the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Oct 2022
Seasonal to interannual variabilities of sea–air CO2 exchange across Tropical Maritime Continent indicated by eddy–permitting coupled OGCM experiment
Abstract. The lack of long–term observational data has limited research on sea–air CO2 exchange variabilities in the Tropical Maritime Continent (TMC). To address the issue, we utilized a three–dimensional high–resolution physical–biogeochemical ocean numerical model and applied it to simulate sea–air CO2 exchange in the region over the last decade (2010–2019). Some key features like atmospheric CO2 source signature and high sea surface pCO2 environment inside the TMC were captured by the model. Within the TMC, model results indicated strong CO2 degassing along the south of Java associated with the seasonal cycle of the upwelling system. Abundant supply of inorganic carbon during upwelling season and strong wind speed results in CO2 degassing that could reach as high as 30 gC m–2 year–1 around the area. In addition to the region acting as a full–year atmospheric CO2 source, the TMC also exhibited interannual modulation in both sea–air CO2 flux and sea surface pCO2 which can be related to the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Large–scale anomalous strong CO2 degassing and high sea surface pCO2 from 2015 to 2016 in response to the 2015/2016 El Niño evolution was observed and dominated by modulation within the TMC. It is further found that modulation of CO2 degassing related to IOD were confined along the west of south of Java with a higher magnitude compared with anomalies related to ENSO which shows larger spatial scale but lower in the magnitude. Study conducted here may provide insight about possible variabilities of sea–air CO2 exchange in the area that still poorly represented in many global–scale modelling and reconstruction efforts.
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Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-1067', Anonymous Referee #1, 28 Nov 2022
Review of “Seasonal to interannual variabilities of sea–air CO2 exchange across Tropical Maritime Continent indicated by eddy–permitting coupled OGCM experiment” by Amri et al submitted to Ocean Science
The paper is a long-standing pending issue in addressing the CO2 fluxes of this very important region. It deserves publication in Ocean Science. However, it has missed the following key elements, which need to be addressed.
Valsala et al., (2020) have done, for the first time, analysis of 60-year long record of sotheastern tropical Indian Ocean CO2 flux variability, pCO2 variability associated with the IOD. The study concluded that, “The IOD leads to a substantial sea-to-air CO2 flux variability in the southeastern tropical Indian Ocean over a broad region (70–105°E, 0–20°S), with more focus near the coast of Java-Sumatra due to the prevailing upwelling dynamics and associated westward propagating anomalies. The sea-to-air CO2 fluxes, surface ocean partial pressure of CO2 (pCO2), the concentration of dissolved inorganic carbon (DIC), and ocean alkalinity (ALK) range as much as ±1.0 mole m−2 year−1, ±20 μatm, ±35 μmole kg−1, and ±22 μmole kg−1 within 80–105°E, 0–10°S due to IOD. The DIC and ALK are significant drivers of pCO2 variability associated with IOD. The roles of temperature (T) and biology are found negligible. A relatively warm T and extremely high freshwater forcing make the southeastern tropical Indian Ocean carbon cycle variability submissive to DIC and ALK evolutions in contrast to the tropical eastern Pacific where changes in DIC and T dominate the pCO2 interannual variability. For the first time, this study provides a most comprehensive and extended analysis for the region while highlighting significant differences in carbon cycle variability of the eastern tropical Indian Ocean compared to that of the other parts of the global oceans.”
This is an important recent work and needs to be cross-discussed in this paper, especially due to the reason the IOD impacts are revisited in this manuscript, and the results have differences. It is always good to have various modelling comparisons so that the community is benefited from knowing how the model performs and differ from each other. Other papers missed addressing are also added below:
Valsala, V., M. G. Sreeush, and K. Chakraborty, (2020), IOD impacts on Indian the Ocean Carbon Cycle, Journal of Geophysical Research, https://doi.org/10.1029/2020JC016485
Chakraborty K., V. Valsala, T. Bhattacharya, J. Ghosh, (2021), Seasonal cycle of surface ocean pCO2 and pH in the northern Indian Ocean and their controlling factors, Progress in Oceanography, Vol.198, doi.org/10.1016/j.pocean.2021.102683
Valsala V., Sreeush M.G., Anju M., Sreenivas P., Tiwari Y.K., Chakraborty K., Sijikumar S., An observing system simulation experiment for Indian Ocean surface pCO2 measurements, Progress in Oceanography, 194: 102570, June 2021, DOI: 10.1016/j.pocean.2021.102570, 1-14
Sreeush, M. G., Valsala, V., Pentakota, S., Prasad, K. V. S. R., and Murtugudde, R (2018), Biological production in the Indian Ocean upwelling zones – Part 1: refined estimation via the use of a variable compensation depth in ocean carbon models, Biogeosciences, 15, 1895-1918, https://doi.org/10.5194/bg-15-1895-2018
- AC2: 'Reply on RC1', Faisal Amri, 07 Dec 2022
-
RC2: 'Comment on egusphere-2022-1067', Anonymous Referee #2, 28 Nov 2022
Amri et al. examined pCO2 and air-sea CO2 fluxes variability over the Tropical Maritime Continent (TMC) using a regional ocean biogeochemical (BGC) model. Surface pCO2 patterns across the TMC have not been well constrained, so this study represents a valuable effort to better understand carbon system dynamics in the region. However, I have three major concerns about the model results and analysis:
1) It is not clear to me whether the model is getting realistic pCO2 patterns or not. The comparison with Bakker et al. (2016), Iida et al. (2022), and LandschuÌtzer et al. (2016) suggests a significant overestimation of surface pCO2, especially in the open ocean region. I wonder to what degree the initial and boundary conditions for the BGC model, derived through an analytical (regression models) approach, were properly resolved. Since the authors do not provide a model validation, neither physic or biogeochemistry –putting aside pCO2 and CO2 fluxes–, it is difficult be confident in their results. I think this study requires a proper model validation, which should include model-data comparisons for horizontal and vertical patterns of temperature, salinity, nutrients, and carbon system variables when available.
2) The authors claim that changes in sDIC and sAlk represent biological processes, which is not correct. DIC and alkalinity also can change due to advection and mixing, and air-sea flux in the case of DIC. This wrong assumption led to a wrong interpretation for the Taylor decomposition analysis. The authors need to revise that interpretation, making clear that process like wind-driven upwelling of DIC-rich subsurface water could play an important role in the pCO2 variability off Java.
3) The analysis of the interannual pCO2 variability is interesting, but the link to ENSO and IOD need a better explanation. If the patterns are properly described, this could be the most interesting part of the study. Please provide a better description. One thing that catch my attention was the negative trend in the CO2 flux. The authors did not offer any explanation for this trend. I wonder whether this a model pCO2 drift or not.
Specific comments.
69: I would rather use the name “regional ocean biogeochemical model” instead of OGCM.
90: I would indicate that “coccolithophores decrease alkalinity, as they produce a body shelf structure made of CaCO3”
122-125: I do not understand why you are indicating this Taylor series decomposition here. Need to explain the motivation.
157: I wonder how your estimated fields for the biogeochemical (BGC) variables compare with the WOA2019 (NO3, PO4, O2). Also, I wonder if you made any comparison between your BGC estimates and BGC fields from reanalysis products (e.g., GLORYS Mercator Ocean).
Table 2. Alkalinity usually co-varies with salinity. I wonder why you left alkalinity as a function of temperature instead of salinity.
188: It would be helpful to show similar map to Fig. 2 (∂pCO2) in Kartadikaria et al (2015). Most likely your model is overestimating pCO2 in the open ocean region.
188: that higher => that were higher
190-197: There is a significant bias in surface pCO2, especially in the open ocean region surrounding the TMC. This likely explains the much greater carbon outgassing you obtained compared to previous studies.
220: I wonder what you consider strong CO2 outgassing. Maybe you could refer to the region(s) with the strongest CO2 outgassing.
236: “The biological processes, represented by SSDIC and SSAlk” This is a wrong statement. Changes in sDIC and sAlk are also affected by advection and mixing, and air-sea flux in the case of sDIC. Besides, I would not expect important biology-driven changes in sAlk.
238-239: This is a wrong conclusion based in the wrong assumption that changes in sDIC and sAlk represent biological processes. Consider the upwelling season off Java during summer. sDIC promotes an increase (and sAlk a decrease) in pCO2. Which biological process could explain this? It is not respiration. Most likely, the signature is associated with the upwelling of subsurface waters with higher DIC and alkalinity concentration than the surface waters. During fall, you have a negative impact of sDIC on pCO2, which could reflect a weakening in coastal upwelling. Remember that in Fig. 5 you are visualizing dpCO2 not pCO2. I would expect a maximum biological uptake of DIC around September. This uptake contributes to decrease sDIC, so its impact should be opposed to the DIC-rich subsurface waters due to upwelling.
Second comment on 238-239, after reading discussion: You mentioned “supply of subsurface inorganic” as a factor impacting pCO2 off Java in the Discussion section (line 339), so I wonder why you did not mention anything of that in the Result section.
268: I wonder why the long-tern negative trend in the fluxes. What does it drive this trend? It may be a model flux drift. Need explanation. Specially if you are highlighting that ENSO and IOD contributed to attenuate this trend.
Figure 6b: It is hard to discriminate the color of the lines. Please increase line width.
270: Why do you think it confirms? You are not stating any mechanisms linking the ENSO or IOD variability.
285: Why are you using standard deviation instead of the mean index value? I got lost.
Figure 8b. I wonder why you did not use a smaller colorbar interval for the flux anomalies.
339: “accelerated gas exchange and an abundant supply of subsurface inorganic”. You should try to mention these two processes when describing Figs. 5 & 6 in the Result section.
350-358: It is not clear to me how the anomalous divergence in the West Pacific affects the air-sea CO2 flux. Could you develop more this idea? I think you need to explain better the counteracting effect of this divergence/convergence with the increased/decreased solar heating during El Nino/La Nina.
Citation: https://doi.org/10.5194/egusphere-2022-1067-RC2 - AC1: 'Reply on RC2', Faisal Amri, 06 Dec 2022
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-1067', Anonymous Referee #1, 28 Nov 2022
Review of “Seasonal to interannual variabilities of sea–air CO2 exchange across Tropical Maritime Continent indicated by eddy–permitting coupled OGCM experiment” by Amri et al submitted to Ocean Science
The paper is a long-standing pending issue in addressing the CO2 fluxes of this very important region. It deserves publication in Ocean Science. However, it has missed the following key elements, which need to be addressed.
Valsala et al., (2020) have done, for the first time, analysis of 60-year long record of sotheastern tropical Indian Ocean CO2 flux variability, pCO2 variability associated with the IOD. The study concluded that, “The IOD leads to a substantial sea-to-air CO2 flux variability in the southeastern tropical Indian Ocean over a broad region (70–105°E, 0–20°S), with more focus near the coast of Java-Sumatra due to the prevailing upwelling dynamics and associated westward propagating anomalies. The sea-to-air CO2 fluxes, surface ocean partial pressure of CO2 (pCO2), the concentration of dissolved inorganic carbon (DIC), and ocean alkalinity (ALK) range as much as ±1.0 mole m−2 year−1, ±20 μatm, ±35 μmole kg−1, and ±22 μmole kg−1 within 80–105°E, 0–10°S due to IOD. The DIC and ALK are significant drivers of pCO2 variability associated with IOD. The roles of temperature (T) and biology are found negligible. A relatively warm T and extremely high freshwater forcing make the southeastern tropical Indian Ocean carbon cycle variability submissive to DIC and ALK evolutions in contrast to the tropical eastern Pacific where changes in DIC and T dominate the pCO2 interannual variability. For the first time, this study provides a most comprehensive and extended analysis for the region while highlighting significant differences in carbon cycle variability of the eastern tropical Indian Ocean compared to that of the other parts of the global oceans.”
This is an important recent work and needs to be cross-discussed in this paper, especially due to the reason the IOD impacts are revisited in this manuscript, and the results have differences. It is always good to have various modelling comparisons so that the community is benefited from knowing how the model performs and differ from each other. Other papers missed addressing are also added below:
Valsala, V., M. G. Sreeush, and K. Chakraborty, (2020), IOD impacts on Indian the Ocean Carbon Cycle, Journal of Geophysical Research, https://doi.org/10.1029/2020JC016485
Chakraborty K., V. Valsala, T. Bhattacharya, J. Ghosh, (2021), Seasonal cycle of surface ocean pCO2 and pH in the northern Indian Ocean and their controlling factors, Progress in Oceanography, Vol.198, doi.org/10.1016/j.pocean.2021.102683
Valsala V., Sreeush M.G., Anju M., Sreenivas P., Tiwari Y.K., Chakraborty K., Sijikumar S., An observing system simulation experiment for Indian Ocean surface pCO2 measurements, Progress in Oceanography, 194: 102570, June 2021, DOI: 10.1016/j.pocean.2021.102570, 1-14
Sreeush, M. G., Valsala, V., Pentakota, S., Prasad, K. V. S. R., and Murtugudde, R (2018), Biological production in the Indian Ocean upwelling zones – Part 1: refined estimation via the use of a variable compensation depth in ocean carbon models, Biogeosciences, 15, 1895-1918, https://doi.org/10.5194/bg-15-1895-2018
- AC2: 'Reply on RC1', Faisal Amri, 07 Dec 2022
-
RC2: 'Comment on egusphere-2022-1067', Anonymous Referee #2, 28 Nov 2022
Amri et al. examined pCO2 and air-sea CO2 fluxes variability over the Tropical Maritime Continent (TMC) using a regional ocean biogeochemical (BGC) model. Surface pCO2 patterns across the TMC have not been well constrained, so this study represents a valuable effort to better understand carbon system dynamics in the region. However, I have three major concerns about the model results and analysis:
1) It is not clear to me whether the model is getting realistic pCO2 patterns or not. The comparison with Bakker et al. (2016), Iida et al. (2022), and LandschuÌtzer et al. (2016) suggests a significant overestimation of surface pCO2, especially in the open ocean region. I wonder to what degree the initial and boundary conditions for the BGC model, derived through an analytical (regression models) approach, were properly resolved. Since the authors do not provide a model validation, neither physic or biogeochemistry –putting aside pCO2 and CO2 fluxes–, it is difficult be confident in their results. I think this study requires a proper model validation, which should include model-data comparisons for horizontal and vertical patterns of temperature, salinity, nutrients, and carbon system variables when available.
2) The authors claim that changes in sDIC and sAlk represent biological processes, which is not correct. DIC and alkalinity also can change due to advection and mixing, and air-sea flux in the case of DIC. This wrong assumption led to a wrong interpretation for the Taylor decomposition analysis. The authors need to revise that interpretation, making clear that process like wind-driven upwelling of DIC-rich subsurface water could play an important role in the pCO2 variability off Java.
3) The analysis of the interannual pCO2 variability is interesting, but the link to ENSO and IOD need a better explanation. If the patterns are properly described, this could be the most interesting part of the study. Please provide a better description. One thing that catch my attention was the negative trend in the CO2 flux. The authors did not offer any explanation for this trend. I wonder whether this a model pCO2 drift or not.
Specific comments.
69: I would rather use the name “regional ocean biogeochemical model” instead of OGCM.
90: I would indicate that “coccolithophores decrease alkalinity, as they produce a body shelf structure made of CaCO3”
122-125: I do not understand why you are indicating this Taylor series decomposition here. Need to explain the motivation.
157: I wonder how your estimated fields for the biogeochemical (BGC) variables compare with the WOA2019 (NO3, PO4, O2). Also, I wonder if you made any comparison between your BGC estimates and BGC fields from reanalysis products (e.g., GLORYS Mercator Ocean).
Table 2. Alkalinity usually co-varies with salinity. I wonder why you left alkalinity as a function of temperature instead of salinity.
188: It would be helpful to show similar map to Fig. 2 (∂pCO2) in Kartadikaria et al (2015). Most likely your model is overestimating pCO2 in the open ocean region.
188: that higher => that were higher
190-197: There is a significant bias in surface pCO2, especially in the open ocean region surrounding the TMC. This likely explains the much greater carbon outgassing you obtained compared to previous studies.
220: I wonder what you consider strong CO2 outgassing. Maybe you could refer to the region(s) with the strongest CO2 outgassing.
236: “The biological processes, represented by SSDIC and SSAlk” This is a wrong statement. Changes in sDIC and sAlk are also affected by advection and mixing, and air-sea flux in the case of sDIC. Besides, I would not expect important biology-driven changes in sAlk.
238-239: This is a wrong conclusion based in the wrong assumption that changes in sDIC and sAlk represent biological processes. Consider the upwelling season off Java during summer. sDIC promotes an increase (and sAlk a decrease) in pCO2. Which biological process could explain this? It is not respiration. Most likely, the signature is associated with the upwelling of subsurface waters with higher DIC and alkalinity concentration than the surface waters. During fall, you have a negative impact of sDIC on pCO2, which could reflect a weakening in coastal upwelling. Remember that in Fig. 5 you are visualizing dpCO2 not pCO2. I would expect a maximum biological uptake of DIC around September. This uptake contributes to decrease sDIC, so its impact should be opposed to the DIC-rich subsurface waters due to upwelling.
Second comment on 238-239, after reading discussion: You mentioned “supply of subsurface inorganic” as a factor impacting pCO2 off Java in the Discussion section (line 339), so I wonder why you did not mention anything of that in the Result section.
268: I wonder why the long-tern negative trend in the fluxes. What does it drive this trend? It may be a model flux drift. Need explanation. Specially if you are highlighting that ENSO and IOD contributed to attenuate this trend.
Figure 6b: It is hard to discriminate the color of the lines. Please increase line width.
270: Why do you think it confirms? You are not stating any mechanisms linking the ENSO or IOD variability.
285: Why are you using standard deviation instead of the mean index value? I got lost.
Figure 8b. I wonder why you did not use a smaller colorbar interval for the flux anomalies.
339: “accelerated gas exchange and an abundant supply of subsurface inorganic”. You should try to mention these two processes when describing Figs. 5 & 6 in the Result section.
350-358: It is not clear to me how the anomalous divergence in the West Pacific affects the air-sea CO2 flux. Could you develop more this idea? I think you need to explain better the counteracting effect of this divergence/convergence with the increased/decreased solar heating during El Nino/La Nina.
Citation: https://doi.org/10.5194/egusphere-2022-1067-RC2 - AC1: 'Reply on RC2', Faisal Amri, 06 Dec 2022
Data sets
Annual global-averaged atmosphere’s CO2 concentration ESRL NOAA https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html
Model code and software
Original model code and necessary tools to prepare the input for simulation experiment Takashi Nakamura https://github.com/NakamuraTakashi
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