Atmospheric and Terrestrial Water Balances of Labrador's Churchill River Basin,as Simulated by the North American Regional Climate Change Assessment Program

In an effort to understand the sources of uncertainty and the physical consistency of climate models from the North American Regional Climate Change Assessment Program (NARCCAP), an ensemble of general circulation models (GCMs) and regional climate models (RCMs) was used to explore climatological water balances for the Churchill River basin in Labrador, Canada. This study quantifies mean atmospheric and terrestrial water balance residuals, as well as their annual cycles. Mean annual atmospheric water balances had consistently higher residuals than the terrestrial water balances due, in part, to the influences of sampling of instantaneous variables and the interpolation of atmospheric data to published pressure levels. Atmospheric and terrestrial water balance residuals for each ensemble member were found to be consistent between base and future periods, implying that they are systemic and not climate dependent. With regard to the annual cycle, no pattern was found across time periods or ensemble members to indicate whether the monthly terrestrial or atmospheric root mean square residual was highest. Because of the interdependence of hydrological cycle components, the complexity of climate models and the variety of methods and processes used by different ensemble members, it was impossible to isolate all causes of the water balance residuals. That being said, the residuals created by interpolating a model's native vertical resolution onto NARCCAP's published pressure levels and the subsequent vertical interpolation were quantified and several other sources were explored. In general, residuals were found to be predominantly functions of the RCM choice (as opposed to the GCM choice) and their respective modelling processes, parameterization schemes, and post-processing.     

区域和全球模式的嵌套技术 及其长期积分试验

将区域模式嵌入澳大利亚CSIRO (Commonwealth Scientific and Industrial Research Organization）的全球模式中,并将其应用于区域模式的长期气候积分试验。模拟结果表明,当区域与全球模式嵌套时,边界吸收问题十分重要,由区域模式得到的高分辨率大尺度环流形式在边界上必须与全球模式提供的强迫一致,同时区域模式必须给出基于模式内部物理过程产生的高分辨信息。因此,在嵌套过程中,必须仔细考虑缓冲区的设置,使大尺度强迫与中尺度特征充分混合,既保持区域模式内外的一致性,又使区域内部中尺度强迫物理过程得到充分发展。将区域模式与澳大利亚CSIRO的9层21波三角形截断谱模式嵌套后,完成了连续3年的区域气候模式积分。模拟结果表明,由于区域模式较好地刻划了区域尺度的地形、下垫面和海岸线分布等的细节特征,模拟的区域气候特征比全球模式有较大的改进,尤其是对季风降水的模拟,区域模式明显改进了全球模式的模拟结果。     

Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP)

We investigate major results of the NARCCAP multiple regional climate model (RCM) experiments driven by multiple global climate models (GCMs) regarding climate change for seasonal temperature and precipitation over North America. We focus on two major questions: How do the RCM simulated climate changes differ from those of the parent GCMs and thus affect our perception of climate change over North America, and how important are the relative contributions of RCMs and GCMs to the uncertainty (variance explained) for different seasons and variables? The RCMs tend to produce stronger climate changes for precipitation: larger increases in the northern part of the domain in winter and greater decreases across a swath of the central part in summer, compared to the four GCMs driving the regional models as well as to the full set of CMIP3 GCM results. We pose some possible process-level mechanisms for the difference in intensity of change, particularly for summer. Detailed process-level studies will be necessary to establish mechanisms and credibility of these results. The GCMs explain more variance for winter temperature and the RCMs for summer temperature. The same is true for precipitation patterns. Thus, we recommend that future RCM-GCM experiments over this region include a balanced number of GCMs and RCMs.     

Extended regional climate model projections for Europe until the mid-twentyfirst century: combining ENSEMBLES and CMIP3

This study aims at sharpening the existing knowledge of expected seasonal mean climate change and its uncertainty over Europe for the two key climate variables air temperature and precipitation amount until the mid-twentyfirst century. For this purpose, we assess and compensate the global climate model (GCM) sampling bias of the ENSEMBLES regional climate model (RCM) projections by combining them with the full set of the CMIP3 GCM ensemble. We first apply a cross-validation in order to assess the skill of different statistical data reconstruction methods in reproducing ensemble mean and standard deviation. We then select the most appropriate reconstruction method in order to fill the missing values of the ENSEMBLES simulation matrix and further extend the matrix by all available CMIP3 GCM simulations forced by the A1B emission scenario. Cross-validation identifies a randomized scaling approach as superior in reconstructing the ensemble spread. Errors in ensemble mean and standard deviation are mostly less than 0.1 K and 1.0 % for air temperature and precipitation amount, respectively. Reconstruction of the missing values reveals that expected seasonal mean climate change of the ENSEMBLES RCM projections is not significantly biased and that the associated uncertainty is not underestimated due to sampling of only a few driving GCMs. In contrast, the spread of the extended simulation matrix is partly significantly lower, sharpening our knowledge about future climate change over Europe by reducing uncertainty in some regions. Furthermore, this study gives substantial weight to recent climate change impact studies based on the ENSEMBLES projections, since it confirms the robustness of the climate forcing of these studies concerning GCM sampling.     

The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability

Various combinations of thirteen regional climate models (RCM) and six general circulation models (GCM) were used in FP6-ENSEMBLES. The response to the SRES-A1B greenhouse gas concentration scenario over Europe, calculated as the difference between the 2021–2050 and the 1961–1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance explained for temperature and precipitation changes over eight European sub-areas. Three sources of uncertainty can be evaluated from the ENSEMBLES database. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30) despite a non-negligible interannual variability. Regional model uncertainty is due to the fact that the RCMs use different techniques to discretize the equations and to represent sub-grid effects. Global model uncertainty is due to the fact that the RCMs have been driven by different GCMs. Two methods are presented to fill the many empty cells of the ENSEMBLES RCM?×?GCM matrix. The first one is based on the same approach as in FP5-PRUDENCE. The second one uses the concept of weather regimes to attempt to separate the contribution of the GCM and the RCM. The variance of the climate response is analyzed with respect to the contribution of the GCM and the RCM. The two filling methods agree that the main contributor to the spread is the choice of the GCM, except for summer precipitation where the choice of the RCM dominates the uncertainty. Of course the implication of the GCM to the spread varies with the region, being maximum in the South-western part of Europe, whereas the continental parts are more sensitive to the choice of the RCM. The third cause of spread is systematically the interannual variability. The total uncertainty about temperature is not large enough to mask the 2021–2050 response which shows a similar pattern to the one obtained for 2071–2100 in PRUDENCE. The uncertainty about precipitation prevents any quantitative assessment on the response at grid point level for the 2021–2050 period. One can however see, as in PRUDENCE, a positive response in winter (more rain in the scenario than in the reference) in northern Europe and a negative summer response in southern Europe.     

On the simulation of Laurentian Great Lakes water levels under projections of global climate change

A new method is proposed to estimate future net basin supplies and lake levels for the Laurentian Great Lakes based on GCM projections of global climate change. The method first dynamically downscales the GCM simulation with a regional climate model, and then bias—corrects the simulated net basin supply in order to be used directly in a river—routing/lake level scheme. This technique addresses two weaknesses in the traditional approach, whereby observed sequences of climate variables are perturbed with fixed ratios or differences derived directly from GCMs in order to run evaporation and runoff models. Specifically, (1) land surface—atmosphere feedback processes are represented, and (2) changes in variability can be analyzed with the new approach. The method is demonstrated with a single, high resolution simulation, where small changes in future mean lake levels for all the upper Great Lakes are found, and an increase in seasonal range—especially for Lake Superior—is indicated. Analysis of a small ensemble of eight lower resolution regional climate model simulations supports these findings. In addition, a direct comparison with the traditional approach based on the same GCM projections used as the driving simulations in this ensemble shows that the new method indicates smaller declines in level for all the upper Great Lakes than has been reported previously based on the traditional method, though median differences are only a few centimetres in each case.     

High resolution regional climate model simulations for Germany: Part II—projected climate changes

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971–2000) and future A1B scenario (2021–2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of ?2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly.     

Improved regional scale processes reflected in projected hydrological changes over large European catchments

For the fourth assessment report of the Intergovernmental Panel on Climate Change (IPCC), the recent version of the coupled atmosphere/ocean general circulation model (GCM) of the Max Planck Institute for Meteorology has been used to conduct an ensemble of transient climate simulations These simulations comprise three control simulations for the past century covering the period 1860–2000, and nine simulations for the future climate (2001–2100) using greenhouse gas (GHG) and aerosol concentrations according to the three IPCC scenarios B1, A1B and A2. For each scenario three simulations were performed. The global simulations were dynamically downscaled over Europe using the regional climate model (RCM) REMO at 0.44° horizontal resolution (about 50 km), whereas the physics packages of the GCM and RCM largely agree. The regional simulations comprise the three control simulations (1950–2000), the three A1B simulations and one simulation for B1 as well as for A2 (2001–2100). In our study we concentrate on the climate change signals in the hydrological cycle and the 2 m temperature by comparing the mean projected climate at the end of the twenty-first century (2071–2100) to a control period representing current climate (1961–1990). The robustness of the climate change signal projected by the GCM and RCM is analysed focussing on the large European catchments of Baltic Sea (land only), Danube and Rhine. In this respect, a robust climate change signal designates a projected change that sticks out of the noise of natural climate variability. Catchments and seasons are identified where the climate change signal in the components of the hydrological cycle is robust, and where this signal has a larger uncertainty. Notable differences in the robustness of the climate change signals between the GCM and RCM simulations are related to a stronger warming projected by the GCM in the winter over the Baltic Sea catchment and in the summer over the Danube and Rhine catchments. Our results indicate that the main explanation for these differences is that the finer resolution of the RCM leads to a better representation of local scale processes at the surface that feed back to the atmosphere, i.e. an improved representation of the land sea contrast and related moisture transport processes over the Baltic Sea catchment, and an improved representation of soil moisture feedbacks to the atmosphere over the Danube and Rhine catchments.     

Gradient in the climate change signal of European discharge predicted by a multi-model ensemble

In order to perform hydrological studies on the PRUDENCE regional climate model (RCM) simulations, a special focus was put on the discharge from large river catchments located in northern and central Europe. The discharge was simulated with a simplified land surface (SL) scheme and the Hydrological Discharge (HD) model. The daily fields of precipitation, 2 m temperature and evapotranspiration from the RCM simulations were used as forcing. Therefore the total catchment water balances are constrained by the hydrological cycle of the different RCMs. The validation of the simulated hydrological cycle from the control simulations shows that the multi-model ensemble mean is closer to the observations than each of the models, especially if different catchments and hydrological variables are considered. Therefore, the multi-model ensemble mean can be used to largely reduce the uncertainty that is introduced by a single RCM. This also provides more confidence in the future projections for the multi-model ensemble means. The scenario simulations predict a gradient in the climate change signal over Northern and Central Europe. Common features are the overall warming and the general increase of evapotranspiration. But while in the northern parts the warming will enhance the hydrological cycle leading to an increased discharge, the large warming, especially in the summer, will slow down the hydrological cycle caused by a drying in the central parts of Europe which is accompanied by a reduction of discharge. The comparison of the changes predicted by the multi-model ensemble mean to the changes predicted by the driving GCM indicates that the RCMs can compensate problems that a driving GCM may have with local scale processes or parameterizations.     

A study on combining global and regional climate model results for generating climate scenarios of temperature and precipitation for the Netherlands

Climate scenarios for the Netherlands are constructed by combining information from global and regional climate models employing a simplified, conceptual framework of three sources (levels) of uncertainty impacting on predictions of the local climate. In this framework, the first level of uncertainty is determined by the global radiation balance, resulting in a range of the projected changes in the global mean temperature. On the regional (1,000–5,000 km) scale, the response of the atmospheric circulation determines the second important level of uncertainty. The third level of uncertainty, acting mainly on a local scale of 10 (and less) to 1,000 km, is related to the small-scale processes, like for example those acting in atmospheric convection, clouds and atmospheric meso-scale circulations—processes that play an important role in extreme events which are highly relevant for society. Global climate models (GCMs) are the main tools to quantify the first two levels of uncertainty, while high resolution regional climate models (RCMs) are more suitable to quantify the third level. Along these lines, results of an ensemble of RCMs, driven by only two GCM boundaries and therefore spanning only a rather narrow range in future climate predictions, are rescaled to obtain a broader uncertainty range. The rescaling is done by first disentangling the climate change response in the RCM simulations into a part related to the circulation, and a residual part which is related to the global temperature rise. Second, these responses are rescaled using the range of the predictions of global temperature change and circulation change from five GCMs. These GCMs have been selected on their ability to simulate the present-day circulation, in particular over Europe. For the seasonal means, the rescaled RCM results obey the range in the GCM ensemble using a high and low emission scenario. Thus, the rescaled RCM results are consistent with the GCM results for the means, while adding information on the small scales and the extremes. The method can be interpreted as a combined statistical–dynamical downscaling approach, with the statistical relations based on regional model output.     

长江流域径流对全球升温1.5℃与2.0℃的响应

全球变暖影响着以流域径流要素为主导的水文水资源系统的变化。长江流域未来水资源量的时空分布对长江大保护与长江经济带的发展意义重大。为探究全球升温1.5℃和2.0℃对长江流域径流变化的影响,使用基于偏差校正的气候模式集合数据驱动两参数月水量平衡模型,比较两种升温情景下径流量的响应差异。结果表明：基于偏差校正的气候模式集合数据可以较好地代表长江流域历史时期（1976—2005年）的年平均降水和年平均蒸散发情势。两参数月水量平衡模型与参数区域化方法相结合能较好地模拟长江流域各子流域的月径流量。升温1.5℃时,无论是年径流量还是季节径流量均呈上升趋势,与历史时期相比,50%以上三级子流域的增幅超过5%;升温2.0℃时,增幅超过8%。这表明升温2.0℃情景下长江流域水资源量将进一步增加。相对于历史时期,升温1.5℃与2.0℃情景下长江流域北部降水量增幅较大;径流量增幅分布格局基本与降水量一致。汉江流域是全流域径流量增幅最显著的区域。     

气候波动和人类活动对南水北调中线工程典型流域径流影响的定量评估

以南水北调中线工程典型流域汉江上游流域和滦河流域为研究对象,采用敏感性分析法、降水?径流双累积曲线法、累积量斜率变化率比较法定量评估了气候波动和人类活动对流域径流变化的影响。结果表明：汉江上游流域和滦河流域变异Ⅰ/Ⅱ期年均径流深相对于基准期分别减少了29.5% / 19.1%和49.8% / 70.0%;对于汉江上游流域,1991-1999年（变异Ⅰ期）气候波动是径流减少的主要影响因素,2000-2008年（变异Ⅱ期）人类活动则是径流减少的主要影响因素,且人类活动对汉江上游流域径流减少的影响逐步增加;对于滦河流域,1980-2010年（变异Ⅰ/Ⅱ期）人类活动一直是径流减少的主要影响因素,且气候波动和人类活动对径流减少的影响贡献率基本保持不变。     

Uncertainties in projected runoff over the conterminous United States

Projections of runoff from global multi-model ensembles provide a valuable basis for the estimation of future hydrological extremes. However, projections suffer from uncertainty that originates from different error sources along the modeling chain. Hydrological impact studies have generally partitioned these error sources into global impact and global climate model (GIM and GCM, respectively) uncertainties, neglecting other sources, including scenarios and internal variability. Using a set of GIMs driven by GCMs under different representative concentration pathways (RCPs), this study aims to partition the uncertainty of future flows coming from GIMs, GCMs, RCPs, and internal variability over the CONterminous United States (CONUS). We focus on annual maximum, median, and minimum runoff, analyzed decadally over the twenty-first century. Results indicate that GCMs and GIMs are responsible for the largest fraction of uncertainty over most of the study area, followed by internal variability and to a smaller extent RCPs. To investigate the influence of the ensemble setup on uncertainty, in addition to the full ensemble, three ensemble configurations are studied using fewer GIMs (excluding least credible GIMs in runoff representation and GIMs accounting for vegetation and CO₂ dynamics), and excluding intermediate RCPs. Overall, the use of fewer GIMs has a minor impact on uncertainty for low and medium flows, but a substantial impact for high flows. Regardless of the number of pathways considered, RCPs always play a very small role, suggesting that improvement of GCMs and GIMs and more informed ensemble selections can yield a reduction of projected uncertainties.     

基于SWAT模型分析淮河流域中上游水量平衡要素对气候变化的响应

本研究在对SWAT模型进行参数化的基础上,采用淮河干流吴家渡和鲁台子水文控制站1971-1990年和1991-2014年的月径流观测数据对SWAT模型进行了率定和验证。模拟效果评估结果显示：不论是率定期还是验证期,Nash-Sutcliffe系数Ens和确定系数R2均>0.8,相对误差Re<1%,模型能够较好地再现月尺度的降雨-径流过程。淮河中上游年径流深线性变化趋势不明显,但子流域空间差异显著,径流深上游及南部呈线性减小趋势,其他子流域呈增大趋势。从年水量平衡要素来看,蒸散量和渗漏量对水量平衡贡献最大。主成分分析表明,平均气温、降水量及蒸散量是淮河中上游水文要素变化的关键因子。剔除人为因素的影响,1971-2014年淮河中上游地区水资源量呈减少趋势,这可能是年平均气温升高、年降 水量略有减少以及年蒸散量减少综合作用的结果。本文研究成果可为淮河中上游水资源管理和相关政策的制定提供技术支撑。     

Future Precipitation Extremes in China under Climate Change and Their Physical Quantification Based on a Regional Climate Model and CMIP5 Model Simulations

The atmospheric water holding capacity will increase with temperature according to Clausius-Clapeyron scaling and affects precipitation.The rates of change in future precipitation extremes are quantified with changes in surface air temperature.Precipitation extremes in China are determined for the 21st century in six simulations using a regional climate model,RegCM4,and 17 global climate models that participated in CMIP5.First,we assess the performance of the CMIP5 models and RCM runs in their simulation of extreme precipitation for the current period(RF:1982-2001).The CMIP5 models and RCM results can capture the spatial variations of precipitation extremes,as well as those based on observations:OBS and XPP.Precipitation extremes over four subregions in China are predicted to increase in the mid-future(MF:2039-58)and far-future(FF:2079-98)relative to those for the RF period based on both the CMIP5 ensemble mean and RCM ensemble mean.The secular trends in the extremes of the CMIP5 models are predicted to increase from 2008 to 2058,and the RCM results show higher interannual variability relative to that of the CMIP5 models.Then,we quantify the increasing rates of change in precipitation extremes in the MF and FF periods in the subregions of China with the changes in surface air temperature.Finally,based on the water vapor equation,changes in precipitation extremes in China for the MF and FF periods are found to correlate positively with changes in the atmospheric vertical wind multiplied by changes in surface specific humidity(significant at the p<0.1 level).     

兰江流域近43年气候变化及对水资源的影响

利用累积距平法对兰江流域近43年（1961-2003年）气温、降水量和径流量资料进行分析，研究兰江流域气候变化及其气候变化对水资源的影响。结果显示：兰江流域近43年来气温、降水量总的趋势是上升的；1990年代是兰江流域气温上升和降水增加最显著的时段，主要表现在冬春气温明显上升，夏季降水量明显增加：兰江流域年径流深与年降水量基本保持同步变化。兰江流域过去43年的气候变化对流域内水资源产生了较大的影响，而且由于兰江流域内水资源空间分布差异较大，致使流域内人均水资源占有量较少的金华地区易受气候变化影响而出现供水紧张。     

多区域气候模式集合对中国径流深的模拟评估和未来变化预估

对5组区域气候模式集合模拟的中国径流深进行评估,并且预估了温室气体高排放情景RCP8.5下的未来变化。结果表明：多区域气候模式集合结果能够基本模拟出径流深的观测特征,对年径流深的空间分布特征模拟较好,但量值存在一定的系统偏差,特别是黄河中游、海河和松辽河存在明显的正偏差,且对全国9个流域片中东南、西南和西北诸河的年内分配总体模拟效果相对较差。未来到21世纪末,全国平均年径流深在各个时段都以增加为主,增加幅度多在5%以内。未来变化存在明显的空间差异,大致表现为“北增南减”的分布特征,但不会改变中国水资源南多北少的空间格局;其中,黄河、西南和西北诸河流域片呈显著的增加趋势,淮河、长江和东南诸河流域片呈现显著的减少趋势,海河、松辽和珠江流域的变化趋势不显著。21世纪末期各地的变化多在±30%以内,且多模式预估的正负变化一致性较高。到21世纪末期,各流域片平均的径流深季节分配总体特征没有明显变化,径流深的最大月份基本维持不变,分配比例的数值有±2%以内的变化,且各季节的增减变化存在明显流域间差异。     

博斯腾湖流域气候变化对开都河径流量的影响

利用线性回归分析法、突变检验法等分析博斯腾湖流域1980～2018年的年均气温、年降水量、年蒸发量等气候因子变化趋势和突变现象及其对开都河径流量的影响.结果表明:1980～2018年博斯腾湖流域年均气温呈波动中上升趋势,其变化速率为0.15℃(10a)-1,年降水量则以0.765mm(10a)-1的速率增加,而年蒸发量...     

Climate change scenarios for precipitation and potential evapotranspiration over central Belgium

In this article, we examine climate model estimations for the future climate over central Belgium. Our analysis is focused mainly on two variables: potential evapotranspiration (PET) and precipitation. PET is calculated using the Penman equation with parameters appropriately calibrated for Belgium, based on RCM data from the European project PRUDENCE database. Next, we proceed into estimating the model capacity to reproduce the reference climate for PET and precipitation. The same analysis for precipitation is also performed based on GCM data from the IPCC AR4 database. Then, the climate change signal is evaluated over central Belgium using RCM and GCM simulations based on several SRES scenarios. The RCM simulations show a clear shift in the precipitation pattern with an increase during winter and a decrease during summer. However, the inclusion of another set of SRES scenarios from the GCM simulations leads to a less clear climate change signal.     

On interpreting hydrological change from regional climate models

Although representation of hydrology is included in all regional climate models (RCMs), the utility of hydrological results from RCMs varies considerably from model to model. Studies to evaluate and compare the hydrological components of a suite of RCMs and their use in assessing hydrological impacts from future climate change were carried out over Europe. This included using different methods to transfer RCM runoff directly to river discharge and coupling different RCMs to offline hydrological models using different methods to transfer the climate change signal between models. The work focused on drainage areas to the Baltic Basin, the Bothnian Bay Basin and the Rhine Basin. A total of 20 anthropogenic climate change scenario simulations from 11 different RCMs were used. One conclusion is that choice of GCM (global climate model) has a larger impact on projected hydrological change than either selection of emissions scenario or RCM used for downscaling.