顺德热岛强度变化及其与城市化发展的关系

根据顺德、鹤山国家气象观测站1984—2013年的气温资料,采用城郊对比法,分析了顺德城区热岛强度的变化特征,初步探讨了热岛强度与城市化发展的关系。结果表明,顺德的热岛强度基本在0.5℃左右,热岛强度的高值体现在平均气温和最低气温上;热岛强度具有明显的季节变化和月变化特征,冬季最强,达0.6℃,夏季最弱,仅为0.3℃,12月最大,达0.7℃,4、5月最小,只有0.3℃;年代际变化上热岛强度呈显著增加趋势,年平均气温计算的热岛强度增幅为0.014℃/年。     

北京夏季高温变化特征及对城市热岛强度的影响

随着全球气候变暖和快速城市化,城市夏季高温及热浪出现频次和强度明显增加,但人口高度集聚的特大城市中夏季高温长期变化特征对城市热岛的影响程度和作用机制仍不甚明了。本文选择京津冀特大型城市群的核心城市北京为研究对象,基于长期气象观测数据计算夏季高温和城市热岛强度,阐明5—8月夏季高温长期变化特征及对城市热岛强度的影响。研究发现,1978—2020年北京城区夏季高温日数、强度和极端高温均呈现显著增加趋势,相伴随的是高温起始时间明显提前,结束时间显著推迟;高温天最高气温热岛强度呈显著降低趋势,而平均气温和最低气温热岛强度则呈轻微下降趋势;5—8月高温天最高、平均和最低气温多年平均热岛强度分别为0.73 ℃、1.61 ℃和2.40 ℃,明显高于非高温天的0.09 ℃、0.80 ℃和1.40 ℃,高温和非高温天热岛强度差值均在0.6 ℃以上,表明夏季高温放大城市热岛强度。预估未来全球变暖和快速城市化背景下北京城市热岛效应将进一步加剧,会形成更频繁和持续更长的夏季高温,给城市居民带来严重的健康风险。     

西宁城市热岛效应分析

西宁作为青藏高原最大的城市,近年来随着城市化的发展,城市热岛效应及其所带来的影响日益明显。本文利用西宁市城市和郊区气象观测站逐小时气温观测资料,分析了西宁市平均气温、最高气温和最低气温日内、候平均热岛强度变化特征,结果显示:(1)相对于郊区,西宁城区平均气温日内变化幅度较小,16—17时(北京时,下同)表现为弱的冷岛效应,冷岛强度为0.034℃,日出前的06—07时热岛强度表现最强,热岛强度最高可达3.01℃;(2)春季和夏季一天中均为热岛效应,且热岛效应日内变化幅度较小,分别为2.76℃和2.12℃。秋季和冬季在日出前的07—08时热岛强度最强,分别为2.89℃和4.14℃,秋季16—17时和冬季15—17时表现为冷岛效应,最大冷岛强度分别为0.34℃和0.53℃;(3)西宁城区1月第3候热岛强度最强为3.40℃,7月第2候热岛强度最弱为1.07℃。其中白天在1月第3候热岛强度最强为0.88℃,9月第1候最弱为0.13℃,热岛强度年内变幅较小仅为0.75℃,而夜晚在1月第3候最强为5.93℃,7月第2候最弱为1.62℃,热岛强度年内变化幅度达4.30℃;(4)西宁城区候平均最高气温在春季和夏季表现为热岛效应,热岛强度平均为0.58℃,而在秋冬季表现为冷岛效应,冷岛强度分别为1.84℃。候平均最低气温全年均表现为热岛效应,其中夏季相对较弱为3.22℃,冬季表现最强达到5.11℃。     

2014-2017年西安市城市热岛、冷岛精细化时空特征分析

选取1971—2017年7个国家级气象站的气温资料,分析年代际气温变化特征及城郊温差、城县温差;选取2014—2017年103个国家考核区域气象站及7个国家级气象站逐时气温资料,利用标准化相对气温法,研究西安市城市热岛、冷岛的年、季平均空间分布特征,以及逐日热岛、冷岛变化规律。结果显示:1971—2017年城区、郊区和郊县气温均呈上升趋势,城区增温速率最大,郊县增温速率最小,进入21世纪后,城市热岛效应较为显著。西安市城市热岛、冷岛现象明显,且均呈"多中心"特征,热岛中心多为老城区及旅游中心,建筑物面积和人口密度占绝对优势;冷岛中心多为地势较高、水域绿被覆盖较大、非人口密集区的秦岭坡脚线附近。城区代表站的年、春季、夏季、秋季基本处于平稳状态,年、春季、夏季06—07时热岛强度最大,秋季、冬季23时热岛强度最大;郊区代表站和郊县代表站的年及四季热岛、冷岛强度均有明显的日变化特征,且变化趋势相反;郊区代表站10时热岛转为冷岛,春、夏季16—17时转为热岛,年及秋、冬两季19—20时转为热岛;郊县代表站年、春季、夏季06—07时冷岛强度最大,秋季、冬季2时冷岛强度最大,08时后冷岛开始减弱,12—13时为最弱后开始增强。     

兰州市近50年城市热岛强度变化特征

利用1956-2005年兰州市日平均气温、日最高气温和日最低气温,分析了近50年兰州市城市热岛效应变化,并利用城区和郊区3种气温的倾向率计算了城市热岛强度倾向率和热岛增温贡献率。结果表明：1956-2005年兰州市3种气温的城郊差均呈逐年上升趋势,平均气温、最高气温和最低气温的倾向率分别为每10年0.371℃、0.169℃和0.654℃,其中,最低气温的城郊差上升最明显。近50年兰州市增温主要发生在后25年（1981-2005年）,前25年除城区最低气温外基本上以降温为主。后25年中,城区年平均气温、最高气温和最低气温倾向率分别为每10年0.789℃、0.997℃和0.625℃,郊区则相应为每10年0.493℃、0.790℃和0.077℃,其中最高气温增温最显著,最低气温增温最少;以年平均、最高和最低气温表示的城市热岛强度的倾向率分别为每10年0.395℃、0.188℃和0.674℃,热岛效应对城区增温的贡献率分别达到87.0%、49.6%和100%。冬季城市和郊区的平均气温和最低气温倾向率最大,但热岛增温贡献率最大的是春、夏季气温,而不是冬季气温;这可能主要与兰州市冬季严重的空气污染有关, 因为其对城市热岛有一定的抑制作用。20世纪80年代以后兰州市热岛效应有增强的趋势,但平均气温和最高气温的热岛增温贡献率除个别季节外有所下降。     

乌鲁木齐城市热岛强度的日变化和季节特征

运用2013—2014年28个自动气象站的逐小时气温观测资料,分析了乌鲁木齐地区气温的日变化特征及季节特征。结果表明:(1)城郊日最高气温出现频率最大的时次均为北京时间17时,出现频率在20%以上。日最低气温出现频率最大的时次为08时,频率在30%以上;(2)城郊年平均气温差异即城市热岛强度在夜晚较大,07时左右达到最大,在1.5℃以上,白天较小,16时左右最小,仅有0.3℃左右;(3)城郊日最高气温出现时间与城区基本一致,但日最低气温出现时间有差别,冬季郊区最低气温出现滞后城区1 h,其他季节保持一致;(4)城区逐小时城市热岛强度日变化可分为3个阶段:08—17时为下降时期,17—22时为迅速上升时期,22—08时为稳定的强热岛时期;(5)候平均气温城市热岛强度年内变化,最大值发生在年终的第72候,为1.53℃,最小值发生在秋末第67候,为0.33℃;(6)综合来看,各季代表月平均城市热岛强度春季(4月)夜晚较强,夏季(7月)夜晚和白天都相对较弱,秋季(9月)夜晚最强,但白天最弱,甚至白天部分时刻(15—18时)出现了负值。冬季白天和晚上都比较强,是四季代表月份平均热岛强度最强的季节。日内变化即日变化,大家公认的。     

北京市朝阳区热岛效应特征分析

利用1961—2014年朝阳、密云、上甸子3个站气温地面观测数据,对朝阳区与郊区温度年际变化、热岛强度年际变化进行了分析,并利用卫星反演数据分析了热岛效应的空间分布特征,得到以下主要结论:1朝阳区在20世纪70年代末城市热岛效应不太明显,之后城市热岛效应开始显现,热岛强度逐年增强,80年代末到90年代初略有减弱,2000年以后热岛强度有明显增强。2朝阳与郊区平均温差达1.5℃左右,为强热岛效应。热岛效应增强使得夏季高温日数增加,冬季低温日数减少。冬季为一年中热岛强度最强的季节。3朝阳区的热岛效应表现为西强东弱,热岛强度逐渐有向东、向南发展的趋势。强热岛区域集中在三环以内的城区。弱热岛区域集中在奥林匹克森林公园等下垫面多为大面积的水体和植被的地区。     

济宁市城市化对气温的影响

利用1981—2010年济宁及周边郊区3县台站的气温资料,研究分析了济宁城区、郊区的气温变化趋势和特点,并探讨了城市化发展对济宁城郊温度的影响。研究发现：（1）近30a来,尽管济宁城区、郊区最高气温、年平均气温、最低气温均呈显著增加趋势,且增温幅度依次增大,但城、郊气温增幅有所差异。其中,年平均气温、最低气温增温幅度郊区高于城区,而最高气温增幅二者相差不大,这表明城市化发展对最低气温的影响最大。此外,一年之中城、郊增暖均表现为：冬季、春季增幅最大,秋季次之,夏季最弱,且城、郊温差逐渐缩小;（2）济宁城市热岛强度总体呈上升趋势,但不同年代、不同季节变化趋势不尽相同。其中,1980年代热岛上升微弱,总体低于平均水平,而1990年代维持在一个较高的水平,2000年以后又明显下降;除秋季外,热岛强度均呈现缓慢上升趋势,其中冬季最强,夏季最弱。城市热岛效应具有明显的季节和日变化特征,表现为：冬半年明显高于夏半年,白天明显低于夜间;（3）济宁市区人口和建成区面积与城市热岛具有很大的相关性,两者的相关系数分别为0．81和0．75。     

乌鲁木齐气温与城市热岛强度日内和季节变化特征

运用2013-2014年28个自动气象站的逐小时气温观测资料,分析了乌鲁木齐地区气温的日变化特征及季节特征。结果表明：1)城郊日最高气温出现频率最大的时次均为北京时间17时,出现频率在20%以上。日最低气温出现频率最大的时次为8时,频率在30%以上;2)年平均城郊气温差异即城市热岛强度在夜晚较大,早上7时左右达到最大,在1.5℃以上,白天较小,16时左右最小,仅有0.3℃左右;3)城郊日最高气温出现时间基本一致,但日最低气温出现时间有差别,冬季郊区最低气温出现滞后城区1小时,其他季节保持一致;4)城区逐小时城市热岛强度日内变化可分为三个阶段：8点到17点为下降时期,17点到22点为迅速上升时期,22点到第二天8点为稳定的强热岛时期;5)侯平均城市热岛强度年内变化,最大值发生在年终的第72候,为1.53℃,最小值发生在第秋末第67候,为0.33℃;6)综合来看,各季代表月平均城市热岛强度春季(4月)夜晚较强,夏季(7月)夜晚和白天都相对较弱,秋季(9月)夜晚最强,但白天最弱,甚至白天部分时刻(15到18点)出现了负值。冬季白天和晚上都比较强,是四季代表月份平均热岛强度最强的季节。     

保定市城市热岛效应特征分析

利用1970-2007年保定市和周边邻近的徐水、满城、望都、高阳4个气象站的气温资料,应用统计学方法,对保定市热岛效应的年代际、年际、季节和日变化特征进行了分析.结果表明:保定市城市热岛效应十分明显,在最近的38 a中,市区比郊区年平均气温偏高0.7 ℃,且这一现象有增强的趋势,平均增长率为0.18 ℃/10a.保定市城市热岛效应主要表现在对最低气温的影响,对最高气温的影响不明显,市区比郊区年平均最低气温偏高1.5 ℃.保定市城市热岛效应四季差异较大,热岛效应最强的季节是冬季,38 a中冬季的平均气温差达到1.0 ℃,最弱的季节是夏季,夏季平均气温差为0.5 ℃.保定市的热岛强度日变化特征是夜间强、白天弱.     

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.     

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.     

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     

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。     

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.     

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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