Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiote...Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.展开更多
Using multi-source reanalysis data,this study examines the relationship between the tropical Pacific-Atlantic SST Dipole Mode(TPA-DM)and summer precipitation in North China(NCSP)on the interannual timescale during the...Using multi-source reanalysis data,this study examines the relationship between the tropical Pacific-Atlantic SST Dipole Mode(TPA-DM)and summer precipitation in North China(NCSP)on the interannual timescale during the period of 1979-2022.The results show that the TPA-DM,the dominant pattern of interannual variability in the tropical Pacific and Atlantic regions,exhibits a significant negative correlation with NCSP.The positive phase of TPA-DM induces subsidence over the Maritime Continent through a zonal circulation pattern,which initiates a Pacific-Japan-like wave train along the East Asian coast.The circulation anomalies lead to moisture deficits and convergence subsidence over North China,leading to below-normal rainfall.Further analysis reveals that cooler SST in the Southern Tropical Atlantic facilitates the persistence of the TPA-DM by stimulating the anomalous Walker circulation associated with wind-evaporation-SST-convection feedback.展开更多
目前普遍使用的基于SWAT(soil and water assessment tool)模型的分布式建模方法仅适用于山地、丘陵等高差较大的地区,对于具有复杂水文结构特点的平原河网区尚无有效的解决方案。该文选择太湖流域湖西区作为研究区,基于SWAT模型探索出...目前普遍使用的基于SWAT(soil and water assessment tool)模型的分布式建模方法仅适用于山地、丘陵等高差较大的地区,对于具有复杂水文结构特点的平原河网区尚无有效的解决方案。该文选择太湖流域湖西区作为研究区,基于SWAT模型探索出一套完整的针对平原河网区的分布式建模方案。该方案采用概化、打断的方式将交叉、环状河网处理成单一的枝状河网,采用按河道分流比例跨子流域调水的方式来还原原始河道的传输过程,采用添加"虚拟水库"的方式来模拟人工圩区内外的水量交换。通过对太湖湖西区2008-2010年的月均径流量进行模拟验证,根据模拟值和观测值计算的荣登桥、胡家圩及宜兴3个水文站的相关系数r和纳什(Nash-Sutcliffe)系数Ens分别为0.94、0.95、0.93和0.84、0.80、0.67,说明了这种建模方法在平原河网区具有较好的适用性。展开更多
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]an Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]+3 种基金a project supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209]a Research Council of Norway funded project(MAPARC)[grant number 328943]a Nansen Center´s basic institutional funding[grant number 342624]the high-performance computing support from the School of Atmospheric Science at Sun Yat-sen University。
文摘Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
基金jointly supported by the Second Tibetan Plateau Scientific Expedition and Research Program[grant number-ber 2019QZKK0103]the National Natural Science Foundation of China[grant number 42293294]the China Meteorological Admin-istration Climate Change Special Program[grant number QBZ202303]。
文摘Using multi-source reanalysis data,this study examines the relationship between the tropical Pacific-Atlantic SST Dipole Mode(TPA-DM)and summer precipitation in North China(NCSP)on the interannual timescale during the period of 1979-2022.The results show that the TPA-DM,the dominant pattern of interannual variability in the tropical Pacific and Atlantic regions,exhibits a significant negative correlation with NCSP.The positive phase of TPA-DM induces subsidence over the Maritime Continent through a zonal circulation pattern,which initiates a Pacific-Japan-like wave train along the East Asian coast.The circulation anomalies lead to moisture deficits and convergence subsidence over North China,leading to below-normal rainfall.Further analysis reveals that cooler SST in the Southern Tropical Atlantic facilitates the persistence of the TPA-DM by stimulating the anomalous Walker circulation associated with wind-evaporation-SST-convection feedback.
文摘目前普遍使用的基于SWAT(soil and water assessment tool)模型的分布式建模方法仅适用于山地、丘陵等高差较大的地区,对于具有复杂水文结构特点的平原河网区尚无有效的解决方案。该文选择太湖流域湖西区作为研究区,基于SWAT模型探索出一套完整的针对平原河网区的分布式建模方案。该方案采用概化、打断的方式将交叉、环状河网处理成单一的枝状河网,采用按河道分流比例跨子流域调水的方式来还原原始河道的传输过程,采用添加"虚拟水库"的方式来模拟人工圩区内外的水量交换。通过对太湖湖西区2008-2010年的月均径流量进行模拟验证,根据模拟值和观测值计算的荣登桥、胡家圩及宜兴3个水文站的相关系数r和纳什(Nash-Sutcliffe)系数Ens分别为0.94、0.95、0.93和0.84、0.80、0.67,说明了这种建模方法在平原河网区具有较好的适用性。
