新疆大风的时空统计特征

统计了1961－1999年39a新疆90个气象观测站的气表－1资料，给出新疆大风的时空分布特征，结果表明：（1）年平均大风日数的高值区在北疆西北部，东疆和南疆西部，阿拉山口，达坂城大风最多，准噶尔盆地中心，塔里木盆地南缘最少。（2）大风年总日数的变化有明显的波动性，大部分地区80年代起大风日数有减少的趋势。（3）春，夏季大风最多，以5，6月最为频敏，大风主要出现在上年10时到午夜23时，半数以上的大风持续时间在1h以上，以0.5h以内最多。     

新疆雪暴天气的气候特征

根据新疆地面气象记录月报表，整理出1961－1999年新疆42个气象观测站的雪暴天气现象资料，统计出新疆雪暴天气的时空分布特征。结果表明：（1）新疆雪暴主要出现在除准噶尔盆地之外的北疆地区及南疆的帕米尔高原上，盆地，平原地区几乎没有雪暴发生。（2）新疆雪暴集中出现在60年代和70年代，1984年后在波动中逐年减少。新疆雪暴集中出现在10－4月，在11月，1月或4月最多。新疆全天都可能有雪暴发生，雪暴出现的时段相对集中在午后，夜晚发生的较少。新疆雪暴持续时间绝大多数在2.5h之内。     

新疆雾天气的分析

利用新疆90个站1961－1999年气表1资料，统计出新疆大雾477天，对应雾天气过程262次，给出了新疆雾常见的3种地面形势并分析了其成因。     

乌鲁木齐城区冬季雾的分布特征及其影响因素

基于乌鲁木齐站（城南）和米东站（城北）2016—2020年冬季（11月—次年2月）的雾、雾日最小能见度、逐时能见度和月平均风速、月平均静风频次、月平均相对湿度等资料，利用统计学方法，分析乌鲁木齐城区冬季雾的分布特征,探讨城南和城北雾分布差异的主要影响因素。结果显示：近5 a冬季城南平均雾日44.6 d，少于城北54.2 d，两地均为1月最多，11月最少；雾日平均最小能见度城南和城北分别为335 m和390 m，城南雾总体强于城北；城南雾高发于17时—19时，常在11时和05时消散；城北雾主要在09时和20时—22时开始，大都在14时前后结束；城南和城北的雾均以持续24 h以内为主，分别占比93.5 %和86.3 %，其中持续3 h内的雾分别占37.2 %和33.3 %，城北的雾持续时间总体长于城南。风速较小、静风较多、相对湿度较大和地理环境是城北冬季雾多于城南的主要影响因素。     

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。     

中国大陆1951—2005年雾与轻雾的长期变化

雾的记录有明确的天气指示意义。通过分析1951—2005年中国大陆743个地面气象站的资料, 对中国大陆雾、轻雾的长期变化趋势有如下认识：我国大陆雾日地理分布基本气候特征呈现东南部多西北部少的特点, 冬半年雾日数多夏半年少。各年代的差异在不同地区不尽一致。西南地区是我国雾日最多的地区，四川盆地一年有雾日20余天；华北平原和东北平原在冬春季节会出现严重的持续性雾天气。长江以南各省的轻雾日数明显多于长江以北地区，而且1980年代以后轻雾日有明显增加；西南地区是我国轻雾日最多的地区，四川盆地一年有轻雾日100余天。     

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。     

新疆夏季降水日变化特征

利用1991-2014年新疆16个国家基准气象站逐时降水资料,分析了新疆夏季不同区域降水日变化基本特征,揭示出新疆夏季降水日变化呈现显著的南、北疆区域差异,有别于我国中东部的一些新事实。结果显示:北疆降水量日变化呈现准单峰型特征,峰值主要发生在傍晚前后（16:00-20:00,地方时,下同）;南疆降水量日变化呈现三峰特征,峰值分别出现在傍晚（17:00-18:00）、午夜后（00:00-01:00）和上午（10:00）。新疆夏季降水事件以6 h以内的短历时性质为主（平均为85%,比例明显高于我国中东部）,而持续12 h以上的较长历时降水事件偶有发生;在天山东麓以外的新疆绝大部分地区,6 h以内短历时降水事件对总降水量的贡献率达54%,高于我国中东部地区。新疆西部和北疆北部降水量日变化主峰的贡献者是2~3 h短持续性降水为主的事件;而天山中-东部降水量日变化峰值则是来自于12 h内各不同持续时间降水事件的大致均等贡献。     

合肥城市发展对气候的影响

利用1968－1999年气象资料，选取肥西站作为对比站，分析了合肥市城市发展对气候的影响。结果表明：城区热岛效应使近30年城乡年平均气温差值升高到0.5℃，年平均最低气温差值升高到0.8-1.0℃，但白天受下垫面和云量影响，城市最高度增幅不明显。同时，热凫效应还使城区霜期缩短，30年来霜期缩短20－40天，城区干岛效应也使年降水量逐年相对减少（主要减少在汛期时段）。30年来年降水量相对减少60－200mm；城区空气日益干燥，每年的雾日数相对急剧减少，30年来减少10－20天。     

清河秋冬季雾变化规律及相关气象要素分析

利用清河国家气象观测站1960—2020年雾日数和2016—2020年逐时地面气象资料,分析清河雾的年、季、月变化规律和各等级雾在一日中生消变化规律、持续时长及与气象要素的相关性。结果表明：清河雾日数呈缓慢增长趋势,线性趋势率为099 d/10 a,秋冬季雾占总雾日的82%,是雾的高发季节;秋冬季浓雾和强浓雾的日变化规律基本一致,强浓雾出现频次最多,累计时间最长,特强浓雾出现频次最少;秋冬季雾的生成时间主要分布在后半夜到清晨,其次是19—20时,11—17时较少有雾生成,消散时间主要在09—12时;雾的浓度越高,持续时间越长,清河秋冬季雾持续时长在5 h以下占比最多,占48%。通过分析秋冬季雾的能见度和气象要素的相关性得出,能见度和相对湿度、10 min平均风速显著相关,当湿度大于90%时,10 min平均风速小于30 m/s时,更利于雾的生成和发展。     

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;