the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Nov 2025
Projected Effects of Climate-induced Changes in Phytoplankton biomass in the Southern South China Sea
Abstract. Phytoplankton lies at the base of the marine pelagic food webs influencing the planet’s net primary production and nutrient cycles. Understanding the impacts of anthropogenic climate change on the phytoplankton dynamics is crucial due to their pivotal role. Numerous studies across various latitudes have investigated the effects of climate change on the world’s oceans, focusing on future projections on plankton. However, despite being one of the largest marginal seas of the world, the impact of such future projections on marine plankton in the South China Sea has rarely been documented. Monsoon derived productive upwelling areas in the South China Sea serve as ideal sites for studying plankton dynamics. In this study, a 3D coupled physical-biogeochemical model is used to examine the response of phytoplankton biomass and selected abiotic and biotic factors to future anthropogenic climate change, focusing on two upwelling areas of the southern South China Sea: Southeast Vietnam and Northwest Sabah. The results show declined phytoplankton biomass associated with warming and nutrient depletion, particularly silicate. However, the grazing pressure by mesozooplankton is predicted to be reduced, suggesting that this studied system is under bottom-up control. This study highlights the anticipated amplification of climate change-induced impacts on the phytoplankton over to higher trophic levels, which may influence both ecosystems and socioeconomics in the region.
- Preprint
(2149 KB) - Metadata XML
-
Supplement
(683 KB) - BibTeX
- EndNote
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-4988', Anonymous Referee #1, 24 Dec 2025
-
RC2: 'Comment on egusphere-2025-4988', Anonymous Referee #2, 12 Jan 2026
This study applies a 3D biophysical model to project the phytoplankton (diatom and non-diatom) and zooplankton biomass compared to the environmental variables in the South China Sea, to understand how the changing climate conditions will affect protist biomass in the region. The manuscript was well organized and clearly written. The rationale behind the study area, model selection seems good. I also appreciate the discussion from line 518 on the ecological limitations of this study. I think that the interpretation of the data needs to be slightly more conservative (see major comments).
Major comments:
The observed and model-stimulated phytoplankton carbon biomass for Sabah in Figure 3 is very different. I think this needs to be highlighted in the discussion for conservative interpretation of results.
Figure 7d: I would not consider the overall non-diatom biomass for Sabah to be decreasing at all. Can you provide the gradient for the trend lines to get a better idea of the trend? I think it is important to distinguish between the average biomass compared to the spatial differences in figure 8. Similarly, I think the discussion lines 462-463 needs to highlight this better.
Figure 10: I am not convinced of the interpretation of the PCA plot. As you mention in the figure caption, the arrows representing the variables that are orthogonal have no relationship, yet the diatom, non-diatom and mesozooplankton variables are orthogonal to the temperature and silicate arrows, especially for the SE Vietnam plot. Yet in lines 443-444, you mention that the diatom biomass have a positive correlation with silicate with a high r value, and a negative correlation to temperature with both variables. This is especially strange since PC1 and PC2 explain 89% of the variance. Can you please double-check this analysis?
Minor comments:
Figure 6: would be good to have the same colour range for the legend for both nitrate and silicate for easy comparison.
Line 251 wrong spelling for Sabah
Line 510: “diatoms have a much higher nutritional value compared to protists”. Please change, as diatoms are protists as well.
Citation: https://doi.org/10.5194/egusphere-2025-4988-RC2
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 178 | 141 | 24 | 343 | 36 | 21 | 20 |
- HTML: 178
- PDF: 141
- XML: 24
- Total: 343
- Supplement: 36
- BibTeX: 21
- EndNote: 20
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Review, «Projected effects of climate-induced changes in phytoplankton biomass in the southern South China Sea.”
In this study, the authors use a 3d coupled physical-geochemical model, specifically the SEAsia model, a configuration of the NEMO model coupled with an earth system model (together representing the ocean circulation component), coupled with the ERSEM biogeochemical model, and all modules were adjusted to the region in question. The study presents projections of phytoplankton biomass (separated as diatom and non-diatom biomass), and mesozooplankton biomass in the southern South China Sea, an area where fisheries are of great socio-economic importance. The study focuses on two selected areas characterized by strong seasonal upwelling and consequently high primary production.
General comments: It is an interesting study which addresses a very important topic, namely the effect of climate change on phytoplankton production and composition, and possible consequences for higher trophic levels. The justification for the chosen areas seems sound. However, the model skill assessment, which is a crucial part of such a study, seems a bit limited, and I think it would strengthen the study if the model skill assessment was done more extensively.
Specific comments:
If access to satellite data was a limiting factor during model skill assessment, I think it would be helpful to the reader if this is explained. Also, why did you only test one value for the carbon-to-Chl-a ratio? According to the Xu et al. 2020 paper, the C: Chl-a ratio in the South China Sea varied from <20 in eutrophic waters (i.e., high chl a per C content), to > 90 in oligotrophic waters (low chl a per C content). It seems that to use c. 67 for these upwelling regions (eutrophic) could inflate the estimated Carbon biomass from Chl a satellite data, c.f. L210 and figure 3 d and h. In line 153 it is supposed to be Carbon: Chl a, not the other way around.
According to the Global Ocean Color website, the variables which can be obtained from this dataset also include phytoplankton functional types (diatoms, dinoflagellates etc.) Is there a reason why you did not compare your model output of diatoms to estimated biomass of diatoms from ocean color?
Throughout the manuscript you should replace the word “observed” with “modeled” or “predicted” when you are describing modeling results, not physical observations. Similarly, in the discussion, you write that there “is a notable decline”, please rephrase so that it is clear that these are predictions.
I think you need to elaborate more in the discussion on the link between temperature, stratification and possible nutrient-depletion in the upper layers, and the corresponding predicted decline in diatom biomass. Since you put a lot of emphasis on the effect of increased stratification on nutrient (silicate) depletion and subsequent decline in diatom biomass, it would have been interesting to see modeled estimates of stratification /pycnocline depth. Is there a way to quantify stratification based on your modeling results? (for instance, calculating the pycnocline depth?)
Some parts of the discussion seem a bit unfocused, and you need to be more clear about which predictions you can actually make based on your modeling data, vs. more general assumptions.
Discussion: L479-L481: Does the oceanographic model take into account river runoff?
L499: You cannot actually conclude from your data that temperature in itself was a controlling factor.
L550-555: Very good that you are making these points, this shows how important your study is.
Technical comments:
Figure 8: where are panels c and d?