基于随机森林模型的雅鲁藏布江流域气温降尺度研究

采用随机森林RF(Random Forest)模型对雅鲁藏布江流域22个站点的日平均气温进行降尺度研究,为了探求在雅鲁藏布江流域更适宜的气温降尺度方法,采用多元线性回归MLR、人工神经网络ANN和支持向量机SVM三种方法作为对比模型,并且采用主成分分析PCA和偏相关分析PAR两种分析方法,进行特征变量筛选。采用纳西效率系数NASH、均方根误差RMSE系数、绝对误差MAE和相关系数r值四种标准来评价模型的模拟效果。结果表明,RF模型的模拟效果要明显优于其他几种方法的模拟结果;采用PAR筛选特征变量的模型计算结果,不仅优于采用PCA筛选特征变量模型的模拟结果,且较稳定,另外,各种模型验证期的NASH效率系数都在0. 86以上,相关系数都在0. 93以上,所用几种模型都能较好地模拟雅江流域平均气温。选取MPI-ESM-LR模式在未来(2016-2050年)两种极端典型浓度路径RCP(Representative Concentration Pathway)排放情景RCP2. 6和RCP8. 5下的试验数据,研究雅鲁藏布江流域未来气温变化趋势表明,雅鲁藏布江流域未来2016-2050年在RCP2. 6和RCP8. 5两种排放情景下,平均气温都呈现出持续上升的趋势,在RCP2. 6排放情景下日平均气温平均上升0. 14℃,在RCP8. 5排放情景下日平均气温平均上升0. 30℃。     

气候模式对东北三省降水模拟能力评估及预估

利用CMIP 5全球气候模式、RegCM 4区域气候模式数据集和中国东北三省162个气象站降水观测资料,评估了CMIP 5和RegCM 4模式对中国东北三省降水的模拟能力,并对RCP 4.5和RCP 8.5温室气体排放情景下东北三省未来降水的变化进行了预估。结果表明:CMIP 5和RegCM 4模式均能较好地模拟东北三省年及四季降水量的变化,可再现东北三省降水量由东南向西向北递减的空间分布形势,但模拟的降水中心偏北,模拟的降水强度偏强;两个模式对夏季降水的模拟优于冬季,对冬季降水的模拟存在较大偏差。总体而言,全球气候模式CMIP 5对东北三省降水的模拟结果较好。对东北三省降水量的预估表明,在RCP 4.5和RCP 8.5情景下,全球气候模式CMIP 5预估东北三省年和四季降水量均呈不同程度的增加,其中对冬季降水量预估的偏差百分率增幅最大。在RCP 8.5情景下,东北三省降水量增幅显著,预估未来东北三省降水增加量基本呈由南向北逐步递减的分布,降水偏差百分率基本呈由西南向东北递减的分布。在RCP 4.5情景下,东北三省降水量增幅较小,预估未来东北三省降水量总体呈由东南向西北递减的分布,降水偏差百分率基本呈由西向东递减的分布。     

基于转移累计概率分布统计降尺度方法的未来降水预估研究:以湖南省为例

基于最新一代CMIP5（Coupled Model Intercomparison Project Phase 5）模式历史情景和未来RCP4.5情景下的模式逐日降水数据,使用转移累计概率分布（CDF-t）统计降尺度方法,从空间变化和时间变率两个方面评估该降尺度方法对湖南日降水量模拟能力的改善效果,并在此基础上对未来降水量变化进行预估。结果表明, CMIP5气候模式由于分辨率较低,无法细致反映湖南地形变化和大气环流影响导致的区域降水变化特征。经过CDF-t统计降尺度处理之后,模式对湖南降水的时、空分布模拟与实况更为接近,绝大部分模式对降水空间结构的模拟能力都有显著提高。基于CDF-t统计降尺度的多模式集合预估结果表明,21世纪湖南省日降水量呈弱的增多趋势（0.95%/（10 a））。21世纪初、中和末期相对于1986—2005年的气候平均态,湖南省日降水量分别增加了4.6%、5%和5.2%。3个时期湖南省日平均降水变化的空间分布存在较强的一致性,皆表现为湖南西北、东北和东南3个地区降水增幅最为显著,且随着辐射强迫的增大,3个地区降水增幅也呈递增趋势。需要指出的是,预估结果在模式之间存在一定差异,并且这种差异随着辐射强迫的增大而增大。      

CMIP5气候模式对云南气候变化模拟评估及未来情景预估

基于8个气候模式和多模式集合数据(21个气候模式简单集合)和观测数据,评估了其在气候基准期内对云南气温、降水的模拟能力,在评估基础上应用多模式集合数据,预估了未来不同排放情景下云南气温、降水的空间变化情况。结果表明:①多模式集合和部分模式能较好的模拟出基准期内气温、降水的年际变化趋势;在空间分布特征上,气候模式(包括多模式集合)对降水的模拟偏差较差,对气温的模拟相对较好;但在月平均气温和月降水的年内分布模拟上,多模式集合数据的模拟效果明显优于8个气候模式数据;②预估结果表明,在未来3种排放情景下云南地区降水呈西增东减的空间部分特征,纵向岭谷地区降水增加幅度为1%～3%,而气温在3种排放情景下则表现为一致的增加,降水和气温均在RCP8.5情景下增幅最大。     

统计降尺度法对华北地区未来区域气温变化情景的预估

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。     

基于CMIP5和RegCM4模式的内蒙古气候变化模拟评估及未来RCPs情景预估

文章利用CMIP5全球气候模式和RegCM4区域气候模式模拟的内蒙古降水量和平均气温的逐月数据,分别将2个气候模式1961—2005年的模拟结果与实际观测值进行对比,综合评估2个气候模式对内蒙古降水量和平均气温的模拟能力,并预估分析3种RCPs情景下2021—2100年内蒙古未来降水量和平均气温的可能变化特征。结果显示:CMIP5模式对年降水量模拟效果优于RegCM4模式,而RegCM4模式对年平均气温的细节模拟更具有优势,总体上CMIP5模式对内蒙古降水量和平均气温均具有良好的模拟能力。未来80年内蒙古气候呈暖湿变化趋势,其中RCP8.5情景增幅最大,年降水量和年平均气温分别增加了21.6%和5.3℃,RCP4.5情景次之,RCP2.6情景增加趋势不明显。四季和各年代的降水量和平均气温也一致呈增加趋势,其中冬季降水量增幅最大,最大可达22.15%,秋季平均气温在RCP2.6和RCP4.5情景下增幅最大,分别为1.50℃和2.22℃,冬季平均气温在RCP8.5情景下增幅最大,为3.67℃;RCP2.6情景下,年降水量和年平均气温分别在21世纪60年代和40年代增幅最大,分别为8.12%和1.57℃,而RCP4.5和RCP8.5情景下则均在21世纪90年代增加幅度最大,最大分别可达18.52%和5.80℃。     

RCPs情景下珠江流域气候变化预估分析

针对珠江流域,分析了在全球气候模式（BCC_CSM1.1）驱动下,区域气候模式RegCM4进行的中国区域气候变化模拟中,珠江流域在RCP4.5和RCP8.5温室气体排放情景下,未来2010—2099年的气候变化。结果表明,RegCM4对珠江流域气候特征具有很强的模拟能力。未来RCPS情景下珠江流域气温将持续增大。与参照时段（1980—1999年）相比,RCP4.5和RCP8.5情景下的年平均温度在2020s分别增加0.7 ℃和0.8 ℃,2050s分别增加1.0 ℃和1.6 ℃,2080s分别增加1.6 ℃和2.9 ℃。而未来年降水并未表现出显著的变化趋势,但不同情景、不同地区预估的降水呈现不同的变化趋势。RCP4.5情景下,流域降水2020s将减少4.3%,2050s和2080s将分别增加0.7%和0.1%;RCP8.5情景下,未来不同时段流域降水均呈减少趋势,2020s、2050s和2080s分别减少1.7%、2.9%和0.2%,表明降水预估具有更大的不确定性。两种排放情景下未来降水在东南沿海增加、西北部减少,变化率为±8%。此外,两种排放情景下未来珠江流域的日平均温度统计特征发生改变,揭示未来高温事件可能增加,同时,大雨级别以上的降水发生频率增加,可能导致洪涝事件增加。      

基于BP-CCA统计降尺度的中亚春季降水的多模式集合模拟与预估

利用中亚地区30个观测台站逐月降水资料及同期ERA-40再分析资料,结合8个CMIP5全球气候模式模拟与未来预估大尺度环流场,使用基于变形典型相关分析的统计降尺度方法（BP-CCA）建立降尺度模型,评估多个气候模式对当前气候下中亚地区春季降水的降尺度模拟能力,并对春季降水进行降尺度集合未来预估。结果表明,建立的降尺度模型能够很好地模拟出交叉检验期内春季降水的时间变化和空间结构：降尺度春季降水与相应观测序列的平均时间相关系数为0.35,最高为0.62,平均空间相关系数为0.87。气候模式对中亚春季降水的模拟能力通过降尺度方法得到了显著提高：8个模式降尺度后模拟的降水气候平均态相对误差绝对值降至0.2%—8%,相比降尺度前减小了10%—60%,模拟的降水量场与相应观测场的空间相关均超过0.77;对比降尺度前多模式集合结果,多模式降尺度集合模拟的相对误差绝对值由64%减小至4%,空间相关系数由0.47增大至0.81,标准化均方根误差降至0.59,且多模式降尺度集合结果优于大部分单个模式降尺度结果。多模式降尺度集合预估结果表明,在RCP4.5排放情景下,21世纪前期（2016—2035年）、中期（2046—2065年）和末期（2081—2100年）的全区平均降水变化率分别为-5.3%、3.0%和17.4%。21世纪前期中亚大部分地区降水呈减少趋势,降水呈增多趋势的站点主要分布在南部。21世纪中期整体降水变化率由减少变为增多趋势,21世纪末期中亚大部分台站降水增多较为明显。21世纪初期和末期可信度高的台站均主要位于中亚西部地区。     

“一带一路”区域未来气候变化预估

利用耦合模式比较计划第5阶段(CMIP5)提供的18个全球气候模式的模拟结果,预估了3种典型浓度路径(RCP2.6、RCP4.5、RCP8.5)下“一带一路”地区平均气候和极端气候的未来变化趋势。结果表明:在温室气体持续排放情景下,“一带一路”地区年平均气温在未来将会持续上升,升温幅度随温室气体浓度的增加而加大。在高温室气体排放情景(RCP8.5)下,到21世纪末期,平均气温将普遍升高5℃以上,其中北亚地区升幅最大,南亚和东南亚地区升幅最小。对于降水的变化,预估该区域大部分地区的年降水量将增加,其中西亚和北亚增加最为明显,而且在21世纪中期,RCP2.6情景下的增幅要比RCP4.5和RCP8.5情景下的偏大,而在21世纪后期,RCP8.5情景下降水的增幅比RCP2.6和RCP4.5情景下的偏大。未来极端温度也将呈升高的趋势,增温幅度高纬度地区大于低纬度地区、高排放情景大于低排放情景。而且在高纬度区域,极端低温的增暖幅度要大于极端高温的增幅。连续干旱日数在北亚和东亚总体呈现减少趋势,而在其他地区则呈增加趋势。极端强降水在“一带一路”区域总体上将增强,增强最明显的地区位于南亚、东南亚和东亚。     

6.25 km高分辨率降尺度数据对21世纪长江经济带极端气候事件及其风险的集合预估

使用基于动力降尺度和统计降尺度方法得到的RCP4.5情景下的6.25 km高分辨率联合降尺度预估数据集,对长江经济带未来极端气候事件及其造成的风险展开评估和预估。结果表明：降尺度预估数据能较好的再现各极端温度指数和大部分极端降水指数的空间分布,但一些极端降水指数的偏差略大。未来长江经济带极端热事件将增加,冷事件减少;长江中游东部和下游的极端降水事件将增加,上游地区东南部发生干旱事件的可能性大。长江经济带以及上游、中游和下游3个分区的高温事件和强降水事件的国内生产总值（GDP）暴露度都将增加;人口暴露度呈先增后降的变化趋势。高温事件的GDP暴露度的分布因子和非线性因子的贡献同样重要,人口暴露度中分布因子的影响更大;强降水事件的暴露度主要取决于GDP或人口分布因子。     

使用岭回归对哈萨克斯坦月平均气温的统计降尺度研究

哈萨克斯坦是世界最大的内陆国家,拥有典型的大陆性气候和多样的地理环境及生态系统,同时哈萨克斯坦的自然环境和人类社会对于气候变化这一全球性问题是敏感的、脆弱的,需要运用科学的研究方法应对气候变化的挑战。通常,区域或局地尺度的气候变化影响研究需要对气候模式输出或再分析资料进行降尺度以获得更细分辨率的气候资料。近年来,大量验证统计降尺度方法在各个地区能力的研究见诸文献,然而在哈萨克斯坦地区验证统计降尺度方法的研究非常少见。本文使用了岭回归的方法对哈萨克斯坦地区11个气象站点1960~2009年的月平均气温进行了统计降尺度研究。结果显示,使用前30年数据和岭回归模型建立大尺度预报因子和观测资料的统计关系可以较好地预测后20年的月平均气温,预测能力在各站各月均有不同程度的差异,地形复杂的站点预测效果较差,夏季预测结果好于冬季;此外,将哈萨克斯坦地区平均来看则与观测数据相吻合。     

Conditional stochastic simulation model for spatial downscaling for assessing the effects of climate change on hydro-meteorological variables

The current study examines the recently proposed “bias correction and stochastic analogues” (BCSA) statistical spatial downscaling technique and attempts to improve it by conditioning coarse resolution data when generating replicates. While the BCSA method reproduces the statistical features of the observed fine data, this existing model does not replicate the observed coarse spatial pattern, and subsequently, the cross-correlation between the observed coarse data and downscaled fine data with the model cannot be preserved. To address the dissimilarity between the BCSA downscaled data and observed fine data, a new statistical spatial downscaling method, “conditional stochastic simulation with bias correction” (BCCS), which employs the conditional multivariate distribution and principal component analysis, is proposed. Gridded observed climate data of mean daily precipitation (mm/day) covering a month at 1/8° for a fine resolution and at 1° for a coarse resolution over Florida for the current and future periods were used to verify and cross-validate the proposed technique. The observed coarse and fine data cover the 50-year period from 1950 to1999, and the future RCP4.5 and RCP8.5 climate scenarios cover the 100-year period from 2000 to 2099. The verification and cross-validation results show that the proposed BCCS downscaling method serves as an effective alternative means of downscaling monthly precipitation levels to assess climate change effects on hydrological variables. The RCP4.5 and RCP8.5 GCM scenarios are successfully downscaled.     

Coupling climate change with hydrological dynamic in Qinling Mountains, China

This study intends to disclose orographic effects on climate and climatic impacts on hydrological regimes in Qinling Mountains under global change background. We integrate a meteorological model (MM5 model, PSU/NCAR, 2005) and a hydrological model (SWAT model, 2005) to couple hydrological dynamic with climate change in Qinling Mountains. Models are calibrated and validated based on the simulation of different combined schemes. Following findings were achieved. Firstly, Qinling Mountains dominantly influence climate, and hydrological process in Weihe River and upper Hanjiang River. Results show that Qinling Mountains lead to a strong north–south gradient precipitation distribution over Qinling Mountains due to orographic effects, and it reduces precipitation from 10–25 mm (December) to 55–80 mm (August) in Weihe River basin, and adds 25–50 mm (December) or 65–112 mm (August) in upper Hanjiang River basin; evapotranspiration (ET) decrease of 21% in Weihe River (August) and increase 10.5% in upper Hanjiang River (July). The Qinling Mountains reduce water yields of 23.5% in Weihe River, and decrease of 11.3% in upper Hanjiang River. Secondly, climate change is responsible for the changes of coupling effects of rainfall, land use and cover, river flow and water resources. It shows that average temperature significantly increased, and precipitation substantially reduced which leads to hydrological process changed greatly from 1950 to 2005: temperature increased and precipitation decreased, climate became drier in the past two decades (1980–2005), high levels of precipitation exists in mid-1950, mid-1970, while other studied periods are in low level states. The inter-annual variation in water yield correlates with surface runoff with an R ² value of 0.63 (Weihe River) and 0.87 (upper Hanjiang River). It shows that variation of annual precipitation was smaller than that of seasonal precipitation.     

Assessment of future climate change over East Asia due to the RCP scenarios downscaled by GRIMs-RMP

This study assesses future climate change over East Asia using the Global/Regional Integrated Model system—Regional Model Program (RMP). The RMP is forced by two types of future climate scenarios produced by the Hadley Center Global Environmental Model version 2 (HG2); the representative concentration pathways (RCP) 4.5 and 8.5 scenarios for the intergovernmental panel on climate change fifth assessment report (AR5). Analyses for the current (1980–2005) climate are performed to evaluate the RMP’s ability to reproduce precipitation and temperature. Two different future (2006–2050) simulations are compared with the current climatology to investigate the climatic change over East Asia centered in Korea. The RMP satisfactorily reproduces the observed seasonal mean and variation of precipitation and temperature. The spatial distribution of the simulated large-scale features and precipitation by the RMP is generally less reflective of current climatic conditions than that is given by the HG2, but their inter-annual variations in East Asia are better captured by the RMP. Furthermore, the RMP shows higher reproducibility of climate extremes including excessive heat wave and precipitation events over South Korea. In the future, strong warming is distinctly coupled with intensified monsoonal precipitation over East Asia. In particular, extreme weather conditions are increased and intensified over South Korea as follows: (1) The frequency of heat wave events with temperature greater than 30 °C is projected to increase by 131 and 111 % in the RCP 8.5 and 4.5 downscaling, relative to the current climate. (2) The RCP 8.5 downscaling shows the frequency and variability of heavy rainfall to increase by 24 and 31.5 %, respectively, while the statistics given by the RCP 4.5 downscaling are similar to those of the current climate.     

A high-resolution 43-year atmospheric hindcast for South America generated with the MPI regional model

An evaluation of the present-day climate in South America simulated by the MPI atmospheric limited area model, REMO, is made. The model dataset was generated by dynamical downscaling from the ECMWF-ERA40 reanalysis and compared to in-situ observations. The model is able to reproduce the low-level summer monsoon circulation but it has some deficiencies in representing the South American Low-Level Jet structure. At upper levels, summer circulation features like the Bolivian High and the associated subtropical jet are well simulated by the model. Sea-level pressure fields are in general well represented by REMO. The model exhibits reasonable skill in representing the general features of the mean seasonal cycle of precipitation. Nevertheless, there is a systematic overestimation of precipitation in both tropical and subtropical regions. Differences between observed and modeled temperature are smaller than 1.5°C over most of the continent, excepting during spring when those differences are quite large. Results also show that the dynamical downscaling performed using REMO introduces some enhancement of the global reanalysis especially in temperature at the tropical regions during the warm season and in precipitation in both the subtropics and extratropics. It is then concluded that REMO can be a useful tool for regional downscaling of global simulations of present and future climates.     

Coupling statistical and dynamical methods for spatial downscaling of precipitation

The resolution of General Circulation Models (GCMs) is too coarse for climate change impact studies at the catchment or site-specific scales. To overcome this problem, both dynamical and statistical downscaling methods have been developed. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. This paper evaluates the improvement in statistical downscaling (SD) predictive power when using predictors from a Regional Climate Model (RCM) over a GCM for downscaling site-specific precipitation. Our approach uses mixed downscaling, combining both dynamic and statistical methods. Precipitation, a critical element of hydrology studies that is also much more difficult to downscale than temperature, is the only variable evaluated in this study. The SD method selected here uses a stepwise linear regression approach for precipitation quantity and occurrence (similar to the well-known Statistical Downscaling Model (SDSM) and called SDSM-like herein). In addition, a discriminant analysis (DA) was tested to generate precipitation occurrence, and a weather typing approach was used to derive statistical relationships based on weather types, and not only on a seasonal basis as is usually done. The existing data record was separated into a calibration and validation periods. To compare the relative efficiency of the SD approaches, relationships were derived at the same sites using the same predictors at a 300km scale (the National Center for Environmental Prediction (NCEP) reanalysis) and at a 45km scale with data from the limited-area Canadian Regional Climate Model (CRCM) driven by NCEP data at its boundaries. Predictably, using CRCM variables as predictors rather than NCEP data resulted in a much-improved explained variance for precipitation, although it was always less than 50?% overall. For precipitation occurrence, the SDSM-like model slightly overestimated the frequencies of wet and dry periods, while these were well-replicated by the DA-based model. Both the SDSM-like and DA-based models reproduced the percentage of wet days, but the wet and dry statuses for each day were poorly downscaled by both approaches. Overall, precipitation occurrence downscaled by the DA-based model was much better than that predicted by the SDSM-like model. Despite the added complexity, the weather typing approach was not better at downscaling precipitation than approaches without classification. Overall, despite significant improvements in precipitation occurrence prediction by the DA scheme, and even going to finer scales predictors, the SD approach tested here still explained less than 50?% of the total precipitation variance. While going to even smaller scale predictors (10–15?km) might improve results even more, such smaller scales would basically transform the direct outputs of climate models into impact models, thus negating the need for statistical downscaling approaches.     

Downscaling projections of climate change in Sao Tome and Principe Islands,Africa

Sao Tome and Principe is a small insular African country extremely vulnerable to rising sea levels and impacts such as inundation, shore line change, and salt water intrusion into underground aquifers. Projections of climate change have considered coarse model resolutions. The objective of this work is to dynamically downscale the global model projections to 4-km resolution and to assess the climate change in the Sao Tome and Principe islands. The global climate projections are provided by the Canadian Earth System Model under two Representative Concentration Pathways greenhouse gas scenarios, RCP4.5 and RCP8.5. The downscaling is produced by the Eta regional climate model. The baseline period is taken between 1971 and 2000, and the future climate period is taken between 2041 and 2070. The 2-m temperature simulations show good agreement with station data. The model simulates temperature more accurately than precipitation. The precipitation simulations systematically show underestimation and delay of the rainy and the dry seasons by about 1 month, a feature inherited from the global climate model. In the middle of the 21st century, projections show the strongest warming in the elevated parts of the Sao Tome Island, especially in February under RCP8.5. Warmer nights and warmer days become more frequent in the islands when compared with those in the present. While under RCP4.5, precipitation increases in the islands; under RCP8.5, it decreases everywhere in both islands. Heavy precipitation rates should increase, especially in the south-southwestern parts of the Sao Tome islands. Detailed spatial variability of the temperature and precipitation changes in the islands can only be revealed at very high spatial model resolution. Implications for the potential energy production from two major river basins are assessed in this work.     

Statistical downscaling of precipitation using local regression and high accuracy surface modeling method

Downscaling precipitation is required in local scale climate impact studies. In this paper, a statistical downscaling scheme was presented with a combination of geographically weighted regression (GWR) model and a recently developed method, high accuracy surface modeling method (HASM). This proposed method was compared with another downscaling method using the Coupled Model Intercomparison Project Phase 5 (CMIP5) database and ground-based data from 732 stations across China for the period 1976–2005. The residual which was produced by GWR was modified by comparing different interpolators including HASM, Kriging, inverse distance weighted method (IDW), and Spline. The spatial downscaling from 1° to 1-km grids for period 1976–2005 and future scenarios was achieved by using the proposed downscaling method. The prediction accuracy was assessed at two separate validation sites throughout China and Jiangxi Province on both annual and seasonal scales, with the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE). The results indicate that the developed model in this study outperforms the method that builds transfer function using the gauge values. There is a large improvement in the results when using a residual correction with meteorological station observations. In comparison with other three classical interpolators, HASM shows better performance in modifying the residual produced by local regression method. The success of the developed technique lies in the effective use of the datasets and the modification process of the residual by using HASM. The results from the future climate scenarios show that precipitation exhibits overall increasing trend from T1 (2011–2040) to T2 (2041–2070) and T2 to T3 (2071–2100) in RCP2.6, RCP4.5, and RCP8.5 emission scenarios. The most significant increase occurs in RCP8.5 from T2 to T3, while the lowest increase is found in RCP2.6 from T2 to T3, increased by 47.11 and 2.12 mm, respectively.

     

Robust intensification of hydroclimatic intensity over East Asia from multi-model ensemble regional projections

This study assesses the hydroclimatic response to global warming over East Asia from multi-model ensemble regional projections. Four different regional climate models (RCMs), namely, WRF, HadGEM3-RA, RegCM4, and GRIMs, are used for dynamical downscaling of the Hadley Centre Global Environmental Model version 2–Atmosphere and Ocean (HadGEM2-AO) global projections forced by the representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Annual mean precipitation, hydroclimatic intensity index (HY-INT), and wet and dry extreme indices are analyzed to identify the robust behavior of hydroclimatic change in response to enhanced emission scenarios using high-resolution (12.5 km) and long-term (1981–2100) daily precipitation. Ensemble projections exhibit increased hydroclimatic intensity across the entire domain and under both the RCP scenarios. However, a geographical pattern with predominantly intensified HY-INT does not fully emerge in the mean precipitation change because HY-INT is tied to the changes in the precipitation characteristics rather than to those in the precipitation amount. All projections show an enhancement of high intensity precipitation and a reduction of weak intensity precipitation, which lead to a possible shift in hydroclimatic regime prone to an increase of both wet and dry extremes. In general, projections forced by the RCP8.5 scenario tend to produce a much stronger response than do those by the RCP4.5 scenario. However, the temperature increase under the RCP4.5 scenario is sufficiently large to induce significant changes in hydroclimatic intensity, despite the relatively uncertain change in mean precipitation. Likewise, the forced responses of HY-INT and the two extreme indices are more robust than that of mean precipitation, in terms of the statistical significance and model agreement.

     

Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model

Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type.