1971—2014年玉门市旅游气候舒适度评价分析

利用玉门市1971—2014年逐日气温、相对湿度、平均风速和日照时数等气象观测资料,运用温湿指数、风寒指数、着衣指数和人体舒适度指数4种旅游气候舒适度评价指数,计算玉门市旅游舒适度指数,确定了玉门市旅游气候舒适期及天数;构建综合舒适度指数模型,并对各指数进行了突变检验。结果表明,玉门市较舒适期为4月下旬中期至9月下旬末,约160 d;舒适期为5月中旬至9月中旬,约124 d。其中,5、6、8月最适宜旅游,7、9月为适宜旅游,11月—次年的3月,由于气候寒冷,不适宜旅游。玉门市的温湿指数舒适期天数从1983年开始明显增多;风寒指数舒适期天数从1990年开始明显增多;着衣指数舒适期天数从1991年开始明显增多;人体舒适度指数舒适期天数从1986年开始明显增多。     

基于熵权法的白山市旅游气候资源分析

利用白山市各观测站1981—2019年的逐日平均气温、平均相对湿度、平均风速及平均日照时数计算白山市逐月温湿指数、风寒指数、穿衣指数,选取熵权法对12个月的3项指数进行权重分配,从而建立旅游气候舒适度评价模型并分析白山市旅游气候资源优势.结果表明:白山市5—9月为舒适期,10月为较舒适期,3月、4月、11月为较不舒适期,1月、2月、12月为不舒适期.6—8月白山市平均舒适天数为63 d,舒适和较舒适的天数占比达95.7％.     

中国大陆九大名山风景区旅游气候舒适度评价

气候舒适度及持续时间的长短,是决定旅游资源质量和旅游季节长短的重要因素。本文采用以温湿指数、风寒指数和着衣指数为基础的综合气候舒适度评价模型,计算了我国具有代表性的九大名山风景区的气候舒适度指数,划分出适宜旅游的等级和时段,并对其随纬度的变化进行了分析。结果表明：位于我国北方的长白山和五台山,气候舒适度年变化呈倒“V”形,舒适期较短,长白山旅游舒适期为6—8月,五台山较舒适期为6—8月;位于我国中部的华山、泰山、黄山和峨眉山,气候舒适度年变化呈倒“U”形,旅游舒适期均为6—9月;位于我国南方的庐山、武夷山和桂林山水,气候舒适度年变化呈“M”形,舒适期较长,庐山旅游舒适期为5—10月,武夷山和桂林山水的旅游舒适期为3—4月与10—11月。     

贵州梵净山旅游气候舒适度分析

利用2011—2019年梵净山区逐日气温、降水、风速和相对湿度等资料,对影响旅游活动的主要气候要素进行分析,并结合旅游实际构建梵净山旅游气候舒适度指数和旅游气象指数预报模型。结果表明:(1)梵净山气温适宜,降雨丰沛、雨热同期,空气湿度大,平均风力为微风,近9 a平均气温、降水量、风速和相对湿度分别为8.4℃、2073.7 mm、3.8 m·s^(-1)和91%。(2)近9 a梵净山年平均较舒适及以上天数206.9 d,其中6—8月为旅游舒适期,4—5月和9—10月为较舒适期,3月和11月为较不舒适期,12月至次年2月为不舒适期。(3)梵净山旅游气象指数预报模型包括舒适度、降雨、云量和灾害性天气4个因子,综合考虑了旅游的安全性、舒适性和观赏性,计算方便合理,可适用于梵净山山岳型旅游气象预报与服务工作。     

梁山县旅游气候资源及舒适度分析

通过对梁山1981—2010年地面气象资料进行统计分析,综合温湿、风效和舒适度指数,对旅游气候资源和舒适度进行了评价分析。结果表明：梁山县旅游气候资源丰富,气候温和,四季皆可旅游。影响旅游气候舒适度的不利因素主要是高低温,而风、降水和日照对旅游的影响利大于弊。采用旅游舒适度指数综合评价得出最佳旅游期是4月、5月、10月,较适宜的月份是3月、6月、9月、11月,不适宜期是12月、1月,较不适宜的月份是2月、7月、8月。气候资料统计分析得出梁山的旅游气候适宜期与指数综合评价得出的结论基本一致。     

贺州市旅游气候舒适度模糊综合评价

选用贺州气象站1982-2011年30a气温、风速和湿度等资料作为影响贺州市旅游舒适度因子,以旬为单位,运用模糊综合评价方法对贺州市气候旅游舒适度分析,结果显示:贺州市总体气候舒适度状况良好,较舒适指数以上天数占57.57%,每年4-6月和9-11月是贺州适宜旅游的季节。最舒适的时段是4月中旬至6月初,9月初至11月初,不舒适时段集中冬季11月底至来年的3月初和盛夏的7月初至8月中旬。     

河北省旅游气候资源评价及变化分析

利用石家庄、秦皇岛、承德气象站1981—2010年逐日平均气温、相对湿度、风速、日照时数等资料,计算了近30年的逐日温湿指数、风寒指数、着衣指数和综合舒适指数,据此对河北省旅游气候资源进行了定量评价。结果表明,各个地区的旅游气候舒适度有一定的差异,河北省最适宜旅游的季节为春季和秋季,其中,4月和10月是石家庄周边旅游的最适宜期,5月和9月是河北沿海和北部山区旅游最适宜期。随着气候变暖,各地最佳旅游期起止时间和综合舒适天数发生一些变化,但最佳旅游期总天数变化不大。     

广西马山旅游气候资源评估

基于1961-2017年马山县气象观测站的逐日气温、降水量、日照时数、风速、相对湿度、总云量等资料,采用人体舒适度指数、温湿指数、风效指数和度假气候指数等4个气候指数,对马山县的旅游气候资源进行定量评估。结果表明:(1)马山县人体舒适期有9个月,属一类气候适宜区;(2)人居环境舒适期有6个月;(3)全年12个月均适宜旅游度假,"特别适宜"的月份高达8个;(4)最为适宜旅游度假期为3月27日至5月1日和10月5日至11月20日。结果可为马山合理开发特色旅游产业提供参考,也可帮助游客选择适合的出行时间。     

近37年成都地区基于温湿指数的气候舒适度变化特征分析

本文利用成都地区14个区(市)县国家级地面气象观测站1980~2016年的日平均气温、日平均相对湿度数据,采用温湿指数对成都地区气候舒适度进行评价分析,结果表明:成都地区4月和10月为非常舒适月份,无极度不舒适月份,春季和秋季为非常舒适季节,夏季为不舒适季节,冬季为较不舒适季节,近37年气候舒适度总体变好。相比,成都西北部、中部和蒲江县的气候舒适度较好。气候舒适度突变多发生在2009~2013年,冬季的突变发生在1984年,冬季气候舒适度向好转向的特征非常明显。     

延安旅游气候舒适度和旅游前景分析

利用延安市12县区1981-2018年气象观测资料,计算温湿指数、风效指数和综合舒适指数,分析延安旅游气候舒适度,得出以下结论:延安市舒适旅游期持续时间较长,5-9月是延安市的最适宜旅游期,是开展乡村游、避暑游的最佳时期;4月和10月是较适宜旅游的时期。冬季室外环境温度低,湿度小,开展传统教育和红色旅游时要注重保暖。     

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.     

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     

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.     

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;