草温与露点温度对结露和结霜指示性分析

利用密云国家基准气候站和电白国家基准气候站2009—2010年自动站的观测记录和人工结露、结霜观测资料,以及平谷国家一般气象站2010年12月至2011年5月自动站的观测记录和天气现象自动化观测资料,分析了结露、结霜与地面气象要素关系。结果表明:诸多气象要素中,草温和草温与露点温度差对露或霜结成具有指示性作用;电白站95.0%结露发生在日最小草温与露点温度差<1.1℃时,密云站95.0%结露发生在日最小草温与露点温度差<1.2℃时,密云站95.0%结霜发生在日最小草温与露点温度差<1.3℃时;结露主要发生在最低草温0.5℃以上,从平谷站自动天气现象观测资料分析可以看出结露发生在0℃以上,结霜发生在0℃以下;很多情况,百叶箱高度上的空气中的水汽含量并未达到饱和,而贴地(或贴近地物)的空气中的水汽含量达到饱和;日最小草温与露点温度差<3.1℃时,电白、密云站不出现露、霜的比率分别是16.8%和11.7%;因土壤湿度南方比北方大,草温与露点温度差为正的比例南方较北方大。     

华北地区玉米田实际蒸散量的计算

以简化农田实际蒸散的计算过程为目的，利用1999年河北定兴县中国气象科学研究院农业气象试验基地的玉米田0~300 cm土壤湿度和气象要素的实测资料以及华北地区5个站1991~1995年0~50 cm土壤湿度和气象资料，探讨由简化参考蒸散模式计算玉米田实际蒸散的可能性；对比了Priestley-Taylor模式和FAO（1998）Penman-Menteith模式的计算结果，以农田试验资料为基础，采用叶面积系数和平均土壤相对湿度为因子，建立了实际蒸散的计算模式。并以华北地区8个站1999~2000年0~50 cm土壤湿度和气象资料进行验证，平均误差一般为10%~15%.     

基于HRCLDAS/CLM陆面数据同化系统的青藏高原地区土壤湿度模拟研究

利用最新的高时空分辨率（1 km、1 h）的中国气象局高分辨率陆面数据同化系统（HRCLDAS-V1.0）大气近地面强迫资料,驱动由NCAR发展的通用陆面模式（CLM）,对青藏高原地区2015年1月1日至9月30日的土壤湿度开展了模拟研究。结果表明模拟得到的高时空分辨率（1 km、1 h）土壤湿度能够体现出青藏高原地区从东南向西北逐渐变低的空间分布特征,较好地表现出各层土壤湿度的时间变化特征,6~9月土壤湿度波动较大,1~5月波动较平缓,上层土壤湿度变幅较大,深层变化较平缓。0~5 cm、0~10 cm和10~40 cm深度土壤湿度模拟结果与观测值的相关系数均在0.8以上,其中0~5 cm土层的相关系数达到0.92,各层土壤湿度观测值与模拟值的均方根误差变化则相反,3个土层土壤湿度模拟结果与观测值的偏差均小于0.04 mm3 mm-3,但模式对于研究时段土壤湿度变化的低值有高估现象,且模拟能力随着土层深度的加深而减弱。     

烘干称重法与自动观测土壤湿度的差异分析

利用2008年12月至2009年2月烘干称重法、自动观测的土壤湿度及同期气象资料进行统计分析得出:(1)在同一气候环境、同一地段烘干称重法与自动观测的土壤湿度变化趋势是一致的,0～10cm土壤湿度一致性最好。(2)烘干称重法测得数据所反映土壤湿度的变化程度大于自动观测。⑶烘干称重法、自动观测的0～50cm土壤湿度与降水、气温相关密切,建立的回归方程通过0.01的显著性检验,采用烘干称重法测得的土壤湿度与气温、降水的响应程度好于自动观测。     

基于GLDAS产品的青藏高原土壤湿度特征分析

选取青藏高原中部那曲地区10个试验点2010年8月至2012年12月的土壤湿度数据与全球陆面数据同化系统(GLDAS)中4个陆面过程模型(NOAH、CLM、VIC、MOSAIC)模拟得到的土壤水分产品进行对比分析,发现NOAH陆面模式资料在青藏高原适用性较好。采用中国科学院青藏高原研究所那曲站10个试验点观测土壤湿度资料和长时间序列的GLDAS陆面模式资料研究青藏高原地区不同深度土壤湿度的时空分布特征。结果表明:那曲地区土壤湿度呈现显著的季节变化特征,一年之中出现两个峰值和两个低值阶段。基于NOAH陆面数据同化产品发现青藏高原土壤湿度的空间分布呈现明显的纬向分布特征,随纬度的升高,土壤湿度值降低;同时,青藏高原中部浅层土壤和中间层土壤湿度有变湿的趋势。0~10 cm、10~40 cm、40~100 cm土壤湿度EOF展开第一模态(EOF1)在高原北部及南部呈反位相分布。     

自动站与人工观测地温资料差异分析

根据青海省海西所属气象站人工与自动站平行观测地温资料,对比分析两系统观测差异,以及差异产生的原因。结果表明:青海海西7站自动观测与人工观测0～320cm地温均存在一定的差异,0～40cm地温自动站普遍高于人工观测值,偏差大部分超出允许范围,0cm偏差较大,越往深层两者差异越小,80～320cm地温偏差基本在允许范围内;大柴旦、冷湖、乌兰站差异较大,茫崖、德令哈、天峻、茶卡站偏差相对较小。两种观测系统仪器测量原理不同、仪器安装位置不同、观测方式的不同、观测时间不同步,导致自动和人工观测资料存在系统性偏差。     

青藏高原春末土壤湿度对初夏降水的影响

为了研究青藏高原(简称高原)春末(5月)土壤湿度与初夏(6月)降水的关系,利用1979-2019年ERA-Interim土壤湿度月平均资料和同时段高原109站观测降水资料,分析了高原春季土壤湿度与汛期(5-9月)降水之间的关系.结果 表明:春末表层(0～28 cm)土壤湿度与高原初夏降水呈显著的正相关,在空间上土壤湿度...     

淮河流域土壤湿度异常的时空分布特征及其与气候异常关系的初步研究

利用中国东部1990~2000年旬平均土壤湿度、降水和气温观测资料，通过对0~50 cm层次土壤湿度进行旋转主分量分析 (REOF)，重点分析了淮河流域土壤湿度的时空分布特征, 并初步研究了土壤湿度与前期、同期和后期不同时段降水与气温的关系。发现春季以30 cm为界，30 cm以上各层土壤湿度异常的第一旋转空间模态十分相似, 其大值中心主要位于淮河流域，而30 cm以下 (30~50 cm) 各层的第二旋转空间模态与之亦十分类似, 因此称该模态为“淮河型”，而夏季和秋季虽然该模态也很显著, 但特征不如春季突出。该模态在各层次土壤中具有明显的持续性特征，均存在40旬左右的显著周期；并与前期和同期降水（气温）呈显著正 (负) 相关关系，与约半年后的降水 (气温) 呈负 (正) 相关关系。     

天气现象自动化观测系统设计

为了实现天气现象自动化观测,针对地面观测规范中34种天气现象,设计了基于图像、光学散射和常规地面气象观测多种技术的天气现象自动化观测系统。它由天气现象传感器和数字处理器两部分组成,天气现象传感器完成天气现象的图像和光学特性采集,数字处理器将模拟图像数字化,对数字图像和光学特性数据进行处理和管理,结合自动站的资料进行天气现象自动化识别。图像采集和识别技术在凝结天气现象自动识别试验中取得初步成效。     

四川盆地土壤湿度时空分布及影响因子分析

基于四川盆地24个农业气象站1996~2012年浅层10cm的旬土壤湿度、温度、降水和日照等观测资料,采用统计分析方法,分析了盆地四季多年土壤湿度时空分布特征以及气象因子对土壤湿度的影响。结果表明:四川盆地浅层土壤湿度空间分布整体呈现南多北少的态势,其大小为春、冬两季偏低;夏、秋相对较高,其中以秋季为一年内最高。时间变化趋势则表现为春夏有微弱的降低趋势,而秋冬呈现微弱上升趋势。小波分析表明,各季节普遍存在4~6年的准周期。从倾向率分析来看,在不同季节表现出减少的区域主要集中在南部及东北部地区,四川盆地其余各地呈现不同程度增加趋势,以绵阳地区变化较为显著,夏、秋两季均达到了1.4%/a。与气象因子相关分析中得出,在相对偏干的盆地西北部,浅层土壤湿度变化受降水的影响最大,而在较为湿润的南部和东北部受日照影响最大。      

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