A multi-scale second-order autoregressive recursive filter approach for the sea ice concentration analysis

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.

On the Association between Spring Arctic Sea Ice Concentration and Chinese Summer Rainfall:A Further Study

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.

An assessment of arctic sea ice concentration retrieval based on “HY-2” scanning radiometer data using field observations during CHINARE-2012 and other satellite instruments

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.

Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010–2018

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.

A variational successive corrections approach for the sea ice concentration analysis

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).

An improved dual-polarized ratio algorithm for sea ice concentration retrieval from passive microwave satellite data and inter-comparison with ASI,ABA and NT2

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.

Arctic sea ice concentration retrieval using the DT-ASI algorithm based on FY-3B/MWRI data

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.

A multi-scale high-order recursive filter approach for the sea ice concentration analysis

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.

The New Minimum of Sea Ice Concentration in the Central Arctic in 2016

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.

Why Are Arctic Sea Ice Concentration in September and Its Interannual Variability Well Predicted over the Barents–East Siberian Seas by CFSv2?

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.

Record Low Sea-Ice Concentration in the Central Arctic during Summer 2010

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.

Arctic sea ice volume export through the Fram Strait: variation and its effect factors

Arctic sea ice export is important for the redistribution of freshwater and sea ice mass.Here,we use the sea ice thickness,sea ice velocity,and sea ice concentration(SIC)to estimate the exported sea ice volume through the Fram Strait from 2011 to 2018.We further analyse the contributions of the sea ice thickness,velocity and concentration to sea ice volume export.Then,the relationships between atmospheric circulation indices(Arctic Oscillation(AO),North Atlantic Oscillation(NAO),and Arctic Dipole(AD))and the sea ice volume export are discussed.Finally,we analyse the impact of wind-driven oceanic circulation indices(Ekman transport(ET))on the sea ice volume export.The sea ice volume export rapidly increases in winter and decreases in spring.The exported sea ice volume in winter is likely to exceed that in spring in the future.Among sea ice thickness,velocity and SIC,the greatest contribution to sea ice export comes from the ice velocity.The exported sea ice volume through the zonal gate of the Fram Strait(which contributes 97%to the total sea ice volume export of the Fram Strait)is much higher than that through the meridional gate(3%)because the sea ice flowing out of the zonal gate has the characteristics of a high thickness(mainly thicker than 1 m),a high velocity(mainly faster than 0.06 m/s)and a high concentration(mainly higher than 80%).The AD and ET explain 53.86%and 38.37%of the variation in sea ice volume export,respectively.

Investigating the impact of atmospheric parameters on sea ice variability in and around Svalbard

Recent research has shown that winter warmings are phenomenally high compared to summer warmings over the poles,especially over the Arctic.Taking the current scenario into account,this paper attempts to understand the atmospheric variables causing sea ice variability over and around the region of Svalbard for seasons;winter,spring,summer and autumn for the span of 42 years(1979-2021).The variability in atmospheric and oceanic parameters namely temperature,precipitation,wind speed,and sea surface salinity are analysed over inter-spatial,inter-seasonal and inter-annual domains.Winters are characterized by inter-annual increasing trend in temperature.During 1981-1990 the rise from the decadal mean is found to be 0.39 K·a^(-1),during 1991-2000 it is 0.20 K·a^(-1),during 2001-2010 it is 0.04 K·a^(-1) and during 2011-2020 it is 0.23 K·a^(-1).Interestingly while considering inter-spatial domains,the region southwest to Svalbard seems to be wetter(0.05 mm·(10 a)-1)compared to its northeast(-0.03 mm·(10 a)-1).Across all the three domains,wind speeds are highest during autumn and then decrease subsequently through summer,spring and are least during winter.Wind is predominantly from the south,and hence it is suspected to carry hot Atlantic air.Additionally,the significant role of salinity in the ocean also plays a key role in governing the fate of sea ice conditions.The long-term forecasts of temperature over seaice of Svalbard are alarming especially for the winter ice(r=-0.84).Correlation matrices between atmospheric and sea ice parameters are shown to gain a better understanding on their inter relation.

Temporal and spatial variation characteristics of Antarctic sea ice and the causes of its record decline during 2015–2016: a review

Satellite observations over the past four decades have shown that the long-term trend of Antarctic sea ice extent(SIE)is opposite to the trend of sea ice extent in the Arctic.Arctic sea ice extent continues to decline while Antarctic SIE is generally on the rise except for a dramatic decline in 2015–2016.Based on the 40-year climatology from 1981 to 2020,Antarctic SIE anomaly in December 2016 is–2.1×10^(6) km^(2),reaching the minimum since 1979.There are many studies on the cause of this record decline.This present review summarizes the spatial and temporal characters of Antarctic sea ice and recaps major findings on the causes of record decline in 2015–2016 from the perspective of direct thermodynamic and dynamic process of atmosphere and ocean as well as the modulation of climate modes.Finally,the challenges and key scientific problems to be solved in the future of Antarctic sea ice research are presented.

Daily sea ice concentration product based on brightness temperature data of FY-3D MWRI in the Arctic

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.

Sea ice concentration over the Antarctic Ocean from satellite pulse altimetry

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.

Role of atmospheric factors in forcing Arctic sea ice variability

The spatial structure of the Arctic sea ice concentration(SIC)variability and the connection to atmospheric as well as radiative forcing during winter and summer for the 1979–2017 period are investigated.The interannual variability with different spatial characteristics of SIC in summer and winter is extracted using the empirical orthogonal function(EOF)analysis.The present study confirms that the atmospheric circulation has a strong influence on the SIC through both dynamic and thermodynamic processes,as the heat flux anomalies in summer are radiatively forced while those in winter contain both radiative and“circulation-induced”components.Thus,atmospheric fluctuations have an explicit and extensive influence to the SIC through complex mechanisms during both seasons.Moreover,analysis of a variety of atmospheric variables indicates that the primary mechanism about specific regional SIC patterns in Arctic marginal seas are different with special characteristics.

Winter sea ice albedo variations in the Bohai Sea of China

Sea ice conditions in the Bohai Sea of China are sensitive to large-scale climatic variations. On the basis of CLARA-A1-SAL data, the albedo variations are examined in space and time in the winter（December, January and February） from 1992 to 2008 in the Bohai Sea sea ice region. Time series data of the sea ice concentration（SIC）, the sea ice extent（SIE） and the sea surface temperature（SST） are used to analyze their relationship with the albedo. The sea ice albedo changed in volatility appears along with time, the trend is not obvious and increases very slightly during the study period at a rate of 0.388% per decade over the Bohai Sea sea ice region.The interannual variation is between 9.93% and 14.50%, and the average albedo is 11.79%. The sea ice albedo in years with heavy sea ice coverage, 1999, 2000 and 2005, is significantly higher than that in other years; in years with light sea ice coverage, 1994, 1998, 2001 and 2006, has low values. For the monthly albedo, the increasing trend（at a rate of 0.988% per decade） in December is distinctly higher than that in January and February. The mean albedo in January（12.90%） is also distinctly higher than that in the other two months. The albedo is significantly positively correlated with the SIC and is significantly negatively correlated with the SST（significance level 90%）.

Aerial observations of sea ice and melt ponds near the North Pole during CHINARE2010

An aerial photography has been used to provide validation data on sea ice near the North Pole where most polar orbiting satellites cannot cover. This kind of data can also be used as a supplement for missing data and for reducing the uncertainty of data interpolation. The aerial photos are analyzed near the North Pole collected during the Chinese national arctic research expedition in the summer of 2010（CHINARE2010）. The result shows that the average fraction of open water increases from the ice camp at approximately 87°N to the North Pole, resulting in the decrease in the sea ice. The average sea ice concentration is only 62.0% for the two flights（16 and 19 August 2010）. The average albedo（0.42） estimated from the area ratios among snow-covered ice,melt pond and water is slightly lower than the 0.49 of HOTRAX 2005. The data on 19 August 2010 shows that the albedo decreases from the ice camp at approximately 87°N to the North Pole, primarily due to the decrease in the fraction of snow-covered ice and the increase in fractions of melt-pond and open-water. The ice concentration from the aerial photos and AMSR-E（The Advanced Microwave Scanning Radiometer-Earth Observing System） images at 87.0°–87.5°N exhibits similar spatial patterns, although the AMSR-E concentration is approximately 18.0%（on average） higher than aerial photos. This can be attributed to the 6.25 km resolution of AMSR-E, which cannot separate melt ponds/submerged ice from ice and cannot detect the small leads between floes. Thus, the aerial photos would play an important role in providing high-resolution independent estimates of the ice concentration and the fraction of melt pond cover to validate and/or supplement space-borne remote sensing products near the North Pole.

A study on the dynamic tie points ASI algorithm in the Arctic Ocean

Sea ice concentration is an important parameter for polar sea ice monitoring. Based on 89 GHz AMSR-E （Advanced Microwave Scanning Radiometer for Earth Observing System） data, a gridded high-resolution passive microwave sea ice concentration product can be obtained using the ASI （the Arctic Radiation And Turbulence Interaction Study （ARTIST） Sea Ice） retrieval algorithm. Instead of using fixed-point values, we developed ASi algorithm based on daily changed tie points, called as the dynamic tie point ASI algorithm in this study. Here the tie points are expressed as the brightness temperature polarization difference of open water and 100% sea ice. In 2010, the yearly-averaged tie points of open water and sea ice in Arctic are estimated to be 50.8 K and 7.8 K, respectively. It is confirmed that the sea ice concentrations retrieved by the dynamic tie point ASI algorithm can increase （decrease） the sea ice concentrations in low-value （high-value） areas. This improved the sea ice concentrations by present retrieval algorithm from microwave data to some extent. Comparing with the products using fixed tie points, the sea ice concentrations retrieved from AMSR-E data by using the dynamic tie point ASI algorithm are closer to those obtained from MODIS （Moderate-resolution Imaging Spectroradiometer） data. In 40 selected cloud-free sample regions, 95% of our results have smaller mean differences and 75% of our results have lower root mean square （RMS） differences compare with those by the fixed tie points.