Reemergence of Antarctic sea ice predictability and its link to deep ocean mixing in global climate models

Abstract

Satellite observations show a small overall increase in Antarctic sea ice extent (SIE) over the period 1979-2015. However, this upward trend needs to be balanced against recent pronounced SIE fluctuations occurring there. In the space of three years, the SIE sank from its highest value ever reached in September 2014 to record low in February 2017. In this work, a set of six state-of-the-art global climate models is used to evaluate the potential predictability of the Antarctic sea ice at such timescales. This first multi-model study of Antarctic sea ice predictability reveals that the ice edge location can potentially be predicted up to three years in advance.However, the ice edge location predictability shows contrasted seasonal performances, with high predictability in winter and no predictability in summer. The reemergence of the predictability from one winter to next is provided by the ocean through its large thermal inertia. Sea surface heat anomalies are stored at depth at the end of the winter and influences the sea ice advance the following year as they resurface.The effectiveness of this mechanism across models is found to depend upon the depth of the mixed layer. One should be very cautious about these potential predictability estimates as there is evidence that the Antarctic sea ice predictability is promoted by deep Southern Ocean convection. We therefore suspect models with excessive convection to show higher sea ice potential predictability results due to an incorrect representation of the Southern Ocean.

Potential predictability of marine ecosystem drivers

“…The second caveat in our study is that we only used one single Earth system model and that our results might depend on the model formulation and resolution. Even though the GFDL-ESM2M model achieves sufficient fidelity in its preindustrial states Dunne et al, 2012Dunne et al, , 2013Laufkötter et al, 2015), it is well known that CMIP5generation models have imperfect representation of biogeochemical and physical processes as well as variability over a range of timescales, ranging from weather variability to ENSO variability (Frölicher et al, 2016;Resplandy et al, 2015) to decadal variability McGregor et al, 2014). Different physical and biogeochemical parameterizations within a given model may change the length of the predictability time horizon.…”

The Year of Polar Prediction in the Southern Hemisphere (YOPP-SH)

“…A corollary is that our understanding of Antarctic sea ice predictability remains limited, too. However, recent studies (Holland et al 2013(Holland et al , 2017Ordoñez et al 2018;Marchi et al 2019) have highlighted several physical predictability mechanisms that suggest potential prediction skill extending for at least a season. It remains to be demonstrated whether these mechanisms can be translated into actual prediction skill (Zampieri et al 2019).…”

Antarctica and the Southern Ocean

“…Endorsed by the Year of Polar Prediction project (Jung et al 2016), SIPN-South provides a focal point for a seasonal forecast of Antarctic sea ice, which is thought to be less predictable than Arctic sea ice. However, recent research (Marchi et al 2018) suggests that, in fact, the large thermal inertia of the Southern Ocean together with atmospheric teleconnections from outside the immediate Antarctic realm (Pope et al 2017) could represent the key factors for Southern Hemisphere (SH) sea ice predictability.…”