韶关地区春季降水的时空变化特征

根据韶关地区8个国家气象观测站1965～2011年共47年逐日降水观测资料,采用经验正交函数(EOF)分解和Morlet小波变换方法,分析了韶关地区近47年春季降水变化特征。结果表明,前3个特征向量占总方差的94.38%,基本上反映了韶关地区春季降水空间分布场的主要特征,其中第1模态为总体一致型,第2模态为南-北差异型,第3模态为东南-中-西北差异型。韶关春季降水空间分布可分为3个区,即南部区:新丰、翁源;中部区:乳源、曲江、仁化、始兴和南雄;北部区:乐昌,分区略呈西南-东北走向。EOF分解的空间场第1模态时间系数能基本反映原场随时间的演变趋势,其时间系数Morlet小波分析显示韶关地区春季降水年际变化明显,主要存在2～3年的周期振荡。     

韶关地区春季降水的时空变化特征

根据韶关地区8个国家气象观测站1965-2011年共47年逐日降水观测资料,采用经验正交函数(EOF)分解和Morlet小波变换方法,分析了韶关地区近47年春季降水变化特征.结果表明,前3个特征向量占总方差的94.38％,基本上反映了韶关地区春季降水空间分布场的主要特征,其中第1模态为总体一致型,第2模态为南-北差异型,第3模态为东南-中-西北差异型.韶关春季降水空间分布可分为3个区即南部区:新丰、翁源;中部区:乳源、曲江、仁化、始兴和南雄;北部区:乐昌,分区略呈西南-东北走向.EOF分解的空间场第1模态时间系数能基本反映原场随时间的演变趋势,其时间系数Morlet小波分析显示韶关地区春季降水年际变化明显,主要存在2～3年的周期振荡.     

近45 a东北地区春季降水异常的气候特征

利用1959-2003年我国东北地区93站春季降水资料，将降水场分成5个区域，并在此基础之上分析了春季降水的时空变化特征，发现：降水量年际变化及长期趋势有明显的区域差异，呈东多西少的分布特征；西部是旱涝易发生区，近45a来降水量略有增多；降水量的周期振荡存在明显的区域差异。     

山东省大雾的气候特征分析

利用1971-2008年山东省112个国家气象观测站（泰山站除外）雾日资料,分析了雾的时空分布及变化特征。结果表明：山东省主要有5个雾区,分别是威海市;青岛附近;鲁东南沿海地区;潍坊到莱阳的半岛内陆地区;包括菏泽、聊城、德州和滨州大部分的山东西部地区）。总体上山东雾日年变化呈现振荡中增加再降低趋势。山东区域具有显著的雾日年变化趋势一致性的特点,山东内陆多地雾日年变化与山东省年平均雾日变化有很好的相关性。辐射雾多出现在秋冬季节,海雾主要出现在春夏季节,特别是4—7月,总体上看山东以内陆辐射雾为主。     

近40 a重庆地区夏季降水的气候特征

利用重庆地区34个测站，1960-2000年6～8月降水量资料，采用EOF，REOF，小波分析及突变分析等方法，对重庆地区夏季降水量的空间分布特征和时间演变规律进行了诊断分析研究。结果表明：重庆地区夏季降水的空间分布既有整体一致的性质，也存在南部和北部及东部和西部相反变化的差异，并且可以把整个重庆地区划分为四个主要的降水分布型：渝西北区、渝东北区、渝东南区及渝西南区。各分布型代表站资料反映出近40a来，重庆地区夏季降水有增加趋势，并且存在着22a、14a、及2～4a的周期变化。     

山东省雷暴时空变化特征

根据1966-2005年山东省的雷暴资料,运用数理统计及自然正交函数展开（EOF）方法,对山东省雷暴气候特征进行了诊断分析。结果表明：山东省年雷暴日数的空间分布呈现出泰沂山区多、半岛沿海和山东西部平原较少的特点;雷暴主要发生在夏季,有明显的日变化;在过去40 a中,山东雷暴日数具有较强的年际、年代际变化,呈明显的波动下降的特点,并有一定的阶段性。     

黄淮海平原地区降水时空分布特征研究

应用时空转换的ＥＯＦ方法分析了黄淮海平原区１９６１－１９９０年３７个测站各季降水距平的特征向量场。结果表明，该区冬、春季降水的空间分布差异较小，而夏、秋季差异较大，分南北反相型和东西反相型，这在一定程度上反映了该区降水的天气气候特征。并由此划分了降水的自然区域。对每个自然区计算其降水距平百分率，根据目前常用的标准，分析了各区各季的降水时间变化序列。     

甘肃省近50a夏季极端强降水量的气候特征

利用甘肃省1960—2006年59个台站逐日降水资料,根据百分位值法定义了不同台站的极端强降水阈值,统计出了夏季（6～8月）逐年逐站极端强降水量,并进行了时空特征诊断。结果表明：一致性异常分布是甘肃省夏季极端强降水量的最主要空间模态;夏季极端强降水量的异常空间分布可分为陇南型、陇中型、河西西部型、河西东部型及陇东型5个关键区;在5个关键区中,陇南区、陇中区近47a来夏季极端强降水量表现出减少趋势,河西东部区与陇东区表现出增加趋势,而河西西部区却经历了少-多-少的抛物线型变化;另外从周期分析来看,甘肃省夏季极端强降水量近47a来在大多数分区存在11～13a和6～8a的振荡周期。     

广东年、季降水量时空变化分布特征

选取广东省86个气象观测站1966～2007年42年的逐月降水资料,应用EOF分析方法对广东省年、季降水特征进行对比分析,结果表明：年、季降水量距平第1特征向量场分布全省位相比较一致,反映全区一致偏少（多）的主要特性,雨季雨量变化的地域差异比干季大,粤西变化梯度大于粤东,山脉对这种变化分布特征的影响十分明显;上半年降水量多寡的空间变化对年雨量的多寡空间变化具有重要影响,上半年雨量的空间变化特征基本上决定了年雨量的空间变化特征。汛期降水的多寡对该年旱涝特征具有决定性影响,但也有例外。     

我国春夏季地气温差的时空变化特征及其与夏季降水的联系

利用1960~2006年我国地温、气温逐日4个时次[02:00(北京时间,下同)、08:00、14:00和20:00]的和中国降水台站观测资料以及NCEP/NCAR再分析资料,分析了我国春季(3~5月)和夏季(6~8月)地气温差的时空变化特征及其与夏季降水的联系。分析结果表明:我国春季地气温差主要存在着3种空间模态分布,第1模态表现出我国西部地区地气温差为正值,我国东部地区从南至北呈现出"-、+、-、+"空间分布特征;而第2模态则呈现"+、-、+"的空间分布特征;第3模态则表现出一致的空间分布特征。我国夏季地气温差同样存在着3种空间模态分布,第1模态表现出我国东部和西部地区夏季地气温差反相的空间分布特征;第2模态则呈现出"-、+、-"的空间分布特征;而第3模态则表现出"+、-、+、-"的空间分布特征。分析结果进一步表明我国春季和夏季地气温差第1模态与我国长江中下游地区夏季降水均存在正相关关系,而与华北地区出现负相关关系。而且,夏季更加显著。这主要是由于我国东部和西部热力差异增强,有利于在我国东部地区出现西北风异常,这说明东亚夏季风偏弱,不利于水汽向北输送,有利于华北地区降水偏少。并且,在我国东南部地区出现水汽辐合和上升运动,从而有利于我国长江中下游地区夏季降水偏多。     

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.     

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.     

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.     

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.     

Information for Authors

正AIMS AND SCOPE Atmospheric and Oceanic Science Letters (AOSL) publishes short research letters on all disciplines of the atmosphere sciences and physical oceanography. Contributions from all over the world are welcome.SUBMISSIONAll submitted     

Information for Authors

AIMS AND SCOPE Atmospheric and Oceanic Science Letters （AOSL） pub- lishes short research letters on all disciplines of the atmos- phere sciences and physical oceanography. Contributions from all over the world are welcome.