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Consistency of Tropospheric Water Vapor between Reanalyses and Himawari-8/AHI Measurements over East Asia

Advances in Atmospheric Sciences 2024年第1期41卷 19-38页

作者： Di DI Jun LI Yunheng XUE Min MIN Bo LI Zhenglong LI Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and TechnologyNanjing 210044China Key laboratory for Aerosol-Cloud-Precipitation China Meteorological AdministrationSchool of Atmospheric PhysicsNanjing University of Information Science and TechnologyNanjing 210044China FYSIC National Satellite Meteorological CenterChina Meteorological AdministrationBeijing 100081China Chengdu University of Information Science and Technology Chengdu 610225China School of Atmospheric Sciences and Guangdong Province Key laboratory for climate Change and Natural Disaster studies Sun Yat-sen University and Southern Laboratory of Ocean Science and EngineeringZhuhai 519082China Cooperative Institute for Meteorological Satellite studies University of Wisconsin–MadisonMadison WI 53706WisconsinUSA Wuxi University Wuxi 214105China

High spatiotemporal resolution radiances from the advanced imagers onboard the new generation of geostationary weather satellites provide a unique opportunity to evaluate the abilities of various reanalysis datasets to depict multilayer tropospheric water vapor(WV),thereby enhancing our understanding of the deficiencies of WV in reanalysis datasets.Based on daily measurements from the Advanced Himawari Imager(AHI)onboard the Himawari-8 satellite in 2016,the bias features of multilayer WV from six reanalysis datasets over East Asia are thoroughly evaluated.The assessments show that wet biases exist in the upper troposphere in all six reanalysis datasets;in particular,these biases are much larger in summer.Overall,we find better depictions of WV in the middle troposphere than in the upper troposphere.The accuracy of WV in the ERA5 dataset is the highest,in terms of the bias magnitude,dispersion,and pattern similarity.The characteristics of the WV bias over the Tibetan Plateau are significantly different from those over other parts of East Asia.In addition,the reanalysis datasets all capture the shift of the subtropical high very well,with ERA5 performing better overall.

关键词： AHI reanalysis dataset multilayer water vapor assessment radiative transfer model

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Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

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Advances in Atmospheric Sciences 2024年第8期41卷 1465-1482页

作者： Jiazhen ZHAO Shengping HE Ke FAN Huijun WANG Fei LI Key laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD) Nanjing University of Information Science & Technology Geophysical Institute University of Bergen and Bjerknes Centre for Climate Research Nansen Environmental and Remote Sensing Center and Bjerknes Centre for climate Research Nansen-Zhu International Research Center Institute of Atmospheric PhysicsChinese Academy of Sciences School of Atmospheric Science Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Southern Marine Science and Engineering Guangdong laboratory (Zhuhai)

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

关键词： model democracy model independence model performance model weighting scheme wintertime newly formed Arctic sea ice

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Potassium isotopic signatures of modern offshore detrital sediments from different climatic regimes and the implications

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Science China Earth Sciences 2024年第2期67卷 405-419页

作者： Jun MU Tianyu CHEN Qian YU Shichao AN Jianfang CHEN Xuefa SHI Weiqiang LI State Key laboratory for Mineral Deposits Research School of Earth Sciences and EngineeringNanjing UniversityNanjing210023China Ministry of Education Key laboratory for Coast and Island Development School of Geography and Ocean ScienceNanjing UniversityNanjing210023China State Key laboratory of Satellite ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural ResourcesHangzhou310012China Key laboratory of Marine Geology and Metallogeny First Institute of OceanographyMinistry of Natural ResourcesQingdao266061China

Potassium isotopes are a novel tracer for continental weathering.Previous K isotope studies on chemical weathering generally targeted weathering profiles under a particular climate region,yet the effects of chemical weathering on K isotopes under different climatic backgrounds remain unclear.Moreover,little is known about the K isotope signatures of modern unconsolidated detrital sediments.Here,we report K isotopic data of surficial seafloor sediments from continental shelves along the east coast of China(ECC),as well as those around the tropical Hainan island in the northern South China Sea.The ECC sediments have a relatively narrow distribution ofδ^(41)K(with reference to NIST3141a)values,which range from(-0.40±0.01)‰to(-0.57±0.04)‰,with an average of(-0.51±0.09)‰.By contrast,δ^(41)K values of Hainan offshore sediments display a larger variation,ranging from(-0.28±0.07)‰to(-0.67±0.02)‰.Theδ^(41)K values of Hainan offshore sediments exhibit negative correlations with the chemical index of alteration(CIA),Al/K,Ti/K,and total iron(FeT),which underlines the control of chemical weathering on K isotopic signatures of detritus inputs into oceans.We also measured Mg isotope compositions for the same samples;interestingly,the variability inδ^(26)Mg of the samples is small(~0.24‰)for all ECC and Hainan offshore sediments,andδ^(26)Mg values do not show clear correlations with indexes of chemical weathering.Our study demonstrates the link between K isotopic variability of detrital sediments and climatic conditions including rainfall intensity,which indicates that K isotopes of the detrital component of marine sediments could be applied to study Earth’s climate in deep time.Theδ^(41)K values of the offshore detrital sediments are significantly less variable than those of pelagic marine sediments,highlighting the importance of distinguishing the effects of diagenesis and neoformation of clay minerals from continental weathering in attempts to study deep-time climate-weathering link by K isotopes in detrital sedimentary recor

关键词： Potassium isotopes Magnesium isotopes Chemical weathering Detrital sediments Climatic regimes Mean annual precipitation

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Leading Pattern of Spring Drought Variability over East Asia and Associated Drivers

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Journal of Tropical Meteorology 2024年第1期30卷 1-10页

作者：赵庆红杨崧田红瑛邓开强 School of Atmospheric Sciences Sun Yat-sen Universityand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)ZhuhaiGuangdong 519082 China Guangdong Province Key laboratory for climate Change and Natural Disaster studies Sun Yat-sen UniversityZhuhaiGuangdong 519082 China College of Atmospheric Sciences Lanzhou UniversityLanzhou 730000 China

Drought events have become more frequent and intense over East Asia in recent decades,leading to huge socioeconomic impacts.Although the droughts have been studied extensively by cases or for individual regions,their leading variability and associated causes remain unclear.Based on the Standardized Precipitation Evapotranspiration Index(SPEI)and ERA5 reanalysis product from 1979 to 2020,this study evealuates the severity of spring droughts in East Asia and investigates their variations and associated drivers.The results indicate that North China and Mongolia have experienced remarkable trends toward dryness during spring in recent decades,while southwestern China has witnessed an opposite trend toward wetness.The first Empirical Orthogonal Function mode of SPEI variability reveals a similar seesawing pattern,with more severe dryness in northwestern China,Mongolia,North China,South Korea,and Japan but increased wetness in Southwestern China and southeast Asia.Further investigation reveals that the anomalously dry(wet)surface in North(Southwestern)China is significantly associated with anomalously high(low)temperature,less(more)precipitation,and reduced(increased)soil moisture during the previous winter and early spring,regulated by an anomalous anticyclone(cyclone)and thus reduced(increased)water vapor convergence.The spring dry-wet pattern in East Asia is also linked to cold sea surface temperature anomalies in the central-eastern Pacific.The findings of this study have important implications for improving the prediction of spring drought events in East Asia.

关键词： drought leading pattern East Asia spring drivers

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ResoNet:Robust and Explainable ENSO Forecasts with Hybrid Convolution and Transformer Networks

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Advances in Atmospheric Sciences 2024年第7期41卷 1289-1298页

作者： Pumeng LYU Tao TANG Fenghua LING Jing-Jia LUO Niklas BOERS Wanli OUYANG Lei BAI Shanghai Artificial Intelligence laboratory Shanghai 200072China Zhejiang Meteorological Observatory Hangzhou 310002China Institute for climate and Application Research(ICAR) Nanjing University of Information Science and TechnologyNanjing 210044China Technical University of Munich Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Recent studies have shown that deep learning(DL)models can skillfully forecast El Niño–Southern Oscillation(ENSO)events more than 1.5 years in advance.However,concerns regarding the reliability of predictions made by DL methods persist,including potential overfitting issues and lack of interpretability.Here,we propose ResoNet,a DL model that combines CNN(convolutional neural network)and transformer architectures.This hybrid architecture enables our model to adequately capture local sea surface temperature anomalies as well as long-range inter-basin interactions across oceans.We show that ResoNet can robustly predict ENSO at lead times of 19 months,thus outperforming existing approaches in terms of the forecast horizon.According to an explainability method applied to ResoNet predictions of El Niño and La Niña from 1-to 18-month leads,we find that it predicts the Niño-3.4 index based on multiple physically reasonable mechanisms,such as the recharge oscillator concept,seasonal footprint mechanism,and Indian ocean capacitor effect.Moreover,we demonstrate for the first time that the asymmetry between El Niño and La Niña development can be captured by ResoNet.Our results could help to alleviate skepticism about applying DL models for ENSO prediction and encourage more attempts to discover and predict climate phenomena using AI methods.

关键词： deep learning ENSO CNN transformer

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Accurate Shortwave Radiation Simulation with a Two-Layer Aerosol Model in Xinjiang Region

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Journal of Meteorological Research 2024年第1期38卷 69-87页

作者： Guan HUANG Yonghang CHEN Qiong LIU Pengtao WANG Qianshan HE Qing HE Shuai LI Weiling SHAO Ting FAN School of Tourism&Research Institute of Human Geography Xi’an International Studies UniversityXi’an 710128 Center for Silk Road and Eurasian Civilization studies Xi’an International Studies UniversityXi’an 710128 College of Environmental Science and Engineering Donghua UniversityShanghai 201620 Shanghai Meteorological Service Shanghai 200030 Shanghai Key laboratory of Meteorology and Health Shanghai 200030 Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration Institute of Desert MeteorologyUrumqi 830002 Xinjiang climate Centre Xinjiang Meteorological ServiceUrumqi 830002

To harness the rich solar energy resources in Xinjiang Region of Northwest China,this study tries to address the issue of lack of downward surface shortwave radiation(DSSR)observations and the need to improve the accuracy of satellite retrieval and numerical simulation of DSSR under varied sky and meteorological conditions.(1)A two-layer aerosol model specific to Xinjiang was developed to capture the vertical distributions of aerosols based on multiple data sources including lidar,GPS sounding,ground meteorological observations,and profiles from the ECMWF reanalysis version 5(ERA5)data.The results show that the ERA5/PBLH(planetary boundary layer height)and ERA5/ALH(aerosol layer height)could be used to establish the two-layer aerosol model and characterize the vertical distribution of aerosols in Xinjiang Region.(2)Using the Santa Barbara Discrete Atmospheric Radiative Transfer(SBDART)model,a localized inverse model of clear-sky DSSR was established.After parameter adjustment and using the optimal combination of input parameters for DSSR simulation together with the two-layer aerosol model,the model-simulated DSSR(DSSRSBD)under clear-sky conditions improved significantly compared to the initial results,with all fitting indices greatly improved.(3)In addition,the study demonstrated that the impact of the two-layer aerosol model on DSSR was more pronounced under dust conditions than clear-sky conditions.(4)Using the localized clear-sky DSSR inversion model and its required parameters,simulations were also conducted to capture the spatiotemporal distribution of DSSR under clear-sky conditions in Xinjiang from 2017 to 2019.The annual average DSSR_(SBD)under clear-sky conditions in Xinjiang during 2017–2019 was 606.78 W m^(-2),while DSSR from CERES(DSSR_(CER))under the same conditions was generally higher(703.95 W m^(-2)).(5)It is found that satellite remote sensing products experienced data loss in high-altitude snow areas,where numerical simulation technology could serve as a valuable complement.

关键词： downward surface shortwave radiation(DSSR) aerosol vertical distribution Clouds and the Earth’s Radiant Energy System(CERES) Santa Barbara Discrete Atmospheric Radiative Transfer(SBDART) Xinjiang

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Slowing down of the summer Southern Hemisphere Annular Mode trend against the background of ozone recovery

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Atmospheric and oceanic Science Letters 2024年第1期17卷 1-8页

作者： Fei Zheng School of Atmospheric Sciences Key Laboratory of Tropical Atmosphere-Ocean SystemMinistry of EducationSun Yat-sen UniversityZhuhaiChina Southern Marine Science and Engineering Guangdong laboratory ZhuhaiChina

20世纪末期,南半球热带外地区经历了显著的气候变化,包括夏季南半球环状模(SAM)的显著上升趋势,伴随着南极半岛的增暖和别林斯高晋海的海冰融化.这些趋势主要是由20世纪末期南极平流层臭氧消耗所驱动的.本文发现,自2001年左右以来,在南... 详细信息

20世纪末期,南半球热带外地区经历了显著的气候变化,包括夏季南半球环状模(SAM)的显著上升趋势,伴随着南极半岛的增暖和别林斯高晋海的海冰融化.这些趋势主要是由20世纪末期南极平流层臭氧消耗所驱动的.本文发现,自2001年左右以来,在南极平流层臭氧恢复的背景下,观测到的夏季SAM的上升趋势已经趋于平缓,验证了前人利用数值模拟预测的夏季SAM上升趋势减缓现象,与SAM在臭氧恢复后趋势只减缓但没有逆转不同,南极地表气温和海冰的趋势发生了逆转.南极半岛由变暖趋势转为降温趋势,别林斯高晋海域的海冰由融化趋势转为增多趋势.

关键词：南半球环状模南极臭氧损耗南极臭氧恢复

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Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

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Advances in Atmospheric Sciences 2024年第7期41卷 1326-1341页

作者： Lu LI Yongjiu DAI Zhongwang WEI Wei SHANGGUAN Nan WEI Yonggen ZHANG Qingliang LI Xian-Xiang LI School of Atmospheric Sciences Sun Yat-sen UniversityGuangzhou 510275China Southern Marine Science and Engineering Guangdong laboratory(Zhuhai) Guangzhou 510275China Guangdong Province Key laboratory for climate Change and Natural Disaster studies Guangzhou 510275China Institute of Surface-Earth System Science School of Earth System ScienceTianjin UniversityTianjin 300072China College of Computer Science and Technology Changchun Normal UniversityChangchun 130123China

Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

关键词： soil moisture forecasting hybrid model deep learning ConvLSTM attention mechanism

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Quantifying climate conditions for the formation of coals and evaporites

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National Science Review 2023年第6期10卷 143-150页

作者： Xiujuan Bao Yongyun Hu Christopher R.Scotese Xiang Li Jiaqi Guo Jiawenjing Lan Qifan Lin Shuai Yuan Mengyu Wei Zhibo Li Kai Man Zihan Yin Jing Han Jian Zhang Qiang Wei Yonggang Liu Jun Yang Ji Nie laboratory for climate and ocean-atmosphere studies Department of Atmospheric and Oceanic Sciences School of Physics Peking University Department of Earth and Planetary Sciences Northwestern University

Coals and evaporites are commonly used as qualitative indicators of wet and dry environments in deep-time climate studies,respectively.Here,we combine geological records with climate simulations to establish quantitative relationships of coals and evaporites with temperature and precipitation over the Phanerozoic.We show that coal records were associated with a median temperature of 25℃ and precipitation of1300 mm yr-1before 250 Ma.Afterwards,coal records appeared with temperatures between 0℃ and 21℃and precipitation of 900 mm yr-1.Evaporite records were associated with a median temperature of 27℃and precipitation of 800 mm yr-1.The most remarkable result is that net precipitation associated with coal and evaporite records remained constant across time.The results here have important implications for quantifying climate conditions for other lithologic indicators of climate and for predicting exogenetic ore deposits.

关键词： deep-time climate evolution coals evaporites temperature precipitation

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Productivity in the Southern ocean Antarctic Zone during the Northern Hemisphere Glaciation(NHG)and its link to atmospheric pCO_(2)

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Science China Earth Sciences 2024年第7期67卷 2242-2252页

作者： Yiming WU Jingteng GUO Xiangyu ZHAO Wenshen XIAO Heng LIU Zhifang XIONG Tiegang LI Key laboratory of Marine Geology and Metallogeny First Institute of OceanographyMinistry of Natural ResourcesQingdao 266061China laboratory for Marine Geology Laoshan LaboratoryQingdao 266237China School of oceanography Shanghai Jiao Tong UniversityShanghai 200030China Key laboratoryof Polar Eosystem and climate Change Shanghai Jiao Tong UniversiyMinistryof EducationShanghai 200030China State Key laboratory of Marine Geology Tongji UniversityShanghai 200092China

A decrease in atmospheric CO_(2)partial pressure(pCO_(2))is considered an important prerequisite for the onset and intensification of Northern Hemisphere Glaciation(NHG).However,how the ocean sequestered missing CO_(2)during the NHG is still uncertain.Changes in surface productivity and deep ventilation in the Southern ocean(SO)have been proposed to explain the variations in atmospheric pCO_(2)over the last eight glacial cycles,but it is unclear whether these mechanisms contributed to the decrease in atmospheric pCO_(2)during the NHG.Using titanium-normalized contents and mass accumulation rates of biogenic opal and total organic carbon from the International ocean Discovery Program(IODP)Expedition 374 Site U1524A,we reconstruct the productivity in the Ross Sea,Antarctica,from 3.3 Ma to 2.4 Ma.The productivity records exhibit a long-term decreasing trend and several distinct phased evolutionary features.Specifically,the local productivity fluctuated dramatically during 3.3-3.0 Ma,decreased gradually during 3.0-2.6 Ma,and remained relatively constant during 2.6-2.4 Ma.By comparing productivity with its potential influences,we infer that the phased and long-term evolutions of productivity were mainly controlled by changes in deep ocean ventilation.Sea ice expansion might have decreased productivity during 3.3-3.0 Ma by light attenuation.Changes in eolian dust input have little effect on productivity.Further analysis revealed no coupling linkage between productivity and atmospheric pCO_(2),indicating that the productivity in the SO Antarctic Zone(AZ)was not the main factor controlling the atmospheric CO_(2)decrease during the NHG.To improve our understanding of the role of SO processes in the NHG,further studies should focus on the potential influences of deep ocean ventilation on atmospheric pCO_(2)in the AZ,and similar studies should also be extended to the sea area in the Subantarctic Zone.

关键词： Ross Sea Biogenic opal Total organic carbon Deep ocean ventilation Sea ice ocean carbon cycle

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