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Volume 46 Issue 6
Jun.  2024
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Article Contents
Lu Yang,Zhao Haibo,Zhao Jiawei, et al. Simulation error diagnosis of the seasonal evolution of sea ice thickness during MOSAiC in-situ observation[J]. Haiyang Xuebao,2024, 46(6):26–39 doi: 10.12284/hyxb2024065
Citation: Lu Yang,Zhao Haibo,Zhao Jiawei, et al. Simulation error diagnosis of the seasonal evolution of sea ice thickness during MOSAiC in-situ observation[J]. Haiyang Xuebao,2024, 46(6):26–39 doi: 10.12284/hyxb2024065

Simulation error diagnosis of the seasonal evolution of sea ice thickness during MOSAiC in-situ observation

doi: 10.12284/hyxb2024065
  • Received Date: 2024-02-06
  • Rev Recd Date: 2024-05-17
  • Available Online: 2024-07-12
  • Publish Date: 2024-06-01
  • The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was conducted from October 2019 to September 2020, yielding complete observations of atmosphere, ocean, sea ice thickness (SIT), and snow thickness. These observations provide new opportunities for the development of sea ice models. In this study, the seasonal evolution of SIT during MOSAiC was simulated using the ICEPACK sea ice model and atmospheric and oceanic forcing observations from two periods without missing data (from November 1, 2019 to May 7, 2020; from June 26 to July 27, 2020). The simulation was compared with SIT observation and the reasons for SIT simulation errors were diagnosed. The results show that, in the winter and spring seasons, the model can reproduce the increase in SIT, but overestimates the transition from submerged snow to sea ice and its contribution to sea ice mass balance. This causes the overestimation of SIT in spring. During the summer season, the combination of two thermodynamic schemes and three melt pond schemes indicates that the model overestimates the sea ice surface melting, resulting in thinner SIT at the end of simulation period. Our research demonstrates that the MOSAiC atmospheric and oceanic observation with all variables needed to force ICEPACK can be used to diagnose current sea ice models and very useful for their future improvements.
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