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Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO

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Advances in atmospheric sciences 2024年第1期41卷 141-154页

作者： Tingyu wang Ping huang Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science&TechnologyNanjing 210044China State Key Laboratory of Numerical Modeling for atmospheric sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of chinese academy of sciences Beijing 100049China

The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key *** studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO *** present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 *** with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern *** CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.

关键词： ENSO diversity deep learning ENSO prediction dynamical forecast system

Excellent alkali resistance of Fe,Ce and sulfates co-modified V_(2)O_(5)-WO_(3)/TiO_(2) catalyst for selective catalytic reduction of NO_(x) with NH_(3)

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Journal of Rare Earths 2024年第9期42卷 1682-1692,I0002页

作者： Biyi huang Xianfang Yi Jianyi Zhang Yuqiu liu Yanting Chen Jinsheng Chen Jinxiu wang Center for Excellence in Regional atmospheric Environment and Key Lab of Urban Environment and HealthInstitute of Urban EnvironmentChinese Academy of SciencesXiamen361021China College of Resources and Environment Fujian Agriculture and Forestry UniversityFuzhou350002China Fujian Key Laboratory of atmospheric Ozone Pollution Prevention Institute of Urban EnvironmentChinese Academy of SciencesXiamen361021China University of chinese academy of sciences Beijing100049China

Improving the alkali resistance of catalysts for selective catalytic reduction of NO_(x) with NH_(3) is still a *** this work,the co-modification with Fe,Ce and sulfates on V_(2)O_(5)-WO_(3)/TiO_(2) catalysts(denoted as xSFeCeVWTi) significantly enhances its alkali resistance with K element as a representative.A series of xSFeCeVWTi catalysts was synthesized by wet impregnation with designed 0.05Fe/V, 1.5Ce/V and different S/V molar ratios *** NO_(x) conversion and K resistance of xSFeCeVWTi catalysts increase with the increase of loading amounts of sulfates but no longer further increase as the sulfates load is excessive to block the pores of catalysts and hinder the adsorption of *** optimal modified catalyst in about 2.2S/V actual loading ratio,corresponding to 10.5SFeCeVWTi sample,shows over 99.0%NO_(x) conversion and N_(2) selectivity at 300-400℃ after *** interaction between Fe,Ce and V improves its redox ability but slightly weakens surface acidity,while the proper amount of sulfate species enriches surface Br?nsted acid sites but attenuates its redox ***,a balance of redox capacity and surface acidity,caused by Fe,Ce and sulfate co-modification,contributes to the excellent K resistance of 10.5SFeCeVWTi ***,the change of physicochemical properties influences the reaction mechanism which follows the Eley-Rideal mechanism on *** *** results show that the co-modification with Fe,Ce and sulfates is a good way to improve the alkali tolerance of V_(2)O_(5)-WO_(3)/TiO_(2) catalyst in industrial applications.

关键词： SCR CeO_(2) Fe_(2)O_(3) SO_(4)^(2-) K resistance K poisoning

Understanding the Low Predictability of the 2015/16 El Niño Event Based on a Deep Learning Model

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Advances in atmospheric sciences 2024年第7期41卷 1313-1325页

作者： Tingyu wang Ping huang Xianke YANG Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China National Space Science Center Chinese Academy of SciencesBeijing 100190China State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China College of Plant Science and Technology Huazhong Agricultural UniversityWuhan 430070China

The 2015/16 El Niño event ranks among the top three of the last 100 years in terms of intensity,but most dynamical models had a relatively low prediction skill for this event before the summer ***,the attribution of this particular event can help us to understand the cause of super El Niño–Southern Oscillation events and how to forecast them *** present study applies attribute methods based on a deep learning model to study the key factors related to the formation of this event.A deep learning model is trained using historical simulations from 21 CMIP6 models to predict the Niño-3.4 *** integrated gradient method is then used to identify the key signals in the North Pacific that determine the evolution of the Niño-3.4 *** crucial signals are then masked in the initial conditions to verify their roles in the *** addition to confirming the key signals inducing the super El Niño event revealed in previous attribution studies,we identify the combined contribution of the tropical North Atlantic and the South Pacific oceans to the evolution and intensity of this event,emphasizing the crucial role of the interactions among them and the North *** approach is also applied to other El Niño events,revealing several new precursor *** study suggests that the deep learning method is useful in attributing the key factors inducing extreme tropical climate events.

关键词： ENSO attribution deep learning ENSO prediction extreme El Niño

A Neural-network-based Alternative Scheme to Include Nonhydrostatic Processes in an atmospheric Dynamical Core

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Advances in atmospheric sciences 2024年第6期41卷 1083-1099,I0002,I0003页

作者： Yang XIA Bin wang Lijuan LI Li liu Jianghao LI Li DONG Shiming XU Yiyuan LI Wenwen XIA Wenyu huang Juanjuan liu Yong wang Hongbo liu Ye PU Yujun HE Kun XIA Ministry of Education Key Laboratory for Earth System Modeling and Department of Earth System ScienceTsinghua UniversityBeijing 100084China State Key Laboratory of Numerical Modeling for atmospheric sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Innovation Group 311020008 Southern Marine Science and Engineering Guangdong LaboratoryZhuhai 519000China Shanghai Ecological Forecasting and Remote Sensing Center Shanghai 200030China Key Laboratory of Earth System Modeling and Prediction China Meteorological AdministrationBeijing 100081China College of Ocean sciences University of Chinese Academy of SciencesBeijing 100049China

Here,a nonhydrostatic alternative scheme(NAS)is proposed for the grey zone where the nonhydrostatic impact on the atmosphere is evident but not large enough to justify the necessity to include an implicit nonhydrostatic solver in an atmospheric dynamical *** NAS is designed to replace this solver,which can be incorporated into any hydrostatic models so that existing well-developed hydrostatic models can effectively serve for a longer *** advances in machine learning(ML)provide a potential tool for capturing the main complicated nonlinear-nonhydrostatic *** this study,an ML approach called a neural network(NN)was adopted to select leading input features and develop the *** NNs were trained and evaluated with 12-day simulation results of dry baroclinic-wave tests by the Weather Research and Forecasting(WRF)*** forward time difference of the nonhydrostatic tendency was used as the target variable,and the five selected features were the nonhydrostatic tendency at the last time step,and four hydrostatic variables at the current step including geopotential height,pressure in two different forms,and potential temperature,***,a practical NAS was developed with these features and trained layer by layer at a 20-km horizontal resolution,which can accurately reproduce the temporal variation and vertical distribution of the nonhydrostatic *** by the NN-based NAS,the improved hydrostatic solver at different horizontal resolutions can run stably for at least one month and effectively reduce most of the nonhydrostatic errors in terms of system bias,anomaly root-mean-square error,and the error of the wave spatial pattern,which proves the feasibility and superiority of this scheme.

关键词： neural network nonhydrostatic alternative scheme atmospheric model dynamical core

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

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Advances in atmospheric sciences 2024年第7期41卷 1299-1312页

作者： Ya wang Gang huang Baoxiang PAN Pengfei LIN Niklas BOERS Weichen TAO Yutong CHEN BO liu Haijie LI State Key Laboratory of Numerical Modeling for atmospheric sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China College of Earth and Planetary sciences University of Chinese Academy of SciencesBeijing 100049China Earth System Modelling School of Engineering and DesignTechnical University of MunichOttobrunn 85521Germany Potsdam institute for Climate Impact Research Potsdam 14412Germany Department of Mathematics and Global Systems institute University of ExeterExeter EX44QEUK CMA Earth System Modeling and Prediction Centre(CEMC) Chinese Academy of Meteorological SciencesBeijing 100081China

Climate models are vital for understanding and projecting global climate change and its associated ***,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future *** these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning ***,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST ***,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to ***,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to *** enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales *** study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

关键词： generative adversarial networks model bias deep learning El Niño-Southern Oscillation marine heatwaves

Recent advances in intermediate-temperature CO_(2) capture: Materials,technologies and applications

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Journal of Energy Chemistry 2024年第3期90卷 435-452,I0010页

作者： Chengbo Zhao Leiming wang Liang huang Nicholas M.Musyoka Tianshan Xue Jabor Rabeah Qiang wang Engineering Research Center for Water Pollution Source Control&Eco-remediation College of Environmental Science and EngineeringBeijing Forestry UniversityBeijing 100083China State Key Laboratory of Efficient Production of Forest Resources Beijing Forestry UniversityBeijing 100083China china Machinery Huanyu Certification and Inspection Co. Ltd.Beijing 102609China Nottingham Ningbo china Beacons of Excellence Research and Innovation institute University of Nottingham Ningbo ChinaNingbo 315100ZhejiangChina institute of atmospheric Environment Chinese Research Academy of Environmental SciencesBeijing 100012China Leibniz-Institut für Katalyse e.V. Albert-Einstein-Str.29aRostock D-18059Germany

Carbon capture,utilization and storage(CCUS) is widely recognized as a vital strategy for mitigating the impact of human induced climate *** various CO_(2) adsorbents,intermediate-temperature solid adsorbents have garnered significant attention due to their potential applications in hydrogen utilization,specifically in the water gas shift,steam reforming and gasification *** processes are crucial for achieving carbon *** laboratory-level studies have showcased the high adsorption capacity of these materials via various synthesis and modification methods,their practical application in complex industrial environments remains challenging,impeding the commercialization *** review aims to critically summarize the recent research progress made in intermediatetemperature(200-400℃) solid CO_(2) adsorbents,particularly focusing on indicators such as cyclability,gas selectivity,and formability,which play vital roles in industrial application ***,we provide an overview of laboratory-level advanced technologies specifically tailored for industrial ***,we highlight several industrial-ready advanced technologies that can pave the way for overcoming the challenges associated with large-scale *** insights provided by this review aim to assist researchers in identifying the most relevant research directions for industrial *** promoting advances in the application of solid adsorbents,we strive to facilitate the ultimate goal of achieving carbon neutrality.

关键词： Intermediate-temperature CO_(2) capture MgO LDHs Industrialization

Toward a Learnable Climate Model in the Artificial Intelligence Era

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Advances in atmospheric sciences 2024年第7期41卷 1281-1288页

作者： Gang huang Ya wang Yoo-Geun HAM Bin MU Weichen TAO Chaoyang XIE State key Laboratory of Numerical Modeling for atmospheric sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China University of chinese academy of sciences Beijing 100049China Department of Environmental Planning Graduate School of Environmental StudiesSeoul National UniversitySeoul 08826South Korea School of Software Engineering Tongji UniversityShanghai 200092China

Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related *** this AI-driven transformation,the foundational role of physics in climate science has occasionally been *** perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”*** controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical ***,we provide illustrative examples to guide future assessments and constraints for AI *** AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online ***,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)*** a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.

关键词： artificial intelligence deep learning learnable climate model

Progress in studies of the climate of humid period and the impacts of changing precession in early-mid Holocene

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Progress in Natural Science:Materials International 2008年第12期18卷 1459-1464页

作者： Jianbin huang~a Shaowu wang~a Xinyu Wen~a Bao Yang~b a Department of atmospheric sciences,School of physics,Peking University,beijing 100871,china b Laboratory for Desert and Desertification,Cold and Arid Regions Environmental and Engineering Research institute,chinese academy of sciences,Lanzhou 730000,china Department of atmospheric sciences School of Physics Peking University Beijing 100871 China Laboratory for Desert and Desertification Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences Lanzhou 730000 China

Studies on the climate of humid period and the impacts of changing precession in the early-mid Holocene are reviewed in this paper. High-resolution proxy data indicated that the African Humid Period,strong summer monsoon from the Arabian Sea to South Asia, northward migration of ITCZ (lntertropical Convergence Zone) over the northern South America,and the humid period of china appeared in 10.5-5.5 kaBP,10.0-6.0 kaBP,10.5 5.4 kaBP,and 11.0-8.0 kaBP,*** studies proved that summer inso- lation over the Northern Hemisphere increased following the changes of precession in the early Holocene,which increased the land-sea temperature contrasts,intensified the summer monsoon circulation in the Northern Hemisphere,and finally induced a humid climate over the area under the influence of summer ***,modeling results underestimated the increase of precipitation and the degree of northward extension of monsoon rain belt compared with palaeo-environmental *** discrepancies between the modeling results and the palaeo-environmental data may be associated with the changes of North Atlantic circulation,sea ice and veg- etation ***,climate of the humid period was not stable,in which several droughts were inlaid on centennial *** this review,perspectives for further studies of the climate change of the humid period in the early-mid Holocene are also proposed.

关键词： Holocene Humid period Precession Summer monsoon

Numerical simulation of the upper ocean currents in South china Sea

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Acta Oceanologica Sinica 1995年第3期14卷 305-317页

作者： Li Rongfeng huang Qizhou and wang Wenzhi(institute of atmospheric physics, chinese academy of sciences, beijing 100080. china)(South china Sea institute of Oceanology, chinese academy of sciences, Guangzhou 510301, china)

The upper ocean currents in the Pacific Ocean are calculated by using an ocean model with higher horizontal resolution. The large current systems in the Pacific Ocean, such as Kuroshio, Oyashio, NEC, SEC, NECC,Califomia Current and East Australia Current, are well simulated. This paper only gives the numerical simulation results of the upper ocean currents of the representative months in four seasons in the South china Sea (SCS). It showsthat the SCS branch of Kuroshio is the most important current in the northem SCS and it is not only the water resourceof the SCS warm current but also a significant part of the overall SCS circulation. There is a relatively strong northeastward flow entering the SCS through the Taiwan Strait throughout the year except for specific months. Some of thenumerical results have been confirmed by the observational evidences.

关键词： Upper ocean current numerical simulation SCS warm current the SCS branch of Kuroshio slope of sealevel