In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,ma...In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.展开更多
海洋温度数据在全球海洋观测和气候研究中发挥着关键作用,质量控制对于确保这些数据的可靠性十分关键,然而,目前在大数据集上的异常数据召回率尚不理想。文章基于Argo温度数据,提出一种基于规则集和多层感知机(rule set and multilayer ...海洋温度数据在全球海洋观测和气候研究中发挥着关键作用,质量控制对于确保这些数据的可靠性十分关键,然而,目前在大数据集上的异常数据召回率尚不理想。文章基于Argo温度数据,提出一种基于规则集和多层感知机(rule set and multilayer perceptron,RS-MLP)的质量控制方法。首先对13种机器学习模型进行对比分析,从中筛选出最优机器学习模型,然后设计了由6种基于规则的质量控制检查模块组成的规则集,最后集成规则集和最优机器学习模型构建出RS-MLP方法,并以南海区域的Argo数据为例评估方法性能。研究结果表明:RS-MLP在351746条温度数据的测试集中真阳性率(true positive rate,TPR)、真阴性率(true negative rate,TNR)和接受者操作特性(receiver operating characteristic,ROC)曲线下面积(area under the curve,AUC)依次能达到93%、96%和95%,并在不同深度层次上的异常数据召回率比较稳定,具有优秀的质量控制性能。展开更多
Understanding the undular tidal bores in the Qiantang River is essential for effective river management and maintenance.While breaking tidal bores have been studied extensively, reports on undular tidal bores in the Q...Understanding the undular tidal bores in the Qiantang River is essential for effective river management and maintenance.While breaking tidal bores have been studied extensively, reports on undular tidal bores in the Qiantang Riverremain limited. Furthermore, observed data on undular tidal bores fulfilling the requirements of short measurementtime intervals, and spring, medium, and neap tide coverage, and providing detailed data for the global vertical stratificationof flow velocity are quite limited. Based on field observations at Qige in the Qiantang estuary, we analyzedthe characteristics of undular tidal bores. The results showed that the flooding amplitude (a) of the first wave isalways larger than its ebbing amplitude (b). Moreover, the vertical distribution of the maximum flood velocity exhibitesthree shapes, influenced by the tidal range, while that of the maximum ebb velocity exhibites a single shape. Duringthe initial phase of the flood tide in the spring and medium tides, the upper water body experiences multiple oscillatingchanges along the flow direction, corresponding to the alternating process of the crest and trough of the tide levelupon the arrival of the tidal bore. The tidal range is a crucial parameter in tidal bore hydrodynamics. By establishingthe relationship between hydrodynamic parameters and tidal range, other hydrodynamic parameters, such as the tidalbore height, maximum flood depth–averaged velocity, maximum flood stratified velocity at the measurement points,and duration of the flood tide current, can be effectively predicted, thereby providing an important reference for rivermanagement and maintenance.展开更多
The thermal front in the oceanic system is believed to have a significant effect on biological activity.During an era of climate change,changes in heat regulation between the atmosphere and oceanic interior can alter ...The thermal front in the oceanic system is believed to have a significant effect on biological activity.During an era of climate change,changes in heat regulation between the atmosphere and oceanic interior can alter the characteristics of this important feature.Using the simulation results of the 3D Regional Ocean Modelling System(ROMS),we identified the location of thermal fronts and determined their dynamic variability in the area between the southern Andaman Sea and northern Malacca Strait.The Single Image Edge Detection(SIED)algorithm was used to detect the thermal front from model-derived temperature.Results show that a thermal front occurred every year from 2002 to 2012 with the temperature gradient at the location of the front was 0.3°C/km.Compared to the years affected by El Ni?o and negative Indian Ocean Dipole(IOD),the normal years(e.g.,May 2003)show the presence of the thermal front at every selected depth(10,25,50,and 75 m),whereas El Ni?o and negative IOD during 2010 show the presence of the thermal front only at depth of 75 m due to greater warming,leading to the thermocline deepening and enhanced stratification.During May 2003,the thermal front was separated by cooler SST in the southern Andaman Sea and warmer SST in the northern Malacca Strait.The higher SST in the northern Malacca Strait was believed due to the besieged Malacca Strait,which trapped the heat and make it difficult to release while higher chlorophyll a in Malacca Strait is due to the freshwater conduit from nearby rivers(Klang,Langat,Perak,and Selangor).Furthermore,compared to the southern Andaman Sea,the chlorophyll a in the northern Malacca Strait is easier to reach the surface area due to the shallower thermocline,which allows nutrients in the area to reach the surface faster.展开更多
Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast s...Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast skill of marine heatwaves(MHWs) over the globe and the predictability sources of the MHWs over the tropical oceans. The MHW forecasts are demonstrated to be skillful on seasonal-annual time scales, particularly in tropical oceans. The forecast skill of the MHWs over the tropical Pacific Ocean(TPO) remains high at lead times of 1–24 months, indicating a forecast better than random chance for up to two years. The forecast skill is subject to the spring predictability barrier of El Nino-Southern Oscillation(ENSO). The forecast skills for the MHWs over the tropical Indian Ocean(TIO), tropical Atlantic Ocean(TAO), and tropical Northwest Pacific(NWP) are lower than that in the TPO. A reliable forecast at lead times of up to two years is shown over the TIO, while a shorter reliable forecast window(less than 17 months) occurs for the TAO and NWP.Additionally, the forecast skills for the TIO, TAO, and NWP are seasonally dependent. Higher skills for the TIO and TAO appear in boreal spring, while a greater skill for the NWP emerges in late summer-early autumn. Further analyses suggest that ENSO serves as a critical source of predictability for MHWs over the TIO and TAO in spring and MHWs over the NWP in summer.展开更多
The complexity of river-tide interaction poses a significant challenge in predicting discharge in tidal rivers.Long short-term memory(LSTM)networks excel in processing and predicting crucial events with extended inter...The complexity of river-tide interaction poses a significant challenge in predicting discharge in tidal rivers.Long short-term memory(LSTM)networks excel in processing and predicting crucial events with extended intervals and time delays in time series data.Additionally,the sequence-to-sequence(Seq2Seq)model,known for handling temporal relationships,adapting to variable-length sequences,effectively capturing historical information,and accommodating various influencing factors,emerges as a robust and flexible tool in discharge forecasting.In this study,we introduce the application of LSTM-based Seq2Seq models for the first time in forecasting the discharge of a tidal reach of the Changjiang River(Yangtze River)Estuary.This study focuses on discharge forecasting using three key input characteristics:flow velocity,water level,and discharge,which means the structure of multiple input and single output is adopted.The experiment used the discharge data of the whole year of 2020,of which the first 80%is used as the training set,and the last 20%is used as the test set.This means that the data covers different tidal cycles,which helps to test the forecasting effect of different models in different tidal cycles and different runoff.The experimental results indicate that the proposed models demonstrate advantages in long-term,mid-term,and short-term discharge forecasting.The Seq2Seq models improved by 6%-60%and 5%-20%of the relative standard deviation compared to the harmonic analysis models and improved back propagation neural network models in discharge prediction,respectively.In addition,the relative accuracy of the Seq2Seq model is 1%to 3%higher than that of the LSTM model.Analytical assessment of the prediction errors shows that the Seq2Seq models are insensitive to the forecast lead time and they can capture characteristic values such as maximum flood tide flow and maximum ebb tide flow in the tidal cycle well.This indicates the significance of the Seq2Seq models.展开更多
Surface Water and Ocean Topography(SWOT)is a next-generation radar altimeter that offers high resolution,wide swath,imaging capabilities.It has provided free public data worldwide since December 2023.This paper aims t...Surface Water and Ocean Topography(SWOT)is a next-generation radar altimeter that offers high resolution,wide swath,imaging capabilities.It has provided free public data worldwide since December 2023.This paper aims to preliminarily analyze the detection capabilities of the Ka-band radar interferometer(KaRIn)and Nadir altimeter(NALT),which are carried out by SWOT for internal solitary waves(ISWs),and to gather other remote sensing images to validate SWOT observations.KaRIn effectively detects ISW surface features and generates surface height variation maps reflecting the modulations induced by ISWs.However,its swath width does not completely cover the entire wave packet,and the resolution of L2/L3 level products(about 2 km)cannot be used to identify ISWs with smaller wavelengths.Additionally,significant wave height(SWH)images exhibit blocky structures that are not suitable for ISW studies;sea surface height anomaly(SSHA)images display systematic leftright banding.We optimize this imbalance using detrending methods;however,more precise treatment should commence with L1-level data.Quantitative analysis based on L3-level SSHA data indicates that the average SSHA variation induced by ISWs ranges from 10 cm to 20 cm.NALTs disturbed by ISWs record unusually elevated SWH and SSHA values,rendering the data unsuitable for analysis and necessitating targeted corrections in future retracking algorithms.For the normalized radar cross section,Ku-band and four-parameter maximum likelihood estimation retracking demonstrated greater sensitivity to minor changes in the sea surface,making them more suitable for ISW detection.In conclusion,SWOT demonstrates outstanding capabilities in ISW detection,significantly advancing research on the modulation of the sea surface by ISWs and remote sensing imaging mechanisms.展开更多
Studying the Arctic sea ice contributes to a comprehensive understanding of the climate system in polar regions and offers valuable insights into the interplay between polar climate change and the global climate and e...Studying the Arctic sea ice contributes to a comprehensive understanding of the climate system in polar regions and offers valuable insights into the interplay between polar climate change and the global climate and environment.One of the key research aspects is the investigation of the temperature,salinity,and density parameters of sea ice to obtain essential insights.During the 11th Chinese National Arctic Research Expedition,acoustic velocity was measured on an ice core at a short-term ice station,however,temperature,salinity,and density were not measured.In the present work,we utilized a genetic algorithm to invert these obtained acoustic velocity data to sea ice temperature,salinity,and density parameters on the basis of the relationship between acoustic velocity and the physical properties of Arctic summer sea ice.We validated the effectiveness of this inversion procedure by comparing its findings with those of other researchers.The results indicate that within the normalized depth range of 0.43-0.94,the ranges for temperature,salinity,and density are -0.48--0.29℃,1.63-3.35,and 793.1-904.1 kg m^(-3),respectively.展开更多
The basic structure and intraseasonal evolution of currents in the southeastern Andaman Sea was analyzed based on data collected in 2017 from two subsurface moorings(C1 and C5).Periodic variation in the upper ocean cu...The basic structure and intraseasonal evolution of currents in the southeastern Andaman Sea was analyzed based on data collected in 2017 from two subsurface moorings(C1 and C5).Periodic variation in the upper ocean currents of the Andaman Sea was investigated by combining observational and satellite data.Mooring observations show that rapid changes of current speed and direction occurred in May and June,with a significant increase in current velocity at the C1 mooring.In the second half of the year,southward flow dominated at the C1 mooring,and alternating northward and southward flows were evident at the C5 mooring during the same period but the northward flow prevailed in boreal winter.In addition,analysis of the power spectra of the upper currents revealed that the tidal period at both moorings is primarily semidiurnal with weaker energy than that of the low-frequency currents.The upper ocean currents at the C1 and C5 moorings exhibited intraseasonal variation of 30-60 d and 120 d,while the zonal current at the C1 mooring exhibited a notable period of approximately 180 d.Further analysis indicated that the variability of currents in the Andaman Sea is influenced primarily by equatorial Kelvin waves and Rossby wave packets.Moreover,our results suggest that equatorial Kelvin waves from the eastern Indian Ocean entered the Andaman Sea in the form of Wyrtki Jets and propagated primarily along two distinct pathways during the observation period.In addition to coastal boundary Kelvin waves,it was found that a branch of the Wyrtki Jet that directly enters the Andaman Sea and flows northward along the slope of the continental shelf,and reflected Rossby wave packets by topography.展开更多
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 Basin.The thickness and tem...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 Basin.The 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 32.5.Comparison 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 2021.This anomalously warm ACW was highly related to the ex-pansion of the Beaufort Gyre in the negative Arctic Oscillation phase.During 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 core.After autumn,F_(w)^(OHF)was reduced(<2 Wm^(-2))except during rare events when it elevated F_(w)^(OHF)slightly.In 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.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.41976193 and 42176243).
文摘In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.
文摘海洋温度数据在全球海洋观测和气候研究中发挥着关键作用,质量控制对于确保这些数据的可靠性十分关键,然而,目前在大数据集上的异常数据召回率尚不理想。文章基于Argo温度数据,提出一种基于规则集和多层感知机(rule set and multilayer perceptron,RS-MLP)的质量控制方法。首先对13种机器学习模型进行对比分析,从中筛选出最优机器学习模型,然后设计了由6种基于规则的质量控制检查模块组成的规则集,最后集成规则集和最优机器学习模型构建出RS-MLP方法,并以南海区域的Argo数据为例评估方法性能。研究结果表明:RS-MLP在351746条温度数据的测试集中真阳性率(true positive rate,TPR)、真阴性率(true negative rate,TNR)和接受者操作特性(receiver operating characteristic,ROC)曲线下面积(area under the curve,AUC)依次能达到93%、96%和95%,并在不同深度层次上的异常数据召回率比较稳定,具有优秀的质量控制性能。
基金supported by the National Natural Science Foundation of China(Grant No.42276176)the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(Grant No.LZJWZ23E090006)+2 种基金the Science and Technology Project of Zhejiang Provincial Department of Water Resources(Grant No.RC2233)the Key Project of Zhejiang Provincial Natural Science Foundation(Grant No.LZJWZ23E090003)the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(Grant No.LZJWY24E090002).
文摘Understanding the undular tidal bores in the Qiantang River is essential for effective river management and maintenance.While breaking tidal bores have been studied extensively, reports on undular tidal bores in the Qiantang Riverremain limited. Furthermore, observed data on undular tidal bores fulfilling the requirements of short measurementtime intervals, and spring, medium, and neap tide coverage, and providing detailed data for the global vertical stratificationof flow velocity are quite limited. Based on field observations at Qige in the Qiantang estuary, we analyzedthe characteristics of undular tidal bores. The results showed that the flooding amplitude (a) of the first wave isalways larger than its ebbing amplitude (b). Moreover, the vertical distribution of the maximum flood velocity exhibitesthree shapes, influenced by the tidal range, while that of the maximum ebb velocity exhibites a single shape. Duringthe initial phase of the flood tide in the spring and medium tides, the upper water body experiences multiple oscillatingchanges along the flow direction, corresponding to the alternating process of the crest and trough of the tide levelupon the arrival of the tidal bore. The tidal range is a crucial parameter in tidal bore hydrodynamics. By establishingthe relationship between hydrodynamic parameters and tidal range, other hydrodynamic parameters, such as the tidalbore height, maximum flood depth–averaged velocity, maximum flood stratified velocity at the measurement points,and duration of the flood tide current, can be effectively predicted, thereby providing an important reference for rivermanagement and maintenance.
基金the Higher Education Ministry research grant,under the Long-Term Research Grant Scheme(No.LRGS/1/2020/UMT/01/1/2)the Universiti Malaysia Terengganu Scholarship(BUMT)。
文摘The thermal front in the oceanic system is believed to have a significant effect on biological activity.During an era of climate change,changes in heat regulation between the atmosphere and oceanic interior can alter the characteristics of this important feature.Using the simulation results of the 3D Regional Ocean Modelling System(ROMS),we identified the location of thermal fronts and determined their dynamic variability in the area between the southern Andaman Sea and northern Malacca Strait.The Single Image Edge Detection(SIED)algorithm was used to detect the thermal front from model-derived temperature.Results show that a thermal front occurred every year from 2002 to 2012 with the temperature gradient at the location of the front was 0.3°C/km.Compared to the years affected by El Ni?o and negative Indian Ocean Dipole(IOD),the normal years(e.g.,May 2003)show the presence of the thermal front at every selected depth(10,25,50,and 75 m),whereas El Ni?o and negative IOD during 2010 show the presence of the thermal front only at depth of 75 m due to greater warming,leading to the thermocline deepening and enhanced stratification.During May 2003,the thermal front was separated by cooler SST in the southern Andaman Sea and warmer SST in the northern Malacca Strait.The higher SST in the northern Malacca Strait was believed due to the besieged Malacca Strait,which trapped the heat and make it difficult to release while higher chlorophyll a in Malacca Strait is due to the freshwater conduit from nearby rivers(Klang,Langat,Perak,and Selangor).Furthermore,compared to the southern Andaman Sea,the chlorophyll a in the northern Malacca Strait is easier to reach the surface area due to the shallower thermocline,which allows nutrients in the area to reach the surface faster.
基金jointly supported by the National Natural Science Foundation of China (Grant Nos.42192562 and 42030605)。
文摘Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast skill of marine heatwaves(MHWs) over the globe and the predictability sources of the MHWs over the tropical oceans. The MHW forecasts are demonstrated to be skillful on seasonal-annual time scales, particularly in tropical oceans. The forecast skill of the MHWs over the tropical Pacific Ocean(TPO) remains high at lead times of 1–24 months, indicating a forecast better than random chance for up to two years. The forecast skill is subject to the spring predictability barrier of El Nino-Southern Oscillation(ENSO). The forecast skills for the MHWs over the tropical Indian Ocean(TIO), tropical Atlantic Ocean(TAO), and tropical Northwest Pacific(NWP) are lower than that in the TPO. A reliable forecast at lead times of up to two years is shown over the TIO, while a shorter reliable forecast window(less than 17 months) occurs for the TAO and NWP.Additionally, the forecast skills for the TIO, TAO, and NWP are seasonally dependent. Higher skills for the TIO and TAO appear in boreal spring, while a greater skill for the NWP emerges in late summer-early autumn. Further analyses suggest that ENSO serves as a critical source of predictability for MHWs over the TIO and TAO in spring and MHWs over the NWP in summer.
基金The National Natural Science Foundation of China under contract Nos 42266006 and 41806114the Jiangxi Provincial Natural Science Foundation under contract Nos 20232BAB204089 and 20202ACBL214019.
文摘The complexity of river-tide interaction poses a significant challenge in predicting discharge in tidal rivers.Long short-term memory(LSTM)networks excel in processing and predicting crucial events with extended intervals and time delays in time series data.Additionally,the sequence-to-sequence(Seq2Seq)model,known for handling temporal relationships,adapting to variable-length sequences,effectively capturing historical information,and accommodating various influencing factors,emerges as a robust and flexible tool in discharge forecasting.In this study,we introduce the application of LSTM-based Seq2Seq models for the first time in forecasting the discharge of a tidal reach of the Changjiang River(Yangtze River)Estuary.This study focuses on discharge forecasting using three key input characteristics:flow velocity,water level,and discharge,which means the structure of multiple input and single output is adopted.The experiment used the discharge data of the whole year of 2020,of which the first 80%is used as the training set,and the last 20%is used as the test set.This means that the data covers different tidal cycles,which helps to test the forecasting effect of different models in different tidal cycles and different runoff.The experimental results indicate that the proposed models demonstrate advantages in long-term,mid-term,and short-term discharge forecasting.The Seq2Seq models improved by 6%-60%and 5%-20%of the relative standard deviation compared to the harmonic analysis models and improved back propagation neural network models in discharge prediction,respectively.In addition,the relative accuracy of the Seq2Seq model is 1%to 3%higher than that of the LSTM model.Analytical assessment of the prediction errors shows that the Seq2Seq models are insensitive to the forecast lead time and they can capture characteristic values such as maximum flood tide flow and maximum ebb tide flow in the tidal cycle well.This indicates the significance of the Seq2Seq models.
基金The National Natural Science Foundation of China under contract Nos U2006207 and 42006164.
文摘Surface Water and Ocean Topography(SWOT)is a next-generation radar altimeter that offers high resolution,wide swath,imaging capabilities.It has provided free public data worldwide since December 2023.This paper aims to preliminarily analyze the detection capabilities of the Ka-band radar interferometer(KaRIn)and Nadir altimeter(NALT),which are carried out by SWOT for internal solitary waves(ISWs),and to gather other remote sensing images to validate SWOT observations.KaRIn effectively detects ISW surface features and generates surface height variation maps reflecting the modulations induced by ISWs.However,its swath width does not completely cover the entire wave packet,and the resolution of L2/L3 level products(about 2 km)cannot be used to identify ISWs with smaller wavelengths.Additionally,significant wave height(SWH)images exhibit blocky structures that are not suitable for ISW studies;sea surface height anomaly(SSHA)images display systematic leftright banding.We optimize this imbalance using detrending methods;however,more precise treatment should commence with L1-level data.Quantitative analysis based on L3-level SSHA data indicates that the average SSHA variation induced by ISWs ranges from 10 cm to 20 cm.NALTs disturbed by ISWs record unusually elevated SWH and SSHA values,rendering the data unsuitable for analysis and necessitating targeted corrections in future retracking algorithms.For the normalized radar cross section,Ku-band and four-parameter maximum likelihood estimation retracking demonstrated greater sensitivity to minor changes in the sea surface,making them more suitable for ISW detection.In conclusion,SWOT demonstrates outstanding capabilities in ISW detection,significantly advancing research on the modulation of the sea surface by ISWs and remote sensing imaging mechanisms.
基金supported by the Fundamental Research Funds for the Central Universities(No.202262012)the National Natural Science Foundation of China(No.42076224)the National Key R&D Program of China(No.2021YFC2801200).
文摘Studying the Arctic sea ice contributes to a comprehensive understanding of the climate system in polar regions and offers valuable insights into the interplay between polar climate change and the global climate and environment.One of the key research aspects is the investigation of the temperature,salinity,and density parameters of sea ice to obtain essential insights.During the 11th Chinese National Arctic Research Expedition,acoustic velocity was measured on an ice core at a short-term ice station,however,temperature,salinity,and density were not measured.In the present work,we utilized a genetic algorithm to invert these obtained acoustic velocity data to sea ice temperature,salinity,and density parameters on the basis of the relationship between acoustic velocity and the physical properties of Arctic summer sea ice.We validated the effectiveness of this inversion procedure by comparing its findings with those of other researchers.The results indicate that within the normalized depth range of 0.43-0.94,the ranges for temperature,salinity,and density are -0.48--0.29℃,1.63-3.35,and 793.1-904.1 kg m^(-3),respectively.
基金Supported by the Laoshan Laboratory(No.LSK 202203003)the National Key R&D Program of China(No.2022YFC3104100)。
文摘The basic structure and intraseasonal evolution of currents in the southeastern Andaman Sea was analyzed based on data collected in 2017 from two subsurface moorings(C1 and C5).Periodic variation in the upper ocean currents of the Andaman Sea was investigated by combining observational and satellite data.Mooring observations show that rapid changes of current speed and direction occurred in May and June,with a significant increase in current velocity at the C1 mooring.In the second half of the year,southward flow dominated at the C1 mooring,and alternating northward and southward flows were evident at the C5 mooring during the same period but the northward flow prevailed in boreal winter.In addition,analysis of the power spectra of the upper currents revealed that the tidal period at both moorings is primarily semidiurnal with weaker energy than that of the low-frequency currents.The upper ocean currents at the C1 and C5 moorings exhibited intraseasonal variation of 30-60 d and 120 d,while the zonal current at the C1 mooring exhibited a notable period of approximately 180 d.Further analysis indicated that the variability of currents in the Andaman Sea is influenced primarily by equatorial Kelvin waves and Rossby wave packets.Moreover,our results suggest that equatorial Kelvin waves from the eastern Indian Ocean entered the Andaman Sea in the form of Wyrtki Jets and propagated primarily along two distinct pathways during the observation period.In addition to coastal boundary Kelvin waves,it was found that a branch of the Wyrtki Jet that directly enters the Andaman Sea and flows northward along the slope of the continental shelf,and reflected Rossby wave packets by topography.
基金supported by the National Natural Science Foundation of China(Nos.42276239 and 41941012)the National Key R&D Program of China(No.2019YFC1509101)the Fundamental Research Funds for the Central Universities(No.202165005).
文摘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 Basin.The 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 32.5.Comparison 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 2021.This anomalously warm ACW was highly related to the ex-pansion of the Beaufort Gyre in the negative Arctic Oscillation phase.During 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 core.After autumn,F_(w)^(OHF)was reduced(<2 Wm^(-2))except during rare events when it elevated F_(w)^(OHF)slightly.In 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.
基金supported by the National Key Research and Development Program of China[grant number 2020YFA0608000]the National Natural Science Foundation of China[grant number 42075141]+2 种基金the Meteorological Joint Funds of the National Natural Science Foundation of China[grant number U2142211]the Key Project Fund of the Shanghai 2020“Science and Technology Innovation Action Plan”for Social Development[grant number 20dz1200702]the first batch of Model Interdisciplinary Joint Research Projects of Tongji University in 2021[grant number YB-21-202110].
