the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Nov 2023
Assessing transient changes in the ocean carbon cycle during the last deglaciation through carbon isotope modeling
Abstract. We conducted a transient numerical experiment on the ocean carbon cycle during the last deglaciation. We used a three-dimensional ocean field from a transient climate model MIROC4m simulation of the last deglaciation as input to an ocean biogeochemical model, which allowed us to evaluate the effects of the gradual warming and the abrupt climate changes associated with the Atlantic Meridional Overturning Circulation during the last deglaciation.
During Heinrich Stadial 1 (HS1), the atmospheric partial pressure of carbon dioxide (pCO2) increased as a result of rising sea surface temperature. Subsequently, during the Bølling–Allerød period, characterized by a rapid strengthening of the Atlantic Meridional Overturning Circulation (AMOC), atmospheric pCO2 showed a decreasing trend. Our decomposition analysis indicates that the declining atmospheric pCO2 in response to the enhanced AMOC during the BA period were primarily driven by an increase in ocean surface alkalinity, although this effect was partially offset by changes in sea surface temperature.
Meantime, we found that our model generally underestimated atmospheric pCO2 changes compared to the ice core data. To understand this, we conducted an analysis of ocean circulation and water masses using radiocarbon and stable carbon isotope signatures (Δ14C and δ13C). We found that the overall changes in the deep water Δ14C in response to the AMOC change are quantitatively consistent with the sediment core data. However, the model underestimates the increased ventilation in the deep ocean and the reduced efficiency of biological carbon export in the Southern Ocean during mid-HS1 compared to estimates derived from sediment core data. In addition, the model underestimates the active ventilation in the North Pacific Intermediate Water during mid-HS1, as suggested by sediment core data. These underestimations in the activation of the deep ocean circulation and the limitation of biological productivity could be the primary reasons why our model exhibits smaller atmospheric pCO2 changes than ice core data.
Our decomposition analysis, which estimates the quantitative contribution to the oceanic pCO2, suggests that changes in alkalinity have played a central role in driving variations and trends in atmospheric pCO2 as the deep ocean circulation changes. This finding may provide valuable insights into the model-dependent response of the ocean carbon cycle to changes in the AMOC, as several previous studies have emphasized the importance of the AMOC in influencing changes in atmospheric pCO2, but the magnitude and direction of these changes have varied widely between studies.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-2526', Anonymous Referee #1, 04 Dec 2023
Kobayashi and colleagues present a novel offline simulation conducted with a carbon isotope-enabled biogeochemical model, forced with climate data from a transient MIROC4m simulation of the last deglaciation. Their investigation focuses on the relationship between changes in atmospheric CO2, stable and radiocarbon, and the varying AMOC during that period. The simulation reveals relatively minor changes in atmospheric CO2 concentration compared to ice core reconstructions, while demonstrating that changes in water mass ventilation and sourcing align with proxy reconstructions. The authors further analyze the simulated changes in pCO2, attributing them to physical (temperature and salinity) and biogeochemical (dissolved inorganic carbon and alkalinity) drivers, revealing complex interactions and compensating effects.
The manuscript is well-written and well-illustrated. While similar studies have been previously conducted with intermediate complexity models, this study represents a significant step towards comprehensive transient simulations with an AOGCM, despite not fully achieving this here. The authors transparently acknowledge certain critical factors during the last deglaciation, such as sea-level rise, ice sheets, and Southern Ocean sea surface temperature biases, which were not accounted for in this study. Considering all these processes for a deglacial simulation is very challenging in such a complex model, making it understandable that they are not fully considered here. Therefore, I recommend the manuscript for publication in Climate of the Past after minor revisions. Detailed comments outlining specific areas for improvement are provided below.
L4: Better introduce the abbreviation AMOC already here than in L7.
L5: Here and in the following, I would replace “atmospheric partial pressure of carbon dioxide” just with “atmospheric concentration of CO2”.
L30: deglacial period not deglaciation period.
L43: investigate not inter.
L80: What is meant by vertical one-dimensional distribution in this context? Previous studies have evaluated the 3D distribution of data.
L87-92: The setup of the model is not fully clear to me. In line 87 it is mentioned that a BGC model was coupled to an ocean model, but in line 90 it is stated that the BGC model was forced with MIROC output. Does this mean that the coupled BGC-ocean model was forced with atmospheric boundary conditions of MIROC? Later on it reads to me as if the BGC model is run entirely in offline mode. Can the authors clarify this in section 2.1?
L91: Can you briefly mention how the MIROC transient simulation was forced, e.g., freshwater fluxes GHG concentrations etc.?
L110: Why were pre-industrial and not LGM values used to initialize these atmospheric values?
L138: Can you give numbers for the AMOC strengths during the LGM and the Holocene?
L146: According to Fig. 2j the Pacific appears to me simulated in rather good agreement with the data. Certainly, much better than the Southern Ocean.
L151: Maybe it is worth mentioning, that indeed the agreement of Kobayashi2021 is much better in the deep Southern Ocean, but quite a bit worse for the mid-depths. It therefore appears to me that it is not as simple as including these processes.
L160: can you give a reason why the SST difference between the LGM and Holocene is so small compared to observations?
L178: There appears to be very little change during the transition from the LGM to HS1, which makes sense, since the AMOC also remains virtually constant. Maybe this needs to be hence slightly rephrased.
L203: As mentioned before, the difference between the LGM and HS1 seems very small in the simulation, because the AMOC also changes very little. In contrast, a larger decrease is observed in the data.
L209: Mention that this is most pronounced in the Atlantic.
L210: It is important to note, that two different things are compared here. The model output has an annual resolution and therefore shows the “true” perturbation magnitude. On the other hand, the reconstructions from marine sediments are smoothed out by processes such as bioturbation, coring artifacts, etc. It is therefore expected that the signal amplitude is bigger in the model than the data for such short perturbations like the YD.
L211: Maybe explicitly mention that this is for the reconstructions.
L224: This is rather surprising to me, as the AMOC strength actually doesn’t change much, but the change in carbon export is rather large in the South Pacific. I’m therefore wondering whether this can really be attributed to an AMOC weakening, or whether other processes dominate this effect? From the pattern and the fact that this negative anomaly persists throughout the deglaciation independent of the AMOC strength, suggest to me that this is primarily a signal of increased iron limitation, which is mentioned in the text.
L227: By which mechanism propagate these changes to the North Pacific?
L304-310: It could also be that the AMOC weakening is too strong in the model.
L334-344: The terrestrial biosphere plays an important role for atmospheric d13C, which I think should be mentioned here as well (see e.g., Jeltsch-Thoemmes et al., 2019, doi:10.5194/cp-15-849-2019).
Fig. 1: I understand the intention to make the model and data timeseries overlap for better comparability. However, I find this in panel c somewhat misleading, as there is a factor of more than two between both y-axes. I would like to see the same increment for both axes as it is done in panel b. Further, can a panel showing global mean surface temperature be added, for instance compared to the data assimilation by Osman et al., 2021 (doi:10.1038/s41586-021-03984-4)?
Fig. 3 and 4: Can you add a similar figure like these two, but for DIC (anomaly?), to better illustrate where the carbon was stored in the course of the deglaciation?
Figs. S4 and S5: The panels are very small and hence hard to read. Can they be made larger, splitting the panels into different rows or columns?
Fig. S7 to S9. I find both these figures very instructive and if possible would like to see them in the main text.
Citation: https://doi.org/10.5194/egusphere-2023-2526-RC1 -
AC1: 'Reply on RC1', Hidetaka Kobayashi, 27 Jan 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2526/egusphere-2023-2526-AC1-supplement.pdf
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AC1: 'Reply on RC1', Hidetaka Kobayashi, 27 Jan 2024
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RC2: 'Comment on egusphere-2023-2526', Anonymous Referee #2, 07 Dec 2023
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AC2: 'Reply on RC2', Hidetaka Kobayashi, 27 Jan 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2526/egusphere-2023-2526-AC2-supplement.pdf
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AC2: 'Reply on RC2', Hidetaka Kobayashi, 27 Jan 2024
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-2526', Anonymous Referee #1, 04 Dec 2023
Kobayashi and colleagues present a novel offline simulation conducted with a carbon isotope-enabled biogeochemical model, forced with climate data from a transient MIROC4m simulation of the last deglaciation. Their investigation focuses on the relationship between changes in atmospheric CO2, stable and radiocarbon, and the varying AMOC during that period. The simulation reveals relatively minor changes in atmospheric CO2 concentration compared to ice core reconstructions, while demonstrating that changes in water mass ventilation and sourcing align with proxy reconstructions. The authors further analyze the simulated changes in pCO2, attributing them to physical (temperature and salinity) and biogeochemical (dissolved inorganic carbon and alkalinity) drivers, revealing complex interactions and compensating effects.
The manuscript is well-written and well-illustrated. While similar studies have been previously conducted with intermediate complexity models, this study represents a significant step towards comprehensive transient simulations with an AOGCM, despite not fully achieving this here. The authors transparently acknowledge certain critical factors during the last deglaciation, such as sea-level rise, ice sheets, and Southern Ocean sea surface temperature biases, which were not accounted for in this study. Considering all these processes for a deglacial simulation is very challenging in such a complex model, making it understandable that they are not fully considered here. Therefore, I recommend the manuscript for publication in Climate of the Past after minor revisions. Detailed comments outlining specific areas for improvement are provided below.
L4: Better introduce the abbreviation AMOC already here than in L7.
L5: Here and in the following, I would replace “atmospheric partial pressure of carbon dioxide” just with “atmospheric concentration of CO2”.
L30: deglacial period not deglaciation period.
L43: investigate not inter.
L80: What is meant by vertical one-dimensional distribution in this context? Previous studies have evaluated the 3D distribution of data.
L87-92: The setup of the model is not fully clear to me. In line 87 it is mentioned that a BGC model was coupled to an ocean model, but in line 90 it is stated that the BGC model was forced with MIROC output. Does this mean that the coupled BGC-ocean model was forced with atmospheric boundary conditions of MIROC? Later on it reads to me as if the BGC model is run entirely in offline mode. Can the authors clarify this in section 2.1?
L91: Can you briefly mention how the MIROC transient simulation was forced, e.g., freshwater fluxes GHG concentrations etc.?
L110: Why were pre-industrial and not LGM values used to initialize these atmospheric values?
L138: Can you give numbers for the AMOC strengths during the LGM and the Holocene?
L146: According to Fig. 2j the Pacific appears to me simulated in rather good agreement with the data. Certainly, much better than the Southern Ocean.
L151: Maybe it is worth mentioning, that indeed the agreement of Kobayashi2021 is much better in the deep Southern Ocean, but quite a bit worse for the mid-depths. It therefore appears to me that it is not as simple as including these processes.
L160: can you give a reason why the SST difference between the LGM and Holocene is so small compared to observations?
L178: There appears to be very little change during the transition from the LGM to HS1, which makes sense, since the AMOC also remains virtually constant. Maybe this needs to be hence slightly rephrased.
L203: As mentioned before, the difference between the LGM and HS1 seems very small in the simulation, because the AMOC also changes very little. In contrast, a larger decrease is observed in the data.
L209: Mention that this is most pronounced in the Atlantic.
L210: It is important to note, that two different things are compared here. The model output has an annual resolution and therefore shows the “true” perturbation magnitude. On the other hand, the reconstructions from marine sediments are smoothed out by processes such as bioturbation, coring artifacts, etc. It is therefore expected that the signal amplitude is bigger in the model than the data for such short perturbations like the YD.
L211: Maybe explicitly mention that this is for the reconstructions.
L224: This is rather surprising to me, as the AMOC strength actually doesn’t change much, but the change in carbon export is rather large in the South Pacific. I’m therefore wondering whether this can really be attributed to an AMOC weakening, or whether other processes dominate this effect? From the pattern and the fact that this negative anomaly persists throughout the deglaciation independent of the AMOC strength, suggest to me that this is primarily a signal of increased iron limitation, which is mentioned in the text.
L227: By which mechanism propagate these changes to the North Pacific?
L304-310: It could also be that the AMOC weakening is too strong in the model.
L334-344: The terrestrial biosphere plays an important role for atmospheric d13C, which I think should be mentioned here as well (see e.g., Jeltsch-Thoemmes et al., 2019, doi:10.5194/cp-15-849-2019).
Fig. 1: I understand the intention to make the model and data timeseries overlap for better comparability. However, I find this in panel c somewhat misleading, as there is a factor of more than two between both y-axes. I would like to see the same increment for both axes as it is done in panel b. Further, can a panel showing global mean surface temperature be added, for instance compared to the data assimilation by Osman et al., 2021 (doi:10.1038/s41586-021-03984-4)?
Fig. 3 and 4: Can you add a similar figure like these two, but for DIC (anomaly?), to better illustrate where the carbon was stored in the course of the deglaciation?
Figs. S4 and S5: The panels are very small and hence hard to read. Can they be made larger, splitting the panels into different rows or columns?
Fig. S7 to S9. I find both these figures very instructive and if possible would like to see them in the main text.
Citation: https://doi.org/10.5194/egusphere-2023-2526-RC1 -
AC1: 'Reply on RC1', Hidetaka Kobayashi, 27 Jan 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2526/egusphere-2023-2526-AC1-supplement.pdf
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AC1: 'Reply on RC1', Hidetaka Kobayashi, 27 Jan 2024
-
RC2: 'Comment on egusphere-2023-2526', Anonymous Referee #2, 07 Dec 2023
-
AC2: 'Reply on RC2', Hidetaka Kobayashi, 27 Jan 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2526/egusphere-2023-2526-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Hidetaka Kobayashi, 27 Jan 2024
Peer review completion
Journal article(s) based on this preprint
Model code and software
Data for journal articles Takashi Obase and Ayako Abe-Ouchi https://cesd.aori.u-tokyo.ac.jp/cesddb/publication/index.html
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Hidetaka Kobayashi
Akira Oka
Takashi Obase
Ayako Abe-Ouchi
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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(4739 KB) - Metadata XML
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Supplement
(3844 KB) - BibTeX
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- Final revised paper