An atmospheric general circulation model(AGCM)is used to analyze the different impact on the Barents Sea(BS)and Greenland Sea(GS)for a perturbation of sea-to-air DMS flux.We compare contemporary anthropogenic S and co...An atmospheric general circulation model(AGCM)is used to analyze the different impact on the Barents Sea(BS)and Greenland Sea(GS)for a perturbation of sea-to-air DMS flux.We compare contemporary anthropogenic S and contemporary DMS sea-to-air flux(as baseline,B00)sulfur emissions,with contemporary anthropogenic S and a perturbed DMS flux(as modified,B01)sulfur emissions.Results show that the global mean surface DMS and DMS vertically integrated concentration all peaked in June and increases more than 63%in BS and increases about 58%in GS.The concentrations of atmospheric sulfur dioxide vertical integral(SO_(2))and sulfate vertical integral(SO_(4))only increase less than 12%in both regions.Sulfur emission(SEM)peaked in June and increased about 67%and 41%in GS and BS,respectively.Aerosol optical depth(AOD)increases less than 4%in GS and in BS.Surface temperature(TSC)peaked in July and reduces 0.25 K and 0.8 K in GS and BS,respectively.Satellite data from 2003 to 2023show that chlorophyll(CHL)concentration in BS exceeds that of GS by 51%.The AOD in GS is only 0.6%higher than in BS.The recent increased rate of DMS surface concentration in BS(from 6%during 1981–2002 to 18.8%in 2003–2023)is mainly caused by elevated CHL concentrations in BS.Finally,the perturbation on DMS flux leads to increase rate of DMS and related sulfur emissions especially in the BS,this tendency will have an offsetting effect on regional warming.展开更多
The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate cha...The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate change.We applied the Spatiotemporal Convolutional Long Short-Term Memory(STConvLSTM)model,integrating key environmental factors including sea surface temperature(SST),sea surface salinity(SSS),and chlorophyll a(Chl a),to predict and analyze sea surface pCO_(2)in the South China Sea.The model demonstrated high accuracy in short-term predictions(1 month),with a mean absolute error(MAE)of 0.394,a root mean square error(RMSE)of 0.659,and a coefficient of determination(R^(2))of 0.998.For long-term predictions(12 months),the model maintained its predictive capability,with an MAE of 0.667,RMSE of 1.255,and R^(2)of 0.994.Feature importance analysis revealed that sea surface pCO_(2)and SST were the main drivers of the model’s predictions,whereas Chl a and SSS had relatively minor impacts.The model’s generalization ability was further validated in the northwest Pacific Ocean and tropical Pacific Ocean,where it successfully captured the spatiotemporal variation in pCO_(2)with small prediction errors.The ST-ConvLSTM model provides an efficient and accurate tool for forecasting and analyzing sea surface pCO_(2)in the South China Sea,offering new insights into global carbon cycling and climate change.This study demonstrates the potential of deep learning in marine science and provides a significant technical support for global changes and marine ecosystem research.展开更多
为了探究海洋锋区中尺度海洋涡旋对区域海气耦合模拟的影响,本文利用区域海气耦合模式,以海气耦合的气候预测系统再分析(Climate Forecast System Reanalysis,CFSR)资料为初始和边界条件,对2000至2009年冬季东海黑潮锋区进行了低空间分...为了探究海洋锋区中尺度海洋涡旋对区域海气耦合模拟的影响,本文利用区域海气耦合模式,以海气耦合的气候预测系统再分析(Climate Forecast System Reanalysis,CFSR)资料为初始和边界条件,对2000至2009年冬季东海黑潮锋区进行了低空间分辨率和涡分辨的高分辨率的耦合数值模拟。两组数值试验中,涡分辨模拟结果在大气边界层高度、海平面气压和低层风场等各物理量的时空分布上均与耦合再分析资料有较强的一致性,而低分辨率模拟结果与再分析资料存在较大偏差。此外,本文综合了海洋对大气的强迫强度与大气对海洋的反馈强度定义了海气耦合指数。再分析资料与涡分辨模拟中的耦合指数均为0.26,而在低分辨率模拟中其仅为0.20。进一步分析表明,涡分辨模拟较好地描述了东海区域内的众多海洋涡旋分布及其与上空海洋性大气边界层之间的耦合过程。特别是在垂直于黑潮主轴的西北风与东南风天气背景下,海洋涡旋在涡分辨模拟中更加显著地加强了黑潮锋区的海气耦合强度,并主导了对低分辨率时模拟结果偏差的修正作用。展开更多
文摘An atmospheric general circulation model(AGCM)is used to analyze the different impact on the Barents Sea(BS)and Greenland Sea(GS)for a perturbation of sea-to-air DMS flux.We compare contemporary anthropogenic S and contemporary DMS sea-to-air flux(as baseline,B00)sulfur emissions,with contemporary anthropogenic S and a perturbed DMS flux(as modified,B01)sulfur emissions.Results show that the global mean surface DMS and DMS vertically integrated concentration all peaked in June and increases more than 63%in BS and increases about 58%in GS.The concentrations of atmospheric sulfur dioxide vertical integral(SO_(2))and sulfate vertical integral(SO_(4))only increase less than 12%in both regions.Sulfur emission(SEM)peaked in June and increased about 67%and 41%in GS and BS,respectively.Aerosol optical depth(AOD)increases less than 4%in GS and in BS.Surface temperature(TSC)peaked in July and reduces 0.25 K and 0.8 K in GS and BS,respectively.Satellite data from 2003 to 2023show that chlorophyll(CHL)concentration in BS exceeds that of GS by 51%.The AOD in GS is only 0.6%higher than in BS.The recent increased rate of DMS surface concentration in BS(from 6%during 1981–2002 to 18.8%in 2003–2023)is mainly caused by elevated CHL concentrations in BS.Finally,the perturbation on DMS flux leads to increase rate of DMS and related sulfur emissions especially in the BS,this tendency will have an offsetting effect on regional warming.
基金Supported by the National Key Research and Development Program of China(No.2023YFC3008202)the National Natural Science Foundation of China(No.42406019)the Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202353066)。
文摘The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate change.We applied the Spatiotemporal Convolutional Long Short-Term Memory(STConvLSTM)model,integrating key environmental factors including sea surface temperature(SST),sea surface salinity(SSS),and chlorophyll a(Chl a),to predict and analyze sea surface pCO_(2)in the South China Sea.The model demonstrated high accuracy in short-term predictions(1 month),with a mean absolute error(MAE)of 0.394,a root mean square error(RMSE)of 0.659,and a coefficient of determination(R^(2))of 0.998.For long-term predictions(12 months),the model maintained its predictive capability,with an MAE of 0.667,RMSE of 1.255,and R^(2)of 0.994.Feature importance analysis revealed that sea surface pCO_(2)and SST were the main drivers of the model’s predictions,whereas Chl a and SSS had relatively minor impacts.The model’s generalization ability was further validated in the northwest Pacific Ocean and tropical Pacific Ocean,where it successfully captured the spatiotemporal variation in pCO_(2)with small prediction errors.The ST-ConvLSTM model provides an efficient and accurate tool for forecasting and analyzing sea surface pCO_(2)in the South China Sea,offering new insights into global carbon cycling and climate change.This study demonstrates the potential of deep learning in marine science and provides a significant technical support for global changes and marine ecosystem research.
文摘为了探究海洋锋区中尺度海洋涡旋对区域海气耦合模拟的影响,本文利用区域海气耦合模式,以海气耦合的气候预测系统再分析(Climate Forecast System Reanalysis,CFSR)资料为初始和边界条件,对2000至2009年冬季东海黑潮锋区进行了低空间分辨率和涡分辨的高分辨率的耦合数值模拟。两组数值试验中,涡分辨模拟结果在大气边界层高度、海平面气压和低层风场等各物理量的时空分布上均与耦合再分析资料有较强的一致性,而低分辨率模拟结果与再分析资料存在较大偏差。此外,本文综合了海洋对大气的强迫强度与大气对海洋的反馈强度定义了海气耦合指数。再分析资料与涡分辨模拟中的耦合指数均为0.26,而在低分辨率模拟中其仅为0.20。进一步分析表明,涡分辨模拟较好地描述了东海区域内的众多海洋涡旋分布及其与上空海洋性大气边界层之间的耦合过程。特别是在垂直于黑潮主轴的西北风与东南风天气背景下,海洋涡旋在涡分辨模拟中更加显著地加强了黑潮锋区的海气耦合强度,并主导了对低分辨率时模拟结果偏差的修正作用。
