ICESat-2 observations of melt ponds on Arctic sea ice

Evolution of Melt Pond Fraction and Depth on Multiyear Ice in 2020 from High Resolution Satellite Observations

“…This region hosts the oldest and thickest ice in the Arctic, and was chosen for the study region as the thick ice can hold melt ponds for a longer period of time without melting through the ice pack as compared to thinner first year ice. The ability to measure melt pond depth on multiyear ice with satellite altimetry was first shown in Farrell et al, 2020. There we demonstrate 339 depths retrievals from 15 ponds and found depths ranging from 0.04 -2.4 m with a modal depth of 0.35 m. In Buckley et al, 2020b, we discussed continued work to track ponds and introduced the DDA-bifurcate (Herzfeld et al, 2017), an additional algorithm to track ponds in ICESat-2 data. The work presented here continues to build on these efforts to create a melt pond database.…”

Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2 ATLAS Data—The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-Bifurcate-Seaice)

“…11), who collected mass-balance measurements during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment conducted October 1997-October 1998. A simple algorithm for depth measurement of ponds has been described and applied in , [Buckley et al, 2020a], [Buckley et al, 2019] (UMD-melt-pond-algorithm (MPA)). Their algorithm requires knowledge where a pond exists, it is not automated and thus cannot be employed for operational processing of ICESat-2 products.…”

“…As a means of evaluation, results from the DDA-bif-seaice will be compared to results from a simple algorithm for analysis of melt ponds in ICESat-2 ATLAS that requires a-priori manual identification of ponds, developed at the University of Maryland (UMD Melt-Pond Algorithm (MPA)). The UMD MPA, described in [Buckley, 2022], [Buckley et al, prep], is not automated and thus cannot be employed for operational processing of ICESat-2 products.…”