夏季贝加尔湖阻塞高压的年际变化及其与我国降水的关系

采用1952—2008年NCEP/NCAR逐日500 hPa高度场再分析资料及中国国家气象中心整编的全国160站降水资料,对夏季贝加尔湖地区阻塞活动的年际变化特征及其同期与我国降水的可能联系,进行了分析。结果表明: 夏季贝加尔湖阻塞区域的阻塞活动具有明显的年际变化;在年际尺度上,夏季贝加尔湖地区阻塞活动偏多(少)对我国内蒙古东北部,华北部分地区,四川盆地与云南地区附近及广东南部的涝(旱)有一定的影响;同时对我国内蒙古中部,秦岭东北部,华北东部及川西高原的旱(涝) 也有一定的影响。     

春季海平面气压指数与我国夏季降水的关系

对近五十年全球海面平气压场和我国夏季水资料进行奇异值分解，得到海平面气压场与夏季降水的分布特征及其反映年际变化的关键区，据此分别构造了春季（3－5月）经向气压指数（IMP),纬向气压指数（IZP）。结果表明；长江中下游夏季旱涝的发生与前期春季的环流异常有关，春季IMP和IZP与长江中下游夏季降水有很好的相关性，并能有效地反映极端降水（旱／涝）情况。在强（弱）气压指数年，长江中下游地区降水偏多（少）。同时两气压指数与华南地区夏季降水有显著的负相关，这与我国东部长江中下游与华南地区降水互相关相一致。因此，春季IMP和IZP具有预报意义。     

南海夏季风强度指数及其变化特征

依据南海夏季风活动的基本特征，设计了一个动力不因子（西南风分量）与热力学因子（OLR）相结合标准化的南海夏季风强度指数Is。并计算出1975－1999年6、7、8中各月及夏季Is的数据，给出了强、弱夏季风月和年。分析了其变化特征和Is与夏季风爆发早晚，及与广东和我国降水的关系。结果表明：近35年来，南海夏季风年际变化有准10年和准3－4年变化周期。南海夏季风爆发早（晚），则该年夏季风大多偏强（弱）。南海夏季风强（弱）年，广东后汛期偏涝（旱），前汛期降水正常或偏旱（正常），我国东北、华北大部和江南大部夏季降水偏多（少），而长江中下游和华北西部以及华西偏少（多）。     

近50 a南京夏季降水的气候特征

利用南京6站近50 a(1960—2009年)的夏季逐日降水资料,应用累积距平、趋势系数、小波分析等方法,分析了南京地区夏季降水和旱、涝年的时空特征。结果表明,南京地区夏季降水量、雨日、暴雨日和暴雨日降水强度均有明显的年代际变化特征,其中降水量和暴雨日数的年代际变化基本一致;各站夏季降水量、雨日和暴雨日均呈增加趋势,总降水量的增加趋势主要是大雨及以上量级降水的贡献;夏季降水量存在2～8 a的周期变化;南京地区旱、涝年的夏季降水量与长江中下游地区有较好的一致性,与华南地区呈相反关系。     

中国南方夏季降水的年代际变率主模态特征及机理研究

在气候变暖背景下,中国南方夏季降水存在明显的年代际变化特征。本文利用1920~2014年的逐月降水,以分析南方夏季降水年代际变率主模态为切入点,以研究南方夏季降水年代际变率空间分布型的年代际变化特征为重点,进一步研究了印度洋、北太平洋及北大西洋海温的年代际变率对南方夏季降水主模态年代际变率的可能影响机制。得到的主要结论包括:（1）指出中国南方夏季降水年代际变率的两个主模态为全区一致型和东西反相型降水模态。两个主导模态在1971/1972年发生了显著的年代际转变,在1925~1971年的第一主模态为东西反相型降水;在1972~2009年的第一主模态为一致型降水。不同主模态对应的海温异常关键区也在1971/1972年发生了相应的年代际变化。（2）揭示了全区一致型和东西反相型降水模态对应的环流场异常特征。一致多（少）型降水对应着中国南海及西北太平洋低空的反气旋（气旋）性异常,有（不）利于水汽自南海向南方地区输送。而贝加尔湖东侧低空的反气旋（气旋）性异常,有（不）利于冷空气向南方输送,并与来自南海地区的水汽在南方地区辐合,有利于南方地区降水一致偏多（少）。东多西少（西多东少）型降水对应着中国东南地区高空的正（负）异常中心,有利于高空辐散（辐合）及异常的上升（下沉）运动,其与南方地区东部低空的气旋（反气旋）性异常共同作用,有利于东部降水偏多（偏少）。与此同时,低空中南半岛反气旋（气旋）性异常及菲律宾地区反气旋（气旋）性异常,不（有）利于水汽自孟加拉湾及南海地区输送向南方地区西部,有利于形成东多西少（西多东少）的降水型。（3）揭示了印度洋海温、北太平洋海温和北大西洋海温协同影响南方地区东西反相型降水和一致型降水的机制。     

贵州夏季降水异常的区域特征

利用贵州省19个测站1951～2000年夏季逐月降水资料,计算了降水量的月平均（区域平均）标准化距平。并进行模糊聚类分析、经验正交函数分解（EOF）和小波分析,研究了贵州夏季降水异常的区域特征。结果表明,贵州夏季降水在近50 a中存在5个明显的气候段：20世纪50年代前期为多雨期;50年代中期到60年代前期为少雨期;60年代中后期为多雨期;70～80年代为少雨期;90年代以后进入多雨期;降水呈增多的趋势。全省一致性是贵州夏季降水的最主要特征,同时还存在区域差异。贵州夏季降水异常有5种空间分布型,即：全省旱（涝）型、东旱（涝）西涝（旱）型、南旱（涝）北涝（旱）型、中东旱（涝）西南涝（旱）型和西南旱（涝）其余涝（旱）型。各型降水具有多时间尺度振荡的特点,存在10～12 a、4～5 a、2～3 a的周期。     

登陆我国台风与华北夏季降水的相关

利用1957-2002年华北104站月降水量、登陆我国台风频数、海平面气压场、850hPa流场、500hPa高度场资料，分析了华北地区夏季降水与登陆我国台风的关系。结果表明：华北中部和东部夏季降水量与登陆我国台风频数存在着显著的正相关，中心在华北中部的河北饶阳和保定一带。在登陆台风特多年，华北中、东部夏季降水增加（偏涝），南部降水减少（偏旱）；登陆台风特少年则相反。逐月分析发现．8月登陆我国台风频数与华北地区同期降水量相关最好，而且显著的正相关仍然位于华北中部和东部。多（少）登陆台风活动年无论是海平面气压距平场、850hPa流场距平、500hPa高度距平场还是冷空气异常，其特征均与华北中、东部夏季降水偏多（少）年相似。     

近50年浙江省旱、涝气候变化及特征

用1951－1999年资料详细研究了浙江省的年、季的旱、涝气候变化特征。主要结果：浙江省夏季降水量表现出稳定的增加，其他季节（特别是秋季）的降水有不同程度的减少。每年在浙江省出现大范围旱（或涝）的可能性很大（约80％）。1985年以后浙江每年都要发生大范围的季节性的旱涝。浙江省年、季降水量旱涝有年代际变化。旱、涝发生的气候频率已经有了明显的变化，特别是夏季。奇异谱分析与最大熵谱分析的结果表明夏季、秋季与年的降水量有明显的长期趋势变化，它们还有10年左右的周期，而冬季降水的2年周期振荡特别明显。     

江苏夏季降水特征及其与太平洋海温的关系

利用江苏省13个代表站41 a(1960-2000年)的夏季降水资料及 NCEP/NCAR月平均再分析资料,运用合成和相关分析方法考察了江苏夏季旱涝年环流特征以及夏季降水异常与太平洋海温的关系.结果表明:江苏夏季降水存在明显年际变化;江苏夏季涝(旱)年500 hPa东亚及西太平洋地区从低纬到中高纬呈"正负正"("负正负")的高度距平分布;江苏夏季降水与东亚夏季风指数反相关;江苏夏季降水的偏多(少)通常与前冬北太平洋海温偏暖(冷)有关.春季赤道东太平洋海温偏暖(冷),夏季降水偏多(少).     

2012年我国夏季降水预测与异常成因分析

本文对2012年我国夏季降水的实况和预测进行简要回顾,发现2012年夏季降水大体呈北方涝、长江旱的分布,主雨带位于黄河流域及其以北,降水异常偏多的区域主要位于西北大部、内蒙古和环渤海湾,黄淮与江淮地区降水偏少,江汉至淮河上游一带干旱严重;预测的主雨带位于华北南部至淮河,较实况偏南。对我国北方降水异常偏多的成因分析表明:2012年夏季欧亚中高纬地区阻塞高压（简称阻高）强盛,同时东北冷涡活动频繁,中高纬500 hPa高度场从西至东呈“+-+”的分布,这种环流形势没有造成长江洪涝是因为东亚夏季风异常偏强,同时西太平洋副热带高压（副高）偏北,冷暖空气对峙于我国北方地区,导致北方降水异常偏多。分析还表明阻高、东北冷涡、东亚夏季风和副高这四个系统的不同配置影响着冷暖气流的对峙位置,进而形成我国夏季的主雨带。最后通过定量和定性判断相结合的方法,选取了2012年夏季降水的最佳相似年和最佳相反年,对比分析了2012年夏季降水与其最佳相似年和最佳相反年的海温演变与东亚夏季风环流系统主要成员的差异:1959年夏季降水作为2012年夏季降水的最佳相似年,虽然海温及东亚夏季风系统关键成员异常不明显,但是和2012年也呈近似相反的特征;而1980年夏季降水作为2012年夏季降水的最佳相反年,海温及东亚夏季风环流系统关键成员和2012年呈显著的反向特征,这些观测事实反映了我国夏季降水与海温及东亚夏季风环流系统关键成员这些主要影响因子之间关系的年代际变化。     

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;