CMIP6HighResMIP对青藏高原气候模拟的评估和预估

高分辨率模式模拟被认为是研究资料相对欠缺的青藏高原地区气候变化的重要方法之一。第六次国际耦合模式比较计划(CMIP6)新增了高分辨率模式比较计划(HighResMIP),但其对青藏高原气候的模拟性能尚未系统评估。本研究分析了6对(更高、较低分辨率)CMIP6 HighResMIP模式对青藏高原当前气候的模拟能力,并集合预估了近期青藏高原气候的变化趋势。相对较粗分辨率模拟,所有(2/3)模式的更高分辨率模拟减少了平均降水(气温)的区域平均偏差。泰勒图涉及指标的综合评估显示,约1/3模式的更高分辨率对平均气温和降水模拟效果优于较低分辨率,其余模式的更高分辨率则接近或者劣于较低分辨率。集合平均结果优于单个模式,且其更高分辨率模拟效果总体优于较低分辨率。更高分辨率模式集合预估显示,相对于1995—2014年,在SSP5-8.5情景下到2021—2040年青藏高原整体呈增温趋势,东南部增温相对较弱;降水从北到南呈增加-减少-增加的变化模态;青藏高原气温将平均增加(0.81±0.91)℃,降水将平均增加(0.05±0.25) mm/d。     

青藏高原春季土壤湿度异常与我国夏季降水的联系

利用 197-2014年 GLDAS-CLM(Global Land Data Assimilation System-the Community Land Mod-el)地表参量数据集、中国区域逐日观测资料格点化数据集(CN05.1)和ERA-nterim大气环流再分析数据,研究青藏高原5月(春季)土壤湿度的异常变化...     

Evaluation of CORDEX regional climate models in simulating temperature and precipitation over the Tibetan Plateau

区域气候模式对研究地形复杂的青藏高原地区气候具有高分辨率的优势。以前的相关研究主要基于单个区域模式,我们评估了CORDEX多区域气候模式对青藏高原气候的模拟能力。结果显示:(1)5个区域气候模式一致模拟出了相似的气温、降水空间模态,但产生了冷偏差和湿偏差。所有区域气候模式未能再现观测的气温、降水趋势空间模态,并且平均高估了气温趋势、低估了降水趋势。综合考虑模拟的气温、降水趋势,多模式集合的结果最优。就单个模式而言,Reg CM4所得趋势最为合理。(2)各区域气候模式结果之间的差异十分显著,表明青藏高原气候模拟具有很大的模式依赖性。这一结果建议当利用单个区域气候模式开展青藏高原气候变化研究时需要谨慎。(3)多区域模式集合预估显示,相对1986–2005年,到2016–2035年气温(降水)将增加1.38±0.09°C(0.8%±4.0%)(RCP4.5)和1.77±0.28°C(7.3%±2.5%)(RCP8.5)。这些结果从多模式角度提高了我们对运用区域气候模式研究青藏高原气候的认识。     

青藏高原未来30～50年A1B情景下气候变化预估

基于政府间气候变化委员会第四次评估报告(IPCC-AR4)所采用的20个气候模式在未来大气温室气体中等排放情景(A1B)下模拟结果的集合平均以及一个全球气候模式模拟输出驱动下的动力降尺度(downscaling)分析结果,对青藏高原地区未来30～50年的气候变化趋势进行了预估研究.结果表明,从2030-2049年相对于1980-1999年气候平均值的变化来看,青藏高原大部分地区年平均地面气温的升温幅度在1.4～2.2℃之间,高海拔地区的增温一般更为显著,西藏西部的冬季增暖将达到2.4℃以上.降水量的变化相对较小,青藏高原大部分地区和全年多数季节降水可能增加,但未来30～50年青藏高原地区降水率增量通常不超过5%.考虑到未来大气温室气体排放程度、多模式集合预估以及区域尺度气候模拟等多方面均可能存在不确定性,这里给出的青藏高原未来气候变化预估结果应适时检验和修正.     

结合模式性能和独立性加权的全球增暖1.5/2℃下中国区域气候的未来预估

基于耦合模式比较计划第6阶段(CMIP6)中的全球气候模式的模拟结果,采用考虑模式性能和独立性结合(Climate model Weighting by Independence and Performance,ClimWIP)的加权方案进行中国区域气候的多模式集合预估及不确定性研究。结果表明,ClimWIP方案在历史阶段的模拟优于等权重方案,降低了多模式模拟的气候态偏差。温度指数的未来预估不确定性较大的区域主要集中在中国北方和青藏高原,而降水指数主要集中在华北和西北地区。ClimWIP方案的预估不确定性与等权重方案相比有所降低。ClimWIP方案预估的温度指数的增温大值区主要集中在中国北方和青藏高原;降水指数在西北和青藏高原增加最为显著。全球额外0.5 ℃增暖时,中国区域平均的温度指数变化更强,平均高于全球0.2 ℃,最低温在东北部分地区的额外增温甚至是全球平均的3倍;总降水额外增加5.2％;强降水额外增加10.5％。全球增暖2 ℃下,中国大部分区域温度指数较当前气候态增加可能超过1.5 ℃(概率>50％),在中国北方和青藏高原的部分地区增温超过1.5 ℃的可能性更大(概率>90％);总降水,强降水和连续干日在西北和华北增加幅度有可能超过10％、25％和-5 d(概率>50％)。     

2001—2010年青藏高原干湿格局及其影响因素分析

基于RFE2.0模型和Penman-Monteith模型,采用潜在蒸散降水比分析了2001—2010年青藏高原生长季(5—9月)干湿气候的时空变化格局,并对其影响因素进行了探讨。结果表明:(1)干旱和半干旱区占整个青藏高原区域的67%,主要集中在高原中部及中部以北;(2)2001—2010年有25%的区域在逐渐变干,北部干旱程度总体上在逐渐减轻,南部及东南部有变干倾向;(3)降水是导致高原区域干湿气候空间格局差异的主要因素,高原干湿气候对潜在蒸散变化的敏感性最强。     

BCC-CSM2-MR全球气候模式对东亚地区降水和气温的模拟评估

全球气候模式BCC-CSM2-MR(Beijing Climate Center-Climate System Model version 2-Medium Resolution)由国家（北京）气候中心自主研发并参与了第六阶段国际耦合模式比较计划,该模式在BCC-CSM1.1m版本基础上对大气辐射传输、深对流过程及重力波等方面进行了优化,因此,该模式对东亚地区降水和气温模拟能力的改进亟需进一步评估。本文主要基于不同格点观测数据集与中国区域站点观测数据,系统对比分析BCC-CSM2-MR、BCC-CSM1.1m两个模式版本对东亚地区季节平均降水（气温）和日极端降水（气温）的模拟能力。结果表明：（1）相比BCC-CSM1.1m,BCC-CSM2-MR改进了对东亚大部分区域季节平均降水的模拟能力,尤其是青藏高原地区夏季平均降水,明显提高了对中国东南地区、朝鲜半岛及日本降水月际变化的模拟性能;（2）BCC-CSM2-MR对东亚地区季节平均气温模拟能力改进不明显,且对东亚大部分区域气温月际变化的模拟误差大于BCC-CSM1.1m;(3)对日极端降水（气温）,BCC-CSM2-MR的模拟能力优于B...     

1998～2018年青藏高原气象学文献计量内容分析——基于CNKI中文核心期刊数据库

青藏高原对全球气候变化尤其是我国天气气候有着十分重要的作用和影响,本文研究以期为青藏高原气象研究及学者提供有价值的参考。主要结论如下:(1)较大范围区域及关联区域天气气候研究是青藏高原气象学的主流方向。(2)论文呈现的研究热点依次为高原气候、降水(雨)和降(积)雪。(3)国内14种主要中文科技核心期刊中,《高原气象》刊发青藏高原论文数最多。(4)高产作者群中7篇以上论文作者有26人,其署名单位总次数靠前的机构是中国科学院寒区旱区环境与工程研究所和中国气象局成都高原气象研究所。(5)综合分析得出青藏高原气象学学科颇受关注且持续影响力较大的学者群体。      

CMIP5和RegCM4.0模式对西南区域未来降水的预估

利用CMIP5全球模式数据集和RegCM4.0区域气候模式进行连续积分获得的模拟数据,对西南区域未来在RCP2.6,RCP4.5和RCP8.5几种温室气体排放情景下年平均降雨、四季降水,极端降雨事件的特征及其相对历史基准期的变化进行预估。结果表明,不同RCP情景下西南区域降水都将呈持续上升趋势,3种情景下西南区域降水在2020—2050年变化特征差别较小,2050年后差别较大,RCP2.6情景下降水变化幅度最小,CMIP5和RegCM4.0模式模拟的西南区域降水变化的地理分布特征基本一致,降水的高值区都位于青藏高原东南部,横断山脉和四川中部,差异在于RegCM4.0模拟的西藏西部的降雨量级更小,而青藏高原东南部、四川中部和贵州的降雨高值区量级更大。未来近期2020—2060年和远期2061—2099年RCP4.5情景下暴雨天数显著减少的区域主要在西藏东南部(0.5~1 d),未来远期2061—2099年RCP4.5情景云南南部和贵州东部区域暴雨天数显著性增加,而RCP8.5情景下上述区域暴雨天数显著性减少。     

IPCC第五次评估报告全球和区域气候预估图集评述

正与以往4次IPCC评估报告~①相比,第五次评估报告(AR5)增加了附录一:"全球和区域气候预估图集"~([1])。该图集是AR5的特色之一,它利用国际耦合模式比较计划第五阶段(CMIP5)~([2])全球气候模式的部分数据,给出了一系列全球和区域气候变化的图形。这些图形显示了全球和若干不同次大陆尺度区域在不同季节的表面气温变化和降水相对变化     

CMIP5全球气候模式对青藏高原地区气候模拟能力评估

青藏高原是气候变化的敏感和脆弱区,全球气候模式对于这一地区气候态的模拟能力如何尚不清楚。为此,本文使用国际耦合模式比较计划第五阶段（CMIP5）的历史模拟试验数据,评估了44 个全球气候模式对1986~2005 年青藏高原地区地表气温和降水两个基本气象要素的模拟能力。结果表明,CMIP5 模式低估了青藏高原地区年和季节平均地表气温,年均平均偏低2.3℃,秋季和冬季冷偏差相对更大;模式可较好地模拟年和季节平均地表气温分布型,但模拟的空间变率总体偏大;地形效应校正能够有效订正地表气温结果。CMIP5 模式对青藏高原地区降水模拟能力较差。尽管它们能够模拟出年均降水自西北向东南渐增的分布型,但模拟的年和季节降水量普遍偏大,年均降水平均偏多1.3 mm d-1,这主要是源于春季和夏季降水被高估。同时,模式模拟的年和季节降水空间变率也普遍大于观测值,尤其表现在春季和冬季。相比较而言,44 个模式集合平均性能总体上要优于大多数单个模式;等权重集合平均方案要优于中位数平均;对择优挑选的模式进行集合平均能够提高总体的模拟能力,其中对降水模拟的改进更为显著。     

Does CMIP6 Inspire More Confidence in Simulating Climate Extremes over China?

Based on climate extreme indices calculated from a high-resolution daily observational dataset in China during1961–2005, the performance of 12 climate models from phase 6 of the Coupled Model Intercomparison Project(CMIP6),and 30 models from phase 5 of CMIP(CMIP5), are assessed in terms of spatial distribution and interannual variability. The CMIP6 multi-model ensemble mean(CMIP6-MME) can simulate well the spatial pattern of annual mean temperature,maximum daily maximum temperature, and minimum daily minimum temperature. However, CMIP6-MME has difficulties in reproducing cold nights and warm days, and has large cold biases over the Tibetan Plateau. Its performance in simulating extreme precipitation indices is generally lower than in simulating temperature indices. Compared to CMIP5, CMIP6 models show improvements in the simulation of climate indices over China. This is particularly true for precipitation indices for both the climatological pattern and the interannual variation, except for the consecutive dry days. The arealmean bias for total precipitation has been reduced from 127%(CMIP5-MME) to 79%(CMIP6-MME). The most striking feature is that the dry biases in southern China, very persistent and general in CMIP5-MME, are largely reduced in CMIP6-MME. Stronger ascent together with more abundant moisture can explain this reduction in dry biases. Wet biases for total precipitation, heavy precipitation, and precipitation intensity in the eastern Tibetan Plateau are still present in CMIP6-MME, but smaller, compared to CMIP5-MME.     

CMIP6不同分辨率全球气候模式对中国降水模拟能力评估

基于参与CMIP6高分辨率模式比较计划（HighResMIP）9个模式组的18个全球气候模式模拟数据,通过与CN05.1观测资料的对比,评估了不同分辨率气候模式对中国区域1961—2014年降水特征的模拟能力。结果表明：低、高分辨率模式均能模拟出中国区域多年平均降水的总体空间分布特征,以及降水冬弱夏强的季节变化特征,但对降水的模拟都存在系统性偏多的误差;与低分辨率模式结果相比,高分辨率模式对降水空间分布的模拟有明显改善,在青藏高原、华北、华南地区降水模拟的系统性偏差明显减小;与低分辨率模式结果相比,高分辨率模式对年循环变化的模拟效果也更好,多年平均1月及9—12月逐月降水以及年降水的模拟误差均有所减小。对于年际、年代际的前两个主导空间模态,低、高分辨率模式大多无法模拟年代际的第一模态,但对于年际前两个模态以及年代际第二模态,分辨率提高可使半数左右模式组的模拟能力有所改善。     

CMIP5全球气候模式对上海极端气温和降水的情景预估

基于国际耦合模式比较计划第五阶段(Coupled Model Intercomparison Project Phase 5,以下简称CMIP5)28个模式的数值模拟结果和1981~2010年华东和上海气温和降水观测数据,评估了该28个气候模式对华东和上海气温和降水的模拟能力,并预估了RCP4.5(Representative Concentration Pathway 4.5)情景下上海2021~2030年极端气温和降水气候的变化趋势和不确定性。结果表明:与观测值相比,模式对华东和上海年平均气温的模拟大多均值偏高、方差偏低;对年总降水量的模拟大多均值偏高,但方差以华东偏高、上海偏低为主;26个模式的气温变化趋势和12个模式的降水变化趋势与观测值相同。选出8个模式的预估结果表明:与2001~2010年相比,2021~2030年上海冬天极端低温的出现日数(冷夜日数)呈减少趋势,不确定性最小;夏天暖夜日数呈增加的趋势,不确定性较小;其他极端气温事件的变化趋势则存在较大的不确定性,冷夜指标的不确定性最大。强降水发生日数和强降水的强度都呈现增加的趋势,且不确定性较小。     

CMIP5全球气候模式对中国地区降水模拟能力的评估

使用多种观测资料和43个参加耦合模式比较计划第五阶段（CMIP5）的全球气候模式模拟数据,评估分析了全球气候模式对中国地区1980-2005年降水特征的模拟能力。结果表明：多数CMIP5模式能够模拟出中国降水由西北向东南递增的分布特点,这与耦合模式比较计划第三阶段（CMIP3）的模式模拟结果类似,但华南地区降水模拟偏少,西部高原地区降水模拟偏多。模式能够较好地模拟出降水冬弱夏强的季节变化特征,但降水模拟系统性偏多。从EOF分析结果来看,多数CMIP5模式可以再现中国地区年平均降水的时空变化特征,集合平均的表现优于CMIP3。多模式集合在月、季、年时间尺度下模拟的平均值优于大部分单个模式的结果。CMIP5中6个中国模式的模拟能力与其他模式相当,其中FGOALS-g2、BCC-CSM1-1-m的模拟能力相对较好。     

Future changes and uncertainties in temperature and precipitation over China based on CMIP5 models

Climate changes in future 21 st century China and their uncertainties are evaluated based on 22 climate models from the Coupled Model Intercomparison Project Phase 5(CMIP5). By 2081–2100, the annual mean surface air temperature(SAT) is predicted to increase by 1.3℃± 0.7℃, 2.6℃± 0.8℃ and 5.2℃± 1.2℃ under the Representative Concentration Pathway(RCP) scenarios RCP2.6, RCP4.5 and RCP8.5, relative to 1986–2005, respectively. The future change in SAT averaged over China increases the most in autumn/winter and the least in spring, while the uncertainty shows little seasonal variation.Spatially, the annual and seasonal mean SAT both show a homogeneous warming pattern across China, with a warming rate increasing from southeastern China to the Tibetan Plateau and northern China, invariant with time and emissions scenario.The associated uncertainty in SAT decreases from northern to southern China. Meanwhile, by 2081–2100, the annual mean precipitation increases by 5% ± 5%, 8% ± 6% and 12% ± 8% under RCP2.6, RCP4.5 and RCP8.5, respectively. The national average precipitation anomaly percentage, largest in spring and smallest in winter, and its uncertainty, largest in winter and smallest in autumn, show visible seasonal variations. Although at a low confidence level, a homogeneous wetting pattern is projected across China on the annual mean scale, with a larger increasing percentage in northern China and a weak drying in southern China in the early 21 st century. The associated uncertainty is also generally larger in northern China and smaller in southwestern China. In addition, both SAT and precipitation usually show larger seasonal variability on the sub-regional scale compared with the national average.     

青藏高原地区地表温度及其取值对大气长波辐射冷却的影响

针对辐射传输模式在青藏高原地区的应用问题，使用Ｌｉｏｕ－Ｏｕ一维辐射传输模式及１９８２年８月￣１９８３年７月青藏高原热源观测实验期间青藏高原地面、高空与卫星观测资料，在高原辐射传输模式中区分了下垫面温度与地表空气温度的作用，并利用卫星观测资料对模式改进后的实际效果进行了验证；分析了地表温度的日变化和季节变化硬度，得到了下垫面温度的简单参数化方法。     

Understanding the Surface Temperature Cold Bias in CMIP5 AGCMs over the Tibetan Plateau

The temperature biases of 28 CMIP5 AGCMs are evaluated over the Tibetan Plateau(TP) for the period 1979–2005. The results demonstrate that the majority of CMIP5 models underestimate annual and seasonal mean surface 2-m air temperatures(T_(as)) over the TP. In addition, the ensemble of the 28 AGCMs and half of the individual models underestimate annual mean skin temperatures(T_s) over the TP. The cold biases are larger in T_(as) than in T_s, and are larger over the western TP. By decomposing the T_s bias using the surface energy budget equation, we investigate the contributions to the cold surface temperature bias on the TP from various factors, including the surface albedo-induced bias, surface cloud radiative forcing, clear-sky shortwave radiation, clear-sky downward longwave radiation, surface sensible heat flux, latent heat flux,and heat storage. The results show a suite of physically interlinked processes contributing to the cold surface temperature bias.Strong negative surface albedo-induced bias associated with excessive snow cover and the surface heat fluxes are highly anticorrelated, and the cancelling out of these two terms leads to a relatively weak contribution to the cold bias. Smaller surface turbulent fluxes lead to colder lower-tropospheric temperature and lower water vapor content, which in turn cause negative clear-sky downward longwave radiation and cold bias. The results suggest that improvements in the parameterization of the area of snow cover, as well as the boundary layer, and hence surface turbulent fluxes, may help to reduce the cold bias over the TP in the models.     

Changes in temperature and precipitation extremes in the CMIP5 ensemble

Twenty-year temperature and precipitation extremes and their projected future changes are evaluated in an ensemble of climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), updating a similar study based on the CMIP3 ensemble. The projected changes are documented for three radiative forcing scenarios. The performance of the CMIP5 models in simulating 20-year temperature and precipitation extremes is comparable to that of the CMIP3 ensemble. The models simulate late 20th century warm extremes reasonably well, compared to estimates from reanalyses. The model discrepancies in simulating cold extremes are generally larger than those for warm extremes. Simulated late 20th century precipitation extremes are plausible in the extratropics but uncertainty in extreme precipitation in the tropics and subtropics remains very large, both in the models and the observationally-constrained datasets. Consistent with CMIP3 results, CMIP5 cold extremes generally warm faster than warm extremes, mainly in regions where snow and sea-ice retreat with global warming. There are tropical and subtropical regions where warming rates of warm extremes exceed those of cold extremes. Relative changes in the intensity of precipitation extremes generally exceed relative changes in annual mean precipitation. The corresponding waiting times for late 20th century extreme precipitation events are reduced almost everywhere, except for a few subtropical regions. The CMIP5 planetary sensitivity in extreme precipitation is about 6 %/°C, with generally lower values over extratropical land.     

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.