Autumn Arctic Pacific Sea Ice Dipole as a Source of Predictability for Subsequent Spring Barents Sea Ice Condition

Abstract

This study uses observational and reanalysis datasets in 1980-2016 to show a close connection between a boreal autumn sea-ice dipole in the Arctic Pacific sector and sea-ice anomalies in the Barents Sea (BS) during the following spring. The September-October Arctic Pacific sea-ice dipole variations are highly correlated with the subsequent April-May BS sea-ice variations (r=0.71). The strong connection between the regional sea-ice variabilities across the Arctic uncovers a new source of predictability for spring BS sea-ice prediction at 7-month lead time. A cross-validated linear regression prediction model using the Arctic Pacific sea-ice dipole with 7-month lead time is demonstrated to have significant prediction skills with 0.54-0.85 anomaly correlation coefficients. The autumn sea-ice dipole, manifested as sea-ice retreat in the Beaufort-Chukchi Seas and expansion in the East Siberian-Laptev Seas, is primarily forced by preceding atmospheric shortwave anomalies from late spring to early autumn. The spring BS sea-ice increases are mostly driven by an ocean-to-sea ice heat flux reduction in preceding months, associated with reduced horizontal ocean heat transport into the BS. The dynamical linkage between the two regional sea-ice anomalies is suggested to involve positive stratospheric polar cap anomalies during autumn and winter, with its center slowly moving toward Greenland. The migration of the stratospheric anomalies is followed in mid-winter by a negative North Atlantic Oscillation-like pattern in the troposphere, leading to reduced ocean heat transport into the BS and sea-ice extent increase.

Arctic Sea Ice Loss Modulates the Surface Impact of Autumn Stratospheric Polar Vortex Stretching Events

“…The effects of Arctic sea ice on the variability of the stratosphere have been widely explored. Numerous studies revealed that the BKS sea ice variability has more effective impacts on the intensity and location of SPV, and the attendant stratospheric signals can persist for approximately 2 months and then extend to the surface (Jaiser et al., 2013; B. M. Kim et al., 2014; J. Kim & Kim, 2020; Liang Y et al., 2023; Nakamura et al., 2016; Peings & Magnusdottir, 2014; Sun et al., 2015; Wu & Smith, 2016; J. Zhang et al., 2016). Further studies also use a nudging method in modeling to highlight the relative importance of the stratospheric process in Eurasian cooling induced by BKS sea ice loss (Ding et al., 2023; Peings, 2019; Xu et al., 2021, 2023; P. Zhang, Wu, & Smith, 2018; P. Zhang, Wu, Simpson, et al., 2018).…”

Exploring Mechanisms for Model‐Dependency of the Stratospheric Response to Arctic Warming

“…There is similar spread within more general climate change studies, such as CMIP5 and CMIP6 (Hall et al., 2021; Karpechko et al., 2022). The Arctic represents one source of uncertainty in SPV projections, which could propagate to uncertainty in the surface response (Liang et al., 2023; Zheng et al., 2023). However, few studies have investigated the extent to which model dependency in the SPV response to climate change is driven by model dependency in the response to Arctic climate change specifically.…”