Articles | Volume 14, issue 3
The Cryosphere, 14, 1083–1104, 2020
https://doi.org/10.5194/tc-14-1083-2020
The Cryosphere, 14, 1083–1104, 2020
https://doi.org/10.5194/tc-14-1083-2020

Research article 25 Mar 2020

Research article | 25 Mar 2020

Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Young Jun Kim et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (26 Nov 2019) by David Schroeder
AR by Jungho Im on behalf of the Authors (26 Nov 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (29 Nov 2019) by David Schroeder
RR by Anonymous Referee #2 (07 Jan 2020)
RR by Anonymous Referee #3 (24 Jan 2020)
ED: Publish subject to minor revisions (review by editor) (04 Feb 2020) by David Schroeder
AR by Jungho Im on behalf of the Authors (11 Feb 2020)  Author's response    Manuscript
ED: Publish as is (26 Feb 2020) by David Schroeder
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Short summary
In this study, we proposed a novel 1-month sea ice concentration (SIC) prediction model with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). The proposed CNN model was evaluated and compared with the two baseline approaches, random-forest and simple-regression models, resulting in better performance. This study also examined SIC predictions for two extreme cases in 2007 and 2012 in detail and the influencing factors through a sensitivity analysis.