Sea Ice Variability and Trends in the Western Indian Ocean Sector of Antarctica During the Past Two Centuries and Its Response to Climatic Modes

Abstract

Antarctic sea ice is a critical component of the ocean‐climate system. Sea ice concentration (SIC) records of various oceanic sectors of Antarctica available since 1979 reveal dramatic changes in recent years; however, there is no consensus yet on drivers of sea ice variability, changes and their future projections. The lack of long‐term SIC records has impeded the quantification of sea ice variability, trends and various forcing factors and mechanisms. Here, we present a long‐term SIC record (1809−1993 CE) for the Western Indian Ocean Sector (WIOS) of Antarctica derived from a new ice core oxygen isotope (δ18O) record together with three available records from Dronning Maud Land (DML), East Antarctica. This long‐term SIC record at annual resolution reveals a significant decline during 1830–1884 CE with a rate of 0.58% ± 0.12% decade−1 followed by moderate increase during 1927–1993 CE with a rate of 0.24% ± 0.11% decade−1. Current satellite‐derived SIC records reveal an increase of 2.59 ± 0.86% decade−1 during 1994–2014 CE, which is unprecedented over the last two centuries as determined by the ice core‐derived SIC record. The satellite and ice core combined SIC record (1809−2019 CE) also reveals a significant increase in interannual variability at El Niño Southern Oscillation band and decadal variability at 16–32 years band in the last few decades. Our investigation suggests that wind‐driven sea‐ice dynamics associated with switching phases of Southern Annular Mode and remote teleconnections of Pacific oscillations largely control the interannual to decadal sea ice variability in this region.

Rapid Warming Over East Antarctica Since the 1940s Caused by Increasing Influence of El Niño Southern Oscillation and Southern Annular Mode

“…The other principal components such as PC3 and PC4 are not significant (eigenvalues <1), and therefore, they were not considered for further investigation. In a previous study, Ejaz et al (2021) demonstrated that PC2 derived from PCA of these records is strongly related to annual sea ice variations in the western Indian Ocean sector of Antarctica. Based on the linear relationship, they have reconstructed past sea ice variability and trend in the western Indian ocean sector of Antarctica during the past two centuries.…”

“…The stable isotopic ratios of melted ice core samples were measured using an "Isoprime" isotope ratio mass spectrometer (GV Instruments, United Kingdom). The analytical details are already discussed elsewhere (Ejaz et al, 2021). Both major ions (Na + and Cl − ) and δ 18 O ice core records were used to establish a robust and reliable chronology.…”

“…Based on such robust constraints, the chronology for the IND33 core was established for the past approximately 200 years (1809-2013 CE) with an uncertainty of ±1 year. The details of the establishment of chronology are discussed elsewhere (Ejaz et al, 2021).…”

“…Several studies have demonstrated that δ 18 O records in ice cores are influenced by several factors such as temperature (Petit et al, 1999;Stenni et al, 2017), sea ice variability (Ejaz et al, 2021), precipitation (Fujita and Abe, 2006), and postdepositional changes related to exchange of water vapor between surface snow and atmosphere through diffusion (Landais et al, 2017). As a result, the δ 18 O records represent a composite signal of various meteorological parameters and/or postdepositional processes and hence cannot be directly used to decipher climate variability.…”

“…As a result, the δ 18 O records represent a composite signal of various meteorological parameters and/or postdepositional processes and hence cannot be directly used to decipher climate variability. A previous study from this region has observed that the selected δ 18 O ice core records are only weakly correlated with ERA5 annual surface air temperature (Ejaz et al, 2021). Therefore, principal component analysis (PCA) was performed on these four δ 18 O records within their common time window (Supplementary Table S2) to deconvolute different climate signals embedded in the δ 18 O records and finally to extract a common temperature signal, which is representative of a large region of DML.…”