. By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.\n

Major conclusions and recommendations regarding the status of global coupled general circulation models are presented here from a workshop convened by the World Climate Research Programme Steering Group on Global Coupled Modelling that was held from 10 to 12 October 1994 at the Scripps Institution of Oceanography, La Jolla, California. The purpose of the workshop was to assess the current state of the art of global coupled modeling on the decadal and longer timescales in terms of methodology and results to identify the major issues and problems facing this activity and to discuss possible alternatives for making progress in light of these problems. This workshop brought together representatives from nearly every group in the world actively involved in formulating and running such models. After presentations by workshop participants, four working groups identified key issues involving 1) initialization and model spinup, 2) strategies and techniques for coupling of model components, 3) flux correction/adjustment, and 4) secular drift and systematic errors. The participants concluded that improved communication between those engaged in this activity will be important to enhance further progress. Consequently, the World Climate Research Programme intends to continue the support of internationally coordinated activities in global coupled modeling.

A coordinated set of global coupled climate model [atmosphere–ocean general circulation model (AOGCM)] experiments for twentieth- and twenty-first-century climate, as well as several climate change commitment and other experiments, was run by 16 modeling groups from 11 countries with 23 models for assessment in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Since the assessment was completed, output from another model has been added to the dataset, so the participation is now 17 groups from 12 countries with 24 models. This effort, as well as the subsequent analysis phase, was organized by the World Climate Research Programme (WCRP) Climate Variability and Predictability (CLIVAR) Working Group on Coupled Models (WGCM) Climate Simulation Panel, and constitutes the third phase of the Coupled Model Intercomparison Project (CMIP3). The dataset is called the WCRP CMIP3 multimodel dataset, and represents the largest and most comprehensive international global coupled climate model experiment and multimodel analysis effort ever attempted. As of March 2007, the Program for Climate Model Diagnostics and Intercomparison (PCMDI) has collected, archived, and served roughly 32 TB of model data. With oversight from the panel, the multimodel data were made openly available from PCMDI for analysis and academic applications. Over 171 TB of data had been downloaded among the more than 1000 registered users to date. Over 200 journal articles, based in part on the dataset, have been published AMERICAN METEOROLOGICAL SOCIETY so far. Though initially aimed at the IPCC AR4, this unique and valuable resource will continue to be maintained for at least the next several years. Never before has such an extensive set of climate model simulations been made available to the international climate science community for study. The ready access to the multimodel dataset opens up these types of model analyses to researchers, including students, who previously could not obtain state-of-the-art climate model output, and thus represents a new era in climate change research. As a direct consequence, these ongoing studies are increasing the body of knowledge regarding our understanding of how the climate system currently works, and how it may change in the future.

The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.

The scientific gaps identified in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) that guided the experiment for its next phase, CMIP6, are identified.

Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.

. Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 °C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017–2018 time frame, and output from the climate model projections made available and analyses performed over the 2018–2020 period.\n

西北干旱区水资源问题突出,全球变暖将进一步加剧其水资源短缺,研究未来气候变化对流域水资源合理分配和使用具有重要意义。本文利用CRU(Climate Research Unit)数据和DCHP（Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections）提供的32个经BCSD降尺度的CMIP5（全球耦合模式比较计划第五阶段）模式气温数据,采用线性倾向估计、滑动平均、M-K(Mann-Kendall)检验及滑动T（MMT）等检验法,以西北干旱区典型流域开都-孔雀河流域为例,通过对1950—2005年的年平均气温、年平均最高气温与年平均最低气温3个指标的变化趋势及突变年份进行检测,评估各模式及模式集合平均对气温变化的模拟能力。研究结果表明：①12个模式能够准确模拟出1950—2005年流域内各气温指标的显著增加趋势,8个模式能够模拟出部分气温指标的增温趋势,但均低估了增温速率,集合平均也存在同样问题;②除FIO-ESM与MPI-ESM-MR能够准确模拟出气温突变时间外,绝大多数模式不能够准确模拟出。基于优选模式的集合平均PM-PLS和PM-EE对突变的模拟能力总体上优于单个模式,其中PM-PLS模拟能力更优;③对PM-PLS模式集合平均进一步评价,发现其能较好地再现流域气温线性趋势的时空变化总体特征,但仍存在增温速率低估的问题。采用气候模式进行未来气候预估仍需加强模式优选及多模式集合平均方法的深入研究。

Global warming will result in serve water shortage, aggravating the existing outstanding water problem in the Kaidu-Kongqi River Basin. Studies on the future climate change will contribute to the rational distribution and utilization of water in the basin. Based on the CRU(Climate Research Unit) dataset and 32 BCSD-downscaled CMIP5 model air temperature dataset from DCHP (Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections), the paper assessed the simulation ability of both 32 models and multi-model ensemble mean through the test of long-term trend and abrupt change of annual average, maximum and minimum air temperature in the Kaidu-Kongqi River Basin over the period of 1950-2005 by using the methods of linear trend calculation, moving average, Mann-Kendall (M-K) test and moving T-test (MMT). Results show that (1) 12 of 32 models are capable of reproducing the significant warming trend of three temperature indicators during 1950-2005, 8 of 32 models can only simulate that of some temperature indicators, but all of them underestimate the warming rate, so does the multi-model ensemble mean. (2) Most models failed to simulate the time of abrupt change accurately except two, FIO-ESM and MPI-ESM-MR. The ensemble mean of preferred models, PM-PLS and PM-EE, are superior to the individual model in simulating abrupt change. Between them, PM-PLS is better. (3) The further evaluation indicates that the multi-model ensemble PM-PLS can better capture the linear trend of spatio-temporal characteristics, but the problem of underestimating the warming rate still exists. It appeals to strengthen the study of model optimum selection and multiple models assemble in the future climate prediction using climate models.

本文利用中国660个站点逐日地面温度资料,评估了参与政府间气候变化专门委员会第五次报告(IPCC AR5)的9个全球气候模式(Global Climate Models, GCMs)及多模式集合(Multi-Model Ensemble, MME)对中国地区气温的模拟精度。结果表明：9个IPCC AR5全球气候模式和MME模拟的中国地区1996-2005年日平均气温与气象站点观测值的相关系数都大于0.86,表明相关性较好;气候模式模拟的中国东南部地区1996-2005年日平均气温的模拟精度较高,模拟值的偏差、平均相对误差、平均绝对误差和均方根误差都比较小;而西部地区的模拟效果较差,模拟精度较低。综合考虑模式模拟值与站点观测值的相关系数、偏差、平均相对误差、平均绝对误差和均方根误差发现,MME在中国地区的气温模拟精度优于大部分单个模式。

This article evaluates the precision of the temperature simulated by nine IPCC AR5 (the Fifth Assessment Report of the Intergovernmental Panel on Climate Change) GCMs (Global Climate Models) and the multi-model ensemble (MME), based on the observed temperature of 660 stations in China from 1996 to 2005. The results show that the correlation coefficients between the average daily temperature simulated by GCMs and station observations in China during 1996-2005 were very high, all above 0.86. The precision of the simulated average daily temperature in the southeast by the 10 models was higher than that in the west, judged by the lower Biases, mean relative errors (MREs), mean absolute errors (MAEs), and root mean square errors (RMSEs) in the southeast as compared to those in the west. The precision of the simulated temperature by IPSL-CM5A-LR, MRI-CGCM3, and NorESM1-M was poorer than that of the others—specially, the Biases, MREs, and RMSEs of the simulation result by IPSL-CM5A-LR, the Biases and RMSEs of the simulation result by MRI-CGCM3, and MREs and RMSEs of the simulation result by NorESM1-M, were larger. Taken into account the Biases, MREs, MAEs, and RMSEs, the simulation precision of MME was the highest.

. The objective of this paper is to identify better performing Coupled Model Intercomparison Project phase 3 (CMIP3) global climate models (GCMs) that reproduce grid-scale climatological statistics of observed precipitation and temperature for input to hydrologic simulation over global land regions. Current assessments are aimed mainly at examining the performance of GCMs from a climatology perspective and not from a hydrology standpoint. The performance of each GCM in reproducing the precipitation and temperature statistics was ranked and better performing GCMs identified for later analyses. Observed global land surface precipitation and temperature data were drawn from the Climatic Research Unit (CRU) 3.10 gridded data set and re-sampled to the resolution of each GCM for comparison. Observed and GCM-based estimates of mean and standard deviation of annual precipitation, mean annual temperature, mean monthly precipitation and temperature and Köppen–Geiger climate type were compared. The main metrics for assessing GCM performance were the Nash–Sutcliffe efficiency (NSE) index and root mean square error (RMSE) between modelled and observed long-term statistics. This information combined with a literature review of the performance of the CMIP3 models identified the following better performing GCMs from a hydrologic perspective: HadCM3 (Hadley Centre for Climate Prediction and Research), MIROCm (Model for Interdisciplinary Research on Climate) (Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change), MIUB (Meteorological Institute of the University of Bonn, Meteorological Research Institute of KMA, and Model and Data group), MPI (Max Planck Institute for Meteorology) and MRI (Japan Meteorological Research Institute). The future response of these GCMs was found to be representative of the 44 GCM ensemble members which confirms that the selected GCMs are reasonably representative of the range of future GCM projections.

The authors have analyzed twentieth-century temperature and precipitation trends and long-term persistence from 19 climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This study is focused on continental areas (60°S–60°N) during 1930–2004 to ensure higher reliability in the observations. A nonparametric trend detection method is employed, and long-term persistence is quantified using the Hurst coefficient, taken from the hydrology literature. The authors found that the multimodel ensemble–mean global land–average temperature trend (0.07°C decade−1) captures the corresponding observed trend well (0.08°C decade−1). Globally, precipitation trends are distributed (spatially) at about zero in both the models and in the observations. There are large uncertainties in the simulation of regional-/local-scale temperature and precipitation trends. The models’ relative performances are different for temperature and precipitation trends. The models capture the long-term persistence in temperature reasonably well. The areal coverage of observed long-term persistence in precipitation is 60% less (32% of land area) than that of temperature (78%). The models have limited capability to capture the long-term persistence in precipitation. Most climate models underestimate the spatial variability in temperature trends. The multimodel ensemble–average trend generally provides a conservative estimate of local/regional trends. The results of this study are generally not biased by the choice of observation datasets used, including Climatic Research Unit Time Series 3.1; temperature data from Hadley Centre/Climatic Research Unit, version 4; and precipitation data from Global Historical Climatology Network, version 2.

Surface air temperature outputs from 16 global climate models participating in the sixth phase of the coupled model intercomparison project (CMIP6) were used to evaluate agreement with observations over the global land surface for the period 1901–2014. Projections of multi-model mean under four different shared socioeconomic pathways were also examined. The results reveal that the majority of models reasonably capture the dominant features of the spatial variations in observed temperature with a pattern correlation typically greater than 0.98, but with large variability across models and regions. In addition, the CMIP6 mean can capture the trends of global surface temperatures shown by the observational data during 1901–1940 (warming), 1941–1970 (cooling) and 1971–2014 (rapid warming). By the end of the 21st century, the global temperature under different scenarios is projected to increase by 1.18 °C/100 yr (SSP1-2.6), 3.22 °C/100 yr (SSP2-4.5), 5.50 °C/100 yr (SSP3-7.0) and 7.20 °C/100 yr (SSP5-8.5), with greater warming projected over the high latitudes of the northern hemisphere and weaker warming over the tropics and the southern hemisphere. Results of probability density distributions further indicate that large increases in the frequency and magnitude of warm extremes over the global land may occur in the future.

Using historical data compiled by the Climate Research Unit, spatial and temporal analysis, trend analysis, empirical orthogonal function (EOF) analysis, and Taylor diagram analysis were applied to test the ability of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models to accurately simulate the annual mean surface air temperature in central Asia from the perspective of the average climate state and climate variability. Results show that each model can reasonably capture the spatial distribution characteristics of the surface air temperature in central Asia but cannot accurately describe the regional details of climate change impacts. Some of the studied models, including CNRM-CM5, GFDL-CM3, and GISS-E2-H, could better simulate the high- and low-value centers and the contour distribution of the surface air temperature. Taylor diagram analysis showed that the root mean square errors of all models were less than 3, the standard deviations were between 8.36 and 13.45, and the spatial correlation coefficients were greater than 0.96. EOF analysis showed that the multi-model ensemble can accurately reproduce the surface air temperature characteristics in central Asia from 1901 to 2005, including the rising periods and the fluctuations of the north and south inversion phases. Overall, this study provides a valuable reference for future climate prediction studies in central Asia.

<p>In this paper, the climate change projections in East China made by the climate models in the IPCC-AR4 were assessed by simulation evaluation and uncertainty analysis. By comparing individual simulation of the 21 IPCC AR4 models with the observations and with each other as well, it is demonstrated that the simulation abilities of different models vary widely. Only models of NCAR-CCSM3 and MRI-CGCM2.3.2 have small root mean square errors for both temperature and precipitation simulations in East China. It has been shown that, under the scenario A1B, multi-model ensemble mean can fairly well illustrate the spatial patterns of annual mean temperature and precipitation. Nevertheless, it can hardly reflect local fine structure of the distributions because of their low spatial resolutions. Moreover, there is a significant systematic deviation in multi-model ensemble mean for temperature projection. It underestimates the annual mean temperature by more than 1.6℃, and the difference between simulation and observation exceeds the extent of the uncertainty which is defined by one-fold standard deviation of inter-models&rsquo; simulations. The standard deviation of annual precipitation is up to 26.7% of the multi-model ensemble mean. Therefore it would be quite questionable if IPCC-AR4 multi-model ensemble means of temperature and precipitation in East China are directly used as climate change projection.</p>

利用国际耦合模式比较计划第六阶段（CMIP6）模拟试验数据， 首先评估了45个全球气候模式对1985 -2014年青藏高原地表气温和降水的模拟能力， 表明CMIP6模式能合理地模拟地表气温的空间分布， 但大部分模式对年和季节平均地表气温的模拟值偏低， 年均偏冷2.1 ℃， 冷偏差在冬季和春季相对更大。CMIP6模式对青藏高原降水的模拟能力较为有限， 尽管它们能模拟出年均降水东多西少的空间分布特征， 但普遍存在高估， 尤其是在春季和夏季， 年均降水较观测偏多397.8 mm·a^-1。基于模拟性能较好的模式， 相比于1995 -2014年， 在共享社会经济路径（SSPs）中等偏低情景SSP2-4.5下， 青藏高原年均地表气温在21世纪90年代上升2.5 ℃， 2015 -2100年的线性趋势平均为0.28 ℃·（10a）^-1， 其中秋季和冬季增幅更大， 高海拔区增暖幅度高于低海拔区。年均降水在21世纪90年代将增加12.8%， 2015 -2100年的线性趋势平均为1.56%·（10a）^-1， 其中春季增幅最大， 高原北部边界区为降水增加的大值区。相较SSP2-4.5情景， SSP5-8.5情景下青藏高原地表气温和降水增幅更大， 21世纪90年代年均地表气温升高5.1 ℃， 降水增加30.2%， 两者在2015 -2100年的线性趋势平均分别为0.64 ℃·（10a）^-1和3.80%·（10a）^-1。整体上， 模式对地表气温和降水预估结果的不确定性均随时间增大。

利用第六次国际耦合模式比较计划（CMIP6）提供的5个气候模式，并结合基于地面气象站的CN05.1气象资料，评估了CMIP6模式对黄河上游地区1961—2014年气温变化的模拟能力。基于7个共享社会经济路径及代表性浓度路径（SSP-RCP）组合情景，结合多模式集合平均预估了2015—2100年黄河上游地区年均气温和季平均气温的时空变化规律。结果表明：多模式集合平均能较好地模拟黄河上游地区历史平均气温的空间分布格局与年变化。7个未来情景一致表明，2015—2100年黄河上游地区年平均气温呈现波动上升趋势［0.03～0.82 ℃?（10a）^-1］。其中，低辐射强迫情景下（SSP1-1.9、SSP1-2.6及SSP4-3.4）气温先呈现增加趋势，21世纪中期到达增幅峰值，之后增温呈现放缓趋势；而中、高辐射强迫情景下（SSP2-4.5、SSP3-7.0、SSP4-6.0及SSP5-8.5）气温表现为持续上升态势。空间上，未来气温增幅显著的区域位于黄河上游西部地区；时间上，呈现夏季增温快，春季增温慢。四季增温的空间分布呈现出一致特征，表现为西部增温强于东部，北部增温强于南部。研究结果可为黄河流域水资源管理及气候变化的适应性研究提供科学依据。

Five global climate models from the latest released Coupled Model Intercomparison Project Phase 6 （CMIP6） and CN05.1 meteorological data are applied to evaluate annual air temperature variations in upper basin of the Yellow River from 1961 to 2014. This study focuses on characterizing the spatiotemporal and annual variations of air temperature across upper basin of the Yellow River under seven future scenarios， combing the shared socioeconomic pathways and the representative concentration pathways （SSP1-1.9， SSP1-2.6， SSP2-4.5， SSP3-7.0， SSP4-3.4， SSP4-6.0 and SSP5-8.5）. The simulation capability of CMIP6 outputs are evaluated during the historical period （1961—2014）. We find that： Multi-model ensemble mean provides good results in characterizing spatial distribution and annual variations of air temperature dynamics across the study area. The average air temperature shows a significant upward trend ［0.03~0.82 ℃?（10a）-1］ under all seven scenarios during 2015—2100. Air temperature increased and reached the peak till the middle 21st century， and showed a slowly increasing trend till the end of the century， under the low forcing scenarios （SSP1-1.9， SSP1-2.6 and SSP4-3.4）. Under the mid and high forcing scenarios （SSP2-4.5， SSP3-7.0， SSP4-6.0 and SSP5-8.5）， the annual mean air temperature showed a continuous rising trend. Regions featured with highest temperature increasing located in western part of upper basin of the Yellow River. Air temperature in summer will rise in a relatively fast speed， while that in spring is slower. Patterns of seasonal air temperature rising shows an obvious spatial distribution， relatively fast in west and slow in east， fast in north and slow in south. In the context of global warming， a reasonable estimation of the future air temperature changes in upper basin of the Yellow River is crucial for the water resources management and study on adaptions to climate change.

Monthly and 3-hourly precipitation data from twentieth-century climate simulations by the newest generation of 18 coupled climate system models are analyzed and compared with available observations. The characteristics examined include the mean spatial patterns, intraseasonal-to-interannual and ENSO-related variability, convective versus stratiform precipitation ratio, precipitation frequency and intensity for different precipitation categories, and diurnal cycle. Although most models reproduce the observed broad patterns of precipitation amount and year-to-year variability, models without flux corrections still show an unrealistic double-ITCZ pattern over the tropical Pacific, whereas the flux-corrected models, especially the Meteorological Research Institute (MRI) Coupled Global Climate Model (CGCM; version 2.3.2a), produce realistic rainfall patterns at low latitudes. As in previous generations of coupled models, the rainfall double ITCZs are related to westward expansion of the cold tongue of sea surface temperature (SST) that is observed only over the equatorial eastern Pacific but extends to the central Pacific in the models. The partitioning of the total variance of precipitation among intraseasonal, seasonal, and longer time scales is generally reproduced by the models, except over the western Pacific where the models fail to capture the large intraseasonal variations. Most models produce too much convective (over 95% of total precipitation) and too little stratiform precipitation over most of the low latitudes, in contrast to 45%–65% in convective form in the Tropical Rainfall Measuring Mission (TRMM) satellite observations. The biases in the convective versus stratiform precipitation ratio are linked to the unrealistically strong coupling of tropical convection to local SST, which results in a positive correlation between the standard deviation of Niño-3.4 SST and the local convective-to-total precipitation ratio among the models. The models reproduce the percentage of the contribution (to total precipitation) and frequency for moderate precipitation (10–20 mm day−1), but underestimate the contribution and frequency for heavy (&amp;gt;20 mm day−1) and overestimate them for light (&amp;lt;10 mm day−1) precipitation. The newest generation of coupled models still rains too frequently, mostly within the 1–10 mm day−1 category. Precipitation intensity over the storm tracks around the eastern coasts of Asia and North America is comparable to that in the ITCZ (10–12 mm day−1) in the TRMM data, but it is much weaker in the models. The diurnal analysis suggests that warm-season convection still starts too early in these new models and occurs too frequently at reduced intensity in some of the models. The results show that considerable improvements in precipitation simulations are still desirable for the latest generation of the world’s coupled climate models.

The representation of tropical precipitation is evaluated across three generations of models participating in phases 3, 5, and 6 of the Coupled Model Intercomparison Project (CMIP). Compared to state-of-the-art observations, improvements in tropical precipitation in the CMIP6 models are identified for some metrics, but we find no general improvement in tropical precipitation on different temporal and spatial scales. Our results indicate overall little changes across the CMIP phases for the summer monsoons, the double-ITCZ bias, and the diurnal cycle of tropical precipitation. We find a reduced amount of drizzle events in CMIP6, but tropical precipitation occurs still too frequently. Continuous improvements across the CMIP phases are identified for the number of consecutive dry days, for the representation of modes of variability, namely, the Madden–Julian oscillation and El Niño–Southern Oscillation, and for the trends in dry months in the twentieth century. The observed positive trend in extreme wet months is, however, not captured by any of the CMIP phases, which simulate negative trends for extremely wet months in the twentieth century. The regional biases are larger than a climate change signal one hopes to use the models to identify. Given the pace of climate change as compared to the pace of model improvements to simulate tropical precipitation, we question the past strategy of the development of the present class of global climate models as the mainstay of the scientific response to climate change. We suggest the exploration of alternative approaches such as high-resolution storm-resolving models that can offer better prospects to inform us about how tropical precipitation might change with anthropogenic warming.

This study investigates the ability of 20 model simulations which contributed to the CMIP6 HighResMIP to simulate precipitation in different monsoon seasons and extreme precipitation events over Peninsular Malaysia. The model experiments utilize common forcing but are run with different horizontal and vertical resolutions. The impact of resolution on the models’ abilities to simulate precipitation and associated environmental fields is assessed by comparing multi-model ensembles at different resolutions with three observed precipitation datasets and four climate reanalyses. Model simulations with relatively high horizontal and vertical resolution exhibit better performance in simulating the annual cycle of precipitation and extreme precipitation over Peninsular Malaysia and the coastal regions. Improvements associated with the increase in horizontal and vertical resolutions are also found in the statistical relationship between precipitation and monsoon intensity in different seasons. However, the increase in vertical resolution can lead to a reduction of annual mean precipitation compared to that from the models with low vertical resolutions, associated with an overestimation of moisture divergence and underestimation of lower-tropospheric vertical ascent in the different monsoon seasons. This limits any improvement in the simulation of precipitation in the high vertical resolution experiments, particularly for the Southwest monsoon season.

我国西南地区的地形地貌非常复杂， 当前的气候模式对该地区降水状况特别是极端降水的模拟技巧是比较低的。本文基于台站和卫星观测的逐日降水资料以及欧洲中心第五代再分析（ERA5）降水资料， 通过与CMIP6高分辨率模式比较计划（HighResMIP）中的12个模式高、 低分辨率模拟结果的对比分析， 评估了当前气候模式对西南地区夏季降水的模拟性能特别是模式水平分辨率对极端降水模拟的影响。结果表明： （1）在夏季降水气候态方面， 各HighResMIP模式模拟与台站观测之间的空间相关系数均超过0.75， 总体性能较CMIP5有明显提升， 但仍有超过半数模式明显低估了四川盆地降水。模式分辨率提高使横断山脉地形陡峭区的降水空间分布和强度更接近观测和ERA5资料， 但对四川盆地降水的改进效果不佳。（2）在夏季极端降水方面， HighResMIP模式对极端降水频率和强度模拟差异较大。CNRM-CM6、 FGOALS-f3、 GFDL和HadGEM-GC31等4个模式对极端降水的各项指标模拟总体较好， 但受气候态模拟偏差影响， 前三者模拟的极端降水在四川盆地偏弱， 而HadGEM-GC31在广西明显偏强。ECMWF-IFS、 EC-Earth3P、 IPSL-CM6A、 MPI-ESM1-2和MRI-AGCM3-2等5个模式中极端降水发生频率明显偏低。提高分辨率可以一定程度改进降水强度的模拟， 主要体现在提高地形陡峭区的降水强度， 但对地形平坦区如四川盆地降水强度改进不大。

Due to complex topography in Southwest China （SWC）， state-of-the-art climate models cannot capture sufficiently the distribution and intensity of summer precipitation， especially extreme rainfall （ER） over SWC.Thus， this study is to evaluate how well the current climate models could reproduce the climate mean summer precipitation and to what extend the horizontal resolutions might impact the ER simulations over SWC， based on daily rain-gauge station-observed， satellite-observed and ERA5-reanalysed rainfall datasets and 12 models available from CMIP6 High-Resolution Model Inter-comparison Project （HighResMIP）.Each HighResMIP model contains one high-resolution and one low-resolution simulation with the same suite of physical processes and external forcing.Results show that almost all models can reproduce the climate-mean state of summer rainfall over SWC， with an area correlation coefficient （ACC） greater than 0.75 between the rain-gauge observed rainfall and simulated rainfall by each model.Over， the performance of CMIP6 HighResMIP models is better than that of CMIP5 models， but over half of the CMIP6 HighResMIP models still underestimate the summer rain amount over the Sichuan Basin.As the model resolution increased， the intensity and spatial pattern of the simulated summer rainfall over the Hengduan mountains are much closer to the observational dataset， especially to the ERA5 reanalysis.However， the underestimating biases over Sichuan Basin are not improved obviously with a higher horizontal resolution.In terms of ER， large spreads exist in the ER intensity and occurrence frequency over SWC among CMIP6 HighResMIP models.The four models， including CNRM-CM6、 FGOALS-f3、 GFDL-CM4 and HadGEM-GC31， exhibit better performances in capturing ER days and percentage.Even so， the first three of the above four models underestimate the ER days over SWC， but HadGEM-GC31 overestimates ER intensity over Guangxi Province.In contrast， the ER frequency is much lower than that of observation in the models ECMWF-IFS， EC-Earth3P， IPSL-CM6A， MPI-ESM1-2 and MRI-AGCM3-2.The higher resolution simulations can improve the simulation in the rainfall intensity to a certain degree， manifesting mainly in enhancing the rainfall intensity over the mountainous region rather than the flat-terrain area such as the Sichuan Basin.

Earth system models are indispensable tools in climate studies. The Coupled Model Intercomparison Project (CMIP) is a coordinated effort of the Earth system modeling community to intercompare existing models. An accurate simulation of surface solar radiation fluxes is of major importance for the accuracy of simulations of the near-surface climate in Earth system models. The present study provides a quantitative assessment of the accuracy and multidecadal changes of surface solar radiation fluxes for model results from two phases of CMIP. The entire archives of phase 5 of CMIP (CMIP5) and its predecessor phase 3 (CMIP3) are analyzed for present-day climate conditions. A relative model ranking is provided, and its uncertainty is quantified using different global observational records. It is shown that the choice of an observational dataset can have a major influence on relative model ranking between CMIP models. However the multidecadal variability of surface solar radiation fluxes, also known as global “dimming” or “brightening,” is largely underestimated by the CMIP models.

Wind stress measurements from the Quick Scatterometer (QuikSCAT) satellite and two atmospheric reanalysis products are used to evaluate the annual mean and seasonal cycle of wind stress simulated by phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). The ensemble CMIP3 and CMIP5 wind stresses are very similar to each other. Generally speaking, there is no significant improvement of CMIP5 over CMIP3. The CMIP ensemble–average zonal wind stress has eastward biases at midlatitude westerly wind regions (30°–50°N and 30°–50°S, with CMIP being too strong by as much as 55%), westward biases in subtropical–tropical easterly wind regions (15°–25°N and 15°–25°S), and westward biases at high-latitude regions (poleward of 55°S and 55°N). These biases correspond to too strong anticyclonic (cyclonic) wind stress curl over the subtropical (subpolar) ocean gyres, which would strengthen these gyres and influence oceanic meridional heat transport. In the equatorial zone, significant biases of CMIP wind exist in individual basins. In the equatorial Atlantic and Indian Oceans, CMIP ensemble zonal wind stresses are too weak and result in too small of an east–west gradient of sea level. In the equatorial Pacific Ocean, CMIP zonal wind stresses are too weak in the central and too strong in the western Pacific. These biases have important implications for the simulation of various modes of climate variability originating in the tropics. The CMIP as a whole overestimate the magnitude of seasonal variability by almost 50% when averaged over the entire global ocean. The biased wind stress climatologies in CMIP not only have implications for the simulated ocean circulation and climate variability but other air–sea fluxes as well.

This study employs multiple reanalysis datasets to evaluate the global shallow and deep soil moisture in Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. The multimodel ensemble mean produces generally reasonable simulations for overall climatology, wet and dry centers, and annual peaks in the melt season at mid- to high latitudes and the rainy season at low latitudes. The simulation capability for shallow soil moisture depends on the relationship between soil moisture and the difference between precipitation and evaporation (P − E). Although most models produce effective simulations in regions where soil moisture is significantly related to the P − E (e.g., Europe, low-latitude Asia, and the Southern Hemisphere), considerable discrepancies between simulated conditions and reanalysis data occur at high elevations and latitudes (e.g., Siberia and the Tibetan Plateau), where cold-season processes play a driving role in soil moisture variability. These discrepancies reflect the lack of information concerning the thaw of snow and frozen ground in the reanalyzed data and the inability of models to simulate these processes. The models also perform poorly in areas of extreme aridity. On a global scale, the majority of models provide consistent and capable simulations owing to the minimal variability in deep soil moisture and limited observational information in reanalysis data. Models with higher spatial resolution do not exhibit closer agreement with the reanalysis data, indicating that spatial resolution is not the first limiting factor for CMIP6 soil moisture simulations.

[1] Land climate is important for human population since it affects inhabited areas. Here we evaluate the realism of simulated evapotranspiration (ET), precipitation, and temperature in the CMIP5 multimodel ensemble on continental areas. For ET, a newly compiled synthesis data set prepared within the Global Energy and Water Cycle Experiment-sponsored LandFlux-EVAL project is used. The results reveal systematic ET biases in the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, with an overestimation in most regions, especially in Europe, Africa, China, Australia, Western North America, and part of the Amazon region. The global average overestimation amounts to 0.17 mm/d. This bias is more pronounced than in the previous CMIP3 ensemble (overestimation of 0.09 mm/d). Consistent with the ET overestimation, precipitation is also overestimated relative to existing reference data sets. We suggest that the identified biases in ET can explain respective systematic biases in temperature in many of the considered regions. The biases additionally display a seasonal dependence and are generally of opposite sign (ET underestimation and temperature overestimation) in boreal summer (June-August).