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Response of warm season secondary pollutants to emissions and meteorology in the North china Plain region during 2018-2022

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atmospheric and Oceanic Science Letters 2024年第4期17卷 58-64页

作者： Yueqi Gao Siyu Wang Zhaolei Zhang Wenxuan Yu Shuai Wang Shengqiang zhu Peng Wang Yan Li Yele Sun Hongliang Zhang Shanghai Key Laboratory of atmospheric Particle Pollution and Prevention Department of Environmental Science&EngineeringFudan UniversityShanghaiChina Department of atmospheric and Oceanic sciences Fudan UniversityShanghaiChina Shanghai Key Laboratory of Ocean-land-atmosphere Boundary Dynamics and Climate Change ShanghaiChina IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health Fudan UniversityShanghaiChina State Key Laboratory of atmospheric Boundary Layer physics and atmospheric Chemistry Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina College of Earth and Planetary sciences University of Chinese Academy of SciencesBeijingChina Institute of Eco-Chongming ShanghaiChina

自2013年起中国空气质量虽改善,但华北平原(NCP)重污染仍存在且二次污染加剧,而人们对其成因和变化了解有限.本研究利用2018-2022年数据,借助CMAQ模型探讨此污染响应.结果显示,在2018-2022年间,PM_(2.5)浓度显著下降31%-37%,O_(3)和NO_... 详细信息

自2013年起中国空气质量虽改善,但华北平原(NCP)重污染仍存在且二次污染加剧,而人们对其成因和变化了解有限.本研究利用2018-2022年数据,借助CMAQ模型探讨此污染响应.结果显示,在2018-2022年间,PM_(2.5)浓度显著下降31%-37%,O_(3)和NO_(2)的年下降速率分别为1%和0.5%SIA和SOA也显著减少,每年分别减少9%和6%PM_(2.5)主要因排放减少而下降,而O_(3)则受气象影响而波动.硫酸盐和铵下降的主因是减排,而硝酸盐对气象变化敏感排放和气象变化对SOA的总体减少同样重要,但人为SOA对排放控制敏感生物SOA易受气象变化影响.研究强调了控制人为排放对缓解NCP地区夏季二次污染的重要性.

关键词： PM_(2.5) 二次污染物人为排放气象华北平原

New Record Ocean Temperatures and Related Climate Indicators in 2023

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Advances in atmospheric sciences 2024年第6期41卷 1068-1082页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH Tim BOYER Michael EMANN Jiang zhu Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Viktor GOURETSKI Gengxin CHEN Alexey MISHONOV Jim REAGAN Karina VON SCHUCKMANN Yuying PAN Zhetao TAN Yujing zhu Wangxu WEI Guancheng LI Qiuping REN Lijuan CAO Yayang LU international Center for Climate and Environment sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of St.Thomas School of EngineeringMinnesota 55105USA NSF National Center for atmospheric research BoulderColorado 80307USA University of Auckland Auckland 1010New Zealand National Oceanic and atmospheric Administration National Centers for Environmental InformationSilver SpringMaryland 20910USA Department of Earth and Environmental Science University of PennsylvaniaPhiladelphiaPennsylvania 19104USA Institute of Oceanology Chinese Academy of SciencesQingdao 266071China National Marine Environmental Forecasting Center Ministry of Natural Resources of ChinaBeijing 100081China Oceanographic Data Center Chinese Academy of SciencesQingdao 266071China College of Oceanography Hohai UniversityNanjing 210098China National Marine Data and Information Service Tianjin 300171China Italian National Agency for New Technologies Energy and Sustainable Economic DevelopmentS.Teresa Research CenterLerici 19032Italy Istituto Nazionale di Geofisica e Vulcanologia Sede di BolognaBologna 40128Italy South china Sea Institute of Oceanology Chinese Academy of SciencesGuangzhou 510301China ESSIC/CISESS-MD University of MarylandCollege ParkMD20740USA Mercator Ocean international Toulouse 31400France Eco-Environmental Monitoring and research Center Pearl River Valley and South China Sea Ecology and Environment AdministrationMinistry of Ecology and EnvironmentPRCGuangzhou 510611China National Meteorological Information Center China Meteorological AdministrationBeijing 100081China international research Center of Big Data for Sustainable Development Goals Beijing 100094China

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human *** 2023,the sea surface temperature(SST)and upper 2000 m ocean heat content(OHC)reached record *** 0–2000 m OHC in 2023 exceeded that of 2022 by 15±10 ZJ(1 Zetta Joules=1021 Joules)(updated IAP/CAS data);9±5 ZJ(NCEI/NOAA data).The Tropical Atlantic Ocean,the Mediterranean Sea,and southern oceans recorded their highest OHC observed since the *** with the onset of a strong El Niño,the global SST reached its record high in 2023 with an annual mean of~0.23℃ higher than 2022 and an astounding>0.3℃ above 2022 values for the second half of *** density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

关键词： ocean heat content salinity stratification global warming climate

Contrast in Secondary Organic Aerosols between the Present Day and the Preindustrial Period:The Importance of Nontraditional Sources and the Changed atmospheric Oxidation Capability

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Advances in atmospheric sciences 2024年第10期41卷 1936-1952,I0002-I0010页

作者： Yingchuan YANG Wenyi YANG Xueshun CHEN Jiawen zhu Huansheng CHEN Yuanlin WANG Wending WANG Lianfang WEI Ying WEI Qian YE Huiyun DU Zichen WU Zhe WANG Jie LI Xiaodong ZENG Zifa WANG State Key Laboratory of atmospheric Boundary Layer physics and atmospheric Chemistry Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China international Center for Climate and Environment sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China chinese academy of Environmental Planning Beijing 100041China University of chinese academy of sciences Beijing 100049China Institute of Urban Meteorology China Meteorology AdministrationBeijing 100089China Unit 93213 of PLA Beijing 100085China

Quantifying differences in secondary organic aerosols(SOAs)between the preindustrial period and the present day is crucial to assess climate forcing and environmental effects resulting from anthropogenic *** lack of vegetation information for the preindustrial period and the uncertainties in describing SOA formation are two leading factors preventing simulation of *** study calculated the online emissions of biogenic volatile organic compounds(VOCs)in the Aerosol and atmospheric Chemistry Model of the Institute of atmospheric physics(IAP-AACM)by coupling the Model of Emissions of Gases and Aerosols from Nature(MEGAN),where the input vegetation parameters were simulated by the IAP Dynamic Global Vegetation Model(IAP-DGVM).The volatility basis set(VBS)approach was adopted to simulate SOA formation from the nontraditional pathways,i.e.,the oxidation of intermediate VOCs and aging of primary organic *** biogenic SOAs(BSOAs)were dominant in SOAs globally in the preindustrial period,the contribution of nontraditional anthropogenic SOAs(ASOAs)to the total SOAs was up to 35.7%.In the present day,the contribution of ASOAs was 2.8 times larger than that in the preindustrial *** contribution of nontraditional sources of SOAs to SOA was as high as 53.1%.The influence of increased anthropogenic emissions in the present day on BSOA concentrations was greater than that of increased biogenic emission *** response of BSOA concentrations to anthropogenic emission changes in the present day was more sensitive than that in the preindustrial *** nontraditional sources and the atmospheric oxidation capability greatly affect the global SOA change.

关键词： IAP-AACM VBS MEGAN secondary organic aerosol anthropogenic and biogenic emissions

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海洋环流模式的并行I/O优化研究

计算机工程与应用 2024年第17期60卷 263-271页

作者：李云龙祝子杰孟祥飞朱小谦林鹏飞刘海龙李庚国家超级计算天津中心天津300457 国防科技大学气象海洋学院长沙410073 中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室北京100000

极端气象日益加剧,严重影响人类的生产生活。如何有效应对未来极端环境变化,更关乎国际竞争。因此,发展高精度高分辨率的地球系统模式推动气候变化研究至关重要。随着模式精度和分辨率的不断提高,I/O性能已成为制约模式计算效率和可扩... 详细信息

极端气象日益加剧,严重影响人类的生产生活。如何有效应对未来极端环境变化,更关乎国际竞争。因此,发展高精度高分辨率的地球系统模式推动气候变化研究至关重要。随着模式精度和分辨率的不断提高,I/O性能已成为制约模式计算效率和可扩展性的瓶颈。选取典型的海洋环流模式LICOM3作为研究对象,基于天河HPC系统,详细测试和分析了模式的I/O逻辑过程,发现在3K进程规模下I/O可占整体运行时间一半以上,是限制模式计算性能的关键因素。为此,设计并实现了主要输出过程的MPI-IO并行优化方案,显著提升了模式运行效率,基本解决了模式I/O瓶颈。测试结果表明,MPI-IO并行优化方案将输出时间占比从48.58%降到2.44%,将输出带宽提升到6.21 GB/s,约是原程序的14倍。此外,进一步分析了影响并行I/O性能的因素,以期为相关工作提供参考和指导。

关键词：并行I/O优化海洋环流模式 MPI-IO

The 2022 report of synergetic roadmap on carbon neutrality and clean air for china:Accelerating transition in key sectors

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Environmental Science and Ecotechnology 2024年第3期19卷 4-18页

作者： Yu Lei Zhicong Yin Xi Lu Qiang Zhang Jicheng Gong Bofeng Cai Cilan Cai Qimin Chai Huopo Chen Renjie Chen Shi Chen Wenhui Chen Jing Cheng Xiyuan Chi Hancheng Dai Xiangzhao Feng Guannan Geng Jianlin Hu Shan Hu Cunrui Huang Tiantian Li Wei Li Xiaomei Li Jun Liu Xin Liu zhu Liu Jinghui Ma Yue Qin Dan Tong Xuhui Wang Xuying Wang Rui Wu Qingyang Xiao Yang Xie Xiaolong Xu Tao Xue Haipeng Yu Da Zhang Ning Zhang Shaohui Zhang Shaojun Zhang Xian Zhang Xin Zhang Zengkai Zhang Bo Zheng Yixuan Zheng Jian Zhou Tong zhu Jinnan Wang Kebin He Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental PlanningBeijing100041China Center for Carbon Neutrality Chinese Academy of Environmental PlanningBeijing100041China Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing210044China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Institute for Carbon Neutrality Tsinghua UniversityBeijing100084China Ministry of Education Key Laboratory for Earth System Modelling Department of Earth System ScienceTsinghua UniversityBeijing100084China State Key Joint Laboratory for Environment Simulation and Pollution Control College of Environmental Sciences and Engineering and Center for Environment and HealthPeking UniversityBeijing100871China National Center for Climate Change Strategy and International CooperationBeijing100035China nansen-zhu international research centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029China School of Public Health Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of HealthFudan UniversityShanghai200032China School of Economics and Management Beijing University of Chemical TechnologyBeijing100029China Department of Earth System Science University of CaliforniaIrvineCA92697USA National Meteorological Center China Meteorological AdministrationBeijing100081China College of Environmental sciences and Engineering Peking UniversityBeijing100871China Policy research Center for Environment and Economy Ministry of Ecology and Environment of the People's Republic of ChinaBeijing100029China Jiangsu Key Laboratory of atmospheric Environment Monitoring

china is now confronting the intertwined challenges of air pollution and climate *** the high synergies between air pollution abatement and climate change mitigation,the chinese government is actively promoting synergetic control of these two *** Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in china by developing and monitoring key *** Synergetic Roadmap 2022 report is the first annual update,featuring 20 indicators across five aspects:synergetic governance system and practices,progress in structural transition,air pollution and associated weather-climate interactions,sources,sinks,and mitigation pathway of atmospheric composition,and health impacts and benefits of coordinated *** to the comprehensive review presented in the 2021 report,the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant *** milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time,a decline in the production of crude steel and cement after years of growth,and the surging penetration of electric ***,in 2022,china issued the first national policy that synergizes abatements of pollution and carbon emissions,marking a new era for china's pollution-carbon *** changes highlight china's efforts to reshape its energy,economic,and transportation structures to meet the demand for synergetic control and sustainable ***,the country has witnessed a slowdown in carbon emission growth,improved air quality,and increased health benefits in recent years.

关键词： Synergetic roadmap Carbon neutrality Clean air

一次北京冰雹形成的数值模拟及微物理机制研究

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大气科学 2024年第5期48卷 1796-1816页

作者：朱好奇平凡谭桂容沈新勇殷蕾南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心南京210044 中国科学院大气物理研究所北京100029 南方海洋科学与工程广东省实验室(珠海) 珠海519082

本文利用WRF模式对2021年6月25日一次发生在北京地区的冰雹天气进行高分辨率数值模拟研究。从雷达反射率和降水来看,数值模拟基本再现了冰雹风暴的宏观特征。在此基础上分析了冰雹风暴不同发展阶段的中尺度热力、动力和微物理特征。最... 详细信息

本文利用WRF模式对2021年6月25日一次发生在北京地区的冰雹天气进行高分辨率数值模拟研究。从雷达反射率和降水来看,数值模拟基本再现了冰雹风暴的宏观特征。在此基础上分析了冰雹风暴不同发展阶段的中尺度热力、动力和微物理特征。最后通过输出微物理过程的中间转化项,着重分析雹粒子和雨滴的质量收支和潜热收支情况,给出冰雹形成的云微物理概念模型图。研究结果表明:本次冰雹过程可分为多单体回波、线状对流和飑线三个阶段。此次冰雹天气主要以雪粒子为雹胚在对流层中高层与雨水和云水发生撞冻作用形成雹粒子,雹粒子生成后不断地撞冻雨水和云水增长,冰晶直接撞冻雨水形成雹的转化率很低。气流在风暴的前部低层流入,多单体阶段的两股弱上升气流不断地合并,而线状对流和飑线阶段低层入流减弱,中高层较干冷气流的流入明显。气流在飑线阶段强烈上升,在高层向前流出。被上升气流抬升的暖湿空气与中高层流入的干冷空气相遇凝结为云水或凝华为冰相粒子,释放大量潜热,导致云内浮力增强,促进云内气流强烈上升,将有更多的云水和冰相粒子生成,同时促进了雹粒子的生成和累积。雪和雹粒子的融化吸收大量潜热,导致融化层升高,因此在飑线阶段有大量过冷雨水生成并与雪粒子发生撞冻作用形成更多的雹粒子。大雹粒子降落产生的更强的水物质拖曳力将促进低层下沉气流增强。下沉运动导致低层雨水蒸发冷却,在近地面形成冷池,与高层的潜热加热配合增强对流,从而正反馈于经向环流。环流导致的垂直上升气流促进低层的水汽抬升至融化层以上凝结形成过冷水和凝华为雪粒子,而垂直下沉气流促进雪和雹降落至融化层以下形成雨水以及雨水在融化层以下蒸发,进而正反馈于云内热力环境。如此反复,产生降雹和强降水。

关键词：冰雹 WRF模式云微物理过程云内热动力场垂直速度诊断

Large potential of strengthening the land carbon sink in china through anthropogenic interventions

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Science Bulletin 2024年第16期69卷 2622-2631页

作者： Xu Yue Hao Zhou Yang Cao Hong Liao Xiaofei Lu Zhen Yu Wenping Yuan zhu Liu Yadong Lei Stephen Sitch Jürgen Knauer Huijun Wang Jiangsu Key Laboratory of atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science&Technology(NUIST)Nanjing 210044China College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410073China Jiangsu Nanjing Environmental Monitoring Center Nanjing 210013China Key Laboratory of Agrometeorology of Jiangsu Province Institute of EcologySchool of Applied MeteorologyNUISTNanjing 210044China Institute of Carbon Neutrality Sino-French Institute for Earth System ScienceCollege of Urban and Environmental SciencesPeking UniversityBeijing 100871China Department of Earth System Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Severe Weather and Key Laboratory of atmospheric Chemistry of CMA Chinese Academy of Meteorological SciencesBeijing 100081China College of Life and Environmental sciences University of ExeterExeter EX44RJUK Hawkesbury Institute for the Environment Western Sydney UniversityPenrith 2751Australia Key Laboratory of Meteorological Disaster Ministry of EducationJoint International Research Laboratory of Climate and Environment ChangeCollaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNUISTNanjing 210044China

The terrestrial ecosystem in china mitigates 21%-45%of the national contemporary fossil fuel CO_(2) emissions every *** and strengthening the land carbon sink is essential for reaching china’s target of carbon ***,this sink is subject to large uncertainties due to the joint impacts of climate change,air pollution,and human ***,we explore the potential of strengthening land carbon sink in china through anthropogenic interventions,including forestation,ozone reduction,and litter removal,taking advantage of a well-validated dynamic vegetation model and meteorological forcings from 16 climate *** anthropogenic interventions,considering Shared Socioeconomic Pathways(SSP)scenarios,the land sink is projected to be 0.26-0.56 Pg C a^(-1) at 2060,to which climate change contributes 0.06-0.13 Pg C a^(-1) and CO_(2) fertilization contributes 0.08-0.44 Pg C a^(-1) with the stronger effects for higher emission *** anthropogenic interventions,under a close-to-neutral emission scenario(SSP1-2.6),the land sink becomes 0.47-0.57 Pg C a^(-1) at 2060,including the contributions of 0.12.Pg C a^(-1) by conservative forestation,0.07 Pg C a^(-1) by ozone pollution control,and 0.06-0.16 Pg C a^(-1) by 20%litter removal over planted *** sink can mitigate 90%-110% of the residue anthropogenic carbon emissions in 2060,providing a solid foundation for the carbon neutrality in china.

关键词： Carbon sink Forestation Ozone Litter removal

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梅雨降水季节预测的多方法比较

大气科学学报 2024年第2期47卷 313-329页

作者：李琳菲杨颖朱志伟王蔚南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心江苏南京210044 中国气象科学研究院灾害天气国家重点实验室北京100086 上海市闵行区气象局上海201199

基于1961—2000年逐月降水观测资料和全球大气再分析资料,分析了6—7月长江中下游(108°~12.°E,27°~33°N)梅雨的时空分布特征。通过观测诊断和数值试验确定了影响梅雨异常偏多的3个前期因子:4—5月平均的西北太平洋海平面气压正异常;... 详细信息

基于1961—2000年逐月降水观测资料和全球大气再分析资料,分析了6—7月长江中下游(108°~12.°E,27°~33°N)梅雨的时空分布特征。通过观测诊断和数值试验确定了影响梅雨异常偏多的3个前期因子:4—5月平均的西北太平洋海平面气压正异常;3月至5月北大西洋海平面气压负变压倾向;1月至4月西伯利亚的2 m温度负倾向。利用这3个具有物理意义的影响因子构建了梅雨季节预测模型,该模型在训练期(1961—2000年)和独立预测期(2001—2022年)均具有显著的预测技巧(相关系数分别为0.79和0.77,均方根误差分别为0.59和0.68)。同时,基于相似的潜在预测因子,对比了利用偏最小二乘回归方法和5种机器学习方法(随机森林、轻量级梯度提升机、自适应提升、类别型特征提升、极端梯度提升)建立的预测模型的技巧。虽然训练期(1961—2000年)偏最小二乘回归和机器学习建模拟合效果更高,但在独立预测期(2001—2022年)上述模型的预测技巧显著降低(相关系数均低于0.44,均方根误差均大于0.93),出现了明显的过拟合问题。本研究强调梅雨的短期气候预测应建立在物理机制基础之上,而使用机器学习方法需谨慎。

关键词：梅雨季节预测物理经验预测模型机器学习

Nonlinear balance constraints in 3DVAR data assimilation

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Science china Earth sciences 2006年第3期49卷 331-336页

作者： zhu Jiang YAN Changxiang international Center for Climate and Environment sciences Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China International Center for Climate and Environment Sciences Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China

In many applications of 3DVAR, the balance constraints can be considered via two main approaches: weak constraint method which adds penalty terms to the cost function; and proper defi- nition of the background error covariance matrix with non-zero cross-correlation sub-matrices. The weak constraint approach requires determining the weighting matrices of the penalty terms. The background error covariance approach does not require determining those additional weighting ma- trices. However, it is only applicable to those linear or linearized balance constraints. A novel ap- proach is proposed based on the background error covariance approach by generalizing the so-called Derber-Bouttier formulation. An assimilation experiment of estimating temperature and salinity from the sea surface dynamic height observation is given to illustrate the proposed treatments of nonlinear balance constraints.

关键词： 3DVAR, linear balance constraint, nonlinear balance constraint, background error covariance matrix, sea surface dynamic height.