SSP“双碳”路径下赣江流域径流变化趋势

根据共享社会经济情景（SSPs）分为“双碳”路径（SSP1-1.9、SSP1-2.6、SSP2-4.5、SSP4-3.4、SSP4-6.0）和“高碳”路径（SSP3-7.0、SSP5-8.5)。在碳达峰（2028—2032年）和碳中和（2058—2062年）两个时期,采用5个气候模式,7个情景驱动SWAT水文模型,分析赣江流域径流演变特征,主要结论如下：1961—2017年赣江流域观测到的年均气温以0.17℃/(10 a)的速率呈显著上升趋势（p<0.01),降水以17 mm/(10 a)的速率呈不显著上升。“双碳”和“高碳”路径下,2021—2100年赣江流域均呈现暖湿态,气温持续变暖,降水有所增加;碳达峰、碳中和时期,“双碳”路径下年径流呈现增加趋势;“双碳”路径下,月径流在汛期呈现增加趋势,枯水期在SSP1-1.9、SSP1-2.6、SSP2-4.5、SSP4-3.4下呈现增加趋势,在SSP4-6.0下呈现减少趋势。“双碳”路径下极端水文事件强度将可能小于“高碳”路径。     

基于CMIP6多模式的长江流域蒸散发预估及影响因素

蒸散发是水文循环和能量传输的中间环节,同时也是联结土壤、植被、大气过程的纽带。基于第六次国际耦合模式比较计划（CMIP6）12个全球气候模式数据,研究了SSP1-2.6、SSP2-4.5和SSP5-8.5三种情景下,长江流域2020-2099年实际蒸散发E_T（Evapotranspiration,简称ET）的时空变化及其影响因素。研究结果表明,在3种气候变化情景下长江流域ET相较基准期（1995-2014年）均存在显著增加趋势,且长江中下游地区增加趋势最为显著;SSP1-2.6情景ET较基准期先快速增加,21世纪60年代之后减缓并趋于平稳,SSP2-4.5和SSP5-8.5情景下均呈持续增加趋势。研究了降水（Precipitation,简称Pr）、气温（Air Temperature,简称T）和叶面积指数L_AI（Leaf Area Index,简称LAI）对长江流域ET的影响;SSP1-2.6和SSP2-4.5情景下,长江流域ET受T影响最为显著,而SSP5-8.5情景下,LAI是影响ET的主导因素。在3种气候情景下,辐射强迫越大,植被增加趋势越显著,对ET的影响越强（SSP5-8.5、SSP2-4.5、SSP1-2.6情景下影响逐渐减弱）,而ET对LAI的敏感性则逐渐降低（SSP1-2.6、SSP2-4.5、SSP5-8.5情景下敏感性逐渐降低）。     

基于CMIP6模式数据的1961-2099年青藏高原降水变化特征分析

青藏高原是海-陆-气相互作用的敏感区域,其降水对当地乃至亚洲水循环起着重要作用,但目前对该区域在21世纪的降水时空演变规律仍认识不足。本文以第六次国际耦合模式比较计划（CMIP6）的25个气候模式模拟数据为基准,结合观测数据评估了各模式对青藏高原历史时期（1961-2014年）降水变化的模拟能力,发现多模式集合平均模拟效果优于多数单模式。由多模式集合平均分析了SSP1-2.6、SSP2-4.5、 SSP3-7.0和SSP5-8.5四种情景下青藏高原2015-2099年降水时空特征,发现未来青藏高原年降水量在时间上呈现增加趋势,在空间上呈现西北向东南递增的特征。相对于参考时段（1995-2014年）,降水增幅在近期（2020-2039年）呈现北正南负的特征,高值区分布在藏北高原中西部和昆仑山区,而在21世纪中期（2040-2059年）和末期（2080-2099年）降水增幅南北相反的特征消失,其高值区分布在南部地区,且排放情景越高,增幅越大,空间差异也越大。到21世纪末,青藏高原年降水量在SSP1-2.6、 SSP2-4.5、 SSP3-7.0和SSP5-8.5情景下较参考时段分别增加约6...     

2℃全球变暖背景下青藏高原平均气候和极端气候事件变化

利用CMIP5耦合模式RCP2.6、RCP4.5和RCP8.5情景预估结果,以1890一1900年为基准气候,确定了2℃全球变暖时间、对应时期青藏高原平均气候和极端气候事件变化幅度,多模式集合平均结果表明:RCP2.6、RCP4.5和RCP8.5情景下2℃全球变暖分别发生在2063年、2040年和2036年;对应着2℃全球变暖,三种情景下青藏高原平均气温分别升高2.99℃、3.22℃和3.28℃,均超过全球2℃的升温水平;年降水量亦增加,分别增加8.35%、7.16%和7.63%。受气温升高和降水量增多影响,RCP4.5情景下霜冻日数、冰封日数减少,暖夜日数、暖昼日数增多;RCP4.5情景下中雨日数、强降水量、降水强度均增加,持续干期天数减少。从各地平均气候和极端气候事件变化结果来看,柴达木盆地是青藏高原气候变化的敏感区。     

云南初夏干湿演变特征和基于CMIP6模式模拟的预估分析

使用1961—2020年的观测数据和2021—2080年的模式预估数据,首先分析了云南初夏干燥度指数（aridity index,AI）的演变特征和影响因子相对贡献,然后采用国际耦合模式比较计划第六阶段（CMIP6）中的20个全球模式,对SSP1-2.6、SSP2-4.5以及SSP5-8.5情景下云南初夏未来干湿变化进行了预估研究。结果表明：(1) 1961—2020年云南初夏气候整体湿润,但为变干燥的趋势,有明显的年代际变化特征,1960s、1970s以及2000s气候相对湿润,其余年代相对干燥,2000s（2010s）为1961年以来最湿润（干燥）的10年。(2) 2021—2080年在3种排放情景下,云南初夏气候较1995—2014年均为变干燥的趋势,SSP1-2.6、SSP2-4.5以及SSP5-8.5情景下,AI分别减少13.9%、17.9%以及24.9%,西南部将可能是湿润度降幅最大值中心。(3) 1961—2020年,降水对云南初夏气候干湿变化的贡献大于潜在蒸散量;而2021—2080年,潜在蒸散量对气候变干燥的贡献大于降水量,且随排放情景的增高和时间推移,其贡献将逐渐增大。     

我国西北降水变化趋势和预估

利用观测资料、GPCC再分析资料和第六次耦合模式比较计划（CMIP6）模拟结果,研究了我国西北地区近几十年及未来降水变化趋势。结果表明,1979—2019年我国西北干旱半干旱区降水在全年各季节均有显著增加,其中秋季增加最多。CMIP6模拟结果显示,随着全球变暖,我国西北地区降水在2015—2100年将继续增加。至21世纪末,在SSP2-4.5和SSP5-8.5情景下,我国西北地区年平均降水量将分别增加约13.7%（37 mm）和25.8%（78 mm),其中降水量增加最多的季节分别为夏季和春季。考虑到西北地区蒸发量也将随全球变暖而增加,模式平均的结果显示西北地区年平均净降水量在两种情景下的增幅分别约1.4%和4.9%,表明我国西北地区未来气候呈现显著的变湿趋势。进一步分析表明,西北地区未来降水增加可能与局地大气低层位势高度降低和上升运动加强有关。     

CMIP5模式对21世纪全球和中国年平均地表气温变化和2℃升温阈值的预估

基于参加国际耦合模式比较计划第5阶段（CMIP5）的29个全球气候模式开展的历史气候模拟和3种典型浓度路径（RCP2.6、RCP4.5、 RCP8.5）下21世纪气候预估的结果,分析了单个模式和多模式集合平均（MME）的21世纪全球与中国年平均地表气温（ASAT）变化特征及2℃升温阈值的出现时间。多模式集合平均的结果显示：全球和中国年平均地表气温均将继续升高,21世纪末的升温幅度随着辐射强迫的增大而增大。RCP2.6情景下,年平均地表气温增幅先升高后降低,全球（中国）年平均地表气温在2056年（2049年）达到升温峰值,21世纪末升温1.74℃（2.12℃）;RCP4.5情景下,年平均地表气温在21世纪前半叶逐渐升高,之后升温趋势减缓,21世纪后期趋于平稳,21世纪末全球（中国）年平均地表气温增幅为2.60℃（3.39℃）;RCP8.5情景下,21世纪年平均地表气温快速升高,21世纪末全球（中国）年平均地表气温增幅为4.75℃（6.55℃）。全球平均的年平均地表气温增幅,在RCP2.6情景下没有超过2℃,RCP4.5和RCP8.5情景下分别在2047和2038年达到2℃。RCP2.6、RCP4.5和RCP8.5情景下中国年平均地表气温增幅连续5 a不低于2℃的时间分别在2032、2033和2027年,明显早于全球平均。任一典型浓度路径情景下,达到2℃升温的时间,北半球同纬度地区早于南半球,同半球高纬度地区早于低纬度地区,同纬度地区陆地早于海洋。3种不同典型浓度路径情景下21世纪全球和中国年平均地表气温将继续升高这一结果是可信的,RCP4.5和RCP8.5情景下全球和中国年平均地表气温增幅超过2℃的结果模式之间有较高的一致性。多模式预估的全球和中国年平均地表气温升幅和不同幅度升温的出现时间均存在一定的不确定性,预估结果的不确定性随预估时间的延长而增大;相同情景下,中国年平均地表气温预估的不确定性大于全球。     

CMIP6模式对青藏高原极端气温指数模拟能力评估及预估

利用第六次国际耦合模式比较计划（CMIP6）28个全球气候模式模拟的历史和多SSP排放情景下的模拟结果以及国家气候中心制作的CN05.1格点化的观测数据,在评估28个全球气候模式对青藏高原极端气温相关指数模拟效果的基础上,预估了多个SSP情景下青藏高原未来极端气温指数的变化趋势。评估结果表明多模式集合平均模拟结果更稳定,且能模拟出极端气温指数的时间分布以及空间分布特征,但与观测相比,不同指数存在不同偏差。预估结果表明,相对于1995-2014年,青藏高原上日最高气温最高值（TXx）、日最低气温最低值（TNn）、暖昼指数（TX90p）未来呈上升趋势,霜冻日数（FD）、冰冻日数（ID）、冷夜指数（TN10p）呈减少趋势,其中高原极端低温比极端高温增温明显,暖昼指数在高原西南部增加明显,霜冻日数、冰冻日数、冷夜指数在高原东南部减少明显。SSP1-1.9情景下,极端气温指数在21世纪的变化幅度较小,随着辐射强迫增大,指数的变化趋势也增大。SSP1-2.6情景下,2030年前中国实现碳达峰时,青藏高原地区TXx、 TNn、 TX90p增长分别不超过1.12℃、0.84℃、 8.4%, FD、 I...     

1.5和2℃升温阈值下中国温度和降水变化的预估

基于CMIP5耦合气候模式模拟结果对1.5和2℃升温阈值时中国温度和降水变化的分析表明,1.5℃升温阈值时,中国年平均升温由南向北加强且在青藏高原地区有所放大,季节尺度上升温的空间分布与其类似,就区域平均而言,RCP2.6、RCP4.5和RCP8.5情景下中国年平均气温分别升高1.83、1.75和1.88℃,气温的季节变幅以冬季升高最为显著;除华南和西南地区外中国大部分地区年平均降水量增多,降水的季节差异明显,以夏季降水的分布模态与年平均降水量的分布最为相似,区域平均的年降水量分别增加5.03%、2.82%和3.27%,季节尺度上以冬季降水增幅最大。2℃升温阈值时,RCP4.5和RCP8.5情景下中国年平均温度的空间分布与1.5℃升温阈值基本一致,中国年平均气温分别升高2.49和2.54℃,季节尺度上气温的变化以秋、冬季增幅最大;中国范围内年平均降水量基本表现为增多趋势,其中,西北和长江中下游部分地区表现为明显的季节差异,区域平均的年降水量分别增加6.26%和5.86%。与1.5℃升温阈值相比较,2℃升温阈值时中国年平均温度在RCP4.5和RCP8.5情景下分别升高0.74和0.76℃,降水则分别增加3.44%和2.59%,空间上温度升高以东北、西北和青藏高原最为显著,降水则在东北、华北、青藏高原和华南地区增加最为明显。      

CMIP5多模式预估的1.5℃升温背景下中国气温和降水变化

本文使用国际耦合模式比较计划第五阶段（CMIP5）中39个全球气候模式的试验数据,预估了相对于工业革命前期全球1.5℃升温背景下中国气温和降水变化。根据多模式中位数预估结果,在不同典型浓度路径（RCPs）情景下,相对于工业革命前期全球1.5℃升温分别发生在2034年（RCP2.6）、2033年（RCP4.5）和2029年（RCP8.5）。全球升温1.5℃时,中国年和季节气温平均上升1.8℃和1.6～2.1℃,其中冬季最强。增温总体上由南向北加强,青藏高原为高值中心。年和各季节增温均超过其自然内部变率,区域平均的信噪比分别为3.4和1.6～2.7。年和季节降水整体上在中国北方增加、华南减少;区域平均的年降水增加1.4%,季节降水增加0.1%～5.1%,冬季增幅最大。年和季节降水变化要远小于其自然内部变率,区域平均的信噪比仅为0.1和0.01～0.2。总体上,模式对气温预估的不确定性较小,对降水的偏大,其中对季节尺度预估的不确定性要高于年平均结果。     

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;