1951-2006年汕头雾变化的气候特征及影响因素分析

利用1951--2006年汕头气候资料,分析大雾天气的气候变化特征。结果表明：汕头年雾日总体呈明显下降趋势。20世纪90年代以前雾日相对偏多,90年代以后雾日明显偏少;大雾的逐月变化呈1峰1谷的特征,峰值出现在3月,谷值出现在8月;下半夜至翌日上午较易出现大雾。起雾时间为04：00—07：00,其中07：00最易起雾,雾消时间为04：00-12：00,09：00-11：00雾最易消散;雾日时静风概率为52％,风速小于等于3m／s的概率超过95％,不利于近地层空气的水平交换;雾日多伴有逆温层存在且逆温层具有底高较低、厚度较厚、强度较强的特点,不利于近地层空气的上下交换,因而雾日空气质量较差。     

石河子垦区大雾气候特征及成因分析

应用石河子垦区1961-2013年的气象观测资料,分析了垦区大雾的气候特征及其成因。结果表明,石河子地区大雾天气主要发生主要出现在10月至翌年3月,南部市区和乌兰乌苏站以12月出现最多,北部莫索湾和炮台站12月最多,具有显季节特征;由北往南逐渐减少,区域差别较大。各地年大雾日数变化趋势不尽相同:石河子站以0.6 d/10a速率呈减少趋势,乌兰乌苏站、炮台站和莫索湾站分别以0.9 d/10a、3.4 d/10a和1.9 d/10a速率呈增加趋势。特殊的地理地形条件和适合的环流条件形成大雾天气。     

1951—2006年汕头雾变化的气候特征及影响因素分析

利用1951—2006年汕头气候资料,分析大雾天气的气候变化特征。结果表明:汕头年雾日总体呈明显下降趋势。20世纪90年代以前雾日相对偏多,90年代以后雾日明显偏少;大雾的逐月变化呈1峰1谷的特征,峰值出现在3月,谷值出现在8月;下半夜至翌日上午较易出现大雾。起雾时间为04:00—07:00,其中07:00最易起雾,雾消时间为04:00—12:00,09:00—11:00雾最易消散;雾日时静风概率为52%,风速小于等于3 m/s的概率超过95%,不利于近地层空气的水平交换;雾日多伴有逆温层存在且逆温层具有底高较低、厚度较厚、强度较强的特点,不利于近地层空气的上下交换,因而雾日空气质量较差。     

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。     

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。     

儋州市大雾天气变化特征及影响因素

利用海南省儋州市1979～2008年常规大雾观测资料,对该市大雾天气发生的频率等主要特征进行了分析,结果表明:儋州市年雾日30a来总体呈明显下降趋势,90年代以后雾日明显偏少;城市热岛效应、气候变暖等因素可能是90年代后儋州市大雾日数明显减少的主要原因;儋州市大雾天气以冬春季发生频率较高,大雾日的月分布呈1峰1谷的特征;相对湿度在81%以上,地表温度在11.0～30.0℃之间,风速小于3 m·s-1时易发生雾.     

近35年南澳岛大雾气候特征及其影响因素分析

利用1975～2009年南澳岛气候资料,分析了大雾的气候特征及影响因素.结果表明:汕头年雾日总体呈明显下降趋势,下降速率为每10年2d.20世纪90年代以前雾日相对偏多,90年代以后雾日明显偏少;大雾主要集中在2～4月,存在春、冬雾的特点;凌晨01:00～09:00是大雾出现的最有利时间;温度在17～26℃正变温阶段、...     

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。     

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%。     

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.     

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     

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.     

Analysis of Atmospheric Boundary Layer Height Characteristics over the Arctic Ocean Using the Aircraft and GPS Soundings

正ERRATUM to: Atmospheric and Oceanic Science Letters, 4(2011), 124-130 On page 126 of the printed edition (Issue 2, Volume 4), Fig. 2 was a wrong figure because the contact author made mistake giving the wrong one. The corrected edition has been updated on our website. The editorial office is sincerely sorry for any     

Index to Vol.31

正AN Junling;see LI Ying et al.;(5),1221—1232AN Junling;see QU Yu et al.;(4),787-800AN Junling;see WANG Feng et al.;(6),1331-1342Ania POLOMSKA-HARLICK;see Jieshun ZHU et al.;(4),743-754Baek-Min KIM;see Seong-Joong KIM et al.;(4),863-878BAI Tao;see LI Gang et al.;(1),66-84BAO Qing;see YANG Jing et al.;(5),1147—1156BEI Naifang;