近50年贵州雾的时空分布及变化

应用贵州84个气象台站50年(1961~2010年)观测资料,对大雾的时空分布,雾日的季节和月频率分布,雾日的年际间变化趋势等特征进行了分析表明,贵州大雾区主要有4个:西部大雾区主要分布在乌蒙山东侧;黔中大雾区主要在开阳和息烽县一带;黔东大雾区主要分布在苗岭山脉周围的县及铜仁的万山特区一带;黔西南大雾区以晴隆为中心。大雾大部分发生在冬季,其次是春季,其后是秋季,夏季发生频率最小。12月、1月和10月出现的雾日为最多;5~7月出现雾日的频率最小。出现大雾的时间主要在早晨,中午和傍晚发生大雾的频率较少。近50年大雾的年际间变化呈现增加趋势(通过0.05的信度检验),但本世纪以来呈现略微减少的趋势。      

西安大雾气候特征及成因分析

为揭示西安地区大雾气候特征及成因,分析西安地区1961-2005年大雾日数和对应的相对湿度、气温.结果表明:西安大雾最多地区是西安城区,秋冬季是大雾的高发季节;西安城区和户县大雾有明显的减少趋势,长安、蓝田大雾有明显的增加趋势,西安冬季大雾变化最明显;西安大雾具有明显的年代际变化;西安大雾变化与相对湿度和气温的变化有一定的相关性.     

我国大雾的时空分布特征及其发生的环流形势

根据1971～2005年35年来714站大雾资料,统计了我国大雾的时空分布特征和环流形势.结果表明:年平均大雾最多的地区主要集中在四川盆地、重庆、云南南部、湖南和江南东部;雾日有明显的季节和月际变化,春、夏季雾的范围较小,秋、冬季雾的范围较大,内陆雾主要为(秋)冬季正态分布型,东北的雾夏季偏多,沿海雾春、夏季较多.雾通常开始于晚上20时(北京时间,下同)至次日早晨8时(以6～7时为最多),结束于8～12时,持续时间大多在1～10 h,持续3h的雾出现的频数最高.近35年雾日的线性趋势表明:江南、华南的雾日变化不明显,其余大部分地区的雾日都呈递减趋势,不同能见度的雾日在1985年前后基本上都呈相反的变化趋势,并且能见度越低的雾日变化越明显.主要考虑地面天气形势我国大范围大雾发生的环流形势可分为均压型和锋前型两大类型.     

四川省大雾时空分布特征研究

采用1986~2007年四川省157个站22年大雾资料,初步统计分析了四川省大雾时空分布特征。结果表明:年平均雾日数最多的主要在四川盆地;雾日有明显的季节和月际变化,春、夏季年均雾日数较少,分布范围较小,秋、冬季年均雾日数较多,分布较广;雾大多开始于晚上20时~次日早上8时,结束于8~12时;其中持续0~3小时的大雾所占比例最大。近22年雾日年际变化趋势:约40%的观测站呈显著下降趋势,且分布集中在四川盆地,有少数的站点呈显著上升趋势。      

西安城区大雾气候特征分析

利用西安站1951—2011年常规气象观测资料,统计分析西安城区大雾气候特征。结果表明:西安城区雾日年际变化较大,平均22.2d/a,1971—1990年是大雾多发期,平均33d/a,大雾以1.7d/10a速率显著减少;大雾主要集中在9月—次年1月,11月为高发期,6月最少,不同等级的雾出现次数与其强度成反比,强浓雾4—8月鲜有发生,主要在10—12月;07:00前后生成的大雾最多,09:00—18:00生成的雾较少,13:00—15:00几乎无大雾;大雾天气主要风向为静风(C),约占66%,次风向为SSW、NE和SW,风速普遍较小,风速≤1m/s的雾次约占总次数的92%,风速较大的雾日,风向以SSW、SW居多;大雾天气相对湿度为80%~100%,相对湿度≥90%的雾日占比88%,夏季成雾湿度高于冬季,平均为95%。     

商丘雾变化的气候特征及天气分型

依据商丘市8个站1961~2004年雾资料,分析了大雾天气的分布和气候变化特征。结果表明:商丘市雾的地理分布是西部睢县至宁陵一带为多雾区,南部柘城至夏邑一带为少雾区。宁陵出现大雾最多,睢县次之,柘城雾日最少。年际变化总体呈上升趋势。月际变化呈“V”型特征,秋冬季雾最多,夏季最少。雾的日变化一般在下半夜到清晨日出前后形成,05:00~06:00最易生成大雾,雾消时间一般在06:00~12:00,日出后07:00~08:00雾最容易消散。最长连雾日一般出现在11至次年1月,而1月出现最长连雾日的次数最多。雾的持续时间3 h以下的短雾最多,12~24 h的最少,没有超过24 h的长雾,连雾时间最长为23.3 h。年最多雾日,宁陵最多为120 d,柘城最少只有32 d,其余各站在40~77 d之间。商丘市雾发生时的地面天气形势主要有大陆高压型、冷锋前暖区型、均压场型和(低压)倒槽型。     

近10 a黔北地区雾的时空分布及能见度与AQI的关系浅析

选取2006—2015年近10 a遵义市14个国家气象站观测资料,分析统计了大雾天气的时空分布,雾日的季节和月频率分布以及区域性大雾年际变化;并通过2015—2017年遵义市市区空气质量指数资料和能见度等地面气象资料,浅析其时间变化特征。结果表明:遵义大雾区主要有西部河谷大雾区、中部偏南大雾区、东部大雾区、北部雾区等4个。遵义市12月—次年1月出现的雾日最多,6—8月出现最少。近10 a区域性大雾天气次数随着年代的增加,总体呈现逐年减少的趋势。遵义秋冬季节空气质量状况不佳,空气中污染颗粒物较多,此时较高的相对湿度有助于形成能见度较差的天气。     

古尔班通古特沙漠南缘近40年大雾变化特征分析—蔡家湖区域

利用新疆蔡家湖气象站1971-2010年大雾天气现象观测资料,分析了该地区近40a大雾天气的年际、年代际、日变化特征以及大雾天气的持续时间特征。研究表明：蔡家湖近40a大雾的年日数年际变化不明显;秋季雾日增多趋势明显,春季和冬季雾日呈减少的趋势;大雾主要出现在冬季,其次为秋季;一日中大雾主要发生在02-08时,其次为8-14时;大雾持续时间大多在3h之内;40a雾的最长持续时间为46．88h,出现在2010年11月;各月平均最长持续时间为14．49h,也出现在11月;最长持续时间季节分布呈秋末和冬季较长,夏季较短;大多月份雾的最长持续时间呈增长的趋势;当出现2d及以上的高湿天气,且日平均气温在一7．O～O℃、日最高气温在一6．0～0℃时,有利于雾的持续。     

威海沿海地区雾的气候特征及相关影响因子分析

利用威海市6个基本气象站40a（1971—2010年）的气象观测资料,对威海沿海地区雾的时空分布特征、气候变化特征和雾过程持续时间等进行了统计分析,探讨了影响沿海雾生成的相关因子,其中还针对典型个例进行了统计分析。结果表明：威海地区雾呈现沿海大于内陆,东部大于西部地区的分布特点;其年代际变化特征表现并不一致,成山头和荣成的年雾日数呈明显的上升趋势,而威海,石岛和文登年雾日数也呈现增长趋势,但变化相对缓慢,只有乳山的年雾日数40a来呈现减小的趋势;除了文登和乳山,其他各站雾日数变化有着明显的季节变化特征,基本上呈春、夏季多、秋、冬季少的分布特点,各站大雾的日变化特征并不一致,其中乳山站日变化特征最为明显,其次是威海站,总体表现为夜间到早晨为大雾多发期,中午为大雾的低发期的特点,而成山头站除了夏季,日变化特征并不明显;各地雾过程出现的雾持续时间各不相同,威海的雾主要以<4h的短时雾为主,成山头雾持续性较长,而乳山站的雾基本在02—08时之间;从风向、风速上来看,大雾主要发生在偏南风的流场下,成山头雾主要出现在3～4级风的情况下,而威海站雾则主要在3级风以下;大雾发生时海温不能高于25℃,且海温在10～25℃之间,海温越接近气温时,大雾更易发生;大雾主要发生在高空脊和西北气流影响下,夏季在弱低槽,弱低涡和副高边缘时大雾也可能发生,地面形势主要为均压场和低压前部型,同时大雾前和大雾期间大气层结稳定,地面湿度大,温度露点差大雾时在0～1℃之间,轻雾时在1～5℃之间。     

近37a乌鲁木齐机场雾的不均匀性特征分析

本文应用1977～2013年乌鲁木齐机场12月~次年(2014年)3月逐时地面观测资料,应用相关气候统计方法探求乌鲁木齐机场雾天气的出现时间及其变化特征,并以大雾集中度(FCD)和大雾集中期(FCP)来表征大雾天气累计出现时间的非均匀性特征,结果表明：⑴1977~2013年,乌鲁木齐机场雾累计出现时间呈明显的上升趋势,大雾的上升速率大于浓雾的上升速率,且大雾天气中浓雾的比率逐年下降;且机场雾的持续时间以低于6h为主;⑵机场雾分为三个阶段：少发期(1977~1991)、调整期(1992~2001)、高发期(2002~2013),且机场雾的突变时间就发生在本世纪00年代初期(2001/2002年);⑶机场大雾累计出现时间和能见度大小有明显的负相关关系,且日变化显著：一天内有两个非常明显的转折时间段,分别为1:00~2:00、和14:00~15:00,即3:00~13:00为一天内易发大雾时间段,14:00~23:00为一天内能见度较好时间段;⑷机场大雾的发生时间相对集中,集中度相对较高。近37a来大雾集中度呈下降趋势,尤其是进入大雾高发期以后,大雾发生次数显著增加,且大雾平均持续时间变化不大,集中度较低;机场大雾集中发生的12月和1月,以周为时间单位计算时,在2002年以前,大雾最多发生在12月第四周;2002以后,大雾最多发生时间开始后移至次年1月份第一周。     

COMPARATIVE ANALYSIS OF VARIOUS DATA OF AIR–SEA TEMPERATURE DIFFERENCE AND ITS VARIATION ACROSS SOUTH CHINA SEA IN THE PAST 35 YEARS

Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter.     

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.     

IMPACT OF ATMOSPHERIC LOW-FREQUENCY OSCILLATION ON THE IMMIGRATION OF NILAPARVATA LUGENS(STL) IN HUNAN AND JIANGXI PROVINCES

Various features of the atmospheric environment affect the number of migratory insects, besides their initial population. However, little is known about the impact of atmospheric low-frequency oscillation(10 to 90 days) on insect migration. A case study was conducted to ascertain the influence of low-frequency atmospheric oscillation on the immigration of brown planthopper, Nilaparvata lugens(Stl), in Hunan and Jiangxi provinces. The results showed the following:(1) The number of immigrating N. lugens from April to June of 2007 through 2016 mainly exhibited a periodic oscillation of 10 to 20 days.(2) The 10-20 d low-frequency number of immigrating N. lugens was significantly correlated with a low-frequency wind field and a geopotential height field at 850 h Pa.(3) During the peak phase of immigration, southwest or south winds served as a driving force and carried N. lugens populations northward, and when in the back of the trough and the front of the ridge, the downward airflow created a favorable condition for N. lugens to land in the study area. In conclusion, the northward migration of N. lugens was influenced by a low-frequency atmospheric circulation based on the analysis of dynamics. This study was the first research connecting atmospheric low-frequency oscillation to insect migration.     

A COMPARATIVE ANALYSIS OF ATMOSPHERIC AND OCEANIC CONDITIONS BEFORE THE OCCURRENCE OF TWO TYPES OF EL NIO EVENTS

The atmospheric and oceanic conditions before the onset of EP El Ni?o and CP El Ni?o in nearly 30 years are compared and analyzed by using 850 hPa wind, 20℃ isotherm depth, sea surface temperature and the Wheeler and Hendon index. The results are as follows: In the western equatorial Pacific, the occurrence of the anomalously strong westerly winds of the EP El Ni?o is earlier than that of the CP El Ni?o. Its intensity is far stronger than that of the CP El Ni?o. Two months before the El Ni?o, the anomaly westerly winds of the EP El Ni?o have extended to the eastern Pacific region, while the westerly wind anomaly of the CP El Ni?o can only extend to the west of the dateline three months before the El Ni?o and later stay there. Unlike the EP El Ni?o, the CP El Ni?o is always associated with easterly wind anomaly in the eastern equatorial Pacific before its onset. The thermocline depth anomaly of the EP El Ni?o can significantly move eastward and deepen. In addition, we also find that the evolution of thermocline is ahead of the development of the sea surface temperature for the EP El Ni?o. The strong MJO activity of the EP El Ni?o in the western and central Pacific is earlier than that of the CP El Ni?o. Measured by the standard deviation of the zonal wind square, the intensity of MJO activity of the EP El Ni?o is significantly greater than that of the CP El Ni?o before the onset of El Ni?o.     

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.     

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.     

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