我国霾日和API分布特征及典型大城市中它们与气象条件关系

利用全国756基准站1994—2013年MICAPS地面常规观测资料和47个城市2002—2012年国家环境保护总局公布的空气污染指数(the Air Pollution Index,API)数据,对北京、南京、重庆沙坪坝、广州4个大城市霾日和API的气象条件进行分析。结果表明:1)我国霾日和API的高值区主要分布在中东部地区,年霾日自1998年开始上升,并持续增长,到2013年达到最多。1月霾日最多,8月霾日最少。2002—2012年期间,与年霾日不同,年API呈下降趋势,12月API最高,8月API最低。四大城市的霾日和API的年际变化和月际变化均存在较大差异。2)一般情况下,霾发生时,北京、南京、重庆沙坪坝、广州对应的相对湿度较高;霾发生时,北京西南风占主导作用,南京和广州均东南风占主导作用,重庆沙坪坝西北风占主导作用,风速多集中在1~4 m/s,负变压及低层大气稳定时更易产生霾日。3)低层稳定度(γ850)对四大城市的API的影响各不相同,北京和重庆沙坪坝与γ850呈负相关,即越稳定,API值越高,南京和广州与γ850呈正相关,即越不稳定,API值越高。4)多元线性回归方程的结果表明:热力因子(相对湿度、γ850)和动力因子(地面风速、24 h变压)对四大城市冬季霾日能见度和冬季API各有贡献。     

1961-2012年江苏省霾日时空变化特征及影响因素分析

利用1961—2012年江苏省69个地面气象站观测资料和2012年苏州市大气气溶胶观测资料,在对霾日进行判识和筛选的基础上,分析江苏省霾日的时空变化特征及霾与气象条件和污染物的关系。结果表明：1961—2012年江苏省各站年总霾日数均呈上升趋势,85％的台站呈极显著上升趋势;江苏省年平均霾日数呈显著上升趋势,其中2011—2012年呈急剧上升趋势;1980年前霾日的空间分布差异不明显,1980年后,沿江和苏南地区为霾的高发区,东部沿海大部地区霾日较少。霾天气主要发生在冬季和春季,以12月和1月发生最多。降水少和风速小有利于霾天气的发生;除SO2外,PM10、PM2.5和NO2等污染物浓度随着霾等级的增加而增大,其中PM2.5浓度增大明显。     

1960—2009年广西霾日时空变化特征

利用1960—2009年广西80个地面观测站资料,运用EOF、线性倾向估计等统计方法,分析了近50年广西霾日数的时空变化特征。结果表明:近50年广西霾日数总体呈上升趋势,与该地区人类活动和经济发展引起的污染排放增长密切相关;空间分布呈现出主要城市及其周边地区霾日多,边远地区及沿海地区霾日少的特点;广西霾天气主要发生在秋、冬季,以轻微霾 (能见度为5~10 km) 为主,且霾天气发生时的相对湿度8成以上介于70%~90%;除了空气污染之外,近50年风速呈下降趋势可能也导致更多的霾天气。     

1981～2015年华北地区持续霾事件的特征及其环流分类研究

根据1981～2015年华北地区地面基本气象要素定时值数据集和地面气候日值数据集站点资料,分别利用14时实测值法和目标区域极端事件的客观识别方法挑选霾日数和持续霾事件,并分析了它们的特征;,然后对持续霾事件进行了分类研究。结果表明:（1）华北地区霾日数空间分布极不均匀,有4个大值区:吕梁山和太行山之间的河谷地区、沿太行山以东的平原、河南北部、环黄海和渤海地区。（2）华北地区共挑选出111个持续霾事件,其中,持续3～5 d的事件最多,占了总数的86.5%,最长的事件可达12 d。（3）持续霾事件和霾日数的空间分布特征相似,且存在明显的年、季变化。1981～2015年持续霾事件数呈增加趋势,冬季增加最显著,其次是秋季和春季,夏季最少。（4）结合地形、霾日数、持续霾事件的分布和环流特征把持续霾事件分为7类,对发生频次较多的4类（华北地区型,河南北部及太行山以东的平原型,河南北部型,河南北部及环渤海、黄海地区型）的环流进行了对比分析。其环流形势的主要特征包括:对流层的中低层华北地区为纬向西风气流或脊前西北气流,我国南部或东南部地区高压西部的西南气流与华北地区的偏西气流产生弱辐合下沉气流;近地面层由于地形的影响形成垂直环流圈,霾最严重的地区一般出现在地形的东坡和垂直环流圈的下沉支。近地面东南气流和低层的西南气流向该地区输送了暖湿空气和污染物。华北地区霾发生位置的不同,主要由低层我国东部或者南部高压的位置和强度,以及局地垂直环流的下沉支的位置决定。这些研究结果可以为华北地区持续性霾的防控提供参考。     

1981~2018年成都平原经济区霾日变化分析

基于1981~2018年成都平原经济区44个气象站点霾日观测资料,分析了该地区霾日的时空演变特征,并利用城市群霾日综合评估模型范式对区域空气质量进行了量化分析。结果表明:（1）成都平原经济区高霾日多集中在德阳、成都和乐山等城市地区,冬季霾日最多,春秋季次之,夏季最少;（2）1991~2000年为成都平原经济区霾日数的高发阶段,2011~2018年霾日数呈明显下降趋势;（3）EOF（Empirical Orthogonal Function）和小波分析均揭示出成都平原经济区霾日数呈明显下降的趋势,其中资阳、成都、遂宁下降最快,仅绵阳北部呈小幅上升趋势。      

西安霾天气时空分布特征与影响因子分析

选用1971—2013年西安7个一般气象站和西安探空站资料,综合人工观测霾日记录、能见度小于10.0km、相对湿度小于80%的标准,分析霾的年代际、季和月的时空分布特征,以及霾天气时风、降水、边界层等影响因子。结果表明:120世纪70年代至21世纪西安年代际平均霾日呈"减-增-减-增"趋势;2西安是关中地区主要大气污染区,霾主要出现在冬季,多发区域基本维持在中东部;城市化对霾天气发生影响显著,呈市区、近郊多,远郊少的特点;3大部分霾持续时间不超过一周;近95%霾出现在3.3m/s风速以下,一半以上霾天气风力小于1级;4霾日与雨日成反比,与连续无降水日数成正比,月均最长连续无降水日数低值区为4—9月,霾日数均不多;各月边界层高度和霾日数呈明显的反相关,冬季边界层低,霾日最多;5特殊的"盆地"地貌、风速逐年减小的气候变化趋势、城市工业污染加重等是造成西安霾天气形成并波动上升的重要影响因子。     

成都及周边地区霾时空分布特征研究

利用成都及周边地区(包括眉山、广汉、绵阳、德阳)1980~2010年的地面气象观测资料,基于多种统计方法对该地区霾日的时空分布特征进行了详细分析。结果表明,(1)成都及周边地区的年平均霾日数整体呈增加趋势,尤其是近5年。霾日的季节变化主要表现为:冬季最多,秋季和春季次之,夏季最少,且夏、秋和冬三季霾日数均呈增加趋势,春季反之。霾日的月变化十分显著,大体呈"V"字型,最多出现在12月,最少出现在6月。(2)年平均霾日的空间分布基本一致,高值区主要分布在成都西南部以及东部的金堂、广汉、德阳、绵阳,低值区分布在成都北部。     

江西省近50年霾天气时空分布特征及成因分析

利用1964—2013年江西省83个站逐日霾观测资料,运用线性倾向估计等统计方法,分析江西省近50年霾时空变化特征及其与气候要素的关系。结果表明:江西霾日数呈赣中北部多、赣南少的特点,赣中的萍乡—宜春—抚州—上饶一带以及赣北北部的九江中部、景德镇北部地区是霾天气多发区,年均霾日数在30 d·a~(-1)以上。冬季霾日数最多,萍乡—宜春—鹰潭地区中北部、南昌—九江的中部以及上饶东部地区超过20 d·a~(-1),春季和秋季次之,夏季最少。12月是霾日数最多的月份,接近全年霾日数的2成。江西省霾日数呈年际增长的趋势,增长率为11 d·(10 a)~(-1),气候趋势系数为0.78,通过0.01的显著性水平检验。霾日数与平均风速和大风日数均呈负相关,而与静风日数呈正相关。近50年平均风速和大风日数呈下降趋势,静风日数呈上升趋势,这可能导致空气中污染物不易扩散而形成更多的霾天气。江西省降水日数呈减少趋势[-6.3 d·(10a)~(-1)],气温呈增暖趋势[0.15℃·(10a)~(-1)],霾日数与气温和降水日数分别呈正、负相关。     

1981—2019年四川省霾日时空变化特征及成因分析

利用1981—2019年气象观测资料,分析了四川霾日的时空变化特征,并分析了污染物排放量和气象条件变化对霾日的影响。结果表明:(1)四川盆地为霾日高发区,年均霾日达53.7 d,其中轻、中、重度霾日数分别为26.9、24.1和2.7 d,川西高原年均霾日数不足1 d。霾日高值区主要分布在盆地的中部、东部及南部,轻、中、重度霾日高值区分布与霾日基本一致。(2)近39 a盆地霾日总体呈下降趋势,气候倾向率为-0.03 d/10 a,霾日数及霾分布范围在20世纪90年代达到最大,进入21世纪后霾日数和霾范围呈减小趋势。(3)霾在冬季发生频繁,冬季年均霾日数达24.7d,且盆地大部地区超过30 d。(4)近39 a盆地共发生持续性霾12 782次,自贡市、德阳市、内江市、乐山市为持续性霾的高发区;盆地共发生区域持续性霾509次,其中10 d的区域持续性霾发生的次数最多,占比为87.8%。(5)盆地霾天气的主要贡献污染物为PM2.5和PM10。二者排放量在20世纪90年代达到最大,进入21世纪后开始减少,21世纪10年代减少最为明显。21世纪10年代前盆地平均气温升高、相对湿度下降,污染物的排放与气象条件的共同作用,导致霾事件出现频率较高。随着城市生态文明的建设与治理,在21世纪10年代,盆地区域污染物排放减少,区域升温率减小,相对湿度显著升高,霾出现频率有所降低。     

广东记录霾日和统计霾日的气候特征及比较

利用广东省86个气象观测站1961—2013年记录霾日资料和1981—2013年统计霾日资料,采用线性趋势分析、计算气候趋势系数等统计诊断方法,分析了广东记录霾日和统计霾日的气候特征并进行比较。结果表明:广东统计霾日与记录霾日的年平均分布非常相似,但是统计霾日数比记录霾日数明显增加10~80 d。广东霾分布可划分为三个区:(1)多霾区:包括珠江三角洲、北部的南雄、东南部的汕头,年统计霾日40.0~144.5d;(2)一般霾区:包括广东中部偏北的部分地区,年统计霾日20.0~39.9 d;(3)少霾区:广东西南部和东部大部分地区,年统计霾日1.0~19.9 d。广东年记录霾日和统计霾日均以11 d(10 a)的速率明显上升,1990年后显著增加,特别是2003年以来上升非常明显,2007年达到最大,但2008年以来逐年波动下降。广东年霾日数增加最明显的区域在珠三角、汕头、南雄等地。1980—2013年的广东平均年记录霾日序列与统计霾日序列的相关系数达0.78,显著相关。分析表明统计霾日比记录霾日总体上更客观合理,并对两者存在差异的原因进行了分析与讨论。     

四会市灰霾天气的变化特征及影响因子

首先利用四会市气象局观测站1959—2012年的地面气象观测资料,对四会地区灰霾天气历史情况进行总结,由于2000年以前灰霾判别标准不统一,阈值偏低造成2000年以前灰霾天数记录偏少,故选择2000—2012年13年的灰霾变化特征、变化规律,及其影响因素进行分析。结果表明：2000年后四会地区的灰霾日数总体呈上升趋势,尤其是近5年来更加明显;灰霾天气具有明显的季节差异,呈现为冬季〉秋季〉春季〉夏季的特征;绝大多数霾天能见度在5~10 km,为轻微灰霾天气,2007年开始小于5 km的轻度或中度灰霾日数也逐渐增加;连续灰霾天气过程增多,尤其是最近3年,灰霾天气中连续灰霾日已超过1/2,灰霾天气持续时间最长达13 d;灰霾天气受气象要素的影响较显著,较小的风速、较大的相对湿度均有利于霾天气的出现;灰霾一定程度上会减少可记录日照长度;近年来大气污染的日益严重,汽车尾气、工业废气、粉尘等大量排放使污染物浓度增大,也可能是灰霾天气频发的一个原因。     

1963—2012年广宁县日照时数的变化特征

利用1963—2012年广宁县逐年、逐月日照,日照百分率,水汽压,相对湿度,总低云量,降水量,轻雾、雾、烟、霾,风速,日照百分率≥60%、日照百分率≤20%等经过严格的筛选、审核,质量可靠的观测资料,综合分析了广宁县日照时数的年、季节、月份变化趋势,同时重点分析了日照时数与总低云量、霾日数的关系。结果表明:广宁县50年的年、季、月日照时数总体都呈减少趋势,导致日照时数减少的最重要原因是低云量和霾日数的显著增加。     

Aerosol characteristics during winter fog at Agra,North India

Simultaneous measurements on physical, chemical and optical properties of aerosols over a tropical semi-arid location, Agra in north India, were undertaken during December 2004. The average concentration of total suspended particulates (TSP) increased by about 1.4 times during intense foggy/hazy days. Concentrations of SO₄ ²⁻, NO₃ ⁻, NH₄ ⁺ and Black Carbon (BC) aerosols increased by 4, 2, 3.5 and 1.7 times, respectively during that period. Aerosols were acidic during intense foggy/hazy days but the fog water showed alkaline nature, mainly due to the neutralizing capacity of NH₄ aerosols. Trajectory analyses showed that air masses were predominantly from NW direction, which might be responsible for transport of BC from distant and surrounding local sources. Diurnal variation of BC on all days showed a morning and an evening peak that were related to domestic cooking and vehicular emissions, apart from boundary layer changes. OPAC (Optical properties of aerosols and clouds) model was used to compute the optical properties of aerosols. Both OPAC-derived and observed aerosol optical depth (AOD) values showed spectral variation with high loadings in the short wavelengths (<1 μm). AOD value at 0.5 μm wavelength was significantly high during intense foggy/hazy days (1.22) than during clear sky or less foggy/hazy days (0.63). OPAC-derived Single scattering albedo (SSA) was 0.84 during the observational period, indicating significant contribution of absorbing aerosols. However, the BC mass fraction to TSP increased by only 1% during intense foggy/hazy days and thereby did not show any impact on SSA during that period. A large increase was observed in the shortwave (SW) atmospheric (ATM) forcing during intense foggy/hazy days (+75.8 W/m²) than that during clear sky or less foggy/hazy days (+38 W/m²), mainly due to increase in absorbing aerosols. Whereas SW forcing at surface (SUF) increased from −40 W/m² during clear sky or less foggy/hazy days to −76 W/m² during intense foggy/hazy days, mainly due to the scattering aerosols like SO₄ ^2-.     

CHARACTERISTICS AND TRENDS OF CLIMATIC EXTREMES IN CHINA DURING 1959–2014

The spatial and temporal variations of daily maximum temperature(Tmax), daily minimum temperature(Tmin), daily maximum precipitation(Pmax) and daily maximum wind speed(WSmax) were examined in China using Mann-Kendall test and linear regression method. The results indicated that for China as a whole, Tmax, Tmin and Pmax had significant increasing trends at rates of 0.15℃ per decade, 0.45℃ per decade and 0.58 mm per decade,respectively, while WSmax had decreased significantly at 1.18 m·s~(-1) per decade during 1959—2014. In all regions of China, Tmin increased and WSmax decreased significantly. Spatially, Tmax increased significantly at most of the stations in South China(SC), northwestern North China(NC), northeastern Northeast China(NEC), eastern Northwest China(NWC) and eastern Southwest China(SWC), and the increasing trends were significant in NC, SC, NWC and SWC on the regional average. Tmin increased significantly at most of the stations in China, with notable increase in NEC, northern and southeastern NC and northwestern and eastern NWC. Pmax showed no significant trend at most of the stations in China, and on the regional average it decreased significantly in NC but increased in SC, NWC and the mid-lower Yangtze River valley(YR). WSmax decreased significantly at the vast majority of stations in China, with remarkable decrease in northern NC, northern and central YR, central and southern SC and in parts of central NEC and western NWC. With global climate change and rapidly economic development, China has become more vulnerable to climatic extremes and meteorological disasters, so more strategies of mitigation and/or adaptation of climatic extremes,such as environmentally-friendly and low-cost energy production systems and the enhancement of engineering defense measures are necessary for government and social publics.     

ANALYSIS OF “BIMODAL PATTERN” STORM ACTIVITY CHARACTERISTICS OF THE BAY OF BENGAL

Storms that occur at the Bay of Bengal (BoB) are of a bimodal pattern, which is different from that of the other sea areas. By using the NCEP, SST and JTWC data, the causes of the bimodal pattern storm activity of the BoB are diagnosed and analyzed in this paper. The result shows that the seasonal variation of general atmosphere circulation in East Asia has a regulating and controlling impact on the BoB storm activity, and the “bimodal period” of the storm activity corresponds exactly to the seasonal conversion period of atmospheric circulation. The minor wind speed of shear spring and autumn contributed to the storm, which was a crucial factor for the generation and occurrence of the “bimodal pattern” storm activity in the BoB. The analysis on sea surface temperature (SST) shows that the SSTs of all the year around in the BoB area meet the conditions required for the generation of tropical cyclones (TCs). However, the SSTs in the central area of the bay are higher than that of the surrounding areas in spring and autumn, which facilitates the occurrence of a “two-peak” storm activity pattern. The genesis potential index (GPI) quantifies and reflects the environmental conditions for the generation of the BoB storms. For GPI, the intense low-level vortex disturbance in the troposphere and high-humidity atmosphere are the sufficient conditions for storms, while large maximum wind velocity of the ground vortex radius and small vertical wind shear are the necessary conditions of storms.     

LOW-FREQUENCY CIRCULATION AND EXTENDED-RANGE FORECAST IN CONNECTION WITH AUTUMN EXTREME HEAVY RAINFALL PROCESS IN HAINAN

Observed daily precipitation data from the National Meteorological Observatory in Hainan province and daily data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-2 dataset from 1981 to 2014 are used to analyze the relationship between Hainan extreme heavy rainfall processes in autumn (referred to as EHRPs) and 10–30 d low-frequency circulation. Based on the key low-frequency signals and the NCEP Climate Forecast System Version 2 (CFSv2) model forecasting products, a dynamical-statistical method is established for the extended-range forecast of EHRPs. The results suggest that EHRPs have a close relationship with the 10–30 d low-frequency oscillation of 850 hPa zonal wind over Hainan Island and to its north, and that they basically occur during the trough phase of the low-frequency oscillation of zonal wind. The latitudinal propagation of the low-frequency wave train in the middle-high latitudes and the meridional propagation of the low-frequency wave train along the coast of East Asia contribute to the ‘north high (cold), south low (warm)’ pattern near Hainan Island, which results in the zonal wind over Hainan Island and to its north reaching its trough, consequently leading to EHRPs. Considering the link between low-frequency circulation and EHRPs, a low-frequency wave train index (LWTI) is defined and adopted to forecast EHRPs by using NCEP CFSv2 forecasting products. EHRPs are predicted to occur during peak phases of LWTI with value larger than 1 for three or more consecutive forecast days. Hindcast experiments for EHRPs in 2015–2016 indicate that EHRPs can be predicted 8–24 d in advance, with an average period of validity of 16.7 d.     

AN ATTRIBUTION ANALYSIS OF CHANGES IN POTENTIAL EVAPOTRANSPIRATION IN THE BEIJING–TIANJIN–HEBEI REGION UNDER CLIMATE CHANGE

Based on the measurements obtained at 64 national meteorological stations in the Beijing–Tianjin–Hebei (BTH) region between 1970 and 2013, the potential evapotranspiration (ET0) in this region was estimated using the Penman–Monteith equation and its sensitivity to maximum temperature (Tmax), minimum temperature (Tmin), wind speed (Vw), net radiation (Rn) and water vapor pressure (Pwv) was analyzed, respectively. The results are shown as follows. (1) The climatic elements in the BTH region underwent significant changes in the study period. Vw and Rn decreased significantly, whereas Tmin, Tmax and Pwv increased considerably. (2) In the BTH region, ET0 also exhibited a significant decreasing trend, and the sensitivity of ET0 to the climatic elements exhibited seasonal characteristics. Of all the climatic elements, ET0 was most sensitive to Pwv in the fall and winter and Rn in the spring and summer. On the annual scale, ET0 was most sensitive to Pwv, followed by Rn, Vw, Tmax and Tmin. In addition, the sensitivity coefficient of ET0 with respect to Pwv had a negative value for all the areas, indicating that increases in Pwv can prevent ET0 from increasing. (3) The sensitivity of ET0 to Tmin and Tmax was significantly lower than its sensitivity to other climatic elements. However, increases in temperature can lead to changes in Pwv and Rn. The temperature should be considered the key intrinsic climatic element that has caused the "evaporation paradox" phenomenon in the BTH region.     

Could the Recent Taal Volcano Eruption Trigger an El Ni?o and Lead to Eurasian Warming?

正The Taal Volcano in Luzon is one of the most active and dangerous volcanoes of the Philippines. A recent eruption occurred on 12 January 2020(Fig. 1a), and this volcano is still active with the occurrence of volcanic earthquakes. The eruption has become a deep concern worldwide, not only for its damage on local society, but also for potential hazardous consequences on the Earth's climate and environment.     

VARIABILITY OF INTER-ANNUAL RELATIONSHIP BETWEEN INDIAN OCEAN DIPOLE AND HUAXI REGION’S AUTUMN PRECIPITATION

The moving-window correlation analysis was applied to investigate the relationship between autumn Indian Ocean Dipole (IOD) events and the synchronous autumn precipitation in Huaxi region, based on the daily precipitation, sea surface temperature (SST) and atmospheric circulation data from 1960 to 2012. The correlation curves of IOD and the early modulation of Huaxi region’s autumn precipitation indicated a mutational site appeared in the 1970s. During 1960 to 1979, when the IOD was in positive phase in autumn, the circulations changed from a “W” shape to an ”M” shape at 500 hPa in Asia middle-high latitude region. Cold flux got into the Sichuan province with Northwest flow, the positive anomaly of the water vapor flux transported from Western Pacific to Huaxi region strengthened, caused precipitation increase in east Huaxi region. During 1980 to 1999, when the IOD in autumn was positive phase, the atmospheric circulation presented a “W” shape at 500 hPa, the positive anomaly of the water vapor flux transported from Bay of Bengal to Huaxi region strengthened, caused precipitation ascend in west Huaxi region. In summary, the Indian Ocean changed from cold phase to warm phase since the 1970s, caused the instability of the inter-annual relationship between the IOD and the autumn rainfall in Huaxi region.     

Revisiting the Concentration Observations and Source Apportionment of Atmospheric Ammonia

正While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly, aerosol ammonium nitrate remains high in East China. As the high nitrate abundances are strongly linked with ammonia, reducing ammonia emissions is becoming increasingly important to improve the air quality of China. Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions, long-term surface observation of ammonia concentrations are sparse. In addition, there is still no consensus on