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Spatio-temporal variability of surface chlorophyll a in the Yellow Sea and the East china Sea based on reconstructions of satellite data of 2001-2020

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Journal of oceanology and Limnology 2024年第2期42卷 390-407页

作者： Weichen XIE Tao WANG Wensheng JIANG Key Laboratory of Marine Environment and Ecology Ocean University of ChinaQingdao 266100China college of oceanic and atmospheric sciences Ocean University of ChinaQingdao 266100China

Chlorophyll-a(Chl-a)concentration is a primary indicator for marine environmental *** spatio-temporal variations of sea surface Chl-a concentration in the Yellow Sea(YS)and the East china Sea(ECS)in 2001-2020 were investigated by reconstructing the MODIS Level 3 products with the data interpolation empirical orthogonal function(DINEOF)*** reconstructed results by interpolating the combined MODIS daily+8-day datasets were found better than those merely by interpolating daily or 8-day ***-a concentration in the YS and the ECS reached its maximum in spring,with blooms occurring,decreased in summer and autumn,and increased in late autumn and early *** performing empirical orthogonal function(EOF)decomposition of the reconstructed data fields and correlation analysis with several potential environmental factors,we found that the sea surface temperature(SST)plays a significant role in the seasonal variation of Chl a,especially during spring and *** increase of SST in spring and the upper-layer nutrients mixed up during the last winter might favor the occurrence of spring *** high sea surface temperature(SST)throughout the summer would strengthen the vertical stratification and prevent nutrients supply from deep water,resulting in low surface Chl-a *** sea surface Chl-a concentration in the YS was found decreased significantly from 2012 to 2020,which was possibly related to the Pacific Decadal Oscillation(PDO).

关键词： chlorophyll a(Chl a) data interpolation empirical orthogonal function(DINEOF) empirical orthogonal function(EOF)analysis Yellow Sea East china Sea

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Classification of Sailboat Tell Tail Based on Deep Learning

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Journal of ocean university of china 2024年第3期23卷 710-720页

作者： CHANG Xiaofeng YU Jintao GAO Ying DING Hongchen LIU Yulong YU Huaming Department of Physical Education Ocean University of ChinaQingdao 266100China college of Big Data Qingdao University of Science and TechnologyQingdao 266061China college of oceanic and atmospheric sciences Ocean University of ChinaQingdao 266100China

The tell tail is usually placed on the triangular sail to display the running state of the air flow on the sail *** is of great significance to make accurate judgement on the drift of the tell tail of the sailboat during sailing for the best sailing *** it is difficult for sailors to keep an eye for a long time on the tell sail for accurate judging its changes,affected by strong sunlight and visual *** this case,we adopt computer vision technology in hope of helping the sailors judge the changes of the tell tail in ease with *** paper proposes for the first time a method to classify sailboat tell tails based on deep learning and an expert guidance system,supported by a sailboat tell tail classification data set on the expert guidance system of interpreting the tell tails states in different sea wind conditions,including the feature extraction *** the expression capabilities that vary with the computational features in different visual tasks,the paper focuses on five tell tail computing features,which are recoded by an automatic encoder and classified by a SVM *** experimental samples were randomly divided into five groups,and four groups were selected from each group as the training set to train the *** remaining one group was used as the test set for *** highest resolution value of the ResNet network was 80.26%.To achieve better operational results on the basis of deep computing features obtained through the ResNet network in the *** method can be used to assist the sailors in making better judgement about the tell tail changes during sailing.

关键词： tell tail sailboat classification deep learning

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Hydrographic Characteristics and oceanic Heat Flux in the Upper Arctic ocean over the Alpha Ridge Observed by the DTOP Platform in 2018 and 2021

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Journal of ocean university of china 2024年第2期23卷 304-316页

作者： WANG Yongjun LI Tao college of oceanic and atmospheric sciences Ocean University of ChinaQingdao 266100China Frontier Science Center for Deep ocean Multispheres and Earth System(FDOMES) and Physical oceanography Laboratory Ocean University of ChinaQingdao 266100China Laoshan Laboratory Qingdao 266237China

In 2018 and 2021,the Drift-Towing ocean Profilers(DTOP)provided extensive temperature and salinity data on the upper 120m ocean through their drifts over the Alpha Ridge north of the Canada *** thickness and temperature maximum of Alaska Coastal Water(ACW)ranged from 20m to 40m and-1.5℃to-0.8℃,respectively,and the salinity generally maintained from 30.2 to *** with World ocean Atlas 2018’s climatology manifested a 40m-thick and warm ACW roughly ex-ceeding the temperature maximum by 0.4–0.5℃in June–August *** anomalously warm ACW was highly related to the ex-pansion of the Beaufort Gyre in the negative Arctic Oscillation *** summer,the under-ice oceanic heat flux F_(w)^(OHF)was elevated,with a maximum value of above 25Wm^(-2).F_(w)^(OHF)was typically low in the freezing season,with an average value of 1.2Wm^(-2).The estimates of upward heat flux contributed by ACW to the sea ice bottom F_(w)^(OHF)were in the range of 3–4Wm^(-2)in June–August 2021,when ACW contained a heat content of more than 80MJm^(-2).The heat loss over this period was driven by a weak stratification upon the ACW layer associated with a surface mixed layer(SML)approaching the ACW *** autumn,F_(w)^(OHF)was reduced(<2 Wm^(-2))except during rare events when it elevated F_(w)^(OHF)*** addition,the intensive and widespread Ekman suction,which created a violent upwelling north of the Canada Basin,was largely responsible for the substantial cooling and thinning of the ACW layer in the summer of 2021.

关键词： Arctic ocean oceanic heat flux Alaska Coastal Water Alpha Ridge

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Improving the Forecasts of Coastal Wind Speeds in Tianjin,china Based on the WRF Model with Machine Learning Algorithms

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Journal of Meteorological Research 2024年第3期38卷 570-585页

作者： Weihang ZHANG Meng TIAN Shangfei HAI Fei WANG Xiadong AN Wanju LI Xiaodong LI Lifang SHENG college of oceanic and atmospheric sciences Ocean University of ChinaQingdao266100China Tianjin Key Laboratory for oceanic Meteorology Tianjin Institute of Meteorological ScienceTianjin300074China CMA Earth System Modeling and Prediction Centre China Meteorological Administration(CMA)Beijing100081China qingdao Meteorological Bureau Qingdao266003China

Characterized by sudden changes in strength,complex influencing factors,and significant impacts,the wind speed in the circum-Bohai Sea area is relatively challenging to *** the western side of Bohai Bay,as the economic center of the circum-Bohai Sea,Tianjin exhibits a high demand for accurate wind *** this study,three machine learning algorithms were employed and compared as post-processing methods to correct wind speed forecasts by the Weather Research and Forecast(WRF)model for *** results showed that the random forest(RF)achieved better performance in improving the forecasts because it substantially reduced the model bias at a lower computing cost,while the support vector machine(SVM)performed slightly worse(especially for stronger winds),but it required an approximately 15 times longer computing *** back propagation(BP)neural network produced an average forecast significantly closer to the observed forecast but insufficiently reduced the *** regard to wind speed frequency forecasting,the RF method commendably corrected the forecasts of the frequency of moderate(force 3)wind speeds,while the BP method showed a desirable capability for correcting the forecasts of stronger(force>6)*** addition,the 10-m u and v components of wind(u_(10)and v_(10)),2-m relative humidity(RH_(2))and temperature(T_(2)),925-hPa u(u925),sea level pressure(SLP),and 500-hPa temperature(T_(500))were identified as the main factors leading to bias in wind speed forecasting by the WRF model in Tianjin,indicating the importance of local dynamical/thermodynamic processes in regulating the wind *** study demonstrates that the combination of numerical models and machine learning techniques has important implications for refined local wind forecasting.

关键词： machine learning Weather Research and Forecast(WRF)model wind speed forecasting coastal region

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Impacts of Water-Sediment Regulation Scheme on Chromophoric Dissolved Organic Matter in the Lower Yellow River

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Journal of ocean university of china 2024年第2期23卷 455-466页

作者： LIU Xiao JIANG Xueyan LIU Qian SUI Juanjuan ZOU Li Key Laboratory of Marine Chemistry Theory and Technology of Ministry of Education Ocean University of ChinaQingdao 266100China college of Chemistry and Chemical Engineering Ocean University of ChinaQingdao 266100China School of Earth sciences University of BristolBristolBS81RJUK North china Sea Environmental Monitoring Center State Oceanic AdministrationQingdao 266033China Key Laboratory of Ecological Prewarning Protection and Restoration of Bohai SeaMinistry of Natural ResourcesQingdao 266033China Key Laboratory of Marine Environment and Ecology of Ministry of Education Ocean University of ChinaQingdao 266100China college of Environmental Science and Engineering Ocean University of ChinaQingdao 266100China

As a river with more than 3000 reservoirs in its watershed,the Yellow River has been affected by dams not only on the sediment load,but also on the water ***-sediment regulation scheme(WSRS),which has been carried out annually in the Yellow River since 2002,is a typical human activity affecting river water *** dissolved organic matter(CDOM)in river is susceptible to changes in ecological and environmental conditions as well as human ***,we report variations in dissolved organic carbon concentrations,compositions and sources of CDOM in time series samples in the lower Yellow River during *** addition,a parallel factor fluorescence analysis(PARAFAC)method is applied to identify different fluorescent components in water samples during WRSR,showing four major components including tryptophan-like component(C1),microbial humic-like component(C2),terrestrial humic-like component(C3)and tyrosine-like component(C4).In general,C1 increased after water regulation,while C2 and C3 increased after sediment regulation,indicating that the water and sediment released by the dam have different effects on CDOM *** the impacts of the dam,source of CDOM in the lower Yellow River is mainly autochthonous related to microbial activities,and is regulated by the terrestrial input during WSRS *** resuspension inhibits microbial activities and reduces the production of autochthonous ***,human activities especially WSRS,as exemplified here,significantly alter the quality and quantity of CDOM in the lower Yellow River,affecting CDOM dynamics and biogeochemical processes in the estuarine environment.

关键词： chromophoric dissolved organic matter(CDOM) water-sediment regulation scheme(WSRS) Yellow River optical properties

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Source apportionment of PM_(2.5)using PMF combined online bulk and single-particle measurements:Contribution of fireworks and biomass burning

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Journal of Environmental sciences 2024年第2期136卷 325-336页

作者： Yanjing Zhang Wenshuai Li Lei Li Mei Li Zhen Zhou Jianzhen Yu Yang Zhou Frontier Science Center for Deep ocean Multispheres and Earth System(FDOMES)and Physical oceanography Laboratory Ocean University of ChinaQingdaoShandong 266100China college of oceanic and atmospheric sciences Ocean University of ChinaQingdaoShandong 266100China Institute of atmospheric Environment Safety and Pollution Control Jinan UniversityGuangdong 510632China Institute of Environment Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Department of Chemistry Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Division of Environment The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina

Fireworks(FW)could significantly worsen air quality in short term during *** to similar tracers with biomass burning(BB),the fast and precise qualification of FW and BB is still *** this study,online bulk and single-particle measurements were combined to investigate the contributions of FW and BB to the overall mass concentrations of PM_(2.5)and specific chemical species by positive matrix factorization(PMF)during the Chinese New Year in Hong Kong in February *** combined information,fresh/aged FW(abundant ^(140)K_(2)NO_(3)^(+)and ^(213)K_(3)SO_(4)^(+)formed from ^(113)K_(2)Cl^(+)discharged by fresh FW)can be extracted from the fresh/aged BB sources,in addition to the Second Aerosol,Vehicles+Road Dust,and Sea Salt *** contributions of FW and BB were investigated during three high particle matter episodes influenced by the pollution transported from the Pearl River Delta *** fresh BB/FW contributed 39.2%and 19.6%to PM_(2.5)during the Lunar Chinese New Year ***,the contributions of aged FW/BB enhanced in the last two episodes due to the aging process,evidenced by high contributions from secondary ***,the fresh BB/FW showed more significant contributions to nitrate(35.1%and15.0%,respectively)compared with sulfate(25.1%and 5.9%,respectively)and OC(14.8%and11.1%,respectively)on *** comparison,the aged FW contributed more to sulfate(13.4%).Overall,combining online bulk and single-particle measurement data can combine both instruments’advantages and provide a new perspective for applying source apportionment of aerosols using PMF.

关键词： Source apportionment PMF Online bulk measurements Single particle analysis Fireworks Biomass burning

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Comprehensive understanding on sources of high levels of fine particulate nitro-aromatic compounds at a coastal rural area in northern china

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Journal of Environmental sciences 2024年第1期135卷 483-494页

作者： Yueru Jiang Xinfeng Wang Min Li Yiheng Liang Zhiyi Liu Jing Chen Tianyi Guan Jiangshan Mu Yujiao Zhu He Meng Yang Zhou Lan Yao Likun Xue Wenxing Wang Environment Research Institute Shandong UniversityQingdao 266237China Department of Environmental Systems Science Swiss Federal Institute of Technology ZurichZurich 8092Switzerland Department of Water Resources and Drinking Water Swiss Federal Institute of Aquatic Science and TechnologyDuebendorf 8600Switzerland qingdao Eco-Environment Monitoring Center of Shandong Province Qingdao 266003China college of oceanic and atmospheric sciences Ocean University of ChinaQingdao 266100China School of Environmental and Geographical sciences Shanghai Normal UniversityShanghai 200234China

Nitro-aromatic compounds(NACs)are among the major components of brown carbon(BrC)in the atmosphere,causing negative impacts on regional climate,air quality,and ecological *** to the extensive origins,it is still a challenge to figure out the contributions and originating regions for different sources of atmospheric ***,field observations on fine particulate NACs were conducted at a coastal rural area in qingdao,china in the winter of 2018 and *** mean total concentrations of fine particulate nitro-aromatic compounds were 125.0±89.5 and 27.7±21.1 ng/m^(3)in the winter of 2018 and 2019,*** the measured eleven NACs,nitrophenols and nitrocatechols were the most abundant *** characteristics and correlation analysis showed that humidity and anthropogenic primary emissions had significant influences on the NAC *** this study,two tracing methods of the improved spatial concentration weighted trajectory(SCWT)model and the receptor model of positive matrix factorization(PMF)were combined to comprehensively understand the origins of NACs in fine particles at coastal *** major sources were identified,including coal combustion,biomass burning,vehicle exhaust,and secondary ***,coal combustion was responsible for about half of the observed nitro-aromatic compounds,followed by biomass burning(~30%).The results by SCWT demonstrated that the coal combustion dominated NACs mainly originated from the Shandong peninsula and the areas to the north and southwest,while those dominated by biomass burning primarily came from local qingdao and the areas to the west.

关键词： Nitro-aromatic compounds Fine particles Source apportionment Originating region Combustion activities

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Application of the Conditional Nonlinear Local Lyapunov Exponent to Second-Kind Predictability

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Advances in atmospheric sciences 2024年第9期41卷 1769-1786页

作者： Ming ZHANG Ruiqiang DING Quanjia ZHONG Jianping LI Deyu LU State Key Laboratory of Earth Surface Processes and Resource Ecology(ESPRE)/Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of EducationBeijing Normal UniversityBeijing 100875China Department of ocean Science Hong Kong University of Science and TechnologyHong KongChina college of oceanic and atmospheric sciences/Frontiers Science Center for Deep ocean Multispheres and Earth System(DOMES)/Key Laboratory of Physical oceanography Ministry of Education/Academy of the Future Ocean/Center for Ocean Carbon NeutralityOcean University of ChinaQingdao 266100China Laoshan Laboratory Qingdao 266237China 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

In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)*** effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent(NLLE)and signal-to-noise ratio methods using a coupled Lorenz *** results show that the CNLLE method is able to capture the slow error growth constrained by external forcings,therefore,it can quantify the predictability limit induced by the external *** this basis,a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable *** spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE *** similarity supports ENSO as the major predictable signal for weather and climate *** addition,a ratio of predictability limit(RPL)calculated by the CNLLE method to that calculated by the NLLE method was *** RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability *** instance,ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian ocean by approximately four months,as well as the predictability limit of sea level pressure over the eastern and western Pacific ***,the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.

关键词： conditional nonlinear local Lyapunov exponent second-kind predictability coupled Lorenz model ENSO

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Using Natural Radionuclides to Trace Sources of Suspended Particles in the Lower Reaches of the Yellow River

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Journal of ocean university of china 2024年第1期23卷 99-108页

作者： ZHANG Han BURNETT CWilliam ZHANG Xiaojie ZHAO Shibin YANG Disong NAN Haiming YU Zhigang XU Bochao Frontiers Science Center for Deep ocean Multispheres and Earth System and Key Laboratory of Marine Chemistry Theory and TechnologyMinistry of EducationOcean University of ChinaQingdao 266100China Laboratory for Marine Ecology and Environmental Science Qingdao National Laboratory for Marine Science and TechnologyQingdao 266071China college of Chemistry and Chemical Engineering Ocean University of ChinaQingdao 266100China Department of Earth Ocean and Atmospheric ScienceFlorida State UniversityTallahasseeFL 32306USA North china Sea Environmental Monitoring Center State Oceanic AdministrationKey Laboratory of Ecological Prewarning and Protection of Bohai SeaMNRQingdao 266033China Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation Qingdao 266033China

Natural radionuclides are powerful tools for understanding the sources and fate of suspended particulate matter(SPM).Particulate matter with different particle sizes behaves differently with respect to adsorption and *** analyzed the activi-ties and distribution characteristics of multiple natural radionuclides(238U,226Ra,40K,228Ra,7Be and 210Pbex)on size-fractionated SPM at the Lijin Hydrographic Station(Huanghe or Yellow River)every month over a one-year *** showed that medium silt(16–32µm)was the main *** expected,the activity of each radionuclide decreased with an increase of particle *** examined the sources of SPM with different particle sizes using activity ratios of 226Ra/238U,228Ra/226Ra,40K/238U and 7Be/210Pbex,and concluded that SPM with different particle sizes originated from different *** results indicate that fine SPM(32µm)was mainly derived from resuspension of riverbed *** high runoff periods,the concentration of SPM increased significantly,and the pro-portion of fine particles originating upstream *** occurring radioactive isotopes,especially on size-fractionated par-ticles,are therefore seen as useful tracers to understand the sources and behaviors of riverine particles transported from land to sea.

关键词： the Yellow River suspended particulate matter size-fractions radionuclides sediment sources water elutriator

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Attribution of Biases of Interhemispheric Temperature Contrast in CMIP6 Models

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Advances in atmospheric sciences 2024年第2期41卷 325-340页

作者： Shiyan ZHANG Yongyun HU Jiankai ZHANG Yan XIA Laboratory for Climate and ocean-Atmosphere Studies Department of Atmospheric and Oceanic SciencesSchool of PhysicsPeking UniversityBeijing 100871China college of atmospheric sciences Lanzhou UniversityLanzhou 730000China School of Systems Science Beijing Normal UniversityBeijing 100875China

One of the basic characteristics of Earth's modern climate is that the Northern Hemisphere(NH) is climatologically warmer than the Southern Hemisphere(SH). Here, model performances of this basic state are examined using simulation results from 26 CMIP6 models. Results show that the CMIP6 models underestimate the contrast in interhemispheric surface temperatures on average(0.8 K for CMIP6 mean versus 1.4 K for reanalysis data mean), and that there is a large intermodel spread, ranging from -0.7 K to 2.3 K. A box model energy budget analysis shows that the contrast in interhemispheric shortwave absorption at the top of the atmosphere, the contrast in interhemispheric greenhouse trapping, and the crossequatorial northward ocean heat transport, are all underestimated in the multimodel mean. By examining the intermodel spread, we find intermodel biases can be tracked back to biases in midlatitude shortwave cloud forcing in AGCMs. Models with a weaker interhemispheric temperature contrast underestimate the shortwave cloud reflection in the SH but overestimate the shortwave cloud reflection in the NH, which are respectively due to underestimation of the cloud fraction over the SH extratropical ocean and overestimation of the cloud liquid water content over the NH extratropical *** that underestimate the interhemispheric temperature contrast exhibit larger double ITCZ biases, characterized by excessive precipitation in the SH tropics. Although this intermodel spread does not account for the multimodel ensemble mean biases, it highlights that improving cloud simulation in AGCMs is essential for simulating the climate realistically in coupled models.

关键词： interhemispheric temperature contrast energy balance shortwave cloud forcing ITCZ CMIP6 AGCM

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