基于CMIP6气候变化情景下南极小须鲸(Balaenoptera bonaerensis)在宇航员海栖息地变化分析

南极小须鲸(Balaenopterabonaerensis)作为顶级捕食者,在南大洋生态系统中起着重要调节作用。目前对南极小须鲸的研究大多集中在捕食和季节性迁移上,在栖息地分布以及气候变化对栖息地影响方面研究亟待补充。基于MaxEnt模型和CMIP6的数据,分析了当前情形以及不同排放情景下,到21世纪中期和21世纪末期宇航员海南极小须鲸栖息地的分布和变化。研究结果表明,南极小须鲸主要分布在宇航员海的东部,当前的高度适生区占整个区域的13.96%。深度、海冰密集度和混合层深度最小值是南极小须鲸分布的主要影响因子,三者的累积贡献为60.5%。气候变化情景下南极小须鲸栖息地呈现缩小的趋势。高排放情景下南极小须鲸的栖息地面积减小更快,从21世纪中期到末期这个时期南极小须鲸的栖息地面积减小速率比从当前到21世纪中期快。到本世纪中期,所有情景下的宇航员海东部仍存在南极小须鲸的栖息地;到本世纪末,中排放情景和高排放情景下的宇航员海已不适合南极小须鲸生存,海冰密集度的减小是造成这一现象的主要原因。     

CAS-ESM2.0 Model Datasets for the CMIP6 Flux-Anomaly-Forced Model Intercomparison Project(FAFMIP)

The second version of the Chinese Academy of Sciences Earth System Model(CAS-ESM2.0)is participating in the Flux-Anomaly-Forced Model Intercomparison Project(FAFMIP)experiments in phase 6 of the Coupled Model Intercomparison Project(CMIP6).The purpose of FAFMIP is to understand and reduce the uncertainty of ocean climate changes in response to increased CO2 forcing in atmosphere-ocean general circulation models(AOGCMs),including the simulations of ocean heat content(OHC)change,ocean circulation change,and sea level rise due to thermal expansion.FAFMIP experiments(including faf-heat,faf-stress,faf-water,faf-all,faf-passiveheat,faf-heat-NA50pct and faf-heat-NA0pct)have been conducted.All of the experiments were integrated over a 70-year period and the corresponding data have been uploaded to the Earth System Grid Federation data server for CMIP6 users to download.This paper describes the experimental design and model datasets and evaluates the preliminary results of CAS-ESM2.0 simulations of ocean climate changes in the FAFMIP experiments.The simulations of the changes in global ocean temperature,Atlantic Meridional Overturning Circulation(AMOC),OHC,and dynamic sea level(DSL),are all reasonably reproduced.     

西风爆发在CMIP5 中的模拟评价

赤道西风爆发现象(西风爆发)是指赤道表面西风突然增大的现象,已有研究表明赤道太平洋西风爆发与ENSO (El Niño-Southern Oscillation) 有密切的关系。本文就西风爆发现象在CMIP5中的模拟情况进行了相关评价,并将其与观测结果进行对比;同时对西风爆发与ENSO的关系、西风爆发与MJO的关系进行了细致的分析与评价。研究结果表明,模式可以很好地再现西风爆发随厄尔尼诺事件发生而向东移动的现象,这主要是由于赤道太平洋西风爆发与赤道太平洋海表温度有很好的对应关系。在大部分模式中,西风爆发领先于厄尔尼诺的发生,并对厄尔尼诺的发展有着相应的影响。与前人的研究结果不同,利用蒙特卡洛验证法证明MJO不能显著地增加西风爆发发生的概率,这一点也在大部分CMIP5模式中有所体现。     

Assessment of Snow Depth over Arctic Sea Ice in CMIP6 Models Using Satellite Data

Snow depth over sea ice is an essential variable for understanding the Arctic energy budget.In this study,we evaluate snow depth over Arctic sea ice during 1993-2014 simulated by 31 models from phase 6 of the Coupled Model Intercomparison Project(CMIP6)against recent satellite retrievals.The CMIP6 models capture some aspects of the observed snow depth climatology and variability.The observed variability lies in the middle of the models’simulations.All the models show negative trends of snow depth during 1993-2014.However,substantial spatiotemporal discrepancies are identified.Compared to the observation,most models have late seasonal maximum snow depth(by two months),remarkably thinner snow for the seasonal minimum,an incorrect transition from the growth to decay period,and a greatly underestimated interannual variability and thinning trend of snow depth over areas with frequent occurrence of multi-year sea ice.Most models are unable to reproduce the observed snow depth gradient from the Canadian Arctic to the outer areas and the largest thinning rate in the central Arctic.Future projections suggest that snow depth in the Arctic will continue to decrease from 2015 to 2099.Under the SSP5-8.5 scenario,the Arctic will be almost snow-free during the summer and fall and the accumulation of snow starts from January.Further investigation into the possible causes of the issues for the simulated snow depth by some models based on the same family of models suggests that resolution,the inclusion of a hightop atmospheric model,and biogeochemistry processes are important factors for snow depth simulation.     

基于CMIP6模式的东南亚极端降水未来预估及热动力成因研究

本文利用26个CMIP6全球气候模式,研究了21世纪末东南亚极端降水事件的变化,通过分解水汽收支方程分析降水变化的动力和热力效应。结果表明,21世纪末(2071—2100年)相对历史参考期(1985—2014年),东南亚大部分地区的气候态降水、极端降水事件的发生频率和强度均显著增加。除大于10 mm降水日数(R10mm)外,其他极端降水指数在SSP5-8.5情景下的变化幅度比SSP2-4.5情景更大。其中强降水量贡献率(R95pTOT)的增长幅度最大,在SSP2-4.5(SSP5-8.5)情景下增加22％(41％)。极端降水变化对气候变暖的响应存在明显的区域性差异。加里曼丹岛将出现更短时集中的极端降水。苏门答腊岛南部的极端降水频率略有减小,且可能发生较强的持续性干旱事件。进一步分析水汽收支方程可知,SSP2-4.5(SSP5-8.5)情景下,热力作用项对P-E(降水减蒸发)的变化贡献为65％(64％),并且模式间一致性更高。而动力作用项对P-E的变化呈抵消趋势,贡献为35％(36％)。这说明相比大尺度环流变化,大气比湿变化引起的水汽辐合是未来东南亚降水量增多的主要因子。     

基于CMIP6模式的热带太平洋海表温度增暖初步评估

热带太平洋是影响全球气候系统的重要区域,热带太平洋海表温度(SST)的长期变化趋势模拟是国际研究领域关注的热点.基于12个参加第六期国际耦合模式比较计划(CMIP6)的模式结果,本研究对1950年至1999年间多模式模拟得到的热带太平洋SST增暖现象进行了初步评估.结果表明,不同模式对热带太平洋SST增暖的模拟能力差别...     

21世纪新疆区域气候暖湿化趋势预估分析

新疆未来暖湿化的预估分析可为区域气候变化减缓和适应提供重要的科学基础。国际耦合模式比较计划第六阶段（CMIP6）全球气候模式在三种共享社会经济路径（SSPs）下的结果显示,新疆地区未来2021~2100年总体呈现气温升高、降水增加的“暖湿化”现象,但这种变化的具体数值和空间分布存在一定差异。其中SSP2-4.5情景下,相对于1995~2014年,预估2021~2040年新疆地区年平均气温将升高1.2℃左右,年平均降水将增加6.8%。对极端事件的预估结果表明,新疆地区未来暖事件将增加,冷事件将减少;极端强降水事件将增多,且高排放情景下的增加更为显著。新疆地区的未来预估分析,将有助于对新疆地区灾害风险时空变化格局的认识,对未来农业方面等风险防范也有重要的指示作用。     

CMIP6年代际试验对中国气温预测能力的初步评估

利用参加第六次国际耦合模式比较计划(CMIP6)年代际气候预测计划(DCPP)的加拿大CanESM5模式和日本MIROC6模式的结果,评估了模式对中国近地面气温的预测能力.在年代际尺度上,两个模式年代际试验对近地面气温的回报技巧均高于历史试验的模拟能力,采用海温初始化可以提高模式对中国近地面气温的年代际预报技巧.对年代...     

长三角城市群夏季高温对未来全球增暖的响应

选取中国东部长江三角洲城市群区域作为研究对象,采用中国区域最新的土地覆盖资料ChinaLC,利用中尺度气象模式WRF(Weather Research and Forecasting Model)对国际耦合模式比较计划第五阶段(CMIP5)中 CESM(Community Earth System Model)气候模式...     

PREDICTION AND UNCERTAINTY OF CLIMATE CHANGE IN CHINA DURING 21ST CENTURY UNDER RCPS

Based on integrated simulations of 26 global climate models provided by the Coupled Model Intercomparison Project(CMIP), this study predicts changes in temperature and precipitation across China in the 21 st century under different representative concentration pathways(RCPs), and analyzes uncertainties of the predictions using Taylor diagrams. Results show that increases of average annual temperature in China using three RCPs(RCP2.6, RCP4.5,RCP8.5) are 1.87 ℃, 2.88 ℃ and 5.51 ℃, respectively. Increases in average annual precipitation are 0.124, 0.214, and 0.323 mm/day, respectively. The increased temperature and precipitation in the 21 st century are mainly contributed by the Tibetan Plateau and Northeast China. Uncertainty analysis shows that most CMIP5 models could predict temperature well, but had a relatively large deviation in predicting precipitation in China in the 21 st century. Deviation analysis shows that more than 80% of the area of China had stronger signals than noise for temperature prediction;however, the area proportion that had meaningful signals for precipitation prediction was less than 20%. Thus, the multi-model ensemble was more reliable in predicting temperature than precipitation because of large uncertainties of precipitation.