the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Jan 2022
Pronounced seasonal and spatial variability in determinants of phytoplankton biomass dynamics along a near–offshore gradient in the southern North Sea
Abstract. Marine phytoplankton biomass dynamics are affected by eutrophication, ocean warming, and ocean acidification. These changing abiotic conditions may impact phytoplankton biomass and its spatiotemporal dynamics. In this study, we used a nutrient–phytoplankton–zooplankton model to quantify the relative importance of bottom-up and top-down determinants on phytoplankton biomass dynamics in the Belgian Part of the North Sea. Using four years (2014–2017) of monthly observations at nine locations of nutrients, solar irradiance, sea surface temperature, chlorophyll-a and zooplankton biomass, we disentangled the monthly, seasonal and yearly variation in phytoplankton biomass dynamics. To quantify how the relative importance of determinants changed along a near–offshore gradient, the analysis was performed for three spatial regions, i.e. nearshore region (< 10 km to the coastline), midshore region (10–30 km), and offshore region (> 30 km). We found that from year 2014 to 2017, phytoplankton biomass dynamics ranged from 1.4 to 23.1 mg Chla m−3. Phytoplankton biomass dynamics follow a general seasonal cycle as in other temperate regional seas, with a distinct spring bloom (5.3–23.1 mg Chla m−3) and a modest autumn bloom (2.9–5.4 mg Chla m−3). This seasonal pattern was most expressed in the nearshore region. The relative contribution of factors determining phytoplankton biomass dynamics varied spatially and temporally. Throughout a calendar year, solar irradiance and zooplankton grazing were the most influential determinants in all regions, i.e. explained 38 %–65 % of the variation in the offshore region, 45 %–71 % in the midshore region, and 56 %–77 % in the nearshore region. In the near- and midshore regions, nutrients are most limiting the phytoplankton production in the month following the spring bloom (44 %–55 %). Nutrients are a determinant throughout the year in the offshore region (27 %–62 %). During winter, sea surface temperature is a determinant in all regions (15 %–17 %). The findings of this study contribute to a better mechanistic understanding of the spatiotemporal dynamics of phytoplankton biomass in the southern North Sea. The parameterized causal relationships allow estimating how the base of the southern North Sea food web will change under future climate change and/or blue economy activities that affect one or more determinants of the phytoplankton biomass dynamics.
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RC1: 'Comment on bg-2022-11', Anonymous Referee #1, 19 Apr 2022
Otero et al. analyzed the dynamics of a simple NPZD model to untangle the co-limitation in time and space of nutrients, light, temperature, and grazing on phytoplankton growth. They used observations of the southern North Sea to validate and constrain their modelling simulations. While I started reading their work with interest, unfortunately, I consider their modelling approach falls short to convince me for the reasons outlined below. I would have appreciated a thorough presentation of the model assumptions, their limitations, how they impact the author's interpretations and how much their analysis advances our understanding of the co-limitation of planktonic communities in coastal ecosystems.
Major points of concern:
The modelling approach has well-known limitations that go beyond what the authors briefly discussed. This aspect in addition to the lack of mathematical formalism and an adequate presentation of the equations and the processes that compose each state variable and what is the rationale to include them is particularly worrisome. Specifically, why their model application includes: A) a single phytoplankton group where their growth is determined by simple Monod kinetics, B) fixed Chl:C:N:P ratios, C) a single zooplankton grazer where their growth is also described by a Monod-like function, D) fixed sinusoidal surface irradiance which is used in a simple Lambert-Beer exponential decay function only applied at 3m depth to determine what they call PAR. The latter is used across their three areas spanning from near-shore to off-shore without any justification. Also, their model does not account even in the simplest terms for vertical mixing or any other type of transport. These are some of the issues I spot in the very superficial description of the model in the appendix. These are openly discussed issues in the marine ecosystem modelling community for the last three decades, see a (non-extensive) selection of the references presenting and discussing all of these various issues (Fasham et al., 1990; Anderson, 1993, 2005, 2010; Flynn, 2003, 2008; Gentleman et al., 2003; Mitra and Flynn, 2006; Hall, 2009; Anderson et al., 2010; Smith et al., 2011, 2014; Bonachela et al., 2016; Flynn and Mitra, 2016). I found these omissions, without justification, a lamentable modelling practice. This is worrisome given that the authors aim to describe the temporal and spatial variability of co-limitation of nutrients, light, temperature, and grazing on phytoplankton growth in a highly dynamic coastal ecosystem, and the variability of all of those factors play a role in the structure of planktonic communities (Cloern et al., 2014; Emeis et al., 2015).
The model to data comparison (or validation) needs to be better presented. If the authors want to provide a stronger case for better model performance. A simple plot of model prediction against observations will go a long way in that endeavour. Also, a comparison with other coastal or regional seas models needs to be discussed, and the literature for the North Sea is quite rich in that respect, e.g. ERSEM, MIRO, HAMSOM, BLOOM-Delf3D and MAECS.
In addition to the issues stated above, it is not clear how co-limitation is untangled in the simple NPZD model, in what they call “relative contribution”. Particularly, how this method compares with other more established approaches, for example, based on Leblig’s law (Klausmeier et al., 2004), dynamic energy budget (Bruggeman and Kooijman, 2007), or acclimation (Wirtz and Kerimoglu, 2016), and how much their observations advance our understanding of resource co-limitation in planktonic communities? These aspects remain unanswered and present a more interesting research venue than just predicting Chla and overinflating the implications of a model with still many reservations.
References
Anderson, T. R. (1993). A spectrally averaged model of light penetration and photosynthesis. Limnol. Oceanogr. 38, 1403–1419.
Anderson, T. R. (2005). Plankton functional type modelling: running before we can walk? J. Plankton Res. 27. doi:10.1093/plankt/fbi076.
Anderson, T. R. (2010). Progress in marine ecosystem modelling and the “unreasonable effectiveness of mathematics”. J. Mar. Syst. 81, 4–11. doi:10.1016/j.jmarsys.2009.12.015.
Anderson, T. R., Gentleman, W. C., and Sinha, B. (2010). Influence of grazing formulations on the emergent properties of a complex ecosystem model in a global ocean general circulation model. Prog. Oceanogr. 87, 201–213. doi:10.1016/j.pocean.2010.06.003.
Bonachela, J. A., Klausmeier, C. A., Edwards, K. F., Litchman, E., and Levin, S. A. (2016). The role of phytoplankton diversity in the emergent oceanic stoichiometry. J. Plankton Res. 38, 1021–1035. doi:10.1093/plankt/fbv087.
Bruggeman, J., and Kooijman, S. A. L. M. (2007). A biodiversity-inspired approach to aquatic ecosystem modeling. Limnol. Oceanogr. 52, 1533–1544. doi:10.4319/lo.2007.52.4.1533.
Cloern, J. E., Foster, S. Q., and Kleckner, A. E. (2014). Phytoplankton primary production in the world’s estuarine-coastal ecosystems. Biogeosciences 11, 2477–2501. doi:10.5194/bg-11-2477-2014.
Emeis, K. C., van Beusekom, J., Callies, U., Ebinghaus, R., Kannen, A., Kraus, G., et al. (2015). The North Sea - A shelf sea in the Anthropocene. J. Mar. Syst. 141, 18–33. doi:10.1016/j.jmarsys.2014.03.012.
Fasham, M. J. R., Ducklow, H. W., and Mckelvie, S. M. (1990). A nitrogen-based model of plankton dynamics in the oceanic mixed layer. J. Mar. Res. 48, 591–639.
Flynn, K. J. (2003). Modelling multi-nutrient interactions in phytoplankton; balancing simplicity and realism. Prog. Oceanogr. 56, 249–279. doi:10.1016/S0079-6611(03)00006-5.
Flynn, K. J. (2008). The importance of the form of the quota curve and control of non-limiting nutrient transport in phytoplankton models. J. Plankton Res. 30, 423–438. doi:10.1093/plankt/fbn007.
Flynn, K. J., and Mitra, A. (2016). Why Plankton Modelers Should Reconsider Using Rectangular Hyperbolic (Michaelis-Menten, Monod) Descriptions of Predator-Prey Interactions. Front. Mar. Sci. 3. doi:10.3389/fmars.2016.00165.
Gentleman, W., Leising, A., Frost, B., Strom, S., and Murray, J. (2003). Functional responses for zooplankton feeding on multiple resources: a review of assumptions and biological dynamics. Deep Sea Res. Part II Top. Stud. Oceanogr. 50, 2847–2875. doi:10.1016/j.dsr2.2003.07.001.
Hall, S. R. (2009). Stoichiometrically explicit food webs: Feedbacks between resource supply, elemental constraints, and species diversity. Annu. Rev. Ecol. Evol. Syst. 40, 503–528. doi:10.1146/annurev.ecolsys.39.110707.173518.
Klausmeier, C., Litchman, E., Daufresne, T., and Levin, S. (2004). Optimal nitrogen-to-phosphorus stoichiometry of phytoplankton. Nature 429, 171–174. doi:1.1029/2001GL014649.
Mitra, A., and Flynn, K. J. (2006). Accounting for variation in prey selectivity by zooplankton. Ecol. Modell. 199, 82–92. doi:10.1016/j.ecolmodel.2006.06.013.
Smith, S. L., Merico, A., Wirtz, K. W., and Pahlow, M. (2014). Leaving misleading legacies behind in plankton ecosystem modelling. J. Plankton Res. 36, 613–620. doi:10.1093/plankt/fbu011.
Smith, S. L., Pahlow, M., Merico, A., and Wirtz, K. W. (2011). Optimality-based modeling of planktonic organisms. Limnol. Oceanogr. 56, 2080–2094. doi:10.4319/lo.2011.56.6.2080.
Wirtz, K. W., and Kerimoglu, O. (2016). Autotrophic stoichiometry emerging from optimality and variable co-limitation. Front. Ecol. Evol. 4. doi:10.3389/fevo.2016.00131.
Citation: https://doi.org/10.5194/bg-2022-11-RC1 -
AC1: 'Reply on RC1 and RC2', Steven Pint, 08 Jul 2022
Dear,
We appreciated and welcomed the comments from the referees and with the response letter attached to this message, we want to share with the editor our intentions on how we will tackle each of the comments. Based on our replies and intentions we hope to be allowed to go to the second phase of the review process, being the submission of a revised manuscript.
Kind regards,
Steven et al.
-
EC1: 'Reply on AC1', Gert Van Hoey, 18 Aug 2022
The intention to revise the manuscript based on the majority of the suggested changes by the reviewers is OK. The revised version can be submitted, so a final discisson on publication can be made. Can currently not be done, as only feedback on the reviewers comments is added.
Citation: https://doi.org/10.5194/bg-2022-11-EC1 -
EC3: 'Reply on AC1', Gert Van Hoey, 18 Aug 2022
Sorry, for my very late reply, but I was on Holiday for 1.5 Month. I hope te process can now proceed further.
Citation: https://doi.org/10.5194/bg-2022-11-EC3
-
EC1: 'Reply on AC1', Gert Van Hoey, 18 Aug 2022
-
AC1: 'Reply on RC1 and RC2', Steven Pint, 08 Jul 2022
-
RC2: 'Comment on bg-2022-11', Anonymous Referee #2, 21 May 2022
The authors of this manuscript have attempted to calibrate and validate their NPZD model with open-access time series data of temperature, nutrients and plankton. The researchers then utilized the model to determine the factors that drive the phytoplankton abundance in the North Sea. Following are the observations and recommendations upon review.
The GAM time-trends show low R2 values for nutrients, while SST is fairly simulated (Table E1). Model validation with observations (Fig 4/F1) show good agreement for offshore region but progressive overestimation closer to the shore, a common caveat in the numerical models. However, the model simulate lag between primary and secondary productivity which is more pronounced near to the shore and also peaks at higher magnitudes (Fig 5 & F2). The model shows nutrients as less limiting factor nearshore (reverse for PAR) for the phytoplankton biomass and the same increases with distance to the shore which is in the agreement with general understanding of the BGC (Appendix G). Inclusion of Appendix H is helpful in depicting seasonal nature of the diatom blooms in the region.
As another referee has covered many important points, I avoid repeat of the same and would rather add to the “discussion” that this journal offers. The North Sea waters are one of the highly contested regions in recent times, and studies on this region could address one or more larger objectives either in terms of robust validation of the model, or addressing climate and/or maritime usage applications, or both (see references). It seems that the authors are well aware of the applications since these are discussed, albeit very briefly.
While divergence of simulation from the observations is common occurrence for the numerical models, the authors could include additional validation such as remote sensing or in-situ observations. Similarly, there is an opportunity to the authors to add a section to depict implications of their findings without listing it as future scope. These implications (relevant to the regional needs) could be in terms of fishery, HABs or maritime use conflict/hazards e.g. issue of invasive species. While these questions are important to address, it would be great value-addition to have it incorporated in the revision of this very manuscript, rather than listing out for the future scope.
Finally, it is appreciable that the authors have presented in commendable English despite not being native speakers.
References:
Chakraborty, K., Nimit, K., Akhand, A., Prakash, S., Paul, A., Ghosh, J., Bhaskar, T.U. and Chanda, A., 2018. Modeling the enhancement of sea surface chlorophyll concentration during the cyclonic events in the Arabian Sea. Journal of Sea Research, 140, pp.22-31.
Chakraborty, K., Kumar, N., Girishkumar, M.S., Gupta, G.V.M., Ghosh, J., Udaya Bhaskar, T.V.S. and Thangaprakash, V.P., 2019. Assessment of the impact of spatial resolution on ROMS simulated upper-ocean biogeochemistry of the Arabian Sea from an operational perspective. Journal of Operational Oceanography, 12(2), pp.116-142.
Citation: https://doi.org/10.5194/bg-2022-11-RC2 -
AC1: 'Reply on RC1 and RC2', Steven Pint, 08 Jul 2022
Dear,
We appreciated and welcomed the comments from the referees and with the response letter attached to this message, we want to share with the editor our intentions on how we will tackle each of the comments. Based on our replies and intentions we hope to be allowed to go to the second phase of the review process, being the submission of a revised manuscript.
Kind regards,
Steven et al.
-
EC2: 'Reply on AC1', Gert Van Hoey, 18 Aug 2022
The same, the revised manuscript can be submitted. I agree that the wishes of the second reviewer were ambitios, so cannot be tackled completely. Therefore, ensure that the goals and intentions of the paper are clear and that further possible steps are limitations are clearly added in the text.
Citation: https://doi.org/10.5194/bg-2022-11-EC2
-
EC2: 'Reply on AC1', Gert Van Hoey, 18 Aug 2022
-
AC1: 'Reply on RC1 and RC2', Steven Pint, 08 Jul 2022
Status: closed
-
RC1: 'Comment on bg-2022-11', Anonymous Referee #1, 19 Apr 2022
Otero et al. analyzed the dynamics of a simple NPZD model to untangle the co-limitation in time and space of nutrients, light, temperature, and grazing on phytoplankton growth. They used observations of the southern North Sea to validate and constrain their modelling simulations. While I started reading their work with interest, unfortunately, I consider their modelling approach falls short to convince me for the reasons outlined below. I would have appreciated a thorough presentation of the model assumptions, their limitations, how they impact the author's interpretations and how much their analysis advances our understanding of the co-limitation of planktonic communities in coastal ecosystems.
Major points of concern:
The modelling approach has well-known limitations that go beyond what the authors briefly discussed. This aspect in addition to the lack of mathematical formalism and an adequate presentation of the equations and the processes that compose each state variable and what is the rationale to include them is particularly worrisome. Specifically, why their model application includes: A) a single phytoplankton group where their growth is determined by simple Monod kinetics, B) fixed Chl:C:N:P ratios, C) a single zooplankton grazer where their growth is also described by a Monod-like function, D) fixed sinusoidal surface irradiance which is used in a simple Lambert-Beer exponential decay function only applied at 3m depth to determine what they call PAR. The latter is used across their three areas spanning from near-shore to off-shore without any justification. Also, their model does not account even in the simplest terms for vertical mixing or any other type of transport. These are some of the issues I spot in the very superficial description of the model in the appendix. These are openly discussed issues in the marine ecosystem modelling community for the last three decades, see a (non-extensive) selection of the references presenting and discussing all of these various issues (Fasham et al., 1990; Anderson, 1993, 2005, 2010; Flynn, 2003, 2008; Gentleman et al., 2003; Mitra and Flynn, 2006; Hall, 2009; Anderson et al., 2010; Smith et al., 2011, 2014; Bonachela et al., 2016; Flynn and Mitra, 2016). I found these omissions, without justification, a lamentable modelling practice. This is worrisome given that the authors aim to describe the temporal and spatial variability of co-limitation of nutrients, light, temperature, and grazing on phytoplankton growth in a highly dynamic coastal ecosystem, and the variability of all of those factors play a role in the structure of planktonic communities (Cloern et al., 2014; Emeis et al., 2015).
The model to data comparison (or validation) needs to be better presented. If the authors want to provide a stronger case for better model performance. A simple plot of model prediction against observations will go a long way in that endeavour. Also, a comparison with other coastal or regional seas models needs to be discussed, and the literature for the North Sea is quite rich in that respect, e.g. ERSEM, MIRO, HAMSOM, BLOOM-Delf3D and MAECS.
In addition to the issues stated above, it is not clear how co-limitation is untangled in the simple NPZD model, in what they call “relative contribution”. Particularly, how this method compares with other more established approaches, for example, based on Leblig’s law (Klausmeier et al., 2004), dynamic energy budget (Bruggeman and Kooijman, 2007), or acclimation (Wirtz and Kerimoglu, 2016), and how much their observations advance our understanding of resource co-limitation in planktonic communities? These aspects remain unanswered and present a more interesting research venue than just predicting Chla and overinflating the implications of a model with still many reservations.
References
Anderson, T. R. (1993). A spectrally averaged model of light penetration and photosynthesis. Limnol. Oceanogr. 38, 1403–1419.
Anderson, T. R. (2005). Plankton functional type modelling: running before we can walk? J. Plankton Res. 27. doi:10.1093/plankt/fbi076.
Anderson, T. R. (2010). Progress in marine ecosystem modelling and the “unreasonable effectiveness of mathematics”. J. Mar. Syst. 81, 4–11. doi:10.1016/j.jmarsys.2009.12.015.
Anderson, T. R., Gentleman, W. C., and Sinha, B. (2010). Influence of grazing formulations on the emergent properties of a complex ecosystem model in a global ocean general circulation model. Prog. Oceanogr. 87, 201–213. doi:10.1016/j.pocean.2010.06.003.
Bonachela, J. A., Klausmeier, C. A., Edwards, K. F., Litchman, E., and Levin, S. A. (2016). The role of phytoplankton diversity in the emergent oceanic stoichiometry. J. Plankton Res. 38, 1021–1035. doi:10.1093/plankt/fbv087.
Bruggeman, J., and Kooijman, S. A. L. M. (2007). A biodiversity-inspired approach to aquatic ecosystem modeling. Limnol. Oceanogr. 52, 1533–1544. doi:10.4319/lo.2007.52.4.1533.
Cloern, J. E., Foster, S. Q., and Kleckner, A. E. (2014). Phytoplankton primary production in the world’s estuarine-coastal ecosystems. Biogeosciences 11, 2477–2501. doi:10.5194/bg-11-2477-2014.
Emeis, K. C., van Beusekom, J., Callies, U., Ebinghaus, R., Kannen, A., Kraus, G., et al. (2015). The North Sea - A shelf sea in the Anthropocene. J. Mar. Syst. 141, 18–33. doi:10.1016/j.jmarsys.2014.03.012.
Fasham, M. J. R., Ducklow, H. W., and Mckelvie, S. M. (1990). A nitrogen-based model of plankton dynamics in the oceanic mixed layer. J. Mar. Res. 48, 591–639.
Flynn, K. J. (2003). Modelling multi-nutrient interactions in phytoplankton; balancing simplicity and realism. Prog. Oceanogr. 56, 249–279. doi:10.1016/S0079-6611(03)00006-5.
Flynn, K. J. (2008). The importance of the form of the quota curve and control of non-limiting nutrient transport in phytoplankton models. J. Plankton Res. 30, 423–438. doi:10.1093/plankt/fbn007.
Flynn, K. J., and Mitra, A. (2016). Why Plankton Modelers Should Reconsider Using Rectangular Hyperbolic (Michaelis-Menten, Monod) Descriptions of Predator-Prey Interactions. Front. Mar. Sci. 3. doi:10.3389/fmars.2016.00165.
Gentleman, W., Leising, A., Frost, B., Strom, S., and Murray, J. (2003). Functional responses for zooplankton feeding on multiple resources: a review of assumptions and biological dynamics. Deep Sea Res. Part II Top. Stud. Oceanogr. 50, 2847–2875. doi:10.1016/j.dsr2.2003.07.001.
Hall, S. R. (2009). Stoichiometrically explicit food webs: Feedbacks between resource supply, elemental constraints, and species diversity. Annu. Rev. Ecol. Evol. Syst. 40, 503–528. doi:10.1146/annurev.ecolsys.39.110707.173518.
Klausmeier, C., Litchman, E., Daufresne, T., and Levin, S. (2004). Optimal nitrogen-to-phosphorus stoichiometry of phytoplankton. Nature 429, 171–174. doi:1.1029/2001GL014649.
Mitra, A., and Flynn, K. J. (2006). Accounting for variation in prey selectivity by zooplankton. Ecol. Modell. 199, 82–92. doi:10.1016/j.ecolmodel.2006.06.013.
Smith, S. L., Merico, A., Wirtz, K. W., and Pahlow, M. (2014). Leaving misleading legacies behind in plankton ecosystem modelling. J. Plankton Res. 36, 613–620. doi:10.1093/plankt/fbu011.
Smith, S. L., Pahlow, M., Merico, A., and Wirtz, K. W. (2011). Optimality-based modeling of planktonic organisms. Limnol. Oceanogr. 56, 2080–2094. doi:10.4319/lo.2011.56.6.2080.
Wirtz, K. W., and Kerimoglu, O. (2016). Autotrophic stoichiometry emerging from optimality and variable co-limitation. Front. Ecol. Evol. 4. doi:10.3389/fevo.2016.00131.
Citation: https://doi.org/10.5194/bg-2022-11-RC1 -
AC1: 'Reply on RC1 and RC2', Steven Pint, 08 Jul 2022
Dear,
We appreciated and welcomed the comments from the referees and with the response letter attached to this message, we want to share with the editor our intentions on how we will tackle each of the comments. Based on our replies and intentions we hope to be allowed to go to the second phase of the review process, being the submission of a revised manuscript.
Kind regards,
Steven et al.
-
EC1: 'Reply on AC1', Gert Van Hoey, 18 Aug 2022
The intention to revise the manuscript based on the majority of the suggested changes by the reviewers is OK. The revised version can be submitted, so a final discisson on publication can be made. Can currently not be done, as only feedback on the reviewers comments is added.
Citation: https://doi.org/10.5194/bg-2022-11-EC1 -
EC3: 'Reply on AC1', Gert Van Hoey, 18 Aug 2022
Sorry, for my very late reply, but I was on Holiday for 1.5 Month. I hope te process can now proceed further.
Citation: https://doi.org/10.5194/bg-2022-11-EC3
-
EC1: 'Reply on AC1', Gert Van Hoey, 18 Aug 2022
-
AC1: 'Reply on RC1 and RC2', Steven Pint, 08 Jul 2022
-
RC2: 'Comment on bg-2022-11', Anonymous Referee #2, 21 May 2022
The authors of this manuscript have attempted to calibrate and validate their NPZD model with open-access time series data of temperature, nutrients and plankton. The researchers then utilized the model to determine the factors that drive the phytoplankton abundance in the North Sea. Following are the observations and recommendations upon review.
The GAM time-trends show low R2 values for nutrients, while SST is fairly simulated (Table E1). Model validation with observations (Fig 4/F1) show good agreement for offshore region but progressive overestimation closer to the shore, a common caveat in the numerical models. However, the model simulate lag between primary and secondary productivity which is more pronounced near to the shore and also peaks at higher magnitudes (Fig 5 & F2). The model shows nutrients as less limiting factor nearshore (reverse for PAR) for the phytoplankton biomass and the same increases with distance to the shore which is in the agreement with general understanding of the BGC (Appendix G). Inclusion of Appendix H is helpful in depicting seasonal nature of the diatom blooms in the region.
As another referee has covered many important points, I avoid repeat of the same and would rather add to the “discussion” that this journal offers. The North Sea waters are one of the highly contested regions in recent times, and studies on this region could address one or more larger objectives either in terms of robust validation of the model, or addressing climate and/or maritime usage applications, or both (see references). It seems that the authors are well aware of the applications since these are discussed, albeit very briefly.
While divergence of simulation from the observations is common occurrence for the numerical models, the authors could include additional validation such as remote sensing or in-situ observations. Similarly, there is an opportunity to the authors to add a section to depict implications of their findings without listing it as future scope. These implications (relevant to the regional needs) could be in terms of fishery, HABs or maritime use conflict/hazards e.g. issue of invasive species. While these questions are important to address, it would be great value-addition to have it incorporated in the revision of this very manuscript, rather than listing out for the future scope.
Finally, it is appreciable that the authors have presented in commendable English despite not being native speakers.
References:
Chakraborty, K., Nimit, K., Akhand, A., Prakash, S., Paul, A., Ghosh, J., Bhaskar, T.U. and Chanda, A., 2018. Modeling the enhancement of sea surface chlorophyll concentration during the cyclonic events in the Arabian Sea. Journal of Sea Research, 140, pp.22-31.
Chakraborty, K., Kumar, N., Girishkumar, M.S., Gupta, G.V.M., Ghosh, J., Udaya Bhaskar, T.V.S. and Thangaprakash, V.P., 2019. Assessment of the impact of spatial resolution on ROMS simulated upper-ocean biogeochemistry of the Arabian Sea from an operational perspective. Journal of Operational Oceanography, 12(2), pp.116-142.
Citation: https://doi.org/10.5194/bg-2022-11-RC2 -
AC1: 'Reply on RC1 and RC2', Steven Pint, 08 Jul 2022
Dear,
We appreciated and welcomed the comments from the referees and with the response letter attached to this message, we want to share with the editor our intentions on how we will tackle each of the comments. Based on our replies and intentions we hope to be allowed to go to the second phase of the review process, being the submission of a revised manuscript.
Kind regards,
Steven et al.
-
EC2: 'Reply on AC1', Gert Van Hoey, 18 Aug 2022
The same, the revised manuscript can be submitted. I agree that the wishes of the second reviewer were ambitios, so cannot be tackled completely. Therefore, ensure that the goals and intentions of the paper are clear and that further possible steps are limitations are clearly added in the text.
Citation: https://doi.org/10.5194/bg-2022-11-EC2
-
EC2: 'Reply on AC1', Gert Van Hoey, 18 Aug 2022
-
AC1: 'Reply on RC1 and RC2', Steven Pint, 08 Jul 2022
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