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The article presents information on a study conducted on the impact of climate change on agriculture. In the study, Community Land Model (CLM)-Crop was validated against observations from two agricultural sites in the U.S., that were planted with maize and soybean. CLM-Crop was compared with the standard CLM3.5, which is associated with crops as grass. The study found that crop yields and productivity had a negative correlation with temperature, and a positive correlation with precipitation.

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]

To study trends in extreme precipitation across the United States over the years 1951–2017, we analyze 10 climate indexes that represent extreme precipitation, such as annual maximum of daily precipitation and annual maximum of consecutive five‐day average precipitation. We consider the gridded data produced by the CLIMDEX project (http://www.climdex.org/gewocs.html), constructed using daily precipitation data. These indexes exhibit spatial and mutual dependence. In this paper, we propose a multivariate spatial skew‐t process for joint modeling of extreme precipitation indexes and discuss its theoretical properties. The model framework allows Bayesian inference while maintaining a computational time that is competitive with common multivariate geostatistical approaches. In a numerical study, we find that the proposed model outperforms several simpler alternatives in terms of various model selection criteria. We apply the proposed model to estimate the average decadal change in the extreme precipitation indexes throughout the United States and find several significant local changes. [ABSTRACT FROM AUTHOR]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

Central Asia is among the most heavily affected regions worldwide by climate change and water shortages. Impacts include changes in precipitation patterns, more frequent temperature extremes and increased aridity causing a negative impact on agricultural production, food availability, and environmental security. To combat this threat, it is important to enhance information literacy among all water users. This can be done through awareness campaigns, blended learning by providing the proper Technical and Vocational Education and Training (TVET) programs and utilizing all available facilities. This will address relevant issues, such as miscommunication, complexities of transboundary water sharing issues, overexploitation of water resources, and poor flood-drought mitigation techniques. Proper and user-friendly lifelong blended learning for scientific information dissemination focusing water issues can provide stronger support to increase awareness among water users and decision policy makers. Worldwide, especially in North America and Australia, information literacy campaigns have proven successful. This strategy can be replicated in the Mountainous Kyrgyz-Kazakh Chu-Talas transboundary river basin. The issues concerning the Mountainous KyrgyzKazakh Chu-Talas transboundary river basin is elaborated and compared with Australian, Canadian, and US river basin management programs. The foresight analysis is presented, as to what would be a rationale to improve water resources more sustainably in Central Asia. Methodologies, programs, technologies, communities-based river basin committees, snow-water collection with agroforestry, and basin-based water market opportunities were analyzed to assess potential applications in Central Asia region. [ABSTRACT FROM AUTHOR]

In a changing climate, there is an interest in predicting how extreme rainfall events may change. Using historical records, several recent papers have evaluated whether high-intensity precipitation scales with temperature in accordance with the Clausius--Clapeyron (C--C) relationship. For varying locations in Europe, these papers have identified both super C--C relationships as well as a breakdown of the C--C relationship under dry conditions. In this paper, a similar analysis is carried out for the United States using data from 14 weather stations clustered in four different hydroclimatic regions: the coastal northeast, interior New York, the central plains, and the western plains. In all regions except interior New York state, 99th percentile 1-h precipitation generally followed the C--C relation. In interior New York, there was evidence that intensity scaled with a super C--C relationship. For the 99.9th percentile precipitation, interior New York displayed some moderate evidence of a super C--C relationship, the western plains showed little relation between precipitation and temperature, and the remainder of sites generally scaled with the C--C relationship. Also, if only July, August, and September precipitation is considered, all stations except those in interior New York have little relation between temperature and precipitation, suggesting that precipitation intensity during summer months may not be well constrained by the C--C relationship. Overall, the C--C relationship (or a variation thereof) does not appear to constrain extreme precipitation in all regions and in all seasons, and its ability to aid in constraining future predictions of extreme precipitation may only be relevant to certain locales and time periods. [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]

Precipitation over the contiguous United States exhibits large multi-decadal oscillations since the early twentieth century, and they often lead to dry (e.g., 1946-1976 and 1999-present) and wet (e.g., 1977-1998) periods and apparent precipitation trends (e.g., from the 1950s to 1990s) over most of the western and central US. The exact cause of these inter-decadal variations is not fully understood. Using observational and reanalysis data and model simulations, this paper examines the influence of the Inter-decadal Pacific Oscillation (IPO) on US precipitation. The IPO is a leading mode of sea surface temperatures (SSTs) seen mostly in the Pacific Ocean. It is found that decadal precipitation variations over much of the West and Central US, especially the Southwest, closely follow the evolution of the IPO ( r = 0.85 during 1923-2010 for the Southwest US), and the dry and wet periods are associated, respectively, with the cold and warm phases of the IPO. In particular, the apparent upward trend from the 1950s-1990s and the dry decade thereafter in precipitation over much of the West and Central US are largely caused by the IPO cycles, which switched to a warm phase around 1977 and back to a cold phase around 1999. An atmospheric model forced with observed SSTs reproduces much of this association of US precipitation with the IPO ( r = 0.95 between smoothed observed and simulated Southwest US precipitation during 1950-2009 and r = 0.88 between the simulated Southwest US precipitation and the IPO). Atmospheric reanalysis and model data both show a strong high (low) pressure center and anti-cyclonic (cyclonic) anomaly circulation over the North Pacific in the lower troposphere during cold (warm) phases of the IPO, which lead to dry and cold northwesterly and northerly winds and below-normal precipitation over much of the West US during IPO cold periods. The IPO induced changes are most pronounced during the boreal cold season. The results reinforce the notion that tropical Pacific SSTs (and the accompanying SST anomalies in the North Pacific) have large impacts on US precipitation and highlight the need to understand and simulate the IPO for decadal prediction of US precipitation. [ABSTRACT FROM AUTHOR]

This paper analyzes surface climate variability in the climate forecast system reanalysis (CFSR) recently completed at the National Centers for Environmental Prediction (NCEP). The CFSR represents a new generation of reanalysis effort with first guess from a coupled atmosphere-ocean-sea ice-land forecast system. This study focuses on the analysis of climate variability for a set of surface variables including precipitation, surface air 2-m temperature (T2m), and surface heat fluxes. None of these quantities are assimilated directly and thus an assessment of their variability provides an independent measure of the accuracy. The CFSR is compared with observational estimates and three previous reanalyses (the NCEP/NCAR reanalysis or R1, the NCEP/DOE reanalysis or R2, and the ERA40 produced by the European Centre for Medium-Range Weather Forecasts). The CFSR has improved time-mean precipitation distribution over various regions compared to the three previous reanalyses, leading to a better representation of freshwater flux (evaporation minus precipitation). For interannual variability, the CFSR shows improved precipitation correlation with observations over the Indian Ocean, Maritime Continent, and western Pacific. The T2m of the CFSR is superior to R1 and R2 with more realistic interannual variability and long-term trend. On the other hand, the CFSR overestimates downward solar radiation flux over the tropical Western Hemisphere warm pool, consistent with a negative cloudiness bias and a positive sea surface temperature bias. Meanwhile, the evaporative latent heat flux in CFSR appears to be larger than other observational estimates over most of the globe. A few deficiencies in the long-term variations are identified in the CFSR. Firstly, dramatic changes are found around 1998-2001 in the global average of a number of variables, possibly related to the changes in the assimilated satellite observations. Secondly, the use of multiple streams for the CFSR induces spurious jumps in soil moisture between adjacent streams. Thirdly, there is an inconsistency in long-term sea ice extent variations over the Arctic regions between the CFSR and other observations with the CFSR showing smaller sea ice extent before 1997 and larger extent starting in 1997. These deficiencies may have impacts on the application of the CFSR for climate diagnoses and predictions. Relationships between surface heat fluxes and SST tendency and between SST and precipitation are analyzed and compared with observational estimates and other reanalyses. Global mean fields of surface heat and water fluxes together with radiation fluxes at the top of the atmosphere are documented and presented over the entire globe, and for the ocean and land separately. [ABSTRACT FROM AUTHOR]

The U.S. Climate Reference Network (USCRN) was deployed between 2001 and 2008 for the purpose of yielding high-quality and temporally stable in situ climate observations in pristine environments over the twenty-first century. Given this mission, USCRN stations are engineered to operate largely autonomously with great reliability and accuracy. A triplicate approach is used to provide redundant measurements of temperature and precipitation at each location, allowing for observations at a specific time to be compared for quality control. This approach has proven to be robust in the most extreme environments, from extreme cold (−49°C) to extreme heat (++52°C), in areas of heavy precipitation (4700 mm yr−1), and in locations impacted by strong winds, freezing rain, and other hazards. In addition to a number of stations enduring extreme winter environments in Alaska and the northern United States, seven of the USCRN stations are located at elevations over 2000 m, including stations on Mauna Loa, Hawaii (3407 m) and on Niwot Ridge above Boulder, Colorado (2996 m). The USCRN temperature instruments and radiation shield have also been installed and run successfully at a station on the Quelccaya Ice Cap in Peru (5670 m). This paper reviews the performance of the USCRN station network during its brief lifetime and the potential utility of its triplicate temperature instrument configuration for measuring climate change at elevation. [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]

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mark 2 global coupled climatic model has been used to generate a 10 000-yr simulation of “present” climate. The resultant dataset has been used to investigate a number of aspects of extremes associated with annual mean rainfall. Multimillennial time series of normalized rainfall amounts for selected points are used to highlight secular variability, spatial variations, and the differences between pluvial and drought conditions. Global distributions are also presented for selected rainfall characteristics, including the frequency of occurrence of specified rainfall anomalies with annual durations, the frequency of occurrence of 5-yr sequences of specified rainfall anomalies, and the maximum and minimum normalized rainfall amounts attained in the simulation. Such features cannot be obtained from observations because of their limited duration. A case study is also made of a megadrought over the southwestern United States, together with an analysis of the associated causal mechanisms. Given the exclusion of all external forcing from the model, it is concluded that the extreme annual mean rainfall extremes presented in the paper are attributable to stochastic events. [ABSTRACT FROM AUTHOR]

In this paper, the authors analyze simulations of present and future climates in the western United States performed with four regional climate models (RCMs) nested within two global ocean–atmosphere climate models. The primary goal here is to assess the range of regional climate responses to increased greenhouse gases in available RCM simulations. The four RCMs used different geographical domains, different increased greenhouse gas scenarios for future-climate simulations, and (in some cases) different lateral boundary conditions. For simulations of the present climate, RCM results are compared to observations and to results of the GCM that provided lateral boundary conditions to the RCM. For future-climate (increased greenhouse gas) simulations, RCM results are compared to each other and to results of the driving GCMs. When results are spatially averaged over the western United States, it is found that the results of each RCM closely follow those of the driving GCM in the same region in both present and future climates. This is true even though the study area is in some cases a small fraction of the RCM domain. Precipitation responses predicted by the RCMs in many regions are not statistically significant compared to interannual variability. Where the predicted precipitation responses are statistically significant, they are positive. The models agree that near-surface temperatures will increase, but do not agree on the spatial pattern of this increase. The four RCMs produce very different estimates of water content of snow in the present climate, and of the change in this water content in response to increased greenhouse gases. [ABSTRACT FROM AUTHOR]

Increase of precipitation intensity is the most definite and detectable hydrological consequence of a warmer climate. Among all U.S. regions, the Northeast has witnessed the strongest increase of extreme precipitation in the past five decades. This study examines the impact of climate changes during 1950–2011 on hydrological processes in the Northeast using the Connecticut River Basin as a case study. In addition to precipitation and river discharge data from observations, the Variable Infiltration Capacity (VIC) hydrological model is used to derive hydrological variables for which long-term observational data are not readily available. Our results show a clear increase of precipitation intensity, with substantial increase in both the number of days with greater than 10 mm precipitation and the simple daily intensity index. From 1950 to 2011, extreme precipitation amount (which is the total amount of precipitation from the upper 1% of daily precipitation) increased substantially, by 240% relative to the 1950 level. The weight of extreme precipitation as a fraction of total precipitation also increased, from about 10.6% in the 1950s to 30.4% in the 2000s. Despite the increase of precipitation extremes, the consecutive dry days experienced a slight decrease. Mean trend analysis shows indications of increasing precipitation amount, increasing discharge, increasing runoff ratios, increasing soil moisture, and a negligible evapotranspiration trend. Our simulations suggest that the basin is entering a wetter regime more subject to meteorological flood conditions than to drought conditions. A companion paper will investigate how these trends may persist or differ in the mid-21st century under continued warming. [ABSTRACT FROM AUTHOR]

Abstract: Hydrological time series increasingly exhibit non-stationarity, e.g., variables such as precipitation and streamflow values do not maintain a consistent mean over long periods, due to natural and anthropogenic changes. Detecting whether such shifts are gradual or abrupt is a growing concern for water resources planning and management. This paper shows that conventional trend and change-point tests do not adequately enable these two types of change to be distinguished. We propose a method for combining the rank correlations of the Mann–Kendall and Pettitt statistics to extract an indicator whose value determines whether a shift observed in a given time series is gradual or abrupt. This method allows the success rate to be independent of the length of record, and it is validated with Monte-Carlo experiments. The limitations caused by the short and noisy nature of hydroclimatic time series are discussed. As an application, the proposed method provides useful insights on changes in hydroclimatic variables in the United States during 1910–2009 using time series from 1217 stations in the United States Hydroclimatic Data Network (USHCN). [Copyright &y& Elsevier]

Summary: Multi-scale temporal variability of precipitation has an established relationship with floods and droughts. In this paper, we present the diagnostics on the ability of 16 General Circulation Models (GCMs) from Bias Corrected and Downscaled (BCSD) World Climate Research Program’s (WCRP’s) Coupled Model Inter-comparison Project Phase 3 (CMIP3) projections and 10 Regional Climate Models (RCMs) that participated in the North American Regional Climate Change Assessment Program (NARCCAP) to represent multi-scale temporal variability determined from the observed station data. Four regions (Los Angeles, Las Vegas, Tucson, and Cimarron) in the Southwest United States are selected as they represent four different precipitation regions classified by clustering method. We investigate how storm properties and seasonal, inter-annual, and decadal precipitation variabilities differed between GCMs/RCMs and observed records in these regions. We find that current GCMs/RCMs tend to simulate longer storm duration and lower storm intensity compared to those from observed records. Most GCMs/RCMs fail to produce the high-intensity summer storms caused by local convective heat transport associated with the summer monsoon. Both inter-annual and decadal bands are present in the GCM/RCM-simulated precipitation time series; however, these do not line up to the patterns of large-scale ocean oscillations such as El Nino/La Nina Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Our results show that the studied GCMs/RCMs can capture long-term monthly mean as the examined data is bias-corrected and downscaled, but fail to simulate the multi-scale precipitation variability including flood generating extreme events, which suggests their inadequacy for studies on floods and droughts that are strongly associated with multi-scale temporal precipitation variability. [Copyright &y& Elsevier]

Both climate change and human activities are known to have induced changes to hydrology. Consequently, quantifying the net impact of human contribution to the streamfiow change is a challenge. In this paper, a decomposition method based on the Budyko hypothesis is used to quantify the climate (i.e., precipitation and potential evaporation change) and direct human impact on mean annual streamflow (MAS) for 413 watersheds in the contiguous United States. The data for annual precipitation, runoff, and potential evaporation are obtained from the international Model Parameter Estimation Experiment (MOPEX), which is often assumed to only include gauges unaffected by human interferences. The data are split into two periods (1948-1970 and 1971-2003) to quantify the change over time. Although climate is found to affect MAS more than direct human impact, the results show that assuming the MOPEX data set to be unaffected by human activities is far from realistic. Climate change causes increasing MAS in most watersheds, while the direct human-induced change is spatially heterogeneous in the contiguous United States, with strong regional patterns, e.g., human activities causing increased MAS in the Midwest and significantly decreased MAS in the High Plains. The climate- and human-induced changes are found to be more severe in arid regions, where water is limited. Comparing the results to a collection of independent data sets indicates that the estimated direct human impacts on MAS in this largely nonurban set of watersheds might be attributed to several human activities, such as cropland expansion, irrigation, and the construction of reservoirs. [ABSTRACT FROM AUTHOR]

A monthly snow accumulation and melt model was used with monthly Precipitation-elevation Regressions on Independent Slopes Model (PRISM) temperature and precipitation data to generate time series of 1 April snow water equivalent (SWE) for 1900 through 2008 in the western United States. Averaged across the western United States, SWE generally was higher than long-term (1900–2008) average conditions during the periods 1900–25, 1944–55, and 1966–82; SWE was lower than long-term average conditions during the periods 1926–43, 1957–65, and 1984–2008. During the period 1900–2008, the temporal pattern in winter precipitation exhibited wetter-than-average and drier-than-average decadal-scale periods with no long-term increasing or decreasing trend. Winter temperature generally was below average from 1900 to the mid-1950s, close to average from the mid-1950s to the mid-1980s, and above average from the mid-1980s to 2008. In general, periods of higher-than-average SWE have been associated with higher precipitation and lower temperature. Since about 1980, western U.S. winter temperatures have been consistently higher than long-term average values, and the resultant lower-than-average SWE values have been only partially offset by periods of higher-than-average precipitation. The post-1980 lower-than-average SWE conditions in the western United States are unprecedented within the context of twentieth-century climate and estimated SWE. [ABSTRACT FROM AUTHOR]

During the last century, much of the United States experienced warming temperatures and changes in amount and intensity of precipitation. Changes in future climate conditions present additional risk to water and watershed managers. The most recent release of U.S. EPA's BASINS watershed modeling system includes a Climate Assessment Tool (CAT) that provides new capabilities for assessing impacts of climate change on water resources. The BASINS CAT provides users with the ability to modify historical climate and conduct systematic sensitivity analyses of specific hydrologic and water quality endpoints to changes in climate using the BASINS models (Hydrologic Simulation Program -- FORTRAN (HSPF)). These capabilities are well suited for addressing questions about the potential impacts of climate change on key hydrologic and water quality goals using the watershed scale at which most important planning decisions are made. This paper discusses the concepts that motivated the CAT development effort; the resulting capabilities incorporated into BASINS CAT; and the opportunities that result from integrating climate assessment capabilities into a comprehensive watershed water quality modeling system. [ABSTRACT FROM AUTHOR]

NW flow snowfall (NWFS) events are common occurrences at higher elevations (1500 to >2000 m a.s.l.) and on windward slopes in the Southern Appalachian Mountain region of the south-eastern USA. The spatial patterns of NWFS are strongly controlled by topography, resulting in pronounced spatial variability. The strong topographic and low-level convective forcing, coupled with low temperatures and strong winds, increases societal impacts. This paper analyzes the relationships between NWFS and various topographic and geographic (TOPO/GEOG) variables in the Southern Appalachians. We identified NWFS events on the basis of low-level wind direction, extracted values for the TOPO/GEOG variables from Digital Elevation Models (DEMs), and developed statistical relationships between NWFS and the TOPO/GEOG variables. Using a multivariate regression model and GIS techniques, we also mapped mean annual NWFS across the region. Results indicate that elevation and NW exposure are most strongly correlated with NWFS; however, the strength of these relationships is mediated by temperature. In the colder NWFS events, the relative importance of elevation is diminished, while NW exposure and distance to a NW slope are relatively more important. Additionally, we demonstrate that multivariate regression modeling in conjunction with GIS techniques can be an effective way to map snowfall patterns associated with specific wind directions, particularly when a strong topographic control is evident. [ABSTRACT FROM AUTHOR]

Impacts modelers and stakeholders use publicly available data sets of downscaled climate projections to assess and design infrastructure for changes in future rainfall extremes. If differences across data sets exist, infrastructure resilience decisions could change depending on which single data set is used. We assess changes in U.S. rainfall extremes from 2044–2099 compared with 1951–2005 based on 227 projections under RCP4.5 and RCP8.5 from five widely used data sets. We show there are large differences in the change magnitude and its spatial structure between data sets. At the continental scale, the data sets show different increases, with high‐end extremes (e.g. 100‐year event) generally increasing more (between 10% and 50%) than low‐end extremes (e.g. 5‐year). These differences largely contribute to the overall uncertainty for small average recurrence intervals (ARIs) extremes (2‐ to 10‐year), while uncertainties due to short record length dominate large ARIs (25‐ to 100‐year). The results indicate that robust infrastructure planning should consider these uncertainties to enable resilient infrastructure under climate change. Plain Language Summary: Observed extreme rainfall magnitudes have increased since 1950, and climate model projections indicate that these increases will continue throughout the 21st century in many areas in the US. Adapting and designing infrastructure for climate change requires future extreme rainfall projections at high resolutions. Global climate model (GCM) output resolution is generally much coarser, and methods have been developed to create relevant climate information at regional scales. Multiple open data sets exist that provide downscaled projections of future rainfall extremes. We analyze changes in future daily extremes using five widely used data sets in climate change impacts assessments and decision making. We found large differences between data sets in how much and where extreme events will intensify by the end of the 21st century. This and other sources of uncertainty need to be considered when designing resilient infrastructure. Key Points: Extreme daily rainfall changes for the United States from five widely used public downscaled climate projection data sets are comparedLarge differences in magnitude and spatial patterns of projected extreme precipitation changes exist between data setsHigh‐end extremes (e.g., 100‐year event) increase 10–50 % faster than low‐end extremes (e.g., 2‐year event) at the continental scale [ABSTRACT FROM AUTHOR]

Trends of extreme precipitation (EP) using various combinations of average return intervals (ARIs) of 1, 2, 5, 10, and 20 years with durations of 1, 2, 5, 10, 20, and 30 days were calculated regionally across the contiguous United States. Changes in the sign of the trend of EP vary by region as well as by ARI and duration, despite the statistically significant upward trends for all combinations of EP thresholds when area averaged across the contiguous United States. Spatially, there is a pronounced east-to-west gradient in the trends of the EP with strong upward trends east of the Rocky Mountains. In general, upward trends are larger and more significant for longer ARIs, but the contribution to the trend in total seasonal and annual precipitation is significantly larger for shorter ARIs because they occur more frequently. Across much of the contiguous United States, upward trends of warm-season EP are substantially larger than those for the cold season and have a substantially greater effect on the annual trend in total precipitation. This result occurs even in areas where the total precipitation is nearly evenly divided between the cold and warm seasons. When compared with short-duration events, long-duration events—for example, 30 days—contribute the most to annual trends. Coincident statistically significant upward trends of EP and precipitable water (PW) occur in many regions, especially during the warm season. Increases in PW are likely to be one of several factors responsible for the increase in EP (and average total precipitation) observed in many areas across the contiguous United States. [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]

The sensitivity of plant production to precipitation underlies the functioning of ecosystems. Studies that relate long-term mean annual precipitation and production across multiple sites (spatial relationship) or examine interannual linkages within a site (temporal relationship) can reveal biophysical controls over ecosystem function but have limited ability to infer responses to extreme changes in precipitation that may become more common under climate change. To overcome limitations of using a single approach, we integrated satellite- and ground-based estimates of production with a standardized, multi-site precipitation manipulation experiment across a grassland elevation gradient in the southwestern USA. The responsiveness of production to changes in precipitation followed the order: temporal (0.06–0.13 g m−2 mm−1) < spatial (0.21 g m−2 mm−1) < experimental relationship (0.25–0.42 g m−2 mm−1), suggesting that spatial and temporal relationships determined with satellite- and ground-based estimates cannot be extrapolated to determine the effect of extreme events. A strong production response to differences in mean annual precipitation across sites reinforces a regional control of water availability. Interannual sensitivity to precipitation was strongest at the low elevation grasslands, and the high elevation mixed conifer meadow had a large reduction in production in a drought year. Extreme experimental drought strongly reduced production in low elevation grasslands, but water addition had mixed effects. High elevation meadows were insensitive to both extreme drought and water addition. Our results highlight the importance of accounting for extreme climate regimes and site-level factors when scaling climate change effects up to regional and global scales. [ABSTRACT FROM AUTHOR]

Climate change effects are already apparent in some Southwestern US forests and are expected to intensify in the coming decades, via direct (temperature, precipitation) and indirect (fire, pests, pathogens) stressors. We grouped Southwestern forests into ten major types to assess their climate exposure by 2070 using two global climate models (GCMs) and two emission scenarios representing wetter or drier conditions and current or lowered emission levels. We estimate future climate exposure over forests covering 370,144 km2 as the location and proportion of each type projected to experience climate conditions that fall outside 99% of those they currently occupy. By late century, 27-77% is climatically exposed under wetter or drier current emission levels, while lowered emission levels produce 10-50% exposure, respectively. This difference points to the benefits of reducing emissions from the RCP8.5 to the RCP4.5 track, with regard to forest retention. Exposed areas common to all four climate futures include central Arizona and the western slope of the Sierra Nevada. Vulnerability assessments also comprise sensitivity and adaptive capacity, which we scored subjectively by forest type according to the number of key stressors they are sensitive to and the resilience conferred by life history traits of their dominant tree species. Under the 2070 RCP8.5 emissions, four forest types are critically and six are highly vulnerable under the hotter GCM; and eight are highly and two moderately vulnerable under the wetter GCM. We discuss forest management adaptation strategies and the barriers to and co-benefits of such plans. [ABSTRACT FROM AUTHOR]

The net surface water budget, precipitation minus evaporation (P = E), shows a clear seasonal cycle in the U.S. Southwest with a net gain of surface water (positive P = E) in the cold half of the year (October-March) and a net loss of water (negative P = E) in the warm half (April-September), with June and July being the driest months of the year. There is a significant shift of the summer drying toward earlier in the year under a CO2 warming scenario, resulting in substantial spring drying (March-May) of the U.S. Southwest from the near-term future to the end of the current century, with gradually increasing magnitude. While the spring drying has been identified in previous studies, its mechanism has not been fully addressed. Using moisture budget analysis, it was found that the drying is mainly due to decreased mean moisture convergence, partially compensated by the increase in transient eddy moisture flux convergence. The decreased mean moisture convergence is further separated into components as a result of changes in circulation (dynamic changes) and changes in atmospheric moisture content (thermodynamic changes). The drying is found to be dominated by the thermodynamic-driven changes in column-averaged moisture convergence, mainly due to increased dry zonal advection caused by the climatological land-ocean thermal contrast, rather than by the well-known "dry get drier" mechanism. Furthermore, the enhanced dry advection in the warming climate is dominated by the robust zonal mean atmospheric warming, leading to equally robust spring drying in the southwestern United States. [ABSTRACT FROM AUTHOR]

Abstract: Phosphorus runoff from agricultural land is a major cause of eutrophication in lakes and reservoirs. Frequency and intensity of extreme precipitation events are increasing in agricultural regions of the Upper Midwestern U.S., and these increases are projected to continue as climate warms. We quantified the linkage between extreme daily precipitation and extreme daily discharge, phosphorus (P) load, and P concentration for Pheasant Branch and the Yahara River, two tributaries of Lake Mendota, Wisconsin, U.S.A. using the generalized Pareto distribution. Although precipitation extremes have increased since 1940, over the shorter period of stream monitoring (1994–2015 for Pheasant Branch and 1991–2015 for Yahara) there is no significant trend in extreme precipitation. Nonetheless a disproportionate number of extreme precipitation events (for example seven of the 11 largest 24‐h events since 1901) occurred during the period of stream monitoring. Daily precipitation extremes were associated with extremes in daily discharge and P load. P load return levels increased steeply and almost linearly with precipitation on log‐log axes. The trend toward more frequent and intense precipitation extremes will increase P loading and intensify the eutrophication of the lake, unless the excessive P enrichment of the watershed is reversed. [ABSTRACT FROM AUTHOR]

ABSTRACT: Climate change has and will continue to impact the magnitude and timing of streamflow throughout the world. While streamflow timing of western United States watersheds has shifted earlier in mountainous snow‐dominated watersheds due to earlier snowmelt, timing for rain‐dominated coastal watersheds has shifted to later in the year. Despite these dissimilarities, coastal watersheds have received little attention in the literature. This research assesses changes in projected climate and hydrology for five small coastal basins (42–718 km2) in Washington, Oregon, and California. Projections of temperature and precipitation from General Circulation Models (GCMs) for Representative Concentration Pathways 4.5 and 8.5 are coupled with the Soil and Water Assessment Tool hydrologic model to simulate future hydrology for each watershed. The response of climate, streamflow discharge, and timing was analysed for each watershed by comparing the projected mid‐21st century (2035–2064) and late‐21st century (2070–2099) to the historical period (1970–1999). While temperature is projected to increase at each watershed from 1 to 6 °C by the end of the 21st century, precipitation varies widely with the median and mean change across all watersheds and GCMs being close to zero (just 2.0 and 3.6%, respectively). Changes in the magnitude of peak winter streamflow discharge differ across the region compared to the historical period. Streamflow timing also features latitudinal divergence, with the southern California watershed projecting later timing by 13 days and all other watersheds indicating smaller shifts (0–7 days) for the end of the 21st century compared to the historical time period, largely from corresponding shifts in precipitation timing. The projected decreases in peak winter streamflow magnitude, particularly in central and southern California, have critical implications for water supply, which is already strained from extensive drought and high demand. This research makes strides to assess new trends across coastal watersheds. [ABSTRACT FROM AUTHOR]

The coastal waters of the southeastern USA contain important protected habitats and natural resources that are vulnerable to climate variability and singular weather events. Water clarity, strongly affected by atmospheric events, is linked to substantial environmental impacts throughout the region. To assess this relationship over the long-term, this study uses an artificial neural network-based time series modeling technique known as non-linear autoregressive models with exogenous input (NARX models) to explore the relationship between climate and a water clarity index (KDI) in this area and to reconstruct this index over a 66-year period. Results show that synoptic-scale circulation patterns, weather types, and precipitation all play roles in impacting water clarity to varying degrees in each region of the larger domain. In particular, turbid water is associated with transitional weather and cyclonic circulation in much of the study region. Overall, NARX model performance also varies-regionally, seasonally and interannually-with wintertime estimates of KDI along the West Florida Shelf correlating to the actual KDI at r > 0.70. Periods of extreme (high) KDI in this area coincide with notable El Niño events. An upward trend in extreme KDI events from 1948 to 2013 is also present across much of the Florida Gulf coast. [ABSTRACT FROM AUTHOR]

In order to understand the changes in precipitation variability associated with the climate shift around mid-1970s, the precipitation regime changes have been analyzed over both China and the USA. Specifically, a new variable is designed based on Benford's Law (BL) to detect precipitation regime shift by using only the first digit information of the datasets. This new variable describes the decadal precipitation regime shift more directly and clearly than the traditional variables, such mean or trend of yearly precipitation amount. It is found that there is an obvious out-phased relation for precipitation regime shift over China and the USA, i.e., a significant shift from the lower to the higher BL's goodness of fit (weaker to stronger precipitation intensity) in the Southern China occurred in 1979, and a significant shift from the higher to the lower BL's goodness of fit (stronger to weaker precipitation intensity) in the USA occurred around 1978. [ABSTRACT FROM AUTHOR]

For 14 alternative climate futures, water yield and snow water equivalent (SWE) throughout the contiguous United States (CONUS) were projected over the twenty-first century using the Variable Infiltration Capacity model (VIC). The futures correspond to climate projections from seven CMIP5 models each forced by two representative concentration pathways for greenhouse gases (RCP4.5 and RCP8.5). With both RCPs, decreases in water yields are projected for roughly two-thirds of the CONUS, and in 60% of that area-mainly at more northern latitudes, where the greatest temperature increases are expected-this occurs despite projected increases in precipitation. The greatest relative decreases in yield are projected for the southern Great Plains and the Southwest, where temperature and precipitation changes combine to decrease yield. Snow accumulation is projected to decrease almost everywhere by the latter half of the century, with the time of peak SWE in some basins projected to occur up to 2 months earlier than it now does. These changes, should they come to pass, will challenge the adaptation capacity of future water management. [ABSTRACT FROM AUTHOR]

El Niño, the Indian Ocean Dipole (IOD), and the Tropical North Atlantic variability (TNA) can influence southeastern South America (SESA) rainfall, particularly during springtime. A recent study has shown that during the last century, there were two synchronization periods (1930s and 1970s) in which different tropical oceans interacted among themselves and, in turn, induced precipitation anomalies over SESA. In this study, we evaluate how this collective influence of the tropical oceans on SESA precipitation observed during the twentieth century, can change in the next century as result of anthropogenic forcing. To do so, we use the output of seven different CMIP5 models and construct a network using as nodes the indices of the Niño3.4, the TNA, the IOD and rainfall over SESA. After evaluating their skill in representing the observed network statistics during the twentieth century, we study changes in the network under RCP4.5 and 8.5 global warming scenarios in the twenty-first century. Focusing on the grand CMIP5 ensemble mean, results suggest that an anthropogenic forcing would increase the number of synchronization periods per century, their time length, and the connectivity between nodes (with the exception of TNA and IOD in RCP8.5). The stronger connectivity of SESA precipitation with the tropical oceans in both scenarios suggest an increase of the oceanic influence on rainfall over SESA as a result of anthropogenic forcing, which would enhance its seasonal predictability. However, these results have to be taken with caution because there is a large disparity in model behavior and thus a large uncertainty in conclusions suggested from the grand ensemble mean. [ABSTRACT FROM AUTHOR]

Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6%-7% K−1, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (1%-2% K−1 in the global average in transient runs). Some other aspect of precipitation events must then change to compensate for this difference. The authors develop a new methodology for identifying individual rainstorms and studying their physical characteristics-including starting location, intensity, spatial extent, duration, and trajectory-that allows identifying that compensating mechanism. This technique is applied to precipitation over the contiguous United States from both radar-based data products and high-resolution model runs simulating 80 years of business-as-usual warming. In the model study the dominant compensating mechanism is a reduction of storm size. In summer, rainstorms become more intense but smaller; in winter, rainstorm shrinkage still dominates, but storms also become less numerous and shorter duration. These results imply that flood impacts from climate change will be less severe than would be expected from changes in precipitation intensity alone. However, these projected changes are smaller than model-observation biases, implying that the best means of incorporating them into impact assessments is via 'data-driven simulations' that apply model-projected changes to observational data. The authors therefore develop a simulation algorithm that statistically describes model changes in precipitation characteristics and adjusts data accordingly, and they show that, especially for summertime precipitation, it outperforms simulation approaches that do not include spatial information. [ABSTRACT FROM AUTHOR]

Coastal ecosystems are dependent on terrestrial freshwater export which is affected by both climate trends and natural climate variability. However, the relative role of these factors is not clear. Here, both climate trends and internal climate variabilities at different time scales are related to variations in terrestrial freshwater export into the eastern United States ( U.S.) coastal region. For the recent 35-year period, the intensified hydro-meteorological processes (annual precipitation or evapotranspiration) may explain the observed streamflow variability in the northeast. However, in the southeast, streamflow is positively correlated with climate variability induced by the Pacific Ocean conditions (El Nino-Southern Oscillation [ENSO] and Pacific Decadal Oscillation) rather than Atlantic Ocean conditions (Atlantic Multi-decadal Oscillation and North Atlantic Oscillation). The centroid location for volume of terrestrial freshwater export integrated along the eastern U.S. has a positive temporal trend and is negatively correlated with ENSO conditions, suggesting the northward trend in freshwater export to U.S. eastern coast may be disturbed by the natural climate variability, especially ENSO conditions, i.e., the center of freshwater mass moves southward (northward) during El Nino (La Nina) years. The results indicate the spatial and temporal variations in freshwater export from the eastern U.S. are affected by both climate change and inter-annual climate variability during the recent 35-year period (1980-2014). [ABSTRACT FROM AUTHOR]

We examine trends and variability in low flows over the eastern U.S. (S. Carolina to Maine) and their attribution in a changing climate. We select 149 out of 4878 USGS stations over the eastern U.S., taking into account data availability and minimal direct management. Annual 7-day low flows (Q7) are computed from the series of daily streamflow records for 1962-2011 and compared to an antecedent precipitation (AP) index calculated over the corresponding basin for each station. In general, a north-south (increasing-decreasing) dipole pattern in low flow trends is associated with trends in AP. The exception is in the southern part of the study area including Virginia and the Carolinas, where moderate increasing trends in AP may have been offset by water withdrawals and increasing potential evapotranspiration (PET) as driven by increasing temperature and vapor pressure deficit. A principal component analysis (PCA) of Q7 and AP indicates that the North Atlantic Oscillation (NAO) and Pacific North America (PNA) pattern show statistically significant correlations for Q7 at 1 and 2 month lead time, respectively, via large-scale pressure patterns. Our findings suggest that the inter-annual variability of low flows has increased due to significant anti-correlation between the NAO and PNA during recent decades, and the future risk of low flow extremes may be further enhanced with temperature driven increases in PET and persistence of the multi-decadal relationship between NAO and PNA. [ABSTRACT FROM AUTHOR]

Time series of U.S. daily heavy precipitation (95th percentile) are analyzed to determine factors responsible for regionality and seasonality in their 1979-2013 trends. For annual conditions, contiguous U.S. trends have been characterized by increases in precipitation associated with heavy daily events across the northern United States and decreases across the southern United States. Diagnosis of climate simulations (CCSM4 and CAM4) reveals that the evolution of observed sea surface temperatures (SSTs) was a more important factor influencing these trends than boundary condition changes linked to external radiative forcing alone. Since 1979, the latter induces widespread, but mostly weak, increases in precipitation associated with heavy daily events. The former induces a meridional pattern of northern U.S. increases and southern U.S. decreases as observed, the magnitude of which closely aligns with observed changes, especially over the south and far west. Analysis of model ensemble spread reveals that appreciable 35-yr trends in heavy daily precipitation can occur in the absence of forcing, thereby limiting detection of the weak anthropogenic influence at regional scales. Analysis of the seasonality in heavy daily precipitation trends supports physical arguments that their changes during 1979-2013 have been intimately linked to internal decadal ocean variability and less so to human-induced climate change. Most of the southern U.S. decrease has occurred during the cold season that has been dynamically driven by an atmospheric circulation reminiscent of teleconnections linked to cold tropical eastern Pacific SSTs. Most of the northeastern U.S. increase has been a warm season phenomenon, the immediate cause for which remains unresolved. [ABSTRACT FROM AUTHOR]

Changes in extreme temperatures, heat waves, and heavy rainfall events have adverse effects on human health, air quality, and water resources. With aerosol-only (AER) and greenhouse gas-only (GHG) simulations from 1860 to 2005 in the GFDL CM3 chemistry-climate model, aerosol-induced versus greenhouse gas-induced changes in temperature (summer) and precipitation (all seasons) extremes over the United States are investigated. Small changes in these extremes in the all forcing simulations reflect cancellations between the effects of increasing anthropogenic aerosols and greenhouse gases. In AER, extreme high temperatures and the number of days with temperatures above the 90th percentile decline over most of the United States. The strongest response occurs in the western United States (−2.0°C and −14 days, regionally averaged) and the weakest response occurs in the southeastern United States (−0.6°C and −4.8 days). An opposite-signed response pattern occurs in GHG (+2.3°C and +11.5 days over the western United States and +1.6°C and +7.2 days over the southeastern United States). The similar spatial response patterns in AER versus GHG suggest a preferred regional mode of response that is largely independent of the type of forcing. Extreme precipitation over the eastern United States decreases in AER, particularly in winter, and increases over the eastern and central United States in GHG, particularly in spring. Over the twenty-first century under the representative concentration pathway 8.5 (RCP8.5) emissions scenario, the patterns of extreme temperature and precipitation associated with greenhouse gas forcing dominate. [ABSTRACT FROM AUTHOR]

Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation-Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5-3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded. [ABSTRACT FROM AUTHOR]

Recent analyses of extreme hydrological events across the United States, including those summarized in the recent U.S. Third National Climate Assessment (May 2014), show that extremely large (extreme) precipitation and streamflow events are increasing over much of the country, with particularly steep trends over the northeastern United States. The authors demonstrate that the increase in extreme hydrological events over the northeastern United States is primarily a warm season phenomenon and is caused more by an increase in frequency than magnitude. The frequency of extreme warm season events peaked during the 2000s; a secondary peak occurred during the 1970s; and the calmest decade was the 1960s. Cold season trends during the last 30-50 yr are weaker. Since extreme precipitation events in this region tend to be larger during the warm season than during the cold season, trend analyses based on annual precipitation values are influenced more by warm season than by cold season trends. In contrast, the magnitude of extreme streamflow events at stations used for climatological analyses tends to be larger during the cold season: therefore, extreme event analyses based on annual streamflow values are overwhelmingly influenced by cold season, and therefore weaker, trends. These results help to explain an apparent discrepancy in the literature, whereby increasing trends in extreme precipitation events appear to be significant and ubiquitous across the region, while trends in streamflow appear less dramatic and less spatially coherent. [ABSTRACT FROM AUTHOR]

An ensemble of future climate projections performed with the regional climate model RegCM4 is used to assess changes in inter-annual variability of precipitation over Southern Mexico and Central America (SMECAM). Two different Global Climate Models (GCMs) from the coupled model intercomparison project phase 5 are used to provide boundary conditions for two different RegCM4 configurations. This results in four regional climate projections extending from 1970 to 2100 for the greenhouse gas representative concentration pathway RCP8.5. The precipitation variability over the SMECAM region and its dependence on the gradient between Atlantic and Pacific sea surface temperature (SST) anomalies are verified by reproducing SST anomaly patterns during wettest and driest years similar to those seen in observational datasets. RegCM4 does a comparably better job than the driving GCMs. This strong relationship between precipitation and SST anomalies does not appear to change substantially under future climate conditions. For the rainy season, June to September, we find a future change in inter-annual variability of precipitation towards a much greater occurrence of very dry seasons over the SMECAM region, with this change being more pronounced in the regional than in the global model projections. A greater warming of the Tropical Northeastern Pacific (TNP) compared to the Tropical North Atlantic (TNA), which causes stronger wind fluxes from the TNA to the TNP through the Caribbean Low Level Jet, is identified as the main process responsible for these drier conditions. [ABSTRACT FROM AUTHOR]

Depending on how disease impacts tree exposure to risk, both the prevalence of disease and disease effects on survival may contribute to patterns of mortality risk across a species' range. Disease may accelerate tree species' declines in response to global change factors, such as drought, biotic interactions, such as competition, or functional traits, such as allometry. To assess the role of disease in mediating mortality risk in quaking aspen (Populus tremuloides), we developed hierarchical Bayesian models for both disease prevalence in live aspen stems and the resulting survival rates of healthy and diseased aspen near the species' southern range limit using 5088 individual trees on 281 United States Forest Service Forest Inventory and Analysis plots in the southwestern United States. We found that disease prevalence depended primarily on tree size, tree allometry, and spatial variation in precipitation, while mortality depended on tree size, allometry, competition, spatial variation in summer temperature, and both temporal and spatial variation in summer precipitation. Disease prevalence was highest in large trees with low slenderness found on dry sites. For healthy trees, mortality decreased with diameter, slenderness, and temporal variation in summer precipitation, but increased with competition and spatial variation in summer temperature. Mortality of diseased trees decreased with diameter and aspen relative basal area and increased with mean summer temperature and precipitation. Disease infection increased aspen mortality, especially in trees of intermediate size and trees on plots at climatic extremes (i.e., cool, wet and warm, dry climates). By examining variation in disease prevalence, mortality of healthy trees, and mortality of diseased trees, we showed that the role of disease in aspen tree mortality depended on the scale of inference. For variation among individuals in diameter, disease tended to expose intermediate-size trees experiencing moderate risk to greater risk. For spatial variation in summer temperature, disease exposed lower risk populations to greater mortality probabilities, but the magnitude of this exposure depended on summer precipitation. Furthermore, the importance of diameter and slenderness in mediating responses to climate supports the increasing emphasis on trait variation in studies of ecological responses to global change. [ABSTRACT FROM AUTHOR]

This manuscript introduces a novel computational science approach for studying the impact of climate variability on precipitation. The approach uses an object-oriented connectivity algorithm that segments gridded near-global satellite precipitation data into four-dimensional (4D) objects (longitude, latitude, time, and intensity). These precipitation systems have distinct spatiotemporal properties that are counted, tracked, described, and stored in a searchable database. A case study of western United States precipitation systems is performed, demonstrating the unique properties and capabilities of this object-oriented database. The precipitation dataset used in the case study is the University of California, Irvine, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) from 1 March 2000 to 1 January 2011. A search of the database for all western United States precipitation systems during this time period returns 626 precipitation systems as objects. By analyzing these systems as segmented objects, joint interactions of the selected climate phenomena 1) Arctic Oscillation (AO), 2) Madden-Julian oscillation (MJO), and 3) El Niño-Southern Oscillation (ENSO) on precipitation can be shown. They directly show the increased/decreased likelihood of having precipitation systems occurring over the western United States (monthly count) during phases of these climate phenomena. It is found that specific climate phenomena impact the monthly count of the events differently, and that the joint interaction of climate phenomena of the AO-MJO and AO-ENSO is important, especially during certain months of the year. It is also found that these interactions impact the physical features of the precipitation systems themselves. [ABSTRACT FROM AUTHOR]

The 100 most severe snowstorms within each of six climate regions east of the Rocky Mountains were analyzed to understand how the frequency of severe snowstorms is associated with seasonal averages of other variables that may be more readily predicted and projected. In particular, temperature, precipitation, and El Niño/La Niña anomalies from 1901 to 2013 were studied. In the southern United States, anomalously cold seasonal temperatures were found to be more closely linked to severe snowstorm development than in the northern United States. The conditional probability of occurrence of one or more severe snowstorms in seasons that are colder than average is 80% or greater in regions of the southern United States, which was found to be statistically significant, while it is as low as 35% when seasonal temperatures are warmer than average. This compares with unconditional probabilities of 55%-60%. For seasons that are wetter (drier) than average, severe snowstorm frequency is significantly greater (less) in the Northern Plains region. An analysis of the seasonal timing of severe snowstorm occurrence found they are not occurring as late in the season in recent decades in the warmest climate regions when compared to the previous 75 years. Since 1977, the median date of occurrence in the last half of the cold season is six or more days earlier in the Southeast, South, and Ohio Valley regions than earlier in the twentieth century. ENSO conditions also were found to have a strong influence on the occurrence of the top 100 snowstorms in the Northeast and Southeast regions. [ABSTRACT FROM AUTHOR]

A number of watersheds are selected from the Hydro-Climate Data Network over southeastern United States to examine possible changes in hydrological time series, e.g. precipitation, introduced by changing climate. Possible changes in monthly precipitation are examined by three different methods to detect second order stationarity, abrupt changes in the variance and smooth changes in quantiles of the time series. An analysis of second order stationarity shows that precipitation in eight of the 56 watersheds display nonstationary behaviour. Change-point analyses reveal that changes in the long-term variance of monthly precipitation are only detected for a few sites. As a complementary analysis tool, quantile regression aims to detect potential changes of different percentiles of the monthly precipitation over time. Several sites show diverging trends in the quantiles, which implies that the range and thus variance of the data, is increasing. As distinct change-points are not identified, this suggests that the effect is small and cumulative. Results are analysed in detail, and possible explanations are provided. This type of thorough analysis provides a basis for understanding the possible redistribution of water cycle. It also provides implications for water resources management and hydrological engineering facility design and planning. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article discusses seasonal and annual mean precipitation extremes experienced in the U.S. in 2013. Topics discussed include the definition of a regional precipitation extreme amid the ongoing climate changes, men and the environment's contribution that lead to the 2013 precipitation extremes and the findings that revealed regions in the U.S. that recorded or near-recorded seasonal or annual precipitation anomalies.

Freshwater delivery is an important factor determining estuarine character and health and may be influenced by large-scale climate oscillations. Variability in freshwater delivery (precipitation and discharge) to the Altamaha River estuary (GA, USA) was examined in relation to indices for several climate signals: the Bermuda High Index (BHI), the Southern Oscillation Index (SOI), the Improved El Niño Modoki Index (IEMI), the North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO), and the Pacific/North American Pattern (PNA). Discharge to this estuary has been linked to key ecosystem properties (e.g., salinity regime, water residence time, nutrient inputs, and marsh processes), so understanding how climate patterns affect precipitation and river discharge will help elucidate how the estuarine ecosystem may respond to climate changes. Precipitation patterns in the Altamaha River watershed were described using empirical orthogonal functions (EOFs) of the combined multidecadal time series of precipitation at 14 stations. The first EOF (67 % of the variance) was spatially uniform, the second EOF (11 %) showed a spatial gradient along the long axis of the watershed (NW-SE), and the third EOF (6 %) showed a NE-SW pattern. We compared the principal components (PCs) associated with these EOFs, monthly standardized anomalies of Altamaha River discharge at the gauge closest to the estuary, and the climate indices. Complex, seasonally alternating patterns emerged. The BHI was correlated with June-January discharge and precipitation PC 1. The SOI was correlated with January-April discharge and precipitation PC 2, and also weakly correlated with PC 1 in November-December. The AMO was correlated with river discharge and precipitation PC 3 mainly in December-February and June. The correlation patterns of precipitation PCs with PDO and PNA were similar to those with SOI, but weaker. There were no consistent relationships with two NAO indices or IEMI. Connections between climate signals and estimates of nutrient loading were consistent with the connections to discharge. The occurrence of tropical storms in the region was strongly related to the BHI but not to the other climate indices, possibly representing the influence of storm tracking more than the rate of storm formation. Comparison with the literature suggests that the patterns found may be typical of southeastern USA estuaries but are likely to be different from those outside the region. [ABSTRACT FROM AUTHOR]

The present study investigates the predictive skill of the North American Multi-Model Ensemble (NMME) system for intraseasonal-to-interannual (ISI) prediction with focus on southeastern U.S. precipitation. The southeastern United States is of particular interest because of the typically short-lived nature of above- and below-normal extended rainfall events allowing for focus on seasonal prediction, as well as the tendency for more predictability in the winter months. Included in this study is analysis of the forecast quality of the NMME system when predicting above- and below-normal rainfall and individual rainfall events, with particular emphasis on results from the 2007 dry period. Both deterministic and probabilistic measures of skill are utilized in order to gain a more complete understanding of how accurately the system predicts precipitation at both short and long lead times and to investigate the multimodel aspect of the system as compared to using an individual predictive model. The NMME system consistently shows low systematic error and relatively high skill in predicting precipitation, particularly in winter months as compared to individual model results. [ABSTRACT FROM AUTHOR]

The article discusses the trends in heavy precipitation in the southern regions of the U.S. It refers to the Intergovernmental Panel on Climate Change (IPCC) report on the changes in global average temperatures that could drive changes in global hydrological cycles. It explains that warmer air can hold more water vapor increasing the moisture that could trigger more precipitation.

Atmospheric rivers (ARs) have, in recent years, been recognized as the cause of the large majority of major floods in rivers all along the U.S. West Coast and as the source of 30%-50% of all precipitation in the same region. The present study surveys the frequency with which ARs have played a critical role as a common cause of the end of droughts on the West Coast. This question was based on the observation that, in most cases, droughts end abruptly as a result of the arrival of an especially wet month or, more exactly, a few very large storms. This observation is documented using both Palmer Drought Severity Index and 6-month Standardized Precipitation Index measures of drought occurrence for climate divisions across the conterminous United States from 1895 to 2010. When the individual storm sequences that contributed most to the wet months that broke historical West Coast droughts from 1950 to 2010 were evaluated, 33%-74% of droughts were broken by the arrival of landfalling AR storms. In the Pacific Northwest, 60%-74% of all persistent drought endings have been brought about by the arrival of AR storms. In California, about 33%-40% of all persistent drought endings have been brought about by landfalling AR storms, with more localized low pressure systems responsible for many of the remaining drought breaks. [ABSTRACT FROM AUTHOR]