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Harmful algae and cyanobacteria blooms are increasing in frequency and intensity in freshwater systems due to anthropogenic impacts such as nutrient loading in watersheds and engineered alterations of natural waterways. There are multiple physical factors that affect the conditions in a freshwater system that contribute to optimal habitats for harmful algae and toxin-producing cyanobacteria. A growing body of research shows that climate change stressors also are impacting water-body conditions that favor harmful algae and cyanobacteria species over other phytoplankton. The overgrowth of these organisms, or a "bloom," increases the opportunity for exposure to toxins by humans, companion animals, livestock, and wildlife. As waters warm and precipitation patterns change over time, exposure to these blooms is projected to increase. Hence, it is important that states and tribes develop monitoring and reporting strategies as well as align governmental policies to protect their citizens and ecosystems within their jurisdiction. Currently, the policies and approaches taken to monitor and report on harmful algae and cyanobacteria blooms vary widely among states, and it is undetermined if any tribes have specific policies on harmful algae blooms. This paper synthesizes research on algal blooms in inland freshwater systems of the United States. This review examines how climate change contributes to trends in bloom frequency or severity and outlines approaches that states and tribes may use to monitor, report, and respond to harmful algae and cyanobacteria blooms. Significance Statement: Inland bodies of freshwater supply drinking water for humans and animals, water for irrigating crops, habitats for aquatic species, places of cultural significance for Indigenous peoples, and other important functions. Many of these bodies of water have been polluted with runoff from industry, including agriculture, and already support harmful algal blooms during warm conditions. Hot extremes associated with climate change are expected to increase the occurrence and duration of harmful algal blooms, and in some places, initiate blooms where none have been recorded previously. These toxic blooms are harmful to people, companion animals, livestock, and wildlife. It is important to review the interconnections among biological, climate, and water systems to monitor blooms and alert the public about their toxin production. [ABSTRACT FROM AUTHOR]

The hydrologic response of different climate-change emission scenarios for the twenty-first century were evaluated in 14 basins from different hydroclimatic regions across the United States using the Precipitation-Runoff Modeling System (PRMS), a process-based, distributed-parameter watershed model. This study involves four major steps: 1) setup and calibration of the PRMS model in 14 basins across the United States by local U.S. Geological Survey personnel; 2) statistical downscaling of the World Climate Research Programme''s Coupled Model Intercomparison Project phase 3 climate-change emission scenarios to create PRMS input files that reflect these emission scenarios; 3) run PRMS for the climate-change emission scenarios for the 14 basins; and 4) evaluation of the PRMS output. This paper presents an overview of this project, details of the methodology, results from the 14 basin simulations, and interpretation of these results. A key finding is that the hydrological response of the different geographical regions of the United States to potential climate change may be very different, depending on the dominant physical processes of that particular region. Also considered is the tremendous amount of uncertainty present in the climate emission scenarios and how this uncertainty propagates through the hydrologic simulations. This paper concludes with a discussion of the lessons learned and potential for future work. [ABSTRACT FROM AUTHOR]

Precipitation is a key link in the global water cycle and a proxy for changing climate; therefore, proper assessment of the urban environment’s impact on precipitation (land use, aerosols, thermal properties) will be increasingly important in ongoing climate diagnostics and prediction, Global Water and Energy Cycle (GWEC) analysis and modeling, weather forecasting, freshwater resource management, urban planning–design, and land–atmosphere–ocean interface processes. These facts are particularly critical if current projections for global urban growth are accurate. The goal of this paper is to provide a concise review of recent (1990–present) studies related to how the urban environment affects precipitation. In addition to providing a synopsis of current work, recent findings are placed in context with historical investigations such as Metropolitan Meteorological Experiment (METROMEX) studies. Both observational and modeling studies of urban-induced rainfall are discussed. Additionally, a discussion of the relative roles of urban dynamic and microphysical (e.g., aerosol) processes is presented. The paper closes with a set of recommendations for what observations and capabilities are needed in the future to advance our understanding of the processes. [ABSTRACT FROM AUTHOR]

Assessing climate change risk to municipal water supplies is often conducted by hydrologic modeling specific to local watersheds and infrastructure to ensure that outputs are compatible with existing planning frameworks and processes. This study leverages the modeling capacity of an operational National Weather Service River Forecast Center to explore the potential impacts of future climate-driven hydrologic changes on factors important to planning at the Salt Lake City Department of Public Utilities (SLC). Hydrologic modeling results for the study area align with prior research in showing that temperature changes alone will lead to earlier runoff and reduced runoff volume. The sensitivity of average annual flow to temperature varies significantly between watersheds, averaging -3.8% °F-1 and ranging from 21.8% to 26.5% flow reduction per degree Fahrenheit of warming. The largest flow reductions occur during the high water demand months of May-September. Precipitation drives hydrologic response more strongly than temperature, with each 1% precipitation change producing an average 1.9% runoff change of the same sign. This paper explores the consequences of climate change for the reliability of SLC's water supply system using scenarios that include hydrologic changes in average conditions, severe drought scenarios, and future water demand test cases. The most significant water management impacts will be earlier and reduced runoff volume, which threaten the system's ability to maintain adequate streamflow and storage to meet late-summer water demands. [ABSTRACT FROM AUTHOR]

Sea surface height (SSH) variability is presented over the Black Sea during 1993–2005. The 1/4° × 1/4° resolution daily SSH fields are formed using optimal interpolation of available altimeter data. SSH variability reveals distinct maxima in the eastern and western basins, reflecting variations in the corresponding gyres. A joint examination of SSH and sea surface temperature (SST) indicates strong relationship between the two only in winter, with correlations as high as 0.6 or more. This would reflect a steric change in sea surface height due to thermal expansion averaged over a relatively deep winter mixed layer. Newly developed SSH fields also demonstrate a switch to the positive mode of SSH starting from the end of 1996 lasting ≈4 yr. Such a climatic shift is found to be strongly related to large-scale teleconnection patterns. Finally, the daily SSH and SST anomaly fields presented in this paper can supplement various applications in the Black Sea, such as examination of biological production and mesoscale eddy dynamics. [ABSTRACT FROM AUTHOR]

A better understanding of the interannual variability in temperature and precipitation datasets used as forcing fields for hydrologic models will lead to a more complete description of hydrologic model uncertainty, in turn helping scientists study the larger goal of how the Arctic terrestrial systemis responding to global change. Accordingly, this paper investigates temporal and spatial variability in monthly mean (1992--2000) temperature and precipitation datasets over the Western Arctic Linkage Experiment (WALE) study region. The six temperature datasets include 1) the fifth-generation Pennsylvania State University--National Center for Atmospheric Research MesoscaleModel (MM5); 2) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); 3) the Advanced Polar Pathfinderall-sky temperatures (APP); 4) National Centers for Environmental Prediction--National Center for Atmospheric Research (NCEP--NCAR) reanalyses (NCEP1); 5) the Climatic Research Unit/University of East Anglia CRUTEM2v (CRU); and 6) the Matsuura and Wilmott 0.5° × 0.5° Global Surface Air Temperature and Precipitation (MW). Comparisons of monthly precipitation are examined for MM5, ERA-40, NCEP1, CRU, and MW. Results of the temporal analyses indicate significant differences between at least two datasets (for either temperature or precipitation) in almost every month. The largest number of significant differences for temperature occurs in October, when there are five separate groupings; for precipitation, there are four significantly different groupings from March through June, and again in December. Spatial analyses of June temperatures indicate that the greatest dissimilarity is concentrated in the central portion of the study region, with the NCEP1 and APP datasets showing the greatest differences. In comparison, the spatial analysis of June precipitation datasets suggests that the largest dissimilarity is concentrated in the eastern portion of the study region. These results indicate that the choice of forcing datasets likely will have a significant effecton the output from hydrologic models, and several different datasets should beused for a robust hydrologic assessment. [ABSTRACT FROM AUTHOR]

This paper explores the link between the anomalous warming hole in the southeastern United States and a major land-use/land-cover (LULC) change in the region. Land surface and satellite observations were analyzed to estimate the net radiative forcing due to LULC change. Albedo and latent energy were specifically addressed for the dominant LULC change of agriculture to forests. It was assumed that in the energy-limited environment of the region, the partition of changes in available energy due to albedo will mostly impact the sensible heat. The results show that in the southeastern United States, for the period of 1920 to 1992, the changes in sensible (as a result of albedo) and latent energies are in direct competition with each other. In the spring and early summer months, the croplands are in peak production and the latent energy associated with their evapotranspiration (ET) is comparable to that of the forests so the decrease in radiation due to albedo dominates the signal. However, during the late summer and fall months, most major crops have matured, thus reducing their transpiration rate while forests (particularly evergreens) maintain their foliage and with their deep roots are able to continue to transpire as long as atmospheric conditions are favorable. This later influence of latent energy appears to more than offset the increased radiative forcing from the spring and early summer. Overall, a mean annual net radiative forcing resulting from a LULC change from cropland to forests was estimated to be −1.06 W m−2 and thus a probable contribution to the 'warming hole' over the Southeast during the majority of the twentieth century. [ABSTRACT FROM AUTHOR]

In recent years, increasing attention has been paid to hydropower generation, since it is a renewable, efficient, and reliable source of energy, as well as an effective tool to reduce the atmospheric concentrations of greenhouse gases resulting from human activities. At the same time, however, hydropower is among the most vulnerable industries to global warming, because water resources are closely linked to climate changes. Indeed, the effects of climate change on water availability are expected to affect hydropower generation with special reference to southern countries, which are supposed to face dryer conditions in the next decades. The aim of this paper is to qualitatively assess the impact of future climate change on the hydrological regime of the Alcantara River basin, eastern Sicily (Italy), based on Monte Carlo simulations. Synthetic series of daily rainfall and temperature are generated, based on observed data, through a first-order Markov chain and an autoregressive moving average (ARMA) model, respectively, for the current scenario and two future scenarios at 2025. In particular, relative changes in the monthly mean and standard deviation values of daily rainfall and temperature at 2025, predicted by the Hadley Centre Coupled Model, version 3 (HadCM3) for A2 and B2 greenhouse gas emissions scenarios, are adopted to generate future values of precipitation and temperature. Synthetic series for the two climatic scenarios are then introduced as input into the Identification of Unit Hydrographs and Component Flows from Rainfall, Evapotranspiration and Streamflow Data (IHACRES) model to simulate the hydrological response of the basin. The effects of climate change are investigated by analyzing potential modification of the resulting flow duration curves and utilization curves, which allow a site's energy potential for the design of run-of-river hydropower plants to be estimated. [ABSTRACT FROM AUTHOR]

Air temperature and precipitation outputs from 10 CMIP6 GCMs were input to the Precipitation-Runoff Modeling System hydrologic model to evaluate water and energy responses in three headwater basins to projected climate change over the twenty-first century. The headwater basins (398–801 km2) are located within the Mataura River basin in the South Island of New Zealand. CMIP6 datasets included two emission scenarios [shared socioeconomic pathways (SSPs) SSP2-4.5 and SSP5-8.5]. Half of the 10 GCMs selected in the study have equilibrium climate sensitivity (ECS) values above 4.5°C, which has been considered the upper end of equilibrium climate sensitivity. Modeling results included increased annual air temperature, evapotranspiration, and precipitation by the end of the twenty-first century for both SSP emissions scenarios, both high- and low-ECS GCMs, and all three headwater basins. Monthly precipitation and evapotranspiration totals also increased for all or most months. Monthly streamflow changes generally corresponded with monthly precipitation changes. Snowpack decreased significantly in depth and seasonal duration in all basins. However, streamflow increased for all SSP and ECS groups and basins because increased precipitation was consistently greater than increased evapotranspiration losses. Sources of uncertainty include the GCMs, climate sensitivity, downscaling, bias adjustment, emission scenarios, and the hydrologic model. Simulated hydrologic responses based on climate data from GCMs with ECS values of greater than 4.5°C could be less plausible since previous studies have suggested true ECS ranges from 1.5° to 4.5°C. [ABSTRACT FROM AUTHOR]

Recent trends in precipitation and streamflow in the United States have become a particular focus of hydroclimatic research. The U.S. Hydro-Climatic Data Network (HCDN) has proven to be especially useful for the analysis of long-term streamflow trends. The U.S. Geological Survey (USGS) scientists selected sites for inclusion in the HCDN from the USGS stream-gauge network on the basis of streamflows being relatively free of nonclimatic anthropogenic influences. Consequently, most previous analyses of flow trends at those sites have either implicitly or explicitly attributed the trends to climate change and variability. In this paper, trends in seasonal and annual precipitation, and annual 7-day low, mean, and peak flows are examined for 48 medium-sized HCDN streams in the upper Mississippi (UM) water-resource region over 1939--2008. Using the concept of precipitation elasticity of flow, it is shown that the observed magnitudes of statistically significant increases in mean and low flows were up to a factor of 3 greater than expected from observed precipitation increases alone. Peak flows increased less than expected, and in the case of the Driftless Area at the center of the UM basin, decreased despite increased precipitation. It is proposed that the differences between expected and observed changes in streamflow can be explained by rural land-use changes in this principally agricultural region. [ABSTRACT FROM AUTHOR]

The variations of large-scale climatic controls and surface responses through the annual cycle during strong positive (El Niño) and negative (La Niña) phase ENSO events are analyzed to assess the within-year and among-year variations of climate anomalies. Data from the NCEP–NCAR reanalysis project are presented as small-multiple maps to illustrate the spatial and temporal variability in North American climate associated with extreme phases of ENSO. Temperature, mean sea level pressure, 500-mb geopotential heights, and 850-mb specific humidity have composite-anomaly patterns that exhibit the greatest degree of spatial and temporal coherence. In general, the composite-anomaly patterns for El Niño and La Niña events are of opposite sign, with stronger, more spatially coherent anomalies occurring during El Niño events than during La Niña events. However, the strength and coherency of the precipitation anomaly patterns are reduced in the interior intermountain west during both positive and negative phase of ENSO. The variations in precipitation anomalies are compared to the 500-mb omega and 850-mb specific humidity composite-anomaly patterns, which provide information on the controls of precipitation by large-scale vertical motions and moisture availability thus providing information on the specific mechanisms associated with precipitation variability during ENSO events. [ABSTRACT FROM AUTHOR]

Projected climatic warming has direct implications for future disturbance regimes, particularly fire-dominated ecosystems at high latitudes, where climate warming is expected to be most dramatic. It is important to ascertain the potential range of climate change impacts on terrestrial ecosystems, which is relevant to making projections of the response of the Earth system and to decisions by policymakers and land managers. Computer simulation models that explicitly model climate–fire relationships represent an important research tool for understanding and projecting future relationships. Retrospective model analyses of ecological models are important for evaluating how to effectively couple ecological models of fire dynamics with climate system models. This paper uses a transient landscape-level model of vegetation dynamics, Alaskan Frame-based Ecosystem Code (ALFRESCO), to evaluate the influence of different driving datasets of climate on simulation results. Our analysis included the use of climate data based on first-order weather station observations from the Climate Research Unit (CRU), a statistical reanalysis from the NCEP–NCAR reanalysis project (NCEP), and the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). Model simulations of annual area burned for Alaska and western Canada were compared to historical fire activity (1950–2000). ALFRESCO was only able to generate reasonable simulation results when driven by the CRU climate data. Simulations driven by the NCEP and MM5 climate data produced almost no annual area burned because of substantially colder and wetter growing seasons (May–September) in comparison with the CRU climate data. The results of this study identify the importance of conducting retrospective analyses prior to coupling ecological models of fire dynamics with climate system models. The authors’ suggestion is to develop coupling methodologies that involve the use of anomalies from future climate model simulations to alter the climate data of more trusted historical climate datasets. [ABSTRACT FROM AUTHOR]

Isoprene is emitted from vegetation to the atmosphere in significant quantities, and it plays an important role in the reactions that control tropospheric oxidant concentrations. As future climatic and land-cover changes occur, the spatial and temporal variations, as well as the magnitude of these biogenic isoprene emissions, are expected to change. This paper presents a study of the change in biogenic isoprene emissions that would result from both anthropogenic land-cover and climate-driven changes. Annual global isoprene emissions were estimated to be 522 Tg yr-1 under current climatological and land-cover conditions. When climate-driven land-cover changes are predicted, but climate does not change, total global emissions did not change significantly, although regional impacts were important. However, the use of future temperature and land-cover drivers to estimate isoprene emissions produced a global estimate of 889 Tg yr-1. Anthropogenic land-cover changes, such as urbanization and changes of natural vegetation to plantation forests, can also have substantial impacts on isoprene emissions (i.e., up to 717 Tg yr-1, a 37% increase, when land-cover changes but temperature remains at current-day values). The Model for Ozone and Related Tracers, version 2 (MOZART-2) was run with the different isoprene emission scenarios to simulate the potential changes in global atmospheric chemical composition. The simulated regional surface ozone concentrations changed as much as -9 to 55 ppbv under certain emission and climate scenarios. These results were used to evaluate changes in the ozone production chemistry under different emission scenarios. As expected, the impacts of changing isoprene emissions are regionally dependent, with large changes in China, the Amazon, and the United States. [ABSTRACT FROM AUTHOR]

Understory fires, which burn the floor of standing forests, are one of the most important types of forest impoverishment in the Amazon, especially during the severe droughts of El Niño–Southern Oscillation (ENSO) episodes. However, the authors are aware of no estimates of the areal extent of these fires for the Brazilian Amazon and, hence, of their contribution to Amazon carbon fluxes to the atmosphere. In this paper, the area of forest understory fires for the Brazilian Amazon region is calculated during an El Niño (1998) and a non–El Niño (1995) year based on forest fire scars mapped with satellite images for three locations in eastern and southern Amazonia, where deforestation is concentrated. The three study sites represented a gradient of both forest types and dry season severity. The burning scar maps were used to determine how the percentage of forest that burned varied with distance from agricultural clearings. These spatial functions were then applied to similar forest/climate combinations outside of the study sites to derive an initial estimate for the Brazilian Amazon. Ninety-one percent of the forest area that burned in the study sites was within the first kilometer of a clearing for the non-ENSO year and within the first four kilometers for the ENSO year. The area of forest burned by understory forest fire during the severe drought (ENSO) year (3.9 × 106 ha) was 13 times greater than the area burned during the average rainfall year (0.2 × 106 ha), and twice the area of annual deforestation. Dense forest was, proportionally, the forest type most affected by understory fires during the El Niño year, while understory fires were concentrated in transitional forests during the year of average rainfall. The estimate here of aboveground tree biomass killed by fire ranged from 0.049 to 0.329 Pg during the ENSO and from 0.003 to 0.021 Pg during the non-ENSO year. [ABSTRACT FROM AUTHOR]

Increase in global mean temperature and changes in rainfall amount, pattern, and distribution over the world are all indicative of climate change events. These changes alter the hydroclimatic condition of regions as well as the availability of water resources. In this study, the data generated by 14 general circulation models (GCMs) developed under the Special Report on Emissions Scenarios (SRES) A1B, A2, and B2 are downscaled and utilized to evaluate climate change impact on the hydroclimatic system of the Karaj River basin located in central Iran. The precipitation and temperature of the study region are downscaled using the change factor approach (CFA). The study analyzes future climate data, extreme changes of future climatic conditions of precipitation, and temperature. The Hydrologiska Byråns Vattenbalansavdelning (HBV) model developed by the Swedish Meteorological and Hydrological Institute (SMHI) is used to simulate streamflow under extreme climate change conditions. Two different sources of uncertainty are investigated in this study. First, the model parameters uncertainty is analyzed with the Monte Carlo procedure, and then different datasets of GCMs projection are investigated under the climate of the twentieth-century climate simulation (20C3M). Results show the GCMs projections range can almost capture the historical records during the 1980s through 2000 for the Karaj basin. By applying the HBV model, considerable range of streamflow changes in the future can be projected that will affect the operation scheme of Karaj Reservoir. In this study, the system dynamics (SD) modeling approach is used to simulate the system behavior through time in an integrated fashion and evaluate its overall reliability in supplying water. The results of this study show that the runoff will decrease in the future under the climate change impact. This will result in more than 50% decrease in reliability of the Karaj Reservoir system under the extreme conditions. As a result, this research predicts that the Karaj Reservoir system will face more than 50% decrease in its reliability under the extreme conditions. Consequently, meeting the increasing water demands would be difficult and application of demand management strategies will be unavoidable. [ABSTRACT FROM AUTHOR]

Potential evapotranspiration (PET), the maximum evapotranspiration rate under unlimited water supply, reflects the capacity for transpiration flow and plant primary production. Numerous models have been developed to quantify PET, but there are still large uncertainties in PET estimations. In this study, the authors conducted spatially explicit estimations of daily PET from 1981 to 2010 for eight different land-cover types on the Tibetan Plateau by applying three types of PET models including a combination model (Penman-Monteith), a radiation-based model (Priestley-Taylor), and a temperature-based model (Thornthwaite). This study found that the PET estimated by Thornthwaite model (PETT) was lower than those estimated by Priestley-Taylor (PETPT) and Penman-Monteith models (PETPM). Penman-Monteith model gave the highest estimates of PET on annual and daily scales. The mean annual PET for the whole plateau estimated by these three models varied from 675.1 to 700.5 mm yr−1, and daily PET varied from 1.33 to 1.92 mm day−1. The spatial pattern of PETT did not agree with the PETPT and PETPM, while the latter two agreed well with each other. Because of different model structures and dominant meteorological drivers, the interannual variability of PET varied significantly among the models. PETPT and PETPM showed a transition around 1993 since the dominant meteorological drivers were different before and after 1993. These disagreements among different models suggested that PET models with different algorithms should be used with caution. This study provided a validation to assist those undertaking PET estimations on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

A regional watershed model was developed for watersheds contributing to Long Island Sound, including the Connecticut River basin. The study region covers approximately 40 900 km2, extending from a moderate coastal climate zone in the south to a mountainous northern New England climate zone dominated by snowmelt in the north. The input data indicate that precipitation and temperature have been increasing for the last 46 years (1961-2006) across the region. Minimum temperature has increased more than maximum temperature over the same period (1961-2006). The model simulation indicates that there was an upward trend in groundwater recharge across most of the modeled region. However, trends in increasing precipitation and groundwater recharge are not significant at the 0.05 level if the drought of 1961-67 is removed from the time series. The trend in simulated snowfall is not significant across much of the region, although there is a significant downward trend in southeast Connecticut and in central Massachusetts. To simulate future trends, two input datasets, one assuming high carbon emissions and one assuming low carbon emissions, were developed from GCM forecasts. Under both of the carbon emission scenarios, simulations indicate that historical trends will continue, with increases in groundwater recharge over much of the region and substantial snowfall decreases across Massachusetts, Connecticut, southern Vermont, and southern New Hampshire. The increases in groundwater recharge and decreases in snowfall are most pronounced for the high emission scenario. [ABSTRACT FROM AUTHOR]

Spring snowpack is an important water resource in many river basins in the United States in areas where snowmelt comprises a large part of the annual runoff. Increasing temperatures will likely reduce snowpacks in the future, resulting in more winter runoff and less available water during the summer low-flow season. As part of the National Climate Change Modeling Project by the U.S. Geological Survey, distributed watershed-model output was analyzed to characterize areal extent and water-equivalent volumes of spring snowpack for a warming climate. The output from seven selected watershed models from the mountainous western United States and one model from coastal Maine in the northeastern United States shows a future of declining spring snowpack. Snow-cover area (SCA) and snow-water equivalent (SWE) were used to compare the spring snowpack for current conditions (2006) with three time periods in the future (2030, 2060, and 2090) using three Intergovernmental Panel on Climate Change (IPCC) emission scenarios published in the 2007 Special Report on Emission Scenarios (SRES): A2, B1, and A1B. Distributed SWE and SCA values were sorted into elevation zones in each basin. The change in spring snowpack over time was greater than the change among different emission scenarios, suggesting that, even for a globally reduced carbon emission scenario, large decreases in SWE are likely to occur. The SRES A2 scenario resulted in the greatest decrease in SWE for six of the basins, and the SRES B1 and A1B scenarios resulted in the greatest decrease in one basin each. [ABSTRACT FROM AUTHOR]

The U.S. Geological Survey Precipitation-Runoff Modeling System (PRMS) model was applied to basins in 14 different hydroclimatic regions to determine the sensitivity and variability of the freshwater resources of the United States in the face of current climate-change projections. Rather than attempting to choose a most likely scenario from the results of the Intergovernmental Panel on Climate Change, an ensemble of climate simulations from five models under three emissions scenarios each was used to drive the basin models. Climate-change scenarios were generated for PRMS by modifying historical precipitation and temperature inputs; mean monthly climate change was derived by calculating changes in mean climates from current to various future decades in the ensemble of climate projections. Empirical orthogonal functions (EOFs) were fitted to the PRMS model output driven by the ensemble of climate projections and provided a basis for randomly (but representatively) generating realizations of hydrologic response to future climates. For each realization, the 1.5-yr flood was calculated to represent a flow important for sediment transport and channel geomorphology. The empirical probability density function (pdf) of the 1.5-yr flood was estimated using the results across the realizations for each basin. Of the 14 basins studied, 9 showed clear temporal shifts in the pdfs of the 1.5-yr flood projected into the twenty-first century. In the western United States, where the annual peak discharges are heavily influenced by snowmelt, three basins show at least a 10%% increase in the 1.5-yr flood in the twenty-first century; the remaining two basins demonstrate increases in the 1.5-yr flood, but the temporal shifts in the pdfs and the percent changes are not as distinct. Four basins in the eastern Rockies/central United States show at least a 10%% decrease in the 1.5-yr flood; the remaining two basins demonstrate decreases in the 1.5-yr flood, but the temporal shifts in the pdfs and the percent changes are not as distinct. Two basins in the eastern United States show at least a 10%% decrease in the 1.5-yr flood; the remaining basin shows little or no change in the 1.5-yr flood. [ABSTRACT FROM AUTHOR]

Daily data from climate models submitted to the Fourth Assessment of the Intergovernmental Panel on Climate Change are compared with daily data from observations over Australia by measuring the overlap of the probability density functions (PDFs). The capacity of these models to simulate maximum temperature, minimum temperature, and precipitation is assessed. The resulting skill score is then used to exclude models with relatively poor skill region by region over Australia. The remaining sample of coupled climate models is then used to determine the seasonal changes in these three variables under a high- (A2) and low- (B1) emission scenario for 2050 and 2100. The authors demonstrate that some projected phenomena, such as the projected drying over southwest Western Australia, are robust and not caused by the inclusion of some weak models in earlier assessments. Some other results, such as the projected change in the monsoon, are more consistent among the good climate models. Consistent with earlier work, a consistent pattern of mean warming is identified in the projections. The amount of warming in the 99.7th percentile is not dramatically higher than the warming in the mean. However, while the mean warming is generally least in the south, the amount of warming in the 99.7th percentile is substantially higher along the southern coast of Australia. This is due to a coupling of the temperature response with reduced rainfall, which causes drying and allows extreme maximum temperatures to increase dramatically. The authors show that, in general, the amount of rainfall is projected to change relatively little, but the frequency of rainfall decreases and the intensity of rainfall at the upper tail of the distribution increases. However, the scale of the increase in extreme rainfall is not large on the time scales analyzed here. The range in projected temperature changes among those climate models with skill in simulating the observations is at least twice as large for the 99.7th/0.3rd percentiles as for the mean. For rainfall, the range among the good models is of order 10 times greater in the 99.7th percentile than in the mean. Since the impact of changes in extremes is increasingly recognized as societally important, this result strongly limits the use of climate model data to explore sectors that are vulnerable to extremes. This suggests an evaluation strategy that focuses on model capacity to simulate whole PDFs since capacity to simulate the mean is a necessary but insufficient criterion for determining a model’s value for future projection. [ABSTRACT FROM AUTHOR]

Complex environmental gradients in the White and Inyo Mountains in eastern California produce striking variations in vegetation assemblages over short distances. Vegetation composition is dominated by elevational gradients of temperature and precipitation, but local modifications by geologic substrate, potential insolation, slope, and topographic position create finescale mosaics. Digital elevation models, geologic maps, and field data were used to map current species distributions over 6220 km2 (622 000 ha) of the White and Inyo Mountains. Species–environment relationships of 88 plant species were modeled at a scale of 54 m using canonical correspondence analysis (CCA). CCA models were calibrated from 434 field plots and evaluated with 216 plots using kappa statistics. Vegetation responses to temperature increases of 1°–6°C were modeled by shifting species tolerances along the elevational gradient according to a standard lapse rate [3°C (500 m)-1] while all other factors were kept constant. Ranges of midelevations species tended to fragment onto local peaks, whereas the ranges of many desert species merged across a major pass. In several cases, local geologic features were identified as obstacles to species’ upslope migration. As modeled temperatures increase, species contract to small populations around White Mountain Peak (4342 m) and its north-facing slopes. It is predicted that 10 of 18 modeled alpine and subalpine species will become locally extinct if temperatures increase by 6°C. These scenarios provide a detailed set of hypotheses on the structure of current species ranges and their ability to persist through rapid climate change. [ABSTRACT FROM AUTHOR]

Accurate estimates of the spatial and temporal variation in terrestrial water and energy fluxes and mean states are important for simulating regional hydrology and biogeochemistry in high-latitude regions. Furthermore, it is necessary to develop high-resolution hydroclimatological datasets at finer spatial resolutions than are currently available from global analyses. This study uses a regional climate model (RCM) to develop a hydroclimatological dataset for hydrologic and ecological application in the Western Arctic. The fifth-generation Penn State–NCAR Mesoscale Model (MM5) forced by global reanalysis products at the boundaries is used to perform 12 yr of simulation (1990 through 2001) over the Western Arctic. An analysis that compares the RCM simulations with independent observationally derived data sources is conducted to evaluate the temporal and spatial distribution of the mean states, variability, and trends during the period of simulation. The RCM simulation of sea level pressure agrees well with the reanalysis in terms of mean states, seasonality, and interannual variability. The RCM also simulates major spatial patterns of the observed climatology of surface air temperature (SAT), but RCM SAT is generally colder in the summertime and warmer in the wintertime in comparison with other datasets. Although there are biases in the mean state of SAT, the RCM simulations of the seasonal and interannual variability of SAT are similar to variability in observationally derived datasets. The RCM also simulates general spatial patterns of observed rainfall, but the modeled mean state of precipitation is characterized by large biases relative to observationally derived datasets. In particular, the RCM tends to overestimate coastal region precipitation but underestimates precipitation in the interior of the Western Arctic. The Arctic terrestrial surface climate trends for the period of 1992 to 2001 of the RCM are similar to those derived from observations, with sea level pressure decreasing 0.15 hPa decade-1, SAT increasing 0.10°C decade-1, and precipitation decreasing slightly in the RCM simulations. In summary, the RCM dataset produced in this study represents an improvement over data currently available from large-scale global reanalysis and provides a consistent meteorological forcing dataset for hydrologic and ecological applications. [ABSTRACT FROM AUTHOR]

The potential role of the impacts of land-cover changes (LCCs) in the Australian climate is investigated within the context of increasing CO2 concentrations and temperature. Specifically, it is explored if possible scenarios for LCC can moderate or amplify CO2-induced changes in climate over Australia. The January climate of Australia is simulated under three different land-cover-change scenarios using a high-resolution regional climate model. The land-cover-change scenarios include a steady-state land cover that is equivalent to current land cover, a low-reforestation scenario that recovers approximately 25% of the trees replaced by grasslands within the last 200 yr, and a high-reforestation scenario that recovers at least 75% of the deforested regions. The model was driven by boundary conditions taken from transitory climate simulations from a general circulation model that included two climate scenarios based on two projected scenarios of CO2 concentration increase. The results show that reforestation has the potential to reduce the projected increase in Australian temperatures in 2050 and 2100 by as much as 40% and 20%, respectively. This cooling effect, however, is highly localized and occurs only in regions of reforestation. The results therefore hint that the potential of reforestation to moderate the impact of global warming may be significantly limited by the spatial scale of reforestation. In terms of deforestation, results show that any future land clearing can exacerbate the projected warming in certain regions of Australia. Carbon-related variables are also analyzed and results show that changes in net CO2 flux may be influenced more by soil respiration than by photosynthesis. The results herein encourage studies on the inclusion of land-cover-change scenarios in future climate change projection simulations of the Australian climate. [ABSTRACT FROM AUTHOR]

Based upon trends in observed climate, extreme events are thought to be increasing in frequency and/or magnitude. This change in extreme events is attributed to enhancement of the hydrologic cycle caused by increased greenhouse gas concentrations. Results are presented of relatively long (50 yr) regional climate model simulations of the western United States examining the sensitivity of climate and extreme events to a doubling of preindustrial atmospheric CO2 concentrations. These results indicate a shift in the temperature distribution, resulting in fewer cold days and more hot days; the largest changes occur at high elevations. The rainfall distribution is also affected; total rain increases as a result of increases in rainfall during the spring season and at higher elevations. The risk of flooding is generally increased, as is the severity of droughts and heat waves. These results, combined with results of decreased snowpack and increased evaporation, could further stress the water supply of the western United States. [ABSTRACT FROM AUTHOR]

Changes in Earth’s climate in response to atmospheric greenhouse gas buildup impact the health of terrestrial ecosystems and the hydrologic cycle. The environmental conditions influential to plant and animal life are often mapped as ecoregions, which are land areas having similar combinations of environmental characteristics. This idea is extended to establish regions of similarity with respect to climatic characteristics that evolve through time using a quantitative statistical clustering technique called Multivariate Spatio-Temporal Clustering (MSTC). MSTC was applied to the monthly time series output from a fully coupled general circulation model (GCM) called the Parallel Climate Model (PCM). Results from an ensemble of five 99-yr Business-As-Usual (BAU) transient simulations from 2000 to 2098 were analyzed. MSTC establishes an exhaustive set of recurring climate regimes that form a “skeleton” through the “observations” (model output) throughout the occupied portion of the climate phase space formed by the characteristics being considered. MSTC facilitates direct comparison of ensemble members and ensemble and temporal averages since the derived climate regimes provide a basis for comparison. Moreover, by mapping all land cells to discrete climate states, the dynamic behavior of any part of the system can be studied by its time-varying sequence of climate state occupancy. MSTC is a powerful tool for model developers and environmental decision makers who wish to understand long, complex time series predictions of models. Strong predicted interannual trends were revealed in this analysis, including an increase in global desertification; a decrease in the cold, dry high-latitude conditions typical of North American and Asian winters; and significant warming in Antarctica and western Greenland. [ABSTRACT FROM AUTHOR]

Land-use land-cover change (LULCC) plays an important role in weather and climate systems. Human modifications of land cover include building reservoirs and thus creating artificial lakes for multipurpose use. In this research, the authors have completed a Weather Research and Forecasting (WRF) Model-based assessment of impacts of two large parallel lakes on precipitation. This area is located in the western part of the states of Kentucky and Tennessee and known as the Land between the Lakes (LBL). To determine the impacts, this study has replaced the lakes with grass, deciduous forests, and bare soil and conducted model simulations for three precipitation events of different magnitudes. The analysis suggests that precipitation increased in some cases and reduced in others. One of the key impacts of LULCC in the LBL area is the relocation of precipitation cells and also the timing of precipitation. Local precipitation amounts increased or decreased with these relocations. In summary, establishment of lakes or replacement of lakes with alternate land cover may modify local precipitation in the LBL area. [ABSTRACT FROM AUTHOR]

Lakes have been suggested as an indicator of climate change; however, long-term, systematic records of lake temperature are limited. Satellite remote sensing is capable of supporting lake temperature mapping with the advantage of large-area and systematic observations. The goal of this research application was to assess spatiotemporal trends in lake skin temperature for all lakes over 8 ha across northern New England for the past three decades. Nearly 10 000 Landsat scenes for July, August, and September from 1984 to 2014 were processed using MODTRAN and MERRA parameterizations to generate atmospherically corrected lake skin temperature records. Results show, on average, lakes warmed at a rate of 0.8°C decade-1, with smaller lakes warming at a faster rate. Complementing regression and space--time analyses showed similar results (R² = 0.63) for lake temperature trends and found lakes, on average, are warming faster than daily maximumorminimum air temperature. Nomajor hot spots were found as lake temperature changes were heterogeneous on a local scale and evenly distributed across the region. Maximum and minimum daily temperature, lake size, and elevation were found as significant drivers of lake temperature. This effort provides the first regionally focused and comprehensive spatiotemporal assessment of thousands (n = 3955) of lakes concentrated in one geographic region. The approach is scalable and adaptable to any region for assessing lake temperature trends and potential drivers. [ABSTRACT FROM AUTHOR]

Simulations of future climate change impacts on water resources are subject to multiple and cascading uncertainties associated with different modeling and methodological choices. A key facet of this uncertainty is the coarse spatial resolution of GCM output compared to the finer-resolution information needed by water managers. To address this issue, it is now common practice to apply spatial downscaling techniques, using either higher-resolution regional climate models or statistical approaches applied to GCM output, to develop finer-resolution information. Downscaling, however, can also introduce its own uncertainties into water resources' impact assessments. This study uses watershed simulations in five U.S. basins to quantify the sources of variability in streamflow, nitrogen, phosphorus, and sediment loads associated with the underlying GCM compared to the choice of downscaling method (both statistically and dynamically downscaled GCM output). This study also assesses the specific, incremental effects of downscaling by comparing watershed simulations based on downscaled and nondownscaled GCM model output. Results show that the underlying GCM and the downscaling method each contribute to the variability of simulated watershed responses. The relative contribution of GCM and downscaling method to the variability of simulated responses varies by watershed and season of the year. Results illustrate the potential implications of one key methodological choice in conducting climate change impact assessments for water-the selection of downscaled climate change information. [ABSTRACT FROM AUTHOR]

Human-induced land-use change (LUC) alters the biogeophysical characteristics of the land surface influencing the surface energy balance. The level of atmospheric CO2 is expected to increase in the coming century and beyond, modifying temperature and precipitation patterns and altering the distribution and physiology of natural vegetation. It is important to constrain how CO2-induced climate and vegetation change may influence the regional extent to which LUC alters climate. This sensitivity study uses the HadCM3 coupled climate model under a range of equilibrium forcings to show that the impact of LUC declines under increasing atmospheric CO2, specifically in temperate and boreal regions. A surface energy balance analysis is used to diagnose how these changes occur. In Northern Hemisphere winter this pattern is attributed in part to the decline in winter snow cover and in the summer due to a reduction in latent cooling with higher levels of CO2. The CO2-induced change in natural vegetation distribution is also shown to play a significant role. Simulations run at elevated CO2, yet present-day vegetation show a significantly increased sensitivity to LUC, driven in part by an increase in latent cooling. This study shows that modeling the impact of LUC needs to accurately simulate CO2-driven changes in precipitation and snowfall and incorporate accurate, dynamic vegetation distribution. [ABSTRACT FROM AUTHOR]

Previous studies have shown strong negative correlation between multidecadal signatures in length of day (LOD)-an inverse measure of Earth's rotational rate-and various climate indices. Mechanisms remain elusive. Climate processes are insufficient to explain observed rotational variability, leading many to hypothesize external (astronomical) forcing as a common source for observed low-frequency signatures. Here, an internal source, a core-to-climate, one-way chain of causality, is hypothesized. To test hypothesis feasibility, a recently published, model-estimated forced component is removed from an observed dataset of Northern Hemisphere (NH) surface temperatures to isolate the intrinsic component of climate variability, enhancing its comparison with LOD. To further explore the rotational connection to climate indices, the LOD anomaly record is compared with sea surface temperatures (SSTs)-global and regional. Because climate variability is most intensely expressed in the North Atlantic sector, LOD is compared to the dominant oceanic pattern there-the Atlantic multidecadal oscillation (AMO). Results reveal that the LOD-related signal is more global than regional, being greater in the global SST record than in the AMO or in global-mean (land + ocean) or land-only surface temperatures. Furthermore, the strong (4σ) correlation of LOD with the estimated NH intrinsic component is consistent with the view proffered here, one of an internally generated, core-to-climate process imprinted on both the climate and Earth's rotational rate. While the exact mechanism is not elucidated by this study's results, reported correlations of geomagnetic and volcanic activity with LOD offer prospects to explain observations in the context of a core-to-climate chain of causality. [ABSTRACT FROM AUTHOR]

Measurements of air temperature and precipitation at 35 stations in Hubei Province, China, during 1962-2011 are used to investigate the regional climate change. There is an increasing trend for observed air temperature (0.23°C decade−1), which is slightly higher than that from multiple model simulations/predictions [phase 5 of CMIP (CMIP5) datasets] (0.16°C decade−1). The observed precipitation increases at the rate of 11.4 mm decade−1, while the CMIP5 results indicate a much lower decreasing trend (0.8 mm decade−1) in this region. To examine the ecological responses to the climate changes in Hubei Province, annual gross primary productivity (GPP) and net primary productivity (NPP) products during 2000-10 and leaf area index (LAI) products during 1981-2011 are also analyzed. It is discovered that GPP, NPP, and LAI increase at the rate of 1.8 TgC yr−1 yr−1, 1.1 TgC yr−1 yr−1, and 0.14 m2 m−2 decade−1, respectively. A linear model is further used to conduct the correlation analyses between climatic parameters (i.e., air temperature and precipitation) and ecological indicators (i.e., GPP, NPP, and LAI). The results indicate that the air temperature has a significant positive correlation with LAI ( R2 = 0.311) and GPP ( R2 = 0.189); precipitation is positively correlated with NPP ( R2 = 0.209). Thus, it is concluded that the air temperature exerts a stronger effect on the ecosystem than precipitation in Hubei Province over the past decades. [ABSTRACT FROM AUTHOR]

While estimates of the impact of climate change on health are necessary for health care planners and climate change policy makers, models to produce quantitative estimates remain scarce. This study describes a freely available dynamic simulation model parameterized for three West Nile virus vectors, which provides an effective tool for studying vectorborne disease risk due to climate change. The Dynamic Mosquito Simulation Model is parameterized with species-specific temperature-dependent development and mortality rates. Using downscaled daily weather data, this study estimates mosquito population dynamics under current and projected future climate scenarios for multiple locations across the country. Trends in mosquito abundance were variable by location; however, an extension of the vector activity periods, and by extension disease risk, was almost uniformly observed. Importantly, midsummer decreases in abundance may be offset by shorter extrinsic incubation periods, resulting in a greater proportion of infective mosquitoes. Quantitative descriptions of the effect of temperature on the virus and mosquito are critical to developing models of future disease risk. [ABSTRACT FROM AUTHOR]

This study reconsiders the role of the Agulhas Current in South African climate variability. Here, the Agulhas Current is delimited by its anticyclonic looping flow and cluster analysis of detrended SST anomalies that lead to an area 28°-37°S, 18°-35°E, poleward of South Africa. Regression of detrended Agulhas SST with rainfall anomaly fields in the years 1950-2012 yields a surprising negative influence over the interior. In summer, the negative regression exhibits a northwest axis consistent with reduced cloud band activity. Positive influence is confined to the eastern escarpment in the September-November season when cutoff lows are prevalent. The overall negative influence of the Agulhas SST is confirmed by regression with the vegetation fraction and latent heat flux in the satellite era. Mechanisms of South African rainfall suppression were investigated. The Agulhas SST index is positively related to the multivariate ENSO index at the 1-3-month lead time. Hence, warm years in the Agulhas Current follow Pacific El Niño. Composite ocean analysis shows enhanced westerly winds offshore and a westward extension of warm salty water from the anticyclonic south Indian Ocean gyre. Composite atmospheric analysis exhibits moist uplifted air over the Agulhas Current folding into an equatorward circulation that sinks over the interior plateau. Because Agulhas SST partially follows ENSO, its suppression of interior rainfall is concluded to be passive. [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO) and Arctic Oscillation (AO) climatology (1980-2010) is developed and analyzed across the U.S. Corn Belt using state climate division weather and historic corn yield data using analysis of variance (ANOVA) and correlation analysis. Findings provide insight to agroclimatic conditions under different ENSO and AO episodes and are analyzed with a perspective for potential impacts to agricultural production and planning, with findings being developed into a web-based tool for the U.S. Corn Belt. This study is unique in that it utilizes the oceanic Niño index and explores two teleconnection patterns that influence weather across different spatiotemporal scales. It is found that the AO has a more frequent weak to moderate correlation to historic yields than ENSO when correlated by average subgrowing season index values. Yield anomaly and ENSO and AO episode analysis affirms the overall positive impact of El Niño events on yields compared to La Niña events, with neutral ENSO events in between as found in previous studies. Yields when binned by the AO episode present more uncertainty. While significant temperature and precipitation impacts from ENSO and AO are felt outside of the primary growing season, correlation between threshold variables of episode-specific temperature and precipitation and historic yields suggests that relationships between ENSO and AO and yield are present during specific months of the growing season, particularly August. Overall, spatial climatic variability resulting from ENSO and AO episodes contributes to yield potential at regional to subregional scales, making generalization of impacts difficult and highlighting a continued need for finescale resolution analysis of ENSO and AO signal impacts on corn production. [ABSTRACT FROM AUTHOR]

As is true of many tropical regions, northeastern Puerto Rico is an ecologically sensitive area with biological life that is highly elevation dependent on precipitation and temperature. Climate change has the potential to increase the risk of losing endemic species and habitats. Consequently, it is important to explore the pattern of trends in precipitation and temperature along an elevation gradient. Statistical derivatives of a frequently sampled dataset of precipitation and temperature at 20 sites along an elevation gradient of 1000 m in northeastern Puerto Rico were examined for trends from 2001 to 2013 with nonparametric methods accounting for annual periodic variations such as yearly weather cycles. Overall daily precipitation had an increasing trend of around 0.1 mm day−1 yr−1. The driest months of the annual dry, early, and late rainfall seasons showed a small increasing trend in the precipitation (around 0.1 mm day−1 yr−1). There was strong evidence that precipitation in the driest months of each rainfall season increased faster at higher elevations (0.02 mm day−1 more increase for 100-m elevation gain) and some evidence for the same pattern in precipitation in all months of the year but at half the rate. Temperature had a positive trend in the daily minimum (around 0.02°C yr−1) and a negative trend in the daily maximum whose size is likely an order of magnitude larger than the size of the daily minimum trend. Physical mechanisms behind the trends may be related to climate change; longer-term studies will need to be undertaken in order to assess the future climatic trajectory of tropical forests. [ABSTRACT FROM AUTHOR]

This study develops a new conceptual tool to explore the potential societal consequences of climate change. The conceptual tool delineates three quasi-independent factors that contribute to the societal consequences of climate change: how climate changes; the sensitivity of physical systems, biological resources, and social institutions to climate change; and the degree of human dependence on those systems, resources, and institutions. This conceptual tool, as currently developed, is not predictive, but it enables the exploration of the dependence of climate change risks on key contributing factors. In exploring a range of plausible behaviors for these factors and methods for their synthesis, the authors show that plausible assumptions lead to a wide range in potential societal consequences of climate change. This illustrates that the societal consequences of climate change are currently difficult to constrain and that high-consequence climate change outcomes are not necessarily low probability, as suggested by leading economic analyses. With careful implementation, this new conceptual tool has potential to increase public understanding of climate change risks, to support risk management decision making, or to facilitate communication of climate risks across disciplinary boundaries. [ABSTRACT FROM AUTHOR]

This study uses empirical models to examine the potential impact of climate change, based on a range of 100-yr phase 5 of the Coupled Model Intercomparison Project (CMIP5) projections, on crop water need in Jamaica. As expected, crop water need increases with rising temperature and decreasing precipitation, especially in May-July. Comparing the temperature and precipitation impacts on crop water need indicates that the 25th percentile of CMIP5 temperature change (moderate warming) yields a larger crop water deficit than the 75th percentile of CMIP5 precipitation change (wet winter and dry summer), but the 25th percentile of CMIP5 precipitation change (substantial drying) dominates the 75th percentile of CMIP5 temperature change (extreme warming). Over the annual cycle, the warming contributes to larger crop water deficits from November to April, while the drying has a greater influence from May to October. All experiments decrease crop suitability, with the largest impact from March to August. [ABSTRACT FROM AUTHOR]

The decrease in size the Aral Sea in central Asia, seen as both lower water levels and reduction in areal extent, has been one of the greatest examples of anthropogenic modification of a natural system in recent history. Many studies have monitored the extent and rate of this water loss and provided estimates on the expected life span of the remaining water. However, with little data for groundwater monitoring in the post-Soviet era, it is unclear what the water balance currently is in the remainder of the watershed. Redistribution of water upstream in the watershed including damming to create reservoirs and groundwater recharge from irrigation has not only deprived the sea of water but also increased evapotranspiration and altered local climate patterns. Using Tropical Rainfall Measurement Mission (TRMM) and Global Precipitation Climatology Centre (GPCC) data, rainfall trends for the Aral Sea watershed were analyzed over 10- and 30-yr periods and only minimal changes in rainfall were detected. Using Gravity Recovery and Climate Experiment (GRACE) gravity data from 2003 to 2012, trends in equivalent water mass were determined for the entire watershed. Estimates show up to 14 km3 of equivalent water mass has been lost from the watershed annually from 2002 to 2013. The mass loss throughout the basin is most likely attributable to increased evapotranspiration due to the inefficient irrigation systems and other human modification increasing the need for international cooperation and conservation programs to minimize negative impacts throughout the region. [ABSTRACT FROM AUTHOR]

Singular value decomposition is used to identify the common variability in first leaf dates (FLDs) and 1 April snow water equivalent (SWE) for the western United States during the period 1900-2012. Results indicate two modes of joint variability that explain 57% of the variability in FLD and 69% of the variability in SWE. The first mode of joint variability is related to widespread late winter-spring warming or cooling across the entire west. The second mode can be described as a north-south dipole in temperature for FLD, as well as in cool season temperature and precipitation for SWE, that is closely correlated to the El Niño-Southern Oscillation. Additionally, both modes of variability indicate a relation with the Pacific-North American atmospheric pattern. These results indicate that there is a substantial amount of common variance in FLD and SWE that is related to large-scale modes of climate variability. [ABSTRACT FROM AUTHOR]

Climate change due to anthropogenic greenhouse gases (GHG) is expected to have important impacts on water resources, with a variety of societal impacts. Recent research has shown that applying different methodologies to assess hydrologic impacts can lead to widely diverging projections of water resources. The authors classify methods of projecting hydrologic impacts of climate change into those that estimate potential evapotranspiration (PET) based on air temperature and those that estimate PET based on components of the surface energy budget. In general, air temperature-based methods more frequently show reductions in measures of water resources (e.g., water yield or soil moisture) and greater sensitivity than those using energy budget-based methods. There are significant trade-offs between these two methods in terms of ease of use, input data required, applicability to specific locales, and adherence to fundamental physical constraints: namely, conservation of energy at the surface. Issues of uncertainty in climate projections, stemming from imperfectly known future atmospheric GHG concentrations and disagreement in projections of the resultant climate, are compounded by questions of methodology and input data availability for models that connect climate change to accompanying changes in hydrology. In the joint atmospheric-hydrologic research community investigating climate change, methods need to be developed in which the energy and moisture budgets remain consistent when considering their interaction with both the atmosphere and water resources. This approach should yield better results for both atmospheric and hydrologic processes. [ABSTRACT FROM AUTHOR]

Prolonged droughts and uneven monsoons have adversely affected socioeconomic and environmental conditions of Pakistan, especially of the Punjab province. Analysis of historical (1981-2010) daily minimum and maximum temperatures from five cities in semiarid Punjab, Pakistan, was carried out to evaluate spatial and temporal patterns in thermal regimes. A total of 13 climate change indices were calculated using daily minimum and maximum temperatures and analyzed for trend using RClimDex, a program written in the statistical software package R. A nonparametric Mann-Kendall test and Sen's slope estimates were used to determine the statistical significance and magnitude of a trend, respectively. Observed trends in selected indices during 1981-2010 suggest an overall warming in the region. Over the analysis period, the regionally averaged occurrence of extreme cold (10th percentile) nights and days has decreased by 23.94 nights per decade and 20.61 days per decade, respectively. Occurrence of extreme hot (90th percentile) nights and days has increased by 4.19 nights per decade and 0.92 days per decade, respectively. The number of summer days has increased by almost 3 days per decade on average at four out of the five cities. Multan was the only city where the number of summer days has declined by 5 days per decade. Regionally averaged increase in tropical nights was 8.35 nights per decade. Regional warming will dictate increased crop water requirements in this semiarid region agriculture, which is already under water-scarce conditions, especially in the Faisalabad district, where saline groundwater is not suitable for crops. [ABSTRACT FROM AUTHOR]

Net primary productivity (NPP) is an important component of the carbon cycle and a key indicator of ecosystem performance. The aim of this study is to construct a more accurate regional vegetation NPP estimation model and explore the relationship between NPP and climatic factors (air temperature, rainfall, sunshine hours, relative humidity, air pressure, global radiation, and surface net radiation). As a key variable in NPP modeling, photosynthetically active radiation (PAR) was obtained by finding a linear relationship between PAR and horizontal direct radiation, scattered radiation, and net radiation with high accuracy. The fraction of absorbed photosynthetically active radiation (FPAR) was estimated by enhanced vegetation index (EVI) instead of the widely used normalized difference vegetation index (NDVI). Stress factors of temperature/humidity for different types of vegetation were also considered in the simulation of light use efficiencies (LUE). The authors used EVI datasets of Moderate Resolution Imaging Spectroradiometer (MODIS) from 2001 to 2011 and geographic information techniques to reveal NPP variations in Wuhan. Time lagged serial correlation analysis was employed to study the delayed and continuous effects of climatic factors on NPP. The results showed that the authors' improved model can simulate vegetation NPP in Wuhan effectively, and it may be adopted or used in other regions of the world that need to be further tested. The results indicated that air temperature and air pressure contributed significantly to the interannual changes of plant NPP while rainfall and global radiation were major climatic factors influencing seasonal NPP variations. A significant positive 32-day lagged correlation was observed between seasonal variation of NPP and rainfall (P , 0.01); the influence of changing climate on NPP lasted for 64 days. The impact of air pressure, global radiation, and net radiation on NPP persisted for 48 days, while the effects of sunshine hours and air temperature on NPP only lasted for 16 and 32 days, respectively. [ABSTRACT FROM AUTHOR]

Large-scale environmental, social, and economic impacts of recent weather and climate extremes are raising questions about whether the frequency and intensity of these extremes have been increasing. Here, the authors evaluate trends in climate extremes during the past half century using the U.S. High Plains as a case study. A total of eight different extreme indices and the standardized precipitation index (SPI) were evaluated using daily maximum and minimum temperature and precipitation data from 207 stations and 0.258 gridded data. The 1958-2010 time period was selected to exclude the 1950s and 2011 droughts. Results show general consistency between the station data and gridded data. The annual extreme temperature range (ETR) decreased significantly (p , 0.05) in ;54% of the High Plains, with a spatial mean rate of 20.78C decade21. Decreases in ETR result primarily from increases in annual lowest temperature in ;63% of the stations at a mean rate of ~0.9°C decade21, whereas increases in annual highest temperature were much less. Approximately 43% of the stations showed increasing warm nights (Tmin90) with a spatial mean rate of 0.5% decade21. Precipitation intensity generally did not vary significantly in most grid cells and stations. Significant decreasing trends in consecutive dry days (CDD) were restricted to 21% of the stations in the northern High Plains with a spatial mean of 20.8 days decade21. Areas experiencing severe dry periods (1-month SPI , 21.5) decreased over time from 8% to 4%. The number of dry months (SPI , 0) in each year also decreased. In summary, the ETR is decreasing and low temperatures are increasing. Precipitation extremes are generally not changing in the High Plains; however, high natural climatic variability in this semiarid region makes it difficult to assess climate extremes. [ABSTRACT FROM AUTHOR]

The Amazon rain forest may undergo significant change in response to future climate change. To determine the likelihood and causes of such changes, the authors analyzed the output of 24 models from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) and a dynamic vegetation model, Vegetation-Global-Atmosphere-Soil (VEGAS), driven by these climate output. Their results suggest that the core of the Amazon rain forest should remain largely stable because rainfall in the core of the basin is projected to increase in nearly all models. However, the periphery, notably the southern edge of Amazonia and farther south into central Brazil (SAB), is in danger of drying out, driven by two main processes. First, a decline in precipitation of 11%% during the southern Amazonia's dry season (May-September) reduces soil moisture. Two dynamical mechanisms may explain the forecast reduction in dry season rainfall: 1) a general subtropical drying under global warming when the dry season southern Amazon basin is under the control of subtropical high pressure and 2) a stronger north-south tropical Atlantic sea surface temperature gradient and, to a lesser degree, a warmer eastern equatorial Pacific. The drying corresponds to a lengthening of the dry season by approximately 10 days. The decline in soil moisture occurs despite an increase in precipitation during the wet season, because of nonlinear responses in hydrology associated with the decline in dry season precipitation, ecosystem dynamics, and an increase in evaporative demand due to the general warming. In terms of ecosystem response, higher maintenance cost and reduced productivity under warming may also have additional adverse impact. Although the IPCC models have substantial intermodel variation in precipitation change, these latter two hydroecological effects are highly robust because of the general warming simulated by all models. As a result, when forced by these climate projections, a dynamic vegetation model VEGAS projects an enhancement of fire risk by 20%%-30%% in the SAB region. Fire danger reaches its peak in Amazonia during the dry season, and this danger is expected to increase primarily because of the reduction in soil moisture and the decrease in dry season rainfall. VEGAS also projects a reduction of about 0.77 in leaf area index (LAI) over the SAB region. The vegetation response may be partially mediated by the CO2 fertilization effect, because a sensitivity experiment without CO2 fertilization shows a higher 0.89 decrease in LAI. Southern Amazonia is currently under intense human influence as a result of deforestation and land-use change. Should this direct human impact continue at present rates, added pressure to the region's ecosystems from climate change may subject the region to profound changes in the twenty-first century. [ABSTRACT FROM AUTHOR]

Understanding the effects of climate change on the vegetative growing season is key to quantifying future hydrologic water budget conditions. The U.S. Geological Survey modeled changes in future growing season length at 14 basins across 11 states. Simulations for each basin were generated using five general circulation models with three emission scenarios as inputs to the Precipitation-Runoff Modeling System (PRMS). PRMS is a deterministic, distributed-parameter, watershed model developed to simulate the effects of various combinations of precipitation, climate, and land use on watershed response. PRMS was modified to include a growing season calculation in this study. The growing season was examined for trends in the total length (annual), as well as changes in the timing of onset (spring) and the end (fall) of the growing season. The results showed an increase in the annual growing season length in all 14 basins, averaging 27-47 days for the three emission scenarios. The change in the spring and fall growing season onset and end varied across the 14 basins, with larger increases in the total length of the growing season occurring in the mountainous regions and smaller increases occurring in the Midwest, Northeast, and Southeast regions. The Clear Creek basin, 1 of the 14 basins in this study, was evaluated to examine the growing season length determined by emission scenario, as compared to a growing season length fixed baseline condition. The Clear Creek basin showed substantial variation in hydrologic responses, including streamflow, as a result of growing season length determined by emission scenario. [ABSTRACT FROM AUTHOR]

The impacts of changing land cover on the soil-vegetation-atmosphere system are numerous. With the fraction of land used for farming and grazing expected to increase, extensive alterations to land cover such as replacing forests with cropland will continue. Therefore, quantifying the impact of global land-cover scenarios on the biosphere is critical. The Predicting Ecosystem Goods and Services Using Scenarios boundary layer (PegBL) model is a new global soil-vegetation-boundary layer model designed to quantify these impacts and act as a complementary tool to computationally expensive general circulation models and large-eddy simulations. PegBL provides high spatial resolution and inexpensive first-order estimates of land-cover change on the surface energy balance and atmospheric boundary layer with limited input requirements. The model uses a climatological-data-driven land surface model that contains only the physics necessary to accurately reproduce observed seasonal cycles of fluxes and state variables for natural and agricultural ecosystems. A bulk boundary layer model was coupled to the land model to estimate the impacts of changing land cover on the lower atmosphere. The model most realistically simulated surface-atmosphere dynamics and impacts of land-cover change at tropical rain forest and northern boreal forest sites. Further, simple indices to measure the potential impact of land-cover change on boundary layer climate were defined and shown to be dependent on boundary layer dynamics and geographically similar to results from previous studies, which highlighted the impacts of land-cover change on the atmosphere in the tropics and boreal forest. [ABSTRACT FROM AUTHOR]

Changes in temperature and precipitation projected from five general circulation models, using one late-twentieth-century and three twenty-first-century emission scenarios, were downscaled to three different baseline conditions. Baseline conditions are periods of measured temperature and precipitation data selected to represent twentieth-century climate. The hydrologic effects of the climate projections are evaluated using the Precipitation-Runoff Modeling System (PRMS), which is a watershed hydrology simulation model. The Almanor Catchment in the North Fork of the Feather River basin, California, is used as a case study. Differences and similarities between PRMS simulations of hydrologic components (i.e., snowpack formation and melt, evapotranspiration, and streamflow) are examined, and results indicate that the selection of a specific time period used for baseline conditions has a substantial effect on some, but not all, hydrologic variables. This effect seems to be amplified in hydrologic variables, which accumulate over time, such as soil-moisture content. Results also indicate that uncertainty related to the selection of baseline conditions should be evaluated using a range of different baseline conditions. This is particularly important for studies in basins with highly variable climate, such as the Almanor Catchment. [ABSTRACT FROM AUTHOR]

The mountainous areas of Colorado are used for tourism and recreation, and they provide water storage and supply for municipalities, industries, and agriculture. Recent studies suggest that water supply and tourist industries such as skiing are at risk from climate change. In this study, a distributed-parameter watershed model, the Precipitation-Runoff Modeling System (PRMS), is used to identify the potential effects of future climate on hydrologic conditions for two Colorado basins, the East River at Almont and the Yampa River at Steamboat Springs, and at the subbasin scale for two ski areas within those basins. Climate-change input files for PRMS were generated by modifying daily PRMS precipitation and temperature inputs with mean monthly climate-change fields of precipitation and temperature derived from five general circulation model (GCM) simulations using one current and three future carbon emission scenarios. All GCM simulations of mean daily minimum and maximum air temperature for the East and Yampa River basins indicate a relatively steady increase of up to several degrees Celsius from baseline conditions by 2094. GCM simulations of precipitation in the two basins indicate little change or trend in precipitation, but there is a large range associated with these projections. PRMS projections of basin mean daily streamflow vary by scenario but indicate a central tendency toward slight decreases, with a large range associated with these projections. Decreases in water content or changes in the spatial extent of snowpack in the East and Yampa River basins are important because of potential adverse effects on water supply and recreational activities. PRMS projections of each future scenario indicate a central tendency for decreases in basin mean snow-covered area and snowpack water equivalent, with the range in the projected decreases increasing with time. However, when examined on a monthly basis, the projected decreases are most dramatic during fall and spring. Presumably, ski area locations are picked because of a tendency to receive snow and keep snowpack relative to the surrounding area. This effect of ski area location within the basin was examined by comparing projections of March snow-covered area and snowpack water equivalent for the entire basin with more local projections for the portion of the basin that represents the ski area in the PRMS models. These projections indicate a steady decrease in March snow-covered area for the basins but only small changes in March snow-covered area at both ski areas for the three future scenarios until around 2050. After 2050, larger decreases are possible, but there is a large range in the projections of future scenarios. The rates of decrease for snowpack water equivalent and precipitation that falls as snow are similar at the basin and subbasin scale in both basins. Results from this modeling effort show that there is a wide range of possible outcomes for future snowpack conditions in Colorado. The results also highlight the differences between projections for entire basins and projections for local areas or subbasins within those basins. [ABSTRACT FROM AUTHOR]

The potential effects of long-term urbanization and climate change on the freshwater resources of the Flint River basin were examined by using the Precipitation-Runoff Modeling System (PRMS). PRMS is a deterministic, distributed-parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land cover on streamflow and multiple intermediate hydrologic states. Precipitation and temperature output from five general circulation models (GCMs) using one current and three future climate-change scenarios were statistically downscaled for input into PRMS. Projections of urbanization through 2050 derived for the Flint River basin by the Forecasting Scenarios of Future Land-Cover (FORE-SCE) land-cover change model were also used as input to PRMS. Comparison of the central tendency of streamflow simulated based on the three climate-change scenarios showed a slight decrease in overall streamflow relative to simulations under current conditions, mostly caused by decreases in the surface-runoff and groundwater components. The addition of information about forecasted urbanization of land surfaces to the hydrologic simulation mitigated the decreases in streamflow, mainly by increasing surface runoff. [ABSTRACT FROM AUTHOR]

In an earlier global climate-change study, air temperature and precipitation data for the entire twenty-first century simulated from five general circulation models were used as input to precalibrated watershed models for 14 selected basins across the United States. Simulated daily streamflow and energy output from the watershed models were used to compute a range of statistics. With a side-by-side comparison of the statistical analyses for the 14 basins, regional climatic and hydrologic trends over the twenty-first century could be qualitatively identified. Low-flow statistics (95%% exceedance, 7-day mean annual minimum, and summer mean monthly streamflow) decreased for almost all basins. Annual maximum daily streamflow also decreased in all the basins, except for all four basins in California and the Pacific Northwest. An analysis of the supply of available energy and water for the basins indicated that ratios of evaporation to precipitation and potential evapotranspiration to precipitation for most of the basins will increase. Probability density functions (PDFs) were developed to assess the uncertainty and multimodality in the impact of climate change on mean annual streamflow variability. Kolmogorov--Smirnov tests showed significant differences between the beginning and ending twenty-first-century PDFs for most of the basins, with the exception of four basins that are located in the western United States. Almost none of the basin PDFs were normally distributed, and two basins in the upper Midwest had PDFs that were extremely dispersed and skewed. [ABSTRACT FROM AUTHOR]