Evaluation of Arctic Sea Ice Thickness from a Parameter-Optimized Arctic Sea Ice–Ocean Model

• Published in: Remote Sensing
• Main Authors: Qiaoqiao Zhang, Hao Luo, Chao Min, Yongwu Xiu, Qian Shi, Qinghua Yang
• Format: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2023
• Subjects: sea ice thickness; Arctic; model; parameter optimization; NAOSIM; CS2SMOS; Arctic Ocean; Atlantic Arctic; Atlantic-Arctic; Beaufort Sea; Canadian Archipelago; North Atlantic; Sea ice
• Online Access: https://doi.org/10.3390/rs15102537

Sea ice thickness (SIT) presents comprehensive information on Arctic sea ice changes and their role in the climate system. However, our understanding of SIT is limited by a scarcity of observations and inaccurate model simulations. Based on simultaneous parameter optimization with a micro genetic algorithm, the North Atlantic/Arctic Ocean–Sea Ice Model (NAOSIM) has already demonstrated advantages in Arctic sea ice simulations. However, its performance in simulating pan-Arctic SITs remains unclear. In this study, a further evaluation of Arctic SITs from NAOSIM was conducted based on a comparison with satellite and in situ observations. Generally, NAOSIM can reproduce the annual cycle and downward trend in the sea ice volume. However, deficiencies can still be found in the simulation of SIT spatial patterns. NAOSIM overestimates the SIT of thinner ice (<1.5 m) in the Beaufort Sea, underestimates the SIT of thick ice (>1.5 m) in the central Arctic and is unable to capture the upward trend in the SIT in the north of the Canadian Archipelago as well as to reproduce the intensity of the observed SIT variability. In terms of SIT simulation, NAOSIM performs better as the time approaches the optimization window (2000–2012). Therefore, in the context of rapid changes in Arctic sea ice, how to optimize this model based on limited observations still remains a challenge.