the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Jan 2024
The oxygen deficiency index blueprint allows an economic and quick scan via baseline assessment for forecasting the risk of seasonal oxygen deficiency in the North and Baltic Seas
Abstract. Oxygen deficiency zones (ODZs) in coastal seas can become hazardous to organisms and may have severe ecological and economic consequences for the environment, the fisheries, and the tourism industries. A tight interaction between ventilation and respiration governs marine oxygen levels. Regions with high primary production and a thin water column below the seasonal mixed layer are particularly prone to the formation of oxygen deficiency. In the study of Große et al. (2016) the critical parameters of the oxygen deficiency index (ODI) were identified as stratification and primary production during the formation of oxygen deficiency in the seasonally stratified regions of the North Sea. In order to approach realistic spatio-temporal distributions of ODZs, Große et al. (2016) formulated a depth index serving as a proxy for the thickness of the water column below the mixed layer depth (MLD). Here we propose the further developed ODI to represent two differing hydrographic regimes, the North and the Baltic Seas, by using a density-based criterion of the MLD and the vertical extension of the water column between the seafloor and the bottom layer of the MLD. Moreover, we define the stratification status of the water column using continuous stratification periods of 30 days as our reference period for higher risks of developing ODZs. Different to Große et al. (2016), net primary production is not cumulated over the entire growing season but only over this reference period. With these modifications, the revised ODI offers intuitive, short-term forecasts on the areas at risk of developing oxygen deficiency in high spatio-temporal resolution for the coastal zone of the North and Baltic Seas. This allows an operational forecasting of ODZs to inform responsible authorities and civil services in advance. We propose an economic solution to assess oxygen conditions of the past, the present and test for the risk to developing ODZs in the near future. We are able to run all necessary simulations and calculations for this research on a simple laptop. We mostly used free and open software products and Open Data products. Our data set up consists of: a) Free available netCDF output files of the operational HBM-ERGOM model and b) free available data from the MARNET monitoring network, both operated by the Federal Maritime and Hydrographic Agency (BSH).
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RC1: 'Comment on egusphere-2023-3092', Anonymous Referee #1, 27 Feb 2024
Overall Statements:
The manuscript "The oxygen deficiency index blueprint allows an economic and quick scan via baseline assessment for forecasting the risk of seasonal oxygen deficiency in the North and Baltic Sea" by Alexandra Marki, Xin Li and Simon Jandt-Scheelke presents a new index that uses the results of a 3D biogeochemical model to describe and predict oxygen deficiency in the North and Baltic Seas.
Such a tool is of course particularly valuable because oxygen deficiency in the ocean can have significant consequences for biological and biogeochemical processes.
However, the manuscript still has so many weaknesses that I couldn't understand everything and cancelled on page 17.
The most serious problem is that the underlying model is not able to reproduce the oxygen dynamics at the stations shown. I would suggest training the oxygen index not on the model but on the measurement data.
In many places it is clear that the authors designed the new index more for the Baltic Sea, because the oxygen problems there are more drastic than in the North Sea. Fundamental mechanisms of the North Sea are not properly represented.
The manuscript is still in a very poor state at the moment: some of the formulae are incorrect, some of the sentences are confusing and most of the references refer to grey literature or have major shortcomings in the formal area.
Detailed remarks:
L33: Hansson and Viktorsson report on the Baltic Sea only.
L34-36: Here I expect background information and motivation for the contents of the manuscript and not an advert for the new index.
L54: Cite correctly: (Carstensen and Conley, 2019). Please check all similar citations.
L65-67: Stratification in the North Sea is mainly driven by local heating. Deeper denisity variations can be caused by cold water from the North Atlantic.
L68: Which two-ocean currents do you mean? The water from the north-western border of the North Sea does not meet the water of the English Channel.
L71: Which shifts?
L75: “sink” is misleading. The salty water flows near the ground.
L82: Why is this comparison a solution?
L90-94: This section fits better in the methods section.
L95-101: This section fits better in an outlook section.
L90-101: The structure of the manuscript can be explained at this point.
L109-111: Why do you use a second approach? Motivate!
L124: I do not understand this formula: Which indices are used for totalling? What does the comma in the centre mean?
L148: How is the bathymetry modelled?
L154: “toa”?
L155-156: This duplication of lines makes the entire section incomprehensible.
L162: This formula is wrong: Istrat cannot have one index and later two.
L233: This pre-calibration should be shown in detail. Fig. 5 and Fig. 6 show that the oxygen dynamics are not well represented by the model.
Fig. 2 a-c show no good (negative) correlations between simulated oxygen saturations and simulated indices. How significant are the correlations?
Fig. 2e: The grey scale is not appropriate here.
Fig. 3: The diagrams for MLD and Istrat do not have a suitable colour scale. Why do you sometimes use colours and sometimes B/W scales?
L267: Explain pre-calibration and calibration.
L268: What does “near real-time” mean?
L276: Motivate and explain the use of time-lags.
L561: Brüning et al 2014. Where is this paper published?
L562: Where is this paper published?
L593: Where is this paper published?
L607: Where is this paper published?
L614: Where is this paper published?
Citation: https://doi.org/10.5194/egusphere-2023-3092-RC1 -
AC3: 'Reply on RC1', Alexandra Marki, 13 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We thoroughly started to revising the manuscript according to the comments of all three referees. Each of you had very similar major concerns. Thus, we will briefly elaborate these common concerns and later discuss them once more in depth, in the course of the final author comments.
We agree that the manuscript in its current form needs to be taken special care in providing more background information and clarification of our motivation and aim of our study. We will address this in more detail during the revision and will particularly focus on following our ‘red line’ throughout the manuscript. Furthermore, we will eliminate grey literature and whenever possible substitute it with peer reviewed references.
To improve our graphics, we will switch back from B/W color scale to color vision deficiency (CVD) friendly palettes in order to better identify the patterns of a plot. Also, we will rearrange bin-sizes and bin-groups of the color scales to obtain more shading effects. We also consider to show seasonal or annual means for the ODI and each of the single indexes.
We aimed to develop this ODI toolbox as a blueprint, which allows for forecasting oxygen deficiency zones (ODZs) at an early stage, without being an expert in modelling. The ODI helps to forecast the risk of developing oxygen deficiency zones (ODZs), in order to inform authorities, entities, NGOs, the tourism industry, fisheries in advance. Moreover, the operational model used in this study is only able to forecast maximum seven days of oxygen. We aimed for an intuitive and easily applicable and adaptable approach that allows a longer timespan to forecasting ODZs. Therefore, the ODI helps to efficiently planning research cruises to capture and hunt possible low oxygen areas that are monitored each summer season.
In this study, we never aimed to evaluate the evolution and existence of oxygen deficiency zones, as well as bottom oxygen saturation/-concentration with the underlaying model of this study. Neither did we aim to improving the oxygen and BGC dynamics of this model. This was and is far beyond our studies. There has been no pre-calibration, nor calibration of the model parameters to calculate the ODI.We also see, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore.
We simply use free available outputs, such as salinity, temperature, and NPP from the operational model system at the BSH to calculate our ODI.
Also, we did not aim to improving the ODI of Große et al. 2016, which was developed only for the North Sea. We modified the ODI, because we consider both regimes, the North and the Baltic Seas - two different regimes when considering the main drivers of thermal and haline stratification, respectively.
Moreover, the ODI after Große et al. 2016 was never intended to predict ODZs. The original ODI was developed to estimate Oxygen developments via hind- and now-casts. In this study, the calculated ODI is used for the first time in a forecast model system, the ODI toolbox.
Since the ODI here serves as a blueprint we would like to encourage the scientific community to further calibrate the ODI. Our aim was to demonstrate that with very few inputs and effort, we can develop a forecasting tool that is independent of model types and -versions and the data.
Unfortunatly, none of the monitoring stations in proximity to the German coast could provide all parameters in question over a sufficient period of time and in sufficient quality. Therefore, we by-passed this problem by using model outputs for initial calibration of our ODI in this manuscript. However, as long as we can get all parameters in question, including depth levels, and make these datasets ‘python-edible’ - we can use any dataset of any station, mooring, etc, to calculate the ODI – which we see is one strength of our approach.### our take-home message: “One does not always have to reinvent the wheel – the wheel might be able to do much more than originally intended for.
Use what you have - combine already existing methods, technologies and products, pair it with free available data – keep it simple, share your knowledge and keep it free.”#####Citation: https://doi.org/10.5194/egusphere-2023-3092-AC3 -
AC4: 'Reply on RC1', Alexandra Marki, 14 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We started to revising the manuscript according to the comments of all three referees. Your valuable coments and constructive feedback will help us to improve our manuscript. which we will address whenever possible during the revision. Furthermore, we will particularly focus on following our ‘red line’ throughout the manuscript. Also, we will eliminate grey literature and whenever possible substitute it with peer reviewed references.
For a brief description of the aims and motivation of our study please refer to our AC3.
https://doi.org/10.5194/egusphere-2023-3092-AC3
We do agree that the mechanisms of the North Sea are not properly represented. We see the lack of our manuscript here, since it was not our intention to focus only on the Baltic Sea, we also aimed to raise awareness to the seasonal oxygen deficiency zones in the North-Seas. We will work on the reformulation and add more information on mechanisms of the North-Sea in our revised manuscript.
Although we agree that training the ODI on observational data is an important consideration, this approach could not be analysed in this manuscript because of the scarcity of (qualified) data. Since only a few of the monitoring stations can provide all parameters in question over a sufficient period of time and sufficient quality, we by-passed this problem by using model outputs for initial calibration of our ODI in this manuscript. However, as long as we can get all parameters in question, including depth levels, and make the datasets ‘python-edible’ - we can use any dataset of any station, mooring, etc, to calculate the ODI – which we see is one strength of our approach.
- Please find the revised formulae in the attached PDF.
Detailed remarks:
L33: Hansson and Viktorsson report on the Baltic Sea only.
We agree and added missing references - which reads now:
“… Low levels of oxygen are documented for an increasing number of areas including the North and Baltic Seas (Greenwood et al., 2010; Mahaffey et al., 2023; Williams et al., 2023; Hansson and Viktorsson, 2023). …”
L34-36: Here I expect background information and motivation for the contents of the manuscript and not an advert for the new index.Thank you very much for pointing this out, we do agree and will reformulate the paragraph.
L54: Cite correctly: (Carstensen and Conley, 2019). Please check all similar citations.Thanks for pointing out. We have corrected this throughout the manuscript.
L65-67: Stratification in the North Sea is mainly driven by local heating. Deeper denisity variations can be caused by cold water from the North Atlantic.The Referee is right. We will reformulate this accordingly.
L68: Which two-ocean currents do you mean? The water from the north-western border of the North Sea does not meet the water of the English Channel.We agree, that formulation was not appropriate. In its current form reads as:
‘… However, high salinity waters might flow inshore, whilst low salinity waters might flow offshore, generating also counter directed currents and influence hydrodynamics, when meet each other. …’
L71: Which shifts?
Sorry, improper wording. It now reads as:
‘… Those gradients in salinity are said to be the main reason there for stratification and prevent ventilation of the deep waters. …’
L75: “sink” is misleading. The salty water flows near the ground.Thank you for pointing this our. We reformulated it:
‘… oxygen-rich waters may be down welled and (re-)supply O2 to the oxygen-depleted bottom waters …’
L82: Why is this comparison a solution?We will discuss this in the final ACs.
L90-94: This section fits better in the methods section.
We agree and will shift this part into the methods section.
L95-101: This section fits better in an outlook section.We agree and we will restructure the introduction and outlook.
L90-101: The structure of the manuscript can be explained at this point.Thank you very much, we will restructure this part and provide a new draft in the final ACs.
L109-111: Why do you use a second approach? Motivate!Thank you very much for discovering this residual of an earlier version of this manuscript.
We reformulated this section:
‘ …
2.1 Development of a common Mixed Layer Depth (MLD) formulation for the North and Baltic SeasSince salinity is a key control of stratification in the Baltic Sea, we had to adapt the ODI calculation of Große et al. (2016) by employing an MLD criterion accounting for both temperature and salinity, making it applicable to both the North and the Baltic Seas. Thus, we decided to implement the density-based criterion after De Boyer Montégut et al. (2004), which is defined as a deviation in potential density at any depth (∆σθ = 0.03 kg m-3) from the density in a reference depth (Zref = 10 m). The limit of the Mixed Layer depth is defined as the deepest depth layer, where the deviation does not surpass the threshold of 0.03 kg m-3. For the calculation of the potential density, we use the formulation by Millero and Huang (2009) and corrected the values according to the corrigendum of Millero and Huang (2010). … ‘
L148: How is the bathymetry modelled?
We are currently working on this and will add a small section to explain the bathymetry of the operational model.
L154: “toa”?Thank you for pointing out this typo. It now reads as
" ... stratification directly prior to a date within the forecast ..."
L155-156: This duplication of lines makes the entire section incomprehensible.
Thank you. We have eliminated the duplicate entries.
L233: This pre-calibration should be shown in detail. Fig. 5 and Fig. 6 show that the oxygen dynamics are not well represented by the model.Yes, we agree, that the modelled oxygen dynamics are not well represented at these stations.
As briefly mentioned in the previous posted AC:
In this study, we never aimed to evaluate the evolution and existence of oxygen deficiency zones, as well as bottom oxygen saturation/-concentration with the underlaying model of this study. Neither did we aim to improving the oxygen and BGC dynamics of this model. This was and is far beyond our studies. There has been no pre-calibration, nor calibration of the model parameters to calculate the ODI. We simply use free available outputs, such as salinity, temperature, and NPP from the operational model system at the BSH to calculate our ODI.
We also see, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore.We will work on this paragraph and also try to find a better and proper wording in order to avoid confusion regarding pre-calibration and calibration.
Fig. 2 a-c show no good (negative) correlations between simulated oxygen saturations and simulated indices. How significant are the correlations?We are currently working on it and will discuss it in the final ACs.
Fig. 2e: The grey scale is not appropriate here.
We thank the reviewer for pointing this out. In fact, the colour scale of Fig. 2e has not been updated in the latest version. We will change the colour scale in our revised version.
Fig. 3: The diagrams for MLD and Istrat do not have a suitable colour scale. Why do you sometimes use colours and sometimes B/W scales?We thank the reviewer for pointing this out. As mentioned in the first AC response, we will change the colour scale back to colour vision deficiency (CVD) friendly palettes. B/W scales were chosen for a rather pragmatic approach, since I've learned that shades are perceived better to persons with monochromatic CVD and are also good distinguishable to milder forms of CVD.
L267: Explain pre-calibration and calibration.
We are currently working on extending this paragraph and will show it updated in the final ACs. We also try to find a better and proper wording in order to avoid confusion regarding pre-calibration and calibration. There has been no pre-calibration, nor calibration of the model parameters to calculate the ODI. We simply use free available outputs, such as salinity, temperature, and NPP from the operational model system at the BSH to calculate our ODI.We corrected the References:
L561: Brüning et al 2014. Where is this paper published?
Brüning, T., Kleine, E., Janssen, F., Brüning, T., Kleine, E., and Janssen, F.: Operational ocean forecasting for German coastal waters, Die Küste, 2014, 273–290, 2014.
L562: Where is this paper published?
Carstensen, J. and Conley, D. J.: Baltic Sea Hypoxia Takes Many Shapes and Sizes, Limnology and Oceanography Bulletin, 28, 125-129, https://doi.org/10.1002/lob.10350, 2019.L593: Where is this paper published?
Mahaffey, C., Hull, T., Hunter, W., Greenwood, N., Palmer, M., and Wakelin, S.: Climate Change Impacts on Dissolved Oxygen Concentration in Marine and Coastal Waters around the UK and Ireland, in: MCCIP Science Review 2023, 31pp., doi: 10.14465/2023.reu07.oxy, 2023.L607: Where is this paper published?
Oschlies, A.: A committed fourfold increase in ocean oxygen loss, Nature Communications, 12, 2307, 10.1038/s41467-021-22584-4, 2021.L614: Where is this paper published?
Pörtner, H. O. and Knust, R.: Climate Change Affects Marine Fishes Through the Oxygen Limitation of Thermal Tolerance, Science, 315, 95-97, doi:10.1126/science.1135471, 2007. -
AC8: 'Reply on RC1', Alexandra Marki, 22 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We highly appreciate your comments and recommendation to help us improving our manuscript. We thoroughly are revising the manuscript according to all three referees’ comments. We are taking special care in providing more background information and clarification of our motivation and aim of our study and particularly focus on following our guiding ‘red line’ throughout the manuscript.
In this study, we neither focused on the underlying model of this study nor aimed to improve the oxygen and BGC dynamics of that model. This was and is far beyond our studies. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore in the revised version of this manuscript. Our goal was to create a tool to evaluate the evolution and existence of oxygen deficiency zones - a tool which is not bond to any specific model. In fact, it could be used with any suitable input data.
We also agree, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore in the revised version of the manuscript.
We added three exemplary figures as attachment, by rolling back to a CVS friendly color scale, show two monthly averages of the ODI. We eliminated the satellite image and diminished the borders around the model domain. Moreover, we show an updated time-series figure without the modelled bottom oxygen saturation. Please refer to the uploaded attachment.
Detailed remarks:
L82: Why is this comparison a solution?Thank you very much for asking this question. We rephrased this:
”… A good and economic work-around to address this limitation is the consideration of ecosystem model outputs together with ‘real’ observational monitoring data. This will help to better constrain and detect areas that are susceptible to experience higher ecological risks. ... ”
Fig. 2 a-c show no good (negative) correlations between simulated oxygen saturations and simulated indices. How significant are the correlations?
Thank you very much this question! We see the lack of our manuscript here and that we have to add additional information to this paragraph. We are currently working on it and will extend this paragraph in the revised form of our manuscript.
L148: How is the bathymetry modelled?
The coarser grids in the HBM-ERGOM model of the North- and Baltic Seas are represented by a horizontal resolution of about 5 km and own 36 vertical layers at the deepest point. The finer coastal grid is fully nested and has a horizontal resolution of about 900 m and shows a maximum of 25 vertical layers. All vertical layers have the same vertical partition of which the uppermost 20 meters of the water column, are equally distributed into 2 m steps. Five layers of 3 m each and fourteen layer of 5 m each represent the water column between 20 m and 100 m depth. The vertical resolution below 100 m is coarse and at its maximum has a thickness of up to 200 m (Brüning et al., 2014, Brüning et al., 2021).
The misleading sentence has been changed:
“ …… , hereafter called Bottom Mixed Layer Depth (BMLD), is smaller than the index of the model bottom layer. “
L267: Explain pre-calibration and calibration.
We refer to the pre-calibration of the ODI as the application of different weightings and the use of different approaches to calculate each of the three individual sub-indices. The calibration was then done with a suite of these calculations owning the highest correaltions and applied over a set of different spatial areas, stations and time scales, as shown in the supplement.
L268: What does “near real-time” mean?
Thank you very much for raising this question! We are currently working to extending this section and will integrate the information in our revised manuscript.
L276: Motivate and explain the use of time-lags.
We see the lack of our manuscript here! Thank you very much, we will explain this in our revised manuscript.
References:Brüning, T., Kleine, E., Janssen, F., Brüning, T., Kleine, E., and Janssen, F.: Operational ocean forecasting for German coastal waters, Die Küste, 2014, 273–290, 2014.
Brüning, T., Li, X., Schwichtenberg, F., and Lorkowski, I.: An operational, assimilative model system for hydrodynamic and biogeochemical applications for German coastal waters, Hydrographische Nachrichten, 6-15, 2021
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AC3: 'Reply on RC1', Alexandra Marki, 13 Mar 2024
-
RC2: 'Comment on egusphere-2023-3092', Anonymous Referee #2, 03 Mar 2024
Review of the manuscript by Marki et al. „The oxygen deficiency index blueprint allows an economic and quick scan via baseline assessment for forecasting the risk of seasonal oxygen deficiency in the North and Baltic Seas”
In the manuscript by Marki et al., an oxygen deficiency index presented in the literature is to be further developed in order to be able to describe the ecological status in the Baltic Sea and North Sea. The idea of the index is based on a study by Große et al. (2016) (Title: Looking beyond stratification: a model-based analysis of the biological drivers of oxygen deficiency in the North Sea), who introduced an index for the North Sea based on three sub-indices for stratification, biological production (or organic matter export) and the size of the volume below the thermocline. The index was developed with the help of physical-biogeochemical model data. Große et al. (2016) found the interesting result that the North Sea can be spatially subdivided into three different zones: “(1) a highly productive, non-stratified coastal zone, (2) a productive, seasonally stratified zone with a small sub-thermocline volume, and (3) a productive, seasonally stratified zone with a large subthermocline volume.” The oxygen deficiency index was developed from model data, whereby the bottom oxygen concentrations were validated with the help of observations.
Unfortunately, the aim of the work by Marki et al. is not clear to me. In their manuscript the modified index is compared/calibrated with bottom oxygen saturation concentrations from model results at selected stations and correlations are calculated (Fig. 2). Since the index takes biological production into account, I do not understand why the index is not compared with bottom oxygen concentrations but only with bottom oxygen saturation concentrations. The records in Figure 2 are much too short that they can be used to evaluate the seasonality of the index. An intra-seasonal agreement does not exist. The correlations only result from the fact that both the index and the bottom oxygen saturation concentrations show a summer signal.
The spatial maps are of such poor quality that a comparison between bottom oxygen saturation concentration and the spatial distribution of the index is impossible and they do not make sense because they show only snapshots instead of mean spatial distributions. Furthermore, the comparison of the net primary production from the model results and the net primary production index are not meaningful for the same reasons. For the longer records shown in Figures 5 and 6, the agreement between observations, model results and the index is rather poor.
In general, I wonder what role the index should play. No attempt is made to identify spatial regimes as in Große et al. (2016). Why will the operational model not further be developed to describe and possibly predict oxygen deficiency areas?
The manuscript is not yet ready for publication because there are many small mistakes and the text is difficult to understand in places. The analysis and the quality of the pictures need to be improved and relevant literature should be presented in the introduction. Please avoid the reference to grey literature or unpublished studies.
Citation: https://doi.org/10.5194/egusphere-2023-3092-RC2 -
AC1: 'Reply on RC2', Alexandra Marki, 13 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We thoroughly started to revising the manuscript according to the comments of all three referees. Each of you had very similar major concerns. Thus, we will briefly elaborate these common concerns and later discuss them once more in depth, in the course of the final author comments.
We agree that the manuscript in its current form needs to be taken special care in providing more background information and clarification of our motivation and aim of our study. We will address this in more detail during the revision and will particularly focus on following our ‘red line’ throughout the manuscript. Furthermore, we will eliminate grey literature and whenever possible substitute it with peer reviewed references.
To improve our graphics, we will switch back from B/W color scale to color vision deficiency (CVD) friendly palettes in order to better identify the patterns of a plot. Also, we will rearrange bin-sizes and bin-groups of the color scales to obtain more shading effects. We also consider to show seasonal or annual means for the ODI and each of the single indexes.
We aimed to develop this ODI toolbox as a blueprint, which allows for forecasting oxygen deficiency zones (ODZs) at an early stage, without being an expert in modelling. The ODI helps to forecast the risk of developing oxygen deficiency zones (ODZs), in order to inform authorities, entities, NGOs, the tourism industry, fisheries in advance. Moreover, the operational model used in this study is only able to forecast maximum seven days of oxygen. We aimed for an intuitive and easily applicable and adaptable approach that allows a longer timespan to forecasting ODZs. Therefore, the ODI helps to efficiently planning research cruises to capture and hunt possible low oxygen areas that are monitored each summer season.
In this study, we never aimed to evaluate the evolution and existence of oxygen deficiency zones, as well as bottom oxygen saturation/-concentration with the underlaying model of this study. Neither did we aim to improving the oxygen and BGC dynamics of this model. This was and is far beyond our studies. There has been no pre-calibration, nor calibration of the model parameters to calculate the ODI.We also see, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore.
We simply use free available outputs, such as salinity, temperature, and NPP from the operational model system at the BSH to calculate our ODI.
Also, we did not aim to improving the ODI of Große et al. 2016, which was developed only for the North Sea. We modified the ODI, because we consider both regimes, the North and the Baltic Seas - two different regimes when considering the main drivers of thermal and haline stratification, respectively.
Moreover, the ODI after Große et al. 2016 was never intended to predict ODZs. The original ODI was developed to estimate Oxygen developments via hind- and now-casts. In this study, the calculated ODI is used for the first time in a forecast model system, the ODI toolbox.
Since the ODI here serves as a blueprint we would like to encourage the scientific community to further calibrate the ODI. Our aim was to demonstrate that with very few inputs and effort, we can develop a forecasting tool that is independent of model types and -versions and the data.
Unfortunatly, none of the monitoring stations in proximity to the German coast could provide all parameters in question over a sufficient period of time and in sufficient quality. Therefore, we by-passed this problem by using model outputs for initial calibration of our ODI in this manuscript. However, as long as we can get all parameters in question, including depth levels, and make these datasets ‘python-edible’ - we can use any dataset of any station, mooring, etc, to calculate the ODI – which we see is one strength of our approach.### our take-home message: “One does not always have to reinvent the wheel – the wheel might be able to do much more than originally intended for.
Use what you have - combine already existing methods, technologies and products, pair it with free available data – keep it simple, share your knowledge and keep it free.”#####Citation: https://doi.org/10.5194/egusphere-2023-3092-AC1 -
AC6: 'Reply on RC2', Alexandra Marki, 14 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We very much appreciate your valuable comments and constructive feedback and started to revising the manuscript according to the comments of all three referees. Furthermore, as suggested, we will particularly focus on following our thread throughout the manuscript. Also, we will eliminate grey literature and whenever possible substitute it with peer reviewed references.
For a brief description of the aims and motivation of our study please refer to our AC1.
https://doi.org/10.5194/egusphere-2023-3092-AC1
In the manuscript by Marki et al., an oxygen deficiency index presented in the literature is to be further developed in order to be able to describe the ecological status in the Baltic Sea and North Sea. The idea of the index is based on a study by Große et al. (2016) (Title: Looking beyond stratification: a model-based analysis of the biological drivers of oxygen deficiency in the North Sea), who introduced an index for the North Sea based on three sub-indices for stratification, biological production (or organic matter export) and the size of the volume below the thermocline. The index was developed with the help of physical-biogeochemical model data. Große et al. (2016) found the interesting result that the North Sea can be spatially subdivided into three different zones: “(1) a highly productive, non-stratified coastal zone, (2) a productive, seasonally stratified zone with a small sub-thermocline volume, and (3) a productive, seasonally stratified zone with a large subthermocline volume.” The oxygen deficiency index was developed from model data, whereby the bottom oxygen concentrations were validated with the help of observations.
Unfortunately, the aim of the work by Marki et al. is not clear to me. In their manuscript the modified index is compared/calibrated with bottom oxygen saturation concentrations from model results at selected stations and correlations are calculated (Fig. 2).
Thank you very much for pointing this out. Since all referees had similar concerns, please consult our posted AC1 for a brief explanation of our aims and motivation here:
https://doi.org/10.5194/egusphere-2023-3092-AC1
Please do also not hesitate to follow our ODI toolbox further, throughout our final ACs, our revised manuscript as well as other (live) representations of the ODI toolbox.
Since the index takes biological production into account, I do not understand why the index is not compared with bottom oxygen concentrations but only with bottom oxygen saturation concentrations. The correlations only result from the fact that both the index and the bottom oxygen saturation concentrations show a summer signal.
We are currently working on extending this paragraph. We are considering and evaluating a possible comparison between the ODI and the bottom oxygen concentration to show in our revised manuscript.
Briefly spoken:
The ODI was also compared to modelled bottom oxygen concentration and observational bottom oxygen concentration. Unfortunately, only a few stations offered abundant long term and quality conforming monitoring data to compare the ODI with. Moreover, the MARNET stations along the German Coast represented in this study rarely offered both, bottom oxygen concentration and bottom oxygen saturation data within the same period. Thus, our pragmatic approach was to focus on bottom oxygen saturation in this study.
The records in Figure 2 are much too short that they can be used to evaluate the seasonality of the index. An intra-seasonal agreement does not exist.
Thank you very much for your concern, which was also raised by RC3. We also determined the seasonality over the whole range of 5 years - 2018-2022. The figure is not shown here, but we can show this figure and are working on to extend more background information about seasonality of both marine regimes in our final AC.
The spatial maps are of such poor quality that a comparison between bottom oxygen saturation concentration and the spatial distribution of the index is impossible and they do not make sense because they show only snapshots instead of mean spatial distributions. Furthermore, the comparison of the net primary production from the model results and the net primary production index are not meaningful for the same reasons. For the longer records shown in Figures 5 and 6, the agreement between observations, model results and the index is rather poor.
Thank you for pointing this out. We are currently working on this. We will switch back from B/W color scale to color vision deficiency (CVD) friendly palettes in order to better identify the patterns of a plot. Also, we will rearrange the bin-size and bin-grouping of the color scales. Also, we will consider to show seasonal or annual means for the ODI and each of the single indexes.
We suppose that you expect something similar to Fig. 7a,b in Große et al. 2016 , showing two annual spatial distributions of the ODI.
In general, I wonder what role the index should play. Why will the operational model not further be developed to describe and possibly predict oxygen deficiency areas?
Thank you very much for pointing this out. Since all referees had similar concerns, please consult our posted AC1 for a brief explanation of our aims and motivation here:
https://doi.org/10.5194/egusphere-2023-3092-AC1
In this study, we never aimed to evaluate the evolution and existence of oxygen deficiency zones, as well as bottom oxygen saturation/-concentration with the underlaying model of this study. Neither did we aim to improving the oxygen and BGC dynamics of this model. This was and is far beyond our studies. There has been no pre-calibration, nor calibration of the model parameters to calculate the ODI.
We also see, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore.
No attempt is made to identify spatial regimes as in Große et al. (2016).
Please explain, what exactly do you mean?
We mentioned in L485 the released graphical visualisation of the ODI of areas around the Germans coasts. Spatial regimes around the German coasts of the ODI are updated daily at https://www.bsh.de/DE/THEMEN/Modelle/InfoWas/infowas_node.html. This is part of the ODI toolbox, that has been developed in the InfoWas Project. After the end of the project in April 2023 the ODI toolbox went online by applying the ODI developed. Also, the results of the ODI formulation, amongst others, is shown in the supplements.
The manuscript is not yet ready for publication because there are many small mistakes and the text is difficult to understand in places. The analysis and the quality of the pictures need to be improved and relevant literature should be presented in the introduction. Please avoid the reference to grey literature or unpublished studies.
Thank you very much for your constructive feedback and recommendations. We are currently working on a revised version and will show some recent implementations in our final ACs.
Since all referees had similar concerns, please consult our posted AC1 for a further information of planned changes for the revised manuscript:
https://doi.org/10.5194/egusphere-2023-3092-AC1
References
Große, F., Greenwood, N., Kreus, M., Lenhart, H. J., Machoczek, D., Pätsch, J., Salt, L., and Thomas, H.: Looking beyond stratification: a model-based analysis of the biological drivers of oxygen deficiency in the North Sea, Biogeosciences, 13, 2511-2535, 10.5194/bg-13-2511-2016, 2016.
Citation: https://doi.org/10.5194/egusphere-2023-3092-AC6 -
AC9: 'Reply on RC2', Alexandra Marki, 23 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We highly appreciate your comments and recommendation to help us improving our manuscript. We thoroughly are revising the manuscript according to all three referees’ comments. We are taking special care in providing more background information and clarification of our motivation and aim of our study and particularly focus on following our guiding ‘red line’ throughout the manuscript.
In this study, we neither focused on the underlying model of this study nor aimed to improve the oxygen and BGC dynamics of that model. This was and is far beyond our studies. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore in the revised version of this manuscript. Our goal was to create a tool to evaluate the evolution and existence of oxygen deficiency zones - a tool which is not bond to any specific model. In fact, it could be used with any suitable input data.
We also agree, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore in the revised version of the manuscript.
We added 3 exemplary figures as attachment, by rolling back to a CVS friendly color scale, show two monthly averages of the ODI. We eliminated the satellite image and diminished the borders around the model domain. Moreover, we show an updated time-series figure without the modelled bottom oxygen saturation. Please refer to the uploaded attachment.
Specific Comments:
The correlations only result from the fact that both the index and the bottom oxygen saturation concentrations show a summer signal.
We agree! Thank you very much for pointing us towards the causation of the correlation.
Since the index takes biological production into account, I do not understand why the index is not compared with bottom oxygen concentrations but only with bottom oxygen saturation concentrations. The records in Figure 2 are much too short that they can be used to evaluate the seasonality of the index. An intra-seasonal agreement does not exist.
Please contact the corresponding author (AM) to obtain higher resolved figures: Five years modelled bottom oxygen saturation (top panel) and modelled bottom oxygen concentration (bottom panel) compared to the ODI33rev.
-
AC1: 'Reply on RC2', Alexandra Marki, 13 Mar 2024
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RC3: 'Comment on egusphere-2023-3092', Anonymous Referee #3, 05 Mar 2024
GENERAL COMMENTS:
The manuscript by Alexandra Marki et al. aims to develop an index for oxygen deficiency in the North Sea and Baltic Sea that can be used for forecasting of seasonal oxygen deficiency. While the approach is based on Große et al. (2016), the authors use 3D biogeochemical model data and observations for index calibration and validation.Unfortunately, the motivation and improvements made by this study do not become clear. I do not see the improvements in the methodology compared to Große et al. (2016) and why the newly developed index is better than the former to quantify oxygen deficiency. There should be more elaboration and emphasis on what exactly the differences are and how this improves the prediction. A direct comparison of the old and new index would be helpful. In addition, there is a clear mismatch between model results and observations, which presumably leads to the rare matches between ODI and observations.
In general, the manuscript is written in a very convoluted way, which makes it difficult to read and understand. A common thread would benefit the structure of the manuscript.
This manuscript is not publishable in its current form and requires a new revision.
SPECIFIC COMMENTS:
- The introduction reads like a discussion. Emphasize the motivation and novelty of your research and move the discussion to the end of the manuscript.- I strongly recommend a more in-depth analysis of the correlations between model and observations and the performance of the newly developed index. Show the performance of the ODI also on a regional scale, not only locally at specific stations. Compare the old ODI by Große et al. (2016) with the new ODI and show its improvements and advantages. Would it be possible to estimate an ODI from the observational data to compare ODI performance?
- Model and observations do not seem to fit. If you calibrate the ODI with model data, this will not lead to appropriate ODI predictions. Fix this first, which will likely improve the correlations between ODI and observations.
- The period of 10/2018 to 09/2019 is too short to determine the seasonality and variability of the ODI.
- Snapshots in Figures 2-4 do not give adequate insights into the seasonal stratification or NetPP. Use seasonal averages instead.
- There is a lot of reference to the supplements in the text. If this information is important for your study, you should include some of it in the main text.
- The maps are of poor quality and inappropriate. In general, the satellite background distracts from your data. Use a simple background and concentrate on the important things and messages you want to display. Furthermore, the map section is unnecessarily large, which makes it difficult to see details on the maps. Zoom in closer to the boundaries of your model area. In Figure 1, you are missing a color bar. In Figures 2-4, poor choice of colors and color ranges. Particularly for MLD and NPP the color ranges should be adjusted to make details more visible.
TECHNICAL CORRECTIONS:
- The title is very lenghtly and not catchy. Make it more to the point.- Avoid references in the abstract
- Avoid long confusing sentences
- Don't use "and/or" that often. Be precise: is it "and" or "or"?
- The manuscript contains a number of typos and typographical errors. Please check again!
Citation: https://doi.org/10.5194/egusphere-2023-3092-RC3 -
AC2: 'Reply on RC3', Alexandra Marki, 13 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We thoroughly started to revising the manuscript according to the comments of all three referees. Each of you had very similar major concerns. Thus, we will briefly elaborate these common concerns and later discuss them once more in depth, in the course of the final author comments.
We agree that the manuscript in its current form needs to be taken special care in providing more background information and clarification of our motivation and aim of our study. We will address this in more detail during the revision and will particularly focus on following our ‘red line’ throughout the manuscript. Furthermore, we will eliminate grey literature and whenever possible substitute it with peer reviewed references.
To improve our graphics, we will switch back from B/W color scale to color vision deficiency (CVD) friendly palettes in order to better identify the patterns of a plot. Also, we will rearrange bin-sizes and bin-groups of the color scales to obtain more shading effects. We also consider to show seasonal or annual means for the ODI and each of the single indexes.
We aimed to develop this ODI toolbox as a blueprint, which allows for forecasting oxygen deficiency zones (ODZs) at an early stage, without being an expert in modelling. The ODI helps to forecast the risk of developing oxygen deficiency zones (ODZs), in order to inform authorities, entities, NGOs, the tourism industry, fisheries in advance. Moreover, the operational model used in this study is only able to forecast maximum seven days of oxygen. We aimed for an intuitive and easily applicable and adaptable approach that allows a longer timespan to forecasting ODZs. Therefore, the ODI helps to efficiently planning research cruises to capture and hunt possible low oxygen areas that are monitored each summer season.
In this study, we never aimed to evaluate the evolution and existence of oxygen deficiency zones, as well as bottom oxygen saturation/-concentration with the underlaying model of this study. Neither did we aim to improving the oxygen and BGC dynamics of this model. This was and is far beyond our studies. There has been no pre-calibration, nor calibration of the model parameters to calculate the ODI.We also see, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore.
We simply use free available outputs, such as salinity, temperature, and NPP from the operational model system at the BSH to calculate our ODI.
Also, we did not aim to improving the ODI of Große et al. 2016, which was developed only for the North Sea. We modified the ODI, because we consider both regimes, the North and the Baltic Seas - two different regimes when considering the main drivers of thermal and haline stratification, respectively.
Moreover, the ODI after Große et al. 2016 was never intended to predict ODZs. The original ODI was developed to estimate Oxygen developments via hind- and now-casts. In this study, the calculated ODI is used for the first time in a forecast model system, the ODI toolbox.
Since the ODI here serves as a blueprint we would like to encourage the scientific community to further calibrate the ODI. Our aim was to demonstrate that with very few inputs and effort, we can develop a forecasting tool that is independent of model types and -versions and the data.
Unfortunatly, none of the monitoring stations in proximity to the German coast could provide all parameters in question over a sufficient period of time and in sufficient quality. Therefore, we by-passed this problem by using model outputs for initial calibration of our ODI in this manuscript. However, as long as we can get all parameters in question, including depth levels, and make these datasets ‘python-edible’ - we can use any dataset of any station, mooring, etc, to calculate the ODI – which we see is one strength of our approach.### our take-home message: “One does not always have to reinvent the wheel – the wheel might be able to do much more than originally intended for.
Use what you have - combine already existing methods, technologies and products, pair it with free available data – keep it simple, share your knowledge and keep it free.”#####Citation: https://doi.org/10.5194/egusphere-2023-3092-AC2 -
AC5: 'Reply on RC3', Alexandra Marki, 14 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We highly appreciate your valuable comments and constructive feedback and started to revising the manuscript according to the comments of all three referees. Furthermore, as suggested, we will particularly focus on following our thread throughout the manuscript. Also, we will eliminate grey literature and whenever possible substitute it with peer reviewed references.
For a brief description of the aims and motivation of our study please refer to our AC3.
https://doi.org/10.5194/egusphere-2023-3092-AC2
We will add a more detailed explanation on how the ODI should be read with regards to the observations.
Briefly: The lower the Oxygen – the higher the ODI – resulting in negative correlations. Whilst we do agree that there is a mismatch between the model results and the observation, as well as the intensity of the ODI is not always sound with the observed levels of oxygen saturations - With all respect, we do not agree with the above statement ‘…presumably leads to the rare matches between ODI and observations.” What exactly do you mean with rare matches? The ODI works as it should, please see Fig. 7: The lower the Oxygen saturation – the higher the ODI – resulting in negative correlations.
SPECIFIC COMMENTS:
- The introduction reads like a discussion. Emphasize the motivation and novelty of your research and move the discussion to the end of the manuscript.Thank you very much. We realized that we have to restructure our manuscript in order to follow a common ‘red-line’. Moreover, we emphasized our motivation and aim of the study in the AC2 and are currently working on this.
- I strongly recommend a more in-depth analysis of the correlations between model and observations and the performance of the newly developed index. Show the performance of the ODI also on a regional scale, not only locally at specific stations. Compare the old ODI by Große et al. (2016) with the new ODI and show its improvements and advantages. Would it be possible to estimate an ODI from the observational data to compare ODI performance?
Thank you very much for your recommendations. We see the lack of our manuscript here, since all three referees raised similar concerns. We also see, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore.
As briefly sketched in AC2 ( https://doi.org/10.5194/egusphere-2023-3092-AC2).
Analysing more in-depth the correlations between the model and the observations in this study would only lead to tweak the weighing or change the formulation of the ODI, since we did not pre-calibrate, calibrate, nor did we or will we correct the model parameters to calculate the ODI. This was and is far beyond this study.
Please refer to AC **** further down, briefly talking about data assimilation.
Spatial regimes around the German coasts of the ODI are updated daily at https://www.bsh.de/DE/THEMEN/Modelle/InfoWas/infowas_node.html. This is part of the ODI toolbox, that has been developed in the InfoWas Project. Directly after the project in April 2023, the ODI toolbox went online by applying one of the tested ODI formulations.
Also, we did not aim to improving the ODI of Große et al. 2016, which was developed only for the North Sea. We modified the ODI, because we consider both regimes, the North and the Baltic Seas - two different regimes when considering the main drivers of thermal and haline stratification, respectively.
Moreover, the ODI after Große et al. 2016 was never intended to predict ODZs. The original ODI was developed to estimate Oxygen developments via hind- and now-casts. In this study, the calculated ODI is used for the first time in a forecast model system, the ODI toolbox.Therefore, we never aimed to compare the performance of the old with the new ODI in this study. Nevertheless, we agree, that it could be of great value and beneficial to compare the old and the new ODI formulations. And I would take this ever further and pose this in the outlook section - Why not comparing the old and the new ODI with the eutrophication risk index (EUTRISK, Druon et al. 2004)?
Yes of course! It is possible, to estimate the ODI of observational data, the ODI toolbox is designed for this. As long as we can get all parameters in question, including depth levels, and make the datasets ‘python-edible’ - we can use any dataset of any station, mooring, etc, to calculate the ODI – which we see is one strength of our approach.
Unfortunatly, none of the monitoring stations in proximity to the German coast could provide all parameters in question over a sufficient period of time and in sufficient quality. Therefore, we by-passed this problem by using model outputs for initial calibration of our ODI in this manuscript. However, as long as we can get all parameters in question, including depth levels, and make these datasets ‘python-edible’ - we can use any dataset of any station, mooring, etc, to calculate the ODI – which we see is one strength of our approach.
****
- Model and observations do not seem to fit. If you calibrate the ODI with model data, this will not lead to appropriate ODI predictions. Fix this first, which will likely improve the correlations between ODI and observations.
We do agree. We will reformulated L438FF accordingly in our revised manuscript.
Nevertheless, preliminary results have shown that data assimilation of biogeochemical and satellite data, as well as the introduction of regional buffer regions with varying distances to the stations in overall improves the operational model performance (Sathyanarayanan et al., 2022 and references therein). As a consequence, we also applied this ODI blueprint toolbox to the outputs of the operational HBM-ERGOM model coupled to the parallel data assimilation framework (PDAF). Correlation coefficients between the modelled bottom oxygen saturation, as well as the modelled bottom oxygen concentration and the ODI, improved significantly in these preliminary tests (not shown here, in prep: Marki et al. 2024). Our resume, the better the model outputs agree with the observations, the better the ODI will agree to the observations.
- The period of 10/2018 to 09/2019 is too short to determine the seasonality and variability of the ODI.
Thank you for your concern. We also determined the seasonality over the whole range of 5 years - 2018-2022. The figure is not shown here, but can add this figure.
- Snapshots in Figures 2-4 do not give adequate insights into the seasonal stratification or NetPP. Use seasonal averages instead.
Thanks for the recommendation. We will take this into consideration for our revised manuscript.
- There is a lot of reference to the supplements in the text. If this information is important for your study, you should include some of it in the main text.
Thanks for pointing out. We are currently working on the text and will either sort out, re-arrange or eliminate the reference to the supplements.
- The maps are of poor quality and inappropriate. In general, the satellite background distracts from your data. Use a simple background and concentrate on the important things and messages you want to display. Furthermore, the map section is unnecessarily large, which makes it difficult to see details on the maps. Zoom in closer to the boundaries of your model area. In Figure 1, you are missing a color bar. In Figures 2-4, poor choice of colors and color ranges. Particularly for MLD and NPP the color ranges should be adjusted to make details more visible.
Thank you for pointing out. We will minimize and zoom closer to the boundaries of our modelling area. The B/W areas in Figure 1 represent the whole model regime (big map), whilst the smaller focussed map represents the MARNET stations, thus we did not add a colorbar.
As mentioned in our AC2 (https://doi.org/10.5194/egusphere-2023-3092-AC2 ), we will re-adjust the colors and color ranges or bins. Thus, we will switch back to CVD friendly color palettes in our revised manuscript.
TECHNICAL CORRECTIONS:
- The title is very lenghtly and not catchy. Make it more to the point.Thank you, we do agree the title is rather long. We are open for recommendations, any ideas are warmly welcome. Do you have something particular in your mind?
- Avoid references in the abstract
Thank you for pointing out. We avoided references in the abstract, which now reads like this:
"Oxygen deficiency zones (ODZs) in coastal seas can become hazardous to organisms and may have severe ecological and economic consequences for the environment, the fisheries, and the tourism industries. A tight interaction between ventilation and respiration governs marine oxygen levels. Regions with high primary production and a thin water column below the seasonal mixed layer are particularly prone to the formation of oxygen deficiency. In a former study the critical parameters of the oxygen deficiency index (ODI) were identified as stratification and primary production. In order to approach realistic spatio-temporal distributions of ODZs during the formation of oxygen deficiency in the seasonally stratified regions of the North Sea, a depth index serving as a proxy for the thickness of the water column below the mixed layer depth (MLD) was used. Here we propose a modified ODI to represent two differing hydrographic regimes, the North and the Baltic Seas. We use the density-based criterion of the MLD and the vertical extension of the water column between the seafloor and the bottom layer of the MLD. Moreover, we define the stratification status of the water column using continuous stratification periods of 30 days. This is our reference period for higher risks of developing ODZs. Different to the former study net primary production is not cumulated over the entire growing season but only over this reference period. With these modifications, the modified ODI offers intuitive, short-term forecasts on the areas at risk of developing oxygen deficiency. The high spatio-temporal resolution of the ODI close to the coastal zone of the North and Baltic Seas allows an operational forecasting of ODZs to inform responsible authorities and civil services in advance. We propose an economic solution to assess oxygen conditions of the past, the present and test for the risk to developing ODZs in the near future. We are able to run all necessary simulations and calculations for this research on a simple laptop. We mostly used free and open software products and Open Data products. Our data set up consists of: a) Free available netCDF output files of the operational HBM-ERGOM model and b) free available data from the MARNET monitoring network, both operated by the Federal Maritime and Hydrographic Agency (BSH)."
- Avoid long confusing sentences
The referee is right, we will shorten our sentences and for easier understanding split the long once into more parts.
- Don't use "and/or" that often. Be precise: is it "and" or "or"?
Thanks for pointing out. We will decide for one option, whenever possible.
References
Sathyanarayanan, A., Li, X., van der Lee, E., Marki, A., Lorkowski, I., and Nerger, L.: Influence of temperature and chlorophyll data assimilation on a biogeochemical ocean model for the North and Baltic Seas, EGU General Assembly 2022, Vienna, Austria, EGU22-11341, https://doi.org/10.5194/egusphere-egu22-11341
Druon, J.-N., Schrimpf, W., Dobricic, S., and Stips, A.: Comparative assessment of large-scale marine eutrophication: North Sea area and Adriatic Sea as case studies, Mar. Ecol.-Prog. Ser., 272,1–23, 2004.
Citation: https://doi.org/10.5194/egusphere-2023-3092-AC5 -
AC7: 'Reply on RC3', Alexandra Marki, 22 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We highly appreciate your comments and recommendation to help us improving our manuscript. We thoroughly are revising the manuscript according to all three referees’ comments. We are taking special care in providing more background information and clarification of our motivation and aim of our study and particularly focus on following our guiding ‘red line’ throughout the manuscript.
In this study, we neither focused on the underlying model of this study nor aimed to improve the oxygen and BGC dynamics of that model. This was and is far beyond our studies. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore in the revised version of this manuscript. Our goal was to create a tool to evaluate the evolution and existence of oxygen deficiency zones - a tool which is not bond to any specific model. In fact, it could be used with any suitable input data.
We also agree, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore in the revised version of the manuscript.
We added 3 exemplary figures as attachment, by rolling back to a CVS friendly color scale, show two monthly averages of the ODI. We eliminated the satellite image and diminished the borders around the model domain. Moreover, we show an updated time-series figure without the modelled bottom oxygen saturation. Please refer to the uploaded attachment.
Specific Comments:
- The period of 10/2018 to 09/2019 is too short to determine the seasonality and variability of the ODI.
Please contact the corresponding author (AM) to obtain a higher resolved figure:
-
AC2: 'Reply on RC3', Alexandra Marki, 13 Mar 2024
Status: closed
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RC1: 'Comment on egusphere-2023-3092', Anonymous Referee #1, 27 Feb 2024
Overall Statements:
The manuscript "The oxygen deficiency index blueprint allows an economic and quick scan via baseline assessment for forecasting the risk of seasonal oxygen deficiency in the North and Baltic Sea" by Alexandra Marki, Xin Li and Simon Jandt-Scheelke presents a new index that uses the results of a 3D biogeochemical model to describe and predict oxygen deficiency in the North and Baltic Seas.
Such a tool is of course particularly valuable because oxygen deficiency in the ocean can have significant consequences for biological and biogeochemical processes.
However, the manuscript still has so many weaknesses that I couldn't understand everything and cancelled on page 17.
The most serious problem is that the underlying model is not able to reproduce the oxygen dynamics at the stations shown. I would suggest training the oxygen index not on the model but on the measurement data.
In many places it is clear that the authors designed the new index more for the Baltic Sea, because the oxygen problems there are more drastic than in the North Sea. Fundamental mechanisms of the North Sea are not properly represented.
The manuscript is still in a very poor state at the moment: some of the formulae are incorrect, some of the sentences are confusing and most of the references refer to grey literature or have major shortcomings in the formal area.
Detailed remarks:
L33: Hansson and Viktorsson report on the Baltic Sea only.
L34-36: Here I expect background information and motivation for the contents of the manuscript and not an advert for the new index.
L54: Cite correctly: (Carstensen and Conley, 2019). Please check all similar citations.
L65-67: Stratification in the North Sea is mainly driven by local heating. Deeper denisity variations can be caused by cold water from the North Atlantic.
L68: Which two-ocean currents do you mean? The water from the north-western border of the North Sea does not meet the water of the English Channel.
L71: Which shifts?
L75: “sink” is misleading. The salty water flows near the ground.
L82: Why is this comparison a solution?
L90-94: This section fits better in the methods section.
L95-101: This section fits better in an outlook section.
L90-101: The structure of the manuscript can be explained at this point.
L109-111: Why do you use a second approach? Motivate!
L124: I do not understand this formula: Which indices are used for totalling? What does the comma in the centre mean?
L148: How is the bathymetry modelled?
L154: “toa”?
L155-156: This duplication of lines makes the entire section incomprehensible.
L162: This formula is wrong: Istrat cannot have one index and later two.
L233: This pre-calibration should be shown in detail. Fig. 5 and Fig. 6 show that the oxygen dynamics are not well represented by the model.
Fig. 2 a-c show no good (negative) correlations between simulated oxygen saturations and simulated indices. How significant are the correlations?
Fig. 2e: The grey scale is not appropriate here.
Fig. 3: The diagrams for MLD and Istrat do not have a suitable colour scale. Why do you sometimes use colours and sometimes B/W scales?
L267: Explain pre-calibration and calibration.
L268: What does “near real-time” mean?
L276: Motivate and explain the use of time-lags.
L561: Brüning et al 2014. Where is this paper published?
L562: Where is this paper published?
L593: Where is this paper published?
L607: Where is this paper published?
L614: Where is this paper published?
Citation: https://doi.org/10.5194/egusphere-2023-3092-RC1 -
AC3: 'Reply on RC1', Alexandra Marki, 13 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We thoroughly started to revising the manuscript according to the comments of all three referees. Each of you had very similar major concerns. Thus, we will briefly elaborate these common concerns and later discuss them once more in depth, in the course of the final author comments.
We agree that the manuscript in its current form needs to be taken special care in providing more background information and clarification of our motivation and aim of our study. We will address this in more detail during the revision and will particularly focus on following our ‘red line’ throughout the manuscript. Furthermore, we will eliminate grey literature and whenever possible substitute it with peer reviewed references.
To improve our graphics, we will switch back from B/W color scale to color vision deficiency (CVD) friendly palettes in order to better identify the patterns of a plot. Also, we will rearrange bin-sizes and bin-groups of the color scales to obtain more shading effects. We also consider to show seasonal or annual means for the ODI and each of the single indexes.
We aimed to develop this ODI toolbox as a blueprint, which allows for forecasting oxygen deficiency zones (ODZs) at an early stage, without being an expert in modelling. The ODI helps to forecast the risk of developing oxygen deficiency zones (ODZs), in order to inform authorities, entities, NGOs, the tourism industry, fisheries in advance. Moreover, the operational model used in this study is only able to forecast maximum seven days of oxygen. We aimed for an intuitive and easily applicable and adaptable approach that allows a longer timespan to forecasting ODZs. Therefore, the ODI helps to efficiently planning research cruises to capture and hunt possible low oxygen areas that are monitored each summer season.
In this study, we never aimed to evaluate the evolution and existence of oxygen deficiency zones, as well as bottom oxygen saturation/-concentration with the underlaying model of this study. Neither did we aim to improving the oxygen and BGC dynamics of this model. This was and is far beyond our studies. There has been no pre-calibration, nor calibration of the model parameters to calculate the ODI.We also see, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore.
We simply use free available outputs, such as salinity, temperature, and NPP from the operational model system at the BSH to calculate our ODI.
Also, we did not aim to improving the ODI of Große et al. 2016, which was developed only for the North Sea. We modified the ODI, because we consider both regimes, the North and the Baltic Seas - two different regimes when considering the main drivers of thermal and haline stratification, respectively.
Moreover, the ODI after Große et al. 2016 was never intended to predict ODZs. The original ODI was developed to estimate Oxygen developments via hind- and now-casts. In this study, the calculated ODI is used for the first time in a forecast model system, the ODI toolbox.
Since the ODI here serves as a blueprint we would like to encourage the scientific community to further calibrate the ODI. Our aim was to demonstrate that with very few inputs and effort, we can develop a forecasting tool that is independent of model types and -versions and the data.
Unfortunatly, none of the monitoring stations in proximity to the German coast could provide all parameters in question over a sufficient period of time and in sufficient quality. Therefore, we by-passed this problem by using model outputs for initial calibration of our ODI in this manuscript. However, as long as we can get all parameters in question, including depth levels, and make these datasets ‘python-edible’ - we can use any dataset of any station, mooring, etc, to calculate the ODI – which we see is one strength of our approach.### our take-home message: “One does not always have to reinvent the wheel – the wheel might be able to do much more than originally intended for.
Use what you have - combine already existing methods, technologies and products, pair it with free available data – keep it simple, share your knowledge and keep it free.”#####Citation: https://doi.org/10.5194/egusphere-2023-3092-AC3 -
AC4: 'Reply on RC1', Alexandra Marki, 14 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We started to revising the manuscript according to the comments of all three referees. Your valuable coments and constructive feedback will help us to improve our manuscript. which we will address whenever possible during the revision. Furthermore, we will particularly focus on following our ‘red line’ throughout the manuscript. Also, we will eliminate grey literature and whenever possible substitute it with peer reviewed references.
For a brief description of the aims and motivation of our study please refer to our AC3.
https://doi.org/10.5194/egusphere-2023-3092-AC3
We do agree that the mechanisms of the North Sea are not properly represented. We see the lack of our manuscript here, since it was not our intention to focus only on the Baltic Sea, we also aimed to raise awareness to the seasonal oxygen deficiency zones in the North-Seas. We will work on the reformulation and add more information on mechanisms of the North-Sea in our revised manuscript.
Although we agree that training the ODI on observational data is an important consideration, this approach could not be analysed in this manuscript because of the scarcity of (qualified) data. Since only a few of the monitoring stations can provide all parameters in question over a sufficient period of time and sufficient quality, we by-passed this problem by using model outputs for initial calibration of our ODI in this manuscript. However, as long as we can get all parameters in question, including depth levels, and make the datasets ‘python-edible’ - we can use any dataset of any station, mooring, etc, to calculate the ODI – which we see is one strength of our approach.
- Please find the revised formulae in the attached PDF.
Detailed remarks:
L33: Hansson and Viktorsson report on the Baltic Sea only.
We agree and added missing references - which reads now:
“… Low levels of oxygen are documented for an increasing number of areas including the North and Baltic Seas (Greenwood et al., 2010; Mahaffey et al., 2023; Williams et al., 2023; Hansson and Viktorsson, 2023). …”
L34-36: Here I expect background information and motivation for the contents of the manuscript and not an advert for the new index.Thank you very much for pointing this out, we do agree and will reformulate the paragraph.
L54: Cite correctly: (Carstensen and Conley, 2019). Please check all similar citations.Thanks for pointing out. We have corrected this throughout the manuscript.
L65-67: Stratification in the North Sea is mainly driven by local heating. Deeper denisity variations can be caused by cold water from the North Atlantic.The Referee is right. We will reformulate this accordingly.
L68: Which two-ocean currents do you mean? The water from the north-western border of the North Sea does not meet the water of the English Channel.We agree, that formulation was not appropriate. In its current form reads as:
‘… However, high salinity waters might flow inshore, whilst low salinity waters might flow offshore, generating also counter directed currents and influence hydrodynamics, when meet each other. …’
L71: Which shifts?
Sorry, improper wording. It now reads as:
‘… Those gradients in salinity are said to be the main reason there for stratification and prevent ventilation of the deep waters. …’
L75: “sink” is misleading. The salty water flows near the ground.Thank you for pointing this our. We reformulated it:
‘… oxygen-rich waters may be down welled and (re-)supply O2 to the oxygen-depleted bottom waters …’
L82: Why is this comparison a solution?We will discuss this in the final ACs.
L90-94: This section fits better in the methods section.
We agree and will shift this part into the methods section.
L95-101: This section fits better in an outlook section.We agree and we will restructure the introduction and outlook.
L90-101: The structure of the manuscript can be explained at this point.Thank you very much, we will restructure this part and provide a new draft in the final ACs.
L109-111: Why do you use a second approach? Motivate!Thank you very much for discovering this residual of an earlier version of this manuscript.
We reformulated this section:
‘ …
2.1 Development of a common Mixed Layer Depth (MLD) formulation for the North and Baltic SeasSince salinity is a key control of stratification in the Baltic Sea, we had to adapt the ODI calculation of Große et al. (2016) by employing an MLD criterion accounting for both temperature and salinity, making it applicable to both the North and the Baltic Seas. Thus, we decided to implement the density-based criterion after De Boyer Montégut et al. (2004), which is defined as a deviation in potential density at any depth (∆σθ = 0.03 kg m-3) from the density in a reference depth (Zref = 10 m). The limit of the Mixed Layer depth is defined as the deepest depth layer, where the deviation does not surpass the threshold of 0.03 kg m-3. For the calculation of the potential density, we use the formulation by Millero and Huang (2009) and corrected the values according to the corrigendum of Millero and Huang (2010). … ‘
L148: How is the bathymetry modelled?
We are currently working on this and will add a small section to explain the bathymetry of the operational model.
L154: “toa”?Thank you for pointing out this typo. It now reads as
" ... stratification directly prior to a date within the forecast ..."
L155-156: This duplication of lines makes the entire section incomprehensible.
Thank you. We have eliminated the duplicate entries.
L233: This pre-calibration should be shown in detail. Fig. 5 and Fig. 6 show that the oxygen dynamics are not well represented by the model.Yes, we agree, that the modelled oxygen dynamics are not well represented at these stations.
As briefly mentioned in the previous posted AC:
In this study, we never aimed to evaluate the evolution and existence of oxygen deficiency zones, as well as bottom oxygen saturation/-concentration with the underlaying model of this study. Neither did we aim to improving the oxygen and BGC dynamics of this model. This was and is far beyond our studies. There has been no pre-calibration, nor calibration of the model parameters to calculate the ODI. We simply use free available outputs, such as salinity, temperature, and NPP from the operational model system at the BSH to calculate our ODI.
We also see, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore.We will work on this paragraph and also try to find a better and proper wording in order to avoid confusion regarding pre-calibration and calibration.
Fig. 2 a-c show no good (negative) correlations between simulated oxygen saturations and simulated indices. How significant are the correlations?We are currently working on it and will discuss it in the final ACs.
Fig. 2e: The grey scale is not appropriate here.
We thank the reviewer for pointing this out. In fact, the colour scale of Fig. 2e has not been updated in the latest version. We will change the colour scale in our revised version.
Fig. 3: The diagrams for MLD and Istrat do not have a suitable colour scale. Why do you sometimes use colours and sometimes B/W scales?We thank the reviewer for pointing this out. As mentioned in the first AC response, we will change the colour scale back to colour vision deficiency (CVD) friendly palettes. B/W scales were chosen for a rather pragmatic approach, since I've learned that shades are perceived better to persons with monochromatic CVD and are also good distinguishable to milder forms of CVD.
L267: Explain pre-calibration and calibration.
We are currently working on extending this paragraph and will show it updated in the final ACs. We also try to find a better and proper wording in order to avoid confusion regarding pre-calibration and calibration. There has been no pre-calibration, nor calibration of the model parameters to calculate the ODI. We simply use free available outputs, such as salinity, temperature, and NPP from the operational model system at the BSH to calculate our ODI.We corrected the References:
L561: Brüning et al 2014. Where is this paper published?
Brüning, T., Kleine, E., Janssen, F., Brüning, T., Kleine, E., and Janssen, F.: Operational ocean forecasting for German coastal waters, Die Küste, 2014, 273–290, 2014.
L562: Where is this paper published?
Carstensen, J. and Conley, D. J.: Baltic Sea Hypoxia Takes Many Shapes and Sizes, Limnology and Oceanography Bulletin, 28, 125-129, https://doi.org/10.1002/lob.10350, 2019.L593: Where is this paper published?
Mahaffey, C., Hull, T., Hunter, W., Greenwood, N., Palmer, M., and Wakelin, S.: Climate Change Impacts on Dissolved Oxygen Concentration in Marine and Coastal Waters around the UK and Ireland, in: MCCIP Science Review 2023, 31pp., doi: 10.14465/2023.reu07.oxy, 2023.L607: Where is this paper published?
Oschlies, A.: A committed fourfold increase in ocean oxygen loss, Nature Communications, 12, 2307, 10.1038/s41467-021-22584-4, 2021.L614: Where is this paper published?
Pörtner, H. O. and Knust, R.: Climate Change Affects Marine Fishes Through the Oxygen Limitation of Thermal Tolerance, Science, 315, 95-97, doi:10.1126/science.1135471, 2007. -
AC8: 'Reply on RC1', Alexandra Marki, 22 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We highly appreciate your comments and recommendation to help us improving our manuscript. We thoroughly are revising the manuscript according to all three referees’ comments. We are taking special care in providing more background information and clarification of our motivation and aim of our study and particularly focus on following our guiding ‘red line’ throughout the manuscript.
In this study, we neither focused on the underlying model of this study nor aimed to improve the oxygen and BGC dynamics of that model. This was and is far beyond our studies. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore in the revised version of this manuscript. Our goal was to create a tool to evaluate the evolution and existence of oxygen deficiency zones - a tool which is not bond to any specific model. In fact, it could be used with any suitable input data.
We also agree, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore in the revised version of the manuscript.
We added three exemplary figures as attachment, by rolling back to a CVS friendly color scale, show two monthly averages of the ODI. We eliminated the satellite image and diminished the borders around the model domain. Moreover, we show an updated time-series figure without the modelled bottom oxygen saturation. Please refer to the uploaded attachment.
Detailed remarks:
L82: Why is this comparison a solution?Thank you very much for asking this question. We rephrased this:
”… A good and economic work-around to address this limitation is the consideration of ecosystem model outputs together with ‘real’ observational monitoring data. This will help to better constrain and detect areas that are susceptible to experience higher ecological risks. ... ”
Fig. 2 a-c show no good (negative) correlations between simulated oxygen saturations and simulated indices. How significant are the correlations?
Thank you very much this question! We see the lack of our manuscript here and that we have to add additional information to this paragraph. We are currently working on it and will extend this paragraph in the revised form of our manuscript.
L148: How is the bathymetry modelled?
The coarser grids in the HBM-ERGOM model of the North- and Baltic Seas are represented by a horizontal resolution of about 5 km and own 36 vertical layers at the deepest point. The finer coastal grid is fully nested and has a horizontal resolution of about 900 m and shows a maximum of 25 vertical layers. All vertical layers have the same vertical partition of which the uppermost 20 meters of the water column, are equally distributed into 2 m steps. Five layers of 3 m each and fourteen layer of 5 m each represent the water column between 20 m and 100 m depth. The vertical resolution below 100 m is coarse and at its maximum has a thickness of up to 200 m (Brüning et al., 2014, Brüning et al., 2021).
The misleading sentence has been changed:
“ …… , hereafter called Bottom Mixed Layer Depth (BMLD), is smaller than the index of the model bottom layer. “
L267: Explain pre-calibration and calibration.
We refer to the pre-calibration of the ODI as the application of different weightings and the use of different approaches to calculate each of the three individual sub-indices. The calibration was then done with a suite of these calculations owning the highest correaltions and applied over a set of different spatial areas, stations and time scales, as shown in the supplement.
L268: What does “near real-time” mean?
Thank you very much for raising this question! We are currently working to extending this section and will integrate the information in our revised manuscript.
L276: Motivate and explain the use of time-lags.
We see the lack of our manuscript here! Thank you very much, we will explain this in our revised manuscript.
References:Brüning, T., Kleine, E., Janssen, F., Brüning, T., Kleine, E., and Janssen, F.: Operational ocean forecasting for German coastal waters, Die Küste, 2014, 273–290, 2014.
Brüning, T., Li, X., Schwichtenberg, F., and Lorkowski, I.: An operational, assimilative model system for hydrodynamic and biogeochemical applications for German coastal waters, Hydrographische Nachrichten, 6-15, 2021
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AC3: 'Reply on RC1', Alexandra Marki, 13 Mar 2024
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RC2: 'Comment on egusphere-2023-3092', Anonymous Referee #2, 03 Mar 2024
Review of the manuscript by Marki et al. „The oxygen deficiency index blueprint allows an economic and quick scan via baseline assessment for forecasting the risk of seasonal oxygen deficiency in the North and Baltic Seas”
In the manuscript by Marki et al., an oxygen deficiency index presented in the literature is to be further developed in order to be able to describe the ecological status in the Baltic Sea and North Sea. The idea of the index is based on a study by Große et al. (2016) (Title: Looking beyond stratification: a model-based analysis of the biological drivers of oxygen deficiency in the North Sea), who introduced an index for the North Sea based on three sub-indices for stratification, biological production (or organic matter export) and the size of the volume below the thermocline. The index was developed with the help of physical-biogeochemical model data. Große et al. (2016) found the interesting result that the North Sea can be spatially subdivided into three different zones: “(1) a highly productive, non-stratified coastal zone, (2) a productive, seasonally stratified zone with a small sub-thermocline volume, and (3) a productive, seasonally stratified zone with a large subthermocline volume.” The oxygen deficiency index was developed from model data, whereby the bottom oxygen concentrations were validated with the help of observations.
Unfortunately, the aim of the work by Marki et al. is not clear to me. In their manuscript the modified index is compared/calibrated with bottom oxygen saturation concentrations from model results at selected stations and correlations are calculated (Fig. 2). Since the index takes biological production into account, I do not understand why the index is not compared with bottom oxygen concentrations but only with bottom oxygen saturation concentrations. The records in Figure 2 are much too short that they can be used to evaluate the seasonality of the index. An intra-seasonal agreement does not exist. The correlations only result from the fact that both the index and the bottom oxygen saturation concentrations show a summer signal.
The spatial maps are of such poor quality that a comparison between bottom oxygen saturation concentration and the spatial distribution of the index is impossible and they do not make sense because they show only snapshots instead of mean spatial distributions. Furthermore, the comparison of the net primary production from the model results and the net primary production index are not meaningful for the same reasons. For the longer records shown in Figures 5 and 6, the agreement between observations, model results and the index is rather poor.
In general, I wonder what role the index should play. No attempt is made to identify spatial regimes as in Große et al. (2016). Why will the operational model not further be developed to describe and possibly predict oxygen deficiency areas?
The manuscript is not yet ready for publication because there are many small mistakes and the text is difficult to understand in places. The analysis and the quality of the pictures need to be improved and relevant literature should be presented in the introduction. Please avoid the reference to grey literature or unpublished studies.
Citation: https://doi.org/10.5194/egusphere-2023-3092-RC2 -
AC1: 'Reply on RC2', Alexandra Marki, 13 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We thoroughly started to revising the manuscript according to the comments of all three referees. Each of you had very similar major concerns. Thus, we will briefly elaborate these common concerns and later discuss them once more in depth, in the course of the final author comments.
We agree that the manuscript in its current form needs to be taken special care in providing more background information and clarification of our motivation and aim of our study. We will address this in more detail during the revision and will particularly focus on following our ‘red line’ throughout the manuscript. Furthermore, we will eliminate grey literature and whenever possible substitute it with peer reviewed references.
To improve our graphics, we will switch back from B/W color scale to color vision deficiency (CVD) friendly palettes in order to better identify the patterns of a plot. Also, we will rearrange bin-sizes and bin-groups of the color scales to obtain more shading effects. We also consider to show seasonal or annual means for the ODI and each of the single indexes.
We aimed to develop this ODI toolbox as a blueprint, which allows for forecasting oxygen deficiency zones (ODZs) at an early stage, without being an expert in modelling. The ODI helps to forecast the risk of developing oxygen deficiency zones (ODZs), in order to inform authorities, entities, NGOs, the tourism industry, fisheries in advance. Moreover, the operational model used in this study is only able to forecast maximum seven days of oxygen. We aimed for an intuitive and easily applicable and adaptable approach that allows a longer timespan to forecasting ODZs. Therefore, the ODI helps to efficiently planning research cruises to capture and hunt possible low oxygen areas that are monitored each summer season.
In this study, we never aimed to evaluate the evolution and existence of oxygen deficiency zones, as well as bottom oxygen saturation/-concentration with the underlaying model of this study. Neither did we aim to improving the oxygen and BGC dynamics of this model. This was and is far beyond our studies. There has been no pre-calibration, nor calibration of the model parameters to calculate the ODI.We also see, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore.
We simply use free available outputs, such as salinity, temperature, and NPP from the operational model system at the BSH to calculate our ODI.
Also, we did not aim to improving the ODI of Große et al. 2016, which was developed only for the North Sea. We modified the ODI, because we consider both regimes, the North and the Baltic Seas - two different regimes when considering the main drivers of thermal and haline stratification, respectively.
Moreover, the ODI after Große et al. 2016 was never intended to predict ODZs. The original ODI was developed to estimate Oxygen developments via hind- and now-casts. In this study, the calculated ODI is used for the first time in a forecast model system, the ODI toolbox.
Since the ODI here serves as a blueprint we would like to encourage the scientific community to further calibrate the ODI. Our aim was to demonstrate that with very few inputs and effort, we can develop a forecasting tool that is independent of model types and -versions and the data.
Unfortunatly, none of the monitoring stations in proximity to the German coast could provide all parameters in question over a sufficient period of time and in sufficient quality. Therefore, we by-passed this problem by using model outputs for initial calibration of our ODI in this manuscript. However, as long as we can get all parameters in question, including depth levels, and make these datasets ‘python-edible’ - we can use any dataset of any station, mooring, etc, to calculate the ODI – which we see is one strength of our approach.### our take-home message: “One does not always have to reinvent the wheel – the wheel might be able to do much more than originally intended for.
Use what you have - combine already existing methods, technologies and products, pair it with free available data – keep it simple, share your knowledge and keep it free.”#####Citation: https://doi.org/10.5194/egusphere-2023-3092-AC1 -
AC6: 'Reply on RC2', Alexandra Marki, 14 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We very much appreciate your valuable comments and constructive feedback and started to revising the manuscript according to the comments of all three referees. Furthermore, as suggested, we will particularly focus on following our thread throughout the manuscript. Also, we will eliminate grey literature and whenever possible substitute it with peer reviewed references.
For a brief description of the aims and motivation of our study please refer to our AC1.
https://doi.org/10.5194/egusphere-2023-3092-AC1
In the manuscript by Marki et al., an oxygen deficiency index presented in the literature is to be further developed in order to be able to describe the ecological status in the Baltic Sea and North Sea. The idea of the index is based on a study by Große et al. (2016) (Title: Looking beyond stratification: a model-based analysis of the biological drivers of oxygen deficiency in the North Sea), who introduced an index for the North Sea based on three sub-indices for stratification, biological production (or organic matter export) and the size of the volume below the thermocline. The index was developed with the help of physical-biogeochemical model data. Große et al. (2016) found the interesting result that the North Sea can be spatially subdivided into three different zones: “(1) a highly productive, non-stratified coastal zone, (2) a productive, seasonally stratified zone with a small sub-thermocline volume, and (3) a productive, seasonally stratified zone with a large subthermocline volume.” The oxygen deficiency index was developed from model data, whereby the bottom oxygen concentrations were validated with the help of observations.
Unfortunately, the aim of the work by Marki et al. is not clear to me. In their manuscript the modified index is compared/calibrated with bottom oxygen saturation concentrations from model results at selected stations and correlations are calculated (Fig. 2).
Thank you very much for pointing this out. Since all referees had similar concerns, please consult our posted AC1 for a brief explanation of our aims and motivation here:
https://doi.org/10.5194/egusphere-2023-3092-AC1
Please do also not hesitate to follow our ODI toolbox further, throughout our final ACs, our revised manuscript as well as other (live) representations of the ODI toolbox.
Since the index takes biological production into account, I do not understand why the index is not compared with bottom oxygen concentrations but only with bottom oxygen saturation concentrations. The correlations only result from the fact that both the index and the bottom oxygen saturation concentrations show a summer signal.
We are currently working on extending this paragraph. We are considering and evaluating a possible comparison between the ODI and the bottom oxygen concentration to show in our revised manuscript.
Briefly spoken:
The ODI was also compared to modelled bottom oxygen concentration and observational bottom oxygen concentration. Unfortunately, only a few stations offered abundant long term and quality conforming monitoring data to compare the ODI with. Moreover, the MARNET stations along the German Coast represented in this study rarely offered both, bottom oxygen concentration and bottom oxygen saturation data within the same period. Thus, our pragmatic approach was to focus on bottom oxygen saturation in this study.
The records in Figure 2 are much too short that they can be used to evaluate the seasonality of the index. An intra-seasonal agreement does not exist.
Thank you very much for your concern, which was also raised by RC3. We also determined the seasonality over the whole range of 5 years - 2018-2022. The figure is not shown here, but we can show this figure and are working on to extend more background information about seasonality of both marine regimes in our final AC.
The spatial maps are of such poor quality that a comparison between bottom oxygen saturation concentration and the spatial distribution of the index is impossible and they do not make sense because they show only snapshots instead of mean spatial distributions. Furthermore, the comparison of the net primary production from the model results and the net primary production index are not meaningful for the same reasons. For the longer records shown in Figures 5 and 6, the agreement between observations, model results and the index is rather poor.
Thank you for pointing this out. We are currently working on this. We will switch back from B/W color scale to color vision deficiency (CVD) friendly palettes in order to better identify the patterns of a plot. Also, we will rearrange the bin-size and bin-grouping of the color scales. Also, we will consider to show seasonal or annual means for the ODI and each of the single indexes.
We suppose that you expect something similar to Fig. 7a,b in Große et al. 2016 , showing two annual spatial distributions of the ODI.
In general, I wonder what role the index should play. Why will the operational model not further be developed to describe and possibly predict oxygen deficiency areas?
Thank you very much for pointing this out. Since all referees had similar concerns, please consult our posted AC1 for a brief explanation of our aims and motivation here:
https://doi.org/10.5194/egusphere-2023-3092-AC1
In this study, we never aimed to evaluate the evolution and existence of oxygen deficiency zones, as well as bottom oxygen saturation/-concentration with the underlaying model of this study. Neither did we aim to improving the oxygen and BGC dynamics of this model. This was and is far beyond our studies. There has been no pre-calibration, nor calibration of the model parameters to calculate the ODI.
We also see, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore.
No attempt is made to identify spatial regimes as in Große et al. (2016).
Please explain, what exactly do you mean?
We mentioned in L485 the released graphical visualisation of the ODI of areas around the Germans coasts. Spatial regimes around the German coasts of the ODI are updated daily at https://www.bsh.de/DE/THEMEN/Modelle/InfoWas/infowas_node.html. This is part of the ODI toolbox, that has been developed in the InfoWas Project. After the end of the project in April 2023 the ODI toolbox went online by applying the ODI developed. Also, the results of the ODI formulation, amongst others, is shown in the supplements.
The manuscript is not yet ready for publication because there are many small mistakes and the text is difficult to understand in places. The analysis and the quality of the pictures need to be improved and relevant literature should be presented in the introduction. Please avoid the reference to grey literature or unpublished studies.
Thank you very much for your constructive feedback and recommendations. We are currently working on a revised version and will show some recent implementations in our final ACs.
Since all referees had similar concerns, please consult our posted AC1 for a further information of planned changes for the revised manuscript:
https://doi.org/10.5194/egusphere-2023-3092-AC1
References
Große, F., Greenwood, N., Kreus, M., Lenhart, H. J., Machoczek, D., Pätsch, J., Salt, L., and Thomas, H.: Looking beyond stratification: a model-based analysis of the biological drivers of oxygen deficiency in the North Sea, Biogeosciences, 13, 2511-2535, 10.5194/bg-13-2511-2016, 2016.
Citation: https://doi.org/10.5194/egusphere-2023-3092-AC6 -
AC9: 'Reply on RC2', Alexandra Marki, 23 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We highly appreciate your comments and recommendation to help us improving our manuscript. We thoroughly are revising the manuscript according to all three referees’ comments. We are taking special care in providing more background information and clarification of our motivation and aim of our study and particularly focus on following our guiding ‘red line’ throughout the manuscript.
In this study, we neither focused on the underlying model of this study nor aimed to improve the oxygen and BGC dynamics of that model. This was and is far beyond our studies. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore in the revised version of this manuscript. Our goal was to create a tool to evaluate the evolution and existence of oxygen deficiency zones - a tool which is not bond to any specific model. In fact, it could be used with any suitable input data.
We also agree, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore in the revised version of the manuscript.
We added 3 exemplary figures as attachment, by rolling back to a CVS friendly color scale, show two monthly averages of the ODI. We eliminated the satellite image and diminished the borders around the model domain. Moreover, we show an updated time-series figure without the modelled bottom oxygen saturation. Please refer to the uploaded attachment.
Specific Comments:
The correlations only result from the fact that both the index and the bottom oxygen saturation concentrations show a summer signal.
We agree! Thank you very much for pointing us towards the causation of the correlation.
Since the index takes biological production into account, I do not understand why the index is not compared with bottom oxygen concentrations but only with bottom oxygen saturation concentrations. The records in Figure 2 are much too short that they can be used to evaluate the seasonality of the index. An intra-seasonal agreement does not exist.
Please contact the corresponding author (AM) to obtain higher resolved figures: Five years modelled bottom oxygen saturation (top panel) and modelled bottom oxygen concentration (bottom panel) compared to the ODI33rev.
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AC1: 'Reply on RC2', Alexandra Marki, 13 Mar 2024
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RC3: 'Comment on egusphere-2023-3092', Anonymous Referee #3, 05 Mar 2024
GENERAL COMMENTS:
The manuscript by Alexandra Marki et al. aims to develop an index for oxygen deficiency in the North Sea and Baltic Sea that can be used for forecasting of seasonal oxygen deficiency. While the approach is based on Große et al. (2016), the authors use 3D biogeochemical model data and observations for index calibration and validation.Unfortunately, the motivation and improvements made by this study do not become clear. I do not see the improvements in the methodology compared to Große et al. (2016) and why the newly developed index is better than the former to quantify oxygen deficiency. There should be more elaboration and emphasis on what exactly the differences are and how this improves the prediction. A direct comparison of the old and new index would be helpful. In addition, there is a clear mismatch between model results and observations, which presumably leads to the rare matches between ODI and observations.
In general, the manuscript is written in a very convoluted way, which makes it difficult to read and understand. A common thread would benefit the structure of the manuscript.
This manuscript is not publishable in its current form and requires a new revision.
SPECIFIC COMMENTS:
- The introduction reads like a discussion. Emphasize the motivation and novelty of your research and move the discussion to the end of the manuscript.- I strongly recommend a more in-depth analysis of the correlations between model and observations and the performance of the newly developed index. Show the performance of the ODI also on a regional scale, not only locally at specific stations. Compare the old ODI by Große et al. (2016) with the new ODI and show its improvements and advantages. Would it be possible to estimate an ODI from the observational data to compare ODI performance?
- Model and observations do not seem to fit. If you calibrate the ODI with model data, this will not lead to appropriate ODI predictions. Fix this first, which will likely improve the correlations between ODI and observations.
- The period of 10/2018 to 09/2019 is too short to determine the seasonality and variability of the ODI.
- Snapshots in Figures 2-4 do not give adequate insights into the seasonal stratification or NetPP. Use seasonal averages instead.
- There is a lot of reference to the supplements in the text. If this information is important for your study, you should include some of it in the main text.
- The maps are of poor quality and inappropriate. In general, the satellite background distracts from your data. Use a simple background and concentrate on the important things and messages you want to display. Furthermore, the map section is unnecessarily large, which makes it difficult to see details on the maps. Zoom in closer to the boundaries of your model area. In Figure 1, you are missing a color bar. In Figures 2-4, poor choice of colors and color ranges. Particularly for MLD and NPP the color ranges should be adjusted to make details more visible.
TECHNICAL CORRECTIONS:
- The title is very lenghtly and not catchy. Make it more to the point.- Avoid references in the abstract
- Avoid long confusing sentences
- Don't use "and/or" that often. Be precise: is it "and" or "or"?
- The manuscript contains a number of typos and typographical errors. Please check again!
Citation: https://doi.org/10.5194/egusphere-2023-3092-RC3 -
AC2: 'Reply on RC3', Alexandra Marki, 13 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We thoroughly started to revising the manuscript according to the comments of all three referees. Each of you had very similar major concerns. Thus, we will briefly elaborate these common concerns and later discuss them once more in depth, in the course of the final author comments.
We agree that the manuscript in its current form needs to be taken special care in providing more background information and clarification of our motivation and aim of our study. We will address this in more detail during the revision and will particularly focus on following our ‘red line’ throughout the manuscript. Furthermore, we will eliminate grey literature and whenever possible substitute it with peer reviewed references.
To improve our graphics, we will switch back from B/W color scale to color vision deficiency (CVD) friendly palettes in order to better identify the patterns of a plot. Also, we will rearrange bin-sizes and bin-groups of the color scales to obtain more shading effects. We also consider to show seasonal or annual means for the ODI and each of the single indexes.
We aimed to develop this ODI toolbox as a blueprint, which allows for forecasting oxygen deficiency zones (ODZs) at an early stage, without being an expert in modelling. The ODI helps to forecast the risk of developing oxygen deficiency zones (ODZs), in order to inform authorities, entities, NGOs, the tourism industry, fisheries in advance. Moreover, the operational model used in this study is only able to forecast maximum seven days of oxygen. We aimed for an intuitive and easily applicable and adaptable approach that allows a longer timespan to forecasting ODZs. Therefore, the ODI helps to efficiently planning research cruises to capture and hunt possible low oxygen areas that are monitored each summer season.
In this study, we never aimed to evaluate the evolution and existence of oxygen deficiency zones, as well as bottom oxygen saturation/-concentration with the underlaying model of this study. Neither did we aim to improving the oxygen and BGC dynamics of this model. This was and is far beyond our studies. There has been no pre-calibration, nor calibration of the model parameters to calculate the ODI.We also see, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore.
We simply use free available outputs, such as salinity, temperature, and NPP from the operational model system at the BSH to calculate our ODI.
Also, we did not aim to improving the ODI of Große et al. 2016, which was developed only for the North Sea. We modified the ODI, because we consider both regimes, the North and the Baltic Seas - two different regimes when considering the main drivers of thermal and haline stratification, respectively.
Moreover, the ODI after Große et al. 2016 was never intended to predict ODZs. The original ODI was developed to estimate Oxygen developments via hind- and now-casts. In this study, the calculated ODI is used for the first time in a forecast model system, the ODI toolbox.
Since the ODI here serves as a blueprint we would like to encourage the scientific community to further calibrate the ODI. Our aim was to demonstrate that with very few inputs and effort, we can develop a forecasting tool that is independent of model types and -versions and the data.
Unfortunatly, none of the monitoring stations in proximity to the German coast could provide all parameters in question over a sufficient period of time and in sufficient quality. Therefore, we by-passed this problem by using model outputs for initial calibration of our ODI in this manuscript. However, as long as we can get all parameters in question, including depth levels, and make these datasets ‘python-edible’ - we can use any dataset of any station, mooring, etc, to calculate the ODI – which we see is one strength of our approach.### our take-home message: “One does not always have to reinvent the wheel – the wheel might be able to do much more than originally intended for.
Use what you have - combine already existing methods, technologies and products, pair it with free available data – keep it simple, share your knowledge and keep it free.”#####Citation: https://doi.org/10.5194/egusphere-2023-3092-AC2 -
AC5: 'Reply on RC3', Alexandra Marki, 14 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We highly appreciate your valuable comments and constructive feedback and started to revising the manuscript according to the comments of all three referees. Furthermore, as suggested, we will particularly focus on following our thread throughout the manuscript. Also, we will eliminate grey literature and whenever possible substitute it with peer reviewed references.
For a brief description of the aims and motivation of our study please refer to our AC3.
https://doi.org/10.5194/egusphere-2023-3092-AC2
We will add a more detailed explanation on how the ODI should be read with regards to the observations.
Briefly: The lower the Oxygen – the higher the ODI – resulting in negative correlations. Whilst we do agree that there is a mismatch between the model results and the observation, as well as the intensity of the ODI is not always sound with the observed levels of oxygen saturations - With all respect, we do not agree with the above statement ‘…presumably leads to the rare matches between ODI and observations.” What exactly do you mean with rare matches? The ODI works as it should, please see Fig. 7: The lower the Oxygen saturation – the higher the ODI – resulting in negative correlations.
SPECIFIC COMMENTS:
- The introduction reads like a discussion. Emphasize the motivation and novelty of your research and move the discussion to the end of the manuscript.Thank you very much. We realized that we have to restructure our manuscript in order to follow a common ‘red-line’. Moreover, we emphasized our motivation and aim of the study in the AC2 and are currently working on this.
- I strongly recommend a more in-depth analysis of the correlations between model and observations and the performance of the newly developed index. Show the performance of the ODI also on a regional scale, not only locally at specific stations. Compare the old ODI by Große et al. (2016) with the new ODI and show its improvements and advantages. Would it be possible to estimate an ODI from the observational data to compare ODI performance?
Thank you very much for your recommendations. We see the lack of our manuscript here, since all three referees raised similar concerns. We also see, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore.
As briefly sketched in AC2 ( https://doi.org/10.5194/egusphere-2023-3092-AC2).
Analysing more in-depth the correlations between the model and the observations in this study would only lead to tweak the weighing or change the formulation of the ODI, since we did not pre-calibrate, calibrate, nor did we or will we correct the model parameters to calculate the ODI. This was and is far beyond this study.
Please refer to AC **** further down, briefly talking about data assimilation.
Spatial regimes around the German coasts of the ODI are updated daily at https://www.bsh.de/DE/THEMEN/Modelle/InfoWas/infowas_node.html. This is part of the ODI toolbox, that has been developed in the InfoWas Project. Directly after the project in April 2023, the ODI toolbox went online by applying one of the tested ODI formulations.
Also, we did not aim to improving the ODI of Große et al. 2016, which was developed only for the North Sea. We modified the ODI, because we consider both regimes, the North and the Baltic Seas - two different regimes when considering the main drivers of thermal and haline stratification, respectively.
Moreover, the ODI after Große et al. 2016 was never intended to predict ODZs. The original ODI was developed to estimate Oxygen developments via hind- and now-casts. In this study, the calculated ODI is used for the first time in a forecast model system, the ODI toolbox.Therefore, we never aimed to compare the performance of the old with the new ODI in this study. Nevertheless, we agree, that it could be of great value and beneficial to compare the old and the new ODI formulations. And I would take this ever further and pose this in the outlook section - Why not comparing the old and the new ODI with the eutrophication risk index (EUTRISK, Druon et al. 2004)?
Yes of course! It is possible, to estimate the ODI of observational data, the ODI toolbox is designed for this. As long as we can get all parameters in question, including depth levels, and make the datasets ‘python-edible’ - we can use any dataset of any station, mooring, etc, to calculate the ODI – which we see is one strength of our approach.
Unfortunatly, none of the monitoring stations in proximity to the German coast could provide all parameters in question over a sufficient period of time and in sufficient quality. Therefore, we by-passed this problem by using model outputs for initial calibration of our ODI in this manuscript. However, as long as we can get all parameters in question, including depth levels, and make these datasets ‘python-edible’ - we can use any dataset of any station, mooring, etc, to calculate the ODI – which we see is one strength of our approach.
****
- Model and observations do not seem to fit. If you calibrate the ODI with model data, this will not lead to appropriate ODI predictions. Fix this first, which will likely improve the correlations between ODI and observations.
We do agree. We will reformulated L438FF accordingly in our revised manuscript.
Nevertheless, preliminary results have shown that data assimilation of biogeochemical and satellite data, as well as the introduction of regional buffer regions with varying distances to the stations in overall improves the operational model performance (Sathyanarayanan et al., 2022 and references therein). As a consequence, we also applied this ODI blueprint toolbox to the outputs of the operational HBM-ERGOM model coupled to the parallel data assimilation framework (PDAF). Correlation coefficients between the modelled bottom oxygen saturation, as well as the modelled bottom oxygen concentration and the ODI, improved significantly in these preliminary tests (not shown here, in prep: Marki et al. 2024). Our resume, the better the model outputs agree with the observations, the better the ODI will agree to the observations.
- The period of 10/2018 to 09/2019 is too short to determine the seasonality and variability of the ODI.
Thank you for your concern. We also determined the seasonality over the whole range of 5 years - 2018-2022. The figure is not shown here, but can add this figure.
- Snapshots in Figures 2-4 do not give adequate insights into the seasonal stratification or NetPP. Use seasonal averages instead.
Thanks for the recommendation. We will take this into consideration for our revised manuscript.
- There is a lot of reference to the supplements in the text. If this information is important for your study, you should include some of it in the main text.
Thanks for pointing out. We are currently working on the text and will either sort out, re-arrange or eliminate the reference to the supplements.
- The maps are of poor quality and inappropriate. In general, the satellite background distracts from your data. Use a simple background and concentrate on the important things and messages you want to display. Furthermore, the map section is unnecessarily large, which makes it difficult to see details on the maps. Zoom in closer to the boundaries of your model area. In Figure 1, you are missing a color bar. In Figures 2-4, poor choice of colors and color ranges. Particularly for MLD and NPP the color ranges should be adjusted to make details more visible.
Thank you for pointing out. We will minimize and zoom closer to the boundaries of our modelling area. The B/W areas in Figure 1 represent the whole model regime (big map), whilst the smaller focussed map represents the MARNET stations, thus we did not add a colorbar.
As mentioned in our AC2 (https://doi.org/10.5194/egusphere-2023-3092-AC2 ), we will re-adjust the colors and color ranges or bins. Thus, we will switch back to CVD friendly color palettes in our revised manuscript.
TECHNICAL CORRECTIONS:
- The title is very lenghtly and not catchy. Make it more to the point.Thank you, we do agree the title is rather long. We are open for recommendations, any ideas are warmly welcome. Do you have something particular in your mind?
- Avoid references in the abstract
Thank you for pointing out. We avoided references in the abstract, which now reads like this:
"Oxygen deficiency zones (ODZs) in coastal seas can become hazardous to organisms and may have severe ecological and economic consequences for the environment, the fisheries, and the tourism industries. A tight interaction between ventilation and respiration governs marine oxygen levels. Regions with high primary production and a thin water column below the seasonal mixed layer are particularly prone to the formation of oxygen deficiency. In a former study the critical parameters of the oxygen deficiency index (ODI) were identified as stratification and primary production. In order to approach realistic spatio-temporal distributions of ODZs during the formation of oxygen deficiency in the seasonally stratified regions of the North Sea, a depth index serving as a proxy for the thickness of the water column below the mixed layer depth (MLD) was used. Here we propose a modified ODI to represent two differing hydrographic regimes, the North and the Baltic Seas. We use the density-based criterion of the MLD and the vertical extension of the water column between the seafloor and the bottom layer of the MLD. Moreover, we define the stratification status of the water column using continuous stratification periods of 30 days. This is our reference period for higher risks of developing ODZs. Different to the former study net primary production is not cumulated over the entire growing season but only over this reference period. With these modifications, the modified ODI offers intuitive, short-term forecasts on the areas at risk of developing oxygen deficiency. The high spatio-temporal resolution of the ODI close to the coastal zone of the North and Baltic Seas allows an operational forecasting of ODZs to inform responsible authorities and civil services in advance. We propose an economic solution to assess oxygen conditions of the past, the present and test for the risk to developing ODZs in the near future. We are able to run all necessary simulations and calculations for this research on a simple laptop. We mostly used free and open software products and Open Data products. Our data set up consists of: a) Free available netCDF output files of the operational HBM-ERGOM model and b) free available data from the MARNET monitoring network, both operated by the Federal Maritime and Hydrographic Agency (BSH)."
- Avoid long confusing sentences
The referee is right, we will shorten our sentences and for easier understanding split the long once into more parts.
- Don't use "and/or" that often. Be precise: is it "and" or "or"?
Thanks for pointing out. We will decide for one option, whenever possible.
References
Sathyanarayanan, A., Li, X., van der Lee, E., Marki, A., Lorkowski, I., and Nerger, L.: Influence of temperature and chlorophyll data assimilation on a biogeochemical ocean model for the North and Baltic Seas, EGU General Assembly 2022, Vienna, Austria, EGU22-11341, https://doi.org/10.5194/egusphere-egu22-11341
Druon, J.-N., Schrimpf, W., Dobricic, S., and Stips, A.: Comparative assessment of large-scale marine eutrophication: North Sea area and Adriatic Sea as case studies, Mar. Ecol.-Prog. Ser., 272,1–23, 2004.
Citation: https://doi.org/10.5194/egusphere-2023-3092-AC5 -
AC7: 'Reply on RC3', Alexandra Marki, 22 Mar 2024
Dear Referee,
thank you very much for taking the time and effort necessary to reviewing our manuscript. We highly appreciate your comments and recommendation to help us improving our manuscript. We thoroughly are revising the manuscript according to all three referees’ comments. We are taking special care in providing more background information and clarification of our motivation and aim of our study and particularly focus on following our guiding ‘red line’ throughout the manuscript.
In this study, we neither focused on the underlying model of this study nor aimed to improve the oxygen and BGC dynamics of that model. This was and is far beyond our studies. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore in the revised version of this manuscript. Our goal was to create a tool to evaluate the evolution and existence of oxygen deficiency zones - a tool which is not bond to any specific model. In fact, it could be used with any suitable input data.
We also agree, that the integration of modelled bottom oxygen saturation deviates the focus from the ODI and misleads the understanding of the whole manuscript. The oxygen saturation in the bottom layer of the model will be taken out from the study and not shown in the Figures anymore in the revised version of the manuscript.
We added 3 exemplary figures as attachment, by rolling back to a CVS friendly color scale, show two monthly averages of the ODI. We eliminated the satellite image and diminished the borders around the model domain. Moreover, we show an updated time-series figure without the modelled bottom oxygen saturation. Please refer to the uploaded attachment.
Specific Comments:
- The period of 10/2018 to 09/2019 is too short to determine the seasonality and variability of the ODI.
Please contact the corresponding author (AM) to obtain a higher resolved figure:
-
AC2: 'Reply on RC3', Alexandra Marki, 13 Mar 2024
Data sets
operational HBM-ERGOM model data Operational Modelling department, BSH https://www.bsh.de/DE/THEMEN/Modelle/modelle_node.html
Oceanographic data from North West Shelf and from the Baltic Sea Federal Maritime and Hydrographic Agency- Dept. Oceanography https://www.bsh.de/EN/DATA/Climate-and-Sea/Marine_environment_monitoring_network/marine_environment_monitoring_network_node.html
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