Journal of Advances in Modeling Earth Systems (Mar 2024)

A Coordinated Sea‐Ice Assimilation Scheme Jointly Using Sea‐Ice Concentration and Thickness Observations With a Coupled Climate Model

  • X. Liu,
  • J. Yao,
  • S. Zhang,
  • T. Wu,
  • Z. Chen,
  • Y. Fang,
  • M. Chu,
  • J. Yan,
  • W. Jie

DOI
https://doi.org/10.1029/2023MS003608
Journal volume & issue
Vol. 16, no. 3
pp. n/a – n/a

Abstract

Read online

Abstract For jointly assimilating sea‐ice concentration (SIC) and sea‐ice thickness (SIT) observations into a global coupled climate system model consisting of sea‐ice component with multiple sea‐ice categories, we propose a new sea‐ice analysis update scheme in an ensemble assimilation framework and compare it with some previously used schemes. Different from the conventional scheme that often assigns SIC/SIT analysis to multiple sea‐ice categories according to the background ratios and thus directly updates the corresponding variables in model (i.e., direct‐update scheme), the new scheme converts SIC/SIT analysis into ice heating term to adjust the ice enthalpy using model freezing/melting physics and further updates the model sea‐ice state (i.e., enthalpy‐adjusting scheme). It has a capability in particularly adjusting multiple sea‐ice variables in addition to SIC and SIT in a coordinated way, and avoiding the artificial addition or elimination of sea‐ice in analysis that is often adopted in the direct‐update scheme. Evaluated by several sets of experiments assimilating satellite‐derived Arctic sea‐ice observations, the enthalpy‐adjusting scheme performs better than the direct‐update scheme in analysis of the Arctic SIT. Further, 4‐week forecasts after assimilation initialization exhibit slow growth of forecast error. Compared to the direct‐update scheme, the enthalpy‐adjusting scheme initialized forecasts show comparable skills in the SIC but significantly higher skills in the SIT, especially in the Arctic sea‐ice edge areas. These results highlight advantage of the enthalpy‐adjusting scheme that has promise to improve coupled data assimilation and reduce climate prediction uncertainty.

Keywords