Western Indian Ocean SST signal and anomalous Antarctic sea-ice concentration variation

2011 ◽  
Vol 30 (2) ◽  
pp. 9-13 ◽  
Author(s):  
Na Liu ◽  
Zhanhai Zhang ◽  
Hongxia Chen ◽  
Lina Lin
Keyword(s):  
Indian Ocean ◽  
Sea Ice ◽  
Western Indian Ocean ◽  
Sea Ice Concentration ◽  
Concentration Variation ◽  
Antarctic Sea Ice ◽  
Ice Concentration

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.

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 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 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 Aspects of intraseasonal variability of Antarctic sea ice in austral winter related to ENSO and SAM events

2017 ◽  
Vol 63 (241) ◽  
pp. 838-846 ◽  
Author(s):  
KENJI BABA ◽  
JAMES RENWICK
Keyword(s):  
Sea Ice ◽  
El Nino ◽  
Intraseasonal Variability ◽  
Southern Annular Mode ◽  
Wave Number ◽  
Austral Winter ◽  
Sea Ice Concentration ◽  
Annular Mode ◽  
Antarctic Sea Ice ◽  
Ice Concentration

ABSTRACTWe performed an Empirical Orthogonal Function (EOF) analysis to assess the intraseasonal variability of 5–60 day band-pass filtered Antarctic sea-ice concentration in austral winter using a 20-year daily dataset from 1995 to 2014. Zonal wave number 3 dominated in the Antarctic, especially so across the west Antarctic. Results showed the coexistence of stationary and propagating wave components. A spectral analysis of the first two principal components (PCs) showed a similar structure for periods up to 15 days but generally more power in PC1 at longer periods. Regression analysis upon atmospheric fields using the first two PCs of sea-ice concentration showed a coherent wave number 3 pattern. The spatial phase delay between the sea-ice and mean sea-level pressure patterns suggests that meridional flow and associated temperature advection are important for modulating the sea-ice field. EOF analyses carried out separately for El Niño, La Niña and neutral years, and for Southern Annular Mode positive, negative and neutral periods, suggest that the spatial patterns of wave number 3 shift between subsets. The results also indicate that El Niño-Southern Oscillation and Southern Annular Mode affect stationary wave interactions between sea-ice and atmospheric fields on intraseasonal timescales.

scholarly journals Relationships between Antarctic sea-ice concentration, wind stress and temperature temporal variability, and their changes with distance from the coast

1998 ◽  
Vol 27 ◽  
pp. 409-412 ◽  
Author(s):  
Andrew B. Watkins ◽  
Ian Simmonds
Keyword(s):  
Sea Ice ◽  
Wind Stress ◽  
Surface Air Temperature ◽  
Red Shift ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Individual Wave ◽  
Temperature Spectra ◽  
Time Of Year

Daily A ntarrtir sea-ice concentration, wind and temperature data for the years 1988-94 were analyzed using Fourier techniques to determine their temporal spectra for three equal-length “seasons". The percentage of the total variance explained (VE) by each individual wave (i.e. frequency] was calculated, and spatial averages made over six latitudinal hands surrounding Antarctica. Comparisons of trie sea-ice concentration, wind stress and surface air-temperature spectra showed sea-ice concentration VE has smallest, and wind-stress VE greatest, value in the synoptic time-scales. Conversely, in the 20-25 day window the wind-stress VE is smallest, and sea-ice concentration VE greatest. This “red shift” of the sea-ice concentration suggests it has inertia to the wind slress and temperature Forcing. Resulls show the magnitude of the red shift varies with not only time of year, but also distance from the coast, and is dependent upon the position of the Antarctic circumpolar trough.

scholarly journals Modeled Trends in Antarctic Sea Ice Thickness

2014 ◽  
Vol 27 (10) ◽  
pp. 3784-3801 ◽  
Author(s):  
Paul R. Holland ◽  
Nicolas Bruneau ◽  
Clare Enright ◽  
Martin Losch ◽  
Nathan T. Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Order Of Magnitude ◽  
Concentration Changes

Abstract Unlike the rapid sea ice losses reported in the Arctic, satellite observations show an overall increase in Antarctic sea ice concentration over recent decades. However, observations of decadal trends in Antarctic ice thickness, and hence ice volume, do not currently exist. In this study a model of the Southern Ocean and its sea ice, forced by atmospheric reanalyses, is used to assess 1992–2010 trends in ice thickness and volume. The model successfully reproduces observations of mean ice concentration, thickness, and drift, and decadal trends in ice concentration and drift, imparting some confidence in the hindcasted trends in ice thickness. The model suggests that overall Antarctic sea ice volume has increased by approximately 30 km3 yr−1 (0.4% yr−1) as an equal result of areal expansion (20 × 103 km2 yr−1 or 0.2% yr−1) and thickening (1.5 mm yr−1 or 0.2% yr−1). This ice volume increase is an order of magnitude smaller than the Arctic decrease, and about half the size of the increased freshwater supply from the Antarctic Ice Sheet. Similarly to the observed ice concentration trends, the small overall increase in modeled ice volume is actually the residual of much larger opposing regional trends. Thickness changes near the ice edge follow observed concentration changes, with increasing concentration corresponding to increased thickness. Ice thickness increases are also found in the inner pack in the Amundsen and Weddell Seas, where the model suggests that observed ice-drift trends directed toward the coast have caused dynamical thickening in autumn and winter. Modeled changes are predominantly dynamic in origin in the Pacific sector and thermodynamic elsewhere.

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
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