the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Jul 2025
Drivers of Phytoplankton Bloom Interannual Variability in the Amundsen and Pine Island Polynyas
Abstract. The Amundsen Sea Embayment experiences both the highest ice shelf melt rates and the highest biological productivity in West Antarctica. Using 19 years of satellite data and modelling output, we investigated the long-term influence of environmental factors on the phytoplankton bloom in the Amundsen sea (ASP) and Pine Island polynyas (PIP). We tested the prevailing hypothesis that changes in ice shelf melt rate could drive interannual variability in the polynyas’ surface chlorophyll-a (chla) and Net Primary Productivity (NPP). We found that the interannual variability and long-term change in glacial meltwater may play an important role in chla variance in the ASP, but not for NPP. Glacial meltwater does not explain the variability in both chla and NPP in the PIP, where light and temperature are the main drivers. We attribute this to potentially greater amount of iron-enriched meltwater brought to the surface by the meltwater pump downstream of the PIP, and the coastal ocean circulation accumulating and transporting iron towards the ASP.
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Status: open (until 17 Sep 2025)
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RC1: 'Comment on egusphere-2025-3149', Anonymous Referee #1, 18 Aug 2025
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Review of "Drivers of Phytoplankton Bloom Interannual Variability in the Amundsen and Pine Island Polynyas" by Guillaume Liniger et al.
The manuscript presents a valuable study of the phytoplankton blooms and their drivers in the Amundsen and Pine Island Polynyas. Satellite-derived Chl-a and NPP maps were used to characterize phytoplankton abundance and primary productivity in the years 1998 – 2017. Overall, the manuscript is well-written, well-organized, and the main points are clearly articulated. The determination of phenology metrics followed the standard methodologies described in the literature, and the use of Principal Component Analysis (PCA) and the Mann-Kendall test demonstrates good statistical practices. I especially appreciate the application of non-parametric statistical metrics in this study.
I see, however, a few issues that when fixed, could improve the final version of the paper. I present them in the points below:
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The study compares the Amundsen and Pine Island Polynyas, highlighting several differences that appear to arise from variations in local topography, sediment resuspension, and currents (as mentioned in lines 519-520). While these factors were discussed, they were overlooked in the study area's section. To improve the brief description and make it easier for readers to follow the discussion, it would be beneficial to add the following: (a) the bathymetry of the area, which is an important aspect in the analysis of sediment resuspension; (b) contrasts between the polynyas regarding circulation patterns; (c) a brief description of the glaciers analyzed in the study with a particular focus on the differences between them; and (d) information on phytoplankton composition, which determines the demand for nutrients, sensitivity to iron shortages (notably different for diatoms and small flagellates), and the potential for using recycled nutrients. Recent research has indicated changes in the phytoplankton community structure on Antarctica's shelf, including a decline in diatoms sensitive to iron shortages, so I would expect at least a brief characterization of these communities.
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The role of ligands, which are mentioned late in the discussion, seems significant for the availability of iron to phytoplankton. Information in lines 568-570 seems to suggest a possible feedback loop between the biological activity, ligands and the bioavailability of iron, which could be an interesting aspect to consider when analyzing bloom cycles. It might be worth adding a short comment on this topic in the model description around line 190.
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There are at least two GlobColour L3 chl-a products that differ by the averaging method. It would be helpful to provide an ID or DOI number for the dataset. On a similar note, it would be interesting to see a discussion on the strong connection between net primary production (NPP) and chl-a, as chl-a is a key parameter for estimating NPP.
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Under high-nitrate low-iron conditions, literature reported significant variations in the carbon-to-chlorophyll (Cphyto:Chl) ratio from those assumed globally, due to phytoplankton adaptations to iron shortages. Additionally, low-light conditions can alter the carbon-to-chlorophyll ratio. It would be worth including these elements in the discussion as a potential source of uncertainty. Might these differences explain the significant correlation with chl-a in Figure 3 and the lack of correlation with NPP at the same time?
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Lastly, a small editorial note: lines 274-277 contain a repeated sentence.
Citation: https://doi.org/10.5194/egusphere-2025-3149-RC1 -
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