| Arctic sea ice is an important component of the global climate, the Arctic sea ice undergone rapidly decrease with the increase in global warming in recent decades. Previous researches show that in Arctic, the decrease of sea multi-year (MY) ice is faster than the total sea ice. The length of the melt period increased in recent years. And the interannual variability of Arctic sea ice mainly occurred in the edge of central Arctic, which is the edge of the summer sea ice, rather than the edge of winter sea ice in normal meaning. Therefore, it’s meaningful to study on the interannual variability of seasonal sea ice and its interaction with atmosphere. Good quality data are needed to study the seasonal ice, but there are no published data to support the study. Thus it is need to study interannual variability of seasonal ice and relationship with atmosphere factors based on good quality data. Sea ice concentration is an important parameter for polar sea ice monitoring. Based on 89GHz 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. The fixed tie points ASI algorithm overestimates (underestimates) sea ice concentration in high (low) concentration regions. It is confirmed that the sea ice concentrations retrieved by the dynamic tie point ASI algorithm can increase the sea ice concentrations in low-value areas and decrease those in 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 data in the marginal and seasonal ice zones. 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.In recent years, the rapid decline of Arctic sea ice area (SIA) and sea ice extent (SIE), especially for the 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 6.9GHz brightness temperature data and sea ice concentration using 89.0GHz 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 0.75 X 106km2 and 0.79 X 106km2 during January to March,-0.06 X 106km2 and 0.14 X 106km2 during 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 × 106km2 and 0.84 × 106km2, respectively. And comparison with MY SIE obtained from the daily ice age data show that, the mean difference and rms difference are 0.35 × 106km2 and 0.77 × 106km2, 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.The change of Arctic sea ice mainly occurred in the edge of central Arctic region in recent years. In order to study the variability of Arctic seasonal sea ice, the dynamic tie points ASI algorithm and the reference MY ice concentrations are used to get the data of seasonal ice concentrations in autumn and winter. The analysis shows the first two EOF modes of seasonal sea ice anomaly mainly shows the characteristics of sea ice of year 2007 and 2005. The second mode mainly reflects an extreme change of Arctic sea ice in 2005, while the first mode not only reflects the change of Arctic sea ice in 2007, but also reflects a phase shift of seasonal sea ice in winter during 2002-2010. The shift mainly occurs in the Pacific Sector of the Arctic Ocean. The phase of seasonal sea ice anomaly is negative before 2007 and shifts to positive after 2007, and the positive phase continues to 2010. The maximum anomalies of surface temperature in Pacific sector occur in 2007. And the anomaly of Beaufort high helps to reduce the summer sea ice in the Pacific sector, and the weakened westerly jet is in favor of positive Beaufort high anomaly in summer and autumn. Also the clockwise ice velocity distribution is in favor of ice leaving from the Pacific sector, which will lead to the positive seasonal ice anomalies in autumn and winter maintained from 2007 to 2010 in Pacific sector. |
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