Adjoint-based simultaneous state and parameter estimation in an Arctic Sea Ice-Ocean Model using MITgcm (c63m)

Lyu, Guokun; Mu, Longjiang; Koehl, Armin; Lei, Ruibo; Liang, Xi; Liu, Chuanyu

doi:10.5194/gmd-18-9451-2025

Articles | Volume 18, issue 23

https://doi.org/10.5194/gmd-18-9451-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-18-9451-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 23

Development and technical paper

|

03 Dec 2025

Development and technical paper |

| 03 Dec 2025

Adjoint-based simultaneous state and parameter estimation in an Arctic Sea Ice-Ocean Model using MITgcm (c63m)

Guokun Lyu, Longjiang Mu, Armin Koehl, Ruibo Lei, Xi Liang, and Chuanyu Liu

Data sets

MITgcm model developed for state and parameter estimation in a pan-Arctic ocean and sea ice model using MITgcm (c63m) Guokun Lyu et al. https://doi.org/10.5281/zenodo.14584780

SMOS-CryoSat L4 Sea Ice Thickness European Space Agency https://doi.org/10.57780/sm1-4f787c3

In situ validation of Tropical Rainfall Measuring Mission microwave sea surface temperatures (https://data.remss.com/SST/daily/mw_ir/v05.1/netcdf/) C. L. Gentemann et al. https://doi.org/10.1029/2003JC002092

EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates (https://www.metoffice.gov.uk/hadobs/en4/) S. A. Good et al. https://doi.org/10.1002/2013jc009067

Observing and understanding climate change: Monitoring the mass balance, motion, and thickness of Arctic sea ice D. Perovich et al. http://imb-crrel-dartmouth.org/results/

Model code and software

MITgcm model developed for state and parameter estimation in a pan-Arctic ocean and sea ice model Guokun Lyu et al. https://doi.org/10.5281/zenodo.14584929

Short summary

In the sea ice-ocean models, errors in the parameters and missing spatiotemporal variations contribute to the deviations between the simulations and the observations. We extended an adjoint method to optimize spatiotemporally varying parameters together with the atmosphere forcing and the initial conditions using satellite and in-situ observations. Seasonally, this scheme demonstrates a more prominent advantage in mid-autumn and show great potential for accurately reproducing the Arctic changes.