Winter Coastal Divergence as a Predictor for the Minimum Sea Ice Extent in the Laptev Sea

Abstract

Seasonal predictability of the minimum sea ice extent (SIE) in the Laptev Sea is investigated using winter coastal divergence as a predictor. From February to May, the new ice forming in wind-driven coastal polynyas grows to a thickness approximately equal to the climatological thickness loss due to summer thermodynamic processes. Estimating the area of sea ice that is preconditioned to melt enables seasonal predictability of the minimum SIE. Wintertime ice motion is quantified by seeding passive tracers along the coastlines and advecting them with the Lagrangian Ice Tracking System (LITS) forced with sea ice drifts from the Polar Pathfinder dataset for years 1992–2016. LITS-derived landfast ice estimates are comparable to those of the Russian Arctic and Antarctic Research Institute ice charts. Time series of the minimum SIE and coastal divergence show trends of −24.2% and +31.3% per decade, respectively. Statistically significant correlation ( r = −0.63) between anomalies of coastal divergence and the following September SIE occurs for coastal divergence integrated from February to the beginning of May. Using the coastal divergence anomaly to predict the minimum SIE departure from the trend improves the explained variance by 21% compared to hindcasts based on persistence of the linear trend. Coastal divergence anomalies correlate with the winter mean Arctic Oscillation index ( r = 0.69). LITS-derived areas of coastal divergence tend to underestimate the total area covered by thin ice in the CryoSat-2/SMOS (Soil Moisture and Ocean Salinity) thickness dataset, as suggested by a thermodynamic sea ice growth model.

A Mechanism for the Arctic Sea Ice Spring Predictability Barrier

“…The mass export climatology is notably noisier than the thermodynamic terms, reflecting the impact of high‐frequency (daily‐to‐weekly) atmospheric variability on sea ice export. Both models export ice out of the Laptev Sea domain in the winter months, consistent with wind‐driven coastal divergence associated with storms (e.g., Brunette et al., 2019; Krumpen et al., 2013; Preußer et al., 2016; Willmes et al., 2011). Both models import ice in the summer months, with the greater import rates in CESM possibly related to its greater mean thickness in the Central Arctic.…”

“…Interestingly, in their observation‐based analysis, Brunette et al. (2019) also identified early January as a critical time when export‐driven anomalies begin increasing. The export‐driven covariance increases roughly linearly in CESM and sublinearly in FLOR.…”

“…This mechanism builds upon many previous studies that have identified SIT as the key source of predictability for summer SIA (e.g., Blanchard‐Wrigglesworth, Bitz, et al., 2011, 2017; Blockley & Peterson, 2018; Bonan et al., 2019; Bushuk et al., 2017b; Chevallier & Salas, 2012; Collow et al., 2015; Day, Hawkins, et al., 2014; Dirkson et al., 2017; Guemas et al., 2016; Holland et al., 2011, 2019). Earlier work has showed that preconditioning of early summer SIT anomalies via winter and spring ice dynamics is an important source of summer SIA predictability (Babb et al., 2019; Brunette et al., 2019; Itkin & Krumpen, 2017; Krumpen et al., 2013; Williams et al., 2016). Another line of work has demonstrated the importance of melt season ice‐albedo feedback processes in predicting September SIA (Cox et al., 2016; Kapsch et al., 2014; Kwok et al., 2018; Landy et al., 2015; Liu et al., 2015; Petty et al., 2017; Schröder et al., 2014; Zhan & Davies, 2017).…”

“…Statistical prediction methods, based purely on observed quantities, also show evidence of a spring predictability barrier. The study of Brunette et al (2019), which used winter coastal divergence as a predictor of summer Laptev SIE, found maximum skill when coastal divergence was integrated up to the first week of May and a notable drop in skill when integrated to the first week of April. Other statistical prediction systems report skillful detrended SIE predictions for forecasts initialized after 1 May, but not prior to this date, consistent with a spring predictability barrier (Kapsch et al, 2014;Lindsay et al, 2008;Liu et al, 2015;Petty et al, 2017;Schröder et al, 2014;Walsh et al, 2019;Williams et al, 2016;Yuan et al, 2016).…”

A Spring Barrier for Regional Predictions of Summer Arctic Sea Ice

“…In their work, PM forecasts initialized in May are found to lose skill more quickly than forecasts initialized in July. More recently, Brunette et al () present an observational‐based statistical forecast model which uses winter coastal divergence as a predictor of summer minimum SIE in the Laptev Sea. They find maximal skill when considering coastal divergence up to the first week of May and report a drop in skill when the divergence is compared to the first week of April, consistent with a spring predictability barrier.…”