Ice and Snow Thickness Variability and Change in the High Arctic Ocean Observed by In Situ Measurements

Abstract

In April 2017, we collected unique, extensive in situ data of sea ice and snow thickness. At 10 sampling sites, located under a CryoSat‐2 overpass, between Ellesmere Island and 87.1°N mean and modal total ice thicknesses ranged between 2 to 3.4 m and 1.8 to 2.9 m, respectively. Coincident snow thicknesses ranged between 0.3 to 0.47 m (mean) and 0.1 to 0.5 m (mode). The profile spanned the complete multiyear ice zone in the Lincoln Sea, into the first‐year ice zone farther north. Complementary snow thickness measurements near the North Pole showed a mean thickness of 0.31 m. Compared with scarce measurements from other years, multiyear ice was up to 0.75 m thinner than in 2004, but not significantly different from 2011 and 2014. We found excellent agreement with a commonly used snow climatology and with published long‐term ice thinning rates. There was reasonable agreement with CryoSat‐2 thickness retrievals.

Arctic Mission Benefit Analysis: impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance

“…8). For a comparison of CryoSat-2 thickness to in situ observations we refer to Haas et al (2017). The misfit to the CryoSat-2 ice thickness in April 2015 is similar to the misfit to the ITRP shown in Fig.…”

“…Such rigorous assessments can be performed in an efficient manner by the quantitative network design (QND) approach, allowing for an objective evaluation of the added value of observations for a given aspect of a model simulation or forecast. The technique originates from seismology (Hardt and Scherbaum, 1994) and was first applied to the climate system by Rayner et al (1996), who optimised the spatial distribution of in situ observations of atmospheric carbon dioxide to achieve minimum uncertainty in inferred surface fluxes. After an initial QND study that demonstrated the feasibility of the approach for remote sensing of the column-integrated atmospheric carbon dioxide concentration (Rayner and O'Brien, 2001), QND is now routinely applied in the design of CO 2 space missions (e.g., Patra et al, 2003;Houweling et al, 2004;Crisp et al, 2004;Feng et al, 2009;Kadygrov et al, 2009;Kaminski et al, 2010;Hungershoefer et al, 2010;Rayner et al, 2014;Bovensmann et al, 2015).…”

Contrasting Ice Algae and Snow‐Dependent Irradiance Relationships Between First‐Year and Multiyear Sea Ice

“…Several studies have shown consistent snow depth distribution patterns on MYI in relation to the undulating ice surface with more snow accumulating in low points and thinner snow/snow‐free regions on high elevations (e.g., hummocks; Haas et al, ; Iacozza & Barber, ; Lange, Flores, et al, ). Based on this consistent snow distribution pattern for MYI and the identification of high chl a biomass at the bottom of hummocks (Lange, Flores, et al, ), we hypothesize that MYI provides a more stable under‐ice light environment for ice algae compared to FYI.…”

A Distributed Snow‐Evolution Model for Sea‐Ice Applications (SnowModel)

“…However, that climatology was developed using data collected between 1954 and 1991 and was from a limited number of stations. While some studies have shown the Warren climatology is similar to contemporary snow depths over MYI (e.g., Haas et al, 2017), it does not include snow depths over the increasing expanse of seasonal ice currently found throughout the Arctic (e.g., Gerland & Haas, 2011;Hansen et al, 2013;Maslanik et al, 2007Maslanik et al, , 2011Nghiem et al, 2007;Renner et al, 2014). Recent snow and ice field measurements have shifted from MYI (e.g., Sturm et al, 2002bSturm et al, , 2006 to relatively new ice, because the predominant ice types have become younger over much of the Arctic (e.g., Haapala et al, 2013;King et al, 2015;Kwok et al, 2009;Kurtz et al, 2013;Kurtz & Farrell, 2011;Meier et al, 2014;Newman et al, 2014;Webster et al, 2014).…”