1981—2013年6—7月江淮地区切变线及暴雨统计分析北大核心CSCD

为了研究20世纪80年代以来的江淮切变线及暴雨的气候态特征,从而为未来的江淮切变线暴雨的业务预报和科研提供参考,利用欧洲中心风场再分析资料和地面气象站基本气象要素日值数据集(V3.0)的降水资料,通过纬向风的经向切变、相对涡度和纬向0风速线3个客观判据,统计了1981—2013年6—7月江淮地区暴雨、切变线以及切变线暴雨。结果表明:1981—2013年6—7月,江淮地区有30.2 d出现暴雨,有33.2 d出现切变线,22.0d出现切变线暴雨,切变线暴雨日数占切变线日数的近2/3,占暴雨日数的近3/4;6—7月江淮地区出现切变线和暴雨的日数有不显著的年际增长趋势,增长率比江淮切变线暴雨大一个量级,而后者的日数在近33年基本维持不变。江淮地区的切变线日数、暴雨日数和切变线暴雨日数2000年前年际波动较大,2000年后年际波动较小。6—7月江淮地区的暴雨日数、切变线日数和切变线暴雨日数均存在一定的年代际变化特征,且三者的年代际变化特征较为一致,在1981—2007年,江淮地区降水量的年代际变化与暴雨日数、切变线日数和切变线暴雨日数的年代际变化较为一致。1995年前,6—7月江淮切变线暴雨日数存在2—3年的周期,1995年后没有显著的周期。在6月上中旬和7月中下旬,江淮切变线暴雨日数存在2—4 d的周期,在6月下旬到7月上旬,江淮切变线暴雨日数不存在明显周期,切变线暴雨日数在梅雨期内稳定维持,且江淮切变线暴雨最集中发生在6月下旬到7月上旬的梅雨期内,说明梅雨期降水以切变线引发的降水为主。     

1981—2013年6—7月江淮地区切变线及暴雨统计分析北大核心CSCD

为了研究20世纪80年代以来的江淮切变线及暴雨的气候态特征,从而为未来的江淮切变线暴雨的业务预报和科研提供参考,利用欧洲中心风场再分析资料和地面气象站基本气象要素日值数据集(V3.0)的降水资料,通过纬向风的经向切变、相对涡度和纬向0风速线3个客观判据,统计了1981—2013年6—7月江淮地区暴雨、切变线以及切变线暴雨。结果表明:1981—2013年6—7月,江淮地区有30.2 d出现暴雨,有33.2 d出现切变线,22.0d出现切变线暴雨,切变线暴雨日数占切变线日数的近2/3,占暴雨日数的近3/4;6—7月江淮地区出现切变线和暴雨的日数有不显著的年际增长趋势,增长率比江淮切变线暴雨大一个量级,而后者的日数在近33年基本维持不变。江淮地区的切变线日数、暴雨日数和切变线暴雨日数2000年前年际波动较大,2000年后年际波动较小。6—7月江淮地区的暴雨日数、切变线日数和切变线暴雨日数均存在一定的年代际变化特征,且三者的年代际变化特征较为一致,在1981—2007年,江淮地区降水量的年代际变化与暴雨日数、切变线日数和切变线暴雨日数的年代际变化较为一致。1995年前,6—7月江淮切变线暴雨日数存在2—3年的周期,1995年后没有显著的周期。在6月上中旬和7月中下旬,江淮切变线暴雨日数存在2—4 d的周期,在6月下旬到7月上旬,江淮切变线暴雨日数不存在明显周期,切变线暴雨日数在梅雨期内稳定维持,且江淮切变线暴雨最集中发生在6月下旬到7月上旬的梅雨期内,说明梅雨期降水以切变线引发的降水为主。     

1981—2013年6—7月江淮地区切变线及暴雨统计分析

为了研究20世纪80年代以来的江淮切变线及暴雨的气候态特征,从而为未来的江淮切变线暴雨的业务预报和科研提供参考,利用欧洲中心风场再分析资料和地面气象站基本气象要素日值数据集(V3.0)的降水资料,通过纬向风的经向切变、相对涡度和纬向0风速线3个客观判据,统计了1981—2013年6—7月江淮地区暴雨、切变线以及切变线暴雨。结果表明:1981—2013年6—7月,江淮地区有30.2 d出现暴雨,有33.2 d出现切变线,22.0 d出现切变线暴雨,切变线暴雨日数占切变线日数的近2/3,占暴雨日数的近3/4;6—7月江淮地区出现切变线和暴雨的日数有不显著的年际增长趋势,增长率比江淮切变线暴雨大一个量级,而后者的日数在近33年基本维持不变。江淮地区的切变线日数、暴雨日数和切变线暴雨日数2000年前年际波动较大,2000年后年际波动较小。6—7月江淮地区的暴雨日数、切变线日数和切变线暴雨日数均存在一定的年代际变化特征,且三者的年代际变化特征较为一致,在1981—2007年,江淮地区降水量的年代际变化与暴雨日数、切变线日数和切变线暴雨日数的年代际变化较为一致。1995年前,6—7月江淮切变线暴雨日数存在2—3年的周期,1995年后没有显著的周期。在6月上中旬和7月中下旬,江淮切变线暴雨日数存在2—4 d的周期,在6月下旬到7月上旬,江淮切变线暴雨日数不存在明显周期,切变线暴雨日数在梅雨期内稳定维持,且江淮切变线暴雨最集中发生在6月下旬到7月上旬的梅雨期内,说明梅雨期降水以切变线引发的降水为主。      

1960—2018年佛冈县前汛期极端降水的变化特征

利用1960—2018年佛冈县国家观测站4—6月的逐日降水量资料,对前汛期极端降水进行年际和年代际变化分析,结果表明:近59年佛冈县前汛期极端降水量、强度和日数均呈弱的下降趋势。近59年佛冈县前汛期共出现30次连续性极端降水,最长持续时间为3d。前汛期极端降水强度和日数分别在1988和1969年发生减少突变,极端降水量未发生突变。前汛期极端降水量、强度和日数均存在5~8年的年际周期振荡,年代际周期变化则存在差异。     

贵州夏季暴雨的气候特征

利用贵州52个测站的1961-2006年历年夏季（6-8月）逐日降水资料,分析了贵州夏季暴雨的时空分布特征、周期振荡及其突变特征。结果表明：46 a来贵州夏季暴雨量呈增加趋势,并存在明显的年际、年代际变化特征;暴雨日数和暴雨量在1985年发生突变;暴雨日数和暴雨量均存在15 a和准10 a的周期振荡;暴雨日数和暴雨量EOF分解的第一特征向量的荷载场空间分布基本一致,表明全省呈偏多（少）的一致型同位相分布。     

贵州夏季暴雨的气候特征

 利用贵州52个测站的1961-2006年历年夏季（6-8月）逐日降水资料,分析了贵州夏季暴雨的时空分布特征、周期振荡及其突变特征。结果表明：46 a来贵州夏季暴雨量呈增加趋势,并存在明显的年际、年代际变化特征;暴雨日数和暴雨量在1985年发生突变;暴雨日数和暴雨量均存在15 a和准10 a的周期振荡;暴雨日数和暴雨量EOF分解的第一特征向量的荷载场空间分布基本一致,表明全省呈偏多（少）的一致型同位相分布。     

近50a江淮梅雨期暴雨的区域特征

利用江淮地区44个站1954-2003年近50a逐日降水资料，采用模糊聚类和经验正交函数分解（EOF）方法分析了江淮梅雨期暴雨的时空变化特征。结果表明：梅雨期暴雨量存在显著的区域差异，可以分为南、北两个区；梅雨期暴雨量具有显著的候际、月际、年际和年代际变化特征，南区暴雨量峰值出现在6月第5候，北区峰值出现在6月第5候和7月第1候；南区暴雨贡献率大于北区，而北区暴雨集中率大于南区；南区暴雨量长期变化呈显著的上升趋势，而北区变化不明显；南北两区暴雨量均存在不同时间尺度的振荡周期。     

广西近50年暴雨日数变化的小波分析

利用1951—2005年广西降水资料,使用Morlet小波分析方法分析了暴雨和大暴雨日数的年际变化,结果表明均具有明显的周期变化规律特征,都存在着2、4、8、14年周期振荡信号,其中2年周期振荡信号最强,其次为4,8,14年,各周期信号分布的时间区域略有差异。广西暴雨日数变化总趋势为逐渐增多,在1990年代进入高峰期。影响广西暴雨发生的主要天气系统为热带气旋、西南低涡、低空急流、高空槽,其中热带气旋、西南低涡是广西特大暴雨发生的主要原因。     

1960-2005年长江中下游极端降水指数变化特征分析

本文利用长江中下游流域内的81个气象站,对长江中下游极端降水指数的时空变化特征进行分析.结果发现,在时间尺度上,长江中下游地区近46 a来极端降水指数呈上升趋势,其中降水强度上升趋势最明显,各个极端降水指数在年代际尺度上具有相同的变化特征,均存在着12 a左右的周期振荡,在年际尺度上,各极端降水指数变化周期并不一致.大雨日数与其他指数相比突变时间比较早,发生在1979年,其他几个指数突变时间比较接近,出现在1990年前后.在空间变化上,除极端湿天降水量在全区均为上升趋势外,其他几种极端降水指数在江苏东部地区、湖北西北部都存在着极端降水指数的负变化趋势,高值区主要分布在江西大部、湖北东南部、湖南东北部地区.     

近53年江淮流域梅汛期极端降水变化特征

基于1961—2013年江淮流域梅汛期（6—7月）逐日降水资料,利用百分位法确定极端降水阈值,对江淮流域梅汛期极端降水的时空分布及突变特征进行分析,结果表明：95%分位极端降水阈值多在50 mm以上,大值中心主要位于湖北东部到安徽南部一带;平均极端降水强度与阈值大小的空间分布相似。极端降水量和极端降水日数整体呈现由安徽南部向四周递减的空间分布特征,极端降水量约占梅汛期降水总量的1/4~1/3。从季节内分布上看,极端强降水站次在梅汛期呈单峰型分布,各候间差异明显,其中6月第5候到7月第2候最多。极端降水量、极端降水日数以及极端降水量占梅汛期总降水量百分比均具有明显的年际变化,且上升趋势显著;江淮流域梅汛期极端降水量和极端降水站次的这种上升趋势均在1980年发生突变。     

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.