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Numerous circulation indices have been applied in practical climate services focused on regional precipitation. It is beneficial to identify the most influential or decisive indices, but this is difficult with conventional correlation analyses because of the underlying nonlinear mechanisms for precipitation. This paper demonstrates a set of the most influential indices for July–August precipitation in North China, based on the recursive random forest (RRF) method. These decisive circulation indices include the Polar–Eurasia teleconnection, North African subtropical high ridge position, India–Burma trough, Antarctic Oscillation, Northern Hemisphere polar vortex central latitude, North Atlantic Oscillation, and western Pacific subtropical high northern boundary position. Some of these factors have been recognized as directly influential to the regional precipitation, for example, those of the northwestern Pacific subtropical high; however, some are not easily understood. Decision tree (DT) models using these indices were developed to facilitate composite analyses to explain the RRF results. Taking one of the most interesting DT rules as an example, when the North African subtropical high ridge position is sufficiently far south, an anomalous anticyclone occurs in the upstream and an anomalous cyclone in the downstream of North China. This is unfavorable for northward moisture transport in eastern China and hence causes less precipitation in North China than climatology. The present results are not only helpful for improving diagnostic models of regional precipitation, but also enlightening for exploring how global climate change could impact a region by modulating large-scale circulation patterns. [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]

Middle and eastern routes of the South-North Water Diversion Project (SNWDP) of China, which are approximately located within the area 28°-42°N and 110°-122°E, are being constructed. This paper investigates the past climatic variations on various time scales using instrumental and proxy data. It is found that annual mean surface air temperature has increased significantly during the past 50-100 years, and winter and spring temperatures in the northern part of the region have undergone the most significant changes. A much more significant increase occurs for annual mean minimum temperature and extreme low temperature than for annual mean maximum temperature and extreme high temperature. No significant trend in annual precipitation is found for the region as a whole for the last 50 and 100 years, although obvious decadal and spatial variation is detectable. A seesaw pattern of annual and summer precipitation variability between the north and the south of the region is evident. Over the last 100 years, the Haihe River basin has witnessed a significant negative trend of annual precipitation, but no similar trend is detected for the Yangtze and Huaihe River basins. Pan evaporation has significantly decreased since the mid-1960s in the region in spite of the fact that the trend appears to have ended in the early 1990s. The negative trend of pan evaporation is very significant in the plain area between the Yangtze and Yellow Rivers. There was a notable series of dry intervals lasting decades in the north of the region. The northern drought of the past 30 years is not the most severe in view of the past 500 years; however, the southern drought during the period from the 1960s to the 1980s may have been unprecedented. The dryness-wetness index (DWI) shows significant oscillations with periodicities of 9.5 and 20 years in the south and 10.5 and 25 years in the north. Longer periodicities in the DWI series include 160-170- and 70-80-yr oscillations in the north, and 100-150-yr oscillations in the south. The observed climate change could have implications for the construction and management of the SNWDP. The official approval and start of the hydro project was catalyzed by the severe multiyear drought of 1997-2003 in the north, and the operation and management of the project in the future will also be influenced by climate change-in particular by precipitation variability. This paper provides a preliminary discussion of the potential implications of observed climate change for the SNWDP. [ABSTRACT FROM AUTHOR]

Recently, a single-site stochastic precipitation model called ''the mixture of generalized chain-dependent processes conditioned on a climate variable'' was developed. The model can effectively eliminate overdispersion--that is, underestimation in variance of seasonal precipitation total. In this paper, the single-site model is further developed into a multisite stochastic precipitation model by driving a collection of individual single-site models, but with spatial dependence following a method proposed by D. S. Wilks. Specifically, a computationally effective algorithm for estimating the spatial dependence of precipitation occurrence is developed to replace the construction of the empirical curves in the Wilks method. An effective and straightforward approach for correcting the bias of the spatial correlation of precipitation intensity is also proposed. This model is tested on a small network of sites from a significant hydroelectric power generation region of South Island, New Zealand. [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]

Because of the limitations of coarse-resolution general circulation models (GCMs), delta change (DC) methods are generally used to derive scenarios of future climate as inputs into impact models. In this paper, the impact of future climate change on irrigation was investigated over China using the Community Land Model, version 4 (CLM4), which was calibrated against observed irrigation water demand (IWD) at the provincial level. The results show large differences in projected changes of IWD variability, extremes, timing, and regional responses between the DC and bias-corrected (BC) methods. For example, 95th-percentile IWD increased by 62% in the BC method compared to only a 28% increase in the DC method. In addition, a shift of seasonal IWD peaks (averaged over the country) to one month later in the year was projected when using the BC method, whereas no evident changes were predicted when using the DC method. Furthermore, low-percentile runoff has larger impacts in the BC method compared with proportional changes in the DC method, indicating that hydrological droughts seem to be exacerbated by increased climate variability. The discrepancies between the two methods were potentially due to the inability of the DC method to capture the changes in precipitation variability. Therefore, the authors highlight the potential effects of climate variability and the sensitivity to the choice of particular strategy-adjusting climate projection in assessing climate change impacts on irrigation. Some caveats, however, should be placed around interpretation of simulated percentage changes for all of China since a large model bias was found in southern China. [ABSTRACT FROM AUTHOR]

Results from a regional climate model simulation show substantial increases in future flood risk (2040-69) in many Pacific Northwest river basins in the early fall. Two primary causes are identified: 1) more extreme and earlier storms and 2) warming temperatures that shift precipitation from snow to rain dominance over regional terrain. The simulations also show a wide range of uncertainty among different basins stemming from localized storm characteristics. While previous research using statistical downscaling suggests that many areas in the Pacific Northwest are likely to experience substantial increases in flooding in response to global climate change, these initial estimates do not adequately represent the effects of changes in heavy precipitation. Unlike statistical downscaling techniques applied to global climate model scenarios, the regional model provides an explicit, physically based simulation of the seasonality, size, location, and intensity of historical and future extreme storms, including atmospheric rivers. This paper presents climate projections from the ECHAM5/Max Planck Institute Ocean Model (MPI-OM) global climate model dynamically downscaled using the Weather Research and Forecasting (WRF) Model implemented at 12-km resolution for the period 1970-2069. The resulting daily precipitation and temperature data are bias corrected and used as input to a physically based Variable Infiltration Capacity (VIC) hydrologic model. From the daily time step simulations of streamflow produced by the hydrologic model, probability distributions are fit to the extreme events extracted from each water year and flood statistics for various return intervals are estimated. [ABSTRACT FROM AUTHOR]

According to the mean seasonal and annual precipitation from 30 meteorological stations in the periods of 1961-2008, the precipitation trends are analyzed by using the Mann-Kendall (MK) test in the Jinsha River basin (JRB). Both the temporal and spatial distribution characteristics of precipitation trends in different regions in the JRB are studied for the first time in this paper. There is a slight and insignificant increasing trend in seasonal and annual precipitation except for autumn precipitation, and the annual precipitation has increased by 0.7634 mm yr−1 during the last 48 years. The increasing precipitation trends in spring seem more significant than those in the other three seasons, and autumn is the only season showing a slight and insignificant decreasing precipitation trend. There are more than 80% of stations exhibiting increasing trends for annual precipitation, and it goes to 90% for spring precipitation. The increasing precipitation trends in the headwater and the middle reaches are more dominant than those in the upper and lower reaches. The largest increase magnitude occurred in the less precipitation area, while the largest decrease magnitude occurred in the more precipitation area. The increasing trend of minimum precipitation series and decreasing trend of maximum precipitation series could result in a decreasing trend for the range series in the JRB. In general, the increasing trends of precipitation in the tributary (the Yalong River) are more significant than those in the mainstream (the Jinsha River). The results of this study will provide further knowledge for understanding on the climate change in the JRB. [ABSTRACT FROM AUTHOR]

The rapid expansion of irrigation in the Great Plains since World War II has resulted in significant water table declines, threatening the long-term sustainability of the Ogallala Aquifer. As discussed in Part I of this paper, the Weather Research and Forecasting Model (WRF) was modified to simulate the effects of irrigation at subgrid scales. Simulations of nine April-October periods (three drought, three normal, and three pluvial) over the Great Plains were completed to assess the full impact of irrigation on the water budget. Averaged over all simulated years, irrigation over the Great Plains contributes to May-September evapotranspiration increases of approximately 4% and precipitation increases of 1%, with localized increases of up to 20%. Results from these WRF simulations are used along with a backward trajectory analysis to identify where evapotranspiration from irrigated fields falls as precipitation (i.e., irrigation-induced precipitation) and how irrigation impacts precipitation recycling. On average, only 15.8% of evapotranspiration from irrigated fields falls as precipitation over the Great Plains, resulting in 5.11 mm of May-September irrigation-induced precipitation and contributing to 6.71 mm of recycled precipitation. Reductions in nonrecycled precipitation suggest that irrigation reduces precipitation of moisture advected into the region. The heaviest irrigation-induced precipitation is coincident with simulated and observed precipitation increases, suggesting that observed precipitation increases in north-central Nebraska are strongly related to evapotranspiration of irrigated water. Water losses due to evapotranspiration are much larger than irrigation-induced precipitation and recycled precipitation increases, confirming that irrigation results in net water loss over the Great Plains. [ABSTRACT FROM AUTHOR]

Satellite-derived high-resolution precipitation products (HRPP) have been developed to address the needs of the user community and are now available with 0.25° × 0.25° (or less) subdaily resolutions. This paper evaluates a number of commonly available satellite-derived HRPPs covering northwest Europe over a 6-yr period. Precipitation products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center (CPC) morphing (CMORPH) technique, the CPC merged microwave technique, the Naval Research Laboratory (NRL) blended technique, and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) technique. In addition, the Geosynchronous Operational Environmental Satellite (GOES) precipitation index (GPI) and the European Centre for Medium-Range Weather Forecasting (ECMWF) operational forecast model products are included for comparison. Surface reference data from the European radar network is used as ground truth, supported by the Global Precipitation Climatology Centre (GPCC) precipitation gauge analysis and gauge data over the United Kingdom. Measures of correlation, bias ratio, probability of detection, and false alarm ratio are used to evaluate the products. Results show that satellite products generally exhibit a seasonal cycle in correlation, bias ratio, probability of detection, and false alarm ratio, with poorer statistics during the winter. The ECMWF model also shows a seasonal cycle in the correlation, although the results are poorer during the summer, while the bias ratio, probability of detection, and false alarm ratio are consistent through all seasons. Importantly, all the satellite HRPPs underestimate precipitation over northwest Europe in all seasons. [ABSTRACT FROM AUTHOR]

Precipitation has a high spatial variability, and thus some modeling applications require high-resolution data (<10 km). Unfortunately, in some cases, such as meteorological forecasts and future regional climate projections, only spatial averages over large areas are available. While some attention has been given to the disaggregation of mean areal precipitation estimates, the computation of a disaggregated field with a realistic spatial structure remains a difficult task. This paper describes the development of a statistical disaggregation model based on Gibbs sampling. The model disaggregates 45.6-km-resolution rainfall fields to grids with pixel sizes ranging from 3.8 to 22.8 km. The model is conceptually simple, as the algorithm is straightforward to compute with only a few parameters to estimate. The rainfall depth at each grid pixel is related to the depths of the neighboring pixels, while the spatial variability is related to the convective available potential energy (CAPE) field. The model is developed using daily rainfall data over a 40 000-km2 area located in the southeastern United States. Four-kilometer-resolution rainfall estimates obtained from NCEP's stage IV analysis were used to estimate the model parameters (2002-04) and as a reference to validate the disaggregated fields (2005/06). Results show that the model accurately simulates rainfall depths and the spatial structure of the observed field. Because the model has low computational requirements, an ensemble of disaggregated data series can be generated. [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]

This paper presents a validation study for a high-resolution version of the Regional Climate Model version 3 (RegCM3) over the Carpathian basin and its surroundings. The horizontal grid spacing of the model is 10 km--the highest reached by RegCM3. The ability of the model to capture temporal and spatial variability of temperature and precipitation over the region of interest is evaluated using metrics spanning a wide range of temporal (daily to climatology) and spatial (inner domain average to local) scales against different observational datasets. The simulated period is 1961--90. RegCM3 shows small temperature biases but a general overestimation of precipitation, especially in winter; although, this overestimate may be artificially enhanced by uncertainties in observations. The precipitation bias over the Hungarian territory, the authors'' main area of interest, is mostly less than 20%%. The model captures well the observed late twentieth-century decadal-to-interannual and interseasonal variability. On short time scales, simulated daily temperature and precipitation show a high correlation with observations, with a correlation coefficient of 0.9 for temperature and 0.6 for precipitation. Comparison with two Hungarian station time series shows that the model performance does not degrade when going to the 10-km gridpoint scale. Finally, the model reproduces the spatial distribution of dry and wet spells over the region. Overall, it is assessed that this high-resolution version of RegCM3 is of sufficiently good quality to perform climate change experiments over the Carpathian region--and, in particular, the Hungarian territory--for application to impact and adaptation studies. [ABSTRACT FROM AUTHOR]

Overlying the challenge of managing within natural hydroclimatic variability is the likely modification of runoff variability along with average runoff due to anthropogenic enhancement of greenhouse gas concentrations. In this paper analytical models are developed in which runoff mean and variability, the latter defined by the variance (or standard deviation) of annual runoff, are related to the variances and the covariance of annual precipitation and potential evapotranspiration, and the aridity index (mean annual potential evapotranspiration divided by mean annual precipitation). The method was validated using observed runoff data for 699 worldwide catchments. It was concluded that combining the Schreiber function, which relates the ratio of annual actual evapotranspiration to annual precipitation, with the analytical models provided satisfactory estimates of observed annual runoff mean and interannual variability. It was also concluded that estimates of annual runoff variability based on the simplified model of Koster and Suarez were unsatisfactory. By way of illustrating the new methodology, the approach was applied to projected annual values of precipitation from the Hadley Centre Global Environment Model version 1 (HadGEM) and it showed that considerable changes in reservoir yield are likely to occur if climate change projections of precipitation from HadGEM are realistic. Finally, further simplifications of the equations, based on the Schreiber function, are developed to estimate the mean and standard deviation of annual runoff that allow climate analysts to estimate the impact of potential climate changes on annual runoff characteristics and reservoir yield performance without having to resort to the calibration and application of a rainfall-runoff model or rely on the runoff output from general circulation models to examine such characteristics. [ABSTRACT FROM AUTHOR]

The integration of climate change projections into hydrological and other response models used for water resource planning and management is challenging given the varying spatial resolutions of the different models. In general, climate models are generated at spatial ranges of hundreds of kilometers, while hydrological models are generally watershed specific and based on input at the station or local level. This paper focuses on techniques applied to downscale large-scale climate model simulations to the spatial scale required by local response models (hydrological, agricultural, soil). Specifically, results were extracted from a regional climate model (RegCM) simulation focused on the Middle East, which was downscaled to a scale appropriate for input into a local watershed model [the Hydrological Model for Karst Environment (HYMKE)] calibrated for the upper Jordan River catchment. With this application, the authors evaluated the effect of future climate change on the amount and form of precipitation (rain or snow) and its effect on streamflow in the Jordan River and its tributaries—the major water resources in the region. They found that the expected changes in the form of precipitation are nearly insignificant in terms of changing the timing of streamflow. Additionally, the results suggest a future increase in evaporation and decrease in average annual rainfall, supporting expected changes based on global models in this region. [ABSTRACT FROM AUTHOR]

The future rainfall series used to drive hydrological models in most climate change impact studies is informed by global climate models (GCMs). This paper compares future runoff projections in ∼11 000 0.25° grid cells across Australia from a daily rainfall–runoff model driven with future daily rainfall series obtained using three simple scaling methods, informed by 14 GCMs. In the constant scaling and daily scaling methods, the historical daily rainfall series is scaled by the relative difference between GCM simulations for the future and historical climates. The constant scaling method scales all the daily rainfall by the same factor, and the daily scaling method takes into account changes in the daily rainfall distribution by scaling the different daily rainfall amounts differently. In the daily translation method, the GCM future daily rainfall series is translated to a 0.25° gridcell rainfall series using the relationship established between the historical GCM-scale rainfall and 0.25° gridcell rainfall data. The daily scaling and daily translation methods generally give higher extreme and annual runoff than the constant scaling method because they take into account the increase in extreme daily rainfall (which generates significant runoff) simulated by the large majority of the GCMs. However, the difference between the mean annual runoff simulated with future daily rainfall series obtained using the constant versus daily scaling methods is generally less than 5%, which is relatively smaller than the range of runoff results from the different GCMs of 30%–40%. [ABSTRACT FROM AUTHOR]

Despite continued interest in meteorological influences on the thermal variability of river systems, there are few detailed studies of stream temperature dynamics during storm events. This paper reports high-resolution (15 min) water column and streambed temperature data for storm events of contrasting magnitude, duration, and intensity for three streams (draining glacier, snow, and groundwater sources) across an alpine river system during summers 2002 and 2003. The results demonstrate clear spatial and temporal differences in water column and streambed thermal responses to precipitation events and streamflow peaks. Analysis of all storms across the three sites showed a decrease in water column temperature for 75% of events, with significant negative relationships between stream temperature and precipitation magnitude, precipitation intensity, and stream discharge peaks. Temperature decreases of 10.4°C were recorded, but temperature increases were less marked at up to 2.3°C. Temperature response to precipitation was dampened with increasing depth into the streambed at all sites. Spatial and temporal differences in thermal response to storm events were controlled by precipitation and stream discharge peak characteristics (above) plus antecedent basin conditions, which together determine the nature and rapidity of hydrological response. In this steep alpine basin, stream temperature variability appears to be enhanced by quick routing of precipitation to the river channel (i.e., direct precipitation/channel interception, rapid surface flow over impermeable bedrock/thin alpine soils, and subsurface flow through highly weathered scree slopes). This research highlights the need for integrated hydrometeorological research of precipitation event–hydrological response–stream temperature interactions to advance understanding of runoff generation processes driving event-scale thermal dynamics in alpine and other river systems. [ABSTRACT FROM AUTHOR]

This paper attempts to provide some new understanding of the mechanical as well as thermal effects of the Tibetan Plateau (TP) on the circulation and climate in Asia through diagnosis and numerical experiments. The air column over the TP descends in winter and ascends in summer and regulates the surface Asian monsoon flow. Sensible heating on the sloping lateral surfaces appears from the authors’ experiments to be the major driving source. The retarding and deflecting effects of the TP in winter generate an asymmetric dipole zonal-deviation circulation, with a large anticyclone gyre to the north and a cyclonic gyre to the south. Such a dipole deviation circulation enhances the cold outbreaks from the north over East Asia, results in a dry climate in south Asia and a moist climate over the Indochina peninsula and south China, and forms the persistent rainfall in early spring (PRES) in south China. In summer the TP heating generates a cyclonic spiral zonal-deviation circulation in the lower troposphere, which converges toward and rises over the TP. It is shown that because the TP is located east of the Eurasian continent, in summertime the meridional winds and vertical motions forced by the Eurasian continental-scale heating and the TP local heating are in phase over the eastern and central parts of the continent. The monsoon in East Asia and the dry climate in middle Asia are therefore intensified. [ABSTRACT FROM AUTHOR]

Variability in the global distribution of precipitation is recognized as a key element in assessing the impact of climate change for life on earth. The response of precipitation to climate forcings is, however, poorly understood because of discrepancies in the magnitude and sign of climatic trends in satellite-based rainfall estimates. Quantifying and ultimately removing these biases is critical for studying the response of the hydrologic cycle to climate change. In addition, estimates of random errors owing to variability in algorithm assumptions on local spatial and temporal scales are critical for establishing how strongly their products should be weighted in data assimilation or model validation applications and for assigning a level of confidence to climate trends diagnosed from the data. This paper explores the potential for refining assumed drop size distributions (DSDs) in global radar rainfall algorithms by establishing a link between satellite observables and information gleaned from regional validation experiments where polarimetric radar, Doppler radar, and disdrometer measurements can be used to infer raindrop size distributions. By virtue of the limited information available in the satellite retrieval framework, the current method deviates from approaches adopted in the ground-based radar community that attempt to relate microphysical processes and resultant DSDs to local meteorological conditions. Instead, the technique exploits the fact that different microphysical pathways for rainfall production are likely to lead to differences in both the DSD of the resulting raindrops and the three-dimensional structure of associated radar reflectivity profiles. Objective rain-type classification based on the complete three-dimensional structure of observed reflectivity profiles is found to partially mitigate random and systematic errors in DSDs implied by differential reflectivity measurements. In particular, it is shown that vertical and horizontal reflectivity structure obtained from spaceborne radar can be used to reproduce significant differences in Zdr between the easterly and westerly climate regimes observed in the Tropical Rainfall Measuring Mission Large-scale Biosphere–Atmosphere (TRMM-LBA) field experiment as well as the even larger differences between Amazonian rainfall and that observed in eastern Colorado. As such, the technique offers a potential methodology for placing locally observed DSD information into a global framework. [ABSTRACT FROM AUTHOR]

Stationarity is an assumption that permeates training and practice in water-resource engineering. However, with global change, the validity of stationarity as well as uncertainty of nonstationarity in water-resource planning are being questioned; thus, it is critical to evaluate the stationarity of climate variables, especially precipitation. Based on the continuous observation data of precipitation from 1427 stations across China, 593 efficient grid cells (1° × 1°) are constructed, and the annual precipitation stationarities from 1959 to 2018 are analyzed. The evaluated autocorrelation stationarity indicates that 92.24%–96.12% of the grid cells for an autocorrelation coefficient of lag 1–8 years of precipitation are indistinguishable from 0 [90% confidence level (CL)]. The mean stationarity indicates that 97.47% of the grid cells have a stable mean for 30 years (90% CL); beyond the confidence limits, they are mainly located in the northwest of China, where annual precipitation is less, and the average exceeding range is ±3.78 mm. The long-term observation of annual precipitation in Beijing (1819–2018) and Shanghai (1879–2018) also yields autocorrelation and mean stationarities. There is no significant difference in the annual precipitations between the past 20 years (1999–2018) and the past 60 years (1959–2018) over China. Therefore, the annual precipitation in China exhibits a weak stationary behavior that is indistinguishable from the stationary stochastic process. The average variation in precipitation is ±9.55% between 30 successive years and 16.53% between 10 successive years. Therefore, it is valuable and feasible to utilize the historical data of annual precipitation as the basis of water-resources application. [ABSTRACT FROM AUTHOR]

This research introduces a climatology of hourly precipitation characteristics, investigates trends in precipitation hours (PH) and hourly accumulation, and uses four different time series to determine if precipitation intensity is changing across the southeastern United States from 1960 to 2017. Results indicate hourly intensity significantly increased at 44% (22/50) of the stations, accompanied by an increase in average hourly accumulation at 40% of the sites analyzed (20/50). The average duration of precipitation events decreased at 82% (41/50) of the stations. However, the frequency of 90th percentile hourly events and events above station-specific average hourly totals did not show a broad increase similar to hourly intensity. It seems hourly events are becoming heavier on average, while the duration of the average precipitation event is decreasing. Geographically, heavy hourly events are more frequent along the Gulf Coast and decrease inland. PH significantly decreased across South Carolina, Georgia, and northern Florida, mainly due to significant decreases in winter (DJF) and spring (MAM). Decreases in PH during spring were contained to Georgia and South Carolina and were accompanied by a decrease in accumulation. Decreases in PH during winter were more widespread and did not exhibit a broad decrease in accumulation, suggesting winter precipitation across that portion of the region is becoming more intense. [ABSTRACT FROM AUTHOR]

Changes in extreme precipitation due to climate change often require the application of methods to bias correct simulated atmospheric fields, including extremes. Most existing bias correction techniques (i) only focus on the bias in the mean value or on the extreme values separately, and (ii) exclude zero values from analysis, even though their presence is significant in daily precipitation. We developed a copula-based bias correction scheme that is suitable for zero-inflated daily precipitation data to correct the bias in mean as well as in extreme precipitation at any specific statistical quantile. In considering the whole of Germany as a test bed, the proposed scheme is found to work well across the entire study area, including the German Alpine regions. The joint distribution between observed and regional climate model (RCM)-derived precipitation is developed through copulas. In particular, the joint distribution is modified to make it discrete at zero in order to account for zero values. The benefit of considering zero precipitation values is revealed through the improved performance of bias correction both in the mean and extreme values. Second, the quantile that best captures the bias (whether in the mean or any extreme value) is determined for a specific location and varies spatially and seasonally. This relaxation in selecting the location-specific optimal quantile renders the proposed methodology spatially transferable. By acknowledging possible changes in extreme precipitation due to climate change, the proposed scheme is expected to be suitable for climate change impact assessments for extreme events worldwide. [ABSTRACT FROM AUTHOR]

Precipitation on the Tibetan Plateau (TP) showed different spatial changes during 1979–2016, with an increasing trend over the northern Tibetan Plateau (NTP) and a slightly negative trend over the southern Tibetan Plateau (STP). The changes in precipitation moisture sources over the NTP and STP are investigated using the improved Water Accounting Model with an atmospheric reanalysis as well as observational precipitation and evaporation data. The results show the region in the northwest (region NW), ranging from the TP to Europe dominated by the westerlies, provides 38.9% of precipitation moisture for the NTP, and the region in the southeast (region SE), ranging from the TP to the Indian Ocean and Indochina dominated by the Asian monsoons, provides 51.4% of precipitation moisture for the STP. For the precipitation increase over the NTP, the SE and TP are the main contributors, contributing around 35.8% and 51.7% of the increase, respectively. The contributions from the SE and TP to the STP are, however, minor and insignificant. Meanwhile, the NW shows a negative trend of −4.2 ± 2.9 mm yr−1 decade−1 (significant at the 0.01 level), which contributes to the negative precipitation trend over the STP. Results during the wet season indicate that moisture sources from the areas dominated by the Asian monsoons have contributed more precipitated moisture for the NTP, but not for the STP. Further analysis reveals that precipitated moisture originating from the Indian subcontinent has increased for the NTP while it has decreased for the STP during 1979–2016. [ABSTRACT FROM AUTHOR]

Representative methods of statistical disaggregation and dynamical downscaling are compared in terms of their ability to disaggregate precipitation data into hourly resolution in an urban area with complex terrain. The nonparametric statistical Method of Fragments (MoF) uses hourly data from rain gauges to split the daily data at the location of interest into hourly fragments. The high-resolution, convection-permitting Weather Research and Forecasting (WRF) regional climate model is driven by reanalysis data. The MoF can reconstruct the variance, dry proportion, wet hours per month, number and length of wet spells per rainy day, timing of the maximum rainfall burst, and intensities of extreme precipitation with errors of less than 10%. However, the MoF cannot capture the spatial coherence and temporal interday connectivity of precipitation events due to the random elements involved in the algorithm. Otherwise, the statistical method is well suited for filling gaps in subdaily historical records. The WRF Model is able to reproduce dry proportion, lag-1 autocorrelation, wet hours per month, number and length of wet spells per rainy day, spatial correlation, and 6- and 12-h intensities of extreme precipitation with errors of 10% or less. The WRF approach tends to underestimate peak rainfall of 1- and 3-h aggregates but can be used where no observations are available or when areal precipitation data are needed. [ABSTRACT FROM AUTHOR]

The Ecuadorian Andes are characterized by a complex spatiotemporal variability of precipitation. Global circulation models do not have sufficient horizontal resolution to realistically simulate the complex Andean climate and in situ meteorological data are sparse; thus, a high-resolution gridded precipitation product is needed for hydrological purposes. The region of interest is situated in the center of Ecuador and covers three climatic influences: the Amazon basin, the Andes, and the Pacific coast. Therefore, regional climate models are essential tools to simulate the local climate with high spatiotemporal resolution; this study is based on simulations from the Weather Research and Forecasting (WRF) Model. The WRF Model is able to reproduce a realistic precipitation variability in terms of the diurnal cycle and seasonal cycle compared to observations and satellite products; however, it generated some nonnegligible bias in the region of interest. We propose two new methods for precipitation bias correction of the WRF precipitation simulations based on in situ observations. One method consists of modeling the precipitation bias with a Gaussian process metamodel. The other method is a spatial adaptation of the cumulative distribution function transform approach, called CDF-t, based on Voronoï diagrams. The methods are compared in terms of precipitation occurrence and intensity criteria using a cross-validation leave-one-out framework. In terms of both criteria, the Gaussian process metamodel approach yields better results. However, in the upper parts of the Andes (>2000 m), the spatial CDF-t method seems to better preserve the spatial WRF physical patterns. [ABSTRACT FROM AUTHOR]

In this study, the Providing Regional Climates for Impacts Studies (PRECIS) and the Regional Climate Model (RegCM) system as well as the Variable Infiltration Capacity (VIC) macroscale hydrologic model were integrated into a general framework to investigate impacts of future climates on the hydrologic regime of the Athabasca River basin. Regional climate models (RCMs) including PRECIS and RegCM were used to develop ensemble high-resolution climate projections for 1979–2099. RCMs were driven by the boundary conditions from the Hadley Centre Global Environment Model, version 2 with Earth system configurations (HadGEM2-ES); the Second Generation Canadian Earth System Model (CanESM2); and the Geophysical Fluid Dynamics Laboratory Earth System Model with MOM (GFDL-ESM2M) under the representative concentration pathways (RCPs). The ensemble climate simulations were validated through comparison with observations for 1984–2003. The RCMs project increases in temperature, precipitation, and wind speed under RCPs across most of the Athabasca River basin. Meanwhile, VIC was calibrated using the University of Arizona Shuffled Complex Evolution method (SCE-UA). The performance of the VIC model in replicating the characteristics of the observed streamflow was validated for 1994–2003. Changes in runoff and streamflow under RCPs were then simulated by the validated VIC model. The validation results demonstrate that the ensemble-RCM-driven VIC model can effectively reproduce historical climatological and hydrological patterns in the Athabasca River basin. The ensemble-RCM-driven VIC model shows that monthly streamflow is projected to increase in the 2050s and 2080s under RCPs, with notably higher flows expected in the spring for the 2080s. This will have substantial impacts on water balance on the Athabasca River basin, thus affecting the surrounding industry and ecosystems. The developed framework can be applied to other regions for exploration of hydrologic impacts under climate change. [ABSTRACT FROM AUTHOR]

Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data. [ABSTRACT FROM AUTHOR]

The extreme precipitation that resulted in historic flooding in central-northern France began 26 May 2016 and was linked to a large cutoff low. The floods caused some casualties and over a billion euros in damage. To objectively answer the question of whether anthropogenic climate change played a role, a near-real-time "rapid" attribution analysis was performed, using well-established event attribution methods, best available observational data, and as many climate simulations as possible within that time frame. This study confirms the results of the rapid attribution study. We estimate how anthropogenic climate change has affected the likelihood of exceedance of the observed amount of 3-day precipitation in April–June for the Seine and Loire basins. We find that the observed precipitation in the Seine basin was very rare, with a return period of hundreds of years. It was less rare on the Loire—roughly 1 in 20 years. We evaluated five climate model ensembles for 3-day basin-averaged precipitation extremes in April–June. The four ensembles that simulated the statistics agree well. Combining the results reduces the uncertainty and indicates that the probability of such rainfall has increased over the last century by about a factor of 2.2 (>1.4) on the Seine and 1.9 (>1.5) on the Loire due to anthropogenic emissions. These numbers are virtually the same as those in the near-real-time attribution study by van Oldenborgh et al. Together with the evaluation of the attribution of Storm Desmond by Otto et al., this shows that, for these types of events, near-real-time attribution studies are now possible. [ABSTRACT FROM AUTHOR]

This study quantifies the impact of atmospheric rivers (ARs) on precipitation in southern South America. An AR detection algorithm was developed based on integrated water vapor transport (IVT) from 6-hourly CFSR reanalysis data over a 16-yr period (2001–16). AR landfalls were linked to precipitation using a comprehensive observing network that spanned large variations in terrain along and across the Andes from 27° to 55°S, including some sites with hourly data. Along the Pacific (west) coast, AR landfalls are most frequent between 38° and 50°S, averaging 35–40 days yr−1. This decreases rapidly to the south and north of this maximum, as well as to the east of the Andes. Landfalling ARs are more frequent in winter/spring (summer/fall) to the north (south) of ~43°S. ARs contribute 45%–60% of the annual precipitation in subtropical Chile (37°–32°S) and 40%–55% along the midlatitude west coast (37°–47°S). These values significantly exceed those in western North America, likely due to the Andes being taller. In subtropical and midlatitude regions, roughly half of all events with top-quartile precipitation rates occur under AR conditions. Median daily and hourly precipitation in ARs is 2–3 times that of other storms. The results of this study extend knowledge of the key roles of ARs on precipitation, weather, and climate in the South American region. They enable comparisons with other areas globally, provide context for specific events, and support local nowcasting and forecasting. [ABSTRACT FROM AUTHOR]

The Colorado River basin (CRB) is one of the most important watersheds for energy, water, and food security in the United States. CRB water supports 15% of U.S. food production, more than 50 GW of electricity capacity, and one of the fastest growing populations in the United States. Energy–water–food nexus impacts from climate change are projected to increase in the CRB. These include a higher incidence of extreme events, widespread snow-to-rain regime shifts, and a higher frequency and magnitude of climate-driven disturbances. Here, we empirically show how the historical annual streamflow maximum and hydrograph centroid timing relate to temperature, precipitation, and snow. In addition, we show how these hydroclimatic relationships vary with elevation and how the elevation dependence has changed over this historical observational record. We find temperature and precipitation have a relatively weak relation (|r| < 0.3) to interannual variations in streamflow timing and extremes at low elevations (<1500 m), but a relatively strong relation (|r| > 0.5) at high elevations (>2300 m) where more snow occurs in the CRB. The threshold elevation where this relationship is strongest (|r| > 0.5) is moving uphill at a rate of up to 4.8 m yr−1 (p = 0.11) and 6.1 m yr−1 (p = 0.01) for temperature and precipitation, respectively. Based on these findings, we hypothesize where warming and precipitation-related streamflow changes are likely to be most severe using a watershed-scale vulnerability map to prioritize areas for further research and to inform energy, water, and food resource management in the CRB. [ABSTRACT FROM AUTHOR]

Floods in the Mississippi basin can have large negative societal, natural, and economic impacts. Understanding the drivers of floods, now and in the future, is relevant for risk management and infrastructure-planning purposes. We investigate the drivers of 100-yr-return lower Mississippi River floods using a global coupled climate model with an integrated surface water module. The model provides 3400 years of physically consistent data from a static climate, in contrast to available observational data (relatively short records, incomplete land surface data, transient climate). In the months preceding the model's 100-yr floods, as indicated by extreme monthly discharge, above-average rain and snowfall lead to moist subsurface conditions and the buildup of snowpack, making the river system prone to these major flooding events. The meltwater from snowpack in the northern Missouri and upper Mississippi catchments primes the river system, sensitizing it to subsequent above-average precipitation in the Ohio and Tennessee catchments. An ensemble of transient forcing experiments is used to investigate the impacts of past and projected anthropogenic climate change on extreme floods. There is no statistically significant projected trend in the occurrence of 100-yr floods in the model ensemble, despite significant increases in extreme precipitation, significant decreases in extreme snowmelt, and significant decreases in less extreme floods. The results emphasize the importance of considering the fully coupled land–atmosphere system for extreme floods. This initial analysis provides avenues for further investigation, including comparison to characteristics of less extreme floods, the sensitivity to model configuration, the role of human water management, and implications for future flood-risk management. [ABSTRACT FROM AUTHOR]

Accounting for climate change, GCM-based projections and their uncertainty are relevant to study potential impacts on hydrological regimes as well as to analyze, operate, and design water infrastructure. Traditionally, several downscaled and/or bias-corrected GCM projections are individually or jointly used to map the raw GCMs' changes to local stations and evaluate uncertainty. However, the preservation of GCMs' statistical attributes is by no means guaranteed, and thus alternative methods to cope with this issue are needed. This work develops an ensemble technique for the unbiased mapping of GCM changes to local stations, which preserves local climate variability and the GCMs' statistics. In the approach, trend percentiles are extracted from the GCMs to represent the range of future long-term climate conditions to which local climatic variability is added. The approach is compared against a method in which each GCM is individually used to build future climatic scenarios from which percentiles are computed. Both approaches were compared to study future precipitation conditions in three Chilean basins under future climate projections based on 45 GCM runs under the RCP8.5 scenario. Overall, the approaches produce very similar results, even if a few trend percentiles are adopted in the GCM preanalysis. In fact, using 5–10 percentiles produces a mean absolute difference of 0.4% in the estimation of the probabilities of consecutive years under different precipitation thresholds, which is ~60% less than the error obtained using the median trend. Thus, the approach successfully preserves the GCM's statistical attributes while incorporating the range of projected climates. [ABSTRACT FROM AUTHOR]

Land surface water and energy fluxes from the ensemble mean of the Atmospheric Model Intercomparison Project (AMIP) simulations of a Geophysical Fluid Dynamics Laboratory (GFDL) high-resolution climate model (AM2.5) were evaluated using offline simulations of a calibrated land surface model [Princeton Global Forcing (PGF)/VIC] and intercompared with three reanalysis datasets: MERRA-Land, ERA-Interim/Land, and CFSR. Using PGF/VIC as the reference, the AM2.5 precipitation, evapotranspiration, and runoff showed a global positive bias of ~0.44, ~0.27, and ~0.15 mm day−1, respectively. For the energy budget, while the AM2.5 net radiation agreed very well with the PGF/VIC, the AM2.5 improperly partitioned the net radiation, with the latent heat showing positive bias and sensible heat showing negative bias. The AM2.5 net radiation, latent heat, and sensible heat relative to the PGF/VIC had a global negative bias of ~1.42 W m−2, positive bias of ~7.8 W m−2, and negative bias of ~8.7 W m−2, respectively. The three reanalyses show greater biases in net radiation, likely due to the deficiencies in cloud parameterizations. At a regional scale, the biases of the AM2.5 water and energy budget components are mostly comparable to the three reanalyses and PGF/VIC. While the AM2.5 well simulated the actual values of water and energy fluxes, the temporal anomaly correlations of the three reanalyses with PGF/VIC were mostly greater than the AM2.5, partly due to the ensemble mean of the AM2.5 members averaging out the intrinsic variability of the land surface fluxes. The discrepancies among land surface model simulations, reanalyses, and high-resolution climate model simulations demonstrate the challenges in estimating and evaluating land surface hydrologic fluxes at regional-to-global scales. [ABSTRACT FROM AUTHOR]

Mountain precipitation is often strongly underestimated as observations are scarce, biased toward lower-lying locations and prone to wind-induced undercatch, while topographical heterogeneity is large. This presents serious challenges to hydrological modeling for water resource management and climate change impact assessments in mountainous regions of the world, where a large population depends on water supply from the mountains. The headwaters of the Tarim River, covering four remote and highly glacierized Asian mountain ranges, are vital water suppliers to large agricultural communities along the Taklamakan Desert, northwest China. Assessments of future changes to these water towers have been hampered because of the large precipitation uncertainties. In this study, six existing precipitation datasets (observation-based reanalysis datasets, satellite observation datasets, and the output of high-resolution regional climate models) were compared over five headwaters of the Tarim River. The dataset incorporating the highest observation density (APHRODITE) is then corrected by calibrating the glacio-hydrological model Soil and Water Integrated Model-Glacier Dynamics (SWIM-G) to observed discharge, glacier hypsometry, and modeled glacier mass balance. Results show that this form of inverse modeling is able to inform the precipitation correction in such data-scarce conditions. Substantial disagreement of annual mean precipitation between the analyzed datasets, with coefficients of variation in catchment mean precipitation of 68% on average, was found. The model-based precipitation estimates are on average 1.5-4.3 times higher than the APHRODITE data, but fall between satellite-based and regional climate model results. [ABSTRACT FROM AUTHOR]

In this study, a microwave-based multisatellite merged product released from the European Space Agency's Climate Change Initiative (ESA CCI) and two model-based simulations from the Community Land Model 4.5 (CLM4.5) and Global Land Data Assimilation System (GLDAS) were used to investigate interannual variations and trends of soil moisture in China between 1979 and 2010. They were also evaluated using in situ observations from the nationwide agrometeorological network. These three datasets show consistent drying trends for surface soil moisture in northeastern and central China, as well the eastern portion of Inner Mongolia, and wetting trends in the Tibetan Plateau, which are also identified by in situ observations. Trends in the root-zone soil moisture are in line with those of surface soil moisture seen in the CLM4.5 and GLDAS simulations obtained from most areas in China (78%-88%), except for northwestern China and southwest of the Tibetan Plateau. Moreover, the drying trend intensifies with increasing soil depth. Taking the in situ measurements as reference, it is found that ESA CCI has better accuracy in identifying the significant drying trends while CLM4.5 and GLDAS capture wetting trends better. Compared to temperature, precipitation is the primary factor responsible for these trends, which controls the direction of soil moisture changes, while increasing temperatures can also enhance soil drying during periods of decreased precipitation. [ABSTRACT FROM AUTHOR]

The Mekong River is the lifeblood of the Southeast (SE) Asian economies. In situ and satellite-based precipitation are analyzed to assess the amount of water received as precipitation in the river basin (Mekong basin water), in particular, the amount each country receives. Laos, Thailand, and Cambodia contribute ~75% of the basin water during March-September, whereas China's contribution is 10%-15%, except in winter when it rises to 25%. The processing of Mekong basin water into Mekong streamflow entails accounting for the uncertain water losses but, interestingly, interannual variations in Mekong basin water can be processed into Mekong streamflow using a simple hydrologic model, which is validated using monthly river discharge data from four stations. Preliminary evidence for the impact of upbasin dams on downstream flow, especially the timing of peak summer flow, is presented. Characterization of El Niño's influence on SE Asian rainfall reveals significant rainfall reductions in the fall preceding and the spring following El Niño's peak phase (winter); such reductions at the bookends of the dry season in SE Asia (winter) generate droughts, as in 2015-16. The linear trend in twentieth-century rainfall assesses the vulnerability of the region to climate change. The analysis indicates the feasibility of streamflow prediction using a simple hydrologic model driven by high-resolution precipitation observations and forecasts. It raises the prospects of drought prediction based on El Niño's emergence/forecast. Finally, by showing the Mekong to be largely a rain-fed and not snowmelt-fed river, it provides quantitative context for assessing the notion of Chinese control on the lower Mekong via upbasin dams. [ABSTRACT FROM AUTHOR]

Drawing from a NOAA database of hourly precipitation data from 5995 stations in the contiguous United States over the period 1949-2009, the authors investigate possible trends in the variance of the hourly precipitation, averaged over the diurnal and annual cycles and normalized by the square of the mean precipitation at that site. This normalized variance is a measure of storminess, distinct from increases in precipitation attributable to warming. For the 1722 stations surviving quality control with data on at least 80% of days in at least 30 years, the authors compute the rate of change of the logarithm of the normalized variance at each station and set bounds on its mean (over stations) trend. The logarithmic function weights the trends at calm stations equally to those at stormy stations and enhances the statistical power of the mean. The authors find a logarithmic rate of change of the mean normalized variance of (-2.8±1.7)×10-4 yr-1 (±1σ). The 2σ upper bounds on any continentally averaged trend, increasing or decreasing, are about 0.001 yr-1 (doubling or halving times . 1000 years). It is found that the normalized variance in the Los Angeles basin has increased at a statistically significant rate. Thismay be attributable to a decrease in the number of aerosol condensation nuclei. Upper bounds are set on any effect of the 11-yr solar cycle. [ABSTRACT FROM AUTHOR]

Global climate models are beginning to include explicit treatments of irrigation to investigate the coupling between human water use and the natural hydrologic cycle. However, differences in the formulation of irrigation schemes have produced inconsistent results, and thus the impact of irrigation on the climate system remains uncertain. To better understand the influence of irrigation on precipitation, the authors analyze simulations from the irrigation-enabled Community Land Model, version 4 (CLM4), where irrigation is applied only over a region centered on India. The addition of irrigation to the land surface has the anticipated consequence of increasing evapotranspiration locally, despite issues revealed in CLM4 of unrealistically high partitioning of irrigation water to surface runoff and unrealistically fast water drainage through the soil column. These limitations highlight a need to observationally constrain and simultaneously optimize irrigation, runoff, drainage, and evapotranspiration. Nonlocal precipitation changes as a result of Indian irrigation during the premonsoon season are examined through a hindcast framework that reveals robust hydrologic teleconnections to parts of the Arabian Sea, Bay of Bengal, and Japan on short lead times, but with strong dependence on initial synoptic conditions. On longer time scales, many of these teleconnections to Indian irrigation are easily shrouded by internal variability, but a potential geographic action center remains over the meiyu-baiu rainband indicative of a nonlocal bridge mechanism. Many of the sensitivities identified here are distinct from other global models, emphasizing the need for carefully designed irrigation-intercomparison studies. [ABSTRACT FROM AUTHOR]

Using observed and reanalysis data, the pronounced interdecadal variations of Lake Qinghai (LQH) water levels and associated climate factors were diagnosed. From the 1960s to the early 2000s, the water level of LQH in the Tibetan Plateau has experienced a continual decline of 3m but has since increased considerably. A water budget analysis of the LQH watershed suggested that the water vapor flux divergence ∇ · Q is the dominant atmospheric process modulating precipitation and subsequently the lake volume change ΔV. The marked interdecadal variability in ΔV and = ∇ Q was found to be related to the North Pacific (NP) and Pacific decadal oscillation (PDO) modes during the cold season (November-March). Through empirical orthogonal function (EOF) and regression analyses, the water vapor sink over the LQH watershed also responds significantly to the summer Eurasian wave train modulated by the low-frequency variability associated with the cold season NP and PDO modes. Removal of these variability modes (NP, PDO, and the Eurasian wave train) led to a residual uptrend in the hydrological variables of ΔV, ∇ · Q, and precipitation, corresponding to the net water level increase. Attribution analysis using the Coupled Model Intercomparison Project phase 5 (CMIP5) single-forcing experiments shows that the simulations driven by greenhouse gas forcing produced a significant increase in the LQH precipitation, while anthropogenic aerosols generated a minor wetting trend as well. [ABSTRACT FROM AUTHOR]

Long-term changes in the monthly precipitation along the coastal areas of the Sea of Japan are examined using monthly operational observation data from the Japan Meteorological Agency. The monthly precipitation in December significantly increased from the mid-1980s to 2015, even though no remarkable changes were found in January, February, or November. Significant positive trends in the December precipitation extend widely over the coastal areas of the Sea of Japan, and the amounts increase to approximately 50% of the climatological precipitation in December at most observational stations. The interannual variations in other variables, including the monthly accumulated actual sunshine duration, days with lightning detection, and satellite-retrieved outgoing longwave radiation, also show significant trends that are consistent with the precipitation increase in December. The effect of the sea surface temperature (SST) on precipitation change is discussed based on correlation and regression analyses. The interannual variations in the December precipitation averaged over the observational stations near the Sea of Japan are significantly correlated with the SSTs in the prior month (November). However, the SST increase in November is insufficient to account for the increase in precipitation. In addition, it was found that the satellite-retrieved surface wind speed in December has grown stronger in recent years over the Sea of Japan. It is suggested that the stronger wind corresponds to the enhanced monsoonal flow and is the primary cause of the precipitation increase in December. [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]

The Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR) provides a unique set of three-dimensional radar precipitation estimates across much of the globe. Both terrain and climatic conditions can have a strong influence on the reliability of these estimates. Switzerland provides an ideal testbed to evaluate the performance of the DPR in complex terrain: it consists of a mixture of very complex terrain (the Alps) and the far flatter Swiss Plateau. It is also well instrumented, covered with a dense gauge network as well as a network of four dual-polarization C-band weather radars, with the same instrument network used in both the Plateau and the Alps. Here an evaluation of the GPM DPR rainfall rate products against the MeteoSwiss radar rainfall rate product for the first two years of the GPM DPR's operation is presented. Errors in both detection and estimation are considered, broken down by terrain complexity, season, precipitation phase, precipitation type, and precipitation rate. Errors are considered both integrated across the entire domain and spatially, and consistent underestimation of precipitation by GPM is found. This rises to −51% in complex terrain in the winter, primarily due to the predominance of DPR measurements wholly in the solid phase, where problems are caused by lower reflectivities. The smaller vertical extent of precipitation in winter is also likely a cause. Both detection and estimation performance are found to be significantly better in summer than in winter, in liquid than in solid precipitation, and in flatter terrain than in complex terrain. [ABSTRACT FROM AUTHOR]

Watersheds located in the Catskill Mountains of southeastern New York State contribute about 90% of the water to the New York City water supply system. Recent studies show that this region is experiencing increasing trends in total precipitation and extreme precipitation events. To assess the impact of this and other possible climatic changes on the water supply, there is a need to develop future climate scenarios that can be used as input to hydrological and reservoir models. Recently, stochastic weather generators (SWGs) have been used in climate change adaptation studies because of their ability to produce synthetic weather time series. This study examines the performance of a set of SWGs with varying levels of complexity to simulate daily precipitation characteristics, with a focus on extreme events. To generate precipitation occurrence, three Markov chain models (first, second, and third orders) were evaluated in terms of simulating average and extreme wet days and dry/wet spell lengths. For precipitation magnitude, seven models were investigated, including five parametric distributions, one resampling technique, and a polynomial-based curve fitting technique. The methodology applied here to evaluate SWGs combines several different types of metrics that are not typically combined in a single analysis. It is found that the first-order Markov chain performs aswell as higher orders for simulating precipitation occurrence, and two parametric distribution models (skewed normal and mixed exponential) are deemed best for simulating precipitationmagnitudes. The specific models that were found to be most applicable to the region may be valuable in bottom-up vulnerability studies for the watershed, as well as for other nearby basins. [ABSTRACT FROM AUTHOR]

This study analyzes the variation and risk changes of extreme precipitation under nonstationarity conditions using the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) and the Mann-Kendall (MK) test. The extreme precipitation series is extracted from the observations during the second flood season (July-September) from 1960 to 2012 derived from 86 meteorological stations in the southeastern coastal region of China. The trend of mean (Mn) and variance (Var) of extreme precipitation is detected by MK. Ten large-scale circulation variables and four greenhouse gases are selected to construct a climate change index and a human activity index, which are based on principal component analysis. The recurrence risk of extreme precipitation is calculated by GAMLSS while considering climate changes and human activities. The results demonstrate that the nonstationarity characteristic of extreme precipitation is widespread in this region. A significant increasing trend of Mn is found in Shanghai, eastern Zhejiang, and northern and southern Fujian. An enhanced Var is found in eastern Guangdong. A significant positive correlation exists between climate changes/human activities and Mn/Var, especially in Zhejiang and Fujian. Generally, the contribution of climate changes and human activities to Mn is greater than it is to Var. In this region, the precipitation amount of high-frequency (2-yr return period) and low-frequency (100-yr return period) events increases from inland to coastal and from north to south. The government should pay careful attention to these trends because the intensity of extreme precipitation events and their secondary disasters could result in serious losses. [ABSTRACT FROM AUTHOR]

Changes in air temperature and precipitation can modify snowmelt-driven runoff in snowmelt-dominated regimes. This study focuses on climate change impacts on the snow hydrology of the Fraser River basin (FRB) of British Columbia (BC), Canada, using the Variable Infiltration Capacity model (VIC). Statistically downscaled forcing datasets based on 12 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are used to drive VIC for two 30-yr time periods, a historical baseline (1980-2009) and future projections (2040-69: 2050s), under representative concentration pathways (RCPs) 4.5 and 8.5. The ensemble-based VIC simulations reveal widespread and regionally coherent spatial changes in snowfall, snow water equivalent (SWE), and snow cover over the FRB by the 2050s. While the mean precipitation is projected to increase slightly, the fraction of precipitation falling as snow is projected to decrease by nearly 50% in the 2050s compared to the baseline. Snow accumulation and snow-covered area are projected to decline substantially across the FRB, particularly in the Rocky Mountains. Onset of springtime snowmelt in the 2050s is projected to be nearly 25 days earlier than historically, yielding more runoff in the winter and spring for the Fraser River at Hope, BC, and earlier recession to low-flow volumes in summer. The ratio of snowmelt contribution to runoff decreases by nearly 20% in the Stuart and Nautley subbasins of the FRB in the 2050s. The decrease in SWE and loss of snow cover is greater from low to midelevations than in high elevations, where temperatures remain sufficiently cold for precipitation to fall as snow. [ABSTRACT FROM AUTHOR]

Dry conditions in 2013-16 in much of the western United States were responsible for severe drought and led to an exceptional fire season in the Pacific Northwest in 2015. Winter 2015/16 was forecasted to relieve drought in the southern portion of the region as a result of increased precipitation due to a very strong El Nino signal. A student forecasting challenge is summarized in which forecasts of winter hydroclimate across the western United States were made on 1 January 2016 for the winter hydroclimate using several dynamical and statistical forecast methods. They show that the precipitation forecasts had a large spread and none were skillful, while anomalously high observed temperatures were forecasted with a higher skill and precision. The poor forecast performance, particularly for precipitation, is traceable to high uncertainty in the North American Multi-Model Ensemble (NMME) forecast, which appears to be related to the inability of the models to predict an atmospheric blocking pattern over the region. It is found that strong El Nino sensitivities in dynamical models resulted in an overprediction of precipitation in the southern part of the domain. The results suggest the need for a more detailed attribution study of the anomalous meteorological patterns of the 2015/16 El Nino event compared to previous major events. [ABSTRACT FROM AUTHOR]

In this study, the potential future changes of mean and extreme precipitation in the middle and lower Yangtze River basin (MLYRB), eastern China, are assessed using the models of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Historical model simulations are first compared with observations in order to evaluate model performance. In general, the models simulate the precipitation mean and frequency better than the precipitation intensity and extremes, but still have difficulty capturing precipitation patterns over complex terrains. They tend to overestimate precipitation mean, frequency, and intensity while underestimating the extremes. After correcting for model biases, the spatial variation of mean precipitation projected by the multimodel ensemble mean (MME) is improved, so the MME after the bias correction is used to project changes for the years 2021-50 and 2071-2100 relative to 1971-2000 under two emission scenarios: RCP4.5 and RCP8.5. Results show that with global warming, precipitation will become less frequent but more intense over the MLYRB. Relative changes in extremes generally exceed those in mean precipitation. Moreover, increased precipitation extremes are also expected even in places where mean precipitation is projected to decrease in 2021-50. The overall increase in extreme precipitation could potentially lead to more frequent floods in this already flood-prone region. [ABSTRACT FROM AUTHOR]

Postprocessing of climate model outputs is usually performed to remove biases prior to performing climate change impact studies. The evaluation of the performance of bias correction methods is routinely done by comparing postprocessed outputs to observed data. However, such an approach does not take into account the inherent uncertainty linked to natural climate variability and may end up recommending unnecessary complex postprocessing methods. This study evaluates the performance of bias correction methods using natural variability as a baseline. This baseline implies that any bias between model simulations and observations is only significant if it is larger than the natural climate variability. Four bias correction methods are evaluated with respect to reproducing a set of climatic and hydrological statistics. When using natural variability as a baseline, complex bias correction methods still outperform the simplest ones for precipitation and temperature time series, although the differences are much smaller than in all previous studies. However, after driving a hydrological model using the bias-corrected precipitation and temperature, all bias correction methods perform similarly with respect to reproducing 46 hydrological metrics over two watersheds in different climatic zones. The sophisticated distribution mapping correction methods show little advantage over the simplest scaling method. The main conclusion is that simple bias correction methods appear to be just as good as other more complex methods for hydrological climate change impact studies. While sophisticated methods may appear more theoretically sound, this additional complexity appears to be unjustified in hydrological impact studies when taking into account the uncertainty linked to natural climate variability. [ABSTRACT FROM AUTHOR]

Runoff in the Amu Darya River (ADR) in central Asia has been declining steadily since the 1950s. The reasons for this decline are ambiguous, requiring a complete analysis of glaciohydrological processes across the entire data-scarce source region. In this study, grid databases of precipitation from the Asian Precipitation-Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) and temperature from Princeton's Global Meteorological Forcing Dataset (PGMFD) are used to force the distributed, glacier-enhanced Soil and Water Assessment Tool (SWAT) model to simulate glaciohydrological processes for 1951-2007 so as to determine long-term streamflow changes and the primary driving factors in the source region of the ADR. The study suggests that the database was a suitable proxy for temperature and precipitation forcing in simulating glaciohydrological processes in the data-scarce alpine catchment region. The estimated annual streamflow of 72.6 km3 in the upper ADR had a decreasing trend for the period from 1951 to 2007. Change in precipitation, rather than in temperature, dominated the decline in streamflow in either the tributaries or mainstream of the ADR. The streamflow decreased by 15.5% because of the decline in precipitation but only increased by 0.2% as a result of the increase in temperature. Thus, warming temperature had much less effect than declining precipitation on streamflow decline in the ADR in central Asia in 1951-2007. [ABSTRACT FROM AUTHOR]

With limited computational resources, there is a need for computationally frugal models. This is particularly the case for atmospheric sciences, which have long relied on either simplistic analytical solutions or computationally expensive numerical models. The simpler solutions are inadequate for many problems, while the cost of numerical models makes their use impossible for many problems, most notably high-resolution climate downscaling applications spanning large areas, long time periods, and many global climate projections. Here the Intermediate Complexity Atmospheric Research model (ICAR) is presented to provide a new step along the modeling complexity continuum. ICAR leverages an analytical solution for high-resolution perturbations to wind velocities, in conjunction with numerical physics schemes, that is, advection and cloud microphysics, to simulate the atmosphere. The focus of the initial development of ICAR is for predictions of precipitation, and eventually temperature, humidity, and radiation at the land surface. Comparisons between ICAR and the Weather Research and Forecasting (WRF) Model for simulations over an idealized mountain are presented, as well as among ICAR, WRF, and the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) observation-based product for a year-long simulation over the Colorado Rockies. In the ideal simulations, ICAR matches WRF precipitation predictions across a range of environmental conditions with a coefficient of determination r2 of 0.92. In the Colorado Rockies, ICAR, WRF, and PRISM show very good agreement, with differences between ICAR and WRF comparable to the differences between WRF and PRISM in the cool season. For these simulations, WRF required 140-800 times more computational resources than ICAR. [ABSTRACT FROM AUTHOR]