the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Eddy-induced Chlorophyll-a Variations in the Northern Indian Ocean: A Study Using Multi-Source Satellite Data and Deep Learning
Yingjie Liu
Xiaofeng Li
Abstract. Mesoscale eddies, including surface-intensified eddies (SEs) and subsurface-intensified eddies (SSEs), significantly influence phytoplankton distribution in the ocean. Nevertheless, due to the sparse in-situ data, it is still unclear in understanding the characteristics of SSEs and their influence on chlorophyll-a (Chl-a) concentration. Consequently, the study utilized a deep learning model to extract SEs and SSEs in the North Indian Ocean (NIO) from 2000 to 2015, using long time series of satellite-derived sea surface height (SSH) and sea surface temperature (SST) data. The analysis revealed that SSEs accounted for 44 % of the total eddies in the NIO, and their SST signatures exhibited an opposite behavior compared to SEs. Furthermore, by integrating ocean color remote sensing data, the study investigated the contrasting impacts of SEs and SSEs on Chl-a concentration in two basins of the NIO: the Arabian Sea (AS) and the Bay of Bengal (BoB), known for their disparate biological productivity. In the AS, SEs induced Chl-a anomalies that were two to three times higher than those caused by SSEs. Notably, there were no significant differences in Chl-a anomalies induced by the same type of eddies between summer and winter. In contrast, the BoB exhibited distinct seasonal variations, where SEs induced slightly higher Chl-a anomalies than SSEs during the summer, while substantial differences were observed during the winter. Specifically, subsurface-intensified anticyclonic eddies (SSAEs) led to positive Chl-a anomalies, contrasting the negative anomalies induced by surface-intensified anticyclonic eddies (SAEs) with comparable magnitudes. Moreover, while both subsurface-intensified cyclonic eddies (SSCEs) and surface-intensified cyclonic eddies (SCEs) resulted in positive Chl-a anomalies, the magnitude of SSCEs was only one-fourth of that induced by SCEs. The distinct Chl-a anomalies between SEs and SSEs can be attributed to the contrasting subsurface structures revealed by Argo profiles. The upward displacement of isopycnals within SSAEs and the downward displacement within SSCEs in the upper 30–50 meters lead to higher and lower Chl-a concentrations, respectively. The study provides a valuable approach to investigating subsurface eddies and contributes to a comprehensive understanding of their influence on chlorophyll concentration.
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Yingjie Liu and Xiaofeng Li
Status: open (until 23 Sep 2023)
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RC1: 'Comment on egusphere-2023-1440', Anonymous Referee #1, 20 Jul 2023
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The authors have proposed a deep learning-based model for extracting surface-intensified eddies (SEs) and subsurface-intensified eddies (SSEs) using satellite-derived sea surface height and sea surface temperature data from 2000 to 2015 in the North Indian Ocean (NIO). Additionally, the study integrated ocean color remote sensing data to investigate the differential impacts of SEs and SSEs on chlorophyll-a (Chl-a) concentration in the two basins of the NIO: the Arabian Sea and the Bay of Bengal, known for their varying biological productivity. The authors have successfully linked the observed differences in Chl-a anomalies between SEs and SSEs to the contrasting subsurface structures revealed by Argo profiles. Overall, the manuscript provides an effective deep learning-based method for extracting SSEs solely from remote sensing data and contributes to a deeper understanding of the complex interactions between eddy dynamics and biogeochemical processes.
The manuscript is generally well written. But moderate revisions should be made before publication. My concerns and comments are detailed below.
- Consider adding information on the formation mechanism of SSEs in the regions to provide readers with a more comprehensive understanding of SSEs. What’s the dominant mechanism of SSE generating in NIO? What cause the different chlorophyll features between SEs and SSEs? If necessary, please give explanations with Argo/BGC-Argo results.
- The manuscript concluded that SSEs account for nearly 50% of the total eddies, which needs further consideration. The sea surface temperature can be easily disturbed by environment, such as wind speed. Therefore, identifying SSEs according SSTA<0 (or SSTA>0) may increase the noises from low-energy eddies. It is suggested to set threshold for SSEs identification, such as amplitude, lifetime, which should increase the accuracy of SSEs identification.
- The paper proposes an identification method for SEs and SSEs using deep learning, along with validation and analysis of their temperature and chlorophyll characteristics. Consider refining the title to align more accurately with the manuscript's content.
- Line 104: Reword 'as described by Assassi et al. (2016)' to 'as described in the study by Assassi et al. (2016)'.
- Line 114-117: Has the sign of SSρ/SSHA been previously used as an indicator to distinguish SEs and SSEs in any studies? Please provide references if available.
- Line 145: Please include the formula for the dice loss function.
- Line 146: What is the specific definition of accuracy for the DL-based model? Clarify this point.
- Line 167: Provide detailed information on the inversed distance weighting interpolation method.
- Line 258: Revise 'to accurately determine the most intense core's location' to 'to determine the location of the most intense core accurately.'
- Figure 5: The Chl-a anomalies induced by SSAEs and SSCEs displayed in the current color bar are not easily discernible. It is recommended to modify the color bar to enhance the visibility of the differences.
Citation: https://doi.org/10.5194/egusphere-2023-1440-RC1 -
AC1: 'Reply on RC1', Yingjie Liu, 14 Aug 2023
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Dear reviewer,
Many thanks for taking the time to review our manuscript. We highly appreciate your corrections and suggestions, which, along with the comments from the other referees, will have a large positive impact on our study.
Please find our point-by-point response in the supplement.
With thanks and best wishes,
Yingjie Liu
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RC2: 'Reply on AC1', Anonymous Referee #1, 30 Aug 2023
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After the revision, most of my questions got feedback. The manuscript is well-organized and suitable for publication. Other simple questions which are just my curious points: Do the authors have any idea how many chlorophyll profiles from BGC-Argo are associated with SE and SSE eddies? Do SE and SSE exhibit any differences in the vertical chlorophyll distributions?
Citation: https://doi.org/10.5194/egusphere-2023-1440-RC2 -
AC2: 'Reply on RC2', Yingjie Liu, 22 Sep 2023
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Dear reviewer,
Many thanks for taking the time to review our manuscript. Please find our point-by-point response in the supplement.
With thanks and best wishes,
Yingjie Liu
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AC2: 'Reply on RC2', Yingjie Liu, 22 Sep 2023
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RC2: 'Reply on AC1', Anonymous Referee #1, 30 Aug 2023
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AC1: 'Reply on RC1', Yingjie Liu, 14 Aug 2023
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RC3: 'Comment on egusphere-2023-1440', Anonymous Referee #2, 17 Sep 2023
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This is an important work. The major contributions of the authors are 1) developing Deep Learning methods to identify surface-intensified and sub-surface-intensified eddies using satellite observations and 2) gaining new understanding on the role of eddies played in spatiotemporal variations of ocean primary productivity in the Northern Indian Ocean. Thus, this reviewer recommends the submission to be accepted for publication.
Citation: https://doi.org/10.5194/egusphere-2023-1440-RC3 -
AC3: 'Reply on RC3', Yingjie Liu, 22 Sep 2023
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Dear Reviewer,
We sincerely appreciate your insightful feedback and your positive assessment of our work. Your recognition of the substantial contributions made in our study is highly motivating.
Thank you once again for your valuable feedback and support.
Sincerely,
Yingjie LiuCitation: https://doi.org/10.5194/egusphere-2023-1440-AC3
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AC3: 'Reply on RC3', Yingjie Liu, 22 Sep 2023
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Yingjie Liu and Xiaofeng Li
Yingjie Liu and Xiaofeng Li
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