A physics-based Antarctic melt detection technique: combining Advanced Microwave Scanning Radiometer 2, radiative-transfer modeling, and firn modeling

• Published in: The Cryosphere
• Main Authors: M. E. Dattler, B. Medley, C. M. Stevens
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2024
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; Antarc*; Antarctic; Ice Sheet; Ice Shelf; Ice Shelves; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-18-3613-2024; https://doaj.org/article/8e213faf493d4083b18e0d35d40bd82f

Surface melt on ice shelves has been linked to hydrofracture and subsequent ice shelf breakup. Since the 1990s, scientists have been using microwave radiometers to detect melt on ice shelves and ice sheets by applying various statistical thresholding techniques to identify significant increases in brightness temperature that are associated with melt. In this study, instead of using a fixed threshold, we force the Snow Microwave Radiative Transfer model (SMRT) with outputs from the Community Firn Model (CFM) to create a dynamic, physics-based threshold for melt. In the process, we also combine our method with statistical thresholding techniques and produce microwave grain-size information. We run this “hybrid method” across the Larsen C ice shelf as well as 13 sites on the Antarctic Ice Sheet. Melt and non-melt days from the hybrid method and three statistical thresholding techniques match with the surface energy balance within 94 ± 1 %; the effect of melt on the passive microwaves is mostly binary and thus largely detectable by statistical thresholding techniques as well as physics-based techniques. Rather than always replacing statistical thresholding techniques with the hybrid method, we recommend using the hybrid method in studies where the melt volume or grain size is of interest. In this study, we show that the hybrid method can be used to (a) model dry-snow brightness temperatures of Antarctic snow and (b) derive a measure of grain size; therefore, it is an important step forwards towards using firn and radiative-transfer modeling to quantify melt rather than to simply detect melt days.