the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Nov 2023
Greenland Sea primary production in 1998–2022: monitoring and parameterization using satellite and field data
Abstract. Phytoplankton are responsible for releasing half of the World’s oxygen and for removing large amounts of carbon dioxide from the surface waters. Despite many studies on the topic conducted in the past decades, we are still far from a good understanding of ongoing rapid changes in the Arctic Ocean, and how they will affect phytoplankton and the whole ecosystem. An example is the difference in net primary production modeling estimates, which differ two times globally and fifty times when only the Arctic region is considered. Here we aim to improve the quality of Greenland Sea primary production estimates, by testing different versions of primary production model against in-situ data, and then calculating regional estimates and trends for 1998–2022 for those performing best. As a baseline we chose the commonly used global primary production model and tested it with different combinations of empirical relationships and input data. Local empirical relationships were taken from literature and derived from the unpublished Institute of Oceanology of Polish Academy of Sciences measurements across the Fram Strait. For validation we took historical net primary production 14C data from literature, and added to it our own gross primary production O2 measurements to extend the limited validation dataset. The field data showed expected elevated values at the frontal zone together with differences between Arctic and Atlantic-dominated waters, and unexpected good agreement between primary production measured with 14C and O2 evolution methods. From all the model setups, those including local chlorophyll-a profile and local absorption spectrum and using Level 2 photosynthetically active radiation data, reproduced in-situ data best. Our modeled regional annual primary production estimates equal 346 TgC/year for the Nordic Seas region and 342 TgC/year for the Greenland Sea sector of the Arctic defined as 45° W–15° E, 66°33′N–90° N. These values are higher those previously reported. Monthly values show a seasonal cycle with less monthly variability than previously reported, and with peak values observed in May. No significant increase or decrease in primary production was observed when studying regionally averaged trends. The accuracy of the selected here model setups to reproduce the field data in terms of Root Mean Square Difference is poorer than in the related global studies, but better than in the related Arctic studies.
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RC1: 'Comment on egusphere-2023-2495', Anonymous Referee #1, 05 Jan 2024
General comments
The authors compiled in situ NPP and GPP data in the Greenland Sea where number of matchup data is limited, and compared the in situ data with satellite NPP derived from different combinations of input parameters to choose the best combination to input into a satellite NPP model. They clarified that the combination of local chlorophyll a profile, local aph spectrum, and L2 PAR shows the best performance. These analyses and results are important for monitoring the marine ecosystem in the Arctic and are suitable topics for this journal.
However, validation analyses between satellite and in situ were performed only for PP, not for each input parameter, such as CHL and PAR. Even if satellite NPP coincides with in situ, large uncertainties of input parameters remain, and it is difficult to say that a model setup with the best performance on NPP is really accurate. Some parameters are derived from CHL. If CHL is overestimated, Zeu is underestimated. Therefore, validation of input parameters in the study region is recommended. Another issue is that the authors used monthly composited satellite data. Satellite data is monthly average, but in situ data is daily. I'm not sure what the comparison between monthly and daily data means. PAR data has larger uncertainties because climatology from only 2022 data or reanalysis data is used without any validation.
Methods for in situ NPP and GPP measurements and analyses (vertical integration) are not sufficiently described. Overall, methodological explanations are insufficient and identification numbers of data group and in situ data version are complicated. There are also many typos. More careful and substantial revisions are required for better understanding of the results.
Specific comments and technical corrections
L34 "Van Dyiken" should be "van Dijken"
L35 It is too much speculation if there is no evidence.
Figure 1 Please add a mean of vector color to the caption.
L61 Italicize 'a' of chlorophyll-a.
L81 Why did you use only the global primary production models? Development of a model? Tuning?
L105 If the model by Antoine and Morel (1996) is appropriate, it's better to try to obtain the LUTs from Prof. David Antoine.
L121 "For CHL" For satellite CHL
L135-136 What are "local empirical coefficients"? What is the CHL parameterization? What kind of profiles? Not "parameterisation"
L141 "0.7 um GF/F filters" glass fiber filters (Whatman, GF/F)
L141 Please use "°C"
L142 " Filter papers with deposited particles were measured with Lambda 850" Probably you measured absorbance (optical density) of particles with a spectrophotometer, not filter paper.
L145 "Chla" should be CHL concentration. How did you measure CHL concentration?
L150 Super script only for "14". Could you describe the detailed methods to measure PP using 14C (sampling, incubation, duration, temperature control, etc.)?
L155- Could you plot all data on a map? Figure 4?
L157 " PP data were logarithmically transformed (base 10) (Campbell, 1995) before being analysed in this study." Probably this sentence is not required because the equations of model performance metrics include "log". Not "analysed", "analyzed".
L162 "Gross Primary Production" should be "gross primary production".
L165 Illumination with an artificial lamp?
L165-166 No needed "+". Please show the range of water temperature in situ. Figure 4 shows that 4 degree C is found only in the frontal zone.
L170-171 PAR
L174 "darkening film" Neutral density film?
L175 TriOS, not TrioS. Probably, "Hyperspectral irradiance sensor RAMSES ACC (TriOS, Germany)" is the correct information on this sensor. Downwelling and upwelling depend on the direction of sensor detector.
L180 How much accurate are the dissolved oxygen standards?
L189-190 The general value of PQ was used to convert from O2 to C. How much does this affect the results?
L199 The model is depth-resolved but I can not find "z" in the equation (1).
L196-254 If possible, please summarize in a table.
L221 Does this mean that the CHL profile model was tuned by in-situ data?
L226 The term "PUR" is not found in equation (1).
L247 There are no descriptions on space- and time-windows for the matchup.
L249 Method for interpolation is difficult to understand. More details are required.
L282 Linear fit to log-transformed PP?
L292 side to the west.
L298-312 These are only comparison between NPP and GPP in average from different cruises, waters, and integration. At least, statistical test for difference between the two dataset is needed if both data are treated as same quality and quantity when satellite PP is evaluated by in situ data.
L328-332 It is very difficult to understand this chapter because the data groups are not clear. For example, the data group 3 has only CHL profiles but PP model needs other parameters.
L334-377 and Figure 6 are too much complicated and I could not understand how the authors identify the best models without statistical tests or any other metrics.
Caption of Table 1 and 2 should be put above the table.
Figure 7 What is the red line? I guess many readers are not familiar with the Target diagrams.
L407, 425 432, 442, Figure 8, and Table 2: "van Dijken", not Van Dijken.
L466-472 I understand the simultaneous measurement of NPP and GPP is challenging. How do you consider the difference between the two PPs in the study region?
L486 "For the Lee et al (2015) Arctic study" Typo?
L495 Superscript only "14"
Citation: https://doi.org/10.5194/egusphere-2023-2495-RC1 -
AC1: 'Reply on RC1', Aleksandra Cherkasheva, 31 Jan 2024
Dear Anonymous Referee #1,
First of all we would like to thank you for the time you took for reviewing our manuscript and your constructive comments. Our replies are listed below, straight after each of your comments.
Anonymous Referee #1, 05 Jan 2024
General comments
The authors compiled in situ NPP and GPP data in the Greenland Sea where number of matchup data is limited, and compared the in situ data with satellite NPP derived from different combinations of input parameters to choose the best combination to input into a satellite NPP model. They clarified that the combination of local chlorophyll a profile, local aph spectrum, and L2 PAR shows the best performance. These analyses and results are important for monitoring the marine ecosystem in the Arctic and are suitable topics for this journal.
However, validation analyses between satellite and in situ were performed only for PP, not for each input parameter, such as CHL and PAR. Even if satellite NPP coincides with in situ, large uncertainties of input parameters remain, and it is difficult to say that a model setup with the best performance on NPP is really accurate. Some parameters are derived from CHL. If CHL is overestimated, Zeu is underestimated. Therefore, validation of input parameters in the study region is recommended. Another issue is that the authors used monthly composited satellite data. Satellite data is monthly average, but in situ data is daily. I'm not sure what the comparison between monthly and daily data means. PAR data has larger uncertainties because climatology from only 2022 data or reanalysis data is used without any validation.
R: We agree that validation of the input parameters would have been an advantage for our study. However, as we do not have the in-situ data of all the input parameters measured simultaneously with PP (e.g. PAR was not simultaneously measured with PP), we will add the information from the previous studies validating those input parameters in the Arctic (e.g. Konik et al., 2020). We’ve used the monthly data to be able to obtain collocations between satellite and in-situ data, which is a challenging task due to the presence of sea ice and clouds in the area. We have previously shown that for the Fram Strait, where most of our field PP is concentrated, satellite chlorophyll data is available on average for 2 days in a month for the years 1998–2001 and 5 days in a month for the years after 2001 (Cherkasheva et al., 2014). This resulted in 10% of field data being collocated (54 collocations out of 526 field points). For the current study having 10% of collocations would have resulted in four points for the analysis to be based on, which is too few from our point of view. We took the PAR data from trusted sources, the evaluation of PAR algorithms at high northern latitudes was not in the scope of this study, but can be found in e.g. Laliberté et al. (2016). As Eumetsat OLCI Level 2 PAR data for all the months from April until September were available only starting from 2022, we took this only available year.
Methods for in situ NPP and GPP measurements and analyses (vertical integration) are not sufficiently described. Overall, methodological explanations are insufficient and identification numbers of data group and in situ data version are complicated. There are also many typos. More careful and substantial revisions are required for better understanding of the results.
R: We will add more information describing primary production measurements and their vertical integration, as well as revise the results section.
Specific comments and technical corrections
L34 "Van Dyiken" should be "van Dijken" R: corrected
L35 It is too much speculation if there is no evidence. R: changed the wording to give room for other possibilities and added references supporting the point
Figure 1 Please add a mean of vector color to the caption. R: description of the color code was added
L61 Italicize 'a' of chlorophyll-a. R: corrected
L81 Why did you use only the global primary production models? Development of a model? Tuning? R: we used a global primary production model as to our current knowledge there’s no primary production model specifically developed for the Greenland Sea. There are models developed for the other Arctic regions, such as a model by Fernández-Méndez et al. (2015) developed for the Central Arctic Ocean or a model by Hirawake et al. (2012) developed for the Chickchi and Bering Seas. We did not use them as from our point of view these are completely different regions in terms of environmental conditions, and we chose to take a uniform global relationship proven to perform well in the intercomparison study and adapt it for the area. We agree that “development of a model” is not a correct term in this case. Thus we use the wording “development of a model setup”.
L105 If the model by Antoine and Morel (1996) is appropriate, it's better to try to obtain the LUTs from Prof. David Antoine. R: we were not able to obtain LUTs from Prof. David Antoine
L121 "For CHL" For satellite CHL R: corrected
L135-136 What are "local empirical coefficients"? What is the CHL parameterization? What kind of profiles? Not "parameterisation" R: corrected, terms "local empirical coefficients" and “CHL parameterization” were explained
L141 "0.7 um GF/F filters" glass fiber filters (Whatman, GF/F) R: corrected
L141 Please use "°C" R: corrected
L142 " Filter papers with deposited particles were measured with Lambda 850" Probably you measured absorbance (optical density) of particles with a spectrophotometer, not filter paper. R: corrected
L145 "Chla" should be CHL concentration. How did you measure CHL concentration? R: corrected. A section 2.3.2.2 describing CHL concentration measurements was added
L150 Super script only for "14". Could you describe the detailed methods to measure PP using 14C (sampling, incubation, duration, temperature control, etc.)? R: Superscript corrected. As we didn’t measure the PP using 14C ourselves, but obtained the data from the literature, we will add the details on the methods that are available in the five sources used.
L155- Could you plot all data on a map? Figure 4? R: All the data mentioned in this section (field PP) are already plotted on Figure 4. Hopefully we understood this comment correctly, if not please give more details.
L157 " PP data were logarithmically transformed (base 10) (Campbell, 1995) before being analysed in this study." Probably this sentence is not required because the equations of model performance metrics include "log". Not "analysed", "analyzed". R: corrected
L162 "Gross Primary Production" should be "gross primary production". R: corrected
L165 Illumination with an artificial lamp? R: corrected
L165-166 No needed "+". Please show the range of water temperature in situ. Figure 4 shows that 4 degree C is found only in the frontal zone. R: corrected, added the range of water temperature in situ. According to IOCCG Protocol Series (2022) the changes in temperature affect the sensor measurements, thus the temperature needs to be constant throughout the experiments. We chose the value for the constant temperature based on the average temperature of the sampled layer in the area.
L170-171 PAR R: corrected
L174 "darkening film" Neutral density film? R: corrected
L175 TriOS, not TrioS. Probably, "Hyperspectral irradiance sensor RAMSES ACC (TriOS, Germany)" is the correct information on this sensor. Downwelling and upwelling depend on the direction of sensor detector. R: corrected
L180 How much accurate are the dissolved oxygen standards? R: We followed the procedure described in Presens Fixbox 4 manual for calibrating the sensor by the dissolved oxygen standards. There’s no accuracy assessment in this procedure. Note that the primary production is calculated from oxygen gradients and not absolute values, therefore having accurate calibration is not as critical for this case.
L189-190 The general value of PQ was used to convert from O2 to C. How much does this affect the results? R: We will test the influence of different PQ values found in the literature on the result.
L199 The model is depth-resolved but I can not find "z" in the equation (1). R: The term CHLtot is the depth-integrated value, which depends on the chlorophyll vertical profile
L196-254 If possible, please summarize in a table. R: we will add a table
L221 Does this mean that the CHL profile model was tuned by in-situ data? R: Changed to “developed based on the analysis of 1199 profiles ”. The Gaussian profiles were fitted to the mean CHL profile, which was calculated for several categories defined by surface concentration and month. Therefore, the CHL profile model is a fit to in-situ data averaged within several categories. We are not sure that “tuned” is a correct term for it, please correct us if you don’t agree. For more details please see Cherkasheva et al (2013)
L226 The term "PUR" is not found in equation (1). R: PUR, defined as the fraction of PAR that can be absorbed by algae (Morel, 1978) is used instead of PAR in the equation (1) for the cases listed in the group 4. In this case the term “PAR” is substituted by the term “PUR”. We will add this information to the text.
L247 There are no descriptions on space- and time-windows for the matchup. R: we will add the descriptions
L249 Method for interpolation is difficult to understand. More details are required. R: we will add more details
L282 Linear fit to log-transformed PP? R: No, for the basin estimates and trends calculations the PP is not log-transformed. Log-transformation is used when validating the model results against filed PP data. Added more clarification on that.
L292 side to the west. - corrected
L298-312 These are only comparison between NPP and GPP in average from different cruises, waters, and integration. At least, statistical test for difference between the two dataset is needed if both data are treated as same quality and quantity when satellite PP is evaluated by in situ data. R: we will add more statistical characteristics on the difference between the two datasets
L328-332 It is very difficult to understand this chapter because the data groups are not clear. For example, the data group 3 has only CHL profiles but PP model needs other parameters. R: we will add a table describing data groups to section 2.4. Within each of the data groups, all the combinations of other parameters listed in section 2.4 were tested.
L334-377 and Figure 6 are too much complicated and I could not understand how the authors identify the best models without statistical tests or any other metrics. R: We will add more explanation for Figure 6. We have identified the best models following the procedure described in the satellite primary production models intercomparison study by Lee et al. (2015). See lines 270-272: “As in Lee et al. (2015), model versions performing relatively better than the others were selected for further analysis using the two criteria: (1) bias was close to 0 (-0.1<bias<0.1), and (2) Pearson's correlation coefficient (r) was greater than the model average (0.25).”
Caption of Table 1 and 2 should be put above the table. R: corrected
Figure 7 What is the red line? I guess many readers are not familiar with the Target diagrams. R: added the description of the red line
L407, 425 432, 442, Figure 8, and Table 2: "van Dijken", not Van Dijken. R: corrected
L466-472 I understand the simultaneous measurement of NPP and GPP is challenging. How do you consider the difference between the two PPs in the study region? R: As the relationships between GPP and NPP can vary depending on the environment and species (IOCCG Protocol Series, 2022), it is not a common practice to use any factor for conversion between these two different types of primary production measurements. In a way we consider the difference by running the performance test for three different types of datasets: 1) only NPP, 2) only GPP, 3) NPP and GPP. The models that performed best both for the first and the third dataset were chosen for the analysis. No models passed the performance test for the second dataset. Thus, the selected models perform well in reproducing only NPP and NPP combined with GPP.
L486 "For the Lee et al (2015) Arctic study" Typo? R: corrected
L495 Superscript only "14" R: corrected
References
Cherkasheva, A., Nöthig, E.-M., Bauerfeind, E., Melsheimer, C., and Bracher, A.: From the chlorophyll a in the surface layer to its vertical profile: a Greenland Sea relationship for satellite applications. Ocean Science, 9, 431-445, doi:10.5194/os-9-431-2013, 2013
Cherkasheva, A., Bracher, A., Melsheimer, C., Köberle, C., Gerdes, R., Nöthig, E.-M., Bauerfeind, E., Boetius, A. Influence of the physical environment on polar phytoplankton blooms: A case study in the Fram Strait, Journal of Marine Systems, Vol 132, 2014, pp 196-207, ISSN 0924-7963, https://doi.org/10.1016/j.jmarsys.2013.11.008., 2014
Fernández-Méndez, M., C. Katlein, B. Rabe, M. Nicolaus, I. Peeken, K. Bakker, H. Flores, and A. Boetius, Photosynthetic production in the Central Arctic during the record sea-ice minimum in 2012, Biogeosciences, 12, 3525–3549, doi:10.5194/bg-12-3525-2015., 2015
IOCCG Protocol Series: Aquatic Primary Productivity Field Protocols for Satellite Validation and Model Synthesis. Balch, W.M., Carranza, M., Cetinić, I., Chaves, J.E., Duhamel, S., Fassbender, A., Fernandez-Carrera, A., Ferrón, S., García-Martín, E., Goes, J., Gomes, H., Gundersen, K., Halsey, K., Hirawake, T., Isada, T., Juranek, L., Kulk, G., Langdon, C., Letelier, R., López-Sandoval, D., Mannino, A., Marra, J.F., Neale, P., Nicholson, D., Silsbe, G., Stanley, R.H., Vandermeulen, R.A. IOCCG Ocean Optics and Biogeochemistry Protocols for Satellite Ocean Colour Sensor Validation, Volume 7.0, edited by R.A. Vandermeulen, J. E. Chaves, IOCCG, Dartmouth, NS, Canada. doi:http://dx.doi.org/10.25607/OBP-1835, 2022.
Hirawake, T., K. Shinmyo, A. Fujiwara, and S. I. Saitoh, Satellite remote sensing of primary productivity in the Bering and Chukchi Seas using an absorption-based approach, ICES J. Mar. Sci., 69, 1194–1204, 2012
Konik, M., Kowalczuk, P., Zabłocka, M., Makarewicz, A., Meler, J., Zdun, A., Darecki, M. Empirical Relationships between Remote-SensingReflectance and Selected Inherent Optical Properties in Nordic Sea Surface Waters for the MODIS andOLCI Ocean Colour Sensors. Remote Sensing. 12. 2774. 10.3390/rs12172774, 2020.
Laliberté, J., Bélanger, S., Frouin, R. Evaluation of satellite-based algorithms to estimate photosynthetically available radiation (PAR) reaching the ocean surface at high northern latitudes, Remote Sensing of Environment, Vol. 184, 2016, Pp 199-211, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2016.06.014., 2016
Lee, Y. J., et al. An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models, Journal of Geophysical Research Oceans, 120, 6508–6541, doi:10.1002/2015JC011018, 2015.
Morel, A. Available, usable, and stored radiant energy in relation to marine photosynthesis. Deep-Sea Research, 25, 673-688, 1978
Citation: https://doi.org/10.5194/egusphere-2023-2495-AC1
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AC1: 'Reply on RC1', Aleksandra Cherkasheva, 31 Jan 2024
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RC2: 'Comment on egusphere-2023-2495', Anonymous Referee #2, 08 Jan 2024
The subject of the paper is actual enough, especially when talk about recent climate changes in atmospheric CO2 and its influence on polar regions. Melting of sea ice and consequent increasing of open waters in the Arctic Ocean affect total CO2 uptake by marine phytoplankton and values of primary production. Developing of regional models of primary production is a challenge, as we should always take into account local feathers of the processes. In the paper authors have made a really good job on it and provide new regional assessments of primary production and its seasonal dynamic. Furthermore, they used for it an alternative method on a base of optic oxygen measurements.
However, it should be clarified accurately main definitions and differences in GPP, NCP, NPP as well as autotroph and community respiration. My major and minor comments are listed as follows:
L23 – why “unexpected”? It should be clarified.
L35-36 – Did just Rey et al., 2000 write about this? After the 2000 year there are lots of studies have been published on this topic.
L42 – It’s not correct to equal primary production to CO2 uptake. PP can be explained as a rate of synthesis (especially when we talk about PP models as we model the process) of organic matter for which inorganic carbon is needed.
L55 – Figure 1. The area which is discussed in the paper lies outside the boundaries of the figure. It should be extended till 45W at least. A boarder of the Barents Sea lies on the east coast of the Bear Island (Svalbard). It’s not good to label an object on the map outside its borders.
L58 – I can be wrong, but commonly used abbreviation for this is DCM – Deep Chlorophyll Maxima. If it’s so, I would advise not to create entities to keep uniform abbreviation from study to study.
L59 – “but are not detected by ocean colour sensors”. It’s firstly depends on water transparency, and if it’s rather clear in open waters (small chl concentrations), and DCM lies in a layer of the penetration depth, it can be detected by the optic sensor.
L73 – If you talk about Arctic in general you should cite other studies on PP models for this region not just Lee et al., 2015.
L93 – Figure 2. “…Greenland Sea basin estimates and trends” of what? One can guess that here you mean PP, but in the figure caption it should be clarified.
L108 – If globally, there are some other studies on this model and its validation.
L109-110 – I would advise to combine Section 2.2 and 2.4. Otherwise, when read this sentence it’s wanted to see the equation of the model and its description. Furthermore in the next section you talk about the data, and if the description of the model is before, it’s more understandable why you use mentioned data. At the same time, versions of model setup can be kept in the section 2.4.
L123-124, L127 – What “coverage” do you mean? Some average value for the whole period 1998-2022? Data coverage can changes year to year and season to season.
L135 – “1199 profiles”. I would advise to say here some words about them: what seasons, region. It would be better than find out this information in another study. And how did you get this local coefficients? Is it discussed in Cherkasheva et al. (2013)?
L147 – “Chla” isn’t used in this study. This denotement is from Bricaud et al., 1995.
L150, L495 – “the 14C method” is written as superscript.
L160-161 – In what seasons did these cruises take place? It’s said later in the text but not here, when you introduce them. How many data were collected in each cruise?
L162, L194 - "Gross Primary Production". Usually you use it with lowercase letters.
L170-171 – It’s better to clarify, why these depths were chosen.
L179 – Why did you have to incubate for 48 and 72 hours? And for what Chla concentrations? Are you sure that when you incubated more than one day you didn’t have an organic matter destruction by bacteria in your bottles? Did you analyze time variability of O2 in the bottles for these cases?
L183-185 – It’s better to clarify all definitions: CR, NCP and GPP. As well is the difference between NCP and NPP. When you say CR, do you mean respiration of phyto-, zooplankton and bacteria or what? Furthermore, there is a misprint in this sentence: CR – is a difference between dark and control bottle, and NCP is a difference between light and control bottle. When you say “respectively”, you put “light” first.
L199 – What does this coefficient (4.6/12) mean? It should be explained.
L203 – Did you check M&B algorithm on your 1199 Chla profiles?
L252 – Do you mean “measured” and “derived” when use “m” and “d” respectively in your Eqs. 2-4? It should be clarified. Because in Eqs. 5-6 you use “insitu” and “modeled”.
L274 – It’s better to refer for Figure 8, when say about two regions.
L285 – Figure 3. The same comment as for the Figure 1 about the boundaries of the area. What are salinity units? Colour bar for PP isn’t good: all values are in blue colour, and just 2 in red. For my opinion, it should be reduced to 1500 mgCm2day to show PP variability better. Anyway, you can play with colourbar to find out a better solution.
L299 and discussion in this section – If define GPP and NPP correctly, it’s clear that GPP is obviously higher than NPP, because NPP = GPP-R(autotroph). NCP in turn is GPP-R(community). And it’s better to compare NCP assessments with NPP, but multiply the first by a coefficient which represents a part of autotroph respiration.
L306 – You can compare your 2022 data integrated till 60 m with previous assessments.
L326 – Sensitivity analysis was applied in many other studies not just in Lee et al. (2015).
L334 – Do you mean spatial interpolation?
L338-339 – When use this name “NPP interpolated” it is look like NPP was interpolated into a grid, not Chla. It’s better to clarify this in the text or choose another denotement.
L341 – “41 points”. You should refer to Table 1 here. Alternatively, you maybe should to rewrite somehow this part of the text (L340-372) to make it more consistent. You firstly say about dataset with 41 points at L341, but explain, why this dataset has 41 points, just at lines 367-372. It takes much time for readers to figure out.
L345 – Caption to Figure 6. Here you present some indexes: a,b,c,d,e, which are different groups of your model setup. In section 2.4 you present them with numbers: 1,2,3,4,5. I would advise to choose one form. It’s too complicated for readers.
L365 – There is no discussion in this section, why datasets just with GPP have no good agreement with modeled values of PP.
And it’s better to provide correlation coefficients and biases at least for models with the best performance.
L379 – Caption to Figure 7. What is a red line? The same comment as for caption to Figure 6 about groups. Furthermore, both figures are separated for integration depth: euphotic and productive layers. However, in Figure 7 you missed “e”-group.
L391 – Do you mean “Figure 6” in brackets?
L399-400 – Here you present best models in terms of 5 groups, however in Figure 7 there are just 4. The result of this section is the same as for the previous one. It should be mentioned somehow.
L427-428 - Do you have your own assessment of DCM input (in %) to total Chla according to your 1199 Chla profiles? It would be fine if you present it as well.
L435 – You missed a verb in the sentence with Ardyna et al. (2013) before “ten”.
L446 – It’s better to give standard deviation when average (mean) is given.
L450 – “mgC..”
L458 – Figure 10. “mg C…”. Spatial SD should be shown at the figure when use mean values. And I would advise to provide several trends averaged over different regions of the Greenland Sea. Especially it is discussed earlier in the paper that there are different areas in terms of dynamic that can affect PP.
L463-464 – It’s better to correct this sentence taking into account previous comments for the section 3.1. As you compare different types of PP.
Citation: https://doi.org/10.5194/egusphere-2023-2495-RC2 -
AC2: 'Reply on RC2', Aleksandra Cherkasheva, 04 Feb 2024
Dear Anonymous Referee #2,
First of all we would like to thank you for the time you took for reviewing our manuscript and your constructive comments. Our replies are listed below, straight after each of your comments.
Anonymous Referee #2, 08 Jan 2024
The subject of the paper is actual enough, especially when talk about recent climate changes in atmospheric CO2 and its influence on polar regions. Melting of sea ice and consequent increasing of open waters in the Arctic Ocean affect total CO2 uptake by marine phytoplankton and values of primary production. Developing of regional models of primary production is a challenge, as we should always take into account local feathers of the processes. In the paper authors have made a really good job on it and provide new regional assessments of primary production and its seasonal dynamic. Furthermore, they used for it an alternative method on a base of optic oxygen measurements.
However, it should be clarified accurately main definitions and differences in GPP, NCP, NPP as well as autotroph and community respiration. My major and minor comments are listed as follows:
R: we will add the definitions of GPP, NCP and NPP and explain the differences between those terms.
L23 – why “unexpected”? It should be clarified. R: We will delete the terms “expected” and “unexpected” in the abstract and clarify them when they appear later in the text.
L35-36 – Did just Rey et al., 2000 write about this? After the 2000 year there are lots of studies have been published on this topic. R: we will add more references on the deep convective mixing in the Greenland Sea.
L42 – It’s not correct to equal primary production to CO2 uptake. PP can be explained as a rate of synthesis (especially when we talk about PP models as we model the process) of organic matter for which inorganic carbon is needed. R: we will delete the part where primary production is defined to be equal to phytoplankton CO2 uptake
L55 – Figure 1. The area which is discussed in the paper lies outside the boundaries of the figure. It should be extended till 45W at least. A border of the Barents Sea lies on the east coast of the Bear Island (Svalbard). It’s not good to label an object on the map outside its borders. R: we will delete the label of the Barents Sea from this figure and extend the area to 45W.
L58 – I can be wrong, but commonly used abbreviation for this is DCM – Deep Chlorophyll Maxima. If it’s so, I would advise not to create entities to keep uniform abbreviation from study to study. R: To our knowledge, SCM - deep Subsurface Chlorophyll Maxima is a widely used term for the Arctic region, see e.g. Ardyna et al. (2013).
L59 – “but are not detected by ocean colour sensors”. It’s firstly depends on water transparency, and if it’s rather clear in open waters (small chl concentrations), and DCM lies in a layer of the penetration depth, it can be detected by the optic sensor. R: we will change this statement to “mostly not detected by ocean colour sensors”.
L73 – If you talk about Arctic in general you should cite other studies on PP models for this region not just Lee et al., 2015. R: we will add more citations on PP models developed for the Arctic.
L93 – Figure 2. “…Greenland Sea basin estimates and trends” of what? One can guess that here you mean PP, but in the figure caption it should be clarified. R: we will correct this sentence
L108 – If globally, there are some other studies on this model and its validation.R: we will add references for the other cases when this model was applied
L109-110 – I would advise to combine Section 2.2 and 2.4. Otherwise, when read this sentence it’s wanted to see the equation of the model and its description. Furthermore in the next section you talk about the data, and if the description of the model is before, it’s more understandable why you use mentioned data. At the same time, versions of model setup can be kept in the section 2.4. R: we will shift the main equation and model description from section 2.4. to section 2.2.
L123-124, L127 – What “coverage” do you mean? Some average value for the whole period 1998-2022? Data coverage can changes year to year and season to season. R: The coverage was calculated for the test month of August 2022 (defined previously in lines 119-120): “The test month of August 2022 was chosen based on the most recent expedition with available 120 field data”
L135 – “1199 profiles”. I would advise to say here some words about them: what seasons, region. It would be better than find out this information in another study. And how did you get this local coefficients? Is it discussed in Cherkasheva et al. (2013)? R: we will add more description of in situ CHL data used in Cherkasheva et al. (2013). Cherkasheva et al. (2013) study is fully dedicated to the development of the relationship between the surface CHL and its CHL profile, and the retrieval of local coefficients is discussed there in detail.
L147 – “Chla” isn’t used in this study. This denotement is from Bricaud et al., 1995. - R: corrected
L150, L495 – “the 14C method” is written as superscript. R: corrected
L160-161 – In what seasons did these cruises take place? It’s said later in the text but not here, when you introduce them. How many data were collected in each cruise? R: we will shift the information about seasons and data points collected for each of the cruises here
L162, L194 - "Gross Primary Production". Usually you use it with lowercase letters. R: corrected
L170-171 – It’s better to clarify, why these depths were chosen. R: we will clarify it
L179 – Why did you have to incubate for 48 and 72 hours? And for what Chla concentrations? Are you sure that when you incubated more than one day you didn’t have an organic matter destruction by bacteria in your bottles? Did you analyze time variability of O2 in the bottles for these cases? R: We will give the ranges of CHL concentrations for the data points. They were quite low, which led to us performing incubations for more than 24 hours to see a change in oxygen production. Measurements were taken every six hours, which is the time interval that should have captured the organic matter destruction decrease in oxygen concentrations, we did not observe such changes in our data. For the measurements we followed the procedure described in IOCCG Protocol Series (2022). We will add discussion of these points to the text.
L183-185 – It’s better to clarify all definitions: CR, NCP and GPP. As well is the difference between NCP and NPP. When you say CR, do you mean respiration of phyto-, zooplankton and bacteria or what? Furthermore, there is a misprint in this sentence: CR – is a difference between dark and control bottle, and NCP is a difference between light and control bottle. When you say “respectively”, you put “light” first. R: We will clarify the definitions of CR, NCP and GPP, and correct the misprint
L199 – What does this coefficient (4.6/12) mean? It should be explained. R: we will explain it
L203 – Did you check M&B algorithm on your 1199 Chla profiles? R: Yes, we checked it. From Cherkasheva et al. (2013): ”Since the Morel and Berthon (1989) relationship is seasonally averaged, it captured only the early months of the Greenland Sea season (April-June), suggesting the need to use the monthly resolved relationship for the region”
L252 – Do you mean “measured” and “derived” when use “m” and “d” respectively in your Eqs. 2-4? It should be clarified. Because in Eqs. 5-6 you use “insitu” and “modeled”. R: we will clarify the meaning of the subscripts “m” and “d”
L274 – It’s better to refer for Figure 8, when say about two regions. R: we will refer to Figure 8
L285 – Figure 3. The same comment as for the Figure 1 about the boundaries of the area. What are salinity units? Colour bar for PP isn’t good: all values are in blue colour, and just 2 in red. For my opinion, it should be reduced to 1500 mgCm2day to show PP variability better. Anyway, you can play with colourbar to find out a better solution. R: we will test the other options for PP colorbar and add salinity units to the cross-section. The boundaries of the area here are defined by the availability of the in situ data, i.e no PP field data points were available for 25W-45W.
L299 and discussion in this section – If define GPP and NPP correctly, it’s clear that GPP is obviously higher than NPP, because NPP = GPP-R(autotroph). NCP in turn is GPP-R(community). And it’s better to compare NCP assessments with NPP, but multiply the first by a coefficient which represents a part of autotroph respiration. R: We compare NPP to GPP as to our knowledge these are the two types of primary production traditionally measured as relevant to assess global CO2 fluxes (see comparison between NPP and GPP in e.g. Figure 5 in Robinson et al. (2009)), and produced as an output of satellite PP models (Westberry et al., 2023). NCP is not as commonly used, and is not relevant for the type of primary production modeling we work with in this study, as the Morel (1991) model was not designed to model NCP. There are models which were developed specifically to reproduce NCP (e.g. Li and Cassar (2016)). However, testing the NCP model performance and adapting the NCP model for the Greenland Sea was not the goal of this study.
L306 – You can compare your 2022 data integrated till 60 m with previous assessments. R: this is true, thank you for the idea, we have only four data points from 2022, but we will check at least those four
L326 – Sensitivity analysis was applied in many other studies not just in Lee et al. (2015). R: we will add more references for sensitivity analyses of PP models
L334 – Do you mean spatial interpolation? R: yes, will add the word “spatial”
L338-339 – When use this name “NPP interpolated” it is look like NPP was interpolated into a grid, not Chla. It’s better to clarify this in the text or choose another denotement. R: we will add more clarification to the term “NPP interpolated”
L341 – “41 points”. You should refer to Table 1 here. Alternatively, you maybe should to rewrite somehow this part of the text (L340-372) to make it more consistent. You firstly say about dataset with 41 points at L341, but explain, why this dataset has 41 points, just at lines 367-372. It takes much time for readers to figure out. R: we will change the order of sentences to make more clear why the dataset has 41 points. In addition we will refer to Table 1
L345 – Caption to Figure 6. Here you present some indexes: a,b,c,d,e, which are different groups of your model setup. In section 2.4 you present them with numbers: 1,2,3,4,5. I would advise to choose one form. It’s too complicated for readers. R: we will change the numbers in section 2.4 used to denote different groups of model setup to the indexes a,b,c,d,e used here
L365 – There is no discussion in this section, why datasets just with GPP have no good agreement with modeled values of PP. And it’s better to provide correlation coefficients and biases at least for models with the best performance. R: we will add here the interpretation of why datasets just with GPP have no good agreement with modeled values of PP
L379 – Caption to Figure 7. What is a red line? The same comment as for caption to Figure 6 about groups. Furthermore, both figures are separated for integration depth: euphotic and productive layers. However, in Figure 7 you missed the “e”-group. R: we will add the description of the red line, and also add the explanation of how the “e” group is presented here and why euphotic and productive layers are separated. Comment for caption to Figure 6 about groups is covered in the reply to the previous comment
L391 – Do you mean “Figure 6” in brackets? R: No, we mean “Figure 7”. Here we refer to the target diagrams which are presented in Figure 7
L399-400 – Here you present best models in terms of 5 groups, however in Figure 7 there are just 4. The result of this section is the same as for the previous one. It should be mentioned somehow. R: we will write that the results in this section are supporting the results of the previous section. We will explain in which way the five groups are present on Figure 7, and why the indices for only four groups are given
L427-428 - Do you have your own assessment of DCM input (in %) to total Chla according to your 1199 Chla profiles? It would be fine if you present it as well. R: we will add our assessment from Cherkasheva et al (2013): “omission of SCM in primary production models (i.e., when the uniform CHL profile is used) results in an average of about 10 % underestimation for the Greenland Sea”
L435 – You missed a verb in the sentence with Ardyna et al. (2013) before “ten”. R: we will correct this
L446 – It’s better to give standard deviation when average (mean) is given. R: we will add the standard deviation assessment to the text, as the Table 2 is already full with information from our point of view
L450 – “mgC..” R: we will correct it
L458 – Figure 10. “mg C…”. Spatial SD should be shown at the figure when use mean values. And I would advise to provide several trends averaged over different regions of the Greenland Sea. Especially it is discussed earlier in the paper that there are different areas in terms of dynamic that can affect PP. R: We will add the spatial SD to the figure. We agree that calculating trends for several areas within the study area will be a meaningful study, but from our point of view the justification for choosing different subregions and interpretations of trends within them is an extensive effort that could make an independent publication by itself, and is not in the scope of the current publication.
L463-464 – It’s better to correct this sentence taking into account previous comments for the section 3.1. As you compare different types of PP. R: we will change this sentence by adding information about difference in the definitions of GPP and NPP
References
Ardyna, M., M. Babin, M. Gosselin, E. Devred, S. Belanger, A. Matsuoka, and J.-E. Tremblay, Parameterization of vertical chlorophyll a in the Arctic Ocean: Impact of the subsurface chlorophyll maximum on regional, seasonal, and annual primary production estimates, Biogeosciences,10, 4383–4404, 2013
Cherkasheva, A., Nöthig, E.-M., Bauerfeind, E., Melsheimer, C., and Bracher, A.: From the chlorophyll a in the surface layer to its vertical profile: a Greenland Sea relationship for satellite applications. Ocean Science, 9, 431-445, doi:10.5194/os-9-431- 2013, 2013
IOCCG Protocol Series: Aquatic Primary Productivity Field Protocols for Satellite Validation and Model Synthesis. Balch, W.M., Carranza, M., Cetinić, I., Chaves, J.E., Duhamel, S., Fassbender, A., Fernandez-Carrera, A., Ferrón, S., García-Martín, E., Goes, J., Gomes, H., Gundersen, K., Halsey, K., Hirawake, T., Isada, T., Juranek, L., Kulk, G., Langdon, C., Letelier, R., López-Sandoval, D., Mannino, A., Marra, J.F., Neale, P., Nicholson, D., Silsbe, G., Stanley, R.H., Vandermeulen, R.A. IOCCG Ocean Optics and Biogeochemistry Protocols for Satellite Ocean Colour Sensor Validation, Volume 7.0, edited by R.A. Vandermeulen, J. E. Chaves, IOCCG, Dartmouth, NS, Canada. doi:http://dx.doi.org/10.25607/OBP-1835, 2022.
Li and Cassar, Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates, Global Biogeochem. Cycles, 30, 735–752, doi:10.1002/2015GB005314, 2016
Morel, A.: Light and marine photosynthesis: a spectral model with geochemical and climatological implications, Progress in Oceanography, 26, 3, 1991, 263-306, ISSN 0079-6611, doi: 10.1016/0079-6611(91)90004-6, 1991.
Robinson, C., Tilstone, G., Rees, A., Smyth, T., Fishwick, J., Tarran, G., Luz, B., Barkan, E., Efrat, D.: Comparison of in vitro 685 and in situ plankton production determinations. Aquatic Microbial Ecology. 54. 13-34. 10.3354/ame01250, 2009.
Westberry, T.K., Silsbe, G.M., Behrenfeld, M.J. Gross and net primary production in the global ocean: An ocean color remote sensing perspective, Earth-Science Reviews, v. 237, https://doi.org/10.1016/j.earscirev.2023.104322, 2023
Citation: https://doi.org/10.5194/egusphere-2023-2495-AC2
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AC2: 'Reply on RC2', Aleksandra Cherkasheva, 04 Feb 2024
Status: closed
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RC1: 'Comment on egusphere-2023-2495', Anonymous Referee #1, 05 Jan 2024
General comments
The authors compiled in situ NPP and GPP data in the Greenland Sea where number of matchup data is limited, and compared the in situ data with satellite NPP derived from different combinations of input parameters to choose the best combination to input into a satellite NPP model. They clarified that the combination of local chlorophyll a profile, local aph spectrum, and L2 PAR shows the best performance. These analyses and results are important for monitoring the marine ecosystem in the Arctic and are suitable topics for this journal.
However, validation analyses between satellite and in situ were performed only for PP, not for each input parameter, such as CHL and PAR. Even if satellite NPP coincides with in situ, large uncertainties of input parameters remain, and it is difficult to say that a model setup with the best performance on NPP is really accurate. Some parameters are derived from CHL. If CHL is overestimated, Zeu is underestimated. Therefore, validation of input parameters in the study region is recommended. Another issue is that the authors used monthly composited satellite data. Satellite data is monthly average, but in situ data is daily. I'm not sure what the comparison between monthly and daily data means. PAR data has larger uncertainties because climatology from only 2022 data or reanalysis data is used without any validation.
Methods for in situ NPP and GPP measurements and analyses (vertical integration) are not sufficiently described. Overall, methodological explanations are insufficient and identification numbers of data group and in situ data version are complicated. There are also many typos. More careful and substantial revisions are required for better understanding of the results.
Specific comments and technical corrections
L34 "Van Dyiken" should be "van Dijken"
L35 It is too much speculation if there is no evidence.
Figure 1 Please add a mean of vector color to the caption.
L61 Italicize 'a' of chlorophyll-a.
L81 Why did you use only the global primary production models? Development of a model? Tuning?
L105 If the model by Antoine and Morel (1996) is appropriate, it's better to try to obtain the LUTs from Prof. David Antoine.
L121 "For CHL" For satellite CHL
L135-136 What are "local empirical coefficients"? What is the CHL parameterization? What kind of profiles? Not "parameterisation"
L141 "0.7 um GF/F filters" glass fiber filters (Whatman, GF/F)
L141 Please use "°C"
L142 " Filter papers with deposited particles were measured with Lambda 850" Probably you measured absorbance (optical density) of particles with a spectrophotometer, not filter paper.
L145 "Chla" should be CHL concentration. How did you measure CHL concentration?
L150 Super script only for "14". Could you describe the detailed methods to measure PP using 14C (sampling, incubation, duration, temperature control, etc.)?
L155- Could you plot all data on a map? Figure 4?
L157 " PP data were logarithmically transformed (base 10) (Campbell, 1995) before being analysed in this study." Probably this sentence is not required because the equations of model performance metrics include "log". Not "analysed", "analyzed".
L162 "Gross Primary Production" should be "gross primary production".
L165 Illumination with an artificial lamp?
L165-166 No needed "+". Please show the range of water temperature in situ. Figure 4 shows that 4 degree C is found only in the frontal zone.
L170-171 PAR
L174 "darkening film" Neutral density film?
L175 TriOS, not TrioS. Probably, "Hyperspectral irradiance sensor RAMSES ACC (TriOS, Germany)" is the correct information on this sensor. Downwelling and upwelling depend on the direction of sensor detector.
L180 How much accurate are the dissolved oxygen standards?
L189-190 The general value of PQ was used to convert from O2 to C. How much does this affect the results?
L199 The model is depth-resolved but I can not find "z" in the equation (1).
L196-254 If possible, please summarize in a table.
L221 Does this mean that the CHL profile model was tuned by in-situ data?
L226 The term "PUR" is not found in equation (1).
L247 There are no descriptions on space- and time-windows for the matchup.
L249 Method for interpolation is difficult to understand. More details are required.
L282 Linear fit to log-transformed PP?
L292 side to the west.
L298-312 These are only comparison between NPP and GPP in average from different cruises, waters, and integration. At least, statistical test for difference between the two dataset is needed if both data are treated as same quality and quantity when satellite PP is evaluated by in situ data.
L328-332 It is very difficult to understand this chapter because the data groups are not clear. For example, the data group 3 has only CHL profiles but PP model needs other parameters.
L334-377 and Figure 6 are too much complicated and I could not understand how the authors identify the best models without statistical tests or any other metrics.
Caption of Table 1 and 2 should be put above the table.
Figure 7 What is the red line? I guess many readers are not familiar with the Target diagrams.
L407, 425 432, 442, Figure 8, and Table 2: "van Dijken", not Van Dijken.
L466-472 I understand the simultaneous measurement of NPP and GPP is challenging. How do you consider the difference between the two PPs in the study region?
L486 "For the Lee et al (2015) Arctic study" Typo?
L495 Superscript only "14"
Citation: https://doi.org/10.5194/egusphere-2023-2495-RC1 -
AC1: 'Reply on RC1', Aleksandra Cherkasheva, 31 Jan 2024
Dear Anonymous Referee #1,
First of all we would like to thank you for the time you took for reviewing our manuscript and your constructive comments. Our replies are listed below, straight after each of your comments.
Anonymous Referee #1, 05 Jan 2024
General comments
The authors compiled in situ NPP and GPP data in the Greenland Sea where number of matchup data is limited, and compared the in situ data with satellite NPP derived from different combinations of input parameters to choose the best combination to input into a satellite NPP model. They clarified that the combination of local chlorophyll a profile, local aph spectrum, and L2 PAR shows the best performance. These analyses and results are important for monitoring the marine ecosystem in the Arctic and are suitable topics for this journal.
However, validation analyses between satellite and in situ were performed only for PP, not for each input parameter, such as CHL and PAR. Even if satellite NPP coincides with in situ, large uncertainties of input parameters remain, and it is difficult to say that a model setup with the best performance on NPP is really accurate. Some parameters are derived from CHL. If CHL is overestimated, Zeu is underestimated. Therefore, validation of input parameters in the study region is recommended. Another issue is that the authors used monthly composited satellite data. Satellite data is monthly average, but in situ data is daily. I'm not sure what the comparison between monthly and daily data means. PAR data has larger uncertainties because climatology from only 2022 data or reanalysis data is used without any validation.
R: We agree that validation of the input parameters would have been an advantage for our study. However, as we do not have the in-situ data of all the input parameters measured simultaneously with PP (e.g. PAR was not simultaneously measured with PP), we will add the information from the previous studies validating those input parameters in the Arctic (e.g. Konik et al., 2020). We’ve used the monthly data to be able to obtain collocations between satellite and in-situ data, which is a challenging task due to the presence of sea ice and clouds in the area. We have previously shown that for the Fram Strait, where most of our field PP is concentrated, satellite chlorophyll data is available on average for 2 days in a month for the years 1998–2001 and 5 days in a month for the years after 2001 (Cherkasheva et al., 2014). This resulted in 10% of field data being collocated (54 collocations out of 526 field points). For the current study having 10% of collocations would have resulted in four points for the analysis to be based on, which is too few from our point of view. We took the PAR data from trusted sources, the evaluation of PAR algorithms at high northern latitudes was not in the scope of this study, but can be found in e.g. Laliberté et al. (2016). As Eumetsat OLCI Level 2 PAR data for all the months from April until September were available only starting from 2022, we took this only available year.
Methods for in situ NPP and GPP measurements and analyses (vertical integration) are not sufficiently described. Overall, methodological explanations are insufficient and identification numbers of data group and in situ data version are complicated. There are also many typos. More careful and substantial revisions are required for better understanding of the results.
R: We will add more information describing primary production measurements and their vertical integration, as well as revise the results section.
Specific comments and technical corrections
L34 "Van Dyiken" should be "van Dijken" R: corrected
L35 It is too much speculation if there is no evidence. R: changed the wording to give room for other possibilities and added references supporting the point
Figure 1 Please add a mean of vector color to the caption. R: description of the color code was added
L61 Italicize 'a' of chlorophyll-a. R: corrected
L81 Why did you use only the global primary production models? Development of a model? Tuning? R: we used a global primary production model as to our current knowledge there’s no primary production model specifically developed for the Greenland Sea. There are models developed for the other Arctic regions, such as a model by Fernández-Méndez et al. (2015) developed for the Central Arctic Ocean or a model by Hirawake et al. (2012) developed for the Chickchi and Bering Seas. We did not use them as from our point of view these are completely different regions in terms of environmental conditions, and we chose to take a uniform global relationship proven to perform well in the intercomparison study and adapt it for the area. We agree that “development of a model” is not a correct term in this case. Thus we use the wording “development of a model setup”.
L105 If the model by Antoine and Morel (1996) is appropriate, it's better to try to obtain the LUTs from Prof. David Antoine. R: we were not able to obtain LUTs from Prof. David Antoine
L121 "For CHL" For satellite CHL R: corrected
L135-136 What are "local empirical coefficients"? What is the CHL parameterization? What kind of profiles? Not "parameterisation" R: corrected, terms "local empirical coefficients" and “CHL parameterization” were explained
L141 "0.7 um GF/F filters" glass fiber filters (Whatman, GF/F) R: corrected
L141 Please use "°C" R: corrected
L142 " Filter papers with deposited particles were measured with Lambda 850" Probably you measured absorbance (optical density) of particles with a spectrophotometer, not filter paper. R: corrected
L145 "Chla" should be CHL concentration. How did you measure CHL concentration? R: corrected. A section 2.3.2.2 describing CHL concentration measurements was added
L150 Super script only for "14". Could you describe the detailed methods to measure PP using 14C (sampling, incubation, duration, temperature control, etc.)? R: Superscript corrected. As we didn’t measure the PP using 14C ourselves, but obtained the data from the literature, we will add the details on the methods that are available in the five sources used.
L155- Could you plot all data on a map? Figure 4? R: All the data mentioned in this section (field PP) are already plotted on Figure 4. Hopefully we understood this comment correctly, if not please give more details.
L157 " PP data were logarithmically transformed (base 10) (Campbell, 1995) before being analysed in this study." Probably this sentence is not required because the equations of model performance metrics include "log". Not "analysed", "analyzed". R: corrected
L162 "Gross Primary Production" should be "gross primary production". R: corrected
L165 Illumination with an artificial lamp? R: corrected
L165-166 No needed "+". Please show the range of water temperature in situ. Figure 4 shows that 4 degree C is found only in the frontal zone. R: corrected, added the range of water temperature in situ. According to IOCCG Protocol Series (2022) the changes in temperature affect the sensor measurements, thus the temperature needs to be constant throughout the experiments. We chose the value for the constant temperature based on the average temperature of the sampled layer in the area.
L170-171 PAR R: corrected
L174 "darkening film" Neutral density film? R: corrected
L175 TriOS, not TrioS. Probably, "Hyperspectral irradiance sensor RAMSES ACC (TriOS, Germany)" is the correct information on this sensor. Downwelling and upwelling depend on the direction of sensor detector. R: corrected
L180 How much accurate are the dissolved oxygen standards? R: We followed the procedure described in Presens Fixbox 4 manual for calibrating the sensor by the dissolved oxygen standards. There’s no accuracy assessment in this procedure. Note that the primary production is calculated from oxygen gradients and not absolute values, therefore having accurate calibration is not as critical for this case.
L189-190 The general value of PQ was used to convert from O2 to C. How much does this affect the results? R: We will test the influence of different PQ values found in the literature on the result.
L199 The model is depth-resolved but I can not find "z" in the equation (1). R: The term CHLtot is the depth-integrated value, which depends on the chlorophyll vertical profile
L196-254 If possible, please summarize in a table. R: we will add a table
L221 Does this mean that the CHL profile model was tuned by in-situ data? R: Changed to “developed based on the analysis of 1199 profiles ”. The Gaussian profiles were fitted to the mean CHL profile, which was calculated for several categories defined by surface concentration and month. Therefore, the CHL profile model is a fit to in-situ data averaged within several categories. We are not sure that “tuned” is a correct term for it, please correct us if you don’t agree. For more details please see Cherkasheva et al (2013)
L226 The term "PUR" is not found in equation (1). R: PUR, defined as the fraction of PAR that can be absorbed by algae (Morel, 1978) is used instead of PAR in the equation (1) for the cases listed in the group 4. In this case the term “PAR” is substituted by the term “PUR”. We will add this information to the text.
L247 There are no descriptions on space- and time-windows for the matchup. R: we will add the descriptions
L249 Method for interpolation is difficult to understand. More details are required. R: we will add more details
L282 Linear fit to log-transformed PP? R: No, for the basin estimates and trends calculations the PP is not log-transformed. Log-transformation is used when validating the model results against filed PP data. Added more clarification on that.
L292 side to the west. - corrected
L298-312 These are only comparison between NPP and GPP in average from different cruises, waters, and integration. At least, statistical test for difference between the two dataset is needed if both data are treated as same quality and quantity when satellite PP is evaluated by in situ data. R: we will add more statistical characteristics on the difference between the two datasets
L328-332 It is very difficult to understand this chapter because the data groups are not clear. For example, the data group 3 has only CHL profiles but PP model needs other parameters. R: we will add a table describing data groups to section 2.4. Within each of the data groups, all the combinations of other parameters listed in section 2.4 were tested.
L334-377 and Figure 6 are too much complicated and I could not understand how the authors identify the best models without statistical tests or any other metrics. R: We will add more explanation for Figure 6. We have identified the best models following the procedure described in the satellite primary production models intercomparison study by Lee et al. (2015). See lines 270-272: “As in Lee et al. (2015), model versions performing relatively better than the others were selected for further analysis using the two criteria: (1) bias was close to 0 (-0.1<bias<0.1), and (2) Pearson's correlation coefficient (r) was greater than the model average (0.25).”
Caption of Table 1 and 2 should be put above the table. R: corrected
Figure 7 What is the red line? I guess many readers are not familiar with the Target diagrams. R: added the description of the red line
L407, 425 432, 442, Figure 8, and Table 2: "van Dijken", not Van Dijken. R: corrected
L466-472 I understand the simultaneous measurement of NPP and GPP is challenging. How do you consider the difference between the two PPs in the study region? R: As the relationships between GPP and NPP can vary depending on the environment and species (IOCCG Protocol Series, 2022), it is not a common practice to use any factor for conversion between these two different types of primary production measurements. In a way we consider the difference by running the performance test for three different types of datasets: 1) only NPP, 2) only GPP, 3) NPP and GPP. The models that performed best both for the first and the third dataset were chosen for the analysis. No models passed the performance test for the second dataset. Thus, the selected models perform well in reproducing only NPP and NPP combined with GPP.
L486 "For the Lee et al (2015) Arctic study" Typo? R: corrected
L495 Superscript only "14" R: corrected
References
Cherkasheva, A., Nöthig, E.-M., Bauerfeind, E., Melsheimer, C., and Bracher, A.: From the chlorophyll a in the surface layer to its vertical profile: a Greenland Sea relationship for satellite applications. Ocean Science, 9, 431-445, doi:10.5194/os-9-431-2013, 2013
Cherkasheva, A., Bracher, A., Melsheimer, C., Köberle, C., Gerdes, R., Nöthig, E.-M., Bauerfeind, E., Boetius, A. Influence of the physical environment on polar phytoplankton blooms: A case study in the Fram Strait, Journal of Marine Systems, Vol 132, 2014, pp 196-207, ISSN 0924-7963, https://doi.org/10.1016/j.jmarsys.2013.11.008., 2014
Fernández-Méndez, M., C. Katlein, B. Rabe, M. Nicolaus, I. Peeken, K. Bakker, H. Flores, and A. Boetius, Photosynthetic production in the Central Arctic during the record sea-ice minimum in 2012, Biogeosciences, 12, 3525–3549, doi:10.5194/bg-12-3525-2015., 2015
IOCCG Protocol Series: Aquatic Primary Productivity Field Protocols for Satellite Validation and Model Synthesis. Balch, W.M., Carranza, M., Cetinić, I., Chaves, J.E., Duhamel, S., Fassbender, A., Fernandez-Carrera, A., Ferrón, S., García-Martín, E., Goes, J., Gomes, H., Gundersen, K., Halsey, K., Hirawake, T., Isada, T., Juranek, L., Kulk, G., Langdon, C., Letelier, R., López-Sandoval, D., Mannino, A., Marra, J.F., Neale, P., Nicholson, D., Silsbe, G., Stanley, R.H., Vandermeulen, R.A. IOCCG Ocean Optics and Biogeochemistry Protocols for Satellite Ocean Colour Sensor Validation, Volume 7.0, edited by R.A. Vandermeulen, J. E. Chaves, IOCCG, Dartmouth, NS, Canada. doi:http://dx.doi.org/10.25607/OBP-1835, 2022.
Hirawake, T., K. Shinmyo, A. Fujiwara, and S. I. Saitoh, Satellite remote sensing of primary productivity in the Bering and Chukchi Seas using an absorption-based approach, ICES J. Mar. Sci., 69, 1194–1204, 2012
Konik, M., Kowalczuk, P., Zabłocka, M., Makarewicz, A., Meler, J., Zdun, A., Darecki, M. Empirical Relationships between Remote-SensingReflectance and Selected Inherent Optical Properties in Nordic Sea Surface Waters for the MODIS andOLCI Ocean Colour Sensors. Remote Sensing. 12. 2774. 10.3390/rs12172774, 2020.
Laliberté, J., Bélanger, S., Frouin, R. Evaluation of satellite-based algorithms to estimate photosynthetically available radiation (PAR) reaching the ocean surface at high northern latitudes, Remote Sensing of Environment, Vol. 184, 2016, Pp 199-211, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2016.06.014., 2016
Lee, Y. J., et al. An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models, Journal of Geophysical Research Oceans, 120, 6508–6541, doi:10.1002/2015JC011018, 2015.
Morel, A. Available, usable, and stored radiant energy in relation to marine photosynthesis. Deep-Sea Research, 25, 673-688, 1978
Citation: https://doi.org/10.5194/egusphere-2023-2495-AC1
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AC1: 'Reply on RC1', Aleksandra Cherkasheva, 31 Jan 2024
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RC2: 'Comment on egusphere-2023-2495', Anonymous Referee #2, 08 Jan 2024
The subject of the paper is actual enough, especially when talk about recent climate changes in atmospheric CO2 and its influence on polar regions. Melting of sea ice and consequent increasing of open waters in the Arctic Ocean affect total CO2 uptake by marine phytoplankton and values of primary production. Developing of regional models of primary production is a challenge, as we should always take into account local feathers of the processes. In the paper authors have made a really good job on it and provide new regional assessments of primary production and its seasonal dynamic. Furthermore, they used for it an alternative method on a base of optic oxygen measurements.
However, it should be clarified accurately main definitions and differences in GPP, NCP, NPP as well as autotroph and community respiration. My major and minor comments are listed as follows:
L23 – why “unexpected”? It should be clarified.
L35-36 – Did just Rey et al., 2000 write about this? After the 2000 year there are lots of studies have been published on this topic.
L42 – It’s not correct to equal primary production to CO2 uptake. PP can be explained as a rate of synthesis (especially when we talk about PP models as we model the process) of organic matter for which inorganic carbon is needed.
L55 – Figure 1. The area which is discussed in the paper lies outside the boundaries of the figure. It should be extended till 45W at least. A boarder of the Barents Sea lies on the east coast of the Bear Island (Svalbard). It’s not good to label an object on the map outside its borders.
L58 – I can be wrong, but commonly used abbreviation for this is DCM – Deep Chlorophyll Maxima. If it’s so, I would advise not to create entities to keep uniform abbreviation from study to study.
L59 – “but are not detected by ocean colour sensors”. It’s firstly depends on water transparency, and if it’s rather clear in open waters (small chl concentrations), and DCM lies in a layer of the penetration depth, it can be detected by the optic sensor.
L73 – If you talk about Arctic in general you should cite other studies on PP models for this region not just Lee et al., 2015.
L93 – Figure 2. “…Greenland Sea basin estimates and trends” of what? One can guess that here you mean PP, but in the figure caption it should be clarified.
L108 – If globally, there are some other studies on this model and its validation.
L109-110 – I would advise to combine Section 2.2 and 2.4. Otherwise, when read this sentence it’s wanted to see the equation of the model and its description. Furthermore in the next section you talk about the data, and if the description of the model is before, it’s more understandable why you use mentioned data. At the same time, versions of model setup can be kept in the section 2.4.
L123-124, L127 – What “coverage” do you mean? Some average value for the whole period 1998-2022? Data coverage can changes year to year and season to season.
L135 – “1199 profiles”. I would advise to say here some words about them: what seasons, region. It would be better than find out this information in another study. And how did you get this local coefficients? Is it discussed in Cherkasheva et al. (2013)?
L147 – “Chla” isn’t used in this study. This denotement is from Bricaud et al., 1995.
L150, L495 – “the 14C method” is written as superscript.
L160-161 – In what seasons did these cruises take place? It’s said later in the text but not here, when you introduce them. How many data were collected in each cruise?
L162, L194 - "Gross Primary Production". Usually you use it with lowercase letters.
L170-171 – It’s better to clarify, why these depths were chosen.
L179 – Why did you have to incubate for 48 and 72 hours? And for what Chla concentrations? Are you sure that when you incubated more than one day you didn’t have an organic matter destruction by bacteria in your bottles? Did you analyze time variability of O2 in the bottles for these cases?
L183-185 – It’s better to clarify all definitions: CR, NCP and GPP. As well is the difference between NCP and NPP. When you say CR, do you mean respiration of phyto-, zooplankton and bacteria or what? Furthermore, there is a misprint in this sentence: CR – is a difference between dark and control bottle, and NCP is a difference between light and control bottle. When you say “respectively”, you put “light” first.
L199 – What does this coefficient (4.6/12) mean? It should be explained.
L203 – Did you check M&B algorithm on your 1199 Chla profiles?
L252 – Do you mean “measured” and “derived” when use “m” and “d” respectively in your Eqs. 2-4? It should be clarified. Because in Eqs. 5-6 you use “insitu” and “modeled”.
L274 – It’s better to refer for Figure 8, when say about two regions.
L285 – Figure 3. The same comment as for the Figure 1 about the boundaries of the area. What are salinity units? Colour bar for PP isn’t good: all values are in blue colour, and just 2 in red. For my opinion, it should be reduced to 1500 mgCm2day to show PP variability better. Anyway, you can play with colourbar to find out a better solution.
L299 and discussion in this section – If define GPP and NPP correctly, it’s clear that GPP is obviously higher than NPP, because NPP = GPP-R(autotroph). NCP in turn is GPP-R(community). And it’s better to compare NCP assessments with NPP, but multiply the first by a coefficient which represents a part of autotroph respiration.
L306 – You can compare your 2022 data integrated till 60 m with previous assessments.
L326 – Sensitivity analysis was applied in many other studies not just in Lee et al. (2015).
L334 – Do you mean spatial interpolation?
L338-339 – When use this name “NPP interpolated” it is look like NPP was interpolated into a grid, not Chla. It’s better to clarify this in the text or choose another denotement.
L341 – “41 points”. You should refer to Table 1 here. Alternatively, you maybe should to rewrite somehow this part of the text (L340-372) to make it more consistent. You firstly say about dataset with 41 points at L341, but explain, why this dataset has 41 points, just at lines 367-372. It takes much time for readers to figure out.
L345 – Caption to Figure 6. Here you present some indexes: a,b,c,d,e, which are different groups of your model setup. In section 2.4 you present them with numbers: 1,2,3,4,5. I would advise to choose one form. It’s too complicated for readers.
L365 – There is no discussion in this section, why datasets just with GPP have no good agreement with modeled values of PP.
And it’s better to provide correlation coefficients and biases at least for models with the best performance.
L379 – Caption to Figure 7. What is a red line? The same comment as for caption to Figure 6 about groups. Furthermore, both figures are separated for integration depth: euphotic and productive layers. However, in Figure 7 you missed “e”-group.
L391 – Do you mean “Figure 6” in brackets?
L399-400 – Here you present best models in terms of 5 groups, however in Figure 7 there are just 4. The result of this section is the same as for the previous one. It should be mentioned somehow.
L427-428 - Do you have your own assessment of DCM input (in %) to total Chla according to your 1199 Chla profiles? It would be fine if you present it as well.
L435 – You missed a verb in the sentence with Ardyna et al. (2013) before “ten”.
L446 – It’s better to give standard deviation when average (mean) is given.
L450 – “mgC..”
L458 – Figure 10. “mg C…”. Spatial SD should be shown at the figure when use mean values. And I would advise to provide several trends averaged over different regions of the Greenland Sea. Especially it is discussed earlier in the paper that there are different areas in terms of dynamic that can affect PP.
L463-464 – It’s better to correct this sentence taking into account previous comments for the section 3.1. As you compare different types of PP.
Citation: https://doi.org/10.5194/egusphere-2023-2495-RC2 -
AC2: 'Reply on RC2', Aleksandra Cherkasheva, 04 Feb 2024
Dear Anonymous Referee #2,
First of all we would like to thank you for the time you took for reviewing our manuscript and your constructive comments. Our replies are listed below, straight after each of your comments.
Anonymous Referee #2, 08 Jan 2024
The subject of the paper is actual enough, especially when talk about recent climate changes in atmospheric CO2 and its influence on polar regions. Melting of sea ice and consequent increasing of open waters in the Arctic Ocean affect total CO2 uptake by marine phytoplankton and values of primary production. Developing of regional models of primary production is a challenge, as we should always take into account local feathers of the processes. In the paper authors have made a really good job on it and provide new regional assessments of primary production and its seasonal dynamic. Furthermore, they used for it an alternative method on a base of optic oxygen measurements.
However, it should be clarified accurately main definitions and differences in GPP, NCP, NPP as well as autotroph and community respiration. My major and minor comments are listed as follows:
R: we will add the definitions of GPP, NCP and NPP and explain the differences between those terms.
L23 – why “unexpected”? It should be clarified. R: We will delete the terms “expected” and “unexpected” in the abstract and clarify them when they appear later in the text.
L35-36 – Did just Rey et al., 2000 write about this? After the 2000 year there are lots of studies have been published on this topic. R: we will add more references on the deep convective mixing in the Greenland Sea.
L42 – It’s not correct to equal primary production to CO2 uptake. PP can be explained as a rate of synthesis (especially when we talk about PP models as we model the process) of organic matter for which inorganic carbon is needed. R: we will delete the part where primary production is defined to be equal to phytoplankton CO2 uptake
L55 – Figure 1. The area which is discussed in the paper lies outside the boundaries of the figure. It should be extended till 45W at least. A border of the Barents Sea lies on the east coast of the Bear Island (Svalbard). It’s not good to label an object on the map outside its borders. R: we will delete the label of the Barents Sea from this figure and extend the area to 45W.
L58 – I can be wrong, but commonly used abbreviation for this is DCM – Deep Chlorophyll Maxima. If it’s so, I would advise not to create entities to keep uniform abbreviation from study to study. R: To our knowledge, SCM - deep Subsurface Chlorophyll Maxima is a widely used term for the Arctic region, see e.g. Ardyna et al. (2013).
L59 – “but are not detected by ocean colour sensors”. It’s firstly depends on water transparency, and if it’s rather clear in open waters (small chl concentrations), and DCM lies in a layer of the penetration depth, it can be detected by the optic sensor. R: we will change this statement to “mostly not detected by ocean colour sensors”.
L73 – If you talk about Arctic in general you should cite other studies on PP models for this region not just Lee et al., 2015. R: we will add more citations on PP models developed for the Arctic.
L93 – Figure 2. “…Greenland Sea basin estimates and trends” of what? One can guess that here you mean PP, but in the figure caption it should be clarified. R: we will correct this sentence
L108 – If globally, there are some other studies on this model and its validation.R: we will add references for the other cases when this model was applied
L109-110 – I would advise to combine Section 2.2 and 2.4. Otherwise, when read this sentence it’s wanted to see the equation of the model and its description. Furthermore in the next section you talk about the data, and if the description of the model is before, it’s more understandable why you use mentioned data. At the same time, versions of model setup can be kept in the section 2.4. R: we will shift the main equation and model description from section 2.4. to section 2.2.
L123-124, L127 – What “coverage” do you mean? Some average value for the whole period 1998-2022? Data coverage can changes year to year and season to season. R: The coverage was calculated for the test month of August 2022 (defined previously in lines 119-120): “The test month of August 2022 was chosen based on the most recent expedition with available 120 field data”
L135 – “1199 profiles”. I would advise to say here some words about them: what seasons, region. It would be better than find out this information in another study. And how did you get this local coefficients? Is it discussed in Cherkasheva et al. (2013)? R: we will add more description of in situ CHL data used in Cherkasheva et al. (2013). Cherkasheva et al. (2013) study is fully dedicated to the development of the relationship between the surface CHL and its CHL profile, and the retrieval of local coefficients is discussed there in detail.
L147 – “Chla” isn’t used in this study. This denotement is from Bricaud et al., 1995. - R: corrected
L150, L495 – “the 14C method” is written as superscript. R: corrected
L160-161 – In what seasons did these cruises take place? It’s said later in the text but not here, when you introduce them. How many data were collected in each cruise? R: we will shift the information about seasons and data points collected for each of the cruises here
L162, L194 - "Gross Primary Production". Usually you use it with lowercase letters. R: corrected
L170-171 – It’s better to clarify, why these depths were chosen. R: we will clarify it
L179 – Why did you have to incubate for 48 and 72 hours? And for what Chla concentrations? Are you sure that when you incubated more than one day you didn’t have an organic matter destruction by bacteria in your bottles? Did you analyze time variability of O2 in the bottles for these cases? R: We will give the ranges of CHL concentrations for the data points. They were quite low, which led to us performing incubations for more than 24 hours to see a change in oxygen production. Measurements were taken every six hours, which is the time interval that should have captured the organic matter destruction decrease in oxygen concentrations, we did not observe such changes in our data. For the measurements we followed the procedure described in IOCCG Protocol Series (2022). We will add discussion of these points to the text.
L183-185 – It’s better to clarify all definitions: CR, NCP and GPP. As well is the difference between NCP and NPP. When you say CR, do you mean respiration of phyto-, zooplankton and bacteria or what? Furthermore, there is a misprint in this sentence: CR – is a difference between dark and control bottle, and NCP is a difference between light and control bottle. When you say “respectively”, you put “light” first. R: We will clarify the definitions of CR, NCP and GPP, and correct the misprint
L199 – What does this coefficient (4.6/12) mean? It should be explained. R: we will explain it
L203 – Did you check M&B algorithm on your 1199 Chla profiles? R: Yes, we checked it. From Cherkasheva et al. (2013): ”Since the Morel and Berthon (1989) relationship is seasonally averaged, it captured only the early months of the Greenland Sea season (April-June), suggesting the need to use the monthly resolved relationship for the region”
L252 – Do you mean “measured” and “derived” when use “m” and “d” respectively in your Eqs. 2-4? It should be clarified. Because in Eqs. 5-6 you use “insitu” and “modeled”. R: we will clarify the meaning of the subscripts “m” and “d”
L274 – It’s better to refer for Figure 8, when say about two regions. R: we will refer to Figure 8
L285 – Figure 3. The same comment as for the Figure 1 about the boundaries of the area. What are salinity units? Colour bar for PP isn’t good: all values are in blue colour, and just 2 in red. For my opinion, it should be reduced to 1500 mgCm2day to show PP variability better. Anyway, you can play with colourbar to find out a better solution. R: we will test the other options for PP colorbar and add salinity units to the cross-section. The boundaries of the area here are defined by the availability of the in situ data, i.e no PP field data points were available for 25W-45W.
L299 and discussion in this section – If define GPP and NPP correctly, it’s clear that GPP is obviously higher than NPP, because NPP = GPP-R(autotroph). NCP in turn is GPP-R(community). And it’s better to compare NCP assessments with NPP, but multiply the first by a coefficient which represents a part of autotroph respiration. R: We compare NPP to GPP as to our knowledge these are the two types of primary production traditionally measured as relevant to assess global CO2 fluxes (see comparison between NPP and GPP in e.g. Figure 5 in Robinson et al. (2009)), and produced as an output of satellite PP models (Westberry et al., 2023). NCP is not as commonly used, and is not relevant for the type of primary production modeling we work with in this study, as the Morel (1991) model was not designed to model NCP. There are models which were developed specifically to reproduce NCP (e.g. Li and Cassar (2016)). However, testing the NCP model performance and adapting the NCP model for the Greenland Sea was not the goal of this study.
L306 – You can compare your 2022 data integrated till 60 m with previous assessments. R: this is true, thank you for the idea, we have only four data points from 2022, but we will check at least those four
L326 – Sensitivity analysis was applied in many other studies not just in Lee et al. (2015). R: we will add more references for sensitivity analyses of PP models
L334 – Do you mean spatial interpolation? R: yes, will add the word “spatial”
L338-339 – When use this name “NPP interpolated” it is look like NPP was interpolated into a grid, not Chla. It’s better to clarify this in the text or choose another denotement. R: we will add more clarification to the term “NPP interpolated”
L341 – “41 points”. You should refer to Table 1 here. Alternatively, you maybe should to rewrite somehow this part of the text (L340-372) to make it more consistent. You firstly say about dataset with 41 points at L341, but explain, why this dataset has 41 points, just at lines 367-372. It takes much time for readers to figure out. R: we will change the order of sentences to make more clear why the dataset has 41 points. In addition we will refer to Table 1
L345 – Caption to Figure 6. Here you present some indexes: a,b,c,d,e, which are different groups of your model setup. In section 2.4 you present them with numbers: 1,2,3,4,5. I would advise to choose one form. It’s too complicated for readers. R: we will change the numbers in section 2.4 used to denote different groups of model setup to the indexes a,b,c,d,e used here
L365 – There is no discussion in this section, why datasets just with GPP have no good agreement with modeled values of PP. And it’s better to provide correlation coefficients and biases at least for models with the best performance. R: we will add here the interpretation of why datasets just with GPP have no good agreement with modeled values of PP
L379 – Caption to Figure 7. What is a red line? The same comment as for caption to Figure 6 about groups. Furthermore, both figures are separated for integration depth: euphotic and productive layers. However, in Figure 7 you missed the “e”-group. R: we will add the description of the red line, and also add the explanation of how the “e” group is presented here and why euphotic and productive layers are separated. Comment for caption to Figure 6 about groups is covered in the reply to the previous comment
L391 – Do you mean “Figure 6” in brackets? R: No, we mean “Figure 7”. Here we refer to the target diagrams which are presented in Figure 7
L399-400 – Here you present best models in terms of 5 groups, however in Figure 7 there are just 4. The result of this section is the same as for the previous one. It should be mentioned somehow. R: we will write that the results in this section are supporting the results of the previous section. We will explain in which way the five groups are present on Figure 7, and why the indices for only four groups are given
L427-428 - Do you have your own assessment of DCM input (in %) to total Chla according to your 1199 Chla profiles? It would be fine if you present it as well. R: we will add our assessment from Cherkasheva et al (2013): “omission of SCM in primary production models (i.e., when the uniform CHL profile is used) results in an average of about 10 % underestimation for the Greenland Sea”
L435 – You missed a verb in the sentence with Ardyna et al. (2013) before “ten”. R: we will correct this
L446 – It’s better to give standard deviation when average (mean) is given. R: we will add the standard deviation assessment to the text, as the Table 2 is already full with information from our point of view
L450 – “mgC..” R: we will correct it
L458 – Figure 10. “mg C…”. Spatial SD should be shown at the figure when use mean values. And I would advise to provide several trends averaged over different regions of the Greenland Sea. Especially it is discussed earlier in the paper that there are different areas in terms of dynamic that can affect PP. R: We will add the spatial SD to the figure. We agree that calculating trends for several areas within the study area will be a meaningful study, but from our point of view the justification for choosing different subregions and interpretations of trends within them is an extensive effort that could make an independent publication by itself, and is not in the scope of the current publication.
L463-464 – It’s better to correct this sentence taking into account previous comments for the section 3.1. As you compare different types of PP. R: we will change this sentence by adding information about difference in the definitions of GPP and NPP
References
Ardyna, M., M. Babin, M. Gosselin, E. Devred, S. Belanger, A. Matsuoka, and J.-E. Tremblay, Parameterization of vertical chlorophyll a in the Arctic Ocean: Impact of the subsurface chlorophyll maximum on regional, seasonal, and annual primary production estimates, Biogeosciences,10, 4383–4404, 2013
Cherkasheva, A., Nöthig, E.-M., Bauerfeind, E., Melsheimer, C., and Bracher, A.: From the chlorophyll a in the surface layer to its vertical profile: a Greenland Sea relationship for satellite applications. Ocean Science, 9, 431-445, doi:10.5194/os-9-431- 2013, 2013
IOCCG Protocol Series: Aquatic Primary Productivity Field Protocols for Satellite Validation and Model Synthesis. Balch, W.M., Carranza, M., Cetinić, I., Chaves, J.E., Duhamel, S., Fassbender, A., Fernandez-Carrera, A., Ferrón, S., García-Martín, E., Goes, J., Gomes, H., Gundersen, K., Halsey, K., Hirawake, T., Isada, T., Juranek, L., Kulk, G., Langdon, C., Letelier, R., López-Sandoval, D., Mannino, A., Marra, J.F., Neale, P., Nicholson, D., Silsbe, G., Stanley, R.H., Vandermeulen, R.A. IOCCG Ocean Optics and Biogeochemistry Protocols for Satellite Ocean Colour Sensor Validation, Volume 7.0, edited by R.A. Vandermeulen, J. E. Chaves, IOCCG, Dartmouth, NS, Canada. doi:http://dx.doi.org/10.25607/OBP-1835, 2022.
Li and Cassar, Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates, Global Biogeochem. Cycles, 30, 735–752, doi:10.1002/2015GB005314, 2016
Morel, A.: Light and marine photosynthesis: a spectral model with geochemical and climatological implications, Progress in Oceanography, 26, 3, 1991, 263-306, ISSN 0079-6611, doi: 10.1016/0079-6611(91)90004-6, 1991.
Robinson, C., Tilstone, G., Rees, A., Smyth, T., Fishwick, J., Tarran, G., Luz, B., Barkan, E., Efrat, D.: Comparison of in vitro 685 and in situ plankton production determinations. Aquatic Microbial Ecology. 54. 13-34. 10.3354/ame01250, 2009.
Westberry, T.K., Silsbe, G.M., Behrenfeld, M.J. Gross and net primary production in the global ocean: An ocean color remote sensing perspective, Earth-Science Reviews, v. 237, https://doi.org/10.1016/j.earscirev.2023.104322, 2023
Citation: https://doi.org/10.5194/egusphere-2023-2495-AC2
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AC2: 'Reply on RC2', Aleksandra Cherkasheva, 04 Feb 2024
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