Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay early spring sea ice thickness

Abstract

Abstract. In the Arctic, multi-year sea ice is being rapidly replaced by seasonal sea ice. Baffin Bay, situated between Greenland and Canada, is part of the seasonal ice zone. In this study, we present a long-term multi-mission assessment (2003–2020) of spring sea ice thickness in Baffin Bay from satellite altimetry and sea ice charts. Sea ice thickness within Baffin Bay is calculated from Envisat, ICESat, CryoSat-2, and ICESat-2 freeboard estimates, alongside a proxy from the ice chart stage of development that closely matches the altimetry data. We study the sensitivity of sea ice thickness results estimated from an array of different snow depth and snow density products and methods for redistributing low-resolution snow data onto along-track altimetry freeboards. The snow depth products that are applied include a reference estimated from the Warren climatology, a passive microwave snow depth product, and the dynamic snow scheme SnowModel-LG. We find that applying snow depth redistribution to represent small-scale snow variability has a considerable impact on ice thickness calculations from laser freeboards but was unnecessary for radar freeboards. Decisions on which snow loading product to use and whether to apply snow redistribution can lead to different conclusions on trends and physical mechanisms. For instance, we find an uncertainty envelope around the March mean sea ice thickness of 13 % for different snow depth/density products and redistribution methods. Consequently, trends in March sea ice thickness from 2003–2020 range from −23 to 17 cm per decade, depending on which snow depth/density product and redistribution method is applied. Over a longer timescale, since 1996, the proxy ice chart thickness product has demonstrated statistically significant thinning within Baffin Bay of 7 cm per decade. Our study provides further evidence for long-term asymmetrical trends in Baffin Bay sea ice thickness (with −17.6 cm per decade thinning in the west and 10.8 cm per decade thickening in the east of the bay) since 2003. This asymmetrical thinning is consistent for all combinations of snow product and processing method, but it is unclear what may have driven these changes.

Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection

“…Recent studies leveraging newly generated Arctic snow reconstructions and satellite-derived data products, including the joint ICESat-2-CryoSat-2-derived snow depths, are helping collectively provide new insights into snow depth variability and its impacts on sea ice thickness and its contribution to total thickness uncertainty (Zhou et al, 2021;Mallett et al, 2021;Glissenaar et al, 2021;Kacimi and Kwok, 2022a). While these datasets, including NESOSIM, are still generally limited by a lack of contemporary ground-truth data for assessing data accuracy, the creation of new operational, i.e.…”

The role of oceanic heat flux in reducing thermodynamic ice growth in Nares Strait and promoting earlier collapse of the ice bridge

“…Another type of uncertainty related to estimating sea ice thickness from h is connected to the presence of snow and, more importantly, to the generally unknown spatial and temporal variability of its accumulation rate. The existing climatology or estimates of snow depths in the Arctic either do not cover the study area (Warren et al, 1999;Kwok et al, 2020b) or are too coarse to provide good spatial coverage in Nares Strait (Rostosky et al, 2018;Glissenaar et al, 2021). Using DMSP SSM/I-SSMIS brightness temperatures, Landy et al (2017) reported > 0.3 m mean snow depth in the central Kane Basin by the end of winter.…”

Sub-kilometre scale distribution of snow depth on Arctic sea ice from Soviet drifting stations

“…To account for the observed variability of snow depth on scales below a gridcell (e.g. Farrell and others, 2012), a sub-grid scale snow depth distribution must be employed (see Petty and others, 2020; Glissenaar and others, 2021, for impacts on sea-ice thickness retrievals). For instance, the amount of shortwave solar radiation incident on the ice surface in a multi-kilometre gridcell is sensitive to the fractional coverage of snow which is optically thin ( 15 cm for dry snow; Warren, 2019).…”