商丘市气温日较差变化特征分析

利用商丘市8站1961-2006年逐日最高、最低气温资料,计算并分析了商丘市年、季及月气温日较差的线性变化趋势,结果表明:年最低气温呈明显上升趋势,最高气温上升趋势不显著,气温日较差呈显著减小趋势;春、夏、冬季季平均气温日较差均呈减小趋势,其中春季和冬季的减小趋势显著,秋季呈弱的增加趋势;全年有10个月的气温日较差呈减小趋势,其中1、3、5、8月气温日较差显著减小,1月减小幅度最大.城市化发展对气温日较差变化有一定影响,距城区较近的台站平均气温日较差显著减小,而距城区较远的台站气温日较差减小趋势不显著.     

城市化进程对气温变化的分析——以北京、郑州、南京、杭州城市为例

基于城市热岛效应概念的基础之上,针对北京、郑州、南京、杭州4个城市,采用以城郊对比方法(UMR)为主,结合相关系数权重方法来建立对比站序列,分析1980—2009年30a内年平均气温、日较差年平均气温、最高气温年平均、最低气温年平均的变化特点,以及这4个要素20世纪80、90年代和21世纪00年代上升或下降趋势的比较,分析了城市化与气候变化之间的关系。结果表明:城市化使得城市有明显的增温效应,对最低气温年平均的增温效果最为明显,年平均气温次之,最高气温年平均最不明显;日较差年平均气温有下降趋势,表明城市化使得夜间气温上升,日较差减小;在年代际的比较中20世纪90年代的城市化对气温的影响最为显著。     

城市化对华北地区最高, 最低气温和日较差变化趋势的影响

利用华北地区255个一般站和国家基本、 基准站1961\_2000年的实测资料, 经过质量检验和均一性订正后, 将所有台站根据人口和台站地理位置分为5个类别, 分析了这5个类别台站和国家基本、 基准站地面平均气温、 最高、 最低气温的年和季节变化趋势以及城市化影响。结果表明： 华北全部台站的年平均气温、 最高、 最低气温均呈增加趋势, 且以最低气温上升最为明显, 导致年平均日较差呈现明显下降。就城市化影响而言, 平均气温、 最低气温变化趋势中城市热岛效应加强因素的影响明显, 但城市化对最高气温趋势影响微弱, 个别台站和季节甚至可能造成降温。在国家基本、 基准站观测的年平均气温和年平均最低气温上升趋势中, 城市化造成的增温分别为0.11℃·(10a)-1和0.20℃·(10a)-1, 对全部增温的贡献率分别达39.3%和52.6%。各类台站的四季平均气温和最低气温序列中城市化影响均造成增温。城市化增温以冬季为最大, 夏季最小。城市化还导致乡村站以外的各类台站日较差减小, 近40年华北地区国家基本、 基准站年平均和秋、 冬季平均气温日较差明显下降均由城市化影响造成的。     

云南不同气候带极端气温变化趋势

利用1975-2008年云南6个气候代表站的最高气温、最低气温和平均气温资料,分析了气温序列的变化趋势,探讨了可能的形成原因.结果显示,各代表站的气温总体上呈上升趋势,但气温变化率的大小与其所处的气候带之间没有明显的规律性差异.代表高原气候的香格里拉站其气温增暖趋势最突出,代表北亚热带、中亚热带和南亚热带站点的气温与其有相似的变化趋势,主要表现为最低气温增温速率最大,平均气温次之,最高气温最小,而气温日较差(DTR:Differenceof Temperature Range)有明显减小趋势.温带和北热带代表站气温的变化则有不同的特征,温带的变化表现为最高气温显著上升,最低气温上升速率略小于平均气温的上升速率,DTR变化不显著;北热带的变化表现为平均气温增温速率最大,最低气温次之,最高气温最小,DTR变化不显著.对各站相对暖日、冷日、暖夜、冷夜和霜日的逐年统计分析表明,总体上云南的冷事件在减少,而暖事件增多.     

华中区域1960～2005年平均最高、最低气温及气温日较差的变化特征

利用华中区域(河南、湖北、湖南3省)42站1960～2005年逐月平均最高、最低气温资料,计算并详细分析了该区域年(季、月)平均最高、最低气温和气温日较差的线性变化趋势、突变性及周期性特征。结果发现:1)华中区域年平均最高、最低气温均呈现上升趋势,年平均气温日较差呈减小趋势,其中年平均最低气温变化最显著。2)平均最高气温在春、秋、冬均呈上升趋势;平均最低气温四季均呈上升趋势,其中春、冬季变化显著;平均气温日较差在夏、冬季下降趋势较为明显,其中以冬季降幅最大。3)全年有4个月平均最高气温呈下降趋势,其中8月最为显著;平均最低气温在冬、春季为明显上升趋势,其他月变化趋势不显著;平均气温日较差在冬、夏季呈明显下降趋势,其中1月最为显著。4)年平均最高、最低气温在20世纪90年代经历了一次由冷变暖的明显突变;四季中,平均最高气温春、冬季突变显著,平均最低气温春、夏季突变显著。5)年平均最高、最低气温存在显著的2～4a周期变化。     

东北平原腹地城市化与城市热岛的可能驱动因子

用近50年逐日气象观测记录和城市发展统计资料分析了东北平原腹地3个不同规模城市,即大城市长春,中等城市延吉和山区小城镇临江的年和季节平均气温、最高最低气温等,发现其差别很大,其排序不一定同城市化速率(人口和国内生产总值GDP)、土地利用或能见度趋势对应。但气温日较差趋势均与城市化速率成比例。3个城市气温趋势均表现为变暖,且最低气温趋势大于最高气温趋势;冬季变暖强于夏季。这说明影响夜间或冬季气温的才是城市热岛的主要驱动因子。冬、夏最高气温趋势排序一致,除临江外,最低气温趋势排序与最高气温相反。临江冬季最低气温趋势最强,可能与其特殊地理环境造成的弱风气候有关。最低气温趋势排序与城市化速率排序大致相同,最高气温趋势排序相反。综合对比分析表明,这3个城市气候变暖差异的主要因子不可能是土地利用,而是城市人为热释放和空气污染,后者主要影响夜间的射出长波辐射。二者的影响范围趋势应该同城市化速率成比例,对气温趋势有明显影响。其次,不能忽略地理环境因子对气温趋势及其排序的影响。不过,气温日较差在一定程度上可以削弱城市地理因素等的影响,其趋势是一个反映城市化对气候影响的的合适指标。研究还表明,需要重视空气污染对长波辐射的影响并应该在数值模式辐射参数化中加以考虑。     

城市化对宁波地区极端气温及人体舒适度的影响

利用浙江省沿海宁波市鄞州站(城区)和石浦站(海岛)1956—2018年逐日最高气温、最低气温、相对湿度和风速资料,结合宁波1978—2017年城市化进程参数,研究城市化进程对人体舒适度气象指数(BCMI)及相关气象要素的影响。结果表明:(1)宁波沿海城市城区和海岛年平均最高气温、最低气温均呈增高趋势,城市化进程对城区最高气温、最低气温增幅的贡献率分别为32.3%、48.8%;(2)城市化导致城区年平均相对湿度呈减小趋势,海岛站相对湿度变化不明显;(3)城区和海岛年平均风速均呈减小趋势,城区风速突变年份相对更早,但风速的减小主要是气候自然变化所致,与城市化进程关系不大;(4)气温对人体舒适度指数BCMI的影响最大,城区夏季和冬季极端气温下的BCMI均表现出增大趋势,夏季往炎热不舒适方向发展,冬季则往舒适方向发展;(5)宁波城市化进程参数K与城区BCMI表现出明显的正相关性,城市化进程对城区夏季最高气温和冬季最低气温增幅的贡献率分别为57.8%和46.1%。     

全球地表气温日较差长期趋势的演化特征

基于多维集合经验模态分解方法,利用CRU全球地表气温日较差、最高气温和最低气温资料,开展对1951~2019年全球地表气温日较差长期趋势的演化研究。结果表明:全球地表气温日较差主要呈现下降趋势,并以北半球中高纬度地区为甚,空间差异性较大。全球地表最高气温和最低气温的长期演化趋势则主要表现为上升,且最低气温更为剧烈。从纬向平均的角度看,全球地表气温日较差长期趋势主要存在五条负值带,降低趋势在北半球逐渐加强,并存在与之相伴随的冷舌,而在南半球呈先增强后减弱的特征。由瞬时演化速率可知,全球地表气温日较差长期趋势下降的速率整体上趋于减缓,并于后期在澳洲北部、地中海沿岸以及南美洲南部等地转为上升趋势。      

1981-2015年招远市气温变化征分析

文章利用招远国家气象观测站1981-2015年气温观测资料,运用趋势滑动平均和线性倾向估计方法对招远气象站气温变化特征进行了分析.结果表明:1981-2015年招远气象站年平均气温以0.22℃/10a的速率呈明显上升趋势,且具有明显的阶段性变化特征.季节平均气温均以不同速率上升,冬季气温上升趋势最为显著.年平均最高气温、最低气温均呈上升趋势,年平均最低气温上升趋势最显著.受热岛效应的影响,年平均最低气温的上升速率远大于年平均最高气温.除夏季平均最高气温呈缓慢下降趋势,其他季节均呈上升趋势,且冬季平均最低气温上升趋势最为明显.平均最低气温的快速上升使得气温日较差呈减小趋势.     

1981—2015年招远市气温变化特征分析

文章利用招远国家气象观测站1981—2015年气温观测资料,运用趋势滑动平均和线性倾向估计方法对招远气象站气温变化特征进行了分析。结果表明:1981—2015年招远气象站年平均气温以0.22℃/10a的速率呈明显上升趋势,且具有明显的阶段性变化特征。季节平均气温均以不同速率上升,冬季气温上升趋势最为显著。年平均最高气温、最低气温均呈上升趋势,年平均最低气温上升趋势最显著。受热岛效应的影响,年平均最低气温的上升速率远大于年平均最高气温。除夏季平均最高气温呈缓慢下降趋势,其他季节均呈上升趋势,且冬季平均最低气温上升趋势最为明显。平均最低气温的快速上升使得气温日较差呈减小趋势。     

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.     

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.     

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.     

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