the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
CMEMS-LSCE: A global 0.25-degree, monthly reconstruction of the surface ocean carbonate system
Thi-Tuyet-Trang Chau
Marion Gehlen
Nicolas Metzl
Frédéric Chevallier
Abstract. Observation-based data reconstructions of global surface ocean carbonate system variables play an essential role in monitoring the recent status of ocean carbon uptake and ocean acidification as well as their impacts on marine organisms and ecosystems. So far ongoing efforts are directed towards exploring new approaches to describe the complete marine carbonate system and to better recover its fine-scale features. In this respect, our research activities within the Copernicus Marine Environment Monitoring Service (CMEMS) aim at developing a sustainable production chain of observation-derived global ocean carbonate system datasets at high space-time resolution. As the start of the long-term objective, this study introduces a new global 0.25° monthly reconstruction, namely CMEMS-LSCE, for the period 1985–2021. The CMEMS-LSCE reconstruction derives datasets of six carbonate system variables including surface ocean partial pressure of CO2 (pCO2), total alkalinity (AT), total dissolved inorganic carbon (DIC), surface ocean pH, and saturation states with respect to aragonite (Ωar) and calcite (Ωca). Reconstructing pCO2 relies on an ensemble of neural network models mapping gridded observation-based data provided by the Surface Ocean CO2 ATlas (SOCAT). Surface ocean AT is estimated with a multiple linear regression approach, and the remaining carbonate variables are resolved by CO2 system speciation given the reconstructed pCO2 and AT. 1σ-uncertainty associated with these estimates is also provided. Here, σ stands for either ensemble standard deviation of pCO2 estimates or total uncertainty for each of the five other variables propagated through the processing chain with input data uncertainty. We demonstrate that the 0.25°-resolution pCO2 product outperforms a coarser spatial resolution (1°) thanks to a higher data coverage nearshore and a better description of horizontal and temporal variations in pCO2 across diverse ocean basins, particularly in the coastal-open-ocean continuum. Product qualification with observation-based data confirms reliable reconstructions with root-of-mean–square–deviation from observations less than 8 %, 4 %, and 1 % relative to the global mean of pCO2, AT (DIC), and pH. The global average 1σ-uncertainty is below 5 % and 8 % for pCO2 and Ωar (Ωca), 2 % for AT and DIC, and 0.4 % for pH relative to their global mean values. Both model-observation misfit and model uncertainty indicate that coastal data reproduction still needs further improvement, wherein high temporal and horizontal gradients of carbonate variables and representative uncertainty from data sampling would be taken into account in priority. This study also presents a potential use case of the CMEMS-LSCE carbonate data product in tracking the recent state of ocean acidification.
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Thi-Tuyet-Trang Chau et al.
Status: closed
- RC1: 'Comment on essd-2023-146', Anonymous Referee #1, 04 Jun 2023
-
RC2: 'Comment on essd-2023-146', Anonymous Referee #2, 07 Jul 2023
The authors reconstructed 0.25 degree monthly full carbonate system variables during the period 1985-2021 based on surface ocean observation data. Distributions of pCO2 were reconstructed based on the machine learning method established by the authors (Trang-Chau et al. 2022) and those of TA were based on the LIAR method (Carter et al. 2016; 2018). While few reconstructions of full carbonate system variables are available at this moment, a comprehensive understanding of global surface ocean pH distributions is essential for monitoring ocean acidification, which is related to the SDG indicator 14.3.1. This study can enhance researches on the global carbon cycle as well as provide critical information to policymakers and stakeholders. I think this study has sufficient value to be published in this journal, but major concerns listed below should be addressed appropriately. I would like to encourage the authors to improve the study and revise the manuscript for better understanding.
General comment
The concept of this study itself is not novel, and the assessment of uncertainty in the reconstructed fields and the validation of the method become important. The authors derived uncertainty distributions in reconstructed parameters from the spread of 100 model ensemble. They also demonstrated the validity of the method by comparing the result of this study with observation data that were not used for learning and those of time-series points. The time-series used in this study are biasedly located in the subtropical region, so comparing their data with the results of this study does not seem a good indicator of uncertainty. For validation of this method, the authors must take a comparison with other reconstruction(s) into account, if needed.
The authors use external SST and SSS instead of those incorporated in the datasets in the learning process. This seems unusual because the oceanographic condition represented by temperature and salinity considerably affects the ocean biogeochemistry in the observed area. If the authors think the use of external SST/SSS to be essential, they must demonstrate that the impact of differences between external SST/SSS and those in the datasets is negligible.
In addition, the manuscript seems to contain unnecessary sentences and be lengthened. Shortening the manuscript will increase readability.
Specific comment
1. Introduction
This section seems too long and needs to be shortened.
L71
not only “extrapolate” but also “interpolate”.
L91 Table 1 and Appendix A
Table 1 only shows six carbonate system variables and is not necessary. Reference to them in the text is enough. In the same context, Appendix A is also unnecessary because it only contains general explanations of carbonate system variables as written in, e.g., Dickson et al. 2007.
L95-131
All or a part of these explanations had better be transferred to the beginning of the “3 Reconstruction method” section.
L135
Which were used, sea surface height anomaly (SLA) or sea surface dynamic height (SLA+MDT)? Please clarify.
L155
SOCAT’s full name was already mentioned in L66.
L160-165
Using global 0.25 deg binned data derived from SOCAT cruise data is a usual way, even though using 1 deg binned data does not significantly affect the result.
L213
It should be clarified how you dealt with longitude and latitude parameters.
Table 4
This table contains RMSDs and coefficients of determination, and the name “skill score” is not appropriate. RMSDs of r025 are not significantly different from those of r100 according to Table 4, and therefore the authors should not emphasize an improvement of the prediction skill. The results only show that a fine-scale reconstruction was achieved with no adverse effect.
Fig 2-4
The results from the two methods, r100 and r025, have almost the same structure. Please explain the reason why the authors focused on the comparison of them.
L374 Fig. 3
RMSD for the Sea of Japan is suppressed by using data in the subtropical regions (Tsushima warm current area and Kuroshio area) which generally can be estimated more easily. The RMSD must be calculated from data restricted north of the subtropical front.
L403-414
The discrepancy between the estimated and observed pCO2 not only originated from the timescale but also from the method itself. The method cannot express short-term phenomena inherently because it used external SST and SSS instead of those incorporated in the datasets in the learning process.
5.2 Total alkalinity and dissolved inorganic carbon
In the method of this study, discrepancies in estimated and observed DIC were initially derived from pCO2 and TA estimation and propagated via carbonate system calculations. The discussion on uncertainty should be written along with such a concept.
L467-469
The authors attributed a large σ of DYFAMED estimates to a limited number of observations in the Mediterranean, but GLODAPv2 includes alkalinity measurement data in the Mediterranean. Schneider et al. 2007 successfully derived the salinity-alkalinity relationship. The discrepancy in DYFAMED seems to be attributable to salinity discrepancy only.
L539-540
I think that SDG indicator 14.3.1, “Average marine acidity (pH) measured at agreed suite of representative sampling stations”, is worth mentioning here. Global mean pH based on observation can be a proxy for the indicator. In addition, it is also valuable information that the global mean pH becomes 8.0 with one decimal place, not 8.1 often said.
6 Conclusion and discussion
This section had better be titled “Summary”. It does not seem to include discussion.
Carter et al. 2016, Locally interpolated alkalinity regression for global alkalinity estimation, Limnol. Oceanogr. Methods, 14, 268–277, https://doi.org/https://doi.org/10.1002/lom3.10087.
Carter et al. 2018, Updated methods for global locally interpolated estimation of alkalinity, pH, and nitrate, Limnology and Oceanography: Methods, 16, 119–131, https://doi.org/https://doi.org/10.1002/lom3.10232, 2018.
Chau et al. 2022, A seamless ensemble-based reconstruction of surface ocean pCO2 and air–sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, https://doi.org/10.5194/bg-19-1087-2022
Dickson et al (Eds.) 2007, Guide to Best Practices for Ocean CO2 Measurements. PICES Special Publication 3, 191 pp.
Schneider et al. 2007, Alkalinity of the Mediterranean Sea, Geophys. Res. Lett., 34, L15608, doi:10.1029/2006GL028842.
Citation: https://doi.org/10.5194/essd-2023-146-RC2 -
AC1: 'Comment on essd-2023-146', Thi Tuyet Trang Chau, 11 Sep 2023
Dear Editor and the two reviewers,
We would like to thank the two reviewers for reading the manuscript thoroughly and providing constructive feedback. Based on their
comments, we have improved the manuscript. Please kindly find our replies to their comments in the attached file.Best regards
Thi-Tuyet-Trang Chau on behalf of the authors
Status: closed
- RC1: 'Comment on essd-2023-146', Anonymous Referee #1, 04 Jun 2023
-
RC2: 'Comment on essd-2023-146', Anonymous Referee #2, 07 Jul 2023
The authors reconstructed 0.25 degree monthly full carbonate system variables during the period 1985-2021 based on surface ocean observation data. Distributions of pCO2 were reconstructed based on the machine learning method established by the authors (Trang-Chau et al. 2022) and those of TA were based on the LIAR method (Carter et al. 2016; 2018). While few reconstructions of full carbonate system variables are available at this moment, a comprehensive understanding of global surface ocean pH distributions is essential for monitoring ocean acidification, which is related to the SDG indicator 14.3.1. This study can enhance researches on the global carbon cycle as well as provide critical information to policymakers and stakeholders. I think this study has sufficient value to be published in this journal, but major concerns listed below should be addressed appropriately. I would like to encourage the authors to improve the study and revise the manuscript for better understanding.
General comment
The concept of this study itself is not novel, and the assessment of uncertainty in the reconstructed fields and the validation of the method become important. The authors derived uncertainty distributions in reconstructed parameters from the spread of 100 model ensemble. They also demonstrated the validity of the method by comparing the result of this study with observation data that were not used for learning and those of time-series points. The time-series used in this study are biasedly located in the subtropical region, so comparing their data with the results of this study does not seem a good indicator of uncertainty. For validation of this method, the authors must take a comparison with other reconstruction(s) into account, if needed.
The authors use external SST and SSS instead of those incorporated in the datasets in the learning process. This seems unusual because the oceanographic condition represented by temperature and salinity considerably affects the ocean biogeochemistry in the observed area. If the authors think the use of external SST/SSS to be essential, they must demonstrate that the impact of differences between external SST/SSS and those in the datasets is negligible.
In addition, the manuscript seems to contain unnecessary sentences and be lengthened. Shortening the manuscript will increase readability.
Specific comment
1. Introduction
This section seems too long and needs to be shortened.
L71
not only “extrapolate” but also “interpolate”.
L91 Table 1 and Appendix A
Table 1 only shows six carbonate system variables and is not necessary. Reference to them in the text is enough. In the same context, Appendix A is also unnecessary because it only contains general explanations of carbonate system variables as written in, e.g., Dickson et al. 2007.
L95-131
All or a part of these explanations had better be transferred to the beginning of the “3 Reconstruction method” section.
L135
Which were used, sea surface height anomaly (SLA) or sea surface dynamic height (SLA+MDT)? Please clarify.
L155
SOCAT’s full name was already mentioned in L66.
L160-165
Using global 0.25 deg binned data derived from SOCAT cruise data is a usual way, even though using 1 deg binned data does not significantly affect the result.
L213
It should be clarified how you dealt with longitude and latitude parameters.
Table 4
This table contains RMSDs and coefficients of determination, and the name “skill score” is not appropriate. RMSDs of r025 are not significantly different from those of r100 according to Table 4, and therefore the authors should not emphasize an improvement of the prediction skill. The results only show that a fine-scale reconstruction was achieved with no adverse effect.
Fig 2-4
The results from the two methods, r100 and r025, have almost the same structure. Please explain the reason why the authors focused on the comparison of them.
L374 Fig. 3
RMSD for the Sea of Japan is suppressed by using data in the subtropical regions (Tsushima warm current area and Kuroshio area) which generally can be estimated more easily. The RMSD must be calculated from data restricted north of the subtropical front.
L403-414
The discrepancy between the estimated and observed pCO2 not only originated from the timescale but also from the method itself. The method cannot express short-term phenomena inherently because it used external SST and SSS instead of those incorporated in the datasets in the learning process.
5.2 Total alkalinity and dissolved inorganic carbon
In the method of this study, discrepancies in estimated and observed DIC were initially derived from pCO2 and TA estimation and propagated via carbonate system calculations. The discussion on uncertainty should be written along with such a concept.
L467-469
The authors attributed a large σ of DYFAMED estimates to a limited number of observations in the Mediterranean, but GLODAPv2 includes alkalinity measurement data in the Mediterranean. Schneider et al. 2007 successfully derived the salinity-alkalinity relationship. The discrepancy in DYFAMED seems to be attributable to salinity discrepancy only.
L539-540
I think that SDG indicator 14.3.1, “Average marine acidity (pH) measured at agreed suite of representative sampling stations”, is worth mentioning here. Global mean pH based on observation can be a proxy for the indicator. In addition, it is also valuable information that the global mean pH becomes 8.0 with one decimal place, not 8.1 often said.
6 Conclusion and discussion
This section had better be titled “Summary”. It does not seem to include discussion.
Carter et al. 2016, Locally interpolated alkalinity regression for global alkalinity estimation, Limnol. Oceanogr. Methods, 14, 268–277, https://doi.org/https://doi.org/10.1002/lom3.10087.
Carter et al. 2018, Updated methods for global locally interpolated estimation of alkalinity, pH, and nitrate, Limnology and Oceanography: Methods, 16, 119–131, https://doi.org/https://doi.org/10.1002/lom3.10232, 2018.
Chau et al. 2022, A seamless ensemble-based reconstruction of surface ocean pCO2 and air–sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, https://doi.org/10.5194/bg-19-1087-2022
Dickson et al (Eds.) 2007, Guide to Best Practices for Ocean CO2 Measurements. PICES Special Publication 3, 191 pp.
Schneider et al. 2007, Alkalinity of the Mediterranean Sea, Geophys. Res. Lett., 34, L15608, doi:10.1029/2006GL028842.
Citation: https://doi.org/10.5194/essd-2023-146-RC2 -
AC1: 'Comment on essd-2023-146', Thi Tuyet Trang Chau, 11 Sep 2023
Dear Editor and the two reviewers,
We would like to thank the two reviewers for reading the manuscript thoroughly and providing constructive feedback. Based on their
comments, we have improved the manuscript. Please kindly find our replies to their comments in the attached file.Best regards
Thi-Tuyet-Trang Chau on behalf of the authors
Thi-Tuyet-Trang Chau et al.
Data sets
CMEMS-LSCE Thi-Tuyet-Trang Chau, Marion Gehlen, and Frédéric Chevallier https://doi.org/10.14768/a2f0891b-763a-49e9-af1b-78ed78b16982
Thi-Tuyet-Trang Chau et al.
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