Thermodynamic and dynamic contributions to seasonal Arctic sea ice thickness distributions from airborne observations

Abstract

Sea ice thickness is a key parameter in the polar climate and ecosystem. Thermodynamic and dynamic processes alter the sea ice thickness. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provided a unique opportunity to study seasonal sea ice thickness changes of the same sea ice. We analyzed 11 large-scale (∼50 km) airborne electromagnetic sea thickness and surface roughness surveys from October 2019 to September 2020. Data from ice mass balance and position buoys provided additional information. We found that thermodynamic growth and decay dominated the seasonal cycle with a total mean sea ice thickness increase of 1.4 m (October 2019 to June 2020) and decay of 1.2 m (June 2020 to September 2020). Ice dynamics and deformation-related processes, such as thin ice formation in leads and subsequent ridging, broadened the ice thickness distribution and contributed 30% to the increase in mean thickness. These processes caused a 1-month delay between maximum thermodynamic sea ice thickness and maximum mean ice thickness. The airborne EM measurements bridged the scales from local floe-scale measurements to Arctic-wide satellite observations and model grid cells. The spatial differences in mean sea ice thickness between the Central Observatory (<10 km) of MOSAiC and the Distributed Network (<50 km) were negligible in fall and only 0.2 m in late winter, but the relative abundance of thin and thick ice varied. One unexpected outcome was the large dynamic thickening in a regime where divergence prevailed on average in the western Nansen Basin in spring. We suggest that the large dynamic thickening was due to the mobile, unconsolidated sea ice pack and periodic, sub-daily motion. We demonstrate that this Lagrangian sea ice thickness data set is well suited for validating the existing redistribution theory in sea ice models. Our comprehensive description of seasonal changes of the sea ice thickness distribution is valuable for interpreting MOSAiC time series across disciplines and can be used as a reference to advance sea ice thickness modeling.

Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework

“…It is also likely that, in autumn, basal growth contribution to ice production in pack ice is larger than from January to March, as level ice and its snow layer are generally thinner, hence allowing for more heat loss from the ocean in ice covered areas. von Albedyll et al (2022) suggest that the higher contribution of leads to ice production in spring than in winter could be partly resulting from regional differences, as the vessel drifted towards regions with a higher contribution of leads to the ice production. Our model suggests this is likely the case (Figure 11b).…”

“…Fine-scale sea-ice dynamics also impact the sea ice mass balance. Divergent features in the ice, like leads and polynyas, are associated with localised intense ocean heat loss that enhances sea ice production in winter (Kwok, 2006;Wilchinsky et al, 2015;von Albedyll et al, 2022). The magnitude of ice production in leads remains largely uncertain.…”

“…Kwok (2006) have estimated this contribution to 30% of the total ice production in pack ice from November to April in the western part of the Arctic Basin for the period 1997-2000. More recently, von Albedyll et al (2022) also estimated this contribution to be around 30% during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign. These estimates suggest that properly representing ice formation in leads is key to ensuring a realistic magnitude and distribution of ice growth in numerical models.…”

A climatology of thermodynamic vs. dynamic Arctic wintertime sea ice thickness effects during the CryoSat-2 era

“…In this way, our Eulerian deformation term can be considered Lagrangian dynamics. Two Lagrangian studies of dynamics observed along the MOSAiC drift track offer useful context for validating our Eulerian results (von Albedyll et al, 2022;Koo et al, 2021). The comparison is necessarily between our weekly Eulerian deformation term-i.e., Lagrangian dynamics-from closest in space and time to the MOSAiC drift track and dynamics as described within those studies.…”

“…The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC; Nicolaus et al, 2022) presented an opportunity for partitioning thermodynamic and dynamic growth from a Lagrangian perspective along the Transpolar Drift over a full year from October 2019 to September 2020. von Albedyll et al (2022) analyzed data from airborne electromagnetic (AEM) surveys and an ice mass balance buoy network to characterize the annual cycle of both dynamic and thermodynamic sea ice thickness contributions experienced by the ice pack surrounding the MOSAiC drift station. Thermodynamic growth was modeled using ice mass balance buoy temperature profiles and subtracted from overall ice growth captured by the airborne electromagnetic survey data in order to calculate dynamic sea ice effects as a residual.…”