Tracking a Severe Pollution Event in Beijing in December 2016 with the GRAPES–CUACE Adjoint Model

We traced the adjoint sensitivity of a severe pollution event in December 2016 in Beijing using the adjoint model of the GRAPES–CUACE (Global/Regional Assimilation and Prediction System coupled with the China Meteorological Administration Unified Atmospheric Chemistry Environmental Forecasting System). The key emission sources and periods affecting this severe pollution event are analyzed. For comaprison, we define 2000 Beijing Time 3 December 2016 as the objective time when PM_2.5 reached the maximum concentration in Beijing. It is found that the local hourly sensitivity coefficient amounts to a peak of 9.31 μg m^–3 just 1 h before the objective time, suggesting that PM_2.5 concentration responds rapidly to local emissions. The accumulated sensitivity coefficient in Beijing is large during the 20-h period prior to the objective time, showing that local emissions are the most important in this period. The accumulated contribution rates of emissions from Beijing, Tianjin, Hebei, and Shanxi are 34.2%, 3.0%, 49.4%, and 13.4%, respectively, in the 72-h period before the objective time. The evolution of hourly sensitivity coefficient shows that the main contribution from the Tianjin source occurs 1–26 h before the objective time and its peak hourly contribution is 0.59 μg m^–3 at 4 h before the objective time. The main contributions of the Hebei and Shanxi emission sources occur 1–54 and 14–53 h, respectively, before the objective time and their hourly sensitivity coefficients both show periodic fluctuations. The Hebei source shows three sensitivity coefficient peaks of 3.45, 4.27, and 0.71 μg m^–3 at 4, 16, and 38 h before the objective time, respectively. The sensitivity coefficient of the Shanxi source peaks twice, with values of 1.41 and 0.64 μg m^–3 at 24 and 45 h before the objective time, respectively. Overall, the adjoint model is effective in tracking the crucial sources and key periods of emissions for the severe pollution event.     

Changes in Extreme Maximum Temperature Events and Population Exposure in China under Global Warming Scenarios of 1.5 and 2.0°C: Analysis Using the Regional Climate Model COSMO-CLM

We used daily maximum temperature data (1986–2100) from the COSMO-CLM (COnsortium for Small-scale MOdeling in CLimate Mode) regional climate model and the population statistics for China in 2010 to determine the frequency, intensity, coverage, and population exposure of extreme maximum temperature events (EMTEs) with the intensity–area–duration method. Between 1986 and 2005 (reference period), the frequency, intensity, and coverage of EMTEs are 1330–1680 times yr^–1, 31.4–33.3°C, and 1.76–3.88 million km², respectively. The center of the most severe EMTEs is located in central China and 179.5–392.8 million people are exposed to EMTEs annually. Relative to 1986–2005, the frequency, intensity, and coverage of EMTEs increase by 1.13–6.84, 0.32–1.50, and 15.98%–30.68%, respectively, under 1.5°C warming; under 2.0°C warming, the increases are 1.73–12.48, 0.64–2.76, and 31.96%–50.00%, respectively. It is possible that both the intensity and coverage of future EMTEs could exceed the most severe EMTEs currently observed. Two new centers of EMTEs are projected to develop under 1.5°C warming, one in North China and the other in Southwest China. Under 2.0°C warming, a fourth EMTE center is projected to develop in Northwest China. Under 1.5 and 2.0°C warming, population exposure is projected to increase by 23.2%–39.2% and 26.6%–48%, respectively. From a regional perspective, population exposure is expected to increase most rapidly in Southwest China. A greater proportion of the population in North, Northeast, and Northwest China will be exposed to EMTEs under 2.0°C warming. The results show that a warming world will lead to increases in the intensity, frequency, and coverage of EMTEs. Warming of 2.0°C will lead to both more severe EMTEs and the exposure of more people to EMTEs. Given the probability of the increased occurrence of more severe EMTEs than in the past, it is vitally important to China that the global temperature increase is limited within 1.5°C.     

First observation of domoic acid and its isomers in shellfish samples from Shandong Province,China

Journal of Oceanology and Limnology - A total of 133 shellfish samples were collected in seven cities of Shandong Province, China, from May to October, 2019. The domoic acid (DA) concentrations...     

北半球近地层典型区CO_2体积分数时空分布及成因

利用GEOS-Chem全球三维大气化学传输模式,分析了北半球近地层CO2体积分数的时空变化特征及其成因。2006—2010年的5 a的模拟结果表明:北半球中纬度近地层CO2体积分数存在着两个高值中心,即亚洲东部和北美东北部。在季节尺度上,亚洲东部CO2体积分数最大值出现在春季,而北美东北区域CO2体积分数最大值出现在冬季;而两个地区的CO2体积分数最低值都出现在夏季。在年际尺度上,两个区域CO2体积分数的年际变率增幅明显高于北半球其它区域,且CO2体积分数高值出现时间的年际差异较大。另外,模拟分析发现北半球森林、农田、草原典型区域,所对应的CO2体积分数具有不同的季节变化特点,它们的CO2季节内变幅依次减小。进一步分析发现3种不同典型区域的CO2体积分数与叶面积指数(LAI)季节变化,具有很好的负相关性。可见陆地生态系统作为碳汇,对近地层CO2体积分数的季节变化具有重要的作用。而温度和降水是影响LAI的最重要的两个气象因子,它们与CO2体积分数季节变化存在内在联系,模拟结果表明北半球大部分陆地近地层CO2体积分数与温度、降水呈现显著的负相关。     

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一次飑线大风天气过程成因分析

利用常规气象资料、自动站资料、多普勒雷达资料及NCEP再分析资料,分析了2014年3月31日广西大风天气过程的环境背景场、层结结构、垂直风切变场、局地层结特征、物理量配置、雷达回波及地面风场特征,得到一些有意义的结论,可以为此类天气过程预报提供参考。     

利用两类雷达探测资料分析超强台风“威马逊”登陆期间结构演变特征

利用实时多普勒天气雷达、边界层风廓线雷达和自动站资料对超强台风“威马逊”第4次登陆广西沿海时台风结构的演变特征进行研究,结果表明：台风眼区气压呈“漏斗”形变化,具有气压低、风速弱、空气干而暖的特征;登陆过程中眼区保持圆形,半径约为30 km,是典型强台风结构;天气雷达径向速度大风区具有非对称性,右象限大于左象限;风廓线雷达水平风场能够精确、直观地描述台风不同部位经过测站时的垂直结构特征,从低层到高层风向先后经历了“东北风—东风—东南风—南风”的转变过程,风速整体上呈现随高度先增大后减小的特点,其垂直速度和大气折射率结构常数(C2n)能够很好地反映台风的结构及其云和气流的变化特征;两种雷达风场产品的风向一致,但是VWP产品的风速比风廓线雷达的要小,VWP产品出现无效数据时,可以用风廓线雷达产品作为补充。     

印度洋Carlsberg洋脊玄武岩岩石地球化学特征及其地质意义

本文对采自印度洋Carlsberg脊14个站位的新鲜玄武岩样品进行了常量和微量元素组成分析,旨在研究岩浆源区地幔的性质以及岩浆作用过程。研究结果表明:该区玄武岩为典型的源于亏损型地幔的大洋中脊玄武岩,不同样品经历了不同程度的结晶分异作用,演化过程主要受控于橄榄石的结晶分异作用,部分样品中有单斜辉石结晶分异作用的影响,斜长石的结晶分异作用不显著;玄武岩岩浆来源于亏损型尖晶石二辉橄榄岩地幔的熔融,主微量元素组成中尚未见到富集型组分混入的证据;源区地幔不同比例的熔融作用及其后岩浆演化过程的差异是造成不同样品间地球化学性质差异的主要原因,彼此独立的局部岩浆作用过程是岩浆作用差异的主控制因素。Carlsberg脊玄武岩整体与全球标准大洋中脊玄武岩(N-MORB)平均组分相近,不同脊段间岩浆源区地幔的组成、熔融程度(比例)和熔融深度等无明显差异,这种特征向南直到CIR的北段。     

“白鹿”台风(1911)残涡造成广西大范围暴雨成因分析

利用常规气象资料及EC再分析、卫星云图、雷达、中尺度自动站等资料,采用天气动力诊断分析方法,对"白鹿"台风(1911)残涡造成广西大范围暴雨的成因进行分析。结果表明,"白鹿"台风残涡暴雨是在有利的高低空环流形势和强水汽辐合条件共同作用下造成的。有利的环流条件使停编后的"白鹿"残涡强度维持、加强,卫星云图显示"白鹿"南半环云系发展旺盛、密实,雷达回波显示残涡螺旋云带的前端有新生对流发展、传播,从而造成广西大范围暴雨发生。     

济南市中心城区地表热环境时空变化分析

针对目前众多学者主要针对夏季热环境进行研究,分析方法相对单一,且融合地理探测器等多种方法针对不同季节相关分析研究相对较少的问题,该文基于2017-2018年Landsat8遥感数据,反演济南市中心城区四季的地表温度,综合运用景观格局指数法和剖面法分析不同季节热环境空间分布变化;利用相关性分析方法和地理探测器模型针对地表遥感指标和热环境进行相关性和影响力研究.结果表明:济南市中心城区除冬季,地表温度分布东高西低,与 自然地表、工业生产、人口活动等区位要素关联密切;气温骤降与骤增导致温度斑块割裂、聚合;水体、水汽与建筑的交互作用对春、夏、秋季的城市热环境影响最大,水体与不透水面(交互因子:0.392 952)对冬季热环境交互影响最大;城市热环境的空间分异特征受多因子影响,其中植被与水体(交互因子:0.379 927)、植被与水汽(交互因子:0.380 707)协同降温效应最优,建筑与植被(交互因子:0.278 922)协同保温效应最优.