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Some Advances in Studies of the Climatic Impacts of the Southern Hemisphere Annular Mode

Journal of Meteorological Research 2014年第5期28卷 820-835页

作者：郑菲李建平刘婷 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of Sciences College of Global Change and Earth System Science Beijing Normal University University of Chinese Academy of Sciences

The Southern Hemisphere （SH） annular mode （SAM） is the dominant mode of atmospheric circulation in the SH extratropics. The SAM regulates climate in many regions due to its large spatial scale. Exploration of the climatic impacts of the SAM is a new research field that has developed rapidly in recent years. This paper reviews studies of the climatic impact of the SAM on the SH and the Northern Hemisphere （NH）, emphasizing linkages between the SAM and climate in China. Studies relating the SAM to climate change are also discussed. A general survey of these studies have been systematically investigated. On interannual shows that signals of the SAM in the SH climate scales, the SAM can influence the position of storm tracks and the vertical circulation, and modulate the dynamic and thermodynamic driving effects of the surface wind on the underlying surface, thus influencing the SH air-sea-ice coupled system. These influences generally show zonally symmetrical characteristics, but with local features. On climate change scales, the impacts of the SAM on SH climate change show a similar spatial distribution to those on interannual scales. There are also meaningful results on the relationship between the SAM and the NH climate. The SAM is known to affect the East Asian, West African, and North American summer monsoons, as well as the winter monsoon in China. Air-sea interaction plays an important role in these connections in terms of the storage of the SAM signal and its propagation from the SH to the NH. However, compared with the considerable knowledge of the impact of the SAM on the SH climate, the response of the NH climate to the SAM deserves further study, including both a deep understanding of the propagation mechanism of the SAM signal from the SH to the NH and the establishment of a seasonal prediction model based on the SAM.

关键词： Southern Hemisphere annular mode (SAM) monsoon climatic impact climate change inter-action between the Northern and Southern Hemispheres

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Determining the Spectrum of the Nonlinear Local Lyapunov Exponents in a Multidimensional Chaotic system

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Advances in Atmospheric Sciences 2017年第9期34卷 1027-1034页

作者： Ruiqiang DING Jianping LI Baosheng LI State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of Sciences Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information Technology College of Global Change and Earth System Sciences Beijing Normal University University of Chinese Academy of Sciences

For an n-dimensional chaotic system, we extend the definition of the nonlinear local Lyapunov exponent （NLLE） from one- to n-dimensional spectra, and present a method for computing the NLLE spectrum. The method is tested on three chaotic systems with different complexity. The results indicate that the NLLE spectrum realistically characterizes the growth rates of initial error vectors along different directions from the linear to nonlinear phases of error growth. This represents an improvement over the traditional Lyapunov exponent spectrum, which only characterizes the error growth rates during the linear phase of error growth. In addition, because the NLLE spectrum can effectively separate the slowly and rapidly growing perturbations, it is shown to be more suitable for estimating the predictability of chaotic systems, as compared to the traditional Lyapunov exponent spectrum.

关键词： Lyapunov exponent nonlinear local Lyapunov exponent predictability

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Learning to Infer Weather States Using Partial Observations

Journal of Geophysical Research: Machine Learning and Comput...

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Journal of Geophysical Research: Machine Learning and Computation 2025年第1期2卷

作者： Jie Chao Baoxiang Pan Quanliang Chen Shangshang Yang Jingnan Wang Congyi Nai Yue Zheng Xichen Li Huiling Yuan Xi Chen Bo Lu Ziniu Xiao National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing China School of Atmospheric Sciences Chengdu University of Information Technology Chengdu China Key Laboratory of Mesoscale Severe Weather Ministry of Education and School of Atmospheric Sciences Nanjing University Nanjing China College of Computer National University of Defense Technology Hunan China Clustertech LTD Hong Kong China National Climate Center China Meteorological Administration Beijing China

Accurate state estimation of the high-dimensional, chaotic earth's atmosphere marks a Sisyphean task, yet is indispensable for initiating weather forecasts and gauging climate variability. While much effort is devoted to assimilating observations and forecasts to infer weather states, the inherent low-dimensional statistical structure in atmospheric circulation, shaped by geophysical laws and geographic boundaries, is underutilized as an informative prior for state inference, or as reference for assessing representative of existing observations and planning new ones. We realize these potential by learning climatological distribution from climate reanalysis/simulation, using a deep generative model. For a case study of estimating 2 m temperature spatial patterns, the learned distribution faithfully reproduces climatology statistics. A combination of the learned climatological prior with few station observations yields strong posterior of spatial pattern estimates, which are spatially coherent, faithful and adaptive to observational constraints, and uncertainty-aware. This allows us to evaluate each observation's value in reducing pattern estimation uncertainty, and guide optimal observation network design by pinpointing the most informative sites. Our study showcases how generative models can extract and utilize information produced in the chaotic evolution of climate system. Deep generative model enables accurate spatial interpolation of weather variables from sparse observations The generative model produces spatially coherent and uncertainty-aware estimates, effectively constrained by observational data The approach provides a framework to quantify observation impact on uncertainty reduction, enabling optimization of observation networks Accurate estimation of weather conditions across a large area is crucial but challenging due to the complex and chaotic nature of the atmosphere. Traditional methods rely on combining observations with forecasts, which can be comp

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A Long-Time-Step-Permitting Tracer Transport Model on the Regular Latitude–Longitude Grid

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Advances in Atmospheric Sciences 2024年第3期41卷 493-508页

作者： Jianghao LI Li DONG Key Laboratory of Earth System Modeling and Prediction China Meteorological AdministrationBeijing 100081China State key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG) Institute of Atmospheric Physics(IAP)Chinese Academy of SciencesBeijing 100029China State Key Laboratory of Severe Weather(LaSW) Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China

If an explicit time scheme is used in a numerical model, the size of the integration time step is typically limited by the spatial resolution. This study develops a regular latitude–longitude grid-based global three-dimensional tracer transport model that is computationally stable at large time-step sizes. The tracer model employs a finite-volume flux-form semiLagrangian transport scheme in the horizontal and an adaptively implicit algorithm in the vertical. The horizontal and vertical solvers are coupled via a straightforward operator-splitting technique. Both the finite-volume scheme's onedimensional slope-limiter and the adaptively implicit vertical solver's first-order upwind scheme enforce monotonicity. The tracer model permits a large time-step size and is inherently conservative and monotonic. Idealized advection test cases demonstrate that the three-dimensional transport model performs very well in terms of accuracy, stability, and efficiency. It is possible to use this robust transport model in a global atmospheric dynamical core.

关键词： tracer transport numerical stability latitude–longitude grid

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Past, present and future of the carbon cycle

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National Science Review 2014年第1期1卷 18-21页

作者： Zhenghui Xie Ning Zeng Huijun Wang Zheng Lin Xiangjun Tian Binghao Jia The State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) Institute of Atmospheric Physics Chinese Academy of Sciences Department of Atmospheric and Oceanic Science and Earth System Science Interdisciplinary Center University of Maryland

The recent Ninth International Carbon Dioxide Conference(ICDC9)held in Beijing highlighted the importance and urgency of global carbon management,research challenges,and recent eforts made by Chinese scientists in this *** from fossil fuel use for energy and land use increase atmospheric carbon dioxide(CO2)levels,but carbon sinks in the ocean and on land are currently absorbing half of fossil fuel ***,neither it is clear how these

关键词： Past present and future of the carbon cycle

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Simulation and Causes of Eastern Antarctica Surface Cooling Related to Ozone Depletion during Austral Summer in FGOALS-s2

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Advances in Atmospheric Sciences 2014年第5期31卷 1147-1156页

作者： YANG Jing BAO Qing JI Duoying GONG Daoyi MAO Rui ZHANG Ziyin Seong-Joong KIM State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences College of Global Change and Earth System Science Beijing Normal University Beijing Meteorological Bureau Korea Polar Research Institute Incheon 406-130 Korea

Two parallel sets of numerical experiments （an ozone-hole simulation and a non-ozone-hole simulation） were performed to investigate the effect of ozone depletion on surface temperature change using the second spectral version of the Flexible Global Ocean-Atmosphere-Land system model （FGOALS-s2）, focusing on the eastern Antarctica （EA） continent in austral summer. First, we evaluated the ability of the model to simulate the EA surface cooling, and found the model can successfully reproduce the cooling trend of the EA surface, as well as the circulation change circling the South Pole in the past 30 years. Second, we compared the two experiments and discovered that the ozone depletion causes the cooling trend and strengthens the circumpolar westerly flow. We further investigated the causes of the EA surface cooling associated with the ozone hole and found two major contributors. The first is the ozone-hole direct radiation effect （DRE） upon the surface that happens because the decrease of the downward longwave （LW） radiation overcomes the increase of the downward shortwave （SW） radiation under clear sky. The second is the cloud radiation effect （CRE） induced by ozone depletion, which happens because the decreased downward SW radiation overcomes the increased downward LW radiation in the case of increased cloud. Although the CRE is theoretically opposite to the DRE, their final net effect makes comparable contributions to the EA surface cooling. Compared with the surface radiation budget, the surface heat flux budgets have a much smaller contribution. We additionally note that the CRE is basically ascribed to the circulation change.

关键词： ozone depletion eastern Antarctica surface cooling numerical simulation

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Impacts of uncertain cloud-related parameters on Pacific Walker circulation simulation in GAMIL2

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Atmospheric and Oceanic Science Letters 2018年第1期11卷 7-14页

作者： XIE Feng LI Li-Juan WANG Bin XUE Wei State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics （LASG） Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China College of Earth Science University of Chinese Academy of Sciences Beijing China Ministry of Education Key Laboratory for Earth System Modeling Center of Earth System Science （CESS） Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

To investigate the impacts of uncertain parameters on simulated Pacific Walker circulation （PWC）, a large number of perturbed parameter simulations are conducted using GAMIL2 （the Grid-point Atmospheric Model of IAP/LASG, version 2）, and three different PWC indices are *** results show that the influences of some parameters on PWC are dependent on the selected index - a finding supported by the inconsistent responses of different indexes to these parameters. Among the nine parameters, the RH threshold for deep convection （RHCRIT） is the most sensitive in simulating PWC. Increased RHCRIT weakens deep convective heating and stratiform cooling, and strengthens shallow convective heating. Further analysis reveals that uncertain parameters affect the simulated PWC through changing the diabatic heating and vertical motion.

关键词： Walker circulation Pacific uncertainty cloud-relatedparameters GAMIL2 diabaticheating

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Is the atmospheric river operating at a self-organized criticality state?

arXiv

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arXiv 2025年

作者： Wang, Shang Meng, Jun Fang, Sheng Liu, Teng Christensen, Kim Kurths, Jürgen Fan, Jingfang School of Systems Science Institute of Nonequilibrium Systems Beijing Normal University Beijing100875 China Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China Earth System Modelling School of Engineering and Design Technical University of Munich Munich85748 Germany Potsdam Institute for Climate Impact Research Potsdam14412 Germany Blackett Laboratory Centre for Complexity Science Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom Department of Physics Humboldt University Berlin10099 Germany

Atmospheric rivers (ARs) are essential components of the global hydrological cycle, with profound implications for water resources, extreme weather events, and climate dynamics. Yet, the statistical organization and underlying physical mechanisms of AR intensity and evolution remain poorly understood. Here we apply methods from statistical physics to analyze the full life cycle of ARs and identify universal signatures of self-organized criticality (SOC). We demonstrate that AR morphology exhibits nontrivial fractal geometry, while AR event sizes—quantified via integrated water vapor transport—follow robust power-law distributions, displaying finite-size scaling. These scaling behaviors persist under warming scenarios, suggesting that ARs operate near a critical state as emergent, self-regulating systems. Concurrently, we observe a systematic poleward migration and intensification of ARs, linked to thermodynamic amplification and dynamical reorganization. Our findings establish a statistical physics framework for ARs, linking critical phenomena to the spatiotemporal structure of extreme events in a warming climate. © 2025, CC BY.

关键词： Statistical mechanics

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Autumn Snow Cover Variability over Northern Eurasia and Roles of Atmospheric Circulation

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Advances in Atmospheric Sciences 2017年第7期34卷 847-858页

作者： Kunhui YE Renguang WU Institute of Space and Earth Information Science The Chinese University of Hong Kong Hong Kong China Center for Monsoon System Research/State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China

This study analyzes the variability of northern Eurasian snow cover（SC） in autumn and the impacts of atmospheric circulation changes. The region of large SC variability displays a southward shift from September to November, following the seasonal progression of the transition zones of surface air temperature（SAT）. The dominant pattern of SC variability in September and October features a zonal distribution, and that in November displays an obvious west-east contrast. Surface air cooling and snowfall increase are two factors for larger SC. The relative contribution of SAT and snowfall changes to SC, however, varies with the region and depends upon the season. The downward longwave radiation and atmospheric heat advection play important roles in SAT changes. Anomalous convergence of water vapor flux contributes to enhanced *** changes in downward longwave radiation are associated with those in atmospheric water content and column *** in snowfall and the transport of atmospheric moisture determine the atmospheric moisture content in September and October, and the snowfall appears to be a main factor for atmospheric moisture change in November. These results indicate that atmospheric circulation changes play an important role in snow variability over northern Eurasia in autumn. Overall, the coupling between autumn Eurasian snow and atmospheric circulation may not be driven by external forcing.

关键词： Autumn Eurasian snow cover surface air temperature surface heat fluxes atmospheric circulation water vaportransport

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The Relationship between Deterministic and Ensemble Mean Forecast Errors Revealed by Global and Local Attractor Radii

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Advances in Atmospheric Sciences 2019年第3期36卷 271-278,339页

作者： Jie FENG Jianping LI Jing ZHANG Deqiang LIU Ruiqiang DING School of Meteorology University of Oklahoma College of Global Change and Earth System Science (GCESS) Beijing Normal University Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technology Cooperative Institute for Research in the Atmosphere GSD/ESRL/OAR/NOAA Fujian Meteorological Observatory Wuyishan National Park Meteorological Observatory State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information Technology

It has been demonstrated that ensemble mean forecasts, in the context of the sample mean, have higher forecasting skill than deterministic(or single) forecasts. However, few studies have focused on quantifying the relationship between their forecast errors, especially in individual prediction cases. Clarification of the characteristics of deterministic and ensemble mean forecasts from the perspective of attractors of dynamical systems has also rarely been involved. In this paper, two attractor statistics—namely, the global and local attractor radii(GAR and LAR, respectively)—are applied to reveal the relationship between deterministic and ensemble mean forecast errors. The practical forecast experiments are implemented in a perfect model scenario with the Lorenz96 model as the numerical results for verification. The sample mean errors of deterministic and ensemble mean forecasts can be expressed by GAR and LAR, respectively, and their ratio is found to approach2^(1/2) with lead time. Meanwhile, the LAR can provide the expected ratio of the ensemble mean and deterministic forecast errors in individual cases.

关键词： attractor radius ensemble forecasting ensemble mean forecast error saturation

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