A Mechanism for the Arctic Sea Ice Spring Predictability Barrier

Abstract

The decline of Arctic sea ice extent has created a pressing need for accurate seasonal predictions of regional summer sea ice. Recent work has shown evidence for an Arctic sea ice spring predictability barrier, which may impose a sharp limit on regional forecasts initialized prior to spring. However, the physical mechanism for this barrier has remained elusive. In this work, we perform a daily sea ice mass (SIM) budget analysis in large ensemble experiments from two global climate models to investigate the mechanisms that underpin the spring predictability barrier. We find that predictability is limited in winter months by synoptically driven SIM export and negative feedbacks from sea ice growth. The spring barrier results from a sharp increase in predictability at melt onset, when ice‐albedo feedbacks act to enhance and persist the preexisting export‐generated mass anomaly. These results imply that ice thickness observations collected after melt onset are particularly critical for summer Arctic sea ice predictions.

The 2017 Reversal of the Beaufort Gyre: Can Dynamic Thickening of a Seasonal Ice Cover During a Reversal Limit Summer Ice Melt in the Beaufort Sea?

“…Because the 2017 reversal did not facilitate a recovery of the Beaufort ice cover, it emphasizes that summer ice melt can only be limited when convergent conditions during winter are complimented by convergent and stable atmospheric conditions during summer. Our analysis of the Beaufort Sea case studies in 2013, 2016, and 2017 strongly support the theory of Bushuk et al., (2020) that synoptically driven ice mass convergence and negative ice growth feedbacks limit seasonal predictions of summer ice area. The increasingly seasonal ice cover of the Beaufort is becoming more sensitive to synoptic events, such as the 2017 reversal, that decouple winter preconditioning from summer ice melt.…”

“…The timing of breakup exerts a substantial influence on the melt season as it determines when solar heating of the upper ocean can begin to drive the ice albedo feedback loop, which amplifies bottom and lateral melt of the ice cover (Babb et al, 2016;Perovich et al, 2008Perovich et al, , 2011. Due to its role in driving sea ice melt, the beginning of the ice-albedo feedback creates a barrier to accurate seasonal sea ice predictions around the Arctic, specifically in the Beaufort (Bonan et al, 2019;Bushuk et al, 2020). Hence, getting a better understanding of how winter dynamics affect breakup, which in turn affects summer melt, is required to expand the capability of seasonal ice predictions.…”

Seasonal Prediction and Predictability of Regional Antarctic Sea Ice

“…We begin by describing the Forecast-oriented Low Ocean Resolution (FLOR) system in this subsection and describe two systems based on the Seamless System for Prediction and Earth System Research (SPEAR) in the following subsection. The FLOR prediction system has been shown to skillfully predict regional SIE in the Arctic (Bushuk et al 2017), which motivates its use for Antarctic sea ice predictions in this study.…”

“…These T/S profiles come from the World Ocean Database (WOD; Levitus et al 2013), the Global Temperature and Salinity Profile Programme (GTSPP; Sun et al 2010), and the Argo program (Roemmich et al 2004). The ECDA system does not explicitly assimilate sea ice data, but the sea ice state is constrained via heat fluxes and interfacial stresses from the ocean and atmosphere, associated with the data assimilation in each of these components (Bushuk et al 2019). The sea ice state variables initialized from ECDA include the ice concentration, thickness, temperature, and snow depth in each ice-thickness category and the sea ice velocity field.…”

“…Sea ice thickness (SIT) has been shown to be the crucial source of predictability for summer sea ice predictions in the Arctic (e.g., Chevallier and Salas y Mélia 2012;Bonan et al 2019) due to the multimonth persistence and relatively large spatial autocorrelation of SIT anomalies (Blanchard-Wrigglesworth and Bitz 2014;Ponsoni et al 2020). Previous work has suggested that the efficacy of SIT as a predictor in the Antarctic is reduced relative to the Arctic due to its thinner ice pack and smaller fraction of multiyear ice (e.g., Holland et al 2013;Ordoñez et al 2018;Marchi et al 2019).…”