为IPCC第五次评估报告提供的全球气候模式预估

IPCC自1990年到2007年的4次评估报告都进行了未来气候变化预估,唱主角的一直是全球气候模式.从对未来气候变化的预估,到气候变化影响和对策评估研究,全球模式起了主导作用.因此,关注全球气候模式预估动态对第五次评估报告是非常重要的一个环节.     

长期气候变化——IPCC第五次评估报告解读

<正>IPCC第五次评估报告(AR5)~①中关于长期气候变化的预估主要基于全球耦合模式比较计划第五阶段(CMIP5)的46个地球系统模式结果,在对模式、情景及不确定性介绍的基础上,给出了21世纪及其后更远时期的气候变化预估结果。与第四次评估报告(AR4)及全球耦合模式比较计划第三阶段(CMIP3)不同的是,AR5预估所使用的温室气体排放情景为典型浓度路径(RCP,AR4主要使用的是SRES),但在相似温室气体浓度的情况下,两者给出的未来气候变化结果差别不大。     

IPCC第六次评估报告中的《图集》

决策者和公众正在越来越多地关注气候变化带来的影响,而这需要更加丰富的、区域尺度上的当前和未来气候状况的精细信息.《图集》与IPCC第六次评估报告(AR6)第一工作组(WGI)报告中其他章节相协调,评估区域气候变化的观测、归因、预估的基本信息,并建立了在线交互图集系统.《图集》包含图集章节和交互图集两部分:图集章节基于新...     

IPCC第五次评估报告中所用的气候模式有进步吗?

<正>IPCC第五次评估报告(AR5)第一工作组报告已经问世,由于气候模式是研究气候变化的机理与成因及预测与预估的重要手段和方法,在这次报告中至少有一半的章都需要用到气候模式,因此政策制定者和公众极其关注的问题是:与以往的评估报告相比,IPCC AR5中所用的气候模式有进步吗?为了回答这个问题,在汇集1250余篇相关参考文献的基础上,AR5对目前参加全球耦合模式比较计划第五阶段(CMIP5)的46个地球系统模式、15个中等复杂程度的地球系统模式(EMIC)以及大批区域动力降尺度气候模式和统计降尺度气候模型     

基于全球及区域气候模式的江苏省降水变化趋势预估

利用气象观测资料,8个全球耦合气候系统模式的集合平均以及区域气候模式(RegCM4)的结果,通过方差分析、相关分析、趋势分析、扰动法等方法对模式性能进行了评估,并对江苏省在未来RCP8.5高端排放情景下降水的变化趋势进行了预估。结果表明,在RCP8.5情景下,至2020、2030和2050年,全球模式模拟的江苏省年平均降水在未来有逐渐增加的趋势,线性增加率约为7 mm/(10 a)。至2050年,江苏省年平均降水量将增加2%左右;区域模式模拟的年平均降水在未来线性增加率为1.5 mm/(10 a),变化不显著。区域模式模拟的夏季降水在未来有所增加,最多可增加20%～30%,但增幅随时间逐渐减小;全球模式模拟的夏季降水比现在有所减少,至2050年,减少了大约10%。区域模式模拟的冬季降水在未来不同时间段均比现在有所减少,同现在相比,最多可减少30%～40%;而全球模式模拟的冬季降水在未来则是先减少,后增加,至2050年,比现在大约增加10%。对于不同季节,总体而言,南部地区降水量的变化较北部地区显著。对于极端降水事件来说,江苏省未来小雨日数将减少,而暴雨日数则微弱增加。但由于全球模式本身的性能、区域模式对全球模式的依赖性以及温室气体排放的不确定性使上述预估结果仍具有不确定性。     

IPCC第五次评估报告不确定性处理方法的介绍

由于自然气候系统极端复杂,具有内在混沌的特性,并包括各种时间尺度的非线性反馈,因此,气候变化无论观测,还是预估都存在不确定性。一些不确定性可用某些数值范围来表述,而另一些不确定性则可表述为专家对某些科学发现认识的信度。每一种不确定性的评估都需要专家进行判断,后一     

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。     

气候现象及其与未来区域气候变化的联系

<正>与IPCC以前的评估报告相比,第五次评估报告(AR5)增加了单独一章,即第十四章~([1]),比较系统地评估了关于气候现象及其与未来区域气候变化关系方面取得的最新研究进展。这是AR5的亮点之一。区域气候是很多复杂的大气过程共同影响的结果。在全球变暖的背景下,这些过程对大尺度强迫的响应有明显的区域特征,所以区域气候变化存在显著差异。与CMIP3模式比,CMIP5模式对大尺度     

对IPCC第五次评估报告气溶胶-云对气候变化影响与响应结论的解读

<正>在气候变化的驱动因子中,气溶胶和云对气候变化的影响程度仍然是不确定性最大的部分。政府间气候变化专门委员会(IPCC)第一工作组(WG1)第五次评估报告(AR5)总结了有关云和气溶胶对气候变化影响的研究,从观测、理论以及模式角度评估了其对气候变化的影响与响应~([1]),得出以下主要结论。     

全球气候变化预估最新研究进展

总结了近5 a来对全球气候变化预估的研究进展,着重分析和介绍近几年全球气候变化预估研究的特点,热点和难点。     

Temperature Extremes from Canadian Regional Climate Model (CRCM) Climate Change Projections

Future climate projections of extreme events can help forewarn society of high-impact events and allow the development of better adaptation strategies. In this study a non-stationary model for Generalized Extreme Value (GEV) distributions is used to analyze the trend in extreme temperatures in the context of a changing climate and compare it with the trend in average temperatures.

The analysis is performed using the climate projections of the Canadian Regional Climate Model (CRCM), under an IPCC SRES A2 greenhouse gas emissions scenario, over North America. Annual extremes in daily minimum and maximum temperatures are analyzed. Significant positive trends for the location parameter of the GEV distribution are found, indicating an expected increase in extreme temperature values. The scale parameter of the GEV distribution, on the other hand, reveals a decrease in the variability of temperature extremes in some continental regions. Trends in the annual minimum and maximum temperatures are compared with trends in average winter and summer temperatures, respectively. In some regions, extreme temperatures exhibit a significantly larger increase than the seasonal average temperatures.

The CRCM projections are compared with those of its driving model and framed in the context of the Coupled Model Intercomparison Project, phase 3 (CMIP3) Global Climate Model projections. This enables us to establish the CRCM position within the CMIP3 climate projection uncertainty range. The CRCM is validated against the HadEX2 dataset in order to assess the CRCM representation of temperature extremes in the present climate. The validation is also framed in the context of CMIP3 validation results. The CRCM cold extremes validate better and are closer to the driving model and CMIP3 projections than the hot extremes.     

中国干湿变化的高分辨率区域气候模式预估

本文使用三个全球气候模式驱动下的高分辨率区域气候模式RegCM4的试验数据,首先评估了RegCM4对参考时段（1986～2005年）中国干燥度指数（AI）的模拟能力,而后根据典型浓度路径中等排放（RCP4.5）情景下RegCM4试验对中国未来干湿变化进行了预估研究。结果表明,RegCM4能够合理模拟中国区域AI的空间分布。两种潜在蒸散发计算方法得到的参考时段AI在空间分布和数值上存在一定差异,尤其是在中国西部高海拔地区和北方地区。在三个全球气候模式驱动场作用下的RegCM4预估试验中,21世纪中期（2046～2065年）和末期（2081～2098年）中国区域平均AI较参考时段分别减小2%～4%和2%～5%,其中西北中部变湿,其他地区均变干。不同地区未来干湿变化的主要影响因素存在差异,西北中部降水变化为主导因素,其他地区主要受控于升温所引起的潜在蒸散发变化。     

全球及中国区域气候变化预估研究主要进展简述

自IPCC评估报告发表以来,气候模式对未来气候变化的预估成为人们重视的问题。因此,本文回顾了IPCC前4次报告对全球未来气候变化预估的主要结论,并对全球及区域气候模式对中国地区的模拟和预估结果进行了分类总结,得出区域气候模式由于分辨率更高、对特殊地形的模拟能力更强,因此比全球气候模式的模拟和预估结果更准确;同时讨论了目前存在的问题及今后研究的方向。     

论区域气候模式与全球模式嵌套时边界区的选择

做了3个试验,第一个试验只用大气环流模式(GCM),主要考察GCM的性能并确定其误差的区域分布.后两个为对比试验,一个试验中,将区域气候模式(RCM)(NjU-RCM)的侧边界放在全球模式(L9R15)中我们感兴趣的区域,未考虑侧边界区GCM的误差大小,另一个试验中,RCM的侧边界位置根据GCM预报误差的空间分布选取,使其落在GCM预报误差较小的区域.3个试验都对1998年5、6、7月份中国区域的降水过程进行了模拟和比较.结果表明:单独使用GCM的效果最差;当用GCM-RCM嵌套模式对区域气候进行预测时,GCM侧边界值的误差对RCM的模拟结果有显著的影响,嵌套侧边界若选择在GCM系统性误差较小的地区,模拟或预测效果会有明显的改进.     

Uncertainty in Regional Climate Model Mean Runoff Projections under Climate Change: Case Study of Labrador's Churchill River Basin

An ensemble of seven climate models from the North American Regional Climate Change Assessment Program (NARCCAP) was used to examine uncertainty in simulated runoff changes from a base period (1971–2000) to a future period (2041–2070) for the Churchill River basin, Labrador, Canada. Three approximations for mean annual runoff from each ensemble member were included in the analysis: (i) atmospheric moisture convergence, (ii) the balance between precipitation and evaporation, and (iii) instantaneous runoff output from respective land-surface schemes. Using data imputation (i.e., reconstruction) and variance decomposition it was found that choice of regional climate model (RCM) made the greatest contribution to uncertainty in the climate change signal, whereas the boundary forcing of a general circulation model (GCM) played a smaller, though non-negligible, role. It was also found that choice of runoff approximation made a substantial contribution to uncertainty, falling between the contribution from RCM and GCM choice. The NARCCAP output and imputed data were used to calculate mean and median annual changes and results were presented via probability distribution functions to facilitate decision making. Mean and median increases in runoff for the basin were found to be 11.2% and 8.9%, respectively.     

A Demonstration of the Uncertainty in Projections of UK Climate Change Resulting from Regional Model Formulation

Regional climate models (RCMs) are now commonly used to downscale climate change projections provided by global coupled models to resolutions that can be utilised at national and finer scales. Although this extra tier of complexity adds significant value, it inevitably contributes a further source of uncertainty, due to the regional modelling uncertainties involved. Here, an initial attempt is made to estimate the uncertainty that arises from typical variations in RCM formulation, focussing on changes in UK surface air temperature (SAT) and precipitation projected for the late twenty-first century. Data are provided by a relatively large suite of RCM and global model integrations with widely varying formulations. It is found that uncertainty in the formulation of the RCM has a relatively small, but non-negligible, impact on the range of possible outcomes of future UK seasonal mean climate. This uncertainty is largest in the summer season. It is also similar in magnitude to that of large-scale internal variations of the coupled climate system, and for SAT, it is less than the uncertainty due to the emissions scenario, whereas for precipitation it is probably larger. The largest source of uncertainty, for both variables and in all seasons, is the formulation of the global coupled model. The scale-dependency of uncertainty due to RCM formulation is also explored by considering its impact on projections of the difference in climate change between the north and south of the UK. Finally, the implications for the reliability of UK seasonal mean climate change projections are discussed.     

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。     

5个海气耦合模式模拟东亚区域气候能力的初步分析

分析了CGCM1、CSIRO MK2、ECHAM4、HadCM2和GFDL共5个海气耦合模式模拟的东亚地区地面气温和降水量的多年平均值,并与观测值进行了比较,以初步考察全球模式对区域气候的模拟能力.结果表明,所有的模式对东亚地区地面气温及年变化的模拟结果均较好,但在整个模拟区域地面气温模拟值偏低,冬半年模拟结果较夏半年差;模式能模拟出东亚地区降水的时空分布特征,但模拟的区域性差别比较大,模式对西北、东北地区的模拟效果较差,就季节变化而言,冬、春模拟降水偏大.比较结果显示,ECHAM4和HadCM2对东亚地区地面气温和降水场的模拟效果优于其他模式.     

Near-Term Projections of Global and Regional Land Mean Temperature Changes Considering Both the Secular Trend and Multidecadal Variability

Near-term climate projections are needed by policymakers; however, these projections are difficult because internally generated climate variations need to be considered. In this study, temperature change scenarios in the near-term period 2017–35 are projected at global and regional scales based on a refined multi-model ensemble approach that considers both the secular trend (ST) and multidecadal variability (MDV) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. The ST and MDV components are adaptively extracted from each model simulation by using the ensemble empirical mode decomposition (EEMD) filter, reconstructed via the Bayesian model averaging (BMA) method for the historical period 1901–2005, and validated for 2006–16. In the simulations of the “medium” representative concentration pathways scenario during 2017–35, the MDV-modulated temperature change projected via the refined approach displays an increase of 0.44°C (90% uncertainty range from 0.30 to 0.58°C) for global land, 0.48°C (90% uncertainty range from 0.29 to 0.67°C) for the Northern Hemispheric land (NL), and 0.29°C (90% uncertainty range from 0.23 to 0.35°C) for the Southern Hemispheric land (SL). These increases are smaller than those projected by the conventional arithmetic mean approach. The MDV enhances the ST in 13 of 21 regions across the world. The largest MDV-modulated warming effect (46%) exists in central America. In contrast, the MDV counteracts the ST in NL, SL, and eight other regions, with the largest cooling effect (220%) in Alaska.