the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Oct 2024
Marine carbon dynamics in a coral reef ecosystem of Southern Taiwan
Abstract. The ocean is the planet’s largest carbon reservoir and plays a crucial role in regulating atmospheric CO2 levels, especially in the face of climate change. In coral reef ecosystems, understanding the carbonate system is critical for predicting and mitigating the impact of ocean acidification on these vulnerable marine ecosystems, especially as atmospheric CO2 concentrations continue to rise. This study measured pCO2 over space and time in Nanwan Bay, a coral reef ecosystem in southern Taiwan, to identify factors that influence its variation. The results showed that mean surface water pCO2 values varied seasonally, with values of 393.7 (±10.8), 406.3 (±16.1), 399.2 (±18.6), and 366.9 (±14.5) μatm in spring, summer, fall, and winter, respectively. These seasonal mean differences (ΔpCO2) relative to atmospheric pCO2 were 7.7 (±10.8), 29.3 (±16.1), 21.2 (±18.6), and -16.1 (±14.5) µatm, respectively. These findings suggest that the Nanwan Bay is a highly dynamic coral reef ecosystem, exhibiting both spatial and seasonal variability in carbon exchange. The carbonate system parameters of the surface water in this high-biodiversity, sub-tropical marine ecosystem was influenced not only by seasonal temperature variation but also by vertical mixing, intermittent upwelling, and biological effects.
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Status: open (until 26 Dec 2024)
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RC1: 'Comment on egusphere-2024-3273', Anonymous Referee #1, 20 Nov 2024
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Overall, this manuscript characterizes the seawater carbonate chemistry variability, CO2 flux dynamics, and exposure of a nearshore marine ecosystem in the southern tip of Taiwan. These observations provide a short and sweet narrative of the dynamics of the system. In general, the nearshore is not often adequately characterized in terms of seasonal ocean acidification, hypoxia, and climate change dynamics, so this article would be a contribution to the literature and the study would provide a useful dataset for validating numerical model estimates of ocean conditions in the geographic region. Ultimately, however, I was left underwhelmed by this paper and felt that claims were presented without any relevant data to support it.
Main Comments:
The abstract and introduction allude to the discussion of the influence of vertical mixing, intermittent upwelling, and biological effects on carbonate chemistry parameters, however, the results do not show any mechanisms/observations for how these drivers. For example, Chen et al., 2005 demonstrated an enhanced eddy induced upwelling signal during a spring, flood tide in late-February. However, data presented in this study was not displayed in such a way to convince me that any of the observed variability was due to upwelling.
Additionally, I am doubtful that the upwelling (especially eddy induced upwelling) drives vertical mixing of the entire water column. The same study (Chen et al., 2005) showed shoaling of lower temperature, higher nitrate and Chl a seawater to ~30 m depth in the same region. I would expect that a clear upwelling signal would be represented by enhanced water column stratification – with ocean warming at the surface and upwelling at depth.
In general, the color scheme of the figures is not easy to see, I would recommend the authors to use a different color scheme for the figures or at the very least change the blue outline to black.
The authors do a good job discussing the effect of wind speed on their calculations, but I do not believe that it is appropriate to use an average monthly wind speed for a single day of pCO2 sampling. You can only say for a specific date and time that this was the pCO2 and air sea flux.
Lastly, I am not convinced that the temporal sampling resolution (March, 2011; July, 2011; October, 2011; January 2023) is a good enough representation of the seasonal variability in such a dynamic environment. To improve the quality of the manuscript, I would recommend the authors to utilize any moorings, hindcast models, satellite products, or additional time series from the region to complement the dataset and provide a more robust correlation to the various mechanisms.
Minor Comments:
Why did the authors decide to use Wanninkhof (1992) for wind speed when Ho et al., (2006) is more appropriate for the region?
Table 1: Is all this information useful for the study or is there a better way to show this?
Figure 4: Where are the sites located and is it appropriate to interpolate across these sites given the course spatial resolution?
Figure 5: Was only referenced once; is it appropriately discussed within the manuscript or does the figure have little value.
Figure 6: Where did the data from Tew et al., 2014 come from?
Figure 7: A bit confusing, does this show that the nT effects increase pCO2 while the T effects decrease pCO2?
Lines 111-115: The two sentences in a row are redundant.
Line 175: Was pH converted to T scale or kept in NBS?
Line 243: “spring and winter [water masses] are intermediate between the two.” I do not see this.
Lines 275-277: I do not see the evidence.
Line 320: Use of Takahashi et al. 2002 should be discussed earlier during the methods
Line 365: Chl a does not drive photosynthesis; Chl a is a proxy for phytoplankton biomass.
Line 408: Is this greater than the error that was introduced by the calculation?
Lines 410-415 and 429-433: Text seems too much like a list. Can be presented in a better format.
Citation: https://doi.org/10.5194/egusphere-2024-3273-RC1
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