Ice bottom evolution derived from thermistor string-based ice mass balance buoy observations

• Published in: International Journal of Digital Earth
• Main Authors: Zeliang Liao, Yubing Cheng, Ying Jiang, Mengmeng Li, Bin Cheng, Stein Sandven
• Format: Article in Journal/Newspaper
• Language: English
• Published: Taylor & Francis Group 2023
• Subjects: sea ice thickness; lake ice thickness; ice-bottom evolution; nonlinear filtering; Mathematical geography. Cartography; GA1-1776; Antarctic; Arctic; Boreal Lake; Antarc*; Sea ice
• Online Access: https://doi.org/10.1080/17538947.2023.2242326; https://doaj.org/article/d0e7890df7254631937ea1750d2092e4

Digital information on sea ice extent, thickness, volume, and distribution is crucial for understanding Earth's climate system. The Snow and Ice Mass Balance Apparatus (SIMBA) is used to determine snow and ice temperatures in Arctic, Antarctic, ice-covered seas, and boreal lakes. Snow depth and ice thickness are derived from SIMBA temperature regimes (SIMBA_ET and SIMBA_HT). In warm conditions, SIMBA_ET temperature-based ice thickness may have errors due to the isothermal vertical profile. SIMBA_HT provides a visible ice-bottom interface for manual quantification. We propose an unmanned approach, combining neural networks, wavelet analysis, and Kalman filtering (NWK), to mathematically establish NWK and retrieve ice bottoms from various SIMBA_HT datasets. In the Arctic, NWK-derived total thickness showed a bias range of −5.64 cm to 4.01 cm and a correlation coefficient of 95%−99%. For Baltic Sea ice, values ranged from 1.31 cm to 2.41 cm (88%−98% correlation), and for boreal lake ice, −0.7 cm to 2.6 cm (75%−83% correlation). During ice growth, thermal equilibrium, and melting, the bias varied from −3.93 cm to 2.37 cm, −1.92 cm to 0.04 cm, and −4.90 cm to 3.96 cm, with correlation coefficients of 76%−99%. These results demonstrate NWK's robustness in retrieving ice bottom evolution in different water environments.