2012年5月大气环流和天气分析

2012年5月大气环流特征为:北半球极涡呈单极型,中心位于北地群岛附近,较常年偏强,中高纬环流多波动,引导多股冷空气影响我国;西太平洋副热带高压比常年同期略偏强,东亚大槽较常年同期明显偏强,南支槽位于90°E附近,较常年略偏强。2012年5月,全国平均气温为16.8℃,较常年同期(15.8℃)偏高1.0℃,与常年相比全国大部地区气温接近常年同期或偏高。全国平均降水量为66.1mm,较常年同期(64.4mm)略偏多2.63%。月内出现5次主要的降水过程。甘肃、湖南等地出现极端降水过程,云南干旱有所缓解。四川、云南、贵州和广西4省(区)出现极端高温天气;全国多个地区遭受风雹灾害。     

2012年2月大气环流和天气分析

2012年2月大气环流特征为：北半球极涡呈双极型,中心分别位于加拿大东北部和鄂霍茨克海附近,后者强度较常年平均明显偏强;中高纬环流呈经向型,乌拉尔山附近维持阻塞高压活动,强度偏强;平均南支槽位于90°E附近,接近多年平均位置,强度偏弱。2012年2月全国平均气温-3.2℃,较1月-7.2℃明显回暖,但比常年同期（-2.0℃）偏低1.2℃。全国平均降水量为14.5 mm,较1月16.8 mm减少2.3 mm,较常年同期（17.6mm）偏少3.1 mm。月内出现三次全国范围的中等以上强度冷空气过程,以及三次降水过程。西藏南部出现极端降水事件,云南和四川西南部的气象干旱发展,南方大部继续低温阴雨寡照天气。     

2011年11月大气环流和天气分析

2011年11月大气环流主要特征是：北半球极涡呈单极性分布,主体位于北极圈内,强度和常年同期接近。中高纬呈4波型,其中欧洲大槽强度较常年偏强,位置偏东;东亚大槽强度偏弱,且位置偏东,这不利于引导冷空气影响我国,月内我国出现了5次主要的冷空气过程,11月全国平均气温为3.9℃,比常年同期（2.1℃）偏高1.8℃。南支槽位于100°E附近,强度接近常年,副高的位置偏西,有利西南暖湿空气向我国的输送。11月,我国出现6次主要的降水过程,全国平均降水量为28.3mm,较常年同期（18.0 mm）偏多57.3%,为近20年来的最大值。北方大部地区降水较常年偏多,其中西北地区东部、内蒙古南部、华北西部、江南东南部和华南中东部较常年同期偏多2倍以上。     

2014年6月大气环流和天气分析

2014年6月大气环流特征为:北半球极涡呈双极型,较常年略偏强;西太平洋副热带高压强度较常年同期偏强,南海季风爆发较常年偏晚3候。2014年6月,全国平均气温为20.5℃,较常年同期(20.0℃)偏高0.5℃,与常年相比全国大部地区气温接近常年同期或偏高。南方地区人梅开始晚,降雨量偏少,江淮地区出现空梅。江淮、黄淮、长江中下游降水偏少20%以上。全国平均降水量为100.7 mm,较常年同期(99.3 mm)略偏多1.4%。月内出现4次主要的降水过程。南方强降水天气较多,多地遭受洪涝灾害,云南干旱有所缓解,东北、华北等地多阵性降水;全国22个省(区、市)遭受风雹灾害。全国101个站发生极端高温事件。今年第7号台风海贝思在广东登陆,是今年第一个登陆我国的台风。     

2014年1月大气环流和天气分析

2014年1月大气环流主要特征如下：北半球极涡呈偶极型分布,位置较常年同期明显偏南;欧亚中高纬环流经向度较小;南支槽平均位置位于70°E附近,位置较常年同期明显偏西;同时,西太平洋副热带高压较常年同期略偏弱。1月,全国平均降水量为6.2 mm,较常年同期（13.2 mm）偏少53.0%。全国平均气温为-3.4℃,较常年同期（-5.0℃）偏高1.6℃。月内,我国出现1次主要冷空气过程和4次主要降水过程。北方冬麦区降水偏少,气象干旱持续;中东部地区出现大范围雾或霾天气;南方地区出现低温雨雪天气     

2012年4月大气环流和天气分析

2012年4月大气环流特征为:北半球极涡呈单极型分布,强度接近常年;中高纬度环流呈4波型分布,乌拉尔山阻塞高压尤其偏强,有利于冷空气影响我国;中低纬度地区南支槽前多波动,西太平洋副热带高压强度稍偏强。4月全国平均温度为11.7℃,较常年同期(10.7℃)偏高1.0℃。全国平均降水量为45.6 mm,较常年同期(43.1 mm)偏多2.5 mm(偏多5.8%)。月内我国共出现了8次大到暴雨降水过程,华南、江南多强对流并伴随雷暴大风和冰雹天气,北方出现6次沙尘过程。     

2015年6月大气环流和天气分析

2015年6月大气环流特征为：北半球极涡呈双极型,较常年略偏强;西太平洋副热带高压强度接近常年同期。2015年6月,全国平均气温为20.3℃,较常年同期（20.0℃）偏高0.3℃,北方（南方）地区气温总体较常年同期偏低（高）。全国平均降水量为104.8 mm,较常年同期（99.3 mm）略偏多5.5%。月内出现8次主要的降水过程。江淮、江南北部等地强降水天气较多,多地遭受洪涝灾害,云南西部干旱发展,海南干旱有所缓解,东北地区等地多阵性降水;全国21个省（区、市）遭受风雹灾害。全国64个站发生极端高温事件。今年第8号台风鲸鱼在广东登陆,是今年第一个登陆我国的台风。     

2019年1月大气环流和天气分析

2019年1月大气环流主要特征为：北半球极涡呈偶极型分布,环流呈三波型,南支槽平均位置大致位于90°E附近,同时,西太平洋副热带高压较常年同期位置偏西,强度偏强。本月,全国平均降水量为14.0 mm,较常年同期（13.5 mm）偏多4%,月内出现三次较强降水过程,江南和西南部分地区降水明显,全国共有35站日降水量达到极端事件标准。全国平均气温为-4.1℃,较常年同期（-5.0℃）偏高0.9℃,共出现4次冷空气过程。本月共发生2次大范围雾 霾天气过程。     

2013年11月大气环流和天气分析

2013年11月大气环流主要特征是：北半球极涡呈单极型分布,主体位于北极圈内,强度较常年同期偏强;欧亚中高纬环流经向度较小;南支槽平均位置大致位于80°E附近,同时,副热带高压较常年同期偏强,位置偏西、偏北。11月,全国平均降水量为22.5 mm,较常年同期（18.8 mm）偏多19.7%。全国平均气温为3.6℃,较常年同期（2.9℃）偏高0.7℃。月内,我国出现3次冷空气过程和6次较强降水过程。东北地区出现了两次强降雪过程,部分地区出现暴雪到大暴雪;台风海燕给华南地区带来较大风雨影响;北方冬麦区及江南大部出现明显降水,干旱缓解;由于冷空气势力弱,中东部地区雾霾天气频发,全国平均雾霾日数为4.3天,为1961年以来历史同期最多。     

2015年1月大气环流和天气分析

2015年1月大气环流主要特征为：北半球极涡呈偶极型分布,中心气压均较常年偏低。欧亚中高纬环流呈三波型,以纬向环流为主;西太平洋副热带高压和南支槽的强度接近常年平均水平。1月全国平均气温为-3.1℃,较常年同期（-5.0℃）偏高1.9℃,为1961年以来同期最高。全国平均降水量14.4 mm,较常年同期(13.2 mm)偏多气候中心原数据为9.0,但计算为9.1,但分布极不均匀,西南地区、西北地区和华南南部降水偏多,而华北至江南一带则明显偏少,北京等地几乎无降水。月内仅在上旬出现了1次全国范围中等强度冷空气过程,而降水过程有5次。上旬后期,云南等地出现了创历史极值的强雨雪天气,气象干旱得到有效缓解。月末中东部地区出现入冬以来最大范围雨雪过程,贵州等地出现冻雨。中东部月内共出现3次大范围雾 霾天气。     

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.     

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.     

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.     

全球贸易隐含碳变化及其影响分析

基于最新的GTAP8 (Global Trade Analysis Project）数据库,使用投入产出法,分析了2004年到2007年全球贸易变化下南北集团贸易隐含碳变化及对全球碳排放的影响。结果显示,随着发展中国家进出口规模扩张,全球贸易隐含碳流向的重心逐渐向发展中国家转移。2004年到2007年,发达国家高端设备制造业和服务业出口以及发展中国家资源、能源密集型行业及中低端制造业出口的趋势加强,该过程的生产转移导致全球碳排放增长4.15亿t,占研究时段全球贸易隐含碳增量的63%。未来发展中国家的出口隐含碳比重还将进一步提高。贸易变化带来的南北集团隐含碳流动变化对全球应对气候变化行动的影响日益突出,发达国家对此负有重要责任。     

Retrieval of outgoing longwave radiation from COMS narrowband infrared imagery

Hourly outgoing longwave radiation(OLR) from the geostationary satellite Communication Oceanography Meteorological Satellite(COMS) has been retrieved since June 2010. The COMS OLR retrieval algorithms are based on regression analyses of radiative transfer simulations for spectral functions of COMS infrared channels. This study documents the accuracies of OLRs for future climate applications by making an intercomparison of four OLRs from one single-channel algorithm(OLR12.0using the 12.0 μm channel) and three multiple-channel algorithms(OLR10.8+12.0using the 10.8 and 12.0 μm channels; OLR6.7+10.8using the 6.7 and 10.8 μm channels; and OLR All using the 6.7, 10.8, and 12.0 μm channels). The COMS OLRs from these algorithms were validated with direct measurements of OLR from a broadband radiometer of the Clouds and Earth's Radiant Energy System(CERES) over the full COMS field of view [roughly(50°S–50°N, 70°–170°E)] during April 2011.Validation results show that the root-mean-square errors of COMS OLRs are 5–7 W m-2, which indicates good agreement with CERES OLR over the vast domain. OLR6.7+10.8and OLR All have much smaller errors(～ 6 W m-2) than OLR12.0and OLR10.8+12.0(～ 8 W m-2). Moreover, the small errors of OLR6.7+10.8and OLR All are systematic and can be readily reduced through additional mean bias correction and/or radiance calibration. These results indicate a noteworthy role of the6.7 μm water vapor absorption channel in improving the accuracy of the OLRs. The dependence of the accuracy of COMS OLRs on various surface, atmospheric, and observational conditions is also discussed.     

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;