Mesoscale eddies play a pivotal role in deciphering the intricacies of ocean dynamics and the transport of heat,salt,and nutrients.Accurate representation of these eddies in ocean models is essential for improving mod...Mesoscale eddies play a pivotal role in deciphering the intricacies of ocean dynamics and the transport of heat,salt,and nutrients.Accurate representation of these eddies in ocean models is essential for improving model predictions.In this study,we propose a convolutional neural network(CNN)that combines data-driven techniques with physical principles to develop a robust and interpretable parameterization scheme for mesoscale eddies in ocean modeling.We use a highresolution reanalysis dataset to extract subgrid eddy momentum and then applying machine learning algorithms to identify patterns and correlations.To ensure physical consistency,we have introduced conservation of momentum constraints in our CNN parameterization scheme through soft and hard constraints.The interpretability analysis illustrate that the pre-trained CNN parameterization shows promising results in accurately solving the resolved mean velocity and effectively capturing the representation of unresolved subgrid turbulence processes.Furthermore,to validate the CNN parameterization scheme offline,we conduct simulations using the Massachusetts Institute of Technology general circulation model(MITgcm)ocean model.A series of experiments is conducted to compare the performance of the model with the CNN parameterization scheme and high-resolution simulations.The offline validation demonstrates the effectiveness of the CNN parameterization scheme in improving the representation of mesoscale eddies in the MITgcm ocean model.Incorporating the CNN parameterization scheme leads to better agreement with high-resolution simulations and a more accurate representation of the kinetic energy spectra.展开更多
Sea level has been rising gradually in recent decades.Against this background,this study utilizes synchronous multialtimeter measurements to investigate variations in wind and wave fields relative to sea level anomaly...Sea level has been rising gradually in recent decades.Against this background,this study utilizes synchronous multialtimeter measurements to investigate variations in wind and wave fields relative to sea level anomaly(SLA)in the China Seas and its adjacent waters.The validation between Haiyang-2(HY-2)measurement proceeded to be geophysical data records(GDR)and moored buoys indicate that HY-2 scatterometer-measured wind speed outperforms that derived from altimeter,with lower root-mean-squared error(RMSE)(1.87 m/s vs.2.03 m/s),smaller bias(−0.06 m/s vs.0.47 m/s),same correlation(COR)(0.84),and reduced scatter index(SI)(0.27 vs.0.29).Conversely,GDR product from HY-2 altimeter demonstrates reliable accuracy of significant wave height(SWH)(RMSE:0.37 m,bias:−0.03 m,COR:0.92,SI:0.30).Further time series analysis of HY-2 data reveals synchronized oscillations among SLA,wind speed and SWH with SLA strongly influencing wind speed under extreme conditions.Seasonal and regional disparities are evident:wind speed positively correlates with SLA in spring but shows a negative correlation in summer,while autumn and winter exhibit weak correlations.Periodic linkages between SWH and SLA are prominent in summer and autumn.In addition,the regional analysis shows that the Bohai Sea experiences declining autumn/winter wind speeds with higher SLA but without consistent SWH trends,while the Yellow Sea demonstrates summer covariation among wind speed,SWH and SLA.The East China Sea maintains synchronized SLA-wind speed-SWH relationship throughout spring,summer and winter,while the South China Sea shows alignment only in spring.The largest SLA,wind speed and SWH variations occur in the East China Sea and South China Sea,primarily driven by vigorous energy exchanges processes with the open ocean.These findings highlight distinct response mechanisms of regional marine dynamics to SLA,shaped by localized hydrological-climatic interactions.展开更多
基金The National Key Research and Development Program of China under contract No.2021YFC3101602the National Natural Science Foundation of China under contract Nos 42176017 and 41976019.
文摘Mesoscale eddies play a pivotal role in deciphering the intricacies of ocean dynamics and the transport of heat,salt,and nutrients.Accurate representation of these eddies in ocean models is essential for improving model predictions.In this study,we propose a convolutional neural network(CNN)that combines data-driven techniques with physical principles to develop a robust and interpretable parameterization scheme for mesoscale eddies in ocean modeling.We use a highresolution reanalysis dataset to extract subgrid eddy momentum and then applying machine learning algorithms to identify patterns and correlations.To ensure physical consistency,we have introduced conservation of momentum constraints in our CNN parameterization scheme through soft and hard constraints.The interpretability analysis illustrate that the pre-trained CNN parameterization shows promising results in accurately solving the resolved mean velocity and effectively capturing the representation of unresolved subgrid turbulence processes.Furthermore,to validate the CNN parameterization scheme offline,we conduct simulations using the Massachusetts Institute of Technology general circulation model(MITgcm)ocean model.A series of experiments is conducted to compare the performance of the model with the CNN parameterization scheme and high-resolution simulations.The offline validation demonstrates the effectiveness of the CNN parameterization scheme in improving the representation of mesoscale eddies in the MITgcm ocean model.Incorporating the CNN parameterization scheme leads to better agreement with high-resolution simulations and a more accurate representation of the kinetic energy spectra.
基金The National Natural Science Foundation of China under contract No.42376174the Natural Science Foundation of Shanghai under contract No.23ZR1426900。
文摘Sea level has been rising gradually in recent decades.Against this background,this study utilizes synchronous multialtimeter measurements to investigate variations in wind and wave fields relative to sea level anomaly(SLA)in the China Seas and its adjacent waters.The validation between Haiyang-2(HY-2)measurement proceeded to be geophysical data records(GDR)and moored buoys indicate that HY-2 scatterometer-measured wind speed outperforms that derived from altimeter,with lower root-mean-squared error(RMSE)(1.87 m/s vs.2.03 m/s),smaller bias(−0.06 m/s vs.0.47 m/s),same correlation(COR)(0.84),and reduced scatter index(SI)(0.27 vs.0.29).Conversely,GDR product from HY-2 altimeter demonstrates reliable accuracy of significant wave height(SWH)(RMSE:0.37 m,bias:−0.03 m,COR:0.92,SI:0.30).Further time series analysis of HY-2 data reveals synchronized oscillations among SLA,wind speed and SWH with SLA strongly influencing wind speed under extreme conditions.Seasonal and regional disparities are evident:wind speed positively correlates with SLA in spring but shows a negative correlation in summer,while autumn and winter exhibit weak correlations.Periodic linkages between SWH and SLA are prominent in summer and autumn.In addition,the regional analysis shows that the Bohai Sea experiences declining autumn/winter wind speeds with higher SLA but without consistent SWH trends,while the Yellow Sea demonstrates summer covariation among wind speed,SWH and SLA.The East China Sea maintains synchronized SLA-wind speed-SWH relationship throughout spring,summer and winter,while the South China Sea shows alignment only in spring.The largest SLA,wind speed and SWH variations occur in the East China Sea and South China Sea,primarily driven by vigorous energy exchanges processes with the open ocean.These findings highlight distinct response mechanisms of regional marine dynamics to SLA,shaped by localized hydrological-climatic interactions.
