scholarly journals Variability in Antarctic sea ice from 1998-2017

2018 ◽  
Author(s):  
Zhankai Wu ◽  
Xingdong Wang
Keyword(s):  
Sea Ice ◽  
First Year ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Melt ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
The Antarctic ◽  
Melt Duration ◽  
Multiyear Ice

This study was based on the daily sea ice concentration data from the National Snow and Ice Data Center (Cooperative Institute for Research in Environmental Sciences, Boulder, CO, USA) from 1998 to 2017. The Antarctic sea ice was analysed from the total sea ice area (SIA), first year ice area, first year ice melt duration, and multiyear ice area. On a temporal scale, the changes in sea ice parameters were studied over the whole 20 years and for two 10-year periods. The results showed that the total SIA increased by 0.0083×106 km2 yr-1 (+2.07% dec-1) between 1998 and 2017. However, the total SIA in the two 10-year periods showed opposite trends, in which the total SIA increased by 0.026×106 km2 yr-1 between 1998 and 2007 and decreased by 0.0707×106 km2 yr-1 from 2008 to 2017. The first year ice area increased by 0.0059×106 km2 yr-1 and the melt duration decreased by 0.0908 days yr-1 between 1998 and 2017. The multiyear ice area increased by 0.0154×106 km2 yr-1 from 1998 to 2017, and the increase in the last 10 years was about 12.1% more than that in the first 10 years. On a spatial scale, the Entire Antarctica was divided into two areas, namely West Antarctica (WA) and East Antarctica (EA), according to the spatial change rate of sea ice concentration. The results showed that WA had clear warming in recent years; the total sea ice and multiyear ice areas showed a decreasing trend; multiyear ice area sharply decreased and reached the lowest value in 2017, and accounted for only about 10.1% of the 20-year average. However, the total SIA and multiyear ice area all showed an increased trend in EA, in which the multiyear ice area increased by 0.0478×106 km2 yr-1. Therefore, Antarctic sea ice presented an increasing trend, but there were different trends in WA and EA. Different sea ice parameters in WA and EA showed an opposite trend from 1998 to 2007. However, the total SIA, first year ice area, and multiyear ice area all showed a decreasing trend from 2008-2017, especially the total sea ice and first year ice, which changed almost the same in 2014-2017. In summary, although the Antarctic sea ice has increased slightly over time, it has shown a decreasing trend in recent years.

scholarly journals Variability in Antarctic sea ice from 1998-2017

2018 ◽  
Author(s):  
Zhankai Wu ◽  
Xingdong Wang
Keyword(s):  
Sea Ice ◽  
First Year ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Melt ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
The Antarctic ◽  
Melt Duration ◽  
Multiyear Ice

This study was based on the daily sea ice concentration data from the National Snow and Ice Data Center (Cooperative Institute for Research in Environmental Sciences, Boulder, CO, USA) from 1998 to 2017. The Antarctic sea ice was analysed from the total sea ice area (SIA), first year ice area, first year ice melt duration, and multiyear ice area. On a temporal scale, the changes in sea ice parameters were studied over the whole 20 years and for two 10-year periods. The results showed that the total SIA increased by 0.0083×106 km2 yr-1 (+2.07% dec-1) between 1998 and 2017. However, the total SIA in the two 10-year periods showed opposite trends, in which the total SIA increased by 0.026×106 km2 yr-1 between 1998 and 2007 and decreased by 0.0707×106 km2 yr-1 from 2008 to 2017. The first year ice area increased by 0.0059×106 km2 yr-1 and the melt duration decreased by 0.0908 days yr-1 between 1998 and 2017. The multiyear ice area increased by 0.0154×106 km2 yr-1 from 1998 to 2017, and the increase in the last 10 years was about 12.1% more than that in the first 10 years. On a spatial scale, the Entire Antarctica was divided into two areas, namely West Antarctica (WA) and East Antarctica (EA), according to the spatial change rate of sea ice concentration. The results showed that WA had clear warming in recent years; the total sea ice and multiyear ice areas showed a decreasing trend; multiyear ice area sharply decreased and reached the lowest value in 2017, and accounted for only about 10.1% of the 20-year average. However, the total SIA and multiyear ice area all showed an increased trend in EA, in which the multiyear ice area increased by 0.0478×106 km2 yr-1. Therefore, Antarctic sea ice presented an increasing trend, but there were different trends in WA and EA. Different sea ice parameters in WA and EA showed an opposite trend from 1998 to 2007. However, the total SIA, first year ice area, and multiyear ice area all showed a decreasing trend from 2008-2017, especially the total sea ice and first year ice, which changed almost the same in 2014-2017. In summary, although the Antarctic sea ice has increased slightly over time, it has shown a decreasing trend in recent years.

scholarly journals Variability of Arctic Sea Ice (1979–2016)

Water ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 23 ◽  
Author(s):  
Zhankai Wu ◽  
Xingdong Wang
Keyword(s):  
Sea Ice ◽  
Data Center ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
First Year ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Multiyear Ice

This study is based on the daily sea ice concentration data from the National Snow and Ice Data Center (NSIDC; Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USA) from 1979 to 2016. The Arctic sea ice is analyzed from the total sea ice area, first year ice extent, multiyear ice area, and the variability of sea ice concentration in different ranges. The results show that the total sea ice area decreased by 0.0453 × 106 km2·year−1 (−0.55%/year) between 1979 and 2016, and the variability of the sea ice area from 1997 to 2016 is significantly larger than that from 1979 to 1996. The first-year ice extent increased by 0.0199 × 106 km2·year−1 (0.36%/year) from 1997 to 2016. The multiyear ice area decreased by 0.0711 × 106 km2·year−1 (−0.66%/year) from 1979 to 2016, of which in the last 20 years is about 30.8% less than in 1979–1996. In terms of concentration, we have focused on comparing 1979–1996 and 1997–2016 in different ranges. Sea ice concentration between 0.9–1 accounted for about 39.57% from 1979 to 1996, while from 1997–2016, it accounted for only 27.75%. However, the sea ice of concentration between 0.15–0.4 exhibits no significant trend changes.

scholarly journals Comparison between AMSR-E ASI sea-ice concentration product, MODIS and pseudo-ship observations of the Antarctic sea-ice edge

2015 ◽  
Vol 56 (69) ◽  
pp. 45-52 ◽  
Author(s):  
Xi Zhao ◽  
Haoyue Su ◽  
Alfred Stein ◽  
Xiaoping Pang
Keyword(s):  
Sea Ice ◽  
Average Distance ◽  
Sea Ice Concentration ◽  
Earth Observing System ◽  
Antarctic Sea Ice ◽  
Spatial Coverage ◽  
Ice Concentration ◽  
Ice Edge ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Antarctic

AbstractThe performance of passive microwave sea-ice concentration products in the marginal ice zone and at the ice edge draws much attention in accuracy assessments. In this study, we generated 917 pseudo-ship observations from four Moderate Resolution Imaging Spectroradiometer (MODIS) images based on the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol to assess the quality of the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) ARTIST (Arctic Radiation and Turbulence Interaction STudy) Sea Ice (ASI) concentrations at the ice edge in Antarctica. The results indicate that the ASI pixels in the pseudo-ASPeCt observations have a mean ice concentration of 13% and are significantly different from the well-established 15% threshold. The average distance between the pseudo-ice edge and the 15% threshold contour is ~10 km. The correlation between the sea-ice concentration (SIC), SICASI and SICMODIS values at the ice edge was considerably lower than the high coefficients obtained from a transect analysis. Underestimation of SICASI occurred in summer, whereas no clear bias was observed in winter. The proposed method provides an opportunity to generate a new source of reference data in which the spatial coverage is wider and more flexible than in traditional in situ observations.

scholarly journals Impact of surface wind biases on the Antarctic sea ice concentration budget in climate models

2016 ◽  
Vol 105 ◽  
pp. 60-70 ◽  
Author(s):  
O. Lecomte ◽  
H. Goosse ◽  
T. Fichefet ◽  
P.R. Holland ◽  
P. Uotila ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Surface Wind ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
The Antarctic

Retrieving the antarctic sea-ice concentration based on AMSR-E 89 GHz data

2013 ◽  
Vol 32 (9) ◽  
pp. 38-43
Author(s):  
Qinglong Yu ◽  
Hui Wang ◽  
Liying Wan ◽  
Haibo Bi
Keyword(s):  
Sea Ice ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
The Antarctic

scholarly journals Antarctic sea ice detection using a shipborne GPS reflectometry setup

2021 ◽  
pp. 002029402110130
Author(s):  
Yun Zhang ◽  
Dehao Ma ◽  
Wanting Meng ◽  
Xiangfang Xie ◽  
Shuhu Yang ◽  
...  
Keyword(s):  
Sea Ice ◽  
Sea Water ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Left Handed ◽  
Ice Concentration ◽  
Global Positioning ◽  
Antarctic Research ◽  
Ice Detection

The feasibility of Antarctic sea ice detection based on shipborne global positioning system reflectometry (GPS-R) technology is shown in this paper. Because the permittivity of sea water is much higher than that of sea ice, the reflected left-handed circular polarized (LHCP) GPS signal (RL) reflection coefficient of sea water is markedly higher than that of sea ice. The polarization ratio of RL to the direct right-handed circular polarized (RHCP) GPS signal (DR) is used to distinguish between sea water and sea ice in this paper. The experiment was performed on the ship “XueLong” for approximately 9 days from December 2014 to January 2015 during the 31st Chinese National Antarctic Research Expedition (CHINARE 31). The sea ice concentration data with a 25 km × 25 km spatial resolution derived from the National Snow and Ice Data Center (NSIDC) are used for validation and some pictures of sea ice taken from “XueLong” are shown for comparison. The polarization ratios (RL/DR) are calculated, and the correlation coefficient between the polarization ratios (RL/DR) and the sea ice concentrations is −0.66.

scholarly journals Observed Evidence of an Impact of the Antarctic Sea Ice Dipole on the Antarctic Oscillation

2011 ◽  
Vol 24 (16) ◽  
pp. 4508-4518 ◽  
Author(s):  
Qigang Wu ◽  
Xiangdong Zhang
Keyword(s):  
Sea Ice ◽  
Geopotential Height ◽  
Lead Time ◽  
Antarctic Oscillation ◽  
Atmospheric Variability ◽  
Sea Ice Concentration ◽  
Maximum Covariance Analysis ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
The Antarctic

Abstract A lagged maximum covariance analysis (MCA) is applied to investigate the linear covariability between monthly sea ice concentration (SIC) and 500-mb geopotential height (Z500) in the Southern Hemisphere (SH). The dominant signal is the atmospheric forcing of SIC anomalies throughout the year, but statistically significant covariances are also found between austral springtime Z500 and prior SIC anomalies up to four months earlier. The MCA pattern is characterized by an Antarctic dipole (ADP)-like pattern in SIC and a positively polarized Antarctic Oscillation (AAO) in Z500. Such long lead-time covariance suggests the forcing of the AAO by persistent ADP-like SIC anomalies. The leading time of SIC anomalies provides an implication for skillful predictability of springtime atmospheric variability.

scholarly journals Mesoscale Modeling of the Atmosphere over Antarctic Sea Ice: A Late-Autumn Case Study

2008 ◽  
Vol 136 (4) ◽  
pp. 1457-1474 ◽  
Author(s):  
Teresa Valkonen ◽  
Timo Vihma ◽  
Martin Doble
Keyword(s):  
Boundary Layer ◽  
Sea Ice ◽  
Air Temperature ◽  
Wind Fields ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Late Autumn ◽  
Antarctic Sea Ice ◽  
Cold Air Outbreak ◽  
Ice Concentration

Abstract Atmospheric flow over Antarctic sea ice was simulated applying a polar version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (Polar MM5). The simulation period in late autumn lasted for 48 h, starting as northerly warm airflow over the Weddell Sea ice cover and turning to a southwesterly cold-air outbreak. The model results were validated against atmospheric pressure and wind and air temperature observations made by five buoys drifting with the sea ice. Four different satellite-derived sea ice concentration datasets were applied to provide lower boundary conditions for Polar MM5. During the period of the cold-air outbreak, the modeled air temperatures were highly sensitive to the sea ice concentration: the largest differences in the modeled 2-m air temperature reached 13°C. The experiments applying sea ice concentration data based on the bootstrap and Arctic Radiation and Turbulence Interaction Study (ARTIST) algorithms yielded the best agreement with observations. The cumulative fetch over open water correlated with the bias of the modeled air temperature. The sea ice concentration data affected the simulated air temperature in the lower atmospheric boundary layer, but above it the temperature and wind fields were more strongly controlled by the boundary layer scheme applied in Polar MM5. Analysis nudging applying four-dimensional data assimilation had a positive effect on the pressure and wind fields but negative or no effect on the air temperature fields. The results suggest that applying a sea ice model to update sea ice fields frequently throughout atmospheric model simulations will likely lead to important improvements in forecasts.

Comparison of sea ice classification methods based on satellite scatterometer and radiometer data in the Weddell Sea, Antarctica

2019 ◽  
Vol 31 (3) ◽  
pp. 150-164
Author(s):  
Xiaoping Pang ◽  
Xiang Gao ◽  
Qing Ji
Keyword(s):  
Sea Ice ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Weddell Sea ◽  
First Year ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Type Information

AbstractInformation on sea ice type is an important factor for deriving sea ice parameters from satellite remote sensing data, such as sea ice concentration, extent and thickness. In this study, sea ice in the Weddell Sea was classified by the histogram threshold (HT) method, the Spreen model (SM) method from satellite scatterometer data and the strong contrast (SC) method from radiometer data, and this information was compared with Antarctic Sea Ice Processes and Climate (ASPeCt) sea ice-type ship-based observations. The results show that all three methods can distinguish the multi-year (MY) ice and first-year (FY) ice using Ku-band scatterometer data and radiometer data during the ice growth season, while C-band scatterometer data are not suitable for MY ice and FY ice discrimination using HT and SM methods. The SM model has a smaller MY ice classification extent than the HT method from scatterometer data. The classification accuracy of the SM method is the higher compared to ship-based observations. It can be concluded that the SM method is a promising method for discriminating MY ice from FY ice. These results provide a reference for further retrieval of long-term sea ice-type information for the whole of Antarctica.

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