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Influence of the NAO on Wintertime Surface Air Temperature over East Asia:Multidecadal Variability and Decadal Prediction

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Advances in Atmospheric Sciences 2022年第4期39卷 625-642页

作者： Jianping LI Tiejun XIE Xinxin TANG Hao WANG Cheng SUN Juan FENG Fei ZHENG Ruiqiang DING Frontiers Science Center for Deep Ocean Multispheres and Earth System-Key Laboratory of Physical Oceanography-Institute for Advanced Ocean Studies-Academy of the Future Ocean Ocean University of ChinaQingdao 266100China Laboratory for Ocean Dynamics and Climate Pilot Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China College of Global Change and Earth System Sciences Beijing Normal UniversityBeijing 100875China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal UniversityBeijing 100875China

In this paper,we investigate the influence of the winter NAO on the multidecadal variability of winter East Asian surface air temperature(EASAT)and EASAT decadal *** observational analysis shows that the winter EASAT and East Asian minimum SAT(EAmSAT)display strong in-phase fluctuations and a significant 60-80-year multidecadal variability,apart from a long-term warming *** winter EASAT experienced a decreasing trend in the last two decades,which is consistent with the occurrence of extremely cold events in East Asia winters in recent *** winter NAO leads the detrended winter EASAT by 12-18 years with the greatest significant positive correlation at the lead time of 15 *** analysis shows that ENSO may affect winter EASAT interannual variability,but does not affect the robust lead relationship between the winter NAO and *** present the coupled oceanic-atmospheric bridge(COAB)mechanism of the NAO influences on winter EASAT multidecadal variability through its accumulated delayed effect of~15 years on the Atlantic Multidecadal Oscillation(AMO)and Africa-Asia multidecadal teleconnection(AAMT)*** NAO-based linear model for predicting winter decadal EASAT is constructed on the principle of the COAB mechanism,with good hindcast *** winter EASAT for 2020-34 is predicted to keep on fluctuating downward until~2025,implying a high probability of occurrence of extremely cold events in coming winters in East Asia,followed by a sudden turn towards sharp *** predicted 2020/21 winter EASAT is almost the same as the 2019/20 winter.

关键词： winter East Asian surface air temperature North Atlantic Oscillation Atlantic Multidecadal Oscillation Africa-Asia multidecadal teleconnection pattern coupled oceanic-atmospheric bridge multidecadal variability

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Prediction of ENSO using multivariable deep learning

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Atmospheric and Oceanic Science Letters 2023年第4期16卷 51-56页

作者： Yue Chen Xiaomeng Huang Jing-Jia Luo Yanluan Lin Jonathon S.Wright Youyu Lu Xingrong Chen Hua Jiang Pengfei Lin Ministry of Education Key Laboratory for Earth System Modeling and Department for Earth System Science Tsinghua UniversityBeijingChina Institute for Climate and Application Research(ICAR)/CICFEM/KLME/ILCEC Nanjing University of Information Science and TechnologyNanjingChina Ocean and Ecosystem Sciences Division Fisheries and Oceans CanadaBedford Institute of OceanographyDartmouthNova ScotiaCanada National Marine Environmental Forecasting Center State Oceanic AdministrationBeijingChina State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina

本文基于残差神经网络和观测数据构建了一套深度学习多因子预报测模型,以改进厄尔尼诺-南方涛动(ENSO)的预报.该模型基于最大信息系数进行因子时空特征提取,并根据泰勒图的评估标准可自动确定关键预报因子进行预报.该模型在超前8个月以... 详细信息

本文基于残差神经网络和观测数据构建了一套深度学习多因子预报测模型,以改进厄尔尼诺-南方涛动(ENSO)的预报.该模型基于最大信息系数进行因子时空特征提取,并根据泰勒图的评估标准可自动确定关键预报因子进行预报.该模型在超前8个月以内的预报性能要优于当前传统的业务预报模式.2011–2018年间,该模型的预报性能优于多模式集成预报的结果.在超前6个月预报时效上,模型预报相关性可达0.82,标准化后的均方根误差仅为0.58°C,多模式集成预报的相关性和标准化后的均方根误差分别为0.70和0.73°C.该模型春季预报障碍问题有所缓解,并且自动选取的关键预报因子可用于解释热带和副热带热动力过程对于ENSO变化的影响.

关键词： ENSO预报深度学习春季预报障碍多维时空预报因子

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Seismic and geological evidence of hidden faults in the Yinpan Reservoir area based on a dense seismic array

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Science China earth Sciences 2024年第7期67卷 2401-2407页

作者： Guangyao YIN Huai ZHANG Liwen GONG Q ipeng BAI Yicun GUO Bing ZHANG Yaolin SHI Key Laboratoryof Earth System Numerical Modeling and Application(Chinese Academyof Sciences) College of Earth and Planetary SciencesUniversity of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Computational Geodynamics Chinese Academy of SciencesBeijing 100049China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519080China Beijing Yanshan Critical Zone National Field Scientific Observation and Research Station Beijing 101408China Chongqing Earthquake Agency Chongqing 401147 China Institute of Earthquake Forecasting China Earthquake AdministrationBeijing 100036China Hangzhou Applied Acoustics Research Institute Hangzhou 311100China State Key Laboratory of Geodesy and Earths Dynamics Innovation Academy for Precision Measurement Science and TechnologyCASWuhan 430077China

*** November 23,2017,an M_(S)5.0 earthquake occurred in Wulong,Chongqing,China,resulting in tens of casualties and considerable economic ***,the epicenter of this earthquake was located in the transition zone from the... 详细信息

关键词： earthquake southeast seismic

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CONDITIONAL NONLINEAR OPTIMAL PERTURBATIONS: application IN TROPICAL CYCLONE FORECASTS

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Journal of Tropical Meteorology 2019年第4期25卷 421-436页

作者： TIAN Hong-jun LI Ze-chun WANG Dong-liang WANG Bin Nanjing University of Information Science and Technology Nanjing 210044 China Shanghai Central Meteorological Observatory Shanghai 200030 China National Meteorological Centre Beijing 100081 China Shanghai Typhoon Institute CMAShanghai 200030 China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of ScienceBeijing 100029 China Ministry of Education Key Laboratory for Earth System Modeling Tsinghua UniversityBeijing 100084 China

Considering the feature of tropical cyclones(TCs) that strong positive vorticity exists in the lower layers of troposphere, this study proposed to use vorticity at 850 hPa as cost function to find the conditional nonlinear optimal perturbation(CNOP), which was largely different from previous studies using total energy of perturbed forecast variables. The CNOP was obtained by an ensemble-based approach. All of the sensitive areas determined by CNOP with vorticity at 850 hPa as cost function for the three cases were located over the TC core region and its vicinity. The impact of the CNOP-based adaptive observations on TC forecasts was evaluated with three cases via observational system simulation experiments(OSSEs). Results showed obvious improvements in TC intensity or track forecasts due to the CNOP-based adaptive observations, which were related to the main error source of the verification area, i.e.,intensity error or location error.

关键词： conditional nonlinear optimal perturbations tropical cyclone intensity track

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Comparison of Nonlinear Local Lyapunov Vectors with Bred Vectors, Random Perturbations and Ensemble Transform Kalman Filter Strategies in a Barotropic Model

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Advances in Atmospheric Sciences 2016年第9期33卷 1036-1046页

作者： Jie FENG Ruiqiang DING Jianping LI Deqiang LIU State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 College of Earth Science University of Chinese Academy of SciencesBeijing 100049 Global Systems Division Earth System Research Laboratory/Oceanic and Atmospheric Research/National Oceanic and Atmospheric AdministrationBoulderCO80305USA Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information TechnologyChengdu 610225 College of Global Change and Earth System Sciences Beijing Normal UniversityBeijing 100875 Joint Center for Global Change Studies Beijing 100875 Fujian Meteorological Observatory Fuzhou 350001

The breeding method has been widely used to generate ensemble perturbations in ensemble forecasting due to its simple concept and low computational cost. This method produces the fastest growing perturbation modes to catch the growing components in analysis errors. However, the bred vectors （BVs） are evolved on the same dynamical flow, which may increase the dependence of perturbations. In contrast, the nonlinear local Lyapunov vector （NLLV） scheme generates flow-dependent perturbations as in the breeding method, but regularly conducts the Gram-Schmidt reorthonormalization processes on the perturbations. The resulting NLLVs span the fast-growing perturbation subspace efficiently, and thus may grasp more com- ponents in analysis errors than the BVs. In this paper, the NLLVs are employed to generate initial ensemble perturbations in a barotropic quasi-geostrophic model. The performances of the ensemble forecasts of the NLLV method are systematically compared to those of the random pertur- bation （RP） technique, and the BV method, as well as its improved version--the ensemble transform Kalman filter （ETKF） method. The results demonstrate that the RP technique has the worst performance in ensemble forecasts, which indicates the importance of a flow-dependent initialization scheme. The ensemble perturbation subspaces of the NLLV and ETKF methods are preliminarily shown to catch similar components of analysis errors, which exceed that of the BVs. However, the NLLV scheme demonstrates slightly higher ensemble forecast skill than the ETKF scheme. In addition, the NLLV scheme involves a significantly simpler algorithm and less computation time than the ETKF method, and both demonstrate better ensemble forecast skill than the BV scheme.

关键词： ensemble forecasting bred vector nonlinear local Lyapunov vector ensemble transform Kalman filter

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Coupling a Terrestrial Biogeochemical Model to the Common Land Model

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Advances in Atmospheric Sciences 2011年第5期28卷 1129-1142页

作者：施小英毛嘉富王应平戴永久唐旭利 State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing 100081 Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge TN 37831 USA State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 CSIRO Marine and Atmospheric Research Aspendale Victoria Australia State Key Laboratory of Earth Surface Processes and Resource Ecology College of Global Change and Earth System Science Beijing Normal University Beijing 100875 South China Botanical Garden the Chinese Academy of Sciences Guangzhou 510650

A terrestrial biogeochemical model （CASACNP） was coupled to a land surface model （the Common Land Model,CoLM） to simulate the dynamics of carbon substrate in soil and its limitation on soil *** combined model,CoLM CASACNP,was able to predict long-term carbon sources and sinks that CoLM alone could *** coupled model was tested using measurements of belowground respiration and surface fluxes from two forest *** combined model simulated reasonably well the diurnal and seasonal variations of net ecosystem carbon exchange,as well as seasonal variation in the soil respiration rate of both the forest sites chosen for this ***,the agreement between model simulations and actual measurements was poorer under dry *** model should be tested against more measurements before being applied globally to investigate the feedbacks between the carbon cycle and climate change.

关键词： CoLM CASACNP model terrestrial carbon cycle carbon flux

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Verification of Seasonal Prediction by the Upgraded China Multi-Model Ensemble Prediction system (CMMEv2.0)

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Journal of Meteorological Research 2024年第5期38卷 880-900页

作者： Jie WU Hong-Li REN Jianghua WAN Jingpeng LIU Jinqing ZUO Changzheng LIU Ying LIU Yu NIE Chongbo ZHAO Li GUO Bo LU Lijuan CHEN Qing BAO Jingzhi SU Lin WANG Jing-Jia LUO Xiaolong JIA Qingchen CHAO CMA Key Laboratory of Climate Prediction Studies National Climate CentreChina Meteorological Administration(CMA)Beijing 100081 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science&TechnologyNanjing 210044 State Key Laboratory of Severe Weather and Institute of Tibetan Plateau Meteorology Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 CMA Earth System Modeling and Prediction Centre China Meteorological AdministrationBeijing 100081

Based on a combination of six Chinese climate models and three international operational models,the China multimodel ensemble(CMME)prediction system has been upgraded into its version 2(CMMEv2.0)at the national Climate Centre(NCC)of the China Meteorological Administration(CMA)by including new model members and expanding prediction products.A comprehensive assessment of the performance of the upgraded CMME during its hindcast(1993–2016)and real-time prediction(2021–present)periods is conducted in this *** results demonstrate that CMMEv2.0 outperforms all the individual models by capturing more realistic equatorial sea surface temperature(SST)*** exhibits better prediction skills for precipitation and 2-m temperature anomalies,and the improvements in prediction skill of CMMEv2.0 are significant over East *** superiority of CMMEv2.0 can be attributed to its better projection of El Niño–Southern Oscillation(ENSO;with the temporal correlation coefficient score for Niño3.4 index reaching 0.87 at 6-month lead)and ENSO-related *** for the real-time prediction in recent three years,CMMEv2.0 has also yielded relatively stable skills;it successfully predicted the primary rainbelt over northern China in summers of 2021–2023 and the warm conditions in winters of 2022/*** that,ensemble sampling experiments indicate that the CMMEv2.0 skills become saturated after the ensemble model number increased to 5–6,indicating that selection of only an optimal subgroup of ensemble models could benefit the prediction performance,especially over the extratropics,yet the underlying reasons await future investigation.

关键词： China multi-model ensemble(CMME)prediction system predictability source El Niño-Southern Oscillation(ENSO) real-time forecast verification

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Decadal Indian Ocean Dipolar Variability and Its Relationship with the Tropical Pacific

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Advances in Atmospheric Sciences 2017年第11期34卷 1282-1289页

作者： Yun YANG Jianping LI Lixin WU Yu KOSAKA Yan DU Cheng SUN Fei XIE Juan FENG State Key Laboratory of Earth Surface Processes and Resource Ecology and College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technologyqingdao 266237China Physical Oceanography Laboratory/Qingdao Collaborative Innovation Center of Marine Science and Technology Ocean University of Chinaqingdao 266003China Research Center for Advanced Science and Technology University of TokyoTokyo153-8904Japan State Key Laboratory of Tropical Oceanography South China Sea Institute of OceanologyChinese Academy of Sciencesguangzhou 510301China

A robust decadal Indian Ocean dipolar variability （DIOD） is identified in observations and found to be related to tropical Pacific decadal variability （TPDV）. A Pacific Ocean-global atmosphere （POGA） experiment, with fixed radiative forcing, is conducted to evaluate the DIOD variability and its relationship with the TPDV. In this experiment, the sea surface temperature anomalies are restored to observations over the tropical Pacific, but left as interactive with the atmosphere elsewhere. The TPDV-forced DIOD, represented as the ensemble mean of 10 simulations in POGA, accounts for one third of the total variance. The forced DIOD is triggered by anomalous Walker circulation in response to the TPDV and develops following Bjerknes feedback. Thermocline anomalies do not exhibit a propagating signal, indicating an absence of oceanic planetary wave adjustment in the subtropical Indian Ocean. The DIOD-TPDV correlation differs among the 10 simulations, with a low correlation corresponding to a strong internal DIOD independent of the TPDV. The variance of this internal DIOD depends on the background state in the Indian Ocean, modulated by the thermocline depth off Sumatra/Java.

关键词： Indian Ocean dipole decadal variability tropical Pacific decadal variability

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Impact of Perturbation Schemes on the Ensemble Prediction in a Coupled Lorenz Model

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Advances in Atmospheric Sciences 2023年第3期40卷 501-513页

作者： Qian ZOU Quanjia ZHONG Jiangyu MAO Ruiqiang DING Deyu LU Jianping LI Xuan LI State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Earth Science University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal UniversityBeijing 100875China Frontiers Science Center for Deep Ocean Multispheres and Earth System(FDOMES)/Key Laboratory of Physical Oceanography/Institute for Advanced Ocean Studies Ocean University of ChinaQingdao 266100China Laboratory for Ocean Dynamics and Climate Pilot Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences Fudan UniversityShanghai 200438China

Based on a simple coupled Lorenz model,we investigate how to assess a suitable initial perturbation scheme for ensemble forecasting in a multiscale system involving slow dynamics and fast *** initial perturbation approaches are used in the ensemble forecasting experiments:the random perturbation(RP),the bred vector(BV),the ensemble transform Kalman filter(ETKF),and the nonlinear local Lyapunov vector(NLLV)*** show that,regardless of the method used,the ensemble averages behave indistinguishably from the control forecasts during the first few time *** to different error growth in different time-scale systems,the ensemble averages perform better than the control forecast after very short lead times in a fast subsystem but after a relatively long period of time in a slow *** to the coupled dynamic processes,the addition of perturbations to fast variables or to slow variables can contribute to an improvement in the forecasting skill for fast variables and slow *** the initial perturbation approaches,the NLLVs show higher forecasting skill than the BVs or RPs *** NLLVs and ETKFs had nearly equivalent prediction skill,but NLLVs performed best by a narrow *** particular,when adding perturbations to slow variables,the independent perturbations(NLLVs and ETKFs)perform much better in ensemble *** results are simply implied in a real coupled air–sea *** the prediction of oceanic variables,using independent perturbations(NLLVs)and adding perturbations to oceanic variables are expected to result in better performance in the ensemble prediction.

关键词： ensemble prediction nonlinear local Lyapunov vector(NLLV) ensemble transform Kalman filter(ETKF) coupled air-sea models

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Strengthening of the annual temperature cycle in the mid-latitudes of Northern Hemisphere

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Discover Geoscience 2024年第1期2卷 1-6页

作者： Tang, Tao Lee, Xuhui Cao, Junji Science Center for Earth System Numerical Simulation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China School of the Environment Yale University New Haven USA

The annual temperature cycle (ATC), defined as the surface temperature difference between summer and winter, plays a crucial role in controlling the phenology of biological systems (Sparks and Menzel in Int J Climatol 22(14):1715–25, 2002) and in global and regional circulation patterns (Schott and McCreary in Prog Oceanogr 51(1):1–123, 2001), (Adam et al. in J Clim 29(9): 3219–3230, 2016). Contrary to previous studies which report a decreasing trend in the ATC worldwide (Mann and Park in Geophys Res Lett 23(10):1111–4, 1996), (Wallace and Osborn in Climate Res 22(1):1–11, 2002), recent studies suggest an increasing ATC trend in the mid-latitude regions (Wang and Dillon in Nat Clim Chang 4(11):988–92, 2014). Although the trend may bear anthropogenic fingerprints, the underlying mechanism remains elusive. Using atmospheric reanalysis data and observational records, we confirm that the increasing trend in the ATC occurred during 1980–2020, mainly in the West North America, Mediterranean region and part of North Asia. The main driver of this ATC change is the faster increase in the incoming radiation on the land surface in the summer as a result of the buildup of water vapor and reduction in cloudiness. This increase was further amplified by continental dryness in the summer.

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