Determination of humidity and temperature fluctuations based on MOZAIC data and parametrisation of persistent contrail coverage for general circulation models

Humidity and temperature fluctuations at pressure levels between 166 and 290 hPa on the grid scale of general circulation models for a region coveredn by the routes of airliners, mainly over the Atlantic, have been determined by evaluation of the data obtained with almost 2000 flights within the MOZAIC programme. It is found that the distributions of the fluctuations cannot be modelled by Gaussian distributions, because large fluctuations appear with a relatively high frequency. Lorentz distributions were used for the analytical representation of the fluctuation distributions. From these a joint probability distribution has been derived for simultaneous temperature and humidity fluctuations. This function together with the criteria for the formation and persistence of contrails are used to derive the maximum possible fractional coverage of persistent contrails in a grid cell of a GCM. This can be employed in a statistical formulation of contrail appearance in a climate model.     

Radiative forcing by contrails

A parametric study of the instantaneous radiative impact of contrails is presented using three different radiative transfer models for a series of model atmospheres and cloud parameters. Contrails are treated as geometrically and optically thin plane parallel homogeneous cirrus layers in a static atmosphere. The ice water content is varied as a function of ambient temperature. The model atmospheres include tropical, mid-latitude, and subarctic summer and winter atmospheres. Optically thin contrails cause a positive net forcing at top of the atmosphere. At the surface the radiative forcing is negative during daytime. The forcing increases with the optical depth and the amount of contrail cover. At the top of the atmosphere, a mean contrail cover of 0.1% with average optical depth of 0.2 to 0.5 causes about 0.01 to 0.03 Wm⁻² daily mean instantaneous radiative forcing. Contrails cool the surface during the day and heat the surface during the night, and hence reduce the daily temperature amplitude. The net effect depends strongly on the daily variation of contrail cloud cover. The indirect radiative forcing due to particle changes in natural cirrus clouds may be of the same magnitude as the direct one due to additional cover.     

Greenhouse effects of aircraft emissions as calculated by a radiative transfer model

With a radiative transfer model, assessments are made of the radiative forcing in northern mid-latitudes due to aircraft emissions up to 1990. Considered are the direct climate effects from the major combustion products carbon dioxide, nitrogen dioxide, water vapor and sulphur dioxide, as well as the indirect effect of ozone production from NO_x emissions. Our study indicates a local radiative forcing at the tropopause which should be negative in summer (-0.5 to 0.0 W/m²) and either negative or positive in winter (-0.3 to 0.2 W/m²). To these values the indirect effect of contrails has to be added, which for the North Atlantic Flight Corridor covers the range -0.2 to 0.3 W/m² in summer and 0.0 to 0.3 W/m² in winter. Apart from optically dense non-aged contrails during summer, negative forcings are due to solar screening by sulphate aerosols. The major positive contributions come from contrails, stratospheric water vapor in winter and ozone in summer. The direct effect of NO₂ is negligible and the contribution of CO₂ is relatively small.     

Downscaling two versions of a general circulation model to estimate local hydroclimatic factors under climate change

Abstract

The regional hydroclimatological effect of global climate change has been estimated and compared using a semi-empirical downscaling method with two versions (T21 and T42) of the general circulation model (GCM) developed at the Max Planck Institute for Meteorology, Germany. The comparisons were performed with daily mean temperature and daily precipitation amounts for the continental climate of the state of Nebraska, USA. Both the T21 and the T42 versions resulted in an increase of daily mean temperature under a 2 x C02 climatess. The magnitude of warming was substantially greater for T21 than for T42, except for February and June and at some stations in July where the T42 model suggested greater warming. Both GCMs resulted in a slight decrease in precipitation frequency and an increase in the amount of precipitation on wet days. Here, the T42 model again led to smaller changes. Different locations within Nebraska exhibited somewhat different temperature and precipitation responses with both GCM versions.     

A distribution law for relative humidity in the upper troposphere and lower stratosphere derived from three years of MOZAIC measurements

Data from three years of MOZAIC measurements made it possible to determine a distribution law for the relative humidity in the upper troposphere and lower stratosphere. Data amounting to 13.5% of the total were obtained in regions with ice supersaturation. Troposphere and stratosphere are distinguished by an ozone concentration of 130 ppbv as threshold. The probability of measuring a certain amount of ice supersaturation in the troposphere decreases exponentially with the degree of ice supersaturation. The probability of measuring a certain relative humidity in the stratosphere (both with respect to water and ice) decreases exponentially with the relative humidity. A stochastic model that naturally leads to the exponential distribution is provided. Mean supersaturation in the troposphere is about 15%, whereas ice nucleation requires 30% supersaturation on the average. This explains the frequency of regions in which aircraft induce persistent contrails but which are otherwise free of clouds. Ice supersaturated regions are 3-4 K colder and contain more than 50% more vapour than other regions in the upper troposphere. The stratospheric air masses sampled are dry, as expected, having mean relative humidity over water of 12% and over ice of 23%, respectively. However, 2% of the stratospheric data indicate ice supersaturation. As the MOZAIC measurements have been obtained on commercial flights mainly between Europe and North America, the data do not provide a complete global picture, but the exponential character of the distribution laws found is probably valid globally. Since water vapour is the most important greenhouse gas and since it might enhance the anthropogenic greenhouse effects via positive feedback mechanisms, it is important to represent its distribution correctly in climate models. The discovery of the distribution law of the relative humidity makes possible simple tests to show whether the hydrological cycle in climate models is represented in an adequate way or not.     

Distributed modelling of future changes in hydrological processes of Spencer Creek watershed

The hydrologic impact of climate change has been largely assessed using mostly conceptual hydrologic models. This study investigates the use of distributed hydrologic model for the assessment of the climate change impact for the Spencer Creek watershed in Southern Ontario (Canada). A coupled MIKE SHE/MIKE 11 hydrologic model is developed to represent the complex hydrologic conditions in the Spencer Creek watershed, and later to simulate climate change impact using Canadian global climate model (CGCM 3·1) simulations. Owing to the coarse resolution of GCM data (daily GCM outputs), statistical downscaling techniques are used to generate higher resolution data (daily precipitation and temperature series). The modelling results show that the coupled model captured the snow storage well and also provided good simulation of evapotranspiration (ET) and groundwater recharge. The simulated streamflows are consistent with the observed flows at different sites within the catchment. Using a conservative climate change scenario, the downscaled GCM scenarios predicted an approximately 14–17% increase in the annual mean precipitation and 2–3 °C increase in annual mean maximum and minimum temperatures for the 2050s (i.e., 2046–2065). When the downscaled GCM scenarios were used in the coupled model, the model predicted a 1–5% annual decrease in snow storage for 2050s, approximately 1–10% increase in annual ET, and a 0·5–6% decrease in the annual groundwater recharge. These results are consistent with the downscaled temperature results. For future streamflows, the coupled model indicated an approximately 10–25% increase in annual streamflows for all sites, which is consistent with the predicted changes in precipitation. Overall, it is shown that distributed hydrologic modelling can provide useful information not only about future changes in streamflow but also changes in other key hydrologic processes such as snow storage, ET, and groundwater recharge, which can be particularly important depending on the climatic region of concern. The study results indicate that the coupled MIKE SHE/MIKE 11 hydrologic model could be a particularly useful tool for understanding the integrated effect of climate change in complex catchment scale hydrology. Copyright © 2010 John Wiley & Sons, Ltd.     

On the regional climatic impact of contrails: microphysical and radiative properties of contrails and natural cirrus clouds

The impact of contrail-induced cirrus clouds on regional climate is estimated for mean atmospheric conditions of southern Germany in the months of July and October. This is done by use of a regionalized one-dimensional radiative convective model (RCM). The influence of an increased ice cloud cover is studied by comparing RCM results representing climatological values with a modified case. In order to study the sensitivity of this effect on the radiative characteristics of the ice cloud, two types of additional ice clouds were modelled: cirrus and contrails, the latter cloud type containing a higher number of smaller and less of the larger cloud particles. Ice cloud parameters are calculated on the basis of a particle size distribution which covers the range from 2 to 2000 m, taking into consideration recent measurements which show a remarkable amount of particles smaller than 20 m. It turns out that a 10% increase in ice cloud cover leads to a surface temperature increase in the order of 1K, ranging from 1.1 to 1.2K in July and from 0.8 to 0.9K in October depending on the radiative characteristics of the air-traffic-induced ice clouds. Modelling the current contrail cloud cover which is near 0.5% over Europe yields a surface temperature increase in the order of 0.05 K.     

Assessment of future precipitation indices in the Shikoku region using a statistical downscaling model

In this study, we used the statistical downscaling model (SDSM) to estimate mean and extreme precipitation indices under present and future climate conditions for Shikoku, Japan. Specifically, we considered the following mean and extreme precipitation indices: mean daily precipitation, R10 (number of days with precipitation >10 mm/day), R5d (annual maximum precipitation accumulated over 5 days), maximum dry-spell length (MaDSL), and maximum wet-spell length (MaWSL). Initially, we calibrated the SDSM model using the National Center for environmental prediction (NCEP) reanalysis dataset and daily time series of precipitation for ten locations in Shikoku which were acquired from the surface weather observation point dataset. Subsequently, we used the validated SDSM, using data from NCEP and outputs form general circulation models (GCM), to predict future precipitation indices. Specifically, the HadCM3 GCM was run under the special report on emissions scenarios (SRES) A2 and B2 scenarios, and the CGCM3 GCM was run under the SRES A2 and A1B scenarios. The results showed that: (1) the SDSM can reasonably be used to simulate mean and extreme precipitation indices in the Shikoku region; (2) the values of annual R10 were predicated to decrease in the future in northern Shikoku under all climate scenarios; conversely, the values of annual R10 were predicted to increase in the future in the range of 0–15 % in southern and western Shikoku. The values of annual MaDSL were predicted to increase in northern Shikoku, and the values of annual MaWSL were predicted to decrease in northeastern Shikoku; (3) the spatial variation of precipitation indices indicated the potential for an increased occurrence of drought across northeastern Shikoku and an increased occurrence of flood events in the southwestern part of Shikoku, especially under the A2 and A1B scenarios; (4) characteristics of future precipitation may differ between the northern and southern sides of the Shikoku Mountains. Regional variations in extreme precipitation indices were not notably evident in the B2 scenario compared to the other scenarios.     

Hydrological impacts of climate change predicted for an inland lake catchment in Ontario by using monthly water balance analyses

Potential hydrological impacts of climate change on long‐term water balances were analysed for Harp Lake and its catchment. Harp Lake is located in the boreal ecozone of Ontario, Canada. Two climate change scenarios were used. One was based on extrapolation of long‐term trends of monthly temperature and precipitation from a 129‐year data record, and another was based on a Canadian general circulation model (GCM) predictions. A monthly water balance model was calibrated using 26 years of hydrological and meteorological data, and the model was used to calculate hydrological impact under two climate change scenarios. The first scenario with a warmer and wetter climate predicted a smaller magnitude of change than the second scenario. The first scenario showed an increase in evaporation each month, an increase in catchment runoff in summer, fall and winter, but a decrease in spring, resulting in a slight increase in lake level. Annual runoff and lake level would increase because the precipitation change overrides evaporation change. The second scenario with a warmer, drier climate predicted a greater change, and indicated that evaporation would increase each month, runoff would increase in many months, but would decrease in spring, causing the lake level to decrease slightly. Annual runoff and lake level would decrease because evaporation change overrides precipitation change. In both scenarios, the water balance changes in winter and spring are pronounced. Copyright © 2009 John Wiley & Sons, Ltd.     

Does an upper limit to river water temperature apply in all places?

There remains continued use of non‐linear, logistic regression models for predicting water temperature from air temperature. A dominant feature of these non‐linear models is an upper bound on river water temperature. This upper bound is often attributed to a large increase in evaporative cooling at high air temperatures, but the exact conditions under which such an increase may occur have not been thoroughly explored. To better understand the appropriateness of the non‐linear model for predicting river water temperatures, it is essential to understand the physical basis for the upper bound and when it should and should not be included in the statistical model. This paper applies and validates an energy balance model against 8 river systems spread across different climate regions of the United States. The energy balance model is then used to develop a diagram relating vapour pressure deficit and air temperature to water temperature. With knowledge of present or future vapour pressure deficit (difference between saturation and actual vapour content in the atmosphere) conditions in a given climate, the diagram can be used to predict the likelihood of an upper bound in the air–water temperature relationship. This investigation offers a fundamental physical explanation of the most appropriate form of statistical models that should be used for predicting future water temperature from air temperature in different geographic regions with different climate conditions. In general, climatic regions that have only a slight increase in vapour pressure deficit with increasing air temperature (typically humid regions) would not be expected to have an upper bound. Conversely, climatic regions in which vapour pressure deficit sharply increases with increasing air temperature (typically arid regions) would be expected to have an upper bound.     

Hydrological extremes in a southwestern Ontario river basin under future climate conditions/Extrêmes hydrologiques dans un basin versant du sud-ouest de l'Ontario sous conditions climatiques futures

Abstract

The global climate change may have serious impacts on the frequency, magnitude, location and duration of hydrological extremes. Changed hydrological extremes will have important implications on the design of future hydraulic structures, flood-plain development, and water resource management. This study assesses the potential impact of a changed climate on the timing and magnitude of hydrological extremes in a densely populated and urbanized river basin in southwestern Ontario, Canada. An ensemble of future climate scenarios is developed using a weather generating algorithm, linked with GCM outputs. These climate scenarios are then transformed into basin runoff by a semi-distributed hydrological model of the study area. The results show that future maximum river flows in the study area will be less extreme and more variable in terms of magnitude, and more irregular in terms of seasonal occurrence, than they are at present. Low flows may become less extreme and variable in terms of magnitude, and more irregular in terms of seasonal occurrence. According to the evaluated scenarios, climate change may have favourable impacts on the distribution of hydrological extremes in the study area.     

气候变化对长江流域汉江和赣江径流的影响

近几年来,国家气候中心己经建立了中国主要四大流域气候对水资源影响评估的模式框架.本文拟进一步证明其中之一的两参数分布式月水量平衡水文模式对长江之上汉江和赣江两子流域径流的模拟能力,结果表明该水文模式对目前气候条件下径流模拟效果较好,运行稳定,可用于实时业务运行.在此基础上,利用ECHAM4和HadCM2两GCM(General Circulation Model)未来气候情景模拟结果及目前实测气候情况,对汉江和赣江两子流域的径流对未来气候变化的敏感性进行评估.经检验,两GCM对未来气候,特别是降水情景模拟存在一定差异,因此,造成径流对气候变化的响应不同,这充分反映了全球模式模拟结果不确定性在气候变化影响研究中的重要性.     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.     

Estimation of catchment yield and associated uncertainties due to climate change in a mountainous catchment in Australia

This paper examines the impacts of climate change on future water yield with associated uncertainties in a mountainous catchment in Australia using a multi‐model approach based on four global climate models (GCMs), 200 realisations (50 realisations from each GCM) of downscaled rainfalls, 2 hydrological models and 6 sets of model parameters. The ensemble projections by the GCMs showed that the mean annual rainfall is likely to reduce in the future decades by 2–5% in comparison with the current climate (1987–2012). The results of ensemble runoff projections indicated that the mean annual runoff would reduce in future decades by 35%. However, considerable uncertainty in the runoff estimates was found as the ensemble results project changes of the 5^th (dry scenario) and 95^th (wet scenario) percentiles by ?73% to +27%, ?73% to +12%, ?77% to +21% and ?80% to +24% in the decades of 2021–2030, 2031–2040, 2061–2070 and 2071–2080, respectively. Results of uncertainty estimation demonstrated that the choice of GCMs dominates overall uncertainty. Realisation uncertainty (arising from repetitive simulations for a given time step during downscaling of the GCM data to catchment scale) of the downscaled rainfall data was also found to be remarkably high. Uncertainty linked to the choice of hydrological models was found to be quite small in comparison with the GCM and realisation uncertainty. The hydrological model parameter uncertainty was found to be lowest among the sources of uncertainties considered in this study. Copyright © 2015 John Wiley & Sons, Ltd.     

Regional impacts of climate change on irrigation water demands

This paper presents an approach to model the expected impacts of climate change on irrigation water demand in a reservoir command area. A statistical downscaling model and an evapotranspiration model are used with a general circulation model (GCM) output to predict the anticipated change in the monthly irrigation water requirement of a crop. Specifically, we quantify the likely changes in irrigation water demands at a location in the command area, as a response to the projected changes in precipitation and evapotranspiration at that location. Statistical downscaling with a canonical correlation analysis is carried out to develop the future scenarios of meteorological variables (rainfall, relative humidity (RH), wind speed (U₂), radiation, maximum (Tmax) and minimum (Tmin) temperatures) starting with simulations provided by a GCM for a specified emission scenario. The medium resolution Model for Interdisciplinary Research on Climate GCM is used with the A1B scenario, to assess the likely changes in irrigation demands for paddy, sugarcane, permanent garden and semidry crops over the command area of Bhadra reservoir, India. Results from the downscaling model suggest that the monthly rainfall is likely to increase in the reservoir command area. RH, Tmax and Tmin are also projected to increase with small changes in U₂. Consequently, the reference evapotranspiration, modeled by the Penman–Monteith equation, is predicted to increase. The irrigation requirements are assessed on monthly scale at nine selected locations encompassing the Bhadra reservoir command area. The irrigation requirements are projected to increase, in most cases, suggesting that the effect of projected increase in rainfall on the irrigation demands is offset by the effect due to projected increase/change in other meteorological variables (viz., Tmax and Tmin, solar radiation, RH and U₂). The irrigation demand assessment study carried out at a river basin will be useful for future irrigation management systems. Copyright © 2012 John Wiley & Sons, Ltd.     

Biased estimates of the isotope ratios of steady‐state evaporation from the assumption of equilibrium between vapour and precipitation

Stable water isotope ratios are measured as a tracer of environmental processes in materials such as leaves, soils, and lakes. Water in these archives may experience evaporation, which increases the abundance of heavy isotopologues proportionally to the gradients in humidity and isotope ratio between the evaporating water and the surrounding atmosphere. The isotope ratio of the atmosphere has been difficult to measure until recently, and measurements remain scarce. As a result, several assumptions have been adopted to estimate isotope ratios of atmospheric water vapour. Perhaps the most commonly employed assumption in terrestrial environments is that water vapour is in isotopic equilibrium with precipitation. We evaluate this assumption using an eight‐member ensemble of general circulation model (GCM) simulations that include explicit calculation of isotope ratios in precipitation and vapour. We find that across the model ensemble, water vapour is typically less depleted in heavy isotopologues than expected if it were in equilibrium with annual precipitation. Atmospheric vapour likely possesses higher‐than‐expected isotope ratios because precipitation isotope ratios are determined by atmospheric conditions that favour condensation, which do not reflect atmospheric mixing and advection processes outside of precipitation events. The effect of this deviation on theoretical estimates of isotope ratios of evaporating waters scales with relative humidity. As a result, the equilibrium assumption gives relatively accurate estimates of the isotope ratios of evaporating waters in low latitudes but performs increasingly poorly at increasing latitudes. Future studies of evaporative water pools should include measurements of atmospheric isotope ratios or constrain potential bias with isotope‐enabled GCM simulations.     

Impact of climate change on water resources in Yongdam Dam Basin,Korea

The main purpose of this study is to investigate and evaluate the impact of climate change on the runoff and water resources of Yongdam basin, Korea. First, we construct global climate change scenarios using the YONU GCM control run and transient experiments, then transform the YONU GCM grid-box predictions with coarse resolution of climate change into the site-specific values by statistical downscaling techniques. The downscaled values are used to modify the parameters of a stochastic weather generator model for the simulation of the site-specific daily weather time series. The weather series is fed into a semi-distributed hydrological model called SLURP to simulate the streamflows associated with other water resources for the condition of 2CO₂. This approach is applied to the Yongdam dam basin in the southern part of Korea. The results show that under the condition of 2CO₂, about 7.6% of annual mean streamflow is reduced when it is compared with the current condition. Seasonal streamflows in the winter and autumn are increased, while streamflow in the summer is decreased. However, the seasonality of the simulated series is similar to the observed pattern An erratum to this article can be found at     

Regional precipitation and temperature scenarios for climate change

Abstract

To investigate the consequences of climate change on the water budget in small catchments, it is necessary to know the change of local precipitation and temperature. General Circulation Models (GCM) cannot provide regional climate parameters yet, because of their coarse resolution and imprecise modelling of precipitation. Therefore downscaling of precipitation and temperature has to be carried out from the GCM grids to a small scale of a few square kilometres. Daily rainfall and temperature are modelled as processes conditioned on atmospheric circulation. Rainfall is linked to the circulation patterns (CPs) using conditional probabilities and conditional rainfall amount distribution. Both temperature and precipitation are downscaled to several locations simultaneously taking into account the CP dependent spatial correlation. Temperature is modelled using a simple autoregressive approach, conditioned on atmospheric circulation and local areal precipitation. The model uses the classification scheme of the German Weather Service and a fuzzy rule-based classification. It was applied in the Aller catchment for validation using observed rainfall and temperature, and observed classified geopotential pressure heights. GCM scenarios of the ECHAM model were used to make climate change predictions (using classified GCM geopotential heights); simulated values agree fairly well with historical data. Results for different GCM scenarios are shown.     

1) I. A. H. S. / A. I. H. S.

Abstract

A simple method is used to study the response of runoff in the Sahel to climate change. The statistical characteristics of rainfall are calculated over the western part of the Sahel for the period 1961–1990, using the BADOPLU network. Daily rainfall is simulated using a Markov process with Weibull distribution for rainfall depths. Runoff is modelled using a conceptual SCS model and the curve numbers are calculated for West Africa. Climate change is provided by simulations using the Arpège GCM (Scenario A1B), and a perturbation method is used on the parameters which describe the rainfall. Changes in rainfall are assumed to occur through increases in frequency, not intensity. Using Arpège, runoff is mainly found to increase, in depth and in number of events, by the end of the 21st century. Changes in evaporation and land use are not included in the analysis. The impact of this 21st century potential climate change (rainfall) on the runoff is found to be of the same magnitude as the impact of changes in land use.     

Spatial uncertainty in bias corrected climate change projections and hydrogeological impacts

The question of which climate model bias correction methods and spatial scales for correction are optimal for both projecting future hydrological changes as well as removing initial model bias has so far received little attention. For 11 climate models (CMs), or GCM/RCM – Global/Regional Climate Model pairing, this paper analyses the relationship between complexity and robustness of three distribution‐based scaling (DBS) bias correction methods applied to daily precipitation at various spatial scales. Hydrological simulations are forced by CM inputs to assess the spatial uncertainty of groundwater head and stream discharge given the various DBS methods. A unique metric is devised, which allows for comparison of spatial variability in climate model bias and projected change in precipitation. It is found that the spatial variability in climate model bias is larger than in the climate change signals. The magnitude of spatial bias seen in precipitation inputs does not necessarily correspond to the magnitude of biases seen in hydrological outputs. Variables that integrate basin responses over time and space are more sensitive to mean spatial biases and less so on extremes. Hydrological simulations forced by the least parameterized DBS approach show the highest error in mean and maximum groundwater heads; however, the most highly parameterised DBS approach shows less robustness in future periods compared with the reference period it was trained in. For hydrological impacts studies, choice of bias correction method should depend on the spatial scale at which hydrological impacts variables are required and whether CM initial bias is spatially uniform or spatially varying. Copyright © 2015 John Wiley & Sons, Ltd.