Thirteen Winters (2012–2025) of Penetration-Aware Arctic Sea-Ice Emissivity and Emission Temperature from AMSR2

Abstract. Passive microwave observations enable long-term, all-weather monitoring of Arctic sea ice, but their physical interpretation depends on how surface emission is represented. In conventional satellite emissivity products, emissivity is typically retrieved under a skin-emission assumption, even though low-frequency microwave radiation over sea ice can originate from a deeper, non-isothermal subsurface layer. This study presents a new satellite-derived dataset of low-frequency sea-ice emissivity representative of the microwave-emitting layer and the corresponding emission temperature for wintertime Arctic sea ice. The dataset is generated using a simulation-trained retrieval framework in which penetration-aware sea-ice surface emission parameters are combined with reanalysis atmospheric profiles in radiative transfer calculations to construct forward-consistent training data, and neural-network inversion models are trained separately for emissivity and emission temperature to retrieve each variable independently from top-of-atmosphere brightness temperatures.

With the developed algorithm applied to Advanced Microwave Scanning Radiometer 2 (AMSR2) observations, a 13-winter record (2012/2013–2024/2025) of penetration-aware emissivity and emission temperature is produced over the Arctic Ocean north of 70°N under high sea-ice concentration (Kang, 2026; https://tinyurl.com/5n6843r8). Because neither emissivity nor emission temperature has a direct observational reference, the retrieval is evaluated in observation space via radiative-transfer closure. Brightness temperatures forward-simulated from the retrieved parameters agree closely with AMSR2 observations across all winters (r ≈ 0.99, spread of about 1 K), demonstrating radiative consistency despite training on simulation-based data. Comparable agreement is obtained at the 23.8 GHz horizontally polarized channel, which is not used in the retrieval, supporting cross-channel physical consistency. The dataset is temporally stable across winters and is intended to support radiative-transfer applications, satellite retrieval development, sea-ice characterization, and polar data assimilation.