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

Abstract A strengthening of the Amundsen Sea low from 1979 to 2013 has been shown to largely explain the observed increase in Antarctic sea ice concentration in the eastern Ross Sea and decrease in the Bellingshausen Sea. Here it is shown that while these changes are not generally seen in freely running coupled climate model simulations, they are reproduced in simulations of two independent coupled climate models: one constrained by observed sea surface temperature anomalies in the tropical Pacific and the other by observed surface wind stress in the tropics. This analysis confirms previous results and strengthens the conclusion that the phase change in the interdecadal Pacific oscillation from positive to negative over 1979–2013 contributed to the observed strengthening of the Amundsen Sea low and the associated pattern of Antarctic sea ice change during this period. New support for this conclusion is provided by simulated trends in spatial patterns of sea ice concentrations that are similar to those observed. These results highlight the importance of accounting for teleconnections from low to high latitudes in both model simulations and observations of Antarctic sea ice variability and change.

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

Annals of Glaciology ◽

10.3189/2015aog69a588 ◽

2015 ◽

Vol 56 (69) ◽

pp. 45-52 ◽

Cited By ~ 8

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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Observed Concentration Budgets of Arctic and Antarctic Sea Ice

Journal of Climate ◽

10.1175/jcli-d-16-0121.1 ◽

2016 ◽

Vol 29 (14) ◽

pp. 5241-5249 ◽

Cited By ~ 13

Author(s):

Paul R. Holland ◽

Noriaki Kimura

Keyword(s):

Sea Ice ◽

Climate Models ◽

Arctic Sea Ice ◽

Ice Melting ◽

Antarctic Sea Ice ◽

Arctic Sea ◽

Ice Concentration ◽

Anthropogenic Contribution ◽

Ice Edge ◽

The Antarctic

Abstract In recent decades, Antarctic sea ice has expanded slightly while Arctic sea ice has contracted dramatically. The anthropogenic contribution to these changes cannot be fully assessed unless climate models are able to reproduce them. Process-based evaluation is needed to provide a clear view of the capabilities and limitations of such models. In this study, ice concentration and drift derived from AMSR-E data during 2003–10 are combined to derive a climatology of the ice concentration budget at both poles. This enables an observational decomposition of the seasonal dynamic and thermodynamic changes in ice cover. In both hemispheres, the results show spring ice loss dominated by ice melting. In other seasons ice divergence maintains freezing in the inner pack while advection causes melting at the ice edge, as ice is transported beyond the region where it is thermodynamically sustainable. Mechanical redistribution provides an important sink of ice concentration in the central Arctic and around the Antarctic coastline. This insight builds upon existing understanding of the sea ice cycle gained from ice and climate models, and the datasets may provide a valuable tool in validating such models in the future.

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Variability in Antarctic sea ice from 1998-2017

10.7287/peerj.preprints.27121v1 ◽

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.

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

Acta Oceanologica Sinica ◽

10.1007/s13131-013-0350-0 ◽

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

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Consistent biases in Antarctic sea ice concentration simulated by climate models

10.5194/tc-2017-131 ◽

2017 ◽

Cited By ~ 1

Author(s):

Lettie A. Roach ◽

Samuel M. Dean ◽

James A. Renwick

Keyword(s):

Sea Ice ◽

Climate Models ◽

Regional Distribution ◽

Global Climate Models ◽

Satellite Observations ◽

High Concentration ◽

Sea Ice Concentration ◽

Low Concentration ◽

Antarctic Sea Ice ◽

Ice Concentration

Abstract. The simulation of Antarctic sea ice in global climate models often does not agree with observations. In this study, we examine the compactness of sea ice, as well as the regional distribution of sea ice concentration, in climate models from the latest Coupled Model Intercomparison Project (CMIP5) and in satellite observations. We find substantial differences in concentration values between different sets of satellite observations, requiring careful treatment when comparing to models. As a fraction of total sea ice extent, models simulate too much loose, low-concentration sea ice cover throughout the year, and too little compact, high-concentration cover in the summer. In spite of the differences in physics between models, these tendencies are broadly consistent across the population of 27 CMIP5 simulations, a result not previously highlighted. Targeted model experiments with a coupled ocean – sea ice model show that over-estimation of low-concentration cover is partially determined by choice of constant floe diameter in the lateral melt scheme. This suggests that current sea ice thermodynamics contribute to the inadequate simulation of the low-concentration regime.

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

Journal of Climate ◽

10.1175/2011jcli3965.1 ◽

2011 ◽

Vol 24 (16) ◽

pp. 4508-4518 ◽

Cited By ~ 10

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.

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Variability in Antarctic sea ice from 1998-2017

10.7287/peerj.preprints.27121 ◽

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.

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Consistent biases in Antarctic sea ice concentration simulated by climate models

The Cryosphere ◽

10.5194/tc-12-365-2018 ◽

2018 ◽

Vol 12 (1) ◽

pp. 365-383 ◽

Cited By ~ 17

Author(s):

Lettie A. Roach ◽

Samuel M. Dean ◽

James A. Renwick

Keyword(s):

Sea Ice ◽

Climate Models ◽

Regional Distribution ◽

Global Climate Models ◽

Satellite Observations ◽

High Concentration ◽

Sea Ice Concentration ◽

Low Concentration ◽

Antarctic Sea Ice ◽

Ice Concentration

Abstract. The simulation of Antarctic sea ice in global climate models often does not agree with observations. In this study, we examine the compactness of sea ice, as well as the regional distribution of sea ice concentration, in climate models from the latest Coupled Model Intercomparison Project (CMIP5) and in satellite observations. We find substantial differences in concentration values between different sets of satellite observations, particularly at high concentrations, requiring careful treatment when comparing to models. As a fraction of total sea ice extent, models simulate too much loose, low-concentration sea ice cover throughout the year, and too little compact, high-concentration cover in the summer. In spite of the differences in physics between models, these tendencies are broadly consistent across the population of 40 CMIP5 simulations, a result not previously highlighted. Separating models with and without an explicit lateral melt term, we find that inclusion of lateral melt may account for overestimation of low-concentration cover. Targeted model experiments with a coupled ocean–sea ice model show that choice of constant floe diameter in the lateral melt scheme can also impact representation of loose ice. This suggests that current sea ice thermodynamics contribute to the inadequate simulation of the low-concentration regime in many models.

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

Journal of Glaciology ◽

10.1017/jog.2021.57 ◽

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.

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