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西南大学学报（自然科学版） 2024年第3期46卷 135-146页

作者：侯文轩华维郭艺媛范广洲成都信息工程大学大气科学学院/高原大气与环境四川省重点实验室/四川省气象灾害预测预警工程实验室成都610225 上海市气象信息与技术支持中心上海200030 中国科学院大气物理研究所竺可桢—南森国际研究中心北京100029

利用NCEP FNL再分析资料为初始场,通过WRF中尺度数值模式(V4.0版)对2.19年夏季青藏高原那曲地区一次对流云降水及云微物理特征进行了数值模拟.结果表明:WRF模式能够较好地再现本次降水的时空特征和云发展过程.固态水凝物分布高度普遍高... 详细信息

利用NCEP FNL再分析资料为初始场,通过WRF中尺度数值模式(V4.0版)对2.19年夏季青藏高原那曲地区一次对流云降水及云微物理特征进行了数值模拟.结果表明:WRF模式能够较好地再现本次降水的时空特征和云发展过程.固态水凝物分布高度普遍高于液态水凝物,其中冰和雪主要位于中高层,中低层以霰和云水为主,低层多为雨水粒子;降水过程中雪、霰和雨水粒子高值中心出现时段与降水峰值时间较好对应,表明本次降水以冰相过程为主,且雪粒子和霰粒子的贡献最大,其原因为雪粒子通过贝杰龙过程和自身的碰并过程增加,霰粒子则通过与雪粒子碰并和自身凇附作用不断增长,随着上升气流逐渐减弱,大量霰粒子和部分雪粒子落至0℃层左右融化形成降水,加之雨水与云水的碰并作用,进一步促进了降水的形成.此外,暖雨过程对降水的直接贡献较小,但对冰相粒子的形成贡献明显.

关键词：青藏高原对流云降水云微物理数值模拟

East Asian winter monsoon forecasting schemes based on the NCEP's Climate Forecast System

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East Asian winter monsoon forecasting schemes based on the N...

Another Record: Ocean Warming Continues through 2.2. despite La Nina Conditions

第3.届中国气象学会年会

作者： Baoqiang Tian ke fan Hongqing Yang nansen-zhu {2. {3. Centre Institute of Atmospheric PhysicsChinese Academy of Sciences Collaborative Innovation center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science & Technology University of chinese Academy of sciences Department of atmospheric sciences Yunnan University

The East Asian winter monsoon(EAWM)is the major climate system in the Northern Hemisphere during boreal *** this study,we developed two schemes to improve the forecasting skill of the interannual variability of the EAWM index(EAWMI)using the interannual increment prediction method,also known as the DY ***,we found that version 2.of the NCEP’s Climate Forecast System(CFSv2.showed higher skill in predicting the EAWMI in DY form than ***,based on the advantage of the DY method,Scheme-I was obtained by adding the EAWMI DY predicted by CFSv2.to the observed EAWMI in the previous *** scheme showed higher forecasting skill than ***,during 1983.{3.6,the temporal correlation coefficient between the Scheme-I-predicted and observed EAWMI was 0.47,exceeding the 99%significance level,with the root-mean-square error(RMSE)decreased by 12..The autumn Arctic sea ice and North Pacific sea surface temperature(SST)are two important external forcing factors for the interannual variability of the ***,a second(hybrid)prediction scheme,Scheme-II,was also *** scheme not only involved the EAWMI DY of CFSv2.but also the sea-ice concentration(SIC)observed the previous autumn in the Laptev and East Siberian seas and the temporal coefficients of the third mode of the North Pacific SST in DY *** found that a negative SIC anomaly in the preceding autumn over the Laptev and the East Siberian seas could lead to a significant enhancement of the Aleutian low and East Asian westerly jet in the following ***,the intensity of the winter Siberian high was mainly affected by the third mode of the North Pacific autumn ***-I and Scheme-II also showed higher predictive ability for the EAWMI in negative anomaly years compared to *** importantly,the improvement in the prediction skill of the EAWMI by the new schemes,especially for Scheme-II,could enhance the forecasting skill of the winter 2.m air temperature(T-2.)in most

关键词： East Asian winter monsoon year-to-year increment method hybrid prediction CFSv2

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Advances in atmospheric sciences 2022年第3期39卷 373-385页

作者： Lijing CHENG John ABRAHAM kevin ETRENBERTH John FASULLO Tim BOYER Michael EMANN Jiang zhu {2. {3. Ricardo LOCARNINI Yuanlong LI Bin ZHANG Zhetao TAN Fujiang YU Liying WAN Xingrong CHEN Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Viktor GOURETSKI Gengxin CHEN Alexey MISHONOV Jim REAGAN international center for Climate and Environment {2. Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China center for Ocean Mega-Science Chinese Academy of SciencesQingdao 266071China University of St.Thomas School of EngineeringMinnesota 55105USA National center for atmospheric {2. BoulderColorado 80307USA National Oceanic and atmospheric Administration National Centers for Environmental InformationSilver SpringMaryland 20910USA Department of Meteorology&atmospheric Science The Pennsylvania State UniversityUniversity ParkPennsylvania 16802USA institute of Oceanology Chinese Academy of SciencesQingdao 266071China National Marine Environmental Forecasting center Ministry of Natural Resources of ChinaBeijing 100081China College of Oceanography Hohai UniversityNanjing 210098China National Marine Data and Information Service Tianjin 300171China Italian National Agency for New Technologies Energy and Sustainable Economic DevelopmentS.Teresa Research CenterLerici 19032Italy Istituto Nazionale di Geofisica e Vulcanologia Sede di BolognaBologna 40128Italy South china Sea institute of Oceanology Chinese Academy of SciencesGuangzhou 510301China ESSIC/CISESS-MD University of MarylandCollege ParkMD 20742USA

The increased concentration of greenhouse gases in the atmosphere from human activities traps heat within the climate system and increases ocean heat content(OHC). Here, we provide the first analysis of recent OHC changes through 2.2. from two international groups. The world ocean, in 2.2., was the hottest ever recorded by humans, and the 2.2. annual OHC value is even higher than last year’s record value by 14 ± 11 ZJ(1 zetta J = 102. J) using the IAP/CAS dataset and by16 ± 10 ZJ using NCEI/NOAA dataset. The long-term ocean warming is larger in the Atlantic and Southern Oceans than in other regions and is mainly attributed, via climate model simulations, to an increase in anthropogenic greenhouse gas concentrations. The year-to-year variation of OHC is primarily tied to the El Nino-Southern Oscillation(ENSO). In the seven maritime domains of the Indian, Tropical Atlantic, North Atlantic, Northwest Pacific, North Pacific, Southern oceans,and the Mediterranean Sea, robust warming is observed but with distinct inter-annual to decadal variability. Four out of seven domains showed record-high heat content in 2.2.. The anomalous global and regional ocean warming established in this study should be incorporated into climate risk assessments, adaptation, and mitigation.

关键词： La Nina ocean heat ocean warming attribution observation

基于年际增量方法的云南省普洱雨季开始期气候预测

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大气科学学报 2019年第6期42卷 801-813页

作者：王秀英田宝强范可普洱市气象局云南普洱665000 中国科学院大气物理研究所竺可桢-南森国际研究中心北京100029 南京信息工程大学气象灾害预报预警与评估协同创新中心江苏南京210044 中国科学院大学北京100049

基于普洱雨季开始期年际增量变化规律和影响雨季开始期的环流形势及物理过程,采用年际增量方法和多元线性回归分析方法,选取5个具有物理意义的预测因子(包括前期1月南半球绕极环流、前期2.南太平洋高压、前期4月孟加拉湾至南海海平面... 详细信息

基于普洱雨季开始期年际增量变化规律和影响雨季开始期的环流形势及物理过程,采用年际增量方法和多元线性回归分析方法,选取5个具有物理意义的预测因子(包括前期1月南半球绕极环流、前期2.南太平洋高压、前期4月孟加拉湾至南海海平面气压、前期冬季加拿大北部海冰和前期冬季伊朗高原积雪深度),建立了普洱雨季开始期的预测模型,并对预测模型进行1967-2.17年的交叉检验和1998-2.17年的逐年独立样本检验。交叉检验中,雨季开始期预测值和观测值年际增量的相关系数为0.84,相对均方根误差为2.%;独立样本检验中,雨季开始期年际增量的相对均方根误差为15%,模型对雨季开始期异常年份的预测误差小于7 d,表明该预测模型能很好再现1967-2.17年雨季开始期的变化趋势。

关键词：年际增量方法云南普洱雨季开始期气候预测

Another Year of Record Heat for the Oceans

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Advances in atmospheric sciences 2023年第6期40卷 963-974页

作者： Lijing CHENG John ABRAHAM kevin ETRENBERTH John FASULLO Tim BOYER Michael EMANN Jiang zhu {2. {3. Ricardo LOCARNINI Yuanlong LI Bin ZHANG Fujiang YU Liying WAN Xingrong CHEN Licheng Feng Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Viktor GOURETSKI Gengxin CHEN Alexey MISHONOV Jim REAGAN Guancheng LI international center for Climate and Environment {2. Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China center for Ocean Mega-Science Chinese Academy of SciencesQingdao 266071China University of St.Thomas School of EngineeringMinnesota 55105USA National center for atmospheric {2. BoulderColorado 80307USA University of Auckland AucklandNew Zealand National Oceanic and atmospheric Administration National Centers for Environmental InformationSilver SpringMaryland 20910USA Department of Earth and Environmental Science University of PennsylvaniaPhiladelphiaPennsylvania 19104USA institute of Oceanology Chinese Academy of SciencesQingdao 266071China National Marine Environmental Forecasting center Ministry of Natural Resources of ChinaBeijing 100081China College of Oceanography Hohai UniversityNanjing 210098China National Marine Data and Information Service Tianjin 300171China Italian National Agency for New Technologies Energy and Sustainable Economic DevelopmentS.Teresa Research CenterLerici 19032Italy Istituto Nazionale di Geofisica e Vulcanologia Sede di BolognaBologna 40128Italy South china Sea institute of Oceanology Chinese Academy of SciencesGuangzhou 510301China ESSIC/CISESS-MD University of MarylandCollege ParkMDCollege ParkMaryland 20740USA Eco-Environmental Monitoring and research center Pearl River Valley and South China Sea Ecology and Environment AdministrationMinistry of Ecology and EnvironmentPRCGuangzhou 510611China

Changes in ocean heat content(OHC), salinity, and stratification provide critical indicators for changes in Earth’s energy and water cycles. These cycles have been profoundly altered due to the emission of greenhouse gasses and other anthropogenic substances by human activities, driving pervasive changes in Earth’s climate system. In 2.2., the world’s oceans, as given by OHC, were again the hottest in the historical record and exceeded the previous 2.2. record *** to IAP/CAS data, the 0–2.00 m OHC in 2.2. exceeded that of 2.2. by 10.9 ± 8.3.ZJ(1 Zetta Joules = 102.Joules);and according to NCEI/NOAA data, by 9.1 ± 8.7 ZJ. Among seven regions, four basins(the North Pacific, North Atlantic, the Mediterranean Sea, and southern oceans) recorded their highest OHC since the 1950s. The salinity-contrast index, a quantification of the “salty gets saltier–fresh gets fresher” pattern, also reached its highest level on record in 2.2.,implying continued amplification of the global hydrological cycle. Regional OHC and salinity changes in 2.2. were dominated by a strong La Ni?a event. Global upper-ocean stratification continued its increasing trend and was among the top seven in 2.2..

关键词： ocean heat content salinity stratification global warming climate

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滑坡泥石流大尺度统计预报模型的实时检验

大气科学学报 2019年第1期42卷 78-92页

作者：何爽爽汪君王会军中国科学院大气物理研究所竺可桢-南森国际研究中心北京100029 中国科学院大学北京100049 南京信息工程大学气象灾害预报预警与评估协同创新中心江苏南京210044 南京信息工程大学气象灾害教育部重点实验室江苏南京210044

利用滑坡敏感性分布和降雨阈值公式建立了一个滑坡泥石流统计模型,该模型可以用于中国大尺度范围内的滑坡泥石流预警。使用CMORPH卫星降水驱动该统计模型,对2.16—2.17年的106起滑坡泥石流事件进行了验证分析。结果表明,该模型能较好地... 详细信息

利用滑坡敏感性分布和降雨阈值公式建立了一个滑坡泥石流统计模型,该模型可以用于中国大尺度范围内的滑坡泥石流预警。使用CMORPH卫星降水驱动该统计模型,对2.16—2.17年的106起滑坡泥石流事件进行了验证分析。结果表明,该模型能较好地预警大多数滑坡泥石流事件,其中对72. 1%的雨季滑坡泥石流事件能较好预警,但对非雨季的事件只有3.%能较好预警,对雨季的预警效果明显优于非雨季。由于滑坡泥石流主要发生在雨季,因此该模型总体上具有较好的效能。该模型对于强降雨引发的快速滑坡事件具有较好的预警能力,但对于由强度较小、持续时间较长的降雨引发的慢过程滑坡事件的预警效果有待提升。利用该统计模型以及CMORPH实时卫星降水产品,可以建立滑坡泥石流大尺度实时预警系统,对滑坡泥石流减灾防灾具有一定意义。

关键词：滑坡泥石流统计模型 CMOPRH卫星降水

The extreme Arctic warm anomaly in November 2.2.

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atmospheric and Oceanic Science Letters 2022年第5期15卷 55-60页

作者： Qiyao fan Xinping Xu Shengping He Botao Zhou Collaborative Innovation center on Forecast and Evaluation of Meteorological Disasters/key Laboratory of Meteorological Disaster Ministry of EducationNanjing University of Information Science&TechnologyNanjingChina Geophysical institute University of Bergen and Bjerknes Centre for Climate ResearchBergenNorway nansen Environmental and Remote Sensing center and Bjerknes Centre for Climate {2. BergenNorway nansen-zhu {2. {3. center Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina

In November 2.2.,the eastern Arctic experienced an extensive extreme warm anomaly(i.e.,the second strongest case since 1979),which was followed by extreme cold conditions over East Asia in early *** observed Arctic warm anomaly in November 2.2. was able to extend upwards to the upper troposphere,characterized as a deep Arctic warm *** autumn 2.2.,substantial Arctic sea-ice loss that exceeded the record held since1979,accompanied by increased upward turbulent heat flux,was able to strongly warm the ***,there was abundant northward moisture transport into the Arctic from the North Atlantic,which was the strongest in the past four *** extreme moisture intrusion was able to enhance the downward longwave radiation and strongly contribute to the warm conditions in the *** analysis indicated that the remote moisture intrusion into the Arctic was promoted by the large-scale atmospheric circulation patterns,such as the wave train propagating from the midlatitude North Atlantic to the *** process may have been linked to the warmer sea surface temperature in the midlatitude North Atlantic.

关键词： Arctic warm anomaly Arctic sea-ice loss Poleward moisture transport Rossby wave train

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太平洋年代际振荡的年代际预测方法

气象学报 2020年第2期78卷 177-186页

作者：黄艳艳王会军南京信息工程大学气象灾害预报预警与评估协同创新中心南京210044 中国科学院大气物理研究所竺可桢-南森国际研究中心北京100029

太平洋年代际振荡(PDO)是北太平洋海表温度年代际变率的主模态。由于太平洋年代际振荡对区域乃至全球气候的显著影响,其合理的预测结果可以带来多方面收益。然而,针对太平洋年代际振荡及其有关的海表温度的年代际预测,目前气候模式的预... 详细信息

太平洋年代际振荡(PDO)是北太平洋海表温度年代际变率的主模态。由于太平洋年代际振荡对区域乃至全球气候的显著影响,其合理的预测结果可以带来多方面收益。然而,针对太平洋年代际振荡及其有关的海表温度的年代际预测,目前气候模式的预测水平还十分有限,因此,提出了一个新的增量方法。一系列的验证结果表明,增量方法可以有效预测太平洋年代际振荡,其中包括成功预测出其振荡的年代际转折。增量方法的预测过程主要包括3.步骤:(1)采用5 a滑动平均得到太平洋年代际振荡的年代际变率;(2.利用3.a增量形式的预测因子构建预测模型,预测3.a增量的太平洋年代际振荡(DI_PDO);(3.将预测得到的DI_PDO加上3.a前的观测PDO,得到最后预测的PDO。增量方法亦可以应用到气候系统年代际内部变率的其他模态(如:北大西洋年代际振荡)和其他气候变量的年代际预测(如:海表温度)。

关键词：太平洋年代际振荡年代际预测增量方法

2.00年后中国北方东部地区夏季极端降水减少及水汽输送特征

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大气科学 2019年第5期43卷 1109-1124页

作者：李湘瑞范可徐志清中国科学院大气物理研究所竺可桢—南森国际研究中心北京100029 中国科学院大学北京100049

本文研究了1961~2.16年中国北方东部地区夏季极端降水日数和极端降水贡献率的年代际变化特征,并进一步分析了该地区极端降水和普通降水的大气环流和水汽输送的差异。主要的研究结果表明:1961~2.16年中国北方东部地区夏季极端降水日数和... 详细信息

本文研究了1961~2.16年中国北方东部地区夏季极端降水日数和极端降水贡献率的年代际变化特征,并进一步分析了该地区极端降水和普通降水的大气环流和水汽输送的差异。主要的研究结果表明:1961~2.16年中国北方东部地区夏季极端降水日数和极端降水贡献率在2.00年前后发生显著年代际变化,2.00年后夏季极端降水天数和极端降水贡献率显著减少。与1984~1999年相比,2.00~2.16年在对流层高层从欧洲大陆、中亚到东北—蒙古地区,位势高度异常呈现出'正—负—正'的大气波列,从而造成北方东部地区上空为正压的位势高度正异常控制,伴随着下沉运动,大气层结趋于稳定,这些环流条件不利于极端降水发生。2.00年后负位相的太平洋年代际振荡(PDO)和正位相的北大西洋多年代际振荡(AMO)共同加强了北方东部地区上空的正位势高度异常。进一步研究表明,极端降水与普通降水的水汽输送和收支以及关键的局地大气系统存在着显著差异,较普通降水而言,极端降水在南北向水汽输送和收支上更强;北方东部地区低空为较强的闭合低压控制,并不断受到高层高位涡空气下传的影响。

关键词：中国北方东部极端降水日数极端降水贡献率水汽输送年代际变化