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Climate change impact assessment on meteorological variables involves large uncertainties as a result of incomplete knowledge on the future greenhouse gas concentrations and climate model physics, next to the inherent internal variability of the climate system. Given that the alteration in greenhouse gas concentrations is the driver for the change, one expects the impacts to be highly dependent on the considered greenhouse gas scenario (GHS). In this study, we denote this behavior as GHS sensitivity. Due to the climate model related uncertainties, this sensitivity is, at local scale, not always that strong as expected. This paper aims to study the GHS sensitivity and its contributing role to climate scenarios for a case study in Belgium. An ensemble of 160 CMIP5 climate model runs is considered and climate change signals are studied for precipitation accumulation, daily precipitation intensities and wet day frequencies. This was done for the different seasons of the year and the scenario periods 2011–2040, 2031–2060, 2051–2081 and 2071–2100. By means of variance decomposition, the total variance in the climate change signals was separated in the contribution of the differences in GHSs and the other model-related uncertainty sources. These contributions were found dependent on the variable and season. Following the time of emergence concept, the GHS uncertainty contribution is found dependent on the time horizon and increases over time. For the most distinct time horizon (2071–2100), the climate model uncertainty accounts for the largest uncertainty contribution. The GHS differences explain up to 18% of the total variance in the climate change signals. The results point further at the importance of the climate model ensemble design, specifically the ensemble size and the combination of climate models, whereupon climate scenarios are based. The numerical noise, introduced at scales smaller than the skillful scale, e.g. at local scale, was not considered in this study. [ABSTRACT FROM AUTHOR]

This paper analyzes the historical precipitation and temperature trends and the future climate projections with reference to the northern part of Tuscany (Italy). The trends are identified and quantified at monthly and annual scale at gauging stations with data collected for long periods (60–90 years). An ensemble of 13 Regional Climate Models (RCMs), based on two Representative Concentration Pathways (RCP4.5 and RCP8.5), was then used to assess local scale future precipitation and temperature projections and to represent the uncertainty in the results. The historical data highlight a general decrease of the annual rainfall at a mean rate of 22 mm per decade but, in many cases, the tendencies are not statistically significant. Conversely, the annual mean temperature exhibits an upward trend, statistically significant in the majority of cases, with a warming rate of about 0.1 °C per decade. With reference to the model projections and the annual precipitation, the results are not concordant; the deviations between models in the same period are higher than the future changes at medium- (2031–2040) and long-term (2051–2060) and highlight that the model uncertainty and variability is high. According to the climate model projections, the warming of the study area is unequivocal; a mean positive increment of 0.8 °C at medium-term and 1.1 °C at long-term is expected with respect to the reference period (2003–2012) and the scenario RCP4.5; the increments grow to 0.9 °C and 1.9 °C for the RCP8.5. Finally, in order to check the observed climate change signals, the climate model projections were compared with the trends based on the historical data. A satisfactory agreement is obtained with reference to the precipitation; a systematic underestimation of the trend values with respect to the models, at medium- and long-term, is observed for the temperature data. [ABSTRACT FROM AUTHOR]

Summary: Climate change impact studies are often based on coarse scale projections of general circulation models (GCMs) under greenhouse gases emissions scenarios. Outputs from GCMs have then to be downscaled to obtain information relevant to hydrologic studies. This paper investigates the uncertainty in catchment-scale precipitation scenarios due to (1) the emissions scenario, (2) the configuration of the GCM, and (3) the downscaling method. Two emissions scenarios, six global climate models and four downscaling methods are used to build climate change scenarios for three catchment case studies in the UK. Missing combinations are reconstructed by using a variance decomposition algorithm. The highly increasing variance towards the end of the century is shown to be largely due to the spread of results from different GCMs. Multimodel averaged results at the catchment scale generally show an increase in the amplitude of the annual cycle of precipitation, with wetter winters and drier summers. Hydrologists and water resources planners make use of downscaled climate scenarios, often with little regard for the performance of scenario construction methods, for informing decisions on water resources and flood risk management policies and projects. This paper shows that both the downscaling method and the multimodel building scheme applied have a significant impact on the seasonal precipitation regime that may in turn lead to quite different conclusions in impacts assessments. [Copyright &y& Elsevier]

Summary The climate elasticity of runoff is an important indicator that is used to quantify the relationship between changes in runoff and changes in climate variables. It is a function of both climate and catchment characteristics. Recently, Yang and Yang (2011) proposed an analytical derivation of climate elasticity (YY2011), in which a parameter n was used to represent the impact of the catchment characteristics. In China, both climate and catchment characteristics have large spatial variations. To understand the spatial variation of hydrologic response to climate change, this paper divided China into 210 catchments, further calculated the parameter n , and then estimated the climate elasticity and evaluated the contribution of climate change to runoff for each catchment. The results show that n ranges from 0.4 to 3.8 (with a mean of 1.3 and a standard deviation of 0.6), which has a logarithmic relationship with catchment slope; the precipitation elasticity ranges from 1.1 to 4.8 (with a mean of 1.9 and a standard deviation of 0.6), which shows a large regional variation, smaller values (1.1–2.0) mainly appearing in Southern China, the Songhua River basin and the Northwest, and larger values (2.1–4.8) mainly appearing in the Hai River basin, the Liao River basin and the Yellow River basin. In addition, climate contribution to runoff exhibits a large regional variation, the largest positive values (1.1–3.1%/a) occurring in the Northwest, the largest negative values (−1.0 to −0.5%/a) occurring in the Hai River basin and the middle reach of the Yellow River basin. In theory, the YY2011 method is a first-order approximation. The approximation underestimates the precipitation ( P ) contribution to runoff when P increases and overestimates that when P decreases, and the relative error has a median of ∼3% and a maximum of ∼20% when 10% precipitations change in those catchments of China. [ABSTRACT FROM AUTHOR]

Summary: Statistical downscaling of precipitation is required as part of many climate change studies. Statistical downscaling based on regression models requires one to sample from the conditional distribution to preserve the variance of observed precipitation. In this paper, we present a new technique for downscaling precipitation. The proposed method employs quantile regression rather than traditional linear regression models to determine the conditional distribution for a given day. This eliminates the need for some of the assumptions required in standard linear regression, including the assumption of normally-distributed errors with constant variance. The quantile regression model also allows considerable flexibility in selecting predictor variables in that different subsets of predictors can be used for different parts of the conditional distribution. A Bayesian method adapted to quantile regression is used to select predictor variables. The method is illustrated through an application to five weather stations in Canada. It is found that the proposed method has distinct advantages over the conventional regression model for predicting summer precipitation, while for winter precipitation there is not much difference between the two methods. [Copyright &y& Elsevier]

Summary: Uncertainty estimation of climate change impacts has been given a lot of attention in the recent literature. It is generally assumed that the major sources of uncertainty are linked to General Circulation Models (GCMs) and Greenhouse Gases Emissions Scenarios (GGES). However, other sources of uncertainty such as the choice of a downscaling method have been given less attention. This paper focuses on this issue by comparing six downscaling methods to investigate the uncertainties in quantifying the impacts of climate change on the hydrology of a Canadian (Quebec province) river basin. The downscaling methods regroup dynamical and statistical approaches, including the change factor method and a weather generator-based approach. Future (2070–2099, 2085 horizon) hydrological regimes simulated with a hydrological model are compared to the reference period (1970–1999) using the average hydrograph, annual mean discharge, peak discharge and time to peak discharge as criteria. The results show that all downscaling methods suggest temperature increases over the basin for the 2085 horizon. The regression-based statistical methods predict a larger increase in autumn and winter temperatures. Predicted changes in precipitation are not as unequivocal as those of temperatures, they vary depending on the downscaling methods and seasons. There is a general increase in winter discharge (November–April) while decreases in summer discharge are predicted by most methods. Consistently with the large predicted increases in autumn and winter temperature, regression-based statistical methods show severe increases in winter flows and considerable reductions in peak discharge. Across all variables, a large uncertainty envelope was found to be associated with the choice of a downscaling method. This envelope was compared to the envelope originating from the choice of 28 climate change projections from a combination of seven GCMs and three GGES. Both uncertainty envelopes were similar, although the latter was slightly larger. The regression-based statistical downscaling methods contributed significantly to the uncertainty envelope. Overall, results indicate that climate change impact studies based on only one downscaling method should be interpreted with caution. [Copyright &y& Elsevier]

Summary: This paper develops tools to assess regional drought events based on limited periods of record and applies them to regional climate model output to examine potential future changes in drought due to increased greenhouse gases. The drought metric was defined as a monthly time series of standardized 12-month precipitation accumulations. Regional drought events were characterised by determining the severity, area, duration and frequency of dry periods. In order to improve statistical inference of drought events, a drought generator was adopted to synthesis a large number of events trained on the available data. In nearly all cases, the drought generator produces synthetic events with drought characteristics statistically indistinguishable from the training data. Severity–Area–Frequency (SAF) curves and Area–Probability (Area–P) curves could then be constructed from the synthetic data to determine the return period of a drought event of a given severity covering a given area. These techniques were applied to observations and to output from the Hadley Centre regional climate model (HadRM3). HadRM3, when driven by climate reanalysis data, has good correlation with the observed drought metric. HadRM3 also captures the timing of observed dry periods but not the relative seriousness of any particular period. Natural variability is large with regional drought characteristics highly dependent on the time period covered by the training data. Although the synthetic drought characteristics of HadRM3 generally overlap with the observed natural variability; the model slightly overestimates the area of observed drought; underestimate its frequency; and underestimate the severity at any given area. HadRM3 projections of changes in drought characteristics over the second half of the 21st century under increased atmospheric greenhouse gases hint at an increase in the severity of drought. However, any changes are not readily distinguishable from natural variability or projection uncertainty. Therefore it is not yet possible to robustly predict changes in UK meteorological droughts arising from increased greenhouse gasses. [Copyright &y& Elsevier]

Summary: The Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies. [Copyright &y& Elsevier]

Abstract: One of the more important questions in hydrology is: if the climate warms in the future, will there be an intensification of the water cycle and, if so, the nature of that intensification? There is considerable interest in this question because an intensification of the water cycle may lead to changes in water-resource availability, an increase in the frequency and intensity of tropical storms, floods, and droughts, and an amplification of warming through the water vapor feedback. Empirical evidence for ongoing intensification of the water cycle would provide additional support for the theoretical framework that links intensification with warming. This paper briefly reviews the current state of science regarding historical trends in hydrologic variables, including precipitation, runoff, tropospheric water vapor, soil moisture, glacier mass balance, evaporation, evapotranspiration, and growing season length. Data are often incomplete in spatial and temporal domains and regional analyses are variable and sometimes contradictory; however, the weight of evidence indicates an ongoing intensification of the water cycle. In contrast to these trends, the empirical evidence to date does not consistently support an increase in the frequency or intensity of tropical storms and floods. [Copyright &y& Elsevier]