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
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
In our previous study, a statistical linkage between the spring Arctic sea ice concentration （SIC） and the succeeding Chinese summer rainfall during the period 1968-2005 was identified. This linkage is demonstrated ...In our previous study, a statistical linkage between the spring Arctic sea ice concentration （SIC） and the succeeding Chinese summer rainfall during the period 1968-2005 was identified. This linkage is demonstrated by the leading singular value decomposition （SVD） that accounts for 19% of the co-variance. Both spring SIC and Chinese summer rainfall exhibit a coherent interannual variability and two apparent interdecadal variations that occurred in the late 1970s and the early 1990s. The combined impacts of both spring Arctic SIC and Eurasian snow cover on the summer Eurasian wave train may explain their statistical linkage. In this study, we show that evolution of atmospheric circulation anomalies from spring to summer, to a great extent, may explain the spatial distribution of spring and summer Arctic SIC anomalies, and is dynamically consistent with Chinese summer rainfall anomalies in recent decades. The association between spring Arctic SIC and Chinese summer rainfall on interannual time scales is more important relative to interdecadal time scales. The summer Arctic dipole anomaly may serve as the bridge linking the spring Arctic SIC and Chinese summer rainfall, and their coherent interdecadal variations may reflect the feedback of spring SIC variability on the atmosphere. The summer Arctic dipole anomaly shows a closer relationship with the Chinese summer rainfall relative to the Arctic Oscillation.展开更多
The Arctic sea-ice extent has shown a declining trend over the past 30 years. Ice coverage reached historic minima in 2007 and again in 2012. This trend has recently been assessed to be unique over at least the last 1...The Arctic sea-ice extent has shown a declining trend over the past 30 years. Ice coverage reached historic minima in 2007 and again in 2012. This trend has recently been assessed to be unique over at least the last 1450 years. In the summer of 2010, a very low sea-ice concentration(SIC) appeared at high Arctic latitudes—even lower than that of surrounding pack ice at lower latitudes. This striking low ice concentration—referred to here as a record low ice concentration in the central Arctic(CARLIC)—is unique in our analysis period of 2003–15, and has not been previously reported in the literature. The CARLIC was not the result of ice melt, because sea ice was still quite thick based on in-situ ice thickness measurements.Instead, divergent ice drift appears to have been responsible for the CARLIC. A high correlation between SIC and wind stress curl suggests that the sea ice drift during the summer of 2010 responded strongly to the regional wind forcing. The drift trajectories of ice buoys exhibited a transpolar drift in the Atlantic sector and an eastward drift in the Pacific sector,which appeared to benefit the CARLIC in 2010. Under these conditions, more solar energy can penetrate into the open water,increasing melt through increased heat flux to the ocean. We speculate that this divergence of sea ice could occur more often in the coming decades, and impact on hemispheric SIC and feed back to the climate.展开更多
A retrieval algorithm of arctic sea ice concentration （SIC） based on the brightness temperature data of “HY-2” scanning microwave radiometer has been constructed. The tie points of the brightness temperature were ...A retrieval algorithm of arctic sea ice concentration （SIC） based on the brightness temperature data of “HY-2” scanning microwave radiometer has been constructed. The tie points of the brightness temperature were selected based on the statistical analysis of a polarization gradient ratio and a spectral gradient ratio over open water （OW）, first-year ice （FYI）, and multiyear ice （MYI） in arctic. The thresholds from two weather filters were used to reduce atmospheric effects over the open ocean. SIC retrievals from the “HY-2” radiom-eter data for idealized OW, FYI, and MYI agreed well with theoretical values. The 2012 annual SIC was calcu-lated and compared with two reference operational products from the National Snow and Ice Data Center （NSIDC） and the University of Bremen. The total ice-covered area yielded by the “HY-2” SIC was consistent with the results from the reference products. The assessment of SIC with the aerial photography from the fifth Chinese national arctic research expedition （CHINARE） and six synthetic aperture radar （SAR） images from the National Ice Service was carried out. The “HY-2” SIC product was 16% higher than the values de-rived from the aerial photography in the central arctic. The root-mean-square （RMS） values of SIC between “HY-2” and SAR were comparable with those between the reference products and SAR, varying from 8.57% to 12.34%. The “HY-2” SIC is a promising product that can be used for operational services.展开更多
In order to apply satellite data to guiding navigation in the Arctic more effectively,the sea ice concentrations(SIC)derived from passive microwave(PM)products were compared with ship-based visual observations(OBS)col...In order to apply satellite data to guiding navigation in the Arctic more effectively,the sea ice concentrations(SIC)derived from passive microwave(PM)products were compared with ship-based visual observations(OBS)collected during the Chinese National Arctic Research Expeditions(CHINARE).A total of 3667 observations were collected in the Arctic summers of 2010,2012,2014,2016,and 2018.PM SIC were derived from the NASA-Team(NT),Bootstrap(BT)and Climate Data Record(CDR)algorithms based on the SSMIS sensor,as well as the BT,enhanced NASA-Team(NT2)and ARTIST Sea Ice(ASI)algorithms based on AMSR-E/AMSR-2 sensors.The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons.The correlation coefficients(CC),biases and root mean square deviations(RMSD)between PM SIC and OBS SIC were compared in terms of the overall trend,and under mild/normal/severe ice conditions.Using the OBS data,the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness.Our results show that CC values range from 0.89(AMSR-E/AMSR-2 NT2)to 0.95(SSMIS NT),biases range from−3.96%(SSMIS NT)to 12.05%(AMSR-E/AMSR-2 NT2),and RMSD values range from 10.81%(SSMIS NT)to 20.15%(AMSR-E/AMSR-2 NT2).Floe size has a significant influence on the SIC retrievals of the PM products,and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions.Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products.Overall,the best(worst)agreement occurs between OBS SIC and SSMIS NT(AMSR-E/AMSR-2 NT2)SIC in the Arctic summer.展开更多
Sea ice concentration(SIC)is one of the most important indicators when monitoring climate changes in the polar region.With the development of the Chinese satellite technology,the Feng Yun(FY)series has been applied to...Sea ice concentration(SIC)is one of the most important indicators when monitoring climate changes in the polar region.With the development of the Chinese satellite technology,the Feng Yun(FY)series has been applied to retrieve the sea ice parameters in the polar region.In this paper,to improve the SIC retrieval accuracy from the passive microwave(PM)data of the Microwave Radiation Imager(MWRI)aboard on the Feng Yun-3 B(FY-3 B)Satellite,the dynamic tie-point(DT)Arctic Radiation and Turbulence Interaction Study(ARTIST)Sea Ice(ASI)(DT-ASI)SIC retrieval algorithm is applied and obtained Arctic SIC data for nearly 10 a(from November 18,2010 to August 19,2019).Also,by applying a land spillover correction scheme,the erroneous sea ice along coastlines in melt season is removed.The results of FY-3 B/DT-ASI are obviously improved compared to that of FY-3 B/NT2(NASA-Team2)in both SIC and sea ice extent(SIE),and are highly consistent with the results of similar products of AMSR2(Advanced Microwave Scanning Radiometer 2)/ASI and AMSR2/DT-ASI.Compared with the annual average SIC of FY-3 B/NT2,our result is reduced by 2.31%.The annual average SIE difference between the two FY-3 Bs is 1.65×10^(6) km^(2),of which the DT-ASI algorithm contributes 87.9%and the land spillover method contributes12.1%.We further select 58 MODIS(Moderate-resolution Imaging Spectroradiometer)cloud-free samples in the Arctic region and use the tie-point method to retrieve SIC to verify the accuracy of these SIC products.The root mean square difference(RMSD)and mean absolute difference(MAD)of the FY-3 B/DT-ASI and MODIS results are 17.2%and 12.7%,which is close to those of two AMSR2 products with 6.25 km resolution and decreased 8%and 7.2%compared with FY-3 B/NT2.Further,FY-3 B/DT-ASI has the most significant improvement where the SIC is lower than 60%.A high-quality SIC product can be obtained by using the DT-ASI algorithm and our work will be beneficial to promote the application of Feng Yun Satellite.展开更多
In recent years, the rapid decline of Arctic sea ice area （SIA） and sea ice extent （SIE）, especially for the multiyear （MY） ice, has led to significant effect on climate change. The accurate retrieval of MY ice ...In recent years, the rapid decline of Arctic sea ice area （SIA） and sea ice extent （SIE）, especially for the multiyear （MY） ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0 GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square （rms） difference of MY SIA derived from the algorithm of this study are 0.65×10^6 km^2 and 0.69×10^6 km^2 during January to March, -0.06×10^6 km^2 and 0.14×10^6 km^2during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×10^6 km^2 and 0.84×10^6 km^2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang＇s, Lomax＇ and NASA TEAM algorithms.展开更多
The sea ice concentration observation from satellite remote sensing includes the spatial multi-scale information.However,traditional data assimilation methods cannot better extract the valuable information due to the ...The sea ice concentration observation from satellite remote sensing includes the spatial multi-scale information.However,traditional data assimilation methods cannot better extract the valuable information due to the complicated variability of the sea ice concentration in the marginal ice zone.A successive corrections analysis using variational optimization method,called spatial multi-scale recursive filter(SMRF),has been designed in this paper to extract multi-scale information resolved by sea ice observations.It is a combination of successive correction methods(SCM)and minimization algorithms,in which various observational scales,from longer to shorter wavelengths,can be extracted successively.As a variational objective analysis scheme,it gains the advantage over the conventional approaches that analyze all scales resolved by observations at one time,and also,the specification of parameters is more convenient.Results of single-observation experiment demonstrate that the SMRF scheme possesses a good ability in propagating observational signals.Further,it shows a superior performance in extracting multi-scale information in a two-dimensional sea ice concentration(SIC)experiment with the real observations from Special Sensor Microwave/Imager SIC(SSMI).展开更多
The dual-polarized ratio algorithm(DPR)for the retrieval of Arctic sea ice concentration from Advanced Microwave Scanning Radiometer-EOS(AMSR-E)data was improved using a contrast ratio(CR)parameter.In contrast to thre...The dual-polarized ratio algorithm(DPR)for the retrieval of Arctic sea ice concentration from Advanced Microwave Scanning Radiometer-EOS(AMSR-E)data was improved using a contrast ratio(CR)parameter.In contrast to three other algorithms(Artist Sea Ice algorithm,ASI;NASA-Team 2 algorithm,NT2;and AMSR-E Bootstrap algorithm,ABA),this algorithm does not use a series of tie-points or a priori values of brightness temperature of sea-ice constituents,such as open water and 100% sea ice.Instead,it is based on a ratio(a)of horizontally and vertically polarized sea ice emissivity at 36.5 GHz,which can be automatically determined by the CR.aexhibited a clear seasonal cycle:changing slowly during winter,rapidly at other times,and reaching a minimum during summer.The DPR was improved using a seasonala.The systematic diff erences in the Arctic sea ice area over the complete AMSR-E period(2002–2011)were-0.8% ±2.0% between the improved DPR and ASI;-1.3%±1.7% between the improved DPR and ABA;and-0.7% ±1.9% between the improved DPR and NT2.The improved DPR and ASI(or ABA)had small seasonal diff erences.The seasonal diff erences between the improved DPR and NT2 decreased,except in summer.The improved DPR identified extremely low ice concentration regions in the Pacific sector of the central Arctic(north of 83°N)around August 12,2010,which was confirmed by the Chinese National Arctic Research Expedition.A series of high-resolution MODIS images(250 m×250 m)of the Beaufort Sea during summer were used to assess the four algorithms.According to mean bias and standard deviations,the improved DPR algorithm performed equally well with the other three sea ice concentration algorithms.The improved DPR can provide reasonable sea ice concentration data,especially during summer.展开更多
With the development and deployment of observation systems in the ocean,more precise passive and active microwave data are becoming available for the weather forecasting and the climate monitoring.Due to the complicat...With the development and deployment of observation systems in the ocean,more precise passive and active microwave data are becoming available for the weather forecasting and the climate monitoring.Due to the complicated variability of the sea ice concentration(SIC)in the marginal ice zone and the scarcity of high-precision sea ice data,how to use less data to accurately reconstruct the sea ice field has become an urgent problem to be solved.A reconstruction method for gridding observations using the variational optimization technique,called the multi-scale high-order recursive filter(MHRF),which is a combination of Van Vliet fourth-order recursive filter and the three-dimensional variational(3D-VAR)analysis,has been designed in this study to reproduce the refined structure of sea ice field.Compared with the existing spatial multi-scale first-order recursive filter(SMRF)in which left and right filter iterative processes are executed many times,the MHRF scheme only executes the same filter process once to reduce the analysis errors caused by multiple filters and improve the filter precision.Furthermore,the series connected transfer function in the high-order recursive filter is equivalently replaced by the paralleled one,which can carry out the independent filter process in every direction in order to improve the filter efficiency.Experimental results demonstrate that this method possesses a good potential in extracting the observation information to successfully reconstruct the SIC field in computational efficiency.展开更多
In view of the extremely low sea ice concentration(SIC) appeared at high latitudes of the Arctic in the summer of 2010, the changes of SIC in the central Arctic from 2010 to 2017 were investigated in this paper based ...In view of the extremely low sea ice concentration(SIC) appeared at high latitudes of the Arctic in the summer of 2010, the changes of SIC in the central Arctic from 2010 to 2017 were investigated in this paper based on the AMSR-E/AMSR-2 SIC products retrieved by the NT2 algorithm. The results show that the extremely low sea ice concentration in the central Arctic not only occurred in 2010 but also occurred again in 2016, and the daily average sea ice concentration(ASIC) reached a minimum of 0.70, which was significantly lower than the value of 0.78 in 2010 and became a new historical low record. A large area of sea ice in the sector 150°E–180° in 2010 disappeared in 2016, which was the most important difference to produce the new minimum. Also, the ice edge in 2016 retreated into the 85°N circle, whereas in 2010 it was far from the central Arctic. In 2010 and 2016, there were high correlations between the wind stress curl and the relative variation rate of ASIC, which indicates that wind stress curl(WSC) drove the divergence of sea ice. It directly leads to the decrease in the SIC and is the main cause of the extremely low SIC events. The results in this paper show that the decline of Arctic sea ice is represented by not only the reduction of sea ice coverage but also the reduction of SICs. The central Arctic has always been covered by large amount of sea ices, so the drastic reduction of SIC will not only change the structure of the ice field, but also lead to critical climatic effects that deserve further attention.展开更多
A reasonable initial state of ice concentration is essential for accurate short-term forecasts of sea ice using ice-ocean coupled models. In this study, sea ice concentration data are assimilated into an operational i...A reasonable initial state of ice concentration is essential for accurate short-term forecasts of sea ice using ice-ocean coupled models. In this study, sea ice concentration data are assimilated into an operational ice forecast system based on a com- bined optimal interpolation and nudging scheme. The scheme produces a modeled sea ice concentration at every time step, based on the difference between observational and forecast data and on the ratio of observational error to modeled error. The impact and the effectiveness of data assimilation are investigated. Significant improvements to predictions of sea ice extent were obtained through the assimilation of ice concentration, and minor improvements through the adjustment of the upper ocean properties. The assimilation of ice thickness data did not significantly improve predictions. Forecast experiments show that the forecast accuracy is higher in summer, and that the errors on five-day forecasts occur mainly around the marginal ice zone.展开更多
To effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregress...To effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregressive filter used in this study has been attempted to replace the traditional first-order recursive filter used in spatial multi-scale recursive filter(SMRF)method.The experimental results indicate that the MSRF scheme successfully extracts various scale information resolved by observations.Moreover,compared with the SMRF scheme,the MSRF scheme improves computational accuracy and efficiency to some extent.The MSRF scheme can not only propagate to a longer distance without the attenuation of innovation,but also reduce the mean absolute deviation between the reconstructed sea ice concentration results and observations reduced by about 3.2%compared to the SMRF scheme.On the other hand,compared with traditional first-order recursive filters using in the SMRF scheme that multiple filters are executed,the MSRF scheme only needs to perform two filter processes in one iteration,greatly improving filtering efficiency.In the two-dimensional experiment of sea ice concentration,the calculation time of the MSRF scheme is only 1/7 of that of SMRF scheme.This means that the MSRF scheme can achieve better performance with less computational cost,which is of great significance for further application in real-time ocean or sea ice data assimilation systems in the future.展开更多
A daily sea ice concentration(SIC)product in the Arctic,derived from the brightness temperature(TB)data of the Microwave Radiation Imager(MWRI)sensor aboard on the FY-3D satellite,is described in this paper.The MWRI T...A daily sea ice concentration(SIC)product in the Arctic,derived from the brightness temperature(TB)data of the Microwave Radiation Imager(MWRI)sensor aboard on the FY-3D satellite,is described in this paper.The MWRI TB raw swath data were first processed into daily gridded data and then corrected using the Advanced Microwave Scanning Radiometer 2(AMSR2)sensor.An ASI algorithm,which uses daily dynamic tie points,was adopted to calculate daily SIC at 12.5 km polar stereographic projection from January 2018 to June 2020.Our generated MWRI SIC product was compared with the AMSR2 SIC based on the ASI algorithm that uses fixed tie points.For more detailed comparison,we then compared our MWRI SIC with the SIC from the Moderate Resolution Imaging Spectroradiometer(MODIS)data.The mean bias between our MWRI SIC and AMSR2 SIC is 4.24%.The absolute values of biases between the daily MWRI SIC and MODIS SIC range from 0.14%to 10.76%,better than the MWRI SIC product based on the NT2 algorithm published by the Chinese National Satellite Meteorological Center.The results show that our MWRI SIC product has a good quality and can be used as a basic dataset for sea ice extent records.The dataset is available at http://www.dx.doi.org/10.11922/sciencedb.00137.展开更多
Sea ice concentration (SIC) is an important parameter in characterizing sea ice. Limited by the environment and the spatial extent of observation, it is difficult for field work to meet the needs of a large-scale SIC ...Sea ice concentration (SIC) is an important parameter in characterizing sea ice. Limited by the environment and the spatial extent of observation, it is difficult for field work to meet the needs of a large-scale SIC study. However, with its many advantages, such as the ability to make large-scale, high-resolution and long-duration observations, the altimeter can be used to determine SIC on a large scale. Using the correspondence between the satellite pulse altimeter waveform and reflector property, waveform classification is employed. Moreover, this paper develops an algorithm to obtain the SIC from altimeter waveforms. In an actual computation, Pyrz Bay in the Antarctic is taken as an experimental region, and one-year and seasonal SICs are derived from ERS-1/GM waveforms over this study area. Furthermore, altimetric SICs are compared with those of SSMR SSM/I. The results show that the spatial distribution and the regions of maximum SIC determined employing these two methods are consistent. This demonstrates that altimeter data can be used to monitor sea ice.展开更多
To further understand the prediction skill for the interannual variability of the sea ice concentration(SIC)in specific regions of the Arctic,this paper evaluates the NCEP Climate Forecast System version 2(CFSv2),in p...To further understand the prediction skill for the interannual variability of the sea ice concentration(SIC)in specific regions of the Arctic,this paper evaluates the NCEP Climate Forecast System version 2(CFSv2),in predicting the autumn SIC and its interannual variability over the Barents–East Siberian Seas(BES).It is found that CFSv2 presents much better prediction skill for the September SIC over BES than the Arctic as a whole at 1–6-month leads,and high prediction skill for the interannual variability of the SIC over BES is displayed at 1–2-month leads after removing the linear trend.CFSv2 can reasonably reproduce the relationship between the SIC over BES in September and such factors as the surface air temperature(SAT),200-hPa geopotential height,sea surface temperature(SST),and North Atlantic Oscillation.In addition,it is found that the prescribed SIC initial condition in August as an input to CFSv2 is also essential.Therefore,the above atmospheric and oceanic factors,as well as an accurate initial condition of SIC,all contribute to a high prediction skill for SIC over BES in September.Based on a statistical prediction method,the contributions from individual predictability sources are further identified.The high prediction skill of CFSv2 for the interannual variability of SIC over BES is largely attributable to its accurate predictions of the SAT and SST,as well as a better initial condition of SIC.展开更多
The ice-phase microphysical characteristics of a stratiform cloud system over the Qilian Mountains in northwestern China on 15 September 2022 were analyzed via aircraft data.The stratiform cloud system developed under...The ice-phase microphysical characteristics of a stratiform cloud system over the Qilian Mountains in northwestern China on 15 September 2022 were analyzed via aircraft data.The stratiform cloud system developed under southwesterly flows at 500 hPa and was affected locally by topography.Synoptic features and aircraft observations revealed strengthened cloud development on the leeward slope.The ice particle habits and microphysical processes at heights of 6-8 km were investigated.The cloud system was characterized by extremely low supercooled liquid water content at temperatures between−4℃ and−17℃.The ice particle concentrations ranged predominantly from 10 to 30 L^(−1),corresponding to ice water content ranging from 0.01 to 0.05 g m^(−3).Active ice aggregation was observed at temperatures colder than−10°C.The windward side of the cloud system exhibited weaker development and two distinct cloud layers.Intense orographic uplift on the leeward slope enhanced ice particle aggregation.The clouds on the leeside presented lower ice particle concentrations but larger sizes than those on the windward side.The influence of aggregation on the ice particle size distribution was reflected in two main aspects.One aspect was the bimodal spectra at−16℃,with the first peak at 125μm and subpeak at 400-500μm;the other was the broadened size spectra at−13℃ due to significant aggregation of dendrites.展开更多
In this study, we carried out model tests to investigate the ice failure process and the resistance experienced by a transport vessel navigating in the Arctic region in pack ice conditions. We tested different navigat...In this study, we carried out model tests to investigate the ice failure process and the resistance experienced by a transport vessel navigating in the Arctic region in pack ice conditions. We tested different navigation velocities, ice plate sizes, and ice concentrations. During the tests, we closely observed several phenomena, including the modes of interaction of the ice ship and the moving and failure modes of ice. We also measured the vessel resistances under different conditions. The test results indicate that the navigation velocity is a significant determinant of the moving and failure modes of ice. Moreover, vessel resistance is remarkably dependent on the ice concentration and navigation velocity. The variances of the mean and maximum resistance are also compared and discussed in detail.展开更多
On the basis of the investigated data for sea ice physical processes duringthe Second Chinese National Arctic Research Expedition(CHINARE-2003)in the summer of 2003,the seaice dynamical characteristics were analyzed a...On the basis of the investigated data for sea ice physical processes duringthe Second Chinese National Arctic Research Expedition(CHINARE-2003)in the summer of 2003,the seaice dynamical characteristics were analyzed and the parameters describing these characteristicswere given.The new findings discovered from these parameters are:(1)The ice concentration obtainedfrom the investigation is two tenths to three tenths lower compared with that from the NOAA IceChart;and the ice thickness in the summer is 2 m less compared with the results obtained during theFirst Chinese National Arctic Research Expedition in 1999(CHINARE-1999),(2)the standarddeviation of the ice bottom fluctuation is 3.8 times that of the snow surface on the.ice sheet;(3)the maximum speed of the ice floe on which camp CHIS7 is located(CHIS7 floe)is 1300 m/h withrotation and oscillation.The rotation angle increased stepwise,the maximum being 37.8°,whilethe CHIS7 floe moved toward the north-east,and its rotation angle decreased stepwise.While theCHIS7 floe moved south-eastward.The oscillation period of CHIS7 floe is 12.45 h,which isconsistent with that of the inertial current at the same latitude,showing the contribution of theinertial current to the ice floe movement.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 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 Key Basic Research and Development Project of China(Grant Nos2004CB418300 and 2007CB411505)Chinese COPES project(GYHY200706005)the Na-tional Natural Science Foundation of China(Grant No40875052)
文摘In our previous study, a statistical linkage between the spring Arctic sea ice concentration （SIC） and the succeeding Chinese summer rainfall during the period 1968-2005 was identified. This linkage is demonstrated by the leading singular value decomposition （SVD） that accounts for 19% of the co-variance. Both spring SIC and Chinese summer rainfall exhibit a coherent interannual variability and two apparent interdecadal variations that occurred in the late 1970s and the early 1990s. The combined impacts of both spring Arctic SIC and Eurasian snow cover on the summer Eurasian wave train may explain their statistical linkage. In this study, we show that evolution of atmospheric circulation anomalies from spring to summer, to a great extent, may explain the spatial distribution of spring and summer Arctic SIC anomalies, and is dynamically consistent with Chinese summer rainfall anomalies in recent decades. The association between spring Arctic SIC and Chinese summer rainfall on interannual time scales is more important relative to interdecadal time scales. The summer Arctic dipole anomaly may serve as the bridge linking the spring Arctic SIC and Chinese summer rainfall, and their coherent interdecadal variations may reflect the feedback of spring SIC variability on the atmosphere. The summer Arctic dipole anomaly shows a closer relationship with the Chinese summer rainfall relative to the Arctic Oscillation.
基金funded by the Global Change Research Program of China(Grant No.2015CB953900)the Key Program of the National Natural Science Foundation of China(Grant Nos.41330960 and 41406208)+1 种基金the Canada Research Chairs Program,NSERCCanadian Federal IPY Program Office
文摘The Arctic sea-ice extent has shown a declining trend over the past 30 years. Ice coverage reached historic minima in 2007 and again in 2012. This trend has recently been assessed to be unique over at least the last 1450 years. In the summer of 2010, a very low sea-ice concentration(SIC) appeared at high Arctic latitudes—even lower than that of surrounding pack ice at lower latitudes. This striking low ice concentration—referred to here as a record low ice concentration in the central Arctic(CARLIC)—is unique in our analysis period of 2003–15, and has not been previously reported in the literature. The CARLIC was not the result of ice melt, because sea ice was still quite thick based on in-situ ice thickness measurements.Instead, divergent ice drift appears to have been responsible for the CARLIC. A high correlation between SIC and wind stress curl suggests that the sea ice drift during the summer of 2010 responded strongly to the regional wind forcing. The drift trajectories of ice buoys exhibited a transpolar drift in the Atlantic sector and an eastward drift in the Pacific sector,which appeared to benefit the CARLIC in 2010. Under these conditions, more solar energy can penetrate into the open water,increasing melt through increased heat flux to the ocean. We speculate that this divergence of sea ice could occur more often in the coming decades, and impact on hemispheric SIC and feed back to the climate.
基金The International Science and Technology Cooperation Project of China under contract No.2011DFA22260the National Natural Science Foundation of China under contract No.41276191+1 种基金the Public Science and Technology Research Funds Projects of Ocean by the State Oceanic Administration under contract No.201205007-05the Chinese Polar Environment Comprehensive Investigation & Assessment Program by the State Oceanic Administration under contract Nos 2013-02-04 and 2012-04-03-02
文摘A retrieval algorithm of arctic sea ice concentration （SIC） based on the brightness temperature data of “HY-2” scanning microwave radiometer has been constructed. The tie points of the brightness temperature were selected based on the statistical analysis of a polarization gradient ratio and a spectral gradient ratio over open water （OW）, first-year ice （FYI）, and multiyear ice （MYI） in arctic. The thresholds from two weather filters were used to reduce atmospheric effects over the open ocean. SIC retrievals from the “HY-2” radiom-eter data for idealized OW, FYI, and MYI agreed well with theoretical values. The 2012 annual SIC was calcu-lated and compared with two reference operational products from the National Snow and Ice Data Center （NSIDC） and the University of Bremen. The total ice-covered area yielded by the “HY-2” SIC was consistent with the results from the reference products. The assessment of SIC with the aerial photography from the fifth Chinese national arctic research expedition （CHINARE） and six synthetic aperture radar （SAR） images from the National Ice Service was carried out. The “HY-2” SIC product was 16% higher than the values de-rived from the aerial photography in the central arctic. The root-mean-square （RMS） values of SIC between “HY-2” and SAR were comparable with those between the reference products and SAR, varying from 8.57% to 12.34%. The “HY-2” SIC is a promising product that can be used for operational services.
基金The National Major Research High Resolution Sea Ice Model Development Program of China under contract No.2018YFA0605903the National Natural Science Foundation of China under contract Nos 51639003,41876213 and 41906198+1 种基金the Hightech Ship Research Project of China under contract No.350631009the National Postdoctoral Program for Innovative Talent of China under contract No.BX20190051.
文摘In order to apply satellite data to guiding navigation in the Arctic more effectively,the sea ice concentrations(SIC)derived from passive microwave(PM)products were compared with ship-based visual observations(OBS)collected during the Chinese National Arctic Research Expeditions(CHINARE).A total of 3667 observations were collected in the Arctic summers of 2010,2012,2014,2016,and 2018.PM SIC were derived from the NASA-Team(NT),Bootstrap(BT)and Climate Data Record(CDR)algorithms based on the SSMIS sensor,as well as the BT,enhanced NASA-Team(NT2)and ARTIST Sea Ice(ASI)algorithms based on AMSR-E/AMSR-2 sensors.The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons.The correlation coefficients(CC),biases and root mean square deviations(RMSD)between PM SIC and OBS SIC were compared in terms of the overall trend,and under mild/normal/severe ice conditions.Using the OBS data,the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness.Our results show that CC values range from 0.89(AMSR-E/AMSR-2 NT2)to 0.95(SSMIS NT),biases range from−3.96%(SSMIS NT)to 12.05%(AMSR-E/AMSR-2 NT2),and RMSD values range from 10.81%(SSMIS NT)to 20.15%(AMSR-E/AMSR-2 NT2).Floe size has a significant influence on the SIC retrievals of the PM products,and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions.Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products.Overall,the best(worst)agreement occurs between OBS SIC and SSMIS NT(AMSR-E/AMSR-2 NT2)SIC in the Arctic summer.
基金The National Key Research and Development Program of China under contract No.2016YFC1402704the National Natural Science Foundation of China under contract Nos 41941012 and 42076228the Guangdong Basic and Applied Basic Research Foundation under contract No.2019A1515110295。
文摘Sea ice concentration(SIC)is one of the most important indicators when monitoring climate changes in the polar region.With the development of the Chinese satellite technology,the Feng Yun(FY)series has been applied to retrieve the sea ice parameters in the polar region.In this paper,to improve the SIC retrieval accuracy from the passive microwave(PM)data of the Microwave Radiation Imager(MWRI)aboard on the Feng Yun-3 B(FY-3 B)Satellite,the dynamic tie-point(DT)Arctic Radiation and Turbulence Interaction Study(ARTIST)Sea Ice(ASI)(DT-ASI)SIC retrieval algorithm is applied and obtained Arctic SIC data for nearly 10 a(from November 18,2010 to August 19,2019).Also,by applying a land spillover correction scheme,the erroneous sea ice along coastlines in melt season is removed.The results of FY-3 B/DT-ASI are obviously improved compared to that of FY-3 B/NT2(NASA-Team2)in both SIC and sea ice extent(SIE),and are highly consistent with the results of similar products of AMSR2(Advanced Microwave Scanning Radiometer 2)/ASI and AMSR2/DT-ASI.Compared with the annual average SIC of FY-3 B/NT2,our result is reduced by 2.31%.The annual average SIE difference between the two FY-3 Bs is 1.65×10^(6) km^(2),of which the DT-ASI algorithm contributes 87.9%and the land spillover method contributes12.1%.We further select 58 MODIS(Moderate-resolution Imaging Spectroradiometer)cloud-free samples in the Arctic region and use the tie-point method to retrieve SIC to verify the accuracy of these SIC products.The root mean square difference(RMSD)and mean absolute difference(MAD)of the FY-3 B/DT-ASI and MODIS results are 17.2%and 12.7%,which is close to those of two AMSR2 products with 6.25 km resolution and decreased 8%and 7.2%compared with FY-3 B/NT2.Further,FY-3 B/DT-ASI has the most significant improvement where the SIC is lower than 60%.A high-quality SIC product can be obtained by using the DT-ASI algorithm and our work will be beneficial to promote the application of Feng Yun Satellite.
基金The National Natural Science Foundation of China under contract Nos 41330960 and 41276193 and 41206184
文摘In recent years, the rapid decline of Arctic sea ice area （SIA） and sea ice extent （SIE）, especially for the multiyear （MY） ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0 GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square （rms） difference of MY SIA derived from the algorithm of this study are 0.65×10^6 km^2 and 0.69×10^6 km^2 during January to March, -0.06×10^6 km^2 and 0.14×10^6 km^2during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×10^6 km^2 and 0.84×10^6 km^2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang＇s, Lomax＇ and NASA TEAM algorithms.
基金The National Key Research and Development Program of China under contract Nos 2017YFC1404103 and 2016YFC1401701the National Programme on Global Change and Air-Sea Interaction of China under contract GASI-IPOVAI-04the National Natural Science Foundation of China under contract Nos 41876014 and 41606039.
文摘The sea ice concentration observation from satellite remote sensing includes the spatial multi-scale information.However,traditional data assimilation methods cannot better extract the valuable information due to the complicated variability of the sea ice concentration in the marginal ice zone.A successive corrections analysis using variational optimization method,called spatial multi-scale recursive filter(SMRF),has been designed in this paper to extract multi-scale information resolved by sea ice observations.It is a combination of successive correction methods(SCM)and minimization algorithms,in which various observational scales,from longer to shorter wavelengths,can be extracted successively.As a variational objective analysis scheme,it gains the advantage over the conventional approaches that analyze all scales resolved by observations at one time,and also,the specification of parameters is more convenient.Results of single-observation experiment demonstrate that the SMRF scheme possesses a good ability in propagating observational signals.Further,it shows a superior performance in extracting multi-scale information in a two-dimensional sea ice concentration(SIC)experiment with the real observations from Special Sensor Microwave/Imager SIC(SSMI).
基金Supported by the National Natural Science Foundation of China(No.41406208)the Global Change Research of National Important Research Project on Science(No.2015CB953900)+1 种基金the Scientific and Youth Science Funds of Shandong Academy of Sciences,China(No.2013QN042)the Open Research Fund of the State Oceanic Administration of the People’s Republic of China Key Laboratory for Polar Science(No.3KP201203)
文摘The dual-polarized ratio algorithm(DPR)for the retrieval of Arctic sea ice concentration from Advanced Microwave Scanning Radiometer-EOS(AMSR-E)data was improved using a contrast ratio(CR)parameter.In contrast to three other algorithms(Artist Sea Ice algorithm,ASI;NASA-Team 2 algorithm,NT2;and AMSR-E Bootstrap algorithm,ABA),this algorithm does not use a series of tie-points or a priori values of brightness temperature of sea-ice constituents,such as open water and 100% sea ice.Instead,it is based on a ratio(a)of horizontally and vertically polarized sea ice emissivity at 36.5 GHz,which can be automatically determined by the CR.aexhibited a clear seasonal cycle:changing slowly during winter,rapidly at other times,and reaching a minimum during summer.The DPR was improved using a seasonala.The systematic diff erences in the Arctic sea ice area over the complete AMSR-E period(2002–2011)were-0.8% ±2.0% between the improved DPR and ASI;-1.3%±1.7% between the improved DPR and ABA;and-0.7% ±1.9% between the improved DPR and NT2.The improved DPR and ASI(or ABA)had small seasonal diff erences.The seasonal diff erences between the improved DPR and NT2 decreased,except in summer.The improved DPR identified extremely low ice concentration regions in the Pacific sector of the central Arctic(north of 83°N)around August 12,2010,which was confirmed by the Chinese National Arctic Research Expedition.A series of high-resolution MODIS images(250 m×250 m)of the Beaufort Sea during summer were used to assess the four algorithms.According to mean bias and standard deviations,the improved DPR algorithm performed equally well with the other three sea ice concentration algorithms.The improved DPR can provide reasonable sea ice concentration data,especially during summer.
基金The National Key Research and Development Program of China under contract Nos 2018YFC1407402 and 2017YFC1404103the National Programme on Global Change and Air-Sea Interaction(GASI-IPOVAI-04)of Chinathe Open Fund Project of Key Laboratory of Marine Environmental Information Technology,Ministry of Natural Resources。
文摘With the development and deployment of observation systems in the ocean,more precise passive and active microwave data are becoming available for the weather forecasting and the climate monitoring.Due to the complicated variability of the sea ice concentration(SIC)in the marginal ice zone and the scarcity of high-precision sea ice data,how to use less data to accurately reconstruct the sea ice field has become an urgent problem to be solved.A reconstruction method for gridding observations using the variational optimization technique,called the multi-scale high-order recursive filter(MHRF),which is a combination of Van Vliet fourth-order recursive filter and the three-dimensional variational(3D-VAR)analysis,has been designed in this study to reproduce the refined structure of sea ice field.Compared with the existing spatial multi-scale first-order recursive filter(SMRF)in which left and right filter iterative processes are executed many times,the MHRF scheme only executes the same filter process once to reduce the analysis errors caused by multiple filters and improve the filter precision.Furthermore,the series connected transfer function in the high-order recursive filter is equivalently replaced by the paralleled one,which can carry out the independent filter process in every direction in order to improve the filter efficiency.Experimental results demonstrate that this method possesses a good potential in extracting the observation information to successfully reconstruct the SIC field in computational efficiency.
基金funded by the National Natural Science Foundation of China (No.41976022)the Global Change Research Program of China (No.2015CB953900)。
文摘In view of the extremely low sea ice concentration(SIC) appeared at high latitudes of the Arctic in the summer of 2010, the changes of SIC in the central Arctic from 2010 to 2017 were investigated in this paper based on the AMSR-E/AMSR-2 SIC products retrieved by the NT2 algorithm. The results show that the extremely low sea ice concentration in the central Arctic not only occurred in 2010 but also occurred again in 2016, and the daily average sea ice concentration(ASIC) reached a minimum of 0.70, which was significantly lower than the value of 0.78 in 2010 and became a new historical low record. A large area of sea ice in the sector 150°E–180° in 2010 disappeared in 2016, which was the most important difference to produce the new minimum. Also, the ice edge in 2016 retreated into the 85°N circle, whereas in 2010 it was far from the central Arctic. In 2010 and 2016, there were high correlations between the wind stress curl and the relative variation rate of ASIC, which indicates that wind stress curl(WSC) drove the divergence of sea ice. It directly leads to the decrease in the SIC and is the main cause of the extremely low SIC events. The results in this paper show that the decline of Arctic sea ice is represented by not only the reduction of sea ice coverage but also the reduction of SICs. The central Arctic has always been covered by large amount of sea ices, so the drastic reduction of SIC will not only change the structure of the ice field, but also lead to critical climatic effects that deserve further attention.
基金supported by the National Natural Sci-ence Foundation of China(Grant nos.40906099,40930848)the National Science and Technology Supporting Program of China(Grant no.2011BAC 03B02-03-02)the Ocean Public Welfare Scientific Research Project of China(Grant no.2012418007)
文摘A reasonable initial state of ice concentration is essential for accurate short-term forecasts of sea ice using ice-ocean coupled models. In this study, sea ice concentration data are assimilated into an operational ice forecast system based on a com- bined optimal interpolation and nudging scheme. The scheme produces a modeled sea ice concentration at every time step, based on the difference between observational and forecast data and on the ratio of observational error to modeled error. The impact and the effectiveness of data assimilation are investigated. Significant improvements to predictions of sea ice extent were obtained through the assimilation of ice concentration, and minor improvements through the adjustment of the upper ocean properties. The assimilation of ice thickness data did not significantly improve predictions. Forecast experiments show that the forecast accuracy is higher in summer, and that the errors on five-day forecasts occur mainly around the marginal ice zone.
基金The National Key Research and Development Program of China under contract No.2023YFC3107701the National Natural Science Foundation of China under contract No.42375143.
文摘To effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregressive filter used in this study has been attempted to replace the traditional first-order recursive filter used in spatial multi-scale recursive filter(SMRF)method.The experimental results indicate that the MSRF scheme successfully extracts various scale information resolved by observations.Moreover,compared with the SMRF scheme,the MSRF scheme improves computational accuracy and efficiency to some extent.The MSRF scheme can not only propagate to a longer distance without the attenuation of innovation,but also reduce the mean absolute deviation between the reconstructed sea ice concentration results and observations reduced by about 3.2%compared to the SMRF scheme.On the other hand,compared with traditional first-order recursive filters using in the SMRF scheme that multiple filters are executed,the MSRF scheme only needs to perform two filter processes in one iteration,greatly improving filtering efficiency.In the two-dimensional experiment of sea ice concentration,the calculation time of the MSRF scheme is only 1/7 of that of SMRF scheme.This means that the MSRF scheme can achieve better performance with less computational cost,which is of great significance for further application in real-time ocean or sea ice data assimilation systems in the future.
基金the National Key Research and Development Program of China[2018YFC1407100]the National Natural Science Foundation of China[41876223].
文摘A daily sea ice concentration(SIC)product in the Arctic,derived from the brightness temperature(TB)data of the Microwave Radiation Imager(MWRI)sensor aboard on the FY-3D satellite,is described in this paper.The MWRI TB raw swath data were first processed into daily gridded data and then corrected using the Advanced Microwave Scanning Radiometer 2(AMSR2)sensor.An ASI algorithm,which uses daily dynamic tie points,was adopted to calculate daily SIC at 12.5 km polar stereographic projection from January 2018 to June 2020.Our generated MWRI SIC product was compared with the AMSR2 SIC based on the ASI algorithm that uses fixed tie points.For more detailed comparison,we then compared our MWRI SIC with the SIC from the Moderate Resolution Imaging Spectroradiometer(MODIS)data.The mean bias between our MWRI SIC and AMSR2 SIC is 4.24%.The absolute values of biases between the daily MWRI SIC and MODIS SIC range from 0.14%to 10.76%,better than the MWRI SIC product based on the NT2 algorithm published by the Chinese National Satellite Meteorological Center.The results show that our MWRI SIC product has a good quality and can be used as a basic dataset for sea ice extent records.The dataset is available at http://www.dx.doi.org/10.11922/sciencedb.00137.
基金supported by National Key Technology R & D Program (Grant No. 2006BAB18B01)the National Natural Science Foundation of China (Grant No. 40806076)+2 种基金Antarctic Exploration Fundamental Project (Grant No. 14699907111091)Chinese Polar Strategic Research Foundation (Grant No. 20080203)Key Laboratory of Surveying and Mapping Technology on Island and Reef of the State Bureau of Surveying and Mapping (Grant No. 2009B04)
文摘Sea ice concentration (SIC) is an important parameter in characterizing sea ice. Limited by the environment and the spatial extent of observation, it is difficult for field work to meet the needs of a large-scale SIC study. However, with its many advantages, such as the ability to make large-scale, high-resolution and long-duration observations, the altimeter can be used to determine SIC on a large scale. Using the correspondence between the satellite pulse altimeter waveform and reflector property, waveform classification is employed. Moreover, this paper develops an algorithm to obtain the SIC from altimeter waveforms. In an actual computation, Pyrz Bay in the Antarctic is taken as an experimental region, and one-year and seasonal SICs are derived from ERS-1/GM waveforms over this study area. Furthermore, altimetric SICs are compared with those of SSMR SSM/I. The results show that the spatial distribution and the regions of maximum SIC determined employing these two methods are consistent. This demonstrates that altimeter data can be used to monitor sea ice.
基金Supported by the National Key Research and Development Program of China(2022YFE0106800)National Natural Science Foundation of China(42230603)Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(311021001)。
文摘To further understand the prediction skill for the interannual variability of the sea ice concentration(SIC)in specific regions of the Arctic,this paper evaluates the NCEP Climate Forecast System version 2(CFSv2),in predicting the autumn SIC and its interannual variability over the Barents–East Siberian Seas(BES).It is found that CFSv2 presents much better prediction skill for the September SIC over BES than the Arctic as a whole at 1–6-month leads,and high prediction skill for the interannual variability of the SIC over BES is displayed at 1–2-month leads after removing the linear trend.CFSv2 can reasonably reproduce the relationship between the SIC over BES in September and such factors as the surface air temperature(SAT),200-hPa geopotential height,sea surface temperature(SST),and North Atlantic Oscillation.In addition,it is found that the prescribed SIC initial condition in August as an input to CFSv2 is also essential.Therefore,the above atmospheric and oceanic factors,as well as an accurate initial condition of SIC,all contribute to a high prediction skill for SIC over BES in September.Based on a statistical prediction method,the contributions from individual predictability sources are further identified.The high prediction skill of CFSv2 for the interannual variability of SIC over BES is largely attributable to its accurate predictions of the SAT and SST,as well as a better initial condition of SIC.
基金supported by the National Natural Science Foundation of China(Grant Nos.42475100 and 42405091)supported by the CMA Key Innovation Team(Grant No.CMA2022ZD10)+1 种基金the CMA Weather Modification Centre Innovation Team(Grant No.WMC2023IT02)the National Key R&D Program of China(Grant No.2019YFC1510305).
文摘The ice-phase microphysical characteristics of a stratiform cloud system over the Qilian Mountains in northwestern China on 15 September 2022 were analyzed via aircraft data.The stratiform cloud system developed under southwesterly flows at 500 hPa and was affected locally by topography.Synoptic features and aircraft observations revealed strengthened cloud development on the leeward slope.The ice particle habits and microphysical processes at heights of 6-8 km were investigated.The cloud system was characterized by extremely low supercooled liquid water content at temperatures between−4℃ and−17℃.The ice particle concentrations ranged predominantly from 10 to 30 L^(−1),corresponding to ice water content ranging from 0.01 to 0.05 g m^(−3).Active ice aggregation was observed at temperatures colder than−10°C.The windward side of the cloud system exhibited weaker development and two distinct cloud layers.Intense orographic uplift on the leeward slope enhanced ice particle aggregation.The clouds on the leeside presented lower ice particle concentrations but larger sizes than those on the windward side.The influence of aggregation on the ice particle size distribution was reflected in two main aspects.One aspect was the bimodal spectra at−16℃,with the first peak at 125μm and subpeak at 400-500μm;the other was the broadened size spectra at−13℃ due to significant aggregation of dendrites.
基金Supported by the National Nature Science Foundation of China, under Grant No. 51179123 and No. 51279131 and the Special Research Program of Ministry of Industry and Information Technology of China
文摘In this study, we carried out model tests to investigate the ice failure process and the resistance experienced by a transport vessel navigating in the Arctic region in pack ice conditions. We tested different navigation velocities, ice plate sizes, and ice concentrations. During the tests, we closely observed several phenomena, including the modes of interaction of the ice ship and the moving and failure modes of ice. We also measured the vessel resistances under different conditions. The test results indicate that the navigation velocity is a significant determinant of the moving and failure modes of ice. Moreover, vessel resistance is remarkably dependent on the ice concentration and navigation velocity. The variances of the mean and maximum resistance are also compared and discussed in detail.
基金supported by the Na-tional Natural Science Foundation of China under con-tract Nos 40233032 and 40376006China Science and Technology Basement and Social Commonweal Special Project under contract 2003DEB5J057.
文摘On the basis of the investigated data for sea ice physical processes duringthe Second Chinese National Arctic Research Expedition(CHINARE-2003)in the summer of 2003,the seaice dynamical characteristics were analyzed and the parameters describing these characteristicswere given.The new findings discovered from these parameters are:(1)The ice concentration obtainedfrom the investigation is two tenths to three tenths lower compared with that from the NOAA IceChart;and the ice thickness in the summer is 2 m less compared with the results obtained during theFirst Chinese National Arctic Research Expedition in 1999(CHINARE-1999),(2)the standarddeviation of the ice bottom fluctuation is 3.8 times that of the snow surface on the.ice sheet;(3)the maximum speed of the ice floe on which camp CHIS7 is located(CHIS7 floe)is 1300 m/h withrotation and oscillation.The rotation angle increased stepwise,the maximum being 37.8°,whilethe CHIS7 floe moved toward the north-east,and its rotation angle decreased stepwise.While theCHIS7 floe moved south-eastward.The oscillation period of CHIS7 floe is 12.45 h,which isconsistent with that of the inertial current at the same latitude,showing the contribution of theinertial current to the ice floe movement.
