Arctic sea ice concentration(SIC)prediction on a subseasonal scale plays an important role in polar navigation.To reduce the high uncertainty of daily forecasts,three time series prediction models are combined with em...Arctic sea ice concentration(SIC)prediction on a subseasonal scale plays an important role in polar navigation.To reduce the high uncertainty of daily forecasts,three time series prediction models are combined with empirical orthogonal function(EOF)decomposition to forecast Arctic pentad-mean SIC,where each month is divided into six pentad-means–the first five each span five days,and the last encompasses the remaining days,which may vary in length.The models were trained on SIC data from 1989 to2018 and tested from 2019 to 2023,with lead times ranging from 1 to 12 pentad-means.Model skill was evaluated based on SIC spatial patterns,sea ice area(SIA),and the sea ice edge in September from 2019 to 2023.The moving-averaged 2-m temperature helps reduce the long short-term memory model's error in the Beaufort and Chukchi Seas.Based on the models'scores for each EOF time series,weighted ensemble prediction results were obtained.These results outperform two benchmark models across all lead times.In addition,the ensemble prediction better reproduces the seasonal cycle of the SIA,with relative errors ranging from 1.04%to 3.85%.The predicted September ice edge closely matches observations,with binary accuracy consistently above 90%.Forecast models show the lowest errors in the central Arctic,while relatively higher errors appear in the Barents and Kara Seas.展开更多
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
The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navi...The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.展开更多
Drought across Northwest China in late spring has exerted a vital effect on the local climate and agricultural production,and has been alleviated during the past decades.This study explored the influence of the preced...Drought across Northwest China in late spring has exerted a vital effect on the local climate and agricultural production,and has been alleviated during the past decades.This study explored the influence of the preceding Arctic sea ice on the May drought in Northwest China caused by the precipitation deficit.Further analysis indicated that when the Greenland Sea ice concentration is abnormally high during February to April,the dry conditions in Northwest China tend to be alleviated.The increase of sea ice in the Greenland Sea can excite a meridional circulation,which causes sea surface temperature(SST)anomalies in the North Atlantic via the sea-air interaction,manifested as significant warm SST anomalies over the south of Greenland and the subtropical North Atlantic,but negative SST anomalies over the west of the Azores.This abnormal SST pattern maintains to May and triggers a zonal wave train from the North Atlantic through Scandinavia and Central Asia to Northwest China,leading to abnormal cyclones in Northwest China.Consequently,Northwest China experiences a more humid climate than usual.展开更多
基金supported by the National Key Research and Development Program(No.2023YFC2809101)the Laoshan Laboratory Technology Innovation Project(No.LSKJ202202301)。
文摘Arctic sea ice concentration(SIC)prediction on a subseasonal scale plays an important role in polar navigation.To reduce the high uncertainty of daily forecasts,three time series prediction models are combined with empirical orthogonal function(EOF)decomposition to forecast Arctic pentad-mean SIC,where each month is divided into six pentad-means–the first five each span five days,and the last encompasses the remaining days,which may vary in length.The models were trained on SIC data from 1989 to2018 and tested from 2019 to 2023,with lead times ranging from 1 to 12 pentad-means.Model skill was evaluated based on SIC spatial patterns,sea ice area(SIA),and the sea ice edge in September from 2019 to 2023.The moving-averaged 2-m temperature helps reduce the long short-term memory model's error in the Beaufort and Chukchi Seas.Based on the models'scores for each EOF time series,weighted ensemble prediction results were obtained.These results outperform two benchmark models across all lead times.In addition,the ensemble prediction better reproduces the seasonal cycle of the SIA,with relative errors ranging from 1.04%to 3.85%.The predicted September ice edge closely matches observations,with binary accuracy consistently above 90%.Forecast models show the lowest errors in the central Arctic,while relatively higher errors appear in the Barents and Kara Seas.
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supported by the National Natural Science Foundation of China(No.41971339)the SDUST Research Fund(No.2019TDJH103)。
文摘The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.
基金supported by the National Natural Science Foun-dation of China [grant numbers 41991281 and 42005028]。
文摘Drought across Northwest China in late spring has exerted a vital effect on the local climate and agricultural production,and has been alleviated during the past decades.This study explored the influence of the preceding Arctic sea ice on the May drought in Northwest China caused by the precipitation deficit.Further analysis indicated that when the Greenland Sea ice concentration is abnormally high during February to April,the dry conditions in Northwest China tend to be alleviated.The increase of sea ice in the Greenland Sea can excite a meridional circulation,which causes sea surface temperature(SST)anomalies in the North Atlantic via the sea-air interaction,manifested as significant warm SST anomalies over the south of Greenland and the subtropical North Atlantic,but negative SST anomalies over the west of the Azores.This abnormal SST pattern maintains to May and triggers a zonal wave train from the North Atlantic through Scandinavia and Central Asia to Northwest China,leading to abnormal cyclones in Northwest China.Consequently,Northwest China experiences a more humid climate than usual.
