Sea ice extent retrieval with HY-2A scatterometer data and its assessment

A sea ice extent retrieval algorithm over the polar area based on scatterometer data of HY-2A satellite has been established. Four parameters are used for distinguishing between sea ice and ocean with Fisher＇s linear discriminant analysis method. The method is used to generate polar sea ice extent maps of the Arctic and Antarctic regions of the full 2013-2014 from the scatterometer aboard HY-2A （HY-2A-SCAT） backscatter data. The time series of the ice mapped imagery shows ice edge evolution and indicates a similar seasonal change trend with total ice area from DMSP-F17 Special Sensor Microwave Imager/Sounder （SSMIS） sea ice concentration data. For both hemispheres, the HY-2A-SCAT extent correlates very well with SSMIS 15% extent for the whole year period. Compared with Synthetic Aperture Radar （SAR） imagery, the HY-2A-SCAT ice extent shows good correlation with the Sentinel-1 SAR ice edge. Over some ice edge area, the difference is very evident because sea ice edges can be very dynamic and move several kilometers in a single day.

Variation of sea ice extent in different regions of the Arctic Ocean

Sea ice in the Arctic has been reducing rapidly in the past half century due to global warming. This study analyzes the variations of sea ice extent in the entire Arctic Ocean and its sub regions. The results indicate that sea ice extent reduction during 1979-2013 is most significant in summer, following by that in autumn, winter and spring. In years with rich sea ice, sea ice extent anomaly with seasonal cycle removed changes with a period of 4-6 years. The year of 2003-2006 is the ice-rich period with diverse regional difference in this century. In years with poor sea ice, sea ice margin retreats further north in the Arctic. Sea ice in the Fram Strait changes in an opposite way to that in the entire Arctic. Sea ice coverage index in melting-freezing period is an critical indicator for sea ice changes, which shows an coincident change in the Arctic and sub regions. Since 2002, Region C2 in north of the Pacific sector contributes most to sea ice changes in the central Aarctic, followed by C1 and C3. Sea ice changes in different regions show three relationships. The correlation coefficient between sea ice coverage index of the Chukchi Sea and that of the East Siberian Sea is high, suggesting good consistency of ice variation. In the Atlantic sector, sea ice changes are coincided with each other between the Kara Sea and the Barents Sea as a result of warm inflow into the Kara Sea from the Barents Sea. Sea ice changes in the central Arctic are affected by surrounding seas.

Statistical study on the spatial - temporal distribution features of the arctic sea ice extent

On the basis of the arctic monthly mean sea ice extent data set during 1953-1984, the arctic region is divided into eight subregions,and the analyses of empirical orthogonal functions, power spectrum and maximum entropy spectrum are made to indentify the major spatial and temporal features of the sea ice fluctuations within 32-year period. And then, a brief appropriate physical explanation is tentatively suggested. The results show that both seasonal and non-seasonal variations of the sea ice extent are remarkable, and iis mean annual peripheral positions as well as their interannu-al shifting amplitudes are quite different among all subregions. These features are primarily affected by solar radiation, o-cean circulation, sea surface temperature and maritime-continental contrast, while the non-seasonal variations are most possibly affected by the cosmic-geophysical factors such as earth pole shife, earth rotation oscillation and solar activity.

Mechanisms associated with winter intraseasonal extreme sea ice extent in the Weddell Sea

Previous studies have shown evidence of atmospheric extratropical wave trains modulating sea ice area in the Weddell and Amundsen/Bellingshausen seas on intraseasonal time-scales(20–100 d). Here we investigate mechanisms relating intraseasonal extreme sea ice extent and Ekman layer dynamics with emphasis on the Weddell Sea. This study extends from 1989 to 2013 and focuses on the winter season. Wind stress τ is calculated with winds from the Climate Forecast System reanalysis(CFSR) to evaluate momentum transfer between the atmosphere and the Ekman layer. Lag-composites of the anomalies of Ekman transport and the Ekman pumping indicate that divergence of mass in the Ekman layer and upwelling lead the occurrence of extreme sea ice contraction on intraseasonal time-scales in the Weddell Sea. Opposite conditions(i.e., convergence of the mass and downwelling) lead extreme sea ice expansion on intraseasonal time-scales. This study suggests that the Ekman pumping resulting from the anomalous wind stress on intraseasonal time-scales can transport these warmer waters to the surface contributing to sea ice melting. Additionally, high resolution sea ice fraction and ocean currents obtained from satellite and in situ data are used to investigate in detail mechanisms associated with persistent extreme sea ice expansion and contraction on intraseasonal time-scales. These case studies reveal that atmospheric circumpolar waves on intraseasonal time-scales can induce contrasting anomalies of about ±20% in sea ice concentration at the Weddell and western Antarctica Peninsula margins within less than 30 d. This study shows that extreme anomalies in sea ice may lag between 5–25 d(1–5 pentads) the ocean-atmospheric forcing on intraseasonal time-scales.

Impact of the El Nino on the Variability of the Antarctic Sea Ice Extent

In this paper, the spreading way in the southern hemisphere that anomalous warm water piled in tropical eastern Pacific is analysed and then impact of El Nino on the variability of the Antarctic sea ice extent is investigated by using a dataset from 1970 to 2002. The analysis result show that in El Nino event the anomalous warm water piled in tropical eastern Pacific is poleward propagation yet the westward propagation along southern equator current hasn't been discovered . The poleward propagation time of the anomalous warm water is about 1 year or so. El Nino event has a close relationship with the sea ice extent in the Amundsen sea , Bellingshausen sea and Antarctic peninsula .After El Nino appears , there is a lag of two years that the sea ice in the Amundsen sea , Bellingshausea sea, especially in the Antarctic peninsula decreases obviously. The processes that El Nino has influence with Antarctic sea ice extent is the warm water piled in tropical eastern Pacific poleward propagation along off the coast of southern America and cause the anomalous temperature raise in near pole and then lead the sea ice in Amundsen sea , Bellingshausen sea and Antarctic peninsula to decrease where the obvious decrease of the sea ice since 80' decade has close relation to the frequently appearance of El Nino .

Arctic warming trends and their uncertainties based on surface temperature reconstruction under different sea ice extent scenarios

IPCC AR6 states that the neglect of recent rapid warming in the Arctic generally leads to an underestimate of global warming trends.In this study,by reconstructing the surface temperature(ST)datasets in the Arctic under different sea ice extent scenarios(Imax and Imin),we respectively evaluated the annual and seasonal warming trends and their uncertainties from 1900 to 2020.The results show that the reconstructed datasets have a good representation of the ST change trends in the Arctic.In 1900e2020,the annual warming trends in the Arctic(0.17±0.031 and 0.14±0.025℃ per decade under the Imax and Imin reconstruction,respectively)are roughly 1.6e1.8 times the global mean warming trends(0.10±0.008 and 0.09±0.008℃ per decade).While in 1979e2020,the Arctic warming trends(0.66±0.100 and 0.55±0.080℃ per decade)increase to 3.1e3.5 times of the global warming trend(0.19±0.023 and 0.18±0.023℃ per decade)for Imax and Imin,respectively,indicating that the Arctic amplification effect has been significantly enhancing in recent decades.Although the seasonal warming trends are closely related to cloud feedback mechanisms,atmospheric circulation,and ocean circulation,they are not sensitive to the different reconstruction scenarios.

Causes for different spatial distributions of minimum Arctic sea-ice extent in 2007 and 2012

Satellite records show the minimum Arctic sea ice extents （SIEs） were observed in the Septembers of 2007 and 2012, but the spatial distributions of sea ice concentration reduction in these two years were quite different. Atmospheric circulation pattern and the upper-ocean state in summer were investigated to explain the difference. By employing the ice-temperature and ice-specific humidity （SH） positive feedbacks in the Arctic Ocean, this paper shows that in 2007 and 2012 the higher surface air temperature （SAT） and sea level pressure （SLP） accompanied by more surface SH and higher sea surface temperature （SST）, as a consequence, the strengthened poleward wind was favorable for melting summer Arctic sea ice in different regions in these two years. SAT was the dominant factor influencing the distribution of Arctic sea ice melting. The correlation coefficient is -0.84 between SAT anomalies in summer and the Arctic SIE anomalies in autumn. The increase SAT in different regions in the summers of 2007 and 2012 corresponded to a quicker melting of sea ice in the Arctic. The SLP and related wind were promoting factors connected with SAT. Strengthening poleward winds brought warm moist air to the Arctic and accelerated the melting of sea ice in different regions in the summers of 2007 and 2012. Associated with the rising air temperature, the higher surface SH and SST also played a positive role in reducing summer Arctic sea ice in different regions in these two years, which form two positive feedbacks mechanism.

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.

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%）.