Preprints
https://doi.org/10.5194/egusphere-2024-1896
https://doi.org/10.5194/egusphere-2024-1896
03 Jul 2024
 | 03 Jul 2024
Status: this preprint is open for discussion.

Reconstruction of Arctic sea ice thickness (1992–2010) based on a hybrid machine learning and data assimilation approach

Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino

Abstract. Arctic sea ice thickness (SIT) remains one of the most crucial yet challenging parameters to estimate. Satellite data generally presents temporal and spatial discontinuities, which constrain studies focusing on long-term evolution. Since 2011, the combined satellite product CS2SMOS enables more accurate SIT retrievals that significantly decrease modelled SIT errors during assimilation. Can we extrapolate the benefits of data assimilation to past periods lacking accurate SIT observations? In this study, we train a machine learning (ML) algorithm to learn the systematic SIT errors between two versions of the model TOPAZ4 over 2011–2022, with and without CS2SMOS assimilation, to predict the SIT error and extrapolate the SIT prior to 2011. The ML algorithm relies on SIT coming from the two versions of TOPAZ4, various oceanographic variables, and atmospheric forcings from ERA5. Over the test period 2011–2013, the ML method outperforms TOPAZ4 without CS2SMOS assimilation when compared to TOPAZ4 assimilating CS2SMOS. The root mean square error of Arctic averaged SIT decreases from 0.42 to 0.28 meters and the bias from -0.18 to 0.01 meters. Also, despite the lack of observations available for assimilation in summer, our method still demonstrates a crucial improvement in SIT. Relative to independent mooring data in the Central Arctic between 2001 and 2010, mean SIT bias reduces from -1.74 meters to -0.85 meters when using the ML algorithm. Ultimately, the ML-adjusted SIT reconstruction reveals an Arctic mean SIT of 1.61 meters in 1992 compared to 1.08 meters in 2022. This corresponds to a decline in total sea ice volume from 19,690 to 12,700 km3, with an associated trend of -3,153 km3/decade. These changes are accompanied by a distinct shift in SIT distribution. Our innovative approach proves its ability to correct a significant part of the primary biases of the model by combining data assimilation with machine learning. Although this new reconstructed SIT dataset has not yet been assimilated into TOPAZ4, future work could enable the correction to be further propagated to other sea ice and ocean variables.

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Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino

Status: open (until 14 Aug 2024)

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Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino

Data sets

TOPAZ4-ML Sea Ice Thickness (1992–2022) Léo Edel, Jiping Xie, and Laurent Bertino https://zenodo.org/doi/10.5281/zenodo.11191853

Model code and software

GitHub repository for the manuscript Léo Edel https://github.com/LeoEdel/tardis-ml-paper1

Video supplement

TOPAZ4-ML Sea Ice Thickness and Volume (1992–2022) Léo Edel https://doi.org/10.5446/68161

Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino

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Short summary
This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a combination of machine learning and data assimilation. By training a machine learning model on data from 2011–2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes on sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.