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.

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 DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

2021 ◽  
pp. 1-6
Author(s):  
Hao Luo ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Sea Ice ◽  
Coupled Model ◽  
Sea Ice Concentration ◽  
Model Free ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Data Assimilation System ◽  
Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

Arctic sea ice volume budget decomposition satellite product for the CryoSat-2 (2010-2020) period 

2021 ◽  
Author(s):  
Harry Heorton ◽  
Michel Tsamados ◽  
Paul Holland ◽  
Jack Landy
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Volume Growth ◽  
Arctic Sea Ice ◽  
First Year ◽  
Sea Ice Concentration ◽  
Marginal Seas ◽  
Ice Volume ◽  
Arctic Sea ◽  
Ice Concentration

<p><span>We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide the first observational decomposition of the dynamic (advection/divergence) and thermodynamic (melt/growth) drivers of wintertime Arctic sea ice volume change. Ten winter growth seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. Sensitivity to several observational products is performed to provide an estimated uncertainty of the budget calculations. The total thermodynamic ice volume growth and dynamic ice losses are calculated with marked seasonal, inter-annual and regional variations</span><span>. Ice growth is fastest during Autumn, in the Marginal Seas and over first year ice</span><span>. Our budget decomposition methodology can help diagnose the processes confounding climate model predictions of sea ice. We make our product and code available to the community in monthly pan-Arctic netcdft files for the entire October 2010 to April 2020 period.</span></p>

scholarly journals Dissolved organic matter in Antarctic sea ice

2001 ◽  
Vol 33 ◽  
pp. 297-303 ◽  
Author(s):  
David N. Thomas ◽  
Gerhard Kattner ◽  
Ralph Engbrodt ◽  
Virginia Giannelli ◽  
Hilary Kennedy ◽  
...  
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Sea Ice ◽  
Pore Space ◽  
Ice Cores ◽  
Weddell Sea ◽  
Poor Correlation ◽  
First Year ◽  
Mean Values ◽  
Antarctic Sea Ice

AbstractIt has been hypothesized that there are significant dissolved organic matter (DOM) pools in sea-ice systems, although measurements of DOM in sea ice have only rarely been made. The significance of DOM for ice-based productivity and carbon turnover therefore remains highly speculative. DOM within sea ice from the Amundsen and Bellingshausen Seas, Antarctica, in 1994 and the Weddell Sea, Antarctica, in 1992 and 1997 was investigated. Measurements were made on melted sea-ice sections in 1994 and 1997 and in sea-ice brines in 1992. Dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) concentrations in melted ice cores were up to 1.8 and 0.78 mM, respectively, or 30 and 8 times higher than those in surface water concentrations, respectively. However, when concentrations within the brine channel/pore space were calculated from estimated brine volumes, actual concentrations of DOC in brines were up to 23.3 mM and DON up to 2.2 mM, although mean values were 1.8 and 0.15 mM, respectively. There were higher concentrations of DOM in warm, porous summer second-year sea ice compared with colder autumn first-year ice, consistent with the different biological activity supported within the various ice types. However, in general there was poor correlation between DOC and DON with algal biomass and numbers of bacteria within the ice. The mean DOC/DON ratio was 11, although again values were highly variable, ranging from 3 to highly carbon-enriched samples of 95. Measurements made on a limited dataset showed that carbohydrates constitute on average 35% of the DOC pool, with highly variable contributions of 1−99%.

scholarly journals Erroneous sea-ice concentration retrieval in the East Antarctic

2018 ◽  
Vol 59 (76pt2) ◽  
pp. 201-212 ◽  
Author(s):  
Hoi Ming Lam ◽  
Gunnar Spreen ◽  
Georg Heygster ◽  
Christian Melsheimer ◽  
Neal W. Young
Keyword(s):  
Sea Ice ◽  
Microwave Remote Sensing ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Microwave Brightness Temperature ◽  
Antarctic Sea Ice ◽  
Optical Images ◽  
Ice Concentration ◽  
Retrieval Algorithms ◽  
Landfast Sea Ice

ABSTRACTLarge discrepancies have been observed between satellite-derived sea-ice concentrations(IC) from passive microwave remote sensing and those derived from optical images at several locations in the East Antarctic, between February and April 2014. These artefacts, that resemble polynyas in the IC maps, appear in areas where optical satellite data show that there is landfast sea ice. The IC datasets and the corresponding retrieval algorithms are investigated together with microwave brightness temperature, air temperature, snowfall and bathymetry to understand the failure of the IC retrieval. The artefacts are the result of the application of weather filters in retrieval algorithms. These filters use the 37 and 19 GHz channels to correct for atmospheric effects on the retrieval. These channels show significant departures from typical ranges when the artefacts occur. A melt–refreeze cycle with associated snow metamorphism is proposed as the most likely cause. Together, the areas of the artefacts account for up to 0.5% of the Antarctic sea-ice area and thus cause a bias in sea-IC time series. In addition, erroneous sea ICs can adversely affect shipping operations.

scholarly journals An intercomparison between AMSR-E snow-depth and satellite C- and Ku-band radar backscatter data for Antarctic sea ice

2011 ◽  
Vol 52 (57) ◽  
pp. 279-290 ◽  
Author(s):  
Stefan Kern ◽  
Burcu Ozsoy-Cicek ◽  
Sascha Willmes ◽  
Marcel Nicolaus ◽  
Christian Haas ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Weddell Sea ◽  
Late Winter ◽  
First Year ◽  
Radar Backscatter ◽  
Antarctic Sea Ice ◽  
Ridged Ice ◽  
Ice Fraction ◽  
Ku Band

AbstractAdvanced Microwave Scanning Radiometer (AMSR-E) snow-depth data for Antarctic sea ice are compared with ship-based visual observations of snow depth, ice type and ridged-ice fraction, and with satellite C-band and Ku-band radar backscatter observations for two ship cruises into the Weddell Sea (ISPOL 2004–05,WWOS 2006) and one cruise into the Bellingshausen Sea (SIMBA 2007) during late winter/spring. Most (>75%) AMSR-E and ship-based snow-depth observations agree within 0.2 m during WWOS and SIMBA. Remaining observations indicate substantial underestimations of snow depths by AMSR-E data. These underestimations tend to increase with the ridged-ice fraction for WWOS and SIMBA. In areas with large snow depths, a combination of relatively stable low C-band radar backscatter and variable Ku-band radar backscatter is associated with undeformed first-year ice and may indicate snow metamorphism at this time of year during SIMBA. In areas with small snow depths, a combination of relatively stable low Ku-band radar backscatter, high C-band radar backscatter and low C-band radar backscatter standard deviations is associated with rough first-year ice during SIMBA. This information can help to better understand causes of the observed AMSR-E snow-depth bias during late-winter/spring conditions with decreasing average snow depth and to delineate areas where this bias occurs.

scholarly journals Employing Satellite-Derived Sea Ice Concentration to Constrain Upper-Ocean Temperature in a Global Ocean GCM

2008 ◽  
Vol 21 (17) ◽  
pp. 4498-4513 ◽  
Author(s):  
Achim Stössel
Keyword(s):  
Sea Ice ◽  
Deep Ocean ◽  
Global Ocean ◽  
Upper Ocean ◽  
Ocean Temperature ◽  
Freshwater Input ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Ocean Properties

Abstract The quality of Southern Ocean sea ice simulations in a global ocean general circulation model (GCM) depends decisively on the simulated upper-ocean temperature. This is confirmed by assimilating satellite-derived sea ice concentration to constrain the upper-layer temperature of a sea ice–ocean GCM. The resolution of the model’s sea ice component is about 22 km and thus comparable to the pixel resolution of the satellite data. The ocean component is coarse resolution to afford long-term integrations for investigations of the deep-ocean equilibrium response. Besides improving the sea ice simulation considerably, the simulations with constrained upper-ocean temperature yield much more realistic global deep-ocean properties, in particular when combined with glacial freshwater input. Both outcomes are relatively insensitive to the passive-microwave algorithm used to retrieve the ice concentration being assimilated. The sensitivity of the long-term global deep-ocean properties and circulation to the possible freshwater input from ice shelves and to the parameterization of vertical mixing in the Southern Ocean is reevaluated under the new constraint.

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.

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