Robustness of Precipitation Projections in China:Comparison between CMIP5 and CMIP3 Models

Three sources of uncertainty in model projections of precipitation change in China for the 21st century were separated and quantified: internal variability,inter-model variability,and scenario uncertainty.Simulations from models involved in the third phase and the fifth phase of the Coupled Model Intercomparison Project(CMIP3 and CMIP5) were compared to identify improvements in the robustness of projections from the latest generation of models.No significant differences were found between CMIP3 and CMIP5 in terms of future precipitation projections over China,with the two datasets both showing future increases.The uncertainty can be attributed firstly to internal variability,and then to both inter-model and internal variability.Quantification analysis revealed that the uncertainty in CMIP5 models has increased by about 10%–60% with respect to CMIP3,despite significant improvements in the latest generation of models.The increase is mainly due to the increase of internal variability in the initial decades,and then mainly due to the increase of inter-model variability thereafter,especially by the end of this century.The change in scenario uncertainty shows no major role,but makes a negative contribution to begin with,and then an increase later.     

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。     

CMIP5模式降水订正法及未来30年中国降水预估

借助英国气候研究所（Climate Research Unit, CRU）全球陆地格点分析数据集（CRU TS v4.0）月降水资料和24个国际耦合模式比较计划第五阶段（Coupled Model Intercomparison Project Phase 5, CMIP5）模式历史气候模拟及RCP4.5情景下的降水预估数据,设计了多种回归方案并对模式降水预估偏差进行订正。这些方案包括一元回归、一元对数回归、一元差分回归、一元对数差分回归、多元回归、多元对数回归、多元差分回归、多元对数差分回归和简单移除气候漂移等。2006～2015年中国大陆模式降水预估的订正结果表明,一元回归订正法普遍优于多元回归订正和扣除气候漂移订正法,其中一元对数回归法的效果最好,其降水距平同号率（Anomaly Rate, AR）和降水距平百分率相关系数（Anomaly Percentage Correlation Coefficient, APCC）最高,分别达到69%和0.5;而降水距平相关系数（Anomaly Correlation Coefficient, ACC）最高的是一元对数差分回归法。不同回归订正法所得预估结果的距平同号格点分布显示,一元对数回归法在北方优于南方,而一元差分（年际增量）或对数差分回归法在南方优于北方。这直接导致在中国南方区域（95°E以东,35°N以南）一元对数回归或多元对数回归订正结果的AR、ACC和APCC均低于对应的差分/对数差分回归法,在北方和西部地区则与此相反。因此,模式降水的回归订正方案具有区域性,这可能源于不同区域降水序列统计性质的差异。用区域组合回归订正法,即在南方用一元差分回归订正,其余地区用一元对数回归订正,其降水预估场的AR提高到72%,但ACC和APCC均略有下降,原因是差分回归订正增加了预估降水场的方差。对RCP4.5情景下2016～2045年24个模式集合平均降水预估的组合回归订正结果显示,相对于1976～2005年平均,未来30年降水异常大致呈南北少,中间多的格局,其中长江中下游、江南中西部、西南东北部、华南沿海和海南省等地降水偏少10%～20%,淮河流域、三江源区和台湾省降水偏多10%～40%,西北东部、华北和东北大部降水正常或略偏少。从降水百分率方差看,模式群的离散度（不确定度）呈现东部小,西部大的分布特征,说明模式预估的西北中部和青藏高原西部等降水偏少区的不确定性较大;而河套北部、华北南部和江南东部等地对应于2006～2015年检验期的“盲区”（模式与观测降水距平反号）,其降水预估参考价值可能不大,需要引入他法加以改进。     

CMIP3与CMIP5模式对中国西北干旱区气温和降水的模拟能力比较

通过对15组CMIP3和CMIP5两代模式集合平均对中国西北干旱区气温和降水的模拟能力比较,发现CMIP5模式对气温和降水的模拟更接近观测值。CMIP5模式模拟年、春季、夏季、秋季平均气温的相关系数比CMIP3模式分别提升了0.15、0.13、0.24和0.02,冬季下降了0.07。CMIP5模式对西北干旱区的平均气温变化趋势的模拟效果比CMIP3有所提高,对年、春季、夏季、秋季、冬季趋势的模拟偏差比CMIP3分别减少了0.03℃/10a、0.10℃/10a、0.01℃/10a、0.06℃/10a、0.14℃/10a。对西北干旱区平均气温年、季的模拟偏差分布上,CMIP5模式的偏差均比CMIP3低1~2℃。但是天山区年、季节平均气温的模拟与整体模拟偏低情况相反,CMIP3和CMIP5分别偏高3~6℃和1~4℃,对夏季的模拟偏高最严重,分别达到6℃和4℃。CMIP5模式整体对西北干旱区降水量的模拟结果与观测值的平均相关系数与CMIP3相差不大,均不超过0.1,而且偏差仍然较大。CMIP5模式对西北干旱区的降水量的变化趋势模拟效果比CMIP3有所降低,对年、春季、夏季、秋季、冬季趋势的模拟偏差比CMIP3增加了0.67 mm/10a、0.23 mm/10a、0.51 mm/10a、0.11 mm/10a、0.14 mm/10a。CMIP5模式对年、春季、夏季、秋季和冬季的降水量模拟的均方根误差相比CMIP3分别减少77.6 mm、25.5 mm、25.0 mm、18.8 mm和13.9 mm。在空间上,CMIP5模式对年、季节降水模拟仍然偏高,但是比CMIP3有明显缓解;CMIP3和CMIP5模式对夏季天山区年降水量和夏季降水量的模拟也与大部分区域偏高的趋势明显相反,两代模式对夏季天山区的降水模拟均偏低50 mm左右。     

ENSO representation in climate models: from CMIP3 to CMIP5

We analyse the ability of CMIP3 and CMIP5 coupled ocean–atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Niño-Southern Oscillation (ENSO). The CMIP5 multi-model ensemble displays an encouraging 30 % reduction of the pervasive cold bias in the western Pacific, but no quantum leap in ENSO performance compared to CMIP3. CMIP3 and CMIP5 can thus be considered as one large ensemble (CMIP3 + CMIP5) for multi-model ENSO analysis. The too large diversity in CMIP3 ENSO amplitude is however reduced by a factor of two in CMIP5 and the ENSO life cycle (location of surface temperature anomalies, seasonal phase locking) is modestly improved. Other fundamental ENSO characteristics such as central Pacific precipitation anomalies however remain poorly represented. The sea surface temperature (SST)-latent heat flux feedback is slightly improved in the CMIP5 ensemble but the wind-SST feedback is still underestimated by 20–50 % and the shortwave-SST feedbacks remain underestimated by a factor of two. The improvement in ENSO amplitudes might therefore result from error compensations. The ability of CMIP models to simulate the SST-shortwave feedback, a major source of erroneous ENSO in CGCMs, is further detailed. In observations, this feedback is strongly nonlinear because the real atmosphere switches from subsident (positive feedback) to convective (negative feedback) regimes under the effect of seasonal and interannual variations. Only one-third of CMIP3 + CMIP5 models reproduce this regime shift, with the other models remaining locked in one of the two regimes. The modelled shortwave feedback nonlinearity increases with ENSO amplitude and the amplitude of this feedback in the spring strongly relates with the models ability to simulate ENSO phase locking. In a final stage, a subset of metrics is proposed in order to synthesize the ability of each CMIP3 and CMIP5 models to simulate ENSO main characteristics and key atmospheric feedbacks.     

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。     

The Projection of Temperature and Precipitation over China under RCP Scenarios using a CMIP5 Multi-Model Ensemble

Climate changes in 21st century China are described based on the projections of 11 climate models under Representative Concentration Pathway (RCP) scenarios. The results show that warming is expected in all regions of China under the RCP scenarios, with the northern regions showing greater warming than the southern regions. The warming tendency from 2011 to 2100 is 0.06°C/10 a for RCP2.6, 0.24°C/10 a for RCP4.5, and 0.63°C/10 a for RCP8.5. The projected time series of annual temperature have similar variation tendencies as the new greenhouse gas (GHG) emission scenario pathways, and the warming under the lower emission scenarios is less than under the higher emission scenarios. The regional averaged precipitation will increase, and the increasing precipitation in the northern regions is significant and greater than in the southern regions in China. It is noted that precipitation will tend to decrease in the southern parts of China during the period of 2011-2040, especially under RCP8.5. Compared with the changes over the globe and some previous projections, the increased warming and precipitation over China is more remarkable under the higher emission scenarios. The uncertainties in the projection are unavoidable, and further analyses are necessary to develop a better understanding of the future changes over the region.     

CMIP5模式对我国西南地区干湿季降水的模拟和预估

利用降水观测资料, 评估了参加国际耦合模式比较计划第五阶段(CMIP5)的34个全球模式对1986～2005年我国西南地区干湿季降水的模拟能力。结果表明, 34个CMIP5模式中分别有30和25个模式模拟的干季和湿季降水偏多。34个模式对我国西南地区干湿季降水的模拟能力差异较大, 大约半数模式的模拟值与观测值的空间相关系数通过了99%的信度检验, 且标准差之比小于2。利用两个技巧评分标准, 分别挑选出了对干湿季降水模拟最优的9个模式。最优模式集合平均结果要优于34个模式的集合平均, 更要优于大多数单个模式。进一步利用最优的9个模式的集合平均对RCP4.5和RCP8.5两种典型浓度路径下我国西南地区干湿季降水的变化进行了预估。相对于1986～2005年气候平均态, 在21世纪初期(2016～2035年), 我国西南地区干季降水变化表现为川西高原降水增多, 而四川盆地及攀西地区、重庆、贵州和云南的大部分地区降水减少;湿季降水变化表现为川西高原、贵州和广西大部分地区降水增多, 而四川盆地及攀西地区和云南降水减少。在21世纪中期(2046～2065年)和末期(2080～2099年), 西南地区干湿季降水普遍增多。在RCP8.5情景下, 降水的变化幅度要强于RCP4.5情景。     

2006～2013年CMIP5模式中国降水预估误差分析

用第五次耦合模式比较计划（CMIP5）的10个模式模拟结果与英国东安格利亚大学（UEA）气候研究机构（CRU）的最新降水格点分析资料比较,评估了三种典型浓度路径（RCPs）排放情景下模式集合对2006～2013年中国降水预估误差,结果发现模式间年降水预估在西北和东部沿海地区差异较明显,在沿海地区模式降水估计偏少,在西部和北方大部分地区偏多;冬半年大部分地区模式降水明显偏多,部分地区甚至偏多一倍以上;夏半年东部季风区降水估计偏少,但西部仍然偏多。模式降水误差随时间变化,夏半年误差变化明显的区域主要集中在北方和东部地区,冬半年在东北南部、华东及华南等地。此外,提高排放情景对年降水量估计影响明显的地区主要集中在我国西部的部分地区,加剧了西北模式降水估计偏多程度,但对东部地区影响不大。El Ni?o与La Ni?a年的模式降水误差分布相似,仅在沿海部分地区和华北北部差异较明显,逐年误差分布特征也与此相似。各种误差的对比分析表明,模式降水误差可能多来自模式本身存在的问题,如积云对流参数化、固体降水物理过程、地形处理及分辨率等。这些误差特征说明,直接使用CMIP5模式集合情景输出资料估计未来降水的方法存在较大的不确定性,必须对其进行评估,以降低潜在用户或决策者们制定未来规划的风险。     

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。     

ENSO phase-locking to the boreal winter in CMIP3 and CMIP5 models

In this study, the El Nino-Southern Oscillation (ENSO) phase-locking to the boreal winter in CMIP3 and CMIP5 models is examined. It is found that the models that are poor at simulating the winter ENSO peak tend to simulate colder seasonal-mean sea-surface temperature (SST) during the boreal summer and associated shallower thermocline depth over the eastern Pacific. These models tend to amplify zonal advection and thermocline depth feedback during boreal summer. In addition, the colder eastern Pacific SST in the model can reduce the summertime mean local convective activity, which tends to weaken the atmospheric response to the ENSO SST forcing. It is also revealed that these models have more serious climatological biases over the tropical Pacific, implying that a realistic simulation of the climatological fields may help to simulate winter ENSO peak better. The models that are poor at simulating ENSO peak in winter also show excessive anomalous SST warming over the western Pacific during boreal winter of the El Nino events, which leads to strong local convective anomalies. This prevents the southward shift of El Nino-related westerly during boreal winter season. Therefore, equatorial westerly is prevailed over the western Pacific to further development of ENSO-related SST during boreal winter. This bias in the SST anomaly is partly due to the climatological dry biases over the central Pacific, which confines ENSO-related precipitation and westerly responses over the western Pacific.     

Uncertainty in Projection of Climate Extremes: A Comparison of CMIP5 and CMIP6

Climate projections by global climate models(GCMs) are subject to considerable and multi-source uncertainties.This study aims to compare the uncertainty in projection of precipitation and temperature extremes between Coupled Model Intercomparison Project(CMIP) phase 5(CMIP5) and phase 6(CMIP6), using 24 GCMs forced by 3 emission scenarios in each phase of CMIP. In this study, the total uncertainty(T) of climate projections is decomposed into the greenhouse gas emission scenario uncertainty(S, mean inter-scenario variance of the signals over all the models), GCM uncertainty(M, mean inter-model variance of signals over all emission scenarios), and internal climate variability uncertainty(V, variance in noises over all models, emission scenarios, and projection lead times); namely,T = S + M + V. The results of analysis demonstrate that the magnitudes of S, M, and T present similarly increasing trends over the 21 st century. The magnitudes of S, M, V, and T in CMIP6 are 0.94–0.96, 1.38–2.07, 1.04–1.69, and 1.20–1.93 times as high as those in CMIP5. Both CMIP5 and CMIP6 exhibit similar spatial variation patterns of uncertainties and similar ranks of contributions from different sources of uncertainties. The uncertainty for precipitation is lower in midlatitudes and parts of the equatorial region, but higher in low latitudes and the polar region. The uncertainty for temperature is higher over land areas than oceans, and higher in the Northern Hemisphere than the Southern Hemisphere. For precipitation, T is mainly determined by M and V in the early 21 st century, by M and S at the end of the 21 st century; and the turning point will appear in the 2070 s. For temperature, T is dominated by M in the early 21 st century, and by S at the end of the 21 st century, with the turning point occuring in the 2060 s. The relative contributions of S to T in CMIP6(12.5%–14.3% for precipitation and 31.6%–36.2% for temperature) are lower than those in CMIP5(15.1%–17.5% for precipitation and 38.6%–43.8% for temperature). By contrast, the relative contributions of M in CMIP6(50.6%–59.8% for precipitation and 59.4%–60.3% for temperature) are higher than those in CMIP5(47.5%–57.9% for precipitation and 51.7%–53.6% for temperature). The higher magnitude and relative contributions of M in CMIP6 indicate larger difference among projections of various GCMs. Therefore, more GCMs are needed to ensure the robustness of climate projections.     

Present-day and future Amazonian precipitation in global climate models: CMIP5 versus CMIP3

The present study aims at evaluating and comparing precipitation over the Amazon in two sets of historical and future climate simulations based on phase 3 (CMIP3) and 5 (CMIP5) of the Coupled Model Intercomparison Project. Thirteen models have been selected in order to discuss (1) potential improvements in the simulation of present-day climate and (2) the potential reduction in the uncertainties of the model response to increasing concentrations of greenhouse gases. While several features of present-day precipitation—including annual cycle, spatial distribution and co variability with tropical sea surface temperature (SST)—have been improved, strong uncertainties remain in the climate projections. A closer comparison between CMIP5 and CMIP3 highlights a weaker consensus on increased precipitation during the wet season, but a stronger consensus on a drying and lengthening of the dry season. The latter response is related to a northward shift of the boreal summer intertropical convergence zone in CMIP5, in line with a more asymmetric warming between the northern and southern hemispheres. The large uncertainties that persist in the rainfall response arise from contrasted anomalies in both moisture convergence and evapotranspiration. They might be related to the diverse response of tropical SST and ENSO (El Niño Southern Oscillation) variability, as well as to spurious behaviours among the models that show the most extreme response. Model improvements of present-day climate do not necessarily translate into more reliable projections and further efforts are needed for constraining the pattern of the SST response and the soil moisture feedback in global climate scenarios.     

CMIP5部分模式气温和降水模拟结果在北半球及青藏高原的检验

利用北半球和青藏高原的观测资料,通过趋势分析、量值比较及小波分析等方法对已提交历史模拟结果的8个模式进行了比较.结果表明,各模式对北半球气温年变化模拟的较好,一般7、8月气温最高,1月气温最低,不存在相位差问题.各模式模拟的历史气温年际和年代际变化趋势比较一致,气温最大相差2.8℃以上;模拟的1850-2005年气温平均最高和最低值相差可达1.8℃左右;除1个模式外,其余模式都能较准确地模拟出至少有一次气温突变.对北半球降水的模拟,各模式都模拟出了降水的季节变化,但从年际变化趋势来看,4个模式模拟的降水为增大趋势,4个为减小趋势.对青藏高原的模拟,从变化趋势与观测气温的对比来看,8个模式中,除2个模式通过了0.05显著性水平检验外,其余均通过了0.01显著性水平检验;各模式都模拟出了青藏高原的降水中心,但对降水量值的模拟相差较大.     

Understanding the sources of Caribbean precipitation biases in CMIP3 and CMIP5 simulations

We assess the ability of Global Climate Models participating in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) to simulate observed annual precipitation cycles over the Caribbean. Compared to weather station records and gridded observations, we find that both CMIP3 and CMIP5 models can be grouped into three categories: (1) models that correctly simulate a bimodal distribution with two rainfall maxima in May–June and September–October, punctuated by a mid-summer drought (MSD) in July–August; (2) models that reproduce the MSD and the second precipitation maxima only; and (3) models that simulate only one precipitation maxima, beginning in early summer. These categories appear related to model simulation of the North Atlantic Subtropical High (NASH) and sea surface temperature (SST) in the Caribbean Sea and Gulf of Mexico. Specifically, models in category 2 tend to anticipate the westward expansion of the NASH into the Caribbean in early summer. Early onset of NASH results in strong moisture divergence and MSD-like conditions at the time of the May–June observed precipitation maxima. Models in category 3 tend to have cooler SST across the region, particularly over the central Caribbean and the Gulf of Mexico, as well as a weaker Caribbean low-level jet accompanying a weaker NASH. In these models, observed June-like patterns of moisture convergence in the central Caribbean and the Central America and divergence in the east Caribbean and the Gulf of Mexico persist through September. This analysis suggests systematic biases in model structure may be responsible for biases in observed precipitation variability over the Caribbean and more confidence may be placed in the precipitation simulated by the GCMs that are able to correctly simulate seasonal cycles of SST and NASH.     

Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models

There is a long-standing debate on how the El Niño/Southern Oscillation (ENSO) amplitude may change during the twenty-first century in response to global warming. Here we identify the sources of uncertainty in the ENSO amplitude projections in models participating in the Coupled Model Intercomparison Phase 5 (CMIP5) and Phase 6 (CMIP6), and quantify scenario uncertainty, model uncertainty and uncertainty due to internal variability. The model projections exhibit a large spread, ranging from increasing standard deviation of up to 0.6 °C to diminishing standard deviation of up to − 0.4 °C by the end of the twenty-first century. The ensemble-mean ENSO amplitude change is close to zero. Internal variability is the main contributor to the uncertainty during the first three decades; model uncertainty dominates thereafter, while scenario uncertainty is relatively small throughout the twenty-first century. The total uncertainty increases from CMIP5 to CMIP6: while model uncertainty is reduced, scenario uncertainty is considerably increased. The models with “realistic” ENSO dynamics have been analyzed separately and categorized into models with too small, moderate and too large ENSO amplitude in comparison to instrumental observations. The smallest uncertainties are observed in the sub-ensemble exhibiting realistic ENSO dynamics and moderate ENSO amplitude. However, the global warming signal in ENSO-amplitude change is undetectable in all sub-ensembles. The zonal wind-SST feedback is identified as an important factor determining ENSO amplitude change: global warming signal in ENSO amplitude and zonal wind-SST feedback strength are highly correlated across the CMIP5 and CMIP6 models.

     

参加CMIP5计划的四个中国模式模拟的东亚地区降水结构特征及未来变化

在全球变暖的背景下, 降水特征的改变体现为降水总量和降水结构的变化。由于缺乏较为长期、覆盖范围广的较高分辨率逐日降水资料, 过去对东亚降水的研究多关注其降水量的长期趋势和时空变率, 较少涉及降水结构的变化。本文利用当前最新且分辨率最高、覆盖范围最广的逐日亚洲陆地降水数据集(简称APHRODITE)以及四个中国参加第五次国际耦合模式比较计划(简称CMIP5)的模式(BCC-CSM1-1, BNU-ESM, FGOALS-g2和FGOALS-s2), 研究了东亚地区降水结构的观测特征及四个模式的模拟能力。基于此, 通过分析四个模式的未来预估试验, 探讨东亚地区降水结构在全球变暖背景下的变化。结果表明:整个东亚地区的累积降水量呈现出随着降水强度的增加先增加后减小的分布形态;降水频率则是随着强度的增加显著减小。小雨(中雨)呈现出南少北多(南多北少)的形态;强降水则较多分布在华南沿海以及日本南部地区。长期趋势上, 整个东亚地区大体上呈现小雨和30 mm/d以上的大雨增加, 而中等强度降水减少的变化趋势。四个模式对东亚降水结构的气候态模拟能力较好。BCC-CSM1-1和FGOALS-g2能够合理再现观测中各个强度降水的变化趋势, 而其他模式模拟不出中雨的减小趋势。四个模式的未来预估表明, 在全球变暖的背景下, 东亚地区30 mm/d以上的强降水会增加。且降水强度越大, 增加越明显。以30°N为界, 小雨(中雨)在变暖背景下呈现南部增加北部减少(南部减少北部增加)的变化趋势。     

CMIP5和CMIP3对东亚降水和全球热带气旋模拟效果的评估

<正>自IPCC第五次评估报告2013年正式发表以来,对全球气候模式模拟效果的评估有进一步深入的研究,一些最新的评估对CMIP3和CMIP5的大量模式对东亚降水和全球热带气旋的模拟效果做了定量检验~([1-4])。众所周知,东亚地区的降水量和全球热带气旋是气候模式模拟的难点,因此定量评估大量气候模式对这几个变量的模拟效果是值得重视的。1对东亚地区降水量的模拟效果评估选用31个CMIP5模式(其中4个中国模式BCC-