基于CMIP5多模式回报资料的地面气温超级集合研究

利用CMIP5的15个全球气候系统模式对东亚及周边地区(70~150°E,0°~60°N)地面气温的回报结果进行超级集合(简称SUP)试验,以欧洲中期天气预报中心ERA逐月气温资料作为观测值,并采用均方根误差(RMSE)、距平相关系数(ACC)、绝对误差(MAE)对多模式集合平均(EMN)以及超级集合(SUP)的回报结果进行检验和评估。结果表明,超级集合回报结果一定程度上取决于训练期的长度。随训练期长度的增加,距平相关系数呈增大的趋势,均方根误差呈减小的趋势,但训练期达到一定长度后,误差不再有明显的减小,甚至出现误差增长。15个全球气候系统模式对东亚及周边地区的地面气温具有一定的回报能力,可以较好地回报出地面气温的年际变化和空间分布,海洋上回报的均方根误差小于陆地。但不同模式回报的结果不尽相同,在单模式中CCSM4对地面气温的回报效果最好。多模式集成的回报效果优于单模式的回报效果,SUP的回报效果优于EMN,其区域平均的均方根误差比多模式集合平均小0.43℃,超级集合极大地改善了地面气温的回报效果。     

中国6个CMIP5模式对全球降水年际-年代际变率模拟的定量评估

本文利用美国全球降水气候中心(GPCC)的降水资料和中国参加国际第五阶段耦合模式比较计划(CMIP5)的6个气候模式[BCC_CSM1.1、BCC_CSM1.1(m)、BNU-ESM、FGOALS-s2、FGOALS-g2和FIO-ESM]的历史模拟试验的降水数据,采用可以表征降水变率相对和绝对量级的方法,定量评估了6个模式对降水年际-年代际变率的模拟能力。研究表明,观测降水的年际变率一般占总方差的65%~80%,年代际变率占总方差的10%~35%。在CMIP5历史试验中,6个模式平均的降水年际分量方差对总方差的贡献(超过70%)较观测偏强,模拟降水年代际分量的方差对总方差的贡献较小(约为10%~20%)。模式总体低估了全球平均总降水、年际降水和年代际降水的变率,但是高估了年际降水对总降水的贡献、低估了年代际降水对总降水的贡献。与观测相比,6个模式对东亚和澳大利亚地区的年代际降水的模拟都比较好,模拟与观测年代际降水方差的比值为1左右。在非洲、南美洲和海洋性大陆,BCC_CSM1.1模式模拟的降水年代际变率最接近观测;在欧亚和北美,BNU-ESM模式模拟的降水年代际变率与观测最接近。在欧亚大陆上,BCC_CSM1.1模式模拟的降水年际分量与年代际分量的方差比最接近观测;在非洲和美洲,FGOALS-s2模式模拟的降水年际分量与年代际分量的方差比最接近观测。本文的研究结果有助于理解中国当前气候模式对降水年际-年代际变率的模拟能力,以及未来改进模式。     

气候模式的年际变率和可预测性

该文研究了大气物理研究所９层格点大气环流模式模拟的年际气候变率，一种试验采用多年平均的观测海温作为下边界条件，另一种试验是有年际变化的实测海温作为下边界条件，两种试验的年际变率分别为记为Ｖ１和Ｖ２，其比值Ｒ（Ｖ２／Ｖ１）则可代表以实测海温为边界条件的模式可预测性，研究结果显示：两种试验的年际变率差异主要热带区，Ｖ２一般大于Ｖ１，并且更接近实际变率，气温和高度场的可预测性在热带区较高，在热带以外区域     

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...     

东亚地区冬季地面气温延伸期概率预报研究

利用TIGGE资料中的ECMWF、NCEP、UKMO三个中心集合预报系统以及由此构成的多中心集合预报系统所提供的地面2 m气温10~15 d延伸期集合预报产品,建立贝叶斯模式平均(Bayesian Model Averaging,BMA)概率预报模型,对东亚地区冬季地面气温进行延伸期概率预报研究。采用距平相关系数、均方根误差、布莱尔评分、等级概率评分等指标分别对BMA确定性结果与概率预报进行评估。结果表明,BMA方法明显地改进了原始集合预报结果,预报技巧优于原始集合预报,且多中心BMA预报优于单中心BMA预报,最佳滑动训练期取35 d。BMA预报为气温的延伸期概率预报提供了更合理的概率分布,定量描述了预报的不确定性。     

CMIP5模式对中国地区气温模拟能力评估与预估

利用第五次国际耦合模式比较计划（CMIP5）中29个气候模式的气温模拟结果,评估了各模式对中国地区年平均气温的模拟能力,对未来不同典型浓度路径（RCPs）下中国地区气温的可能变化给出了预估。结果表明：各模式能较好地模拟过去100多年中国地区增温趋势和年平均气温的空间分布,从模式间标准差来看,各模式对中国中部、南部气温模拟具有较高的一致性。利用相对均方根误差分析了各模式的模拟能力,对于多时间尺度（月、年）气温的气候平均态,有7个模式表现良好,高于中等水平,5个模式的模拟能力低于中等水平,模式集合平均值的模拟效果优于大多数单个模式。根据29个模式的评估结果,使用模拟性能相对较好的模式分析了未来不同排放情景下中国地区气温变化,21世纪前期,不同排放情景之间的预估结果差别较小,21世纪中期各情景之间的差别逐渐增大,到21世纪后期,3种排放情景的升温差别明显增大。     

CMIP5 模式对中国极端气温及其变化趋势的模拟评估

本文基于中国区域逐日气温资料和CMIP5 中30 个全球气候模式资料,计算了平均日最高气温（TXAV)、平均日最低气温（TNAV)、热浪指数（HWDI)、霜冻日数（FD)、和暖夜指数（TNF90）5 个极端气温指数,评估各模式对中国区域极端气温的气候平均场和趋势的模拟能力。研究结果表明,大部分模式能够较好地模拟出极端指数的气候平均场,其中对TNAV、TXAV 和FD 平均场模拟能力较强,大部分模式平均场与观测场的相关系数超过0.90,但对TNF90 和HWDI 的模拟能力相对较低,相关系数均低于0.70,且各模式的模拟能力存在较大的差异。对极端指数的趋势模拟来说,模式模拟的中国区域平均各极端气温指数的线性变化趋势与观测相同,但大多数模式模拟趋势的强度偏弱。相比于气候平均场,模式对极端气温指数趋势空间场模拟较差,除TNAV 有1/3 的模式平均场与观测场的相关系数超过0.60 外,模式模拟其余指数的相关系数均低于0.60。模拟极端气温气候平均场的能力最优的5 个模式为：IPSL-CM5A-MR、CMCC-CM、IPSL-CM5A-LR、MPI-ESM-MR 和MPI-ESM-P。趋势空间场模拟最好的5 个模式为：MPI-ESM-P、CANESM2、ACCESS1-3、BCC-CSM1-1 和NorESM1-M。模式对极端气温指数的时空模拟能力一致性较差,但基于气候平均场或趋势空间场的优选模式,相比于所有集合模式平均,模拟能力均有一定程度的改善。     

青藏高原气温的年际变率与大气环状波动模

基于1961年3月至2002年2月间青藏高原地区64个台站的地面气温观测资料和ERA40再分析数据集,研究了青藏高原上空气温的年际变率及其与大尺度环流的关系。结果表明除夏季外,高原地面气温与整个北半球副热带、极地对流层的温度和位势高度有显著的同位相变化关系,而与中高纬度对流层有显著的反位相变化关系。其中北半球副热带还有5个分别位于青藏高原、西太平洋、北美西部、大西洋中部、北非到阿拉伯半岛的活动中心。这3条环状活动带和5个副热带活动中心共同组成了一种北半球大气环状波动模,其纬向特征为异常偏强的中纬度西风气流以及热带和高纬度东风气流,并伴有中纬度大气长波槽脊的减弱;经向基本特征为异常偏强的Hadley和Ferrel环流以及副热带下沉气流和中纬度上升气流,垂直方向呈相当正压结构。当这种环状波动模处于正位相时,异常增强的绝热下沉增温效应和减弱的冷空气活动共同使得高原上空对流层中、低层气温异常偏暖。     

CMIP5多模式资料中气温的BMA预测方法研究

利用CMIP5的8个全球气候系统模式对气温的回报结果进行贝叶斯模式平均(简称BMA)试验,并采用均方根误差、距平相关系数、连续等级概率评分等对多模式集合平均(简称EMN)和BMA的回报结果进行检验、评估。结果表明,EMN的回报效果优于8个单模式的回报效果,而BMA的回报效果最好,其区域平均的均方根误差比EMN小0.5℃左右。在此基础上,利用中等排放情景RCP4.5下CMIP5模式中的年际年代际预估资料对2011—2035年的气温进行预估。研究发现东亚地区在2011—2035年气温将普遍升高,海洋上的增暖幅度较小,陆地上的增暖幅度较大,且增暖幅度随纬度升高而增大。青藏高原及中国北方大部分地区气温将明显升高,升温幅度在1℃左右,而南方的升温幅度较小,约为0.3~0.6℃。     

CMIP5西北太平洋气候变率的模拟评估

利用观测海温资料和CMIP5模式模拟结果分析西北太平洋(120°E~120°W,20~60°N)海表温度的气候态和年代际变化特征。结果表明,所选22个模式可以较好地模拟出西北太平洋海表温度的气候特征及其年际、年代际变化特征;模式模拟的海表温度总体标准偏差在黑潮延伸体区域最大;绝大多数模式能模拟出海表温度的第一EOF模态;西北太平洋海表温度具有较明显的年代际振荡现象,13/22的模式模拟的海表温度存在明显的年代际振荡,同时海表温度气候态的模拟偏差对其周期振荡模拟的影响较大,尤其在黑潮延伸体区域。     

基于贝叶斯模型的中国未来气温变化预估及不确定性分析

利用第5次耦合模式比较计划(CMIP5)中35个全球气候模式历史模拟与RCP4.5预估结果,通过贝叶斯模型平均(Bayesian Model Averaging,BMA)对中国气温进行多模式集合研究,给出了中国未来气温变化预估及其不确定性的时空分布。结果表明,中国21世纪冬夏将持续升温,且升温具有冬季高于夏季,北方高于南方的特点。初期(2016—2035年)北方有很大可能(80%)升温超过0.7℃,南方升温相同幅度的概率则超过50%;中期(2046—2065年)北方和南方升温超过1.5℃的概率分别为80%和50%;末期(2081—2100年),北方(南方)有80%(50%)的可能的升温超过2℃。气温预估的不确定性研究发现,无论冬夏,21世纪不同时期升温相对较弱的塔里木盆地、青藏高原南侧和中国东南地区为不确定性低值区,基本低于0.6℃,对应可信度较高,如21世纪初期信噪比超过4;而不确定性的高值区则主要分布在新疆北部、东北平原北部和青藏高原东南侧等升温相对较大的地区,普遍高于1℃,对应可信度较低,如初期信噪比低于2.5。此外,基于信噪比对比发现除青藏高原东部外,其他区域夏季预估的可信度均高于冬季,21世纪末期高于初期,且空间分布特征一致。     

Predictability of the East Asian winter monsoon interannual variability as indicated by the DEMETER CGCMS

The interannual variability of East Asian winter monsoon(EAWM) circulation from the Development of a European Multi-Model Ensemble(MME) System for Seasonal to Inter-Annual Prediction(DEMETER) hindcasts was evaluated against observation reanalysis data.We evaluated the DEMETER coupled general circulation models(CGCMs)’ retrospective prediction of the typical EAWM and its associated atmospheric circulation.Results show that the EAWM can be reasonably predicted with statistically significant accuracy,yet the major bias of the hindcast models is the underestimation of the related anomalies.The temporal correlation coefficient(TCC) of the MME-produced EAWM index,defined as the first EOF mode of 850hPa air temperature within the EAWM domain(20-60 N,90-150 E),was 0.595.This coefficient was higher than those of the corresponding individual models(range:0.39-0.51) for the period 1969-2001;this result indicates the advantage of the super-ensemble approach.This study also showed that the ensemble models can reasonably reproduce the major modes and their interannual variabilities for sea level pressure,geopotential height,surface air temperature,and wind fields in Eurasia.Therefore,the prediction of EAWM interannual variability is feasible using multimodel ensemble systems and that they may also reveal the associated mechanisms of the EAWM interannual variability.     

基于CMIP5资料的东亚夏季环流的BMA预测研究

利用CMIP5的17个全球气候系统模式对500 hPa位势高度场的年代际回报结果,采用距平相关系数、均方根误差、平均绝对误差及连续等级概率评分4种指标,评估了贝叶斯模式平均(Bayesian model average,BMA)预报方法对东亚夏季环流的回报能力,并与最优单模式MIROC5和多模式简单集合平均结果进行了比较。结果表明,BMA方法对东亚夏季500 hPa位势高度场的回报效果是最好的,优于最优单模式MIROC5和简单集合平均的回报结果。BMA模型能产生高集中度的概率密度函数,并包含了多模式集成回报不确定性的定量估计。此外,BMA方法对西太平洋副热带高压的年际变率也有较好的回报效果,对西太平洋副热带高压的预报,选取60~70%概率下的结果更为合理。     

Probabilistic multimodel ensemble prediction of decadal variability of East Asian surface air temperature based on IPCC-AR5 near-term climate simulations

Based on near-term climate simulations for IPCC-AR5 (The Fifth Assessment Report), probabilistic multimodel ensemble prediction (PMME) of decadal variability of surface air temperature in East Asia (20°-50 °N, 100°-145°E) was conducted using the multivariate Gaussian ensemble kernel dressing (GED) methodology. The ensemble system exhibited high performance in hindcasting the decadal (1981-2010) mean and trend of temperature anomalies with respect to 1961-90, with a RPS of 0.94 and 0.88 respectively. The interpretation of PMME for future decades (2006-35) over East Asia was made on the basis of the bivariate probability density of the mean and trend. The results showed that, under the RCP4.5 (Representative Concentration Pathway 4.5 W m-2 ) scenario, the annual mean temperature increases on average by about 1.1-1.2 K and the temperature trend reaches 0.6-0.7 K (30 yr)-1 . The pattern for both quantities was found to be that the temperature increase will be less intense in the south. While the temperature increase in terms of the 30-yr mean was found to be virtually certain, the results for the 30-yr trend showed an almost 25% chance of a negative value. This indicated that, using a multimodel ensemble system, even if a longer-term warming exists for 2006-35 over East Asia, the trend for temperature may produce a negative value. Temperature was found to be more affected by seasonal variability, with the increase in temperature over East Asia more intense in autumn (mainly), faster in summer to the west of 115°E, and faster still in autumn to the east of 115°E.     

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.     

The Project Siberian High in CMIP5 Models

The Siberian high(SH)experienced a decline from the 1970s to 1990s and a recovery in recent years.The evolution of the SH under global warming is unclear.In this study,41 Coupled Model Intercomparison Project Phase 5(CMIP5)climate models are evaluated in terms of their ability to simulate the temporal evolution of the SH in the 19th and 20th centuries and the spatial pattern of the SH during 1981–2005.The results show that 12models can capture the temporal evolution of the SH center intensity(SHCI)for 1872–2005.The linear correlation coefficient between the SHCI from the Twentieth Century Reanalysis and the simulated SHCI from the multi-model ensemble(MME)of the 12 models is 0.3 on annual and inter-annual scales(above the 99%confidence level).On decadal and multi-decadal time scales,the MME also captures the pronounced reduction(between 1981–2000and 1881–1900 period)and the recovery(during1991–2005)of the SH intensity.Finally,the future evolution of the SH is investigated using the MME of the 12models under the+4.5 and+8.5 W m-2 Representative Concentration Pathway(RCP)scenarios(RCP4.5 and RCP8.5).It is shown that the SHCI,similar to the SHCI in the 20th century,has no significant long-term trend in the 21st century under global warming(RCP8.5 scenario).At the end of 21st century(2081–2100),the SH shows stronger interannual variability than the SH at the end of20th century(1981–2000).The increased interannual variability likely favors the increased interannual variability in winter air temperature over midlatitude Eurasia at the end of 21st century.     

An Analysis of Historical and Future Temperature Fluctuations over China Based on CMIP5 Simulations简

The trends and fluctuations of observed and CMIP5-simulated yearly mean surface air temperature over China were analyzed.In general,the historical simulations replicate the observed increase of temperature,but the multi-model ensemble （MME） mean does not accurately reproduce the drastic interannual fluctuations.The correlation coefficient of the MME mean with the observations over all runs and all models was 0.77,which was larger than the largest value （0.65） from any single model ensemble.The results showed that winter temperatures are increasing at a higher rate than summer temperatures,and that winter temperatures exhibit stronger interannual variations.It was also found that the models underestimate the differences between winter and summer rates.The ensemble empirical mode decomposition technique was used to obtain six intrinsic mode functions （IMFs） for the modeled temperature and observations.The periods of the first two IMFs of the MME mean were 3.2 and 7.2,which represented the cycle of 2-7-yr oscillations.The periods of the third and fourth IMFs were 14.7 and 35.2,which reflected a multi-decadal oscillation of climate change.The corresponding periods of the first four IMFs were 2.69,7.24,16.15 and 52.5 in the observed data.The models overestimate the period of low frequency oscillation of temperature,but underestimate the period of high frequency variation.The warming rates from different representative concentration pathways （RCPs） were calculated,and the results showed that the temperature will increase by approximately 0.9℃,2.4℃,3.2℃ and 6.1℃ in the next century under the RCP2.6,RCP4.5,RCP6.0 and RCP8.5 scenarios,respectively.