Mechanisms driving the asymmetric seasonal cycle of Antarctic Sea Ice in the CESM Large Ensemble

Abstract

The yearly paired process of slow growth and rapid melt of some 15 million square kilometers of Antarctic sea ice takes place with a regular asymmetry; the process has been linked to the relationship of the position of the ice edge with the band of low pressure that circles the continent between 60° and 70°S. In autumn, winds to the north of the low-pressure band slow the advancing ice edge. In summer, Ekman divergence created by opposing winds on either side of the low-pressure band opens up warm water regions that rapidly melt sea ice. We use the 40 ensemble members from the CESM-LENS historical run (1920–2005) to examine the relationship between the asymmetry in the annual cycle and the position and intensity of the low-pressure band. CESM-LENS reproduces the magnitude of the annual cycle of Antarctic sea ice extent with a short lag (2 weeks). Melt rate is the characteristic of the annual cycle that varies the most. Our results provide evidence that lower pressure leads to increased melt rates, which supports the importance of the role of divergence in increasing the melt rate of Antarctic sea ice. The role of winds during the growing season remains unquantified.

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“…Regional trends, particularly the observed decrease in the Bellingshausen Sea and the expansion in the Ross Sea, are also not typically captured in the models (Hobbs et al, 2016). However, some models, such as the CESM1 used in this study (Section 2), compare well with observations in, for example, the annual cycle (Eayrs et al, 2020;Raphael et al, 2020), regional and seasonal ice distributions (Jenouvrier et al, 2020), and the relationship of ice to the Amundsen Sea Low (Landrum et al, 2017). Additionally, CESM simulations indicate that internal ocean variability can drive increasing sea ice despite rising GHGs (Singh et al, 2019).…”

“…Therefore, we are not able to consider the influence of climate model structural uncertainty associated with the variations in simulated sea ice concentration across different AOGCMs. Nevertheless, when compared to observational products, CESM1 produces a good simulation of the annual cycle of total Antarctic sea ice extent (Eayrs et al, 2020 ), associated melt and growth rates (Eayrs et al, 2020 ), and processes, such as wind variations, that drive sea ice variability (e.g., Landrum et al, 2017 ), making it a useful tool for our analysis.…”