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1.
Climate change information required for impact studies is of a much finer scale than that provided by Global circulation models (GCMs). This paper presents an application of partial least squares (PLS) regression for downscaling GCMs output. Statistical downscaling models were developed using PLS regression for simultaneous downscaling of mean monthly maximum and minimum temperatures (T max and T min) as well as pan evaporation to lake-basin scale in an arid region in India. The data used for evaluation were extracted from the NCEP/NCAR reanalysis dataset for the period 1948?C2000 and the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1, and COMMIT for the period 2001?C2100. A simple multiplicative shift was used for correcting predictand values. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response. The analysis of downscaling models reveals that (1) the correlation coefficient for downscaled versus observed mean maximum temperature, mean minimum temperature, and pan evaporation was 0.94, 0.96, and 0.89, respectively; (2) an increasing trend is observed for T max and T min for A1B, A2, and B1 scenarios, whereas no trend is discerned with the COMMIT scenario; and (3) there was no trend observed in pan evaporation. In COMMIT scenario, atmospheric CO2 concentrations are held at year 2000 levels. Furthermore, a comparison with neural network technique shows the efficiency of PLS regression method.  相似文献   

2.
The greenhouse gas emissions scenarios published by the IPCC in the Special Report on Emission Scenarios (SRES) continue to serve as a primary basis for assessing future climate change and possible response strategies. These scenarios were developed between 1996 and 1999 and sufficient time has now passed to make it worth examining their consistency with more recent data and projections. The comparison performed in this paper includes population, GDP, energy use, and emissions of CO2, non-CO2 gases and sulfur. We find the SRES scenarios to be largely consistent with historical data for the 1990–2000 period and with recent projections. Exceptions to this general observation include (1) in the long-term, relatively high population growth assumptions; in some regions, particularly in the A2 scenario; (2) in the medium-term, relatively high economic growth assumptions in the LAM (Latin America, Africa and Middle East) region in the A1 scenario; (3) in the short-term, CO2 emissions projections in A1 that are somewhat higher than the range of current scenarios; and (4) substantially higher sulfur emissions in some scenarios than in historical data and recent projections. In conclusion, given the relatively small inconsistencies for use as global scenarios there seems to be no immediate need for a large-scale IPCC-led update of the SRES scenarios that is solely based on the SRES scenario performance vis-a-vis data for the 1990–2000 period and/or more recent projections. Based on reported findings, individual research teams could make, and in some cases already have made, useful updates of the scenarios.  相似文献   

3.
Many impact studies require climate change information at a finer resolution than that provided by global climate models (GCMs). This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely single conjunctive rule learner, decision table, M5 model tree, and REPTree, and explores the impact of climate change on maximum and minimum temperatures (i.e., predictands) of 14 meteorological stations in the Upper Thames River Basin, Ontario, Canada. The data used for evaluation were large-scale predictor variables, extracted from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset and the simulations from third generation Canadian coupled global climate model. Data for four grid points covering the study region were used for developing the downscaling model. M5 model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. Hence, this technique was applied to project predictands generated from GCM using three scenarios (A1B, A2, and B1) for the periods (2046–2065 and 2081–2100). A simple multiplicative shift was used for correcting predictand values. The potential of the downscaling models in simulating predictands was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily predictands from large-scale weather variables. Trend of projected maximum and minimum temperatures was studied for historical as well as downscaled values using GCM and scenario uncertainty. There is likely an increasing trend for T max and T min for A1B, A2, and B1 scenarios while decreasing trend has been observed for B1 scenarios during 2081–2100.  相似文献   

4.
This study provides some guidance on the choice of predictor variables from both reanalysis products and the third version of the Canadian Coupled Global Climate Model (CGCM3) outputs for regression-based statistical downscaling models (SDMs) for climate change application in southern Québec (Canada). Twenty CGCM3 grid points and four surface observation sites in the study area were employed. Twenty-five deseasonalized predictors and four deseasonalized predictands (daily maximum and minimum temperatures, precipitation occurrence and wet day precipitation amount) were used to investigate correlation coefficients among predictors and to evaluate their predictive ability when used in a multiple linear regression (MLR) downscaling model. The basic statistical characteristics of vorticity at 1,000-, 850- and 500-hPa levels, U-component of velocity at 1,000-hPa level, temperature at 2?m (T 2) and wind direction at 1,000- and 500-hPa level of CGCM3 showed a larger difference with those of the NCEP reanalysis data. Therefore, those seven variables require high caution to be included as predictors in statistical downscaling models. Specific humidity at 1,000-, 850- and 500-hPa levels, geopotential height at 850- and 500-hPa levels and T 2 were the most sensitive predictors for future climate conditions (i.e. A1B and A2 emission scenarios). Specific humidity and geopotential height at different levels and T 2 were important explainable predictors for the daily temperatures. Mean sea level pressure, specific humidity, U and V components and divergence showed potential as predictors for daily precipitation. Spatial explained variance of MLRs between predictors of every different CGCM3 grid points and the four predictands showed large values at the CGCM3 grid points located near the observation sites, whereas relatively small values were shown at the CGCM3 grid points located more than 400?km from the sites. The explained variance of the downscaled predictands by predictors of three or four CGCM3 grid points located near the observation site produced 2–5% larger R-squares than those by predictors of the nearest grid point. The results illustrated that the use of predictors from more than one AOGCM grid points located near the observation site can increase the skill of the MLR downscaling models.  相似文献   

5.
De Li Liu  Heping Zuo 《Climatic change》2012,115(3-4):629-666
This paper outlines a new statistical downscaling method based on a stochastic weather generator. The monthly climate projections from global climate models (GCMs) are first downscaled to specific sites using an inverse distance-weighted interpolation method. A bias correction procedure is then applied to the monthly GCM values of each site. Daily climate projections for the site are generated by using a stochastic weather generator, WGEN. For downscaling WGEN parameters, historical climate data from 1889 to 2008 are sorted, in an ascending order, into 6 climate groups. The WGEN parameters are downscaled based on the linear and non-linear relationships derived from the 6 groups of historical climates and future GCM projections. The overall averaged confidence intervals for these significant linear relationships between parameters and climate variables are 0.08 and 0.11 (the range of these parameters are up to a value of 1.0) at the observed mean and maximum values of climate variables, revealing a high confidence in extrapolating parameters for downscaling future climate. An evaluation procedure is set up to ensure that the downscaled daily sequences are consistent with monthly GCM output in terms of monthly means or totals. The performance of this model is evaluated through the comparison between the distributions of measured and downscaled climate data. Kruskall-Wallis rank (K-W) and Siegel-Tukey rank sum dispersion (S-T) tests are used. The results show that the method can reproduce the climate statistics at annual, monthly and daily time scales for both training and validation periods. The method is applied to 1062 sites across New South Wales (NSW) for 9 GCMs and three IPCC SRES emission scenarios, B1, A1B and A2, for the period of 1900–2099. Projected climate changes by 7 GCMs are also analyzed for the A2 emission scenario based on the downscaling results.  相似文献   

6.
Several studies have been devoted to dynamic and statistical downscaling for both climate variability and climate change. This paper introduces an application of temporal neural networks for downscaling global climate model output and autocorrelation functions. This method is proposed for downscaling daily precipitation time series for a region in the Amazon Basin. The downscaling models were developed and validated using IPCC AR4 model output and observed daily precipitation. In this paper, five AOGCMs for the twentieth century (20C3M; 1970–1999) and three SRES scenarios (A2, A1B, and B1) were used. The performance in downscaling of the temporal neural network was compared to that of an autocorrelation statistical downscaling model with emphasis on its ability to reproduce the observed climate variability and tendency for the period 1970–1999. The model test results indicate that the neural network model significantly outperforms the statistical models for the downscaling of daily precipitation variability.  相似文献   

7.
统计降尺度法对华北地区未来区域气温变化情景的预估   总被引：32，自引：1，他引：31  
迄今为止,大部分海气耦合气候模式（AOGCM）的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测。降尺度法已广泛用于弥补AOGCM在这方面的不足。作者采用统计降尺度方法对1月和7月华北地区49个气象观测站的未来月平均温度变化情景进行预估。采用的统计降尺度方法是主分量分析与逐步回归分析相结合的多元线性回归模型。首先,采用1961～2000年的 NCEP再分析资料和49个台站的观测资料建立月平均温度的统计降尺度模型,然后把建立的统计降尺度模型应用于HadCM3 SRES A2 和 B2 两种排放情景, 从而生成各个台站1950～2099年1月份和7月份温度变化情景。结果表明:在当前气候条件下,无论1月还是7月,统计降尺度方法模拟的温度与观测的温度有很好的一致性,而且在大多数台站,统计降尺度模拟气温与观测值相比略微偏低。对于未来气候情景的预估方面,无论1月还是7月,也无论是HadCM3 SRES A2 还是B2排放情景驱动统计模型,结果表明大多数的站点都存在温度的明显上升趋势,同时7月的上升趋势与1月相比偏低。  相似文献   

8.
Climate change scenarios generated by general circulation models have too coarse a spatial resolution to be useful in planning disaster risk reduction and climate change adaptation strategies at regional to river basin scales. This study presents a new non-parametric statistical K-nearest neighbor algorithm for downscaling climate change scenarios for the Rohini River Basin in Nepal. The study is an introduction to the methodology and discusses its strengths and limitations within the context of hindcasting basin precipitation for the period of 1976?C2006. The actual downscaled climate change projections are not presented here. In general, we find that this method is quite robust and well suited to the data-poor situations common in developing countries. The method is able to replicate historical rainfall values in most months, except for January, September, and October. As with any downscaling technique, whether numerical or statistical, data limitations significantly constrain model ability. The method was able to confirm that the dataset available for the Rohini Basin does not capture long-term climatology. Yet, we do find that the hindcasts generated with this methodology do have enough skill to warrant pursuit of downscaling climate change scenarios for this particularly poor and vulnerable region of the world.  相似文献   

9.
Climate changes over China from the present (1990–1999) to future (2046–2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 downscaling corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 downscaling produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility.  相似文献   

10.
Climate change alone influences future levels of tropospheric ozone and their precursors through modifications of gas-phase chemistry, transport, removal, and natural emissions. The goal of this study is to determine at what extent the modes of variability of gas-phase pollutants respond to different climate change scenarios over Europe. The methodology includes the use of the regional modeling system MM5 (regional climate model version)-CHIMERE for a target domain covering Europe. Two full-transient simulations covering from 1991–2050 under the SRES A2 and B2 scenarios driven by ECHO-G global circulation model have been compared. The results indicate that the spatial patterns of variability for tropospheric ozone are similar for both scenarios, but the magnitude of the change signal significantly differs for A2 and B2. The 1991–2050 simulations share common characteristics for their chemical behavior. As observed from the NO2 and α-pinene modes of variability, our simulations suggest that the enhanced ozone chemical activity is driven by a number of parameters, such as the warming-induced increase in biogenic emissions and, to a lesser extent, by the variation in nitrogen dioxide levels. For gas-phase pollutants, the general increasing trend for ozone found under A2 and B2 forcing is due to a multiplicity of climate factors, such as increased temperature, decreased wet removal associated with an overall decrease of precipitation in southern Europe, increased photolysis of primary and secondary pollutants as a consequence of lower cloudiness and increased biogenic emissions fueled by higher temperatures.  相似文献   

11.
全球气候模式对未来中国风速变化预估   总被引：6，自引：0，他引：6  
江滢  罗勇  赵宗慈 《大气科学》2010,34(2):323-336
利用世界气候研究计划之第三次耦合模式比较计划 (WCRP/CMIP3) 提供的, 参加IPCC AR4的19个气候模式和国家气候中心为IPCC第五次报告研发的新一代气候模式 (BCC_CSM1.0.1) 及模式集成, 考虑高排放 (A2)、 中等排放 (A1B) 和低排放 (B1) 三种人类排放情景, 预估21世纪中国近地层 (离地10 m) 风速变化。预估结果表明: (1) 21世纪全国平均的年平均风速呈微弱的减小趋势, 且随着预估情景人类排放的增加, 中国年平均风速减小趋势越显著。 (2) 冬季 (夏季) 全国平均风速呈减小 (增大) 趋势, 人类排放量越多, 冬季 (夏季) 风速减小 (增加) 程度越大。21世纪我国风速夏季 (冬季) 增大 (减小) 与全球变暖的背景下未来亚洲夏季风 (冬季风) 增强 (减弱) 有一定关系。 (3) 与20世纪末期 (1980～1999年) 相比, 21世纪初期 (2011～2030年) 中国区域年平均风速A2情景下略偏小, A1B和B1情景下年平均风速无明显变化; 21世纪中期 (2046～2065年) 和后期 (2080～2099年), 三种排放情景下中国年平均风速均比20世纪末期风速小。 (4) 21世纪初期、 中期和后期均表现为冬季 (夏季) 平均风速比20世纪末期冬季 (夏季) 平均小 (大)。 (5) 夏季中国中北部和东北地区风速偏大, 其余地区风速无明显变化或略偏小; 冬季除了东北北部和西藏东南部外, 中国大部地区风速偏小。绝大部分地区超过50%模式一致地预估上述风速变化形式, 具有一定的可信度。  相似文献   

12.
利用政府间气候变化委员会（IPCC）第4次评估报告提供的13个新一代气候系统模式的模拟结果,分析了不同情景下（高排放SRESA2、中等排放SRESA1B和低排放SRESB1）重庆地区21世纪的气候变化。结果表明：21世纪重庆气候总体有显著变暖、变湿趋势,年平均气温变暖趋势为每100年2．3～4．2℃,年降水增加趋势为每100年5．9％～8．8％。冬季变暖最明显,春季降水增加较显著、秋季减少较明显。在A2、A1B和B1情景下,21世纪后期气温分别比常年偏高3．68、3．28、2．26℃,年降水分别比常年偏多5．24％、5．77％和3．43％。  相似文献   

13.
Climate change and critical thresholds in China’s food security   总被引：2，自引：0，他引：2  
Identification of ‘critical thresholds’ of temperature increase is an essential task for inform policy decisions on establishing greenhouse gas (GHG) emission targets. We use the A2 (medium-high GHG emission pathway) and B2 (medium-low) climate change scenarios produced by the Regional Climate Model PRECIS, the crop model – CERES, and socio-economic scenarios described by IPCC SRES, to simulate the average yield changes per hectare of three main grain crops (rice, wheat, and maize) at 50 km × 50 km scale. The threshold of food production to temperature increases was analyzed based on the relationship between yield changes and temperature rise, and then food security was discussed corresponding to each IPCC SRES scenario. The results show that without the CO2 fertilization effect in the analysis, the yield per hectare for the three crops would fall consistently as temperature rises beyond 2.5 ^C; when the CO2 fertilization effect was included in the simulation, there were no adverse impacts on China’s food production under the projected range of temperature rise (0.9–3.9 ^C). A critical threshold of temperature increase was not found for food production. When the socio-economic scenarios, agricultural technology development and international trade were incorporated in the analysis, China’s internal food production would meet a critical threshold of basic demand (300 kg/capita) while it would not under A2 (no CO2 fertilization); whereas basic food demand would be satisfied under both A2 and B2, and would even meet a higher food demand threshold required to sustain economic growth (400 kg/capita) under B2, when CO2 fertilization was considered.  相似文献   

14.
Monthly mean temperatures at 562 stations in China are estimated using a statistical downscaling technique. The technique used is multiple linear regressions （MLRs） of principal components （PCs）. A stepwise screening procedure is used for selecting the skilful PCs as predictors used in the regression equation. The predictors include temperature at 850 hPa （7）, the combination of sea-level pressure and temperature at 850 hPa （P＋T） and the combination of geo-potential height and temperature at 850 hPa （H＋T）. The downscaling procedure is tested with the three predictors over three predictor domains. The optimum statistical model is obtained for each station and month by finding the predictor and predictor domain corresponding to the highest correlation. Finally, the optimum statistical downscaling models are applied to the Hadley Centre Coupled Model, version 3 （HadCM3） outputs under the Special Report on Emission Scenarios （SRES） A2 and B2 scenarios to construct local future temperature change scenarios for each station and month, The results show that （1） statistical downscaling produces less warming than the HadCM3 output itself; （2） the downscaled annual cycles of temperature differ from the HadCM3 output, but are similar to the observation; （3） the downscaled temperature scenarios show more warming in the north than in the south; （4） the downscaled temperature scenarios vary with emission scenarios, and the A2 scenario produces more warming than the B2, especially in the north of China.  相似文献   

15.
Ian Castles and David Henderson have criticized IPCC’s Special Report on Emissions Scenarios (SRES) (IPCC: 2000, Special Report on Emissions Scenarios, Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge, 595 pp. http://www.grida.no/climate/ipcc/emission/index.htm) for using market exchange rates (MER) instead of purchasing power parities (PPP), when converting regional GDP into a common denominator. The consequence is that poor countries generally appear to be poorer than they actually are. An overstated income gap between the rich and poor countries in the base year gives rise to projections of too high economic growth in the poor countries, because the scenarios are constructed with the aim of reducing the income gap. Castles and Henderson claim that overstated economic growth means that greenhouse gas emissions are overstated as well. However, because closure of the emission-intensity gap between the rich and the poor parts of the world is another important driving force in the scenarios, we argue that the use of MER in the SRES scenarios has not caused an overestimation of the global emission growth because, as far as global emissions are concerned, the overstated income gap is effectively neutralized by the overstated emission-intensity gap.  相似文献   

16.
利用1961—1990年江淮流域逐日降水资料、NCEP/NCAR再分析资料和HadCM3 SRES A1B情景下模式预估资料,采用典型相关分析统计降尺度方法,评估降尺度模型对当前极端降水指数的模拟能力,并对21世纪中期和末期的极端降水变化进行预估。结果表明：通过降尺度能够有效改善HadCM3对区域气候特征的模拟能力,极端降水指数气候平均态相对误差降低了30%～100%,但降尺度结果仍然在冬季存在湿偏差、夏季存在干偏差;在SRES A1B排放情景下,该区域大部分站点的极端强降水事件将增多,强度增大,极端强降水指数的变化幅度高于平均降水指数,且夏季增幅高于冬季;冬季极端降水贡献率（R95t）在21世纪中期和末期的平均增幅分别为14%和25%,夏季则分别增加24%和32%。  相似文献   

17.
Climate change impacts on global agriculture   总被引：1，自引：0，他引：1  
Based on predicted changes in the magnitude and distribution of global precipitation, temperature and river flow under the IPCC SRES A1B and A2 scenarios, this study assesses the potential impacts of climate change and CO2 fertilization on global agriculture. The analysis uses the new version of the GTAP-W model, which distinguishes between rainfed and irrigated agriculture and implements water as an explicit factor of production for irrigated agriculture. Future climate change is likely to modify regional water endowments and soil moisture. As a consequence, the distribution of harvested land will change, modifying production and international trade patterns. The results suggest that a partial analysis of the main factors through which climate change will affect agricultural productivity provide a false appreciation of the nature of changes likely to occur. Our results show that global food production, welfare and GDP fall in the two time periods and SRES scenarios. Higher food prices are expected. No matter which SRES scenario is preferred, we find that the expected losses in welfare are significant. These losses are slightly larger under the SRES A2 scenario for the 2020s and under the SRES A1B scenario for the 2050s. The results show that national welfare is influenced both by regional climate change and climate-induced changes in competitiveness.  相似文献   

18.
Joint variable spatial downscaling   总被引：1，自引：0，他引：1  
Joint Variable Spatial Downscaling (JVSD), a new statistical technique for downscaling gridded climatic variables, is developed to generate high resolution gridded datasets for regional watershed modeling and assessments. The proposed approach differs from previous statistical downscaling methods in that multiple climatic variables are downscaled simultaneously and consistently to produce realistic climate projections. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being downscaled. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. Analysis and comparisons are performed for 20th Century Climate in Coupled Models (20C3M), broadly available for most GCMs. The results show that the proposed downscaling method is able to reproduce the sub-grid climatic features as well as their temporal/spatial variability in the historical periods. Comparisons are also performed for precipitation and temperature with other statistical and dynamic downscaling methods over the southeastern US and show that JVSD performs favorably. The downscaled sequences are used to assess the implications of GCM scenarios for the Apalachicola-Chattahoochee-Flint river basin as part of a comprehensive climate change impact assessment.  相似文献   

19.
Projections for South America of future climate change conditions in mean state and seasonal cycle for temperature during the twenty-first century are discussed. Our analysis includes one simulation of seven Atmospheric-Ocean Global Circulation Models, which participated in the Intergovernmental Panel on Climate Change Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three Special Report on Emissions Scenarios (SRES) A2, A1B, and B1. We developed a statistical method based on neural networks and Bayesian statistics to evaluate the models’ skills in simulating late twentieth century temperature over continental areas. Some criteria [model weight indices (MWIs)] are computed allowing comparing over such large regions how each model captures the temperature large scale structures and contributes to the multi-model combination. As the study demonstrates, the use of neural networks, optimized by Bayesian statistics, leads to two major results. First, the MWIs can be interpreted as optimal weights for a linear combination of the climate models. Second, the comparison between the neural network projection of twenty-first century conditions and a linear combination of such conditions allows the identification of the regions, which will most probably change, according to model biases and model ensemble variance. Model simulations in the southern tip of South America and along the Chilean and Peruvian coasts or in the northern coasts of South America (Venezuela, Guiana) are particularly poor. Overall, our results present an upper bound of potential temperature warming for each scenario. Spatially, in SRES A2, our major findings are that Tropical South America could warm up by about 4°C, while southern South America (SSA) would also undergo a near 2–3°C average warming. Interestingly, this annual mean temperature trend is modulated by the seasonal cycle in a contrasted way according to the regions. In SSA, the amplitude of the seasonal cycle tends to increase, while in northern South America, the amplitude of the seasonal cycle would be reduced leading to much milder winters. We show that all the scenarios have similar patterns and only differ in amplitude. SRES A1B differ from SRES A2 mainly for the late twenty-first century, reaching more or less an 80–90% amplitude compared to SRES A2. SRES B1, however, diverges from the other scenarios as soon as 2025. For the late twenty-first century, SRES B1 displays amplitudes, which are about half those of SRES A2.  相似文献   

20.
The study evaluates statistical downscaling model (SDSM) developed by annual and monthly sub-models for downscaling maximum temperature, minimum temperature, and precipitation, and assesses future changes in climate in the Jhelum River basin, Pakistan and India. Additionally, bias correction is applied on downscaled climate variables. The mean explained variances of 66, 76, and 11 % for max temperature, min temperature, and precipitation, respectively, are obtained during calibration of SDSM with NCEP predictors, which are selected through a quantitative procedure. During validation, average R 2 values by the annual sub-model (SDSM-A)—followed by bias correction using NCEP, H3A2, and H3B2—lie between 98.4 and 99.1 % for both max and min temperature, and 77 to 85 % for precipitation. As for the monthly sub-model (SDSM-M), followed by bias correction, average R 2 values lie between 98.5 and 99.5 % for both max and min temperature and 75 to 83 % for precipitation. These results indicate a good applicability of SDSM-A and SDSM-M for downscaling max temperature, min temperature, and precipitation under H3A2 and H3B2 scenarios for future periods of the 2020s, 2050s, and 2080s in this basin. Both sub-models show a mean annual increase in max temperature, min temperature, and precipitation. Under H3A2, and according to both sub-models, changes in max temperature, min temperature, and precipitation are projected as 0.91–3.15 °C, 0.93–2.63 °C, and 6–12 %, and under H3B2, the values of change are 0.69–1.92 °C, 0.56–1.63 °C, and 8–14 % in 2020s, 2050s, and 2080s. These results show that the climate of the basin will be warmer and wetter relative to the baseline period. SDSM-A, most of the time, projects higher changes in climate than SDSM-M. It can also be concluded that although SDSM-A performed well in predicting mean annual values, it cannot be used with regard to monthly and seasonal variations, especially in the case of precipitation unless correction is applied.  相似文献   

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