Preprints
https://doi.org/10.5194/tc-2021-271
https://doi.org/10.5194/tc-2021-271

  27 Sep 2021

27 Sep 2021

Review status: this preprint is currently under review for the journal TC.

Estimating snow depth on Arctic sea ice based on reanalysis reconstruction and particle filter assimilation

Haili Li1,2,3, Chang-Qing Ke1,2,3, Qinghui Zhu1,2,3, and Xiaoyi Shen1,2,3 Haili Li et al.
  • 1Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China
  • 2Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 210023, China
  • 3Collaborative Innovation Center of South China Sea Studies, Nanjing, 210023, China

Abstract. The snow depth, an essential metric of snowpacks, can modulate sea ice changes and is a necessary input parameter to obtain altimeter-derived sea ice thickness values. In this study, we propose an innovative snow depth retrieval method with the improved NASA Eulerian Snow on Sea Ice Model (INESOSIM) and the particle filter (PF) approach, namely, INESOSIM-PF. Then, we generate daily snow depth estimates with INESOSIM-PF from 2012 to 2020 at a 50-km resolution. With the use of Operation IceBridge (OIB) data, it can be revealed that compared to the NESOSIM-estimated snow depth, the INESOSIM-PF-estimated snow depth is greatly improved, with a root mean square error (RMSE) decrease of 17.97 % (RMSE: 6.73 cm) and a correlation coefficient increase of 11.85 % (r: 0.71). The INESOSIM-PF-estimated snow depth is close to the satellite-derived snow depth, which is applied in data assimilation. With the use of Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) snow buoy data, it can be verified that INESOSIM-PF performs well in the Central Arctic with an RMSE of 9.23 cm. INESOSIM-PF is robust and the snow depth determined with INESOSIM-PF is less influenced by input parameters with a snow depth uncertainty of 0.74 cm. The variations in the monthly and seasonal snow depth estimates retrieved from INESOSIM-PF agree well with those in the estimates retrieved from two other existing algorithms. Based on the presented snow depth estimates, we can retrieve the sea ice thickness and perform long-term snow depth and sea ice analysis. Snow depth estimates improve the understanding of Arctic environmental change and promote the future development of sea ice models.

Haili Li et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-271', Anonymous Referee #1, 18 Oct 2021
  • RC2: 'Comment on tc-2021-271', Alek Petty, 26 Oct 2021

Haili Li et al.

Haili Li et al.

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Short summary
Here, we employ particle filter assimilation to combine snow depth values retrieved from remote sensing with those obtained from reanalysis reconstructions, and INESOSIM-PF is proposed. The results indicate that the proposed method improves the modeled snow depth, and the monthly and seasonal changes in the snow depth are consistent with those in the snow depth determined with two existing snow depth algorithms.