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A fresh look at the deepwater overflow in the Luzon Strait

Acta Oceanologica Sinica 2017年第5期36卷 1-8页

作者： ZHU Yaohua SUN Junchuan WEI Zexun WANG Yonggang FANG Guohong QU Tangdong Key Laboratory of Marine Science and Numerical Modeling The First Institute of OceanographyState Oceanic Administration Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technology Joint Institute for Regional Earth System Science and Engineering University of California

On the basis of the latest version of a U.S. Navy generalized digital environment model （GDEM-V3.0） and World Ocean Atlas （WOA13）, the hydraulic theory is revisited and applied to the Luzon Strait, providing a fresh look at the deepwater overflow there. The result reveals that：（1） the persistent density difference between two sides of the Luzon Strait sustains an all year round deepwater overflow from the western Pacific to the South China Sea （SCS）; （2） the seasonal variability of the deepwater overflow is influenced not only by changes in the density difference between two sides of the Luzon Strait, but also by changes in its upstream layer thickness; （3） the deepwater overflow in the Luzon Strait shows a weak semiannual variability; （4） the seasonal mean circulation pattern in the SCS deep basin does not synchronously respond to the seasonality of the deepwater overflow in the Luzon Strait. Moreover, the deepwater overflow reaches its seasonal maximum in December （based on GDEM-V3.0） or in fall （October-December, based on the WOA13）, accompanied by the lowest temperature of the year on the Pacific side of the Luzon Strait. The seasonal variability of the deepwater overflow is consistent with the existing longest （3.5 a） continuous observation along the major deepwater passage of the Luzon Strait.

关键词： Luzon Strait deepwater overflow seasonal variability South China Sea

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Attributing climate and weather extremes to Northern Hemisphere sea ice and terrestrial snow: progress, challenges and ways forward

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npj Climate and Atmospheric Science 2025年第1期8卷 1-22页

作者： Ye, Kunhui Cohen, Judah Chen, Hans W. Zhang, Shiyue Luo, Dehai Hamouda, Mostafa Essam Atmospheric Oceanic and Planetary Physics University of Oxford Oxford United Kingdom Atmospheric and Environmental Research Lexington United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge United States Department of Space Earth and Environment Chalmers University of Technology Gothenburg Sweden Key Laboratory of Meteorological Disaster Ministry of Education Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Joint International Research Laboratory of Climate and Environment Change Nanjing University of Information Science and Technology Nanjing China Key Laboratory of Earth System Numerical Modeling and Application and Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China and University of Chinese Academy of Sciences Beijing China Astronomy and Meteorology Department Faculty of Science Cairo University Cairo Al Giza Egypt

Sea ice and snow are crucial components of the cryosphere and the climate system. Both sea ice and spring snow in the Northern Hemisphere (NH) have been decreasing at an alarming rate in a changing climate. Changes in NH sea ice and snow have been linked with a variety of climate and weather extremes including cold spells, heatwaves, droughts and wildfires. Understanding of these linkages will benefit the predictions of climate and weather extremes. However, existing work on this has been largely fragmented and is subject to large uncertainties in physical pathways and methodologies. This has prevented further substantial progress in attributing climate and weather extremes to sea ice and snow change, and will potentially risk the loss of a critical window for effective climate change mitigation. In this review, we synthesize the current progress in attributing climate and weather extremes to sea ice and snow change by evaluating the observed linkages, their physical pathways and uncertainties in these pathways, and suggesting ways forward for future research efforts. By adopting the same framework for both sea ice and snow, we highlight their combined influence and the cryospheric feedback to the climate system. We suggest that future research will benefit from improving observational networks, addressing the causality and complexity of the linkages using multiple lines of evidence, adopting large-ensemble approaches and artificial intelligence, achieving synergy between different methodologies/disciplines, widening the context, and coordinated international collaboration. © The Author(s) 2025.

关键词： Atmospheric dynamics Climate change Cryospheric science

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The Flexible Global Ocean-Atmosphere-Land system Model, Grid-point Version 2: FGOALS-g2

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Advances in Atmospheric Sciences 2013年第3期30卷 543-560页

作者：李立娟林鹏飞俞永强王斌周天军刘利刘骥平包庆徐世明黄文誉夏坤普业董理申思刘屹岷胡宁刘咪咪孙文奇史湘军郑伟鹏吴波宋米荣刘海龙张学洪吴国雄薛巍黄小猛杨广文宋振亚乔方利 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Ministry of Education Key Laboratory for Earth System Modeling Center of Earth System Science Tsinghua University Climate Center Hebei Meteorological Administration First Institute of Oceanography State Oceanic Administration

This study mainly introduces the development of the Flexible Global Ocean-Atmosphere-Land system Model： Grid-point Version 2 （FGOALS-g2） and the preliminary evaluations of its performances based on re- sults from the pre-industrial control run and four members of historical runs according to the fifth phase of the Coupled Model Intercomparison Project （CMIP5） experiment design. The results suggest that many obvi- ous improvements have been achieved by the FGOALS-g2 compared with the previous version, FGOALS-gl, including its climatological mean states, climate variability, and 20th century surface temperature evolution. For example, FGOALS-g2 better simulates the frequency of tropical land precipitation, East Asian Monsoon precipitation and its seasonal cycle, MJO and ENSO, which are closely related to the updated cumulus parameterization scheme, as well as the alleviation of uncertainties in some key parameters in shallow and deep convection schemes, cloud fraction, cloud macro/microphysical processes and the boundary layer scheme in its atmospheric model. The annual cycle of sea surface temperature along the equator in the Pacific is significantly improved in the new version. The sea ice salinity simulation is one of the unique characteristics of FGOALS-g2, although it is somehow inconsistent with empirical observations in the Antarctic.

关键词： FGOALS-g2 climatological mean state climate variability 20th century climate monsoon

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Improving Tropical Cyclone Track Forecast Skill Through Assimilating Target Observation Achieved by AI-Based Conditional Nonlinear Optimal Perturbation

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Journal of Geophysical Research: Atmospheres 2025年第9期130卷

作者： Yonghui Li Wansuo Duan Wei Han Hao Li Xiaohao Qin State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Shanghai Academy of Artificial Intelligence for Science (SAIS) Shanghai China State Key Laboratory of Severe Weather Meteorological Science and Technology (LASW) Beijing China CMA Earth System Modeling and Prediction Centre (CEMC) China Meteorological Administration Beijing China Artificial Intelligence Innovation and Incubation Institute Fudan University Shanghai China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China

The artificial intelligence (AI)-based weather forecasting model named FuXi and its data assimilation (DA) system FuXi-En4DVar has been developed for high-efficiently forecasting high-impact weather events such as tropical cyclones (TCs). Besides conventional observations, target observations are essential to further improve initial field accuracy and then increasing high-impact weather event forecasting skills. The identification of the sensitive area, where the additional observations should be deployed, is the key to implementing target observations. In this paper, a sensitive area identification system is established for the FuXi model on the basis of FuXi-En4DVar, based on the fully nonlinear method of conditional nonlinear optimal perturbation (CNOP). The CNOP represents the optimally growing initial perturbation and can be calculated by using the adjoint of numerical models in numerical forecast models, but in the AI-based FuXi model, it is solved by directly using the automatic differential algorithm embedded in the FuXi model. Such an approach of calculating CNOP significantly increases the computational efficiency. Applying this system to the forecasts of 11 TCs demonstrates that the additional target observations can significantly improve TC track forecast skills, as compared with the other additional observations. Moreover, a small number of additional target observations can be expected to achieve the forecast skill comparable to, or even surpassing to, that obtained by tens of times more observations. This validation shows the potential of applying dynamical CNOP to AI-based model for highly effectively identifying the sensitive area for target observations associated with TC forecasting. AI optimization system for conditional nonlinear optimal perturbation (CNOP) is developed for identifying the sensitive areas of tropical cyclones (TC). Assimilating target observation obviously improves TC track forecast skills. The end-to-end forecast is realized in

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Bias characteristics of cloud diurnal variation in the FGOALS-f3-L model

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Atmospheric and Oceanic Science Letters 2025年

作者： Hongtao Yang Guoxing Chen Qing Bao Bian He Department of Atmospheric and Oceanic Sciences Institute of Atmospheric Sciences and CMA-FDU Joint Laboratory of Marine Meteorology Fudan University Shanghai China Shanghai Frontier Science Center of Atmosphere-Ocean Interaction Fudan University Shanghai China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China

Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics. However, it is often overlooked in the evaluation and development of climate models. Thus, this study aims to investigate the daily mean (CFR) and diurnal variation (CDV) of cloud fraction across high-, middle-, low-level, and total clouds in the FGOALS-f3-L general circulation model. The bias of total CDV is decomposed into the model biases in CFRs and CDVs of clouds at all three levels. Results indicate that the model generally underestimates low-level cloud fraction during the daytime and high-/middle-level cloud fraction at nighttime. The simulation biases of low clouds, especially their CDV biases, dominate the bias of total CDV. Compensation effects exist among the bias decompositions, where the negative contributions of underestimated daytime low-level cloud fraction are partially offset by the opposing contributions from biases in high-/middle-level clouds. Meanwhile, the bias contributions have notable land–ocean differences and region-dependent characteristics, consistent with the model biases in these variables. Additionally, the study estimates the influences of CFR and CDV biases on the bias of shortwave cloud radiative effects. It reveals that the impacts of CDV biases can reach half of those from CFR biases, highlighting the importance of accurate CDV representation in climate models. 云量日变化可以调节云辐射效应, 影响大气动力过程, 但在气候模式评估中常被忽视.本研究评估了FGOALS-f3-L模式中高,中,低云及总云云量的日均值和日变化特征.结果表明, 模式普遍低估白天低云云量和夜间中,高云云量.低云云量日变化误差主导总云云量日变化误差.其中, 低云误差造成的负值贡献被中,高云误差的正值贡献部分抵消.误差贡献呈现显著的海陆和区域差异, 与相应云量的模式误差一致.同时, 云量日变化误差对短波云辐射效应误差的影响可达日均云量影响的一半, 突显了在模式中准确表征云量日变化的重要性.

关键词： Cloud fraction Diurnal variation Climate model Model bias dissection Shortwave cloud radiative effects 云量日变化气候模式模式误差分解短波云辐射效应

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EnKF Assimilation of Satellite-retrieved Cloud Water Path to Improve Tropical Cyclone Rainfall Forecast

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Journal of Tropical Meteorology 2021年第3期27卷 201-217页

作者： GAO Xiao-yu LIN Yan-luan YUE Jian Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084 China Center of Numerical Weather Predication China Meteorological AdministrationBeijing 100081 China

Tropical cyclone(TC) rainfall forecast has remained a challenge. To create initial conditions with high quality for simulation, the present study implemented a data assimilation scheme based on the EnKF method to ingest the satellite-retrieved cloud water path(C_(w)) and tested it in WRF. The scheme uses the vertical integration of forecasted cloud water content to transform control variables to the observation space, and creates the correlations between C_(w) and control variables in the flow-dependent background error covariance based on all the ensemble members, so that the observed cloud information can affect the background temperature and humidity. For two typhoons in 2018(Yagi and Rumiba), assimilating C_(w) significantly increases the simulated rainfalls and TC intensities. In terms of the average equitable threat score of daily moderate to heavy rainfall(5-120 mm), the improvements are over 130%, and the dry biases are cut by about 30%. Such improvements are traced down to the fact that C_(w) assimilation increases the moisture content, especially that further away from the TC center, which provides more precipitable water for the rainfall,strengthens the TC and broadens the TC size via latent heat release and internal wind field adjustment.

关键词： tropical cyclone data assimilation EnKF cloud water path

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Past warm intervals inform the future South Asian summer monsoon

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Nature 2025年第8063期641卷 653-659页

作者： Linqiang He Tianjun Zhou Zhun Guo State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Lamont-Doherty Earth Observatory Columbia University Palisades NY USA Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China

In the future, monsoon rainfall over densely populated South Asia is expected to increase, even as monsoon circulation weakens1,2,3. By contrast, past warm intervals were marked by both increased rainfall and a strengthening of monsoon circulation4,5,6, posing a challenge to understanding the response of the South Asian summer monsoon to warming. Here we show consistent South Asian summer monsoon changes in the mid-Pliocene, Last Interglacial, mid-Holocene and future scenarios, characterized by an overall increase in monsoon rainfall, a weakening of the monsoon trough-like circulation over the Bay of Bengal and a strengthening of the monsoon circulation over the northern Arabian Sea, as revealed by a compilation of proxy records and climate simulations. Increased monsoon rainfall is thermodynamically dominated by atmospheric moisture following the rich-get-richer paradigm, and dynamically dominated by the monsoon circulation driven by the enhanced land warming in subtropical western Eurasia and northern Africa. The coherent response of monsoon dynamics across warm climates reconciles past strengthening with future weakening, reinforcing confidence in future projections. Further prediction of South Asian summer monsoon circulation and rainfall by physics-based regression models using past information agrees well with climate model projections, with spatial correlation coefficients of approximately 0.8 and 0.7 under the high-emissions scenario. These findings underscore the promising potential of past analogues, bolstered by palaeoclimate reconstruction, in improving future South Asian summer monsoon projections.

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A Decadal Shift of Summer Surface Air Temperature over Northeast Asia around the Mid-1990s

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Advances in Atmospheric Sciences 2014年第4期31卷 735-742页

作者： CHEN Wei LU Riyu State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Division of Earth Environmental System Pusan National University

This study identifies a decadal shift of summer surface air temperature （SAT） over Northeast Asia,including southeastern parts of Russia,Mongolia and northern China,around the *** results suggest that the SAT over the Northeast Asia experienced a significant warming after 1994 relative to that before *** decadal shift also extends to northern China,and leads to a warmer summer over Northeast China and North China after the *** decadal warming over Northeast Asia is found to concur with the enhancement of South China rainfall around the *** the one hand,both the Northeast Asian SAT and South China rainfall exhibit this mid-1990s decadal shift only in summer,but not in other *** the other hand,both the Northeast Asian SAT and South China rainfall exhibit this mid-1990s decadal shift not only in the summer seasonal mean,but also in each month of summer （June,July and August）.Furthermore,the decadal warming is found to result from an anticyclonic anomaly over Northeast Asia,which can be interpreted as the response to the increased precipitation over South China,according to previous numerical ***,we conclude that the warming shift of summer Northeast Asian SAT around the mid-1990s was a remote response to the increased precipitation over South China.

关键词： surface air temperature Northeast Asia decadal shift mid-1990s South China rainfall

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Determination of the Backward Predictability Limit and Its Relationship with the Forward Predictability Limit

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Advances in Atmospheric Sciences 2019年第6期36卷 669-677页

作者： Xuan LI Ruiqiang DING Jianping LI State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) Institute of Atmospheric Physics Chinese Academy of Sciences College of Earth Science University of Chinese Academy of Sciences Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technology College of Global Change and Earth System Sciences (GCESS) Beijing Normal University

In this work, two types of predictability are proposed—forward and backward predictability—and then applied in the nonlinear local Lyapunov exponent approach to the Lorenz63 and Lorenz96 models to quantitatively estimate the local forward and backward predictability limits of states in phase space. The forward predictability mainly focuses on the forward evolution of initial errors superposed on the initial state over time, while the backward predictability is mainly concerned with when the given state can be predicted before this state happens. From the results, there is a negative correlation between the local forward and backward predictability limits. That is, the forward predictability limits are higher when the backward predictability limits are lower, and vice versa. We also find that the sum of forward and backward predictability limits of each state tends to fluctuate around the average value of sums of the forward and backward predictability limits of sufficient ***, the average value is constant when the states are sufficient. For different chaotic systems, the average value is dependent on the chaotic systems and more complex chaotic systems get a lower average value. For a single chaotic system,the average value depends on the magnitude of initial perturbations. The average values decrease as the magnitudes of initial perturbations increase.

关键词： nonlinear local Lyapunov exponent forward and backward predictability limit negative correlation average value

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Characteristics of pressure gradient force errors in a terrain-following coordinate

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Atmospheric and Oceanic Science Letters 2016年第3期9卷 211-218页

作者： LI Jin-Xi LI Yi-Yuan WANG Bin State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of Sciences College of Earth Science University of Chinese Academy of Sciences Ministry of Education Key Laboratory for Earth System Modeling and Center for Earth System Science Tsinghua University

A terrain-following coordinate （a-coordinate） in which the computational form of pressure gradient force （PGF） is two-term （the so-called classic method） has significant PGF errors near steep terrain. Using the covariant equations of the a-coordinate to create a one-term PGF （the covariant method） can reduce the PGF errors. This study investigates the factors inducing the PGF errors of these two methods, through geometric analysis and idealized experiments. The geometric analysis first demonstrates that the terrain slope and the vertical pressure gradient can induce the PGF errors of the classic method, and then generalize the effect of the terrain slope to the effect of the slope of each vertical layer （φ）. More importantly, a new factor, the direction of PGF （a）, is proposed by the geometric analysis, and the effects of φ and a are quantified by tan φ.tan a. When tan φ.tan a is greater than 1/9 or smaller than -10/9, the two terms of PGF of the classic method are of the same order but opposite in sign, and then the PGF errors of the classic method are large. Finally, the effects of three factors on inducing the PGF errors of the classic method are validated by a series of idealized experiments using various terrain types and pressure fields. The experimental results also demonstrate that the PGF errors of the covariant method are affected little by the three factors.

关键词： Terrain-following coordinatepressure gradient forceerrors direction of pressuregradient slope of eachvertical layer nonlinearvertical pressure gradient pressure gradient alongvertical layer

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