Turbulent Heat Exchange Over Polar Leads Revisited: A Large Eddy Simulation Study

Abstract

An important feature of polar sea ice is the presence of leads, the characteristics of which have been described by several authors, and most recently by Michaelis et al. (2020), Michaelis et al. (2021), and Michaelis and Lüpkes (2022. In the following, we summarize their introductory reviews, as well as that of Tetzlaff et al. (2015), and also consider further literature that is relevant to our work. Leads are openings in sea ice that form due to divergence or shear in ice drifts caused by wind or ocean currents. Leads exhibit a channel-like shape, with widths from a few meters to several kilometers and lengths of a few kilometers to several hundred kilometers (Barry et al., 1993), as shown in numerous satellite images (e.g., Figure 1). Leads contribute significantly to the formation of ice (Eisen & Kottmeier, 2000) and bottom water (Midttun, 1985). Although leads account for a small amount of total ice coverage in polar regions (e.g., Lindsay & Rothrock, 1995;Miles & Barry, 1998), they play an important role in the energy balance. For example, Eisen and Kottmeier (2000) found that leads in the western Weddell Sea contribute up to 70% of net energy transfer into the atmosphere, with net heat flux

Surface Turbulent Fluxes From the MOSAiC Campaign Predicted by Machine Learning

“…This could potentially explain the underestimation of $${u}_{\star }^{2}$$ by both the ANN and bulk algorithm parametrizations (see Figure 1). Finally, turbulent heat fluxes over sea ice are known to be influenced by lead width (e.g., Marcq & Weiss, 2012), although the exact nature of the dependency is a topic of ongoing research (Gryschka et al., 2023). Including the lead‐width distribution as an input to the ANNs could shed additional light on this question.…”

Modulating surface heat flux through sea ice leads improves Arctic sea ice simulation in the coupled EC-Earth3

“…However, these relationships have not yet been found, and so Davy and Gao (2019) employed a fixed fractal dimension derived from satellite observations. Furthermore, how the fluxes from leads depend upon lead width, background wind, and orientation is the subject of ongoing research using turbulence resolving models for the atmospheric boundary layer (Esau, 2007;Gryschka et al, 2023). The results of Davy and Gao (2019) that were used to create the modulation factor employed here depended on the scale sensitivity derived from the model results of Esau (2007).…”

“…The results of Davy and Gao (2019) that were used to create the modulation factor employed here depended on the scale sensitivity derived from the model results of Esau (2007). However, recent work by Gryschka et al (2023) can be very different depending on the roughness of the surrounding sea ice. This uncertainty from the turbulence resolving modelling highlights the need for further research in this direction to reduce the model-form uncertainty.…”

Surface turbulent fluxes from the MOSAiC campaign predicted by machine learning

“…This could potentially explain the underestimation of 𝐴𝐴 𝐴𝐴 2 ⋆ by both the ANN and bulk algorithm parametrizations (see Figure 1). Finally, turbulent heat fluxes over sea ice are known to be influenced by lead width (e.g., Marcq & Weiss, 2012), although the exact nature of the dependency is a topic of ongoing research (Gryschka et al, 2023). Including the lead-width distribution as an input to the ANNs could shed additional light on this question.…”