the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Aug 2025
Impact of waves on phytoplankton activity on the Northwest European Shelf: insights from observations and km-scale coupled models
Abstract. The spring bloom is an annual event in temperate regions of the North Atlantic Ocean in which the abundance of photosynthetic plankton increases dramatically. The timing and intensity of the spring bloom is dependent on underlying physical conditions that control ocean stratification and mixing. Although waves can be an important source of turbulent kinetic energy to the surface mixed layer, they have seldom been considered explicitly in studies of bloom formation. Here, we investigate the role of surface waves in bloom formation using a combination of satellite observations and numerical models. Satellite observations show a positive correlation between wave activity and chlorophyll concentration in the Northwest European shelf (May–September). In the deeper Northeast Atlantic, increased wave activity correlates with lower chlorophyll during periods of high phytoplankton activity (March–May) and higher chlorophyll when activity is low (below 54° N, July–September). We use a first-of-its-kind, km-scale, two-way coupled model system to investigate both the relationship between wave mixing and bloom formation, and the sensitivity of model results to the method by which wave mixing is parameterised. In deep regions, during the spring bloom, a wave mixing event is likely to mix surface chlorophyll to deeper layers, away from light. In contrast, when and where phytoplankton activity is low in deep regions, wave mixing can entrain nutrients, fueling the growth of nutrient starved phytoplankton near the surface. In June to October, in shallow but weakly stratified regions of the shelf, surface chlorophyll tends to be elevated following a wave mixing event, which can bring to the surface both phytoplankton from deeper layers and nutrients. When contrasted with ocean only runs, the two way-coupled ocean-wave model tends to produce greater vertical mixing and a delay in bloom onset. These results indicate bloom dynamics are sensitive to the way in which waves are modelled, and that the role of waves in bloom formation should be considered in future studies.
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Status: open (until 05 Nov 2025)
- RC1: 'Comment on egusphere-2025-3654', Anonymous Referee #1, 25 Aug 2025 reply
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RC2: 'Comment on egusphere-2025-3654', Anonymous Referee #2, 28 Oct 2025
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Comment on “Impact of waves on phytoplankton activity on the Northwest European Shelf: insights from observations and km-scale coupled models”
This study explores the impact of surface waves on primary production by combining satellite observations with numerical modeling. While the manuscript takes a valuable approach to investigating wave–ecosystem interactions, two major concerns should be addressed to reinforce the conclusions. First, the validation of the model results requires further attention. Specifically, whether the model has been tuned to align with observational data, thereby supporting the reliability of the key findings. Second, the mechanisms through which surface waves affect primary production are not fully clarified. A more detailed and cohesive explanation of the relevant physical and biological processes would improve the overall rigor and scientific contribution of the work.
Specific comments:
Line 45: Can the spring–neap tidal signal be identified in the results?
Line 75: Is this study consistent with previous findings? This point should be addressed in the Discussion section.
Line 130: The statement “the shelf break … is generally negative, except in August–September” is not entirely accurate, as the values are not statistically significant from May to July. Please consider revising for clarity and precision.
Line 130: Could the authors provide a dynamical explanation for the contrasting positive and negative correlations observed north and south of the off-shelf region? This would help clarify the underlying physical processes.
Figure 2: Using p < 0.1 as a significance threshold is more permissive than the conventional p < 0.05 standard. The authors may consider justifying this choice.
Figure 4: The blue color appears saturated in this figure, indicating that the bloom onset occurred earlier than April 11. I suggest adjusting the colorbar range accordingly.
Figure 5: Consider adding the storm track to the map to enhance visualization. The same suggestion applies to Figure 10.
Line 160: This paragraph attributes the differences before and after the storm to its impact; however, multiple processes could contribute to these changes and should be acknowledged in the text.
Line 260: It is unclear whether the observations were taken during the same time period as the simulation.
Line 275: The reported spatial correlation of –0.14% appears unreasonable for validation. Please verify this value and clarify how it was calculated.
Line 290: I am concerned about the delayed bloom in the simulations compared to the observations. The delay attributed to wave effects appears relatively small compared to the overall bias with observations. What could be the possible reasons for the delayed bloom in the model? Has any tuning been attempted to reduce the biases in temperature, chlorophyll concentration, or bloom timing?
Figure 8: For chlorophyll, it is recommended to include the satellite-derived values in panels (c) for comparison.
Line 310: The spatial patterns of bloom timing differ notably between the satellite data (Fig. 4) and the model results (Fig. 9). For instance, the observed later bloom onset south of England is not captured by the model. Additionally, the model shows an expansion of late bloom in the off-shelf region between 55–60°N, which is not evident in the satellite observations for the same area.
Line 325: A significant discrepancy in chlorophyll is evident between the satellite and model results on 2018-06-06. Moreover, the storm response differs between the two: in the simulations, chlorophyll decreases in regions with high concentrations and increases where concentrations are low. This inconsistency makes it challenging to identify a unified mechanism through which storms influence chlorophyll. The causes of these differing responses and their underlying mechanisms warrant further discussion.
Figures 8, 11, and 12: Consider adding dashed lines to indicate the timing of storm events.
Line 390: The phrase “Chlorophyll is only increased” should be revised to “Chlorophyll mainly increases”). Consider revising the sentence for clarity: “Off-shelf, increased nutrient concentrations near the surface may be offset by reduced light availability due to a deeper mixed layer and enhanced vertical mixing that transports chlorophyll out of the euphotic zone.” Also, please ensure consistent use of “off shelf” vs. “off-shelf” throughout the manuscript.
Line 400: If reduced light availability were the main factor delaying the bloom, one might expect elevated chlorophyll concentrations either near the surface or below the euphotic layer due to enhanced vertical mixing. However, the results show a consistent decrease in chlorophyll throughout the water column before June. Could this indicate that temperature plays a more dominant role in controlling the timing of the bloom?
Citation: https://doi.org/10.5194/egusphere-2025-3654-RC2
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Review of “Impact of waves on phytoplankton activity on the Northwest European Shelf: insights from observations and km-scale coupled models”
In this manuscript, the authors compared the simulated phytoplankton activity in the Northwest European Shelf between two high-resolution simulations with and without the effect of coupling with an ocean surface wave model. They also compared the model results with satellite observations. They report interesting differences in the response of phytoplankton activity to two-way ocean-wave coupling in different regions (e.g., off-shelf, shelf break, and on-shelf) and discussed the potential mechanisms. I find this manuscript quite interesting. The topic of impact of waves on phytoplankton activity is also important and hasn’t been sufficiently studied. So this study could potentially be a significant contribution to the literature. However, I feel that the description and discussion on the impact of waves in this manuscript are not sufficiently clear and at some places very confusing. I think this manuscript could benefit from a careful revision. Given the significant amount of revisions that are likely required (such as reorganization of some of the materials, additional literature review on relevant wave impact, more careful analyses and discussion on the results, etc.), I would recommend a major revision.
General comments
Specific comments
L69: How is this done implicitly?
L70: There seems to be a gap in the literature review linking a better representation of Stokes drift to enhanced mixing.
L77: It would be helpful to elaborate more on what kind of wave coupling is considered in this study.
L82-83: It would be helpful to be specific on what are the “implicit and explicit representation of wave mixing effects”.
L96: first “section 5” -> “section 4”?
L131-134: Such correlation could also be a result of the influence of wind, available sunlight etc that correlate with wave energy?
Section 2.3: Does the conclusion generalize to other years?
L147: Is there a starting point of an exponential growth?
L152: But how to deal with the different behaviors in the model and satellite data? Is there a way to identify occasions when the algorithm fails?
L166-167: How to attribute the changes in chlorophyll to wave activities? It is unclear from Fig. 5 whether wave activities have an impact on the changes of concentration of chlorophyll.
L182: How is a high resolution helpful to the processes discussed in this study?
L190: “absence” -> “presence”? — the introduction of wave effects in the model is confusing.
L192: Langmuir turbulence is known to enhance vertical mixing significantly more than wave-breaking (e.g., Belcher et al., 2012). Why not try something like the model by Harcourt (2013, 2015)?
L190-194: It would be helpful to discuss a bit on why these wave effects were considered and why these parameterizations were chosen. There are other parameterizations for both wave-induced mixing and wave-modulated momentum flux.
L219-220: It is very confusing to have wave-breaking effects in the ocean only system.
L211-220: This is very confusing given the introduction in L189-194. Not sure if I understand it correctly, but it seems that some of the wave effects are included in the ocean-only system? It would be helpful to reorganize the description of the ocean-only model and wave-ocean coupled model to clarify on what specifically do the authors mean by the wave effects in this study.
L230: Missing section number?
L232: Please define “AMM7”
L233: Is there a better option than extrapolation from nearest neighbor? Why there was a mismatch between the domain of these two configurations? How would such extrapolation in the initial condition affect the results?
L238: “constant value” of what?
L263: “stratififies” -> “stratifies”
L266-269: How would this warm bias affect the results reported here?
L274-276: These statements seem to conflict with numbers in Table 1. For example, spatial correlation for chlorophyll is low in Table 1.
L285: Consistent only on the seasonal time scale (which should be expected)? But there seems to be large differences in the variation of temperature between models and observations?
L287: Maybe labeling the marine heatwave events in the figure will be helpful.
L290-291: I don’t understand this sentence.
L297: There seems to be large variability of both simulated Chl-a and NPP when they started to increase in Feb-Mar. The difference between OCN and WAV in Fig. 8 does not seem to be significant to me. It might be helpful to label the bloom onset time in Fig. 8 to give readers a reference on what does it really mean by the onset delay shown in Fig 9. It also seems to me that this onset delay may strongly depend on the definition of the bloom onset. So it would be helpful to discuss how robust are the results in Fig. 9.
Fig. 8c: Missing Chl-a OBS data after April?
L325-326: Is there a way to distinguish the effect of a later bloom onset (WAV vs. OCN) and the effect of waves during a storm in these results?
L331: What do the authors mean by “while accounting for biases in the models relative to observations”?
L357: It might be helpful to first introduce these two figures before discussing the results shown in these figures.
L373-377: It would be helpful if the authors could elaborate more on why TKE is reduced when wave effects are included in this case. What are the key differences between locally generated wind waves and remotely generated swells in affecting the TKE in this region?
Fig 13 caption: “North Sea” -> “on-shelf” to be consistent with phrases used in other places?
L409-410: Do the authors mean that during these periods atmospheric momentum is used to grow waves rather than generating mixing in the ocean? Do those waves result in stronger mixing later?
L410-412: Please rephrase this sentence.
Section 6: I don’t see why it is necessary to separate the discussion in this section from the discussion of Figs 13-14 in the previous section.
L469-470: I don’t understand this statement. Waves cannot keep growing indefinitely. At some point they will deposit momentum to the ocean (perhaps at a different location)?
L473-474: I don’t think sufficient evidence is provided to support these conclusions — the differences between WAV and OCN are much smaller than the difference between models and satellite observations?