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.

Seasonal Relationships between Large-Scale Climate Variability and Antarctic Sea Ice Concentration

2012 ◽  
Vol 25 (16) ◽  
pp. 5451-5469 ◽  
Author(s):  
Graham R. Simpkins ◽  
Laura M. Ciasto ◽  
David. W. J. Thompson ◽  
Matthew H. England
Keyword(s):  
Climate Variability ◽  
Sea Ice ◽  
Large Scale ◽  
Southern Oscillation ◽  
Principal Component ◽  
Southern Annular Mode ◽  
Enso Cycle ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration

Abstract The observed relationships between anomalous Antarctic sea ice concentration (SIC) and the leading patterns of Southern Hemisphere (SH) large-scale climate variability are examined as a function of season over 1980–2008. Particular emphasis is placed on 1) the interactions between SIC, the southern annular mode (SAM), and El Niño–Southern Oscillation (ENSO); and 2) the contribution of these two leading modes to the 29-yr trends in sea ice. Regression, composite, and principal component analyses highlight a seasonality in SH sea ice–atmosphere interactions, whereby Antarctic sea ice variability exhibits the strongest linkages to the SAM and ENSO during the austral cold season months. As noted in previous work, a dipole in SIC anomalies emerges in relation to the SAM, characterized by centers of action located near the Bellingshausen/Weddell and Amundsen/eastern Ross Seas. The structure and magnitude of this SIC dipole is found to vary considerably as a function of season, consistent with the seasonality of the overlying atmospheric circulation anomalies. Relative to the SAM, the pattern of sea ice anomalies linked to ENSO exhibits a similar seasonality but tends to be weaker in amplitude and more diffuse in structure. The relationships between ENSO and sea ice also exhibit a substantial nonlinear component, highlighting the need to consider both season and phase of the ENSO cycle when diagnosing ENSO–SIC linkages. Trends in SIC over 1980–2008 are not significantly related to trends in either the SAM or ENSO during any season, including austral summer when the trend in the SAM is most pronounced.

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 Interactions between Antarctic sea ice and large-scale atmospheric modes in CMIP5 models

2016 ◽  
Author(s):  
Serena Schroeter ◽  
Will Hobbs ◽  
Nathaniel L. Bindoff
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Coupled Model ◽  
Southern Annular Mode ◽  
Cmip5 Models ◽  
Atmospheric Variability ◽  
Annular Mode ◽  
Antarctic Sea Ice ◽  
Ice Retreat ◽  
Sea Ice Retreat

Abstract. The response of Antarctic sea ice to large-scale patterns of atmospheric variability varies according to sea ice sector and season. In this study, interannual atmosphere-sea ice interactions were explored using observation-based data and compared with simulated interactions by models in the Coupled Model Intercomparison Project Phase 5. Simulated relationships between atmospheric variability and sea ice variability generally reproduced the observed relationships, though more closely during the season of sea ice advance than the season of sea ice retreat. Atmospheric influence on sea ice is known to be strongest during its advance, with the ocean emerging as a dominant driver of sea ice retreat; therefore, while it appears that models are able to capture the dominance of the atmosphere during advance, simulations of ocean-atmosphere-sea ice interactions during retreat require further investigation. A large proportion of model ensemble members overestimated the relative importance of the Southern Annular Mode compared with other modes on high southern latitude climate, while the influence of tropical forcing was underestimated. This result emerged particularly strongly during the season of sea ice retreat. The amplified zonal patterns of the Southern Annular Mode in many models and its exaggerated influence on sea ice overwhelm the comparatively underestimated meridional influence, suggesting that simulated sea ice variability would become more zonally symmetric as a result. Across the seasons of sea ice advance and retreat, 3 of the 5 sectors did not reveal a strong relationship with a pattern of large-scale atmospheric variability in one or both seasons, indicating that sea ice in these sectors may be influenced more strongly by atmospheric variability unexplained by the major atmospheric modes, or by heat exchange in the ocean.

scholarly journals Antarctic Sea Ice Climatology, Variability, and Late Twentieth-Century Change in CCSM4

2012 ◽  
Vol 25 (14) ◽  
pp. 4817-4838 ◽  
Author(s):  
Laura Landrum ◽  
Marika M. Holland ◽  
David P. Schneider ◽  
Elizabeth Hunke
Keyword(s):  
Twentieth Century ◽  
Sea Ice ◽  
Southern Oscillation ◽  
Austral Summer ◽  
Southern Annular Mode ◽  
Cold Bias ◽  
Sea Ice Concentration ◽  
Climate System Model ◽  
Modes Of Variability ◽  
Antarctic Sea Ice

Abstract A preindustrial control run and an ensemble of twentieth-century integrations of the Community Climate System Model, version 4 (CCSM4), are evaluated for Antarctic sea ice climatology, modes of variability, trends, and covariance with related physical variables such as surface temperature and sea level pressure. Compared to observations, the mean ice cover is too extensive in all months. This is in part related to excessively strong westerly winds over ~50°–60°S, which drive a large equatorward meridional ice transport and enhanced ice growth near the continent and also connected with a cold bias in the Southern Ocean. In spite of these biases in the climatology, the model’s sea ice variability compares well to observations. The leading mode of austral winter sea ice concentration exhibits a dipole structure with anomalies of opposite sign in the Atlantic and Pacific sectors. Both the El Niño–Southern Oscillation and the southern annular mode (SAM) project onto this mode. In twentieth-century integrations, Antarctic sea ice area exhibits significant decreasing annual trends in all six ensemble members from 1950 to 2005, in apparent contrast to observations that suggest a modest ice area increase since 1979. Two ensemble members show insignificant changes when restricted to 1979–2005. The ensemble mean shows a significant increase in the austral summer SAM index over 1960–2005 and 1979–2005 that compares well with the observed SAM trend. However, Antarctic warming and sea ice loss in the model are closely connected to each other and not to the trend in the SAM.

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