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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Many hydrological hazards are closely connected to local precipitation (extremes), especially in small and urban catchments. The use of regional climate model (RCM) data for small-scale hydrological climate change impact assessment has long been nearly unfeasible because of the low spatial resolution. The RCM resolution is, however, rapidly increasing, approaching the size of small catchments and thus potentially increasing the applicability of RCM data for this purpose. The objective of this study is to explore to what degree subhourly temporal precipitation statistics in an RCM converge to observed point statistics when gradually increasing the resolution from 50 to 6 km. This study uses precipitation simulated by RCA3 at seven locations in southern Sweden during 1995-2008. A positive impact of higher resolution was most clearly manifested in 10-yr intensity-duration-frequency (IDF) curves. At 50 km the intensities are underestimated by 50%-90%, but at 6 km they are nearly unbiased, when averaged over all locations and durations. Thus, at 6 km, RCA3 apparently generates low-frequency subdaily extremes that resemble the values found in point observations. Also, the reproduction of short-term variability and less extreme maxima were overall improved with increasing resolution. For monthly totals, a slightly increased overestimation with increasing resolution was found. The bias in terms of wet fraction and wet spell characteristics was overall not strongly dependent on resolution. These metrics are, however, influenced by the cutoff threshold used to separate between wet and dry time steps as well as the wet spell definition. [ABSTRACT FROM AUTHOR]

Precipitation extremes and small-scale variability are essential drivers in many climate change impact studies. However, the spatial resolution currently achieved by global climate models (GCMs) and regional climate models (RCMs) is still insufficient to correctly identify the fine structure of precipitation intensity fields. In the absence of a proper physically based representation, this scale gap can be at least temporarily bridged by adopting a stochastic rainfall downscaling technique. In this work, a precipitation downscaling chain is introduced where the global 40-yr ECMWF Re-Analysis (ERA-40) (at about 120-km resolution) is dynamically downscaled using the Protheus RCM at 30-km resolution. The RCM precipitation is then further downscaled using a stochastic downscaling technique, the Rainfall Filtered Autoregressive Model (RainFARM), which has been extended for application to long climate simulations. The application of the stochastic downscaling technique directly to the larger-scale reanalysis field at about 120-km resolution is also discussed. To assess the ability of this approach in reproducing the main statistical properties of precipitation, the downscaled model results are compared with the precipitation data provided by a dense network of 122 rain gauges in northwestern Italy, in the time period from 1958 to 2001. The high-resolution precipitation fields obtained by stochastically downscaling the RCM outputs reproduce well the seasonality and amplitude distribution of the observed precipitation during most of the year, including extreme events and variance. In addition, the RainFARM outputs compare more favorably to observations when the procedure is applied to the RCM output rather than to the global reanalyses, highlighting the added value of reaching high enough resolution with a dynamical model. [ABSTRACT FROM AUTHOR]

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]

Precipitation maxima in global climate model (GCM) simulations are compared with observations in terms of resolution dependence and climate change. The analysis shows the following results: (i) the observed scaling law relating precipitation maxima to duration is basically reproduced but exhibits resolution dependence, (ii) the intensity of precipitation extremes is up to one order of magnitude smaller in the model data, and (iii) the increase of precipitation maxima on short time scales in the warmer climate simulations [representative concentration pathway 8.5 (RCP8.5)] vanishes for monthly time scales. [ABSTRACT FROM AUTHOR]

Daily rainfall data obtained from 1025 rain gauges spread across the country over 51 years (1951-2001) are subjected to correlation analysis to identify homogeneous rainfall zones over India. In contrast to earlier studies, which were based on seasonal/annual rainfall, the present study identifies homogeneous rainfall regions with the help of seasonal [southwest monsoon (SWM) and northeast monsoon (NEM)] and annual rainfall. India is divided into 26 (20) homogeneous rainfall zones using annual and SWM (NEM) rainfall. The delineated homogeneous regions are compared and contrasted with those defined by earlier studies, employing a variety of schemes. The interseries correlations of rainfall within each zone are found to be better when the zones are identified by the present study than by other studies. The tests that are performed to evaluate coherency of zones reveal that the zones are homogeneous not only at different temporal scales (interannual and intraseasonal) but also in terms of rain amount, rain frequency, and rain type. Although the delineation of coherent zones is done using interannual/seasonal rainfall data, these zones exhibit coherency in rainfall variations at intraseasonal scale. Nevertheless, the degree of homogeneity is different for rainfall variations occurring at different temporal scales. Further, the zones show better coherency in excess rainfall years than in deficit rainfall years. Longer-term utility of the delineated zones is studied by examining delineated zones and their coherency in the first and second half of the total data period. Although the regions remain the same in both the periods, the coherency is reduced in the second half, suggesting that the homogeneity of regions may vary in the future. [ABSTRACT FROM AUTHOR]

Operational radar data reveal that precipitation systems occurring on the southern side of the Alps near Locarno, Switzerland, follow seasonal patterns of vertical reflectivity structure. Storms occurring in summer are more convective than winter season storms as indicated by more frequent observation of reflectivity at higher altitudes during summer. Individual precipitation events occurring year-round are classified by comparison to average seasonal vertical reflectivity structure. Seasonal classification of individual storms reveals a transition between winter- and summer-type storms during spring and fall that follows changes in average daily surface temperature. In addition to distinct vertical structure, summer- and winter-type storms have differences in duration, intensity, and interval between storms. Although summer- and winter-type storms result in a similar amount of total precipitation, summer-type storms have shorter duration, and therefore greater intensity. The dependence of storm types on temperature has implications for intensification of the hydrologic cycle due to climate change. Warmer winter, spring, or fall surface temperatures may affect average precipitation intensity by increasing the number of days per year that experience more intense convective precipitation while decreasing the probability of less intense stratiform precipitation. [ABSTRACT FROM AUTHOR]

Several provinces of China experienced an intense drought episode during 2009 and 2010. The drought was particularly severe in southwestern and northern China, where the accumulated precipitation from May 2009 to April 2010 was about 25% less than normal. The decline of accumulated precipitation over northern China was mostly noticeable during the summer season of 2009 and it was comparable to recent dry episodes. The southwestern China drought resulted from a sequence of dry months from summer 2009 to winter 2010, corresponding to the driest event since at least 1951. The suppression of rainfall in summer over both regions was in agreement with a weakened broad-scale South Asian summer monsoon, possibly influenced by an El Niño developing phase, whereas the extremely negative phases of the Arctic Oscillation during the winter of 2010 may have contributed to the persistence of the drought in southwestern China. The assessment of the associated impacts indicates that water reservoirs were severely affected with a ~20% reduction in the nationwide hydroelectrical production during the drought event. Furthermore, an analysis of the normalized difference vegetation index data reveals that large cropland sectors of northern and eastern China experienced up to 8 months of persistently stressed vegetation between May 2009 and July 2010, while southwestern China was relatively less affected. Such different regional vegetative responses are interpreted in terms of the land-cover type, agriculture management, and their dependence on seasonal precipitation and water availability for irrigation. [ABSTRACT FROM AUTHOR]

Global simulations have been conducted with the European Centre for Medium-Range Weather Forecasts operational model run at T1279 resolution for multiple decades representing climate from the late twentieth and late twenty-first centuries. Changes in key components of the water cycle are examined, focusing on variations at short time scales. Metrics of coupling and feedbacks between soil moisture and surface fluxes and between surface fluxes and properties of the planetary boundary layer (PBL) are inspected. Features of precipitation and other water cycle trends from coupled climate model consensus projections are well simulated. Extreme 6-hourly rainfall totals become more intense over much of the globe, suggesting an increased risk for flash floods. Seasonal-scale droughts are projected to escalate over much of the subtropics and midlatitudes during summer, while tropical and winter droughts become less likely. These changes are accompanied by an increase in the responsiveness of surface evapotranspiration to soil moisture variations. Even though daytime PBL depths increase over most locations in the next century, greater latent heat fluxes also occur over most land areas, contributing a larger energy effect per unit mass of air, except over some semiarid regions. This general increase in land-atmosphere coupling is represented in a combined metric as a 'land coupling index' that incorporates the terrestrial and atmospheric effects together. The enhanced feedbacks are consistent with the precipitation changes, but a causal connection cannot be made without further sensitivity studies. Nevertheless, this approach could be applied to the output of traditional climate change simulations to assess changes in land-atmosphere feedbacks. [ABSTRACT FROM AUTHOR]

In examining intense precipitation over the central United States, the authors consider only days with precipitation when the daily total is above 12.7 mm and focus only on these days and multiday events constructed from such consecutive precipitation days. Analyses show that over the central United States, a statistically significant redistribution in the spectra of intense precipitation days/events during the past decades has occurred. Moderately heavy precipitation events (within a 12.7-25.4 mm day−1 range) became less frequent compared to days and events with precipitation totals above 25.4 mm. During the past 31 yr (compared to the 1948-78 period), significant increases occurred in the frequency of 'very heavy' (the daily rain events above 76.2 mm) and extreme precipitation events (defined as daily and multiday rain events with totals above 154.9 mm or 6 in.), with up to 40%% increases in the frequency of days and multiday extreme rain events. Tropical cyclones associated with extreme precipitation do not significantly contribute to the changes reported in this study. With time, the internal precipitation structure (e.g., mean and maximum hourly precipitation rates within each preselected range of daily or multiday event totals) did not noticeably change. Several possible causes of observed changes in intense precipitation over the central United States are discussed and/or tested. [ABSTRACT FROM AUTHOR]

The ability of coupled climate models to simulate the patterns of interannual precipitation variability over the western half of the United States and northern Mexico is investigated by applying principal component analysis to observations and model output. Global Precipitation Climatology Centre (GPCC) observations are compared to the pooled twentieth-century warm- and cold-season precipitation averages simulated by five coupled global climate models included in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The pooled model spatial structures (EOFs) closely match those of the GPCC observations for both halves of the year. Additionally, the twenty-first-century model pooled EOFs are almost identical in spatial extent and amplitude to their twentieth-century counterparts. Thus, the spatial characteristics of large-scale precipitation variability in the western United States are not projected to change in the twenty-first century. When global observed and modeled seasonally averaged sea surface temperature anomalies are correlated with the time series corresponding to the three leading EOFs to discern sources of each mode of precipitation variability, a pattern reminiscent of El Niño is found to be the only significant association. The spatial structures of variability also appear independent of the model-predicted precipitation trend over the twenty-first century, indicating that the mechanisms responsible for the trend are different from those associated with interannual variability. The results of this study lend confidence in the pooled model predictions of seasonal precipitation patterns, and they suggest that future changes will primarily result from the contribution of the mean trend over which statistically stationary interannual variability is superimposed. [ABSTRACT FROM AUTHOR]