Arctic Climate Changes Based on Historical Simulations (1900‒2013) with the CAMS-CSM

The Chinese Academy of Meteorological Sciences Climate System Model (CAMS-CSM) is a newly developed global climate model that will participate in the Coupled Model Intercomparison Project phase 6. Based on historical simulations (1900?2013), we evaluate the model performance in simulating the observed characteristics of the Arctic climate system, which includes air temperature, precipitation, the Arctic Oscillation (AO), ocean temperature/salinity, the Atlantic meridional overturning circulation (AMOC), snow cover, and sea ice. The model?data comparisons indicate that the CAMS-CSM reproduces spatial patterns of climatological mean air temperature over the Arctic (60°?90°N) and a rapid warming trend from 1979 to 2013. However, the warming trend is overestimated south of the Arctic Circle, implying a subdued Arctic amplification. The distribution of climatological precipitation in the Arctic is broadly captured in the model, whereas it shows limited skills in depicting the overall increasing trend. The AO can be reproduced by the CAMS-CSM in terms of reasonable patterns and variability. Regarding the ocean simulation, the model underestimates the AMOC and zonally averaged ocean temperatures and salinity above a depth of 500 m, and it fails to reproduce the observed increasing trend in the upper ocean heat content in the Arctic. The large-scale distribution of the snow cover extent (SCE) in the Northern Hemisphere and the overall decreasing trend in the spring SCE are captured by the CAMS-CSM, while the biased magnitudes exist. Due to the underestimation of the AMOC and the poor quantification of air–sea interaction, the CAMS-CSM overestimates regional sea ice and underestimates the observed decreasing trend in Arctic sea–ice area in September. Overall, the CAMS-CSM reproduces a climatological distribution of the Arctic climate system and general trends from 1979 to 2013 compared with the observations, but it shows limited skills in modeling local trends and interannual variability.     

CAMS-CSM模式及其参与CMIP6的方案

世界气候研究计划(WCRP)组织开展的耦合模式比较计划已实施到第六阶段（CMIP6),中国气象科学研究院发展的气候系统模式CAMS-CSM是注册参加CMIP6的模式之一。除CMIP6要求的气候诊断、评估和描述试验（DECK）以及历史气候模拟试验（Historical）外,CAMS-CSM还计划参加情景模式比较计划（ScenarioMIP)、云反馈模式比较计划（CFMIP)、全球季风模式比较计划（GMMIP）和高分辨率模式比较计划（HighResMIP)这4个模式比较子计划（MIPs)。文中通过介绍CAMS-CSM的基本情况和模拟性能,以及计划参加的CMIP6试验及MIPs,为模式试验数据使用者提供参考。     

Overview of the CMIP6 Historical Experiment Datasets with the Climate System Model CAS FGOALS-f3-L

The three-member historical simulations by the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System model, version f3-L(CAS FGOALS-f3-L), which is contributing to phase 6 of the Coupled Model Intercomparison Project(CMIP6), are described in this study. The details of the CAS FGOALS-f3-L model, experiment settings and output datasets are briefly introduced. The datasets include monthly and daily outputs from the atmospheric, oceanic, land and sea-ice component models of CAS FG...     

BCC-ESM1 Model Datasets for the CMIP6 Aerosol Chemistry Model Intercomparison Project(AerChemMIP)

BCC-ESM1 is the first version of the Beijing Climate Center’s Earth System Model,and is participating in phase 6 of the Coupled Model Intercomparison Project(CMIP6).The Aerosol Chemistry Model Intercomparison Project(AerChemMIP)is the only CMIP6-endorsed MIP in which BCC-ESM1 is involved.All AerChemMIP experiments in priority 1 and seven experiments in priorities 2 and 3 have been conducted.The DECK(Diagnostic,Evaluation and Characterization of Klima)and CMIP historical simulations have also been run as the entry card of CMIP6.The AerChemMIP outputs from BCC-ESM1 have been widely used in recent atmospheric chemistry studies.To facilitate the use of the BCC-ESM1 datasets,this study describes the experiment settings and summarizes the model outputs in detail.Preliminary evaluations of BCC-ESM1 are also presented,revealing that:the climate sensitivities of BCC-ESM1 are well within the likely ranges suggested by IPCC AR5;the spatial structures of annual mean surface air temperature and precipitation can be reasonably captured,despite some common precipitation biases as in CMIP5 and CMIP6 models;a spurious cooling bias from the 1960s to 1990s is evident in BCC-ESM1,as in most other ESMs;and the mean states of surface sulfate concentrations can also be reasonably reproduced,as well as their temporal evolution at regional scales.These datasets have been archived on the Earth System Grid Federation(ESGF)node for atmospheric chemistry studies.     

Simulation of the Northern and Southern Hemisphere Annular Modes by CAMS-CSM

As leading modes of the planetary-scale atmospheric circulation in the extratropics, the Northern Hemisphere(NH)annular mode(NAM) and Southern Hemisphere(SH) annular mode(SAM) are important components of global circulation, and their variabilities substantially impact the climate in mid-high latitudes. A 35-yr(1979-2013) simulation by the climate system model developed at the Chinese Academy of Meteorological Sciences(CAMS-CSM) was carried out based on observed sea surface temperature and sea ice data. The ability of CAMS-CSM in simulating horizontal and vertical structures of the NAM and SAM, relation of the NAM to the East Asian climate, and temporal variability of the SAM is examined and validated against the observational data. The results show that CAMS-CSM captures the zonally symmetric and out-of-phase variations of sea level pressure anomaly between the midlatitudes and polar zones in the extratropics of the NH and SH. The model has also captured the equivalent barotropic structure in tropospheric geopotential height and the meridional shifts of the NH and SH jet systems associated with the NAM and SAM anomalies. Furthermore, the model is able to reflect the variability of northern and southern Ferrel cells corresponding to the NAM and SAM anomalies. The model reproduces the observed relationship of the boreal winter NAM with the East Asian trough and air temperature over East Asia. It also captures the upward trend of the austral summer SAM index during recent decades. However, compared with the observation, the model shows biases in both the intensity and center locations of the NAM's and SAM's horizontal and vertical structures. Specifically, it overestimates their intensities.     

Evaluation of ocean data assimilation in CAS-ESM-C: Constraining the SST field

A weakly coupled assimilation system, in which SST observations are assimilated into a coupled climate model(CASESM-C) through an ensemble optimal interpolation scheme, was established. This system is a useful tool for historical climate simulation, showing substantial advantages, including maintaining the atmospheric feedback, and keeping the oceanic fields from drifting far away from the observation, among others. During the coupled model integration, the bias of both surface and subsurface oceanic fields in the analysis can be reduced compared to unassimilated fields. Based on 30 model years of output from the system, the climatology and interannual variability of the climate system were evaluated. The results showed that the system can reasonably reproduce the climatological global precipitation and SLP, but it still suffers from the double ITCZ problem. Besides, the ENSO footprint, which is revealed by ENSO-related surface air temperature, geopotential height and precipitation during El Ni ?no evolution, is basically reproduced by the system. The system can also simulate the observed SST–rainfall relationships well on both interannual and intraseasonal timescales in the western North Pacific region, in which atmospheric feedback is crucial for climate simulation.     

An Assessment of CAMS-CSM in Simulating Land–Atmosphere Heat and Water Exchanges

The Chinese Academy of Meteorological Sciences (CAMS) has been devoted to developing a climate system model (CSM) to meet demand for climate simulation and prediction for the East Asian region. In this study, we evaluated the performance of CAMS-CSM in regard to sensible heat flux (H), latent heat flux (LE), surface temperature, soil moisture, and snow depth, focusing on the Atmospheric Model Intercomparison Project experiment, with the aim of participating in the Coupled Model Intercomparison Project phase 6. We systematically assessed the simulation results achieved by CAMS-CSM for these variables against various reference products and ground observations, including the FLUXNET model tree ensembles H and LE data, Climate Prediction Center soil moisture data, snow depth climatology data, and Chinese ground observations of snow depth and winter surface temperature. We compared these results with data from the ECMWF Interim reanalysis (ERA-Interim) and Global Land Data Assimilation System (GLDAS). Our results indicated that CAMS-CSM simulations were better than or comparable to ERAInterim reanalysis for snow depth and winter surface temperature at regional scales, but slightly worse when simulating total column soil moisture. The root-mean-square differences of H in CAMS-CSM were all greater than those from the ERA-Interim reanalysis, but less than or comparable to those from GLDAS. The spatial correlations for H in CAMS-CSM were the lowest in nearly all regions, except for North America. CAMS-CSM LE produced the lowest bias in Siberia, North America, and South America, but with the lowest spatial correlation coefficients. Therefore, there are still scopes for improving H and LE simulations in CAMS-CSM, particularly for LE.     

CMIP5模式对中国东北气候模拟能力的评估

利用CN05观测资料和参与IPCC第五次评估报告的45个全球气候系统模式的模拟结果,分析了新一代全球气候模式对中国东北三省(1961~2005年)气温和降水的模拟能力。结果表明:1)绝大多数模式都能较好地模拟出研究区内显著增温的趋势,对气温的年际变化模拟能力则相对有限;2)所有模式均能很好地再现气温气候态的空间分布特征,且多模式集合模拟结果优于绝大多数单个模式,空间相关系数达到了0.96;3)对于降水的模拟结果,模式间差异较大,多模式集合能较好地再现其空间分布规律(空间相关系数为0.86),对降水年际变化及线性变化趋势的模拟能力则较差。总体来说,多模式集合对东北气候的时空变化特征具有一定的模拟能力,且对气温模拟效果优于降水,对空间分布的模拟能力优于时间变化。     

中国地区IPCC A1B情景下21世纪中期气候变化的数值模拟试验

利用MM5V3区域气候模式单向嵌套ECHAM5全球环流模式的结果,对中国地区实际温室气体浓度下当代气候(1981—2000年)及IPCC A1B情景下21世纪中期气候(2041—2060年)分别进行了水平分辨率为50 km的模拟试验。首先检验全球和区域模式对当代气候的模拟情况,结果表明:区域模式对中国地区地面温度和降水空间分布的模拟能力优于全球模式;与实际观测相比,区域模式模拟的地面温度在中国大部分地区偏低,模拟的降水量偏多,降水位置偏北。IPCCA1B情景下中国地区21世纪中期气候变化的模式结果显示:各季节地面温度在全国范围内都将比当代升高1.2～3.9℃,且升温幅度具有北方大于南方、冬季大于夏季的时空分布特征;降水变化具有一定的区域性和季节性,秋季和冬季降水在全国大部分地区都将增加10%～30%,春季和夏季降水则呈现"北方减少、南方增多"的趋势,变化幅度在-10%～10%之间。21世纪中期地面温度和降水变化还具有一定的年际特征:地面温度在中国地区各子区域均表现为上升趋势,升温速率在0.7～0.9℃/10a之间,温度变率也比当代有所增大;降水在西北地区略呈下降趋势,在其它子区域均为上升,降水变率的变化具有区域性特征。     

北京气候中心气候模式1.1版预估中亚地区未来50年地面气温时空变化特征

利用IPCC AR5中BCC-CSM1.1(Beijing Climate Center Climate System Model version 1.1)的历史试验和4类典型 排放路径情景下未来预估试验结果, 在使用CRU(Climatic Research Unit)资料验证BCC-CSM1.1性能的基础上, 采用趋势分 析、滑动平均以及经验正交函数(EOF)等方法, 研究2011-2060年中亚地区年平均气温的时空演变特征。与CRU 资料的对 比分析发现BCC-CSM1.1能较好地模拟过去109a(1901-2009年)中亚地区气温的显着上升趋势及气候态的空间分布特征。 预估试验结果表明, 中亚地区在未来50a整体呈现变暖趋势, 并且, 随着温室气体排放浓度的升高, 气温的升高趋势愈加明 显, 同时增温显着区域也明显增大。经验正交函数分解主要模态还是延续过去的分布特征:经验正交函数分解第1模态及其 所对应的时间系数显示中亚地区年平均地面气温在未来50a(2011-2060年)呈现出全场一致的升高趋势, 升高强度随着温 室气体排放浓度的增加而增强, 进一步的分析表明, 不同典型排放路径下预估的未来50a中亚地区年平均地面气温的经验正 交函数分解第1模态在中亚上空850hPa等压面上均对应有一个反气旋(气旋)性异常环流, 在这个异常环流控制下, 中亚地 区年平均地面气温变化表现为全场一致的特征。经验正交函数分解第2模态呈现出中亚地区地面气温变化南北反位相的基 本特征, 相应的时间系数主要表现为小幅度波动, 变化趋势特征不明显。     

CLIMATE CHANGE DUE TO GREENHOUSE EFFECTS IN CHINA AS SIMULATED BY A REGIONAL CLIMATE MODEL-PART I:EVALUATION OF THE MODEL SIMULATIONS*

Impacts of greenhouse effects(2×CO₂) on climate change over China as simulated by a regional climate model have been investigated.The model was based on RegCM2 and is nested in one-way mode within a global coupled atmosphere-ocean model(CSIRO R₂₁L₉ AOGCM).Two multi-year simulations,the control run with normal CO₂ concentration and the sensitivity run with doubled CO₂ concentration are conducted. As Part I of the publications,results of control run of the CSIRO,i.e.its simulation of present climate in China,are analyzed briefly.It shows that the model can basically reproduce the surface air temperature and precipitation pattern over China.Therefore,its outputs can be used to drive the regional model. Analysis of control run of RegCM shows that with a high resolution,the model improves the simulations of surface air temperature and precipitation in China as compared to the CSIRO model, especially for the precipitation.The spatial correlation coefficient between simulated and observed annual temperature increased from 0.83 in the CSIRO to 0.92 in the RegCM and for annual precipitation from 0.48 in the CSIRO to 0.65 in the RegCM.A similar improvement in the RegCM compared to the CSIRO was found in all simulated months.The main improvement for surface temperature is that RegCM can simulate the fine scale structure of temperature caused by topography.RegCM greatly improved the spatial distribution of precipitation by eliminating the virtual precipitation center in central China,which was simulated by many other GCMs.The precipitation simulated by RegCM in North and Northwest China is smaller than that by CSIRO, which makes it closer to the observation.     

A maximum entropy method for combining AOGCMs for regional intra-year climate change assessment

This paper deals with different responses from various Atmosphere-Ocean Global Climate Models (AOGCMs) at the regional scale. What can be the best use of AOGCMs for assessing the climate change in a particular region? The question is complicated by the consideration of intra-year month-to-month variability of a particular climate variable such as precipitation or temperature in a specific region. A maximum entropy method (MEM), which combines limited information with empirical perspectives, is applied to assessing the probability-weighted multimodel ensemble average of a climate variable at the region scale. The method is compared to and coupled with other two methods: the root mean square error minimization method and the simple multimodel ensemble average method. A mechanism is developed to handle a comprehensive range of model uncertainties and to identify the best combination of AOGCMs based on a balance of two rules: depending equally on all models versus giving higher priority to models more strongly verified by the historical observation. As a case study, the method is applied to a central US region to compute the probability-based average changes in monthly precipitation and temperature projected for 2055, based on outputs from a set of AOGCMs. Using the AOGCM data prepared by international climate change study groups and local climate observation data, one can apply the MEM to precipitation or temperature for a particular region to generate an annual cycle, which includes the effects from both global climate change and local intra-year climate variability.     

湖北分县MOS预报系统建立与评分

在T213L31数值模式产品释用基础上,设计计算并选用模式大气中分别与降水、地面气温和风场有天气学意义的线性化预报因子,采用Kalman滤波和相似-Kalman滤波,在湖北省及邻近范围,建立实时(逐日更新样本)多站点、多时次、多要素的统计MOS预报方程,做定点(分县)、定时(6小时分辨率)和定量降水与概率、地面气温和风场客观预报(MOS预报系统)."系统"经2005、2006年6-8月业务试验表明,各个MOS预报都具稳定性和具有一定的评分水平.从而表明,相似-Kalman滤波在一定程度上消去单纯Kalman滤波"预报滞后"效应,并实现用Kalman滤波或相似-Kalman滤波做定性、定量降水MOS预报,和做地面气温和风场MOS预报,且用了实时历史样本雨/晴预报准确率,当作有/无降水发生概率.     

The Super-large Ensemble Experiments of CAS FGOALS-g3

A super-large ensemble simulation dataset with 110 members has been produced by the fully coupled model FGOALS-g3 developed by researchers at the Institute of Atmospheric Physics, Chinese Academy of Sciences. This is the first dataset of large ensemble simulations with a climate system model developed by a Chinese modeling center. The simulation has the largest realizations up to now worldwide in terms of single-model initial-condition large ensembles. Each member includes a historical experiment (1850–2014) and an experiment (2015–99) under the very high greenhouse gas emissions Shared Socioeconomic Pathway scenario (SSP5-8.5). The dataset includes monthly and daily temperature, precipitation, and other variables, requiring storage of 275 TB. Additionally, the surface air temperature (SAT) and land precipitation simulated by the FGOALS-g3 super-large ensemble have been validated and projected. The ensemble can capture the response of SAT and land precipitation to external forcings well, and the internal variabilities can be quantified. The availability of more than 100 realizations will help researchers to study rare events and improve the understanding of the impact of internal variability on forced climate changes.     

南京信息工程大学气候预测系统1.0版简介

南京信息工程大学气候预测系统1.0版(NUIST CFS1.0)是基于日本海洋科学技术开发机构(JAMSTEC)的SINTEX-F模式发展而来,可以实现对全球气候异常的季节-年际预测。对过去近40 a的集合历史回报预测试验结果的评估发现,该预测系统对热带太平洋和印度洋海温异常具有良好的预测技巧,并且该系统能提前1.5~2 a对ENSO(Nino3.4指数)做出有技巧的预测(即相关系数达0.5),同时也可以提前1~2个季节对印度洋偶极子(IOD)做出有较高技巧的预测,展现了对主要热带气候信号的良好预测技巧。但是与国内外所有动力模式预测系统类似,该系统对东亚地区的气候异常预测还存在较大的不足。考虑到ENSO对东亚地区气候异常的强烈影响,本文尝试去除与ENSO预测相关的系统偏差来初步订正东亚地区夏季温度异常和降水距平百分率的预测结果。对比订正前后的结果表明,这一简单的订正方法有助于提高我国气候异常的预测准确率。同时选取2019年夏季气温异常和降水距平百分率的实时预测结果作为个例进行分析,发现订正能够提供一定的技巧改善,但与观测结果相比仍存在较大偏差,需要在今后的工作中不断改进完善。此外,本文也初步评估了NUIST CFS1.0对我国冬春季的气候预测技巧,并提供了经简单订正后的2019/2020年冬季和2020年春季的实时预测结果。     

Evaluating regional climate model estimates against site-specific observed data in the UK

This paper compares precipitation, maximum and minimum air temperature and solar radiation estimates from the Hadley Centre’s HadRM3 regional climate model (RCM), (50 × 50 km grid cells), with observed data from 15 meteorological station in the UK, for the period 1960–90. The aim was to investigate how well the HadRM3 is able to represent weather characteristics for a historical period (hindcast) for which validation data exist. The rationale was to determine if the HadRM3 data contain systematic errors and to investigate how suitable the data are for use in climate change impact studies at particular locations. Comparing modelled and observed data helps assess and quantify the uncertainty introduced to climate impact studies. The results show that the model performs very well for some locations and weather variable combinations, but poorly for others. Maximum temperature estimations are generally good, but minimum temperature is overestimated and extreme cold events are not represented well. For precipitation, the model produces too many small events leading to a serious under estimation of the number of dry days (zero precipitation), whilst also over- or underestimating the mean annual total. Estimates represent well the temporal distribution of precipitation events. The model systematically over-estimates solar radiation, but does produce good quality estimates at some locations. It is concluded that the HadRM3 data are unsuitable for detailed (i.e. daily time step simulation model based) site-specific impacts studies in their current form. However, the close similarity between modelled and observed data for the historical case raises the potential for using simple adjustment methods and applying these to future projection data.     

BCC_CSM1.1模式对我国气温的模拟和预估

利用我国541个测站1960—2010年气温资料以及国家气候中心参加第5次耦合模式比较计划 (CMIP5) 的气候系统模式BCC_CSM1.1的历史试验和年代际试验结果,评估了该模式对我国近50年气温变化特征的模拟能力, 对模式的年代际试验结果进行了误差订正,并给出未来10~20年我国气温变化的预估。结果表明:历史试验和年代际试验均模拟出了与观测较为一致的增暖趋势,但均没有观测资料的增暖幅度大。其中,历史试验比年代际试验更接近于观测。年代际尺度上,模式对我国东部的模拟要好于西部;年际尺度上,模式的高预报技巧区在我国西北地区西南部和东部、西南地区北部。历史试验和年代际试验对我国气温空间场整体分布模拟较好,误差订正后的年代际试验结果对空间气温场的模拟有更好把握。相对于观测资料得到的1960—2010年0.27℃/10 a的增温速率,模式预估我国2011—2030年平均气温变化速率达到0.48℃/10 a, 上升趋势更加明显。     

Projections of annual mean air temperature and precipitation over the globe and in China during the 21st century by the BCC Climate System Model BCC_CSM1.0

Evaluating the projection capability of climate models is an important task in climate model development and climate change studies. The projection capability of the Beijing Climate Center (BCC) Climate System Model BCC CSM1.0 is analyzed in this study. We focus on evaluating the projected annual mean air temperature and precipitation during the 21st century under three emission scenarios (Special Report on Emission Scenarios (SRES) B1, A1B, and A2) of the BCC CSM1.0 model, along with comparisons with 22 CMIP3 (Coupled Model Intercomparison Project Phase 3) climate models. Air temperature averaged both globally and within China is projected to increase continuously throughout the 21st century, while precipitation increases intermittently under each of the three emission scenarios, with some specific temporal and spatial characteristics. The changes in globally-averaged and China-averaged air temperature and precipitation simulated by the BCC CSM1.0 model are within the range of CMIP3 model results. On average, the changes of precipitation and temperature are more pronounced over China than over the globe, which is also in agreement with the CMIP3 models. The projection capability of the BCC CSM1.0 model is comparable to that of other climate system models. Furthermore, the results reveal that the climate change response to greenhouse gas emissions is stronger over China than in the global mean, which implies that China may be particularly sensitive to climate change in the 21st century.     

Projections of Annual Mean Air Temperature and Precipitation over the Globe and in China During the 21st Century bythe BCC Climate System Model BCC_CSM1.0

Evaluating the projection capability of climate models is an important task in climate model development and climate change studies. The projection capability of the Beijing Climate Center (BCC) Climate System Model BCC_CSM1.0 is analyzed in this study. We focus on evaluating the projected annual mean air temperature and precipitation during the 21st century under three emission scenarios (Special Report on Emission Scenarios (SRES) B1, A1B, and A2) of the BCC_CSM1.0 model, along with comparisons with 22 CMIP3 (Coupled Model Intercomparison Project Phase 3) climate models. Air temperature averaged both globally and within China is projected to increase continuously throughout the 21st century, while precipitation increases intermittently under each of the three emission scenarios, with some specific temporal and spatial characteristics. The changes in globally-averaged and China-averaged air temperature and precipitation simulated by the BCC_CSM1.0 model are within the range of CMIP3 model results. On average, the changes of precipitation and temperature are more pronounced over China than over the globe, which is also in agreement with the CMIP3 models. The projection capability of the BCC_CSM1.0 model is comparable to that of other climate system models. Furthermore, the results reveal that the climate change response to greenhouse gas emissions is stronger over China than in the global mean, which implies that China may be particularly sensitive to climate change in the 21st century.