOR]

Water temperature, dissolved oxygen, and concentrations of salts in surface water bodies can be affected by the natural environment and local human activities such as surface and ground water withdrawals, land use and energy extraction, and variability and long-term trends in atmospheric conditions including temperature and precipitation. Here, we quantify the relationship between 121 indicators of mean and extreme temperature and precipitation and 24 water quality parameters in 57 Texas reservoirs using observational data records covering the period 1960 to 2010. Over time scales ranging from 1 week to 2 years, we find that water temperature, dissolved oxygen, pH, specific conductance, chloride, sulfate, and phosphorus all show consistent correlations with atmospheric predictors, including high and low temperature extremes, dry days, heavy precipitation events, and mean temperature and precipitation. Based on these relationships combined with regional climate projections, we expect climate change to increase water temperatures, decrease dissolved oxygen levels, decrease pH, increase specific conductance, and increase levels of sulfate and chloride in Texas reservoirs. Over decadal time scales, this may affect aquatic ecosystems in the reservoirs, including altering the risk of conditions conducive to algae occurrence, as well as affecting the quality of water available for human consumption and recreation. [ABSTRACT FROM AUTHOR]

Carbon (C) sequestration in forest biomass and soils may help decrease regional C footprints and mitigate future climate change. The efficacy of these practices must be verified by monitoring and by approved calculation methods (i.e., models) to be credible in C markets. Two widely used soil organic matter models - CENTURY and RothC - were used to project changes in SOC pools after clear-cutting disturbance, as well as under a range of future climate and atmospheric carbon dioxide ( CO2) scenarios. Data from the temperate, predominantly deciduous Hubbard Brook Experimental Forest ( HBEF) in New Hampshire, USA, were used to parameterize and validate the models. Clear-cutting simulations demonstrated that both models can effectively simulate soil C dynamics in the northern hardwood forest when adequately parameterized. The minimum postharvest SOC predicted by RothC occurred in postharvest year 14 and was within 1.5% of the observed minimum, which occurred in year 8. CENTURY predicted the postharvest minimum SOC to occur in year 45, at a value 6.9% greater than the observed minimum; the slow response of both models to disturbance suggests that they may overestimate the time required to reach new steady-state conditions. Four climate change scenarios were used to simulate future changes in SOC pools. Climate-change simulations predicted increases in SOC by as much as 7% at the end of this century, partially offsetting future CO2 emissions. This sequestration was the product of enhanced forest productivity, and associated litter input to the soil, due to increased temperature, precipitation and CO2. The simulations also suggested that considerable losses of SOC (8-30%) could occur if forest vegetation at HBEF does not respond to changes in climate and CO2 levels. Therefore, the source/sink behavior of temperate forest soils likely depends on the degree to which forest growth is stimulated by new climate and CO2 conditions. [ABSTRACT FROM AUTHOR]

Habitat connectivity is a landscape attribute critical to the long-term viability of many wildlife species, includ-ing migratory birds. Climate change has the potential to affect habitat connectivity within and across the three main wetland complexes in the Great Plains of North America: the prairie potholes of the northern plains, the Rainwater Basin of Nebraska, and the playas of the southern plains. Here, we use these wetlands as model sys-tems in a graph-theory-based approach to establish links between climatic drivers and habitat connectivity for wildlife in current and projected wetland landscapes and to discern how that capacity can vary as a function of climatic forcing. We also provide a case study of macrosystems ecology to examine how the patterns and processes that determine habitat connectivity fluctuate across landscapes, regions, and continents. [ABSTRACT FROM AUTHOR]

ABSTRACT The asynchronous regional regression model ( ARRM) is a flexible and computationally efficient statistical model that can downscale station-based or gridded daily values of any variable that can be transformed into an approximately symmetric distribution and for which a large-scale predictor exists. This technique was developed to bridge the gap between large-scale outputs from atmosphere-ocean general circulation models ( AOGCMs) and the fine-scale output required for local and regional climate impact assessments. ARRM uses piecewise regression to quantify the relationship between observed and modelled quantiles and then downscale future projections. Here, we evaluate the performance of three successive versions of the model in downscaling daily minimum and maximum temperature and precipitation for 20 stations in North America from diverse climate zones. Using cross-validation to maximize the independent comparison period, historical downscaled simulations are evaluated relative to observations in terms of three different quantities: the probability distributions, giving a visual image of the skill of each model; root-mean-square errors; and bias in nine quantiles that represent both means and extremes. Successive versions of the model show improved accuracy in simulating extremes, where AOGCMs are often most biased and which are frequently the focus of impact studies. Overall, the quantile regression-based technique is shown to be efficient, robust, and highly generalizable across multiple variables, regions, and climate model inputs. [ABSTRACT FROM AUTHOR]

Dynamic hydrochemical models are useful tools for understanding and predicting the interactive effects of climate change, atmospheric CO2, and atmospheric deposition on the hydrology and water quality of forested watersheds. We used the biogeochemical model, PnET-BGC, to evaluate the effects of potential future changes in temperature, precipitation, solar radiation, and atmospheric CO2 on pools, concentrations, and fluxes of major elements at the Hubbard Brook Experimental Forest in New Hampshire, United States. Future climate projections used to run PnET-BGC were generated specifically for the Hubbard Brook Experimental Forest with a statistical technique that downscales climate output (e.g., air temperature, precipitation, solar radiation) from atmosphere-ocean general circulation models (AOGCMs) to a finer temporal and spatial resolution. These climate projections indicate that over the twenty-first century, average air temperature will increase at the site by 1.7°C to 6.5°C with simultaneous increases in annual average precipitation ranging from 4 to 32 cm above the long-term mean (1970-2000). PnET-BGC simulations under future climate change show a shift in hydrology characterized by later snowpack development, earlier spring discharge (snowmelt), greater évapotranspiration, and a slight increase in annual water yield (associated with C02 effects on vegetation). Model results indicate that under elevated temperature, net soil nitrogen mineralization and nitrification markedly increase, resulting in acidification of soil and stream water, thereby altering the quality of water draining from forested watersheds. Invoking a CO2 fertilization effect on vegetation under climate change substantially mitigates watershed nitrogen loss, highlighting the need for a more thorough understanding of CO2 effects on forest vegetation. [ABSTRACT FROM AUTHOR]

Climate change has the potential to alter streamflow regimes, having ecological, economic, and societal implications. In the northeastern United States, it is unclear how climate change may affect surface water supply, which is of critical importance in this densely populated region. The objective of this study was to evaluate the impact of climate change on the timing and quantity of streamflow at small watersheds at the Hubbard Brook Experimental Forest in New Hampshire. The site is ideal for this analysis because of the availability of long-term hydroclimatological records for analyzing past trends and ample data to parameterize and test hydrological models used to predict future trends. In this study, future streamflow projections were developed with the forest watershed model PnET-BGC, driven by climate change scenarios from statistically downscaled outputs of atmospheric-ocean general circulation models. Results indicated that earlier snowmelt and the diminishing snowpack is advancing the timing and reducing the magnitude of peak discharge associated with snowmelt. Past increases in precipitation have caused annual water yield to increase significantly, a trend that is expected to continue under future climate change. Significant declines in evapotranspiration have been observed over the long-term record, although the cause has not been identified. In the future, evapotranspiration is expected to increase in response to a warmer and wetter environment. These increases in evapotranspiration largely offset increases in precipitation, resulting in relatively little change in streamflow. Future work should aim to decrease uncertainty in the climate projections, particularly for precipitation, obtain a better understanding of the effect of CO2 on vegetation, determine if climate-induced changes in tree species composition will influence discharge, and assess the impacts of changing hydrology on downstream water supplies. [ABSTRACT FROM AUTHOR]

1. Streams collect runoff, heat, and sediment from their watersheds, making them highly vulnerable to anthropogenic disturbances such as urbanization and climate change. Forecasting the effects of these disturbances using process-based models is critical to identifying the form and magnitude of likely impacts. Here, we integrate a new biotic model with four previously developed physical models (downscaled climate projections, stream hydrology, geomorphology, and water temperature) to predict how stream fish growth and reproduction will most probably respond to shifts in climate and urbanization over the next several decades. 2. The biotic submodel couples dynamics in fish populations and habitat suitability to predict fish assemblage composition, based on readily available biotic information (preferences for habitat, temperature, and food, and characteristics of spawning) and day-to-day variability in stream conditions. 3. We illustrate the model using Piedmont headwater streams in the Chesapeake Bay watershed of the USA, projecting ten scenarios: Baseline (low urbanization; no on-going construction; and present-day climate); one Urbanization scenario (higher impervious surface, lower forest cover, significant construction activity); four future climate change scenarios [Hadley CM3 and Parallel Climate Models under medium-high (A2) and medium-low (B2) emissions scenarios]; and the same four climate change scenarios plus Urbanization. 4. Urbanization alone depressed growth or reproduction of 8 of 39 species, while climate change alone depressed 22 to 29 species. Almost every recreationally important species (i.e. trouts, basses, sunfishes) and six of the ten currently most common species were predicted to be significantly stressed. The combined effect of climate change and urbanization on adult growth was sometimes large compared to the effect of either stressor alone. Thus, the model predicts considerable change in fish assemblage composition, including loss of diversity. 5. Synthesis and applications. The interaction of climate change and urban growth may entail significant reconfiguring of headwater streams, including a loss of ecosystem structure and services, which will be more costly than climate change alone. On local scales, stakeholders cannot control climate drivers but they can mitigate stream impacts via careful land use. Therefore, to conserve stream ecosystems, we recommend that proactive measures be taken to insure against species loss or severe population declines. Delays will inevitably exacerbate the impacts of both climate change and urbanization on headwater systems. [ABSTRACT FROM AUTHOR]

Historically, extreme hot, dry summers over the South‐Central (SC) United States are dominated by an isolated dome of high pressure centered over the region. Applying self‐organizing map techniques to North American Regional Reanalysis reanalysis and historical and future Coupled Model Intercomparison Project version 5 (CMIP5) simulations, we find that as the world warms, this type of high‐pressure system is likely to become stronger and more frequent—even after removing the effect of surface warming on the expansion of the lower atmosphere. These projected changes appear to be related to self‐reinforcing ocean‐atmosphere interactions in a warming world. Specifically, intensified easterly winds over the Atlantic and Gulf of Mexico drive an increase in the meridional sea surface temperature gradient due to oceanic Ekman transport, which further enhances the ridge of high pressure extending across the SC United States: a dynamical relationship that increases confidence in regional projections of summer drought risk over the SC United States. Plain Language Summary: Water is already a scarce resource in the South‐Central United States. The region's frequent droughts pit its agriculture community against the energy industry, ecosystem managers, and growing municipalities in competition for the region's increasingly over‐allocated water resources. Our study identifies the daily weather patterns associated with historical drought and examines how these are likely to change as the world gets warmer. We find that the main weather pattern associated with drought, an isolated dome of high pressure centered over the South‐Central region, is likely to become stronger and more frequent by the late 21st century under human‐induced climate change. We also find that this appears to be related to self‐reinforcing ocean‐atmosphere interactions: in other words, the warmer the world gets, the stronger this pattern becomes. This is not good news for the region, as it establishes a physical mechanism that explains why projected summer rainfall is expected to decrease, and droughts to become stronger and longer, as climate continues to change. Key Points: Extreme hot and dry summers over the South‐Central United States are historically characterized by an isolated dome of high pressure over the regionFuture projections indicate that this high‐pressure system is likely to become stronger and more frequent, as climate changesProjected changes in the high‐pressure system appear to be related to self‐reinforcing ocean‐atmosphere interactions in a warming world [ABSTRACT FROM AUTHOR]

Mosquitoes are ecologically and economically important invertebrates that transmit numerous diseases and are an integral part of the food web in many terrestrial and aquatic ecosystems. Despite their importance, there have been no central repositories for occurrence or abundance records of mosquitoes in the United States. To enable research dependent on the geographic distribution and abundance of mosquito species, we built a database of occurrence records collected by many individuals and agencies, primarily in the United States and with emphasis on unpublished data in the Midwest region. This resource is invaluable to researchers studying mosquito ecology, disease vectors and pathways, and insect pests. The database contains 542 144 occurrence records, and each record includes information on sex, life stage, sampling method, presence/absence or abundance, and date and location of sampling. The accessibility of this resource will directly impact management and planning for mosquito-borne diseases with region-specific or species-specific modeling. Because there is little centralized ecological information on mosquitoes, this database is foundational to expanding knowledge of mosquito ecology. [ABSTRACT FROM AUTHOR]