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Many cold water‐dependent aquatic organisms are experiencing habitat and population declines from increasing water temperatures. Identifying mechanisms which drive local and regional stream thermal regimes facilitates restoration at ecologically relevant scales. Stream temperatures vary spatially and temporally both within and among river basins. We developed a modeling process to identify statistical relationships between drivers of stream temperature and covariates representing landscape, climate, and management‐related processes. The modeling process was tested in three study areas of the Pacific Northwest United States during the growing season (May [start], August [warmest], September [end]). Across all months and study systems, covariates with the highest relative importance represented the physical landscape (elevation [1st], catchment area [3rd], main channel slope [5th]) and climate covariates (mean monthly air temperature [2nd] and discharge [4th]). Two management covariates (groundwater use [6th] and riparian shade [7th]) also had high relative importance. Across the growing season (for all basins), local reach slope had high relative importance in May, but transitioned to a regional main channel slope covariate in August and September. This modeling process identified regionally similar and locally unique relationships among drivers of stream temperature. High relative importance of management‐related covariates suggested potential restoration actions for each system. [ABSTRACT FROM AUTHOR]

Summary: Drylands of the southwestern United States are rapidly warming, and rainfall is becoming less frequent and more intense, with major yet poorly understood implications for ecosystem structure and function. Thermography‐based estimates of plant temperature can be integrated with air temperature to infer changes in plant physiology and response to climate change. However, very few studies have evaluated plant temperature dynamics at high spatiotemporal resolution in rainfall pulse‐driven dryland ecosystems.We address this gap by incorporating high‐frequency thermal imaging into a field‐based precipitation manipulation experiment in a semi‐arid grassland to investigate the impacts of rainfall temporal repackaging.All other factors held constant, we found that fewer/larger precipitation events led to cooler plant temperatures (1.4°C) compared to that of many/smaller precipitation events. Perennials, in particular, were 2.5°C cooler than annuals under the fewest/largest treatment.We show these patterns were driven by: increased and consistent soil moisture availability in the deeper soil layers in the fewest/largest treatment; and deeper roots of perennials providing access to deeper plant available water. Our findings highlight the potential for high spatiotemporal resolution thermography to quantify the differential sensitivity of plant functional groups to soil water availability. Detecting these sensitivities is vital to understanding the ecohydrological implications of hydroclimate change. [ABSTRACT FROM AUTHOR]

Extreme climate events are societally harmful and have increased in frequency and intensity in recent decades. Indices based on temperature and precipitation are a valuable way to quantify climate extremes. Certain indices are defined relative to percentiles, which are dependent on a climatological baseline period. In this study, indices computed using temperature and precipitation from the Modern Era Retrospective Analysis for Research and Applications, Version 2 are calculated using percentiles from three baseline periods: 1981–2010, 1991–2020 and 1981–2020. Updating the baseline period from 1981 to 2010 to 1991–2020 leads to significant changes in the quantification of temperature and precipitation extremes over the United States over 1980–2021. Using the later baseline period indicates more cold extremes, fewer warm extremes, and fewer but more intense precipitation extremes throughout the US, with regional variation. Changing the baseline period can mislead the public and decision makers, potentially undermining the appropriate response to climate‐related health risks. Plain Language Summary: Indices computed using 2‐m air temperature and precipitation are used to represent extreme climate events such as heat waves, cold waves, heavy precipitation, and drought. Some indices are defined relative to percentile‐based thresholds, which are computed using a baseline climatology period. The baseline climatology is typically a thirty‐year period and is updated every ten years. This study examines how updating the baseline climatology period from 1981–2010 to 1991–2020 affects the quantification of climate extremes in the United States over 1980–2021. In general, since the 1991–2020 period is warmer than 1981–2010 throughout the United States, there are fewer warm extremes detected and more cold extremes detected when it is used as the baseline. The differences are most notable in the southwest and northeast United States. The changes in the precipitation indices vary throughout the country, but in certain parts of the southern and central United States, updating the baseline period leads to the detection of fewer but heavier extreme precipitation events. It is important to communicate the choice of baseline climatology period to prevent misinterpretation of the extreme climate indices and the comparison of different studies. Key Points: Updating the baseline period from 1981–2010 to 1991–2020 leads to significant changes in percentile‐based extreme climate indices in the USTemperature indices show generally increased cold extremes and decreased warm extremes across the US when the baseline period is updatedFor precipitation indices, the later baseline period indicates fewer but more intense extreme events in the south and central US [ABSTRACT FROM AUTHOR]

Streams and rivers are major sources of greenhouse gases (GHGs) to the atmosphere, as carbon and nitrogen are converted and outgassed during transport. Although our understanding of drivers of individual GHG fluxes has improved with numerous site‐specific studies and global‐scale compilations, our ability to parse out interrelated physical and biogeochemical drivers of gas concentrations is limited by a lack of consistently collected, temporally continuous samples of GHGs and their associated drivers. We present a first analysis of such a dataset collected by the National Ecological Observatory Network across 27 streams and rivers across ecoclimatic domains of the United States. Average concentrations of CO2 ranged from 36.9 ± 0.88 to 404 ± 33 μmol L−1, CH4 from 0.003 ± 0.0003 to 4.99 ± 0.72 μmol L−1, and N2O from 0.015 to 0.04 μmol L−1 and spanned ranges of previous global compilations. Both CO2 and CH4 were strongly affected by physical drivers including mean air temperature and stream slope, as well as by dissolved oxygen and total nitrogen concentrations. N2O was exclusively correlated with total nitrogen concentrations. Results suggested that potential for gas exchange dominated patterns in gas concentrations at the site level, but contributions of in‐stream aerobic and anaerobic metabolism, and groundwater also likely varied across sites. The highest gas concentrations as well as highest variability occurred in low‐gradient, warmer, and nonperennial systems. These results are a first step in providing unprecedented, continuous estimates of GHG flux constrained by temporally variable physical and biogeochemical drivers of GHG production. [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]

In June of 2021 the Southwest United States experienced a record‐breaking heatwave. This heatwave came at a time when the region was in severe drought. As drought alters the surface energy budget in ways that affect lower atmosphere temperature and circulations, it is possible that the combined drought‐heat event was a cascading climate hazard, in which preexisting drought exacerbated the heatwave. We apply satellite observation and numerical experiments with the Weather Research and Forecasting (WRF) model to test for land‐atmosphere feedbacks during the heatwave consistent with drought influence. We find a modest positive drought‐heat effect, as WRF simulations that include the drought have marginally higher air temperatures than those that exclude the initial drought conditions, with more substantial effects in wetter, forested areas. Evidence of drought‐heat‐drought‐coupled feedbacks was similarly modest in our simulations, as accounting for drought preconditioning led to a small reduction in simulated precipitation in the region. Plain Language Summary: In June 2021 the Southwest United States was hit by a record‐breaking heatwave at the same time as a severe drought. In this study, we look into the response of both events effect on one another. The WRF computer model is designed to test the observed events besides the use of satellite images during the same time. We find a small positive response between the two events that was more considerable over wetter and green regions. We also find a decrease in rainfall over the study region as a response to the drought‐heatwave conditions. Key Points: The record‐setting 2021 US heatwave struck at a time when surface conditions were anomalously dry, bright, and barren due to droughtSatellite observations indicate that drought led to anomalous land‐atmosphere fluxesModel simulations also indicate a small but systematic positive feedback of drought on heat [ABSTRACT FROM AUTHOR]

Forecasts from NOAA's Global Forecast System (GFS) and the High‐Resolution Rapid Refresh (HRRR) weather models are matched to surface observations for the winter season of November 2019 to March 2020 at 210 airports across the United States. The 2‐m temperature errors, conditioned on observed weather conditions such as cloud cover amount and wind speed, are used to determine the nature of systematic model biases. We observe a strong diurnal cycle in 2‐m temperature errors in the GFS in conditions with ≤50% and ≤25% sky cover, with a 1°C warm bias at night and a 2°C cold bias during the day. The HRRR, which uses a different set of physical parameterizations, does not have a clear diurnal cycle in errors under the same conditions. These results highlight the utility of weather‐conditional comparisons across the diurnal cycle to diagnose sources of model weaknesses and to target model improvements. Plain Language Summary: We evaluate the output from weather forecast models compared to observations at 210 airports across the United States during the November 2019 to March 2020 winter season. We focus on near‐surface air temperature errors in the Global Forecast System (GFS) and High‐Resolution Rapid Refresh (HRRR) weather models for different times of day and subsets of observed weather conditions. The GFS is 1°C too warm at night and 2°C too cold during the day in conditions with ≤50% and ≤25% cloud cover. The daily high and low temperatures have smaller errors in the HRRR model, which has different algorithms than the GFS model. Model refinement and development efforts would benefit from a focus on accurate representation of the diurnal cycle of temperatures as this basic characteristic of weather can reveal strengths and weaknesses in the model physics. Key Points: National Oceanic and Atmospheric Administration (NOAA)'s global forecast system (GFS) model struggles to adequately represent the diurnal cycle of temperatures under observed conditions of ≤50% and 25% cloud coverNOAA's high‐resolution rapid refresh (HRRR) model uses a different physics suite and does not have a strong diurnal cycle of temperature errors under the same conditionsExamination of errors using similar weather conditions helps to constrain the portion of model physics that can yield larger forecast errors [ABSTRACT FROM AUTHOR]

Amid reports from western US wildland fire managers that, compared to when many started their careers, fires are burning longer throughout the day before reducing in intensity overnight, we examined decadal changes in nighttime vapor pressure deficits over the western United States with a focus on the summer fire season. We calculated changes using a recently updated observation‐assimilating reanalysis (ERA5) available at hourly resolution over the 1980–2019 period. Analysis identifies the proximate cause (atmospheric temperature vs. moisture content) of the observed changes and the extent to which they have been captured in climate model simulations. Increases in nighttime vapor pressure deficits of >50% in 40 years are evident over foothills of mountains ranges adjacent to arid plateaus. The largest observed increases greatly exceed the forced climate‐model response. Correlation analysis reveals a broad link between the variability of western US summer‐nighttime vapor pressure deficit and the Pacific Decadal Oscillation. Plain Language Summary: Western US wildland fire managers have reported that fires are burning longer into the night and increasing in intensity earlier in the morning compared to when many started their careers. Increasing nighttime vapor pressure deficit—a measure of the drying power of air—is widely suspected to be responsible for these perceived changes in fire behavior. It is also suspected that human‐caused increases in nighttime temperatures are responsible. We used a recently released observation‐based data set to quantify the extent to which nighttime vapor pressure deficits have changed over the last 40 years and determine the proximate cause for the observed changes (particularly whether temperature effects on their own can explain them). We also explored how well the observed changes are captured in climate model simulations. Results highlight large increases in nighttime vapor pressure deficit over the foothills of mountain ranges next to arid plateaus, where the observation‐based increases greatly exceed the climate model projections, and both temperature and humidity play a role in driving the observed changes. Key Points: Nighttime vapor pressure deficit increased >50% in 40 years over the foothills of the Sierra Nevada and Rocky MountainsSaturation vapor pressure increases caused by rising temperatures and decreases in actual vapor pressure account for these changesThe magnitudes of the changes in the observation‐based records greatly exceed those projected by climate forecast models [ABSTRACT FROM AUTHOR]

Climate change is causing rapid warming and altered precipitation patterns in mountain watersheds, both of which influence the timing of ice breakup in mountain lakes. To enable predictions of ice breakup in the future, we analyzed a dataset of mountain lake ice breakup dates derived from remote sensing and historical downscaled climate data. We evaluated drivers of ice breakup, constructed a predictive statistical model, and developed projections of mountain lake ice breakup date with global climate models. Using Random Forest analysis, we determined that winter and spring cumulative snow fraction (portion of precipitation falling as snow) and air temperature are the strongest predictors of ice breakup on mountain lakes. Interactions between precipitation, cumulative winter air temperature and lake surface area indicate that shifts in air temperature and precipitation affect smaller lakes (< 2 km²) more than larger lakes (> 2-10 km²). A linear mixed effects model (RMSE of 18 d), applied with an ensemble of 15 global climate models, projected that end-of-century ice breakup in mountain lakes will be earlier by 25 ± 4 and 61 ± 5 (mean ± SE) days for representative concentration pathways 4.5 and 8.5, respectively. [ABSTRACT FROM AUTHOR]

Winter climate warming is rapidly leading to changes in snow depth and soil temperatures across mid‐ and high‐latitude ecosystems, with important implications for survival and distribution of species that overwinter beneath the snow. Amphibians are a particularly vulnerable group to winter climate change because of the tight coupling between their body temperature and metabolic rate. Here, we used a mechanistic microclimate model coupled to an animal biophysics model to predict the spatially explicit effects of future climate change on the wintering energetics of a freeze‐tolerant amphibian, the Wood Frog (Lithobates sylvaticus), across its distributional range in the eastern United States. Our below‐the‐snow microclimate simulations were driven by dynamically downscaled climate projections from a regional climate model coupled to a one‐dimensional model of the Laurentian Great Lakes. We found that warming soil temperatures and decreasing winter length have opposing effects on Wood Frog winter energy requirements, leading to geographically heterogeneous implications for Wood Frogs. While energy expenditures and peak body ice content were predicted to decline in Wood Frogs across most of our study region, we identified an area of heightened energetic risk in the northwestern part of the Great Lakes region where energy requirements were predicted to increase. Because Wood Frogs rely on body stores acquired in fall to fuel winter survival and spring breeding, increased winter energy requirements have the potential to impact local survival and reproduction. Given the geographically variable and intertwined drivers of future under‐snow conditions (e.g., declining snow depths, rising air temperatures, shortening winters), spatially explicit assessments of species energetics and risk will be important to understanding the vulnerability of subnivium‐adapted species. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) play a crucial role in the hydroclimate of the U.S. west coast. While the impact of large‐scale sea surface temperature (SST) patterns on ARs is well recognized, the specific roles of local SST on ARs have not been systematically investigated. This study analyzed the landfalling ARs from two regional climate simulations that differ only in the local SST. With local SST warming, AR frequency increases across the U.S. west coast, with comparable contributions from the spatial expansion of ARs and emergence of new ARs. AR landfalling area, inland intrusion distance, and intensity also increase, with landfalling area showing the largest sensitivity. Although local SST warming only increases the AR moisture by ∼0.4%/K, AR‐related precipitation increases by 3%/K, demonstrating the important impact of local SST warming on intensifying storms. Hence, local SST is important for accurate depiction of ARs and their precipitation in the western United States. Plain Language Summary: Atmospheric rivers (ARs) have important effects on extreme precipitation and flooding in the U.S. west coast. Local sea surface temperature (SST) may intensify ARs and their impacts by changing atmospheric temperature and moisture. This study compares two regional climate simulations of the western United States that differ only in the local SST. Analysis shows that local SST warming increases the frequency of ARs across the U.S. west coast as ARs expand to cover larger areas and more ARs emerge with increasing moisture. AR landfalling area, inland intrusion distance, and intensity also increase. Although local SST warming only increases AR moisture by 0.4%/K, extreme precipitation associated with ARs increases by a much larger rate of 3%/K as SST warming intensifies storms. Hence, local SST has important effects on ARs and extreme precipitation in the western United States. Key Points: Local SST warming increases AR frequencies across the U.S. west coast by expanding AR spatial coverage and increasing AR occurrencesAR landfalling area, inland intrusion distance, and intensity all increase with local SST warming, and landfalling area increases the mostAlthough AR moisture increases only by 0.4%/K of local SST warming, extreme precipitation increases by 3%/K in the west coast [ABSTRACT FROM AUTHOR]

The United States Great Lakes Region (USGLR) is a critical geographic area for future bioenergy production. Switchgrass (Panicum virgatum) is widely considered a carbon (C)‐neutral or C‐negative bioenergy production system, but projected increases in air temperature and precipitation due to climate change might substantially alter soil organic C (SOC) dynamics and storage in soils. This study examined long‐term SOC changes in switchgrass grown on marginal land in the USGLR under current and projected climate, predicted using a process‐based model (Systems Approach to Land‐Use Sustainability) extensively calibrated with a wealth of plant and soil measurements at nine experimental sites. Simulations indicate that these soils are likely a net C sink under switchgrass (average gain 0.87 Mg C ha−1 year−1), although substantial variation in the rate of SOC accumulation was predicted (range: 0.2–1.3 Mg C ha−1 year−1). Principal component analysis revealed that the predicted intersite variability in SOC sequestration was related in part to differences in climatic characteristics, and to a lesser extent, to heterogeneous soils. Although climate change impacts on switchgrass plant growth were predicted to be small (4%–6% decrease on average), the increased soil respiration was predicted to partially negate SOC accumulations down to 70% below historical rates in the most extreme scenarios. Increasing N fertilizer rate and decreasing harvest intensity both had modest SOC sequestration benefits under projected climate, whereas introducing genotypes better adapted to the longer growing seasons was a much more effective strategy. Best‐performing adaptation scenarios were able to offset >60% of the climate change impacts, leading to SOC sequestration 0.7 Mg C ha−1 year−1 under projected climate. On average, this was 0.3 Mg C ha−1 year−1 more C sequestered than the no adaptation baseline. These findings provide crucial knowledge needed to guide policy and operational management for maximizing SOC sequestration of future bioenergy production on marginal lands in the USGLR. [ABSTRACT FROM AUTHOR]

To determine hydrologic changes in a warmer climate, we impose precipitation and potential evaporation (Eo) perturbations on hydrologic response functions constructed from precipitation and satellite soil moisture observations across the United States. Despite nonlinearities in the evaporation (E) and drainage (D) responses and opposing‐sign perturbations, changes in individual fluxes are superposable. Empirical frameworks (Budyko) can misrepresent changes in E/D partitioning by neglecting shifts/trends in hydrologic regime and subseasonal precipitation dynamics. E/D both increase to balance mean precipitation (P¯) increases, and increased Eo reduces soil moisture. E and D are generally more elastic to changes in P¯ than Eo. The results suggest that (1) the impacts of regional hydrologic perturbations may allow for simple superposition/scaling, (2) changes in timing/intensity of precipitation may have substantial impacts on mean moisture states and fluxes, and (3) changes to the distribution of surface moisture states are likely more relevant for E/D partitioning than common aridity indices. Plain Language Summary: We use satellite observations of soil moisture and expected increase in air temperature to determine how evaporation and soil drainage (to groundwater recharge and rivers/streamflow) will change in a warmer climate. The impacts of drier air, more rainfall, and more extreme rainfall (drier dry days and wetter wet days) can largely be considered separately and then added, which will help when predicting a specific location's water balance using scenarios from global climate models. In typical scenarios, soils are likely to dry, evaporation is likely to increase, and—when precipitation increases—drainage to groundwater/streams is likely to increase as well. Evaporation and drainage are relatively more sensitive to changes in precipitation and humidity in the Western United States than the East, and the Corn Belt is particularly susceptible to changes in precipitation intensity. Common methods of determining changes in evaporation and drainage, which neglect changes in soil moisture, may have large errors in global change scenarios. Key Points: Aridity indices (Budyko) fail to capture effects of daily dynamics and precipitation extremes on long‐term hydroclimate changeClimate perturbations to evaporation/drainage are superposable despite nonlinear flux mechanisms and mixed‐sign effectsLong‐term soil drying tied to trends in increased evaporation, decreased drainage to groundwater and streamflow [ABSTRACT FROM AUTHOR]

Anticipated future increases in air temperature and regionally variable changes in precipitation will have direct and cascading effects on United States (U.S.) water quality. In this paper, and a companion paper by Coffey et al., we review technical literature addressing the responses of different water quality attributes to historical and potential future changes in air temperature and precipitation. The goal is to document how different attributes of water quality are sensitive to these drivers, to characterize future risk to inform management responses, and to identify research needs to fill gaps in our understanding. Here we focus on potential changes in streamflow, water temperature, and salt water intrusion (SWI). Projected changes in the volume and timing of streamflow vary regionally, with general increases in northern and eastern regions of the U.S., and decreases in the southern Plains, interior Southwest, and parts of the Southeast. Water temperatures have increased throughout the U.S. and are expected to continue to increase in the future, with the greatest changes in locations where high summer air temperatures occur together with low streamflow volumes. In coastal areas, especially the mid‐Atlantic and Gulf coasts, SWI to rivers and aquifers could be exacerbated by sea level rise, storm surges, and altered freshwater runoff. Management responses for reducing risks to water quality should consider strategies and practices robust to a range of potential future conditions. Research Impact Statement: Climate change effects on water quality will vary in different regional and watershed settings and could present a risk to human health and the environment. [ABSTRACT FROM AUTHOR]

In this paper we review the published, scientific literature addressing the response of nutrients, sediment, pathogens, and cyanobacterial blooms to historical and potential future changes in air temperature and precipitation. The goal is to document how different attributes of water quality are sensitive to these drivers, to characterize future risk, to inform management responses, and to identify research needs to fill gaps in our understanding. Results suggest that anticipated future changes present a risk of water quality and ecosystem degradation in many United States locations. Understanding responses is, however, complicated by inherent high spatial and temporal variability, interactions with land use and water management, and dependence on uncertain changes in hydrology in response to future climate. Effects on pollutant loading in different watershed settings generally correlate with projected changes in precipitation and runoff. In all regions, increased heavy precipitation events are likely to drive more episodic pollutant loading to water bodies. The risk of algal blooms could increase due to an expanded seasonal window of warm water temperatures and the potential for episodic increases in nutrient loading. Increased air and water temperatures are also likely to affect the survival of waterborne pathogens. Responding to these challenges requires understanding of vulnerabilities, and management strategies to reduce risk. Research Impact Statement: Climate change will have direct and cascading effects on water quality that vary in different regional and watershed settings, and could present a risk to human health and the environment. [ABSTRACT FROM AUTHOR]

Hydrologic intensity is often quantified using precipitation without directly incorporating atmospheric water demand. We develop a hydrologic intensity index called the surplus deficit intensity (SDI) index that accounts for variation in supply and demand. SDI is the standardized sum of standardized surplus intensity (mean of daily surplus when supply > demand) and deficit time (mean of consecutive days when demand > supply). Using an observational ensemble of global daily precipitation and atmospheric water demand during 1979–2017, we document widespread hydrologic intensification (SDI; +0.11 z‐score per decade) driven primarily by increased surplus intensity. Using a climate model ensemble of the United States, hydrologic intensification is projected for the mid‐21st century (+0.86 in z‐score compared to 1971–2000), producing greater apparent intensification when compared to an index that does not explicitly incorporate demand. While incorporating demand had a minor effect on observed hydrologic intensification, it doubles hydrological intensification for the mid‐21st century. Plain Language Summary: Increasing air temperatures have resulted in an intensification, or acceleration, of the terrestrial hydrologic cycle, which is defined as an increase of the water fluxes (precipitation and evapotranspiration) between the surface and the atmosphere. Efforts to quantify hydrologic intensification have traditionally only considered "supply" variables such as precipitation rates without considering "demand" that varies over space and time. For this work we develop a method to quantify hydrologic intensification using supply and demand. This approach shows widespread hydrologic intensification from 1979–2017 across much of the global land surface, which is expected to continue into the future. We additionally compare this new method to a previously developed method that uses only supply without explicitly incorporating demand. Incorporating demand results in a notable hydrologic intensification compared to methods that only use supply or a simplified representation of supply and demand. This work suggests that demand needs to be considered when quantifying hydrologic intensification. Key Points: We develop a hydrologic intensity metric that incorporates daily supply and demand using precipitation and reference evapotranspirationWidespread hydrologic intensification occurred over the past four decades and will continue into the futureAdding demand couples intensity metrics to ecohydrologic conditions and produces greater increases than metrics with static demand [ABSTRACT FROM AUTHOR]

Despite the widespread application of statistical downscaling tools, uncertainty remains regarding the role of model formulation in determining model skill for daily maximum and minimum temperature (Tmax and Tmin), and precipitation occurrence and intensity. Impacts of several key aspects of statistical transfer function form on model skill are evaluated using a framework resistant to model overspecification. We focus on: (a) model structure: simple (generalized linear models, GLMs) versus complex (artificial neural networks, ANNs) models. (b) Predictor selection: Fixed number of predictors chosen a priori versus stepwise selection of predictors and inclusion of grid point values versus predictors derived from application of principal components analysis (PCA) to spatial fields. We also examine the influence of domain size on model performance. For precipitation downscaling, we consider the role of the threshold used to characterize a wet day and apply three approaches (Poisson and Gamma distributions in GLM and ANN) to downscale wet‐day precipitation amounts. While no downscaling formulation is optimal for all predictands and at 10 locations representing diverse U.S. climates, and due to the exclusion of variance inflation all of the downscaling formulations fail to reproduce the range of observed variability, models with larger suites of prospective predictors generally have higher skill. For temperature downscaling, ANNs generally outperform GLM, with greater improvements for Tmin than Tmax. Use of PCA‐derived predictors does not systematically improve model skill, but does improve skill for temperature extremes. Model skill for precipitation occurrence generally increases as the wet‐day threshold increases and models using PCA‐derived predictors tend to outperform those based on grid cell predictors. Each model for wet‐day precipitation intensity overestimates annual total precipitation and underestimates the proportion derived from extreme precipitation events, but ANN‐based models and those with larger predictor suites tend to have the smallest bias. [ABSTRACT FROM AUTHOR]

ABSTRACT This article introduces a web-based climate information resource: an El Niño Southern Oscillation ENSO-based monthly climatology for the United States, available on . Climate variability affects land managers and decision makers in a variety of ways. The ENSO is one of the leading drivers of seasonal and year-to-year climate variability. Understanding the historical rainfall and temperature anomalies associated with ENSO can be an important way for decision makers to learn how they might anticipate climate variability. We used the ENSO record from 1950 to 2013 to develop an ENSO-specific climatology. This allows decision makers to pinpoint the combinations of location and month in which there are substantial ENSO-associated rainfall or temperature anomalies. Learning about the timing of historical ENSO-driven climate impacts can be an important first step for decision makers to develop management adjustments that could make the most positive outcomes based on the available climate information. [ABSTRACT FROM AUTHOR]

ABSTRACT The widespread influence of El Niño-Southern Oscillation ( ENSO) phases on regional climate is well known to contribute to climate anomalies around the world. Understanding the effects of climate variability given the ENSO phases is an important component to improve crop management and reduce climate risk due to potential changes in temperature and precipitation patterns. Recognizing the spatial structure in precipitation and temperature under different ENSO phases is fundamental for comprehensive environmental assessments through decisions and policies that consider climate variability at regional scales. The objective of this study was to investigate if there is evidence of changes to the spatial structure of precipitation and temperature associated with the three categories of ENSO phases: El Niño, La Niña and neutral years in two study areas, the Southeast (Alabama, Florida and Georgia) and the Midwest (Nebraska) United States. Daily data of maximum temperature, minimum temperatures and precipitation (1981-2010) from 407 weather stations were analysed. The spatial structure was measured by analysing the monthly correlation matrices calculated from daily observations. To determine the potential changes to spatial structures of precipitation and temperature, the spatial structure was compared with using the complete 30 year period divided by the three categories of the ENSO phases. The results showed statistically significant differences in the spatial structures of maximum and minimum temperature and precipitation (amount and distribution) among the different ENSO phases and between the combinations of two kinds of different ENSO phases. Statistically significant changes in spatial correlation at seasonal and monthly timescales among the different ENSO phases were also observed in both regions. The statistical differences were not uniform in both regions, and some region-variable-month combinations showed no significance. Knowledge of spatial structure among some combinations of different ENSO phases at each region would be useful to improve further applications of weather/climate data in other disciplines such as geo-spatial weather generator. [ABSTRACT FROM AUTHOR]

ABSTRACT Isobaric equivalent temperature ( TE) is the temperature that an air parcel would have if all associated water vapour were condensed and the resulting latent heat used to increase the temperature of the parcel. It is therefore an ideal metric for assessing changes in (1) total near-surface heat content associated with both temperature and moisture content and (2) the joint behaviour of temperature and humidity, which is relevant to both lower atmospheric stability and human heat stress during extreme temperature events. We present results from an analysis of 50 years (1961-2010) of daily TE and its temperature and moisture components at seven stations in the central United States. The annual means of daily TEmax and TEmin increased at all stations during the period of analysis with the largest changes occurring in TEmin, largely as a result of increasing minimum air temperature. At western locations significant increases in the annual mean TEmax were also observed, resulting from a combination of increases in Tmax and humidity. Despite small summer ( JJA) trends in maximum air temperature, summer TE trends were generally larger than their annual counterparts. The timing of the observed variations and the resulting spatial pattern are consistent with observed changes in meridional moisture flux associated with the Great Plains low-level jet. Heat waves in the region were found to be characterized by increasing TEmin, primarily resulting from increases in minimum air temperature. At western stations, heat waves were also characterized by increasing TEmax as a result of positive trends in humidity. In most cases, equivalent temperature provides a perspective on local environmental change that differs from what is provided by consideration of temperature alone. [ABSTRACT FROM AUTHOR]

We investigate the time trends in the maximum and minimum temperatures in the United States from 1895 to 2017 using techniques that allow for fractional integration in the detrended series. In doing so we get more accurate estimates of the trends than those obtained using standard methods that impose either stationarity I(0) or nonstationarity I(1). Our results reveal evidence of significant positive trends in both maximum and minimum temperatures, while the difference between them show a significant negative trend as a consequence of the higher increase in the minimum temperatures. Evidence of stationary long memory behavior is also found in the three series examined. [ABSTRACT FROM AUTHOR]

Daily minimum temperature ( Tmin) has increased faster than daily maximum temperature ( Tmax) in many parts of the world, leading to decreases in diurnal temperature range ( DTR). Projections suggest that these trends are likely to continue in many regions, particularly in northern latitudes and in arid regions. Despite wide speculation that asymmetric warming has different impacts on plant and ecosystem production than equal-night-and-day warming, there has been little direct comparison of these scenarios. Reduced DTR has also been widely misinterpreted as a result of night-only warming, when in fact Tmin occurs near dawn, indicating higher morning as well as night temperatures. We report on the first experiment to examine ecosystem-scale impacts of faster increases in Tmin than in Tmax, using precise temperature controls to create realistic diurnal temperature profiles with gradual day-night temperature transitions and elevated early morning as well as night temperatures. Studying a constructed grassland ecosystem containing species native to Oregon, USA, we found that the ecosystem lost more carbon at elevated than ambient temperatures, but remained unaffected by the 3 °C difference in DTR between symmetric warming (constantly ambient + 3.5 °C) and asymmetric warming (dawn Tmin = ambient + 5 °C, afternoon Tmax = ambient + 2 °C). Reducing DTR had no apparent effect on photosynthesis, probably because temperatures were most different in the morning and late afternoon when light was low. Respiration was also similar in both warming treatments, because respiration temperature sensitivity was not sufficient to respond to the limited temperature differences between asymmetric and symmetric warming. We concluded that changes in daily mean temperatures, rather than changes in Tmin/ Tmax, were sufficient for predicting ecosystem carbon fluxes in this reconstructed Mediterranean grassland system. [ABSTRACT FROM AUTHOR]

The article discusses a study on the equivalent temperature and temperature from 1979-2005 and its relation to land cover types in the U.S. It says that study used the data from National Centers for Environmental Prediction North American Regional Reanalysis, Advanced Very High Resolution Radiometer (AVHRR), National Land Cover Database, and Normalized Difference Vegetation Index (NDVI). Results show that land cover types affect moisture availability and temperature in lower atmosphere.

Biological soil crusts (biocrusts) are an integral part of the soil system in arid regions worldwide, stabilizing soil surfaces, aiding vascular plant establishment, and are significant sources of ecosystem nitrogen and carbon. Hydration and temperature primarily control ecosystem CO2 flux in these systems. Using constructed mesocosms for incubations under controlled laboratory conditions, we examined the effect of temperature (5–35 °C) and water content (WC, 20–100%) on CO2 exchange in light (cyanobacterially dominated) and dark (cyanobacteria/lichen and moss dominated) biocrusts of the cool Colorado Plateau Desert in Utah and the hot Chihuahuan Desert in New Mexico. In light crusts from both Utah and New Mexico, net photosynthesis was highest at temperatures >30 °C. Net photosynthesis in light crusts from Utah was relatively insensitive to changes in soil moisture. In contrast, light crusts from New Mexico tended to exhibit higher rates of net photosynthesis at higher soil moisture. Dark crusts originating from both sites exhibited the greatest net photosynthesis at intermediate soil water content (40–60%). Declines in net photosynthesis were observed in dark crusts with crusts from Utah showing declines at temperatures >25 °C and those originating from New Mexico showing declines at temperatures >35 °C. Maximum net photosynthesis in all crust types from all locations were strongly influenced by offsets in the optimal temperature and water content for gross photosynthesis compared with dark respiration. Gross photosynthesis tended to be maximized at some intermediate value of temperature and water content and dark respiration tended to increase linearly. The results of this study suggest biocrusts are capable of CO2 exchange under a wide range of conditions. However, significant changes in the magnitude of this exchange should be expected for the temperature and precipitation changes suggested by current climate models. [ABSTRACT FROM AUTHOR]

The article presents a study on the temperature and precipitation extreme in South America based on the Providing Regional Climate for Impacts Studies (PRECIS) regional climate modeling (RCM) system. The study compared the simulation trends in the 1961-1990 of the HadRM3P, the RCM within PRECIS, with the actual observations. It reveals that HadRM3P simulated trends show the similarity in the patterns of negative trends in the cold nights in South America and Southeastern South America.

Climate models predict, and empirical evidence confirms, that more extreme precipitation regimes are occurring in tandem with warmer atmospheric temperatures. These more extreme rainfall patterns are characterized by increased event size separated by longer within season drought periods and represent novel climatic conditions whose consequences for different ecosystem types are largely unknown. Here, we present results from an experiment in which more extreme rainfall patterns were imposed in three native grassland sites in the Central Plains Region of North America, USA. Along this 600 km precipitation–productivity gradient, there was strong sensitivity of temperate grasslands to more extreme growing season rainfall regimes, with responses of aboveground net primary productivity (ANPP) contingent on mean soil water levels for different grassland types. At the mesic end of the gradient (tallgrass prairie), longer dry intervals between events led to extended periods of below-average soil water content, increased plant water stress and reduced ANPP by 18%. The opposite response occurred at the dry end (semiarid steppe), where a shift to fewer, but larger, events increased periods of above-average soil water content, reduced seasonal plant water stress and resulted in a 30% increase in ANPP. At an intermediate mixed grass prairie site with high plant species richness, ANPP was most sensitive to more extreme rainfall regimes (70% increase). These results highlight the inherent complexity in predicting how terrestrial ecosystems will respond to forecast novel climate conditions as well as the difficulties in extending inferences from single site experiments across biomes. Even with no change in annual precipitation amount, ANPP responses in a relatively uniform physiographic region differed in both magnitude and direction in response to within season changes in rainfall event size/frequency. [ABSTRACT FROM AUTHOR]

The article presents the study on air temperature impacts over Eastern North America and Europe during the low-frequency modes of North Atlantic sea surface temperature (SST). The dominant mode of SST, known as the Atlantic multi-decadal oscillation, is a multi-decadal signal, while the second mode, commonly related to North Atlantic Oscillation (NAO), shows quasi-decadal range of spectral energy. The study examined several data sets such as the North Atlantic SSTs and mean air temperature. The study found that the during the high phases of the leading mode of SST, air temperature impacts over the eastern U.S. and Northern Europe exhibit the same signs as the SST anomalies and are comparable to the SST and land temperature impacts linked with the NAO in the second mode.

Inland saltgrass [Distichlis spicata var. stricta (L.) Greene], native to the western United States, has potential for use as a turfgrass and a revegetation species on saline sites. This study was conducted (i) to evaluate the effect of seeding date, seeding rate, and seed treatments on saltgrass establishment; and (ii) to determine the required accumulative growing degree days (GDD) for saltgrass to establish adequate cover (80%) after seeding. Seeding dates were 15 May, 15 June, and 15 July in 2006. Machine scarification and stratification broke saltgrass seed dormancy equally well. Saltgrass seeded in May established adequate coverage (≥80%) in September even using the lowest seeding rate (74 kg ha-1). For plots seeded in June, only the higher seeding rates (123 and 170 kg ha-1) established adequate coverage (≥80%) by the end of growing season. For plots seeded in July, however, even the highest seeding rate failed to establish adequate coverage in September. The accumulated GDD to achieve adequate coverage was 1748, 1663, and 1435 for 74, 123, and 172 kg ha-1 seeding rates, respectively. [ABSTRACT FROM AUTHOR]

The article investigates the impact of large scale climate circulations on snow variability as well as wintertime precipitation and temperature variables in eastern United States. Two snowfall variables: total seasonal snowfall (TSF) and number of snow days (NSD) as well as data from 124 snowfall stations were examined using principal component analysis. It was found that the North Atlantic Oscillation is the leading mode of variability for both TSF and NSD, while the second mode of variability for TSF and NSD, are the Pacific/North American pattern (PNA) and a dipole pattern, respectively. Findings reveal that these patterns provide prospects for seasonal predictions of snow variables.

The article presents a study which aims to compare the time-series analyses of urban and rural temperature trends in proximity to the most populous metropolitan areas of the U.S. It also examines the temperature data from the urban and proximate rural stations for 50 large metropolitan areas in the U.S. to determine the mean decadal rate of change in urban temperatures, rural temperatures, as well as heat island intensity between 1951 and 2000. The study reveals that the mean decadal rate of change in the heat island intensity of large cities in the U.S. over the five decades is 0.05 degree Celsius and indicates a division concerning the temperature trends between cities in the northeastern and southern regions in the U.S.

Partitioning ions between the solid and solution phase is one of the most important processes controlling nutrient mobility and bioavailability Despite this, less research has focused on the interactions of nutrient anions at soil interfaces, although variable-charge components are present to some extent in nearly all soils. The objective of this study was to develop equations using commonly measured soil properties (particle size analysis, organic matter content, and extractable Fe and Al fractions) to predict sorption isotherms for NO3-, SO42-, and H2PO4-. Six subsurface soils, ranging spatially and temporally from heavily weathered Oxisols of the tropics to a recently glaciated Entisol from the U.S. Pacific Northwest, were used to generate sorption isotherms of the three macronutrient anions using initial solution concentrations from 0.1 to 5 mmol L-1. Before batch sorption experiments, soils were saturated with KCl, rinsed free of excess salts, and adjusted to pH = 4.0 ± 0.1 to eliminate the confounding effects of competing ions or differing pH regimes. Almost all soils from temperate latitudes had a greater capacity to sorb anions than the Oxisols included in this study for comparison. This was particularly true for the soils with volcanic parent materials from the U.S. Pacific Northwest. For any given soil, the capacity to sorb the macronutrient anions was in the order H2PO4- > SO42-> NO3-. Multiple regression analyses generally suggest that the electrostatic sorption of NO3- and SO42- is positively related to the presence of active Al fractions and negatively correlated with organic C content. [ABSTRACT FROM AUTHOR]

The article reports on the study about the synchronization between the surface air temperatures in National Centers for Environmental Prediction (NCEP) and the North Sea—Caspian pattern (NCP). It is investigated through teleconnection patterns based on phase synchronization, which findings are compared with those of the classical cross-correlation method in cases that shows linear spatial cross-dependency. The method shows that the experiential intra-phase relationships between the large-scale surface air temperature series and the NCP Index have improved, and assures that the major modes of mid and high Northern Hemisphere variability in climate are qualified. In relation, the result of statistics relative to the matter is mentioned.

A method to estimate the time of observation employed at US Cooperative Observer Network stations has been developed using rapid update cycle model analyses. This method uses the day‐to‐day variability in model temperature biases to estimate observation schedules on a time scale of weeks, making it ideal for use in ‘real‐time’ applications. Observation time estimates from a two‐category system (morning and ‘non‐morning’) and three‐category system (morning, afternoon and midnight) were both evaluated. The performance of the two‐category system was compared with existing techniques that employ this system on monthly time scales. The results were comparable, showing dependence on season and climatological characteristics, but reveal an ability to reach high levels of accuracy (>90% of stations have observation schedules correctly estimated) over similar time periods (10–50 days). To our knowledge, the evaluation of three‐category estimation performance for the time scales investigated has not been documented. Accuracy remained high for morning and midnight stations (>90%), and decreased for stations with afternoon observation schedules (85–65%). Additionally, the three‐category estimation technique was extended to four categories in order to identify observers who shift temperature records temporally. The accuracy of detecting shifted records within the context of the four‐category estimation technique was comparable to the performance of the three‐category system, with shifted observations correctly identified more than 75% of the time in most cases. Copyright © 2005 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Examines the characteristics of snow covered duration in the U.S. Effect of the snow cover on the environmental and societal systems; Impact of the snow cover on the near-surface air temperatures; Variability of the snow cover duration in both space and time.

Microclimatic data are required for many problems in pure and applied ecology. This data includes aboveground convective and radiative conditions as well as soil temperature and moisture. In cold regions, the connection between above‐ and belowground microclimates via snow cover is also critically important. Here I describe a data set of hourly microclimates for the continental United States, simulated from the years 1979 to 2017 across a grid of 2,287 locations approximately 60 km apart. The data were generated with the NicheMapR microclimate model, driven by 0.04° gridded daily meteorological forcing data (air temperature, wind speed, humidity, solar radiation, air pressure, and rainfall). The aboveground microclimate variables include horizontal plane solar radiation, solar zenith angle, sky temperature (from which downwelling longwave radiation can be computed), air temperature, relative humidity, and wind speed at 1 and 200 cm height, and snow depth. The belowground variables include soil temperature, pore humidity, soil moisture, and soil water potential for 0, 2.5, 5, 10, 15, 20, 30, 50, 100, and 200 cm belowground. The computations are for four shade levels (0%, 50%, 75%, and 90%). The predictions are validated against detailed soil temperature, soil moisture, and snow observations and show enhanced performance over existing microclimatic data for the United States. The data set can be used for a wide variety of applications, including the computation of heat and water budgets of organisms, the potential for vegetation growth, and the computation of stress and growth indices. The use of daily forcing data also allows assessments of the consequences of extreme events including heat waves and drought. Example applications are provided for computing plant growth potential, lizard egg development and body temperature, and mammalian energy and water requirements. No copyright or proprietary restrictions are associated with the use of this data set other than citation of this Data Paper. [ABSTRACT FROM AUTHOR]

This study shows that the mean monthly temperature and precipitation for Zahedan from January to March are predictable. They have oscillatory behavior in relation to El Niño-Southern Oscillation (ENSO) phases. The 200hPa vector wind composite mean maps from January to March show that a jet streak oscillates between 27°N and 30°N, to the west of Iran. These oscillations result in strong contour gradients of 1000hPa geopotential height during strong La Niña events; by contrast, they result in weak contour gradients during El Niño events in the southeast of the country. The composite forecasts for Zahedan based on El Niño/La Niña seem to be skillful for the strong events and show some skill for neutral events. The applicability of the forecasts in water management, agriculture, health, and other fields is considerable. [ABSTRACT FROM AUTHOR]