ICESat-2 observations of melt ponds on Arctic sea ice
Journal Article
Overview
abstract
During the Arctic summer season, snow atop the sea ice melts and pools; into low-lying areas on the surface. These melt ponds reduce surface; albedo and increase solar absorption in the Arctic Ocean. Throughout the; summer, melt ponds grow, drain, and connect, through a complex drainage; system. Current melt pond schemes in sea ice models, such as the; level-ice scheme in the Los Alamos Sea Ice Model (CICE), rely on a; linear relationship between pond depth and fraction to predict the; evolution of pond growth as the snow and sea ice melt. Although the; inclusion of melt ponds in models has been shown to improve forecasts of; end-of-summer sea ice extent, observations of melt pond depth and; fraction guiding these models are from SHEBA, a spatially-limited field; campaign which occurred over 20 years ago. Until recently, melt ponds; characteristics have been difficult to resolve from spaceborne platforms; due to their small size (10s - 100s m in diameter), and; indistinguishable radiometric similarity to open water. Here we show; that new, high-resolution laser altimetry measurements from ICESat-2; (IS2), combined with coincident high-resolution satellite imagery,; provides a three-dimensional view of the melting sea ice cover. IS2,; launched in September 2018, has now observed two summer melt seasons in; the Arctic. IS2 operates at 532 nm, a wavelength that penetrates low; turbidity water, and can therefore be used to capture the bathymetry of; shallow water features. Building on previous work, we demonstrate IS2’s; ability to detect and measure melt ponds on multiyear sea ice. We; validate the existence of melt ponds with high resolution (10 m) visible; imagery from the Sentinel-2 (S2) MultiSpectral Instrument. We apply the; “density dimension algorithm – bifurcate” (DDA-bifurcate), an; auto-adaptive algorithm utilizing data aggregation with the ability to; track two surfaces, as well as a second algorithm that tracks melt pond; surface and bottom, to derive melt pond depth for dozens of melt ponds; in 2019 and 2020. Applying a sea ice surface classification algorithm to; S2 imagery, we are able to determine melt pond fraction. We compare our; findings of coincident melt pond fraction and depth with the melt pond; parameterization used in the level-ice scheme in CICE. We discuss our; results in the context of the existing literature on pond depth and; volume.
