the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
08 Jul 2021
08 Jul 2021
Effect of the Atlantic Meridional Overturning Circulation on Atmospheric pCO2 Variations
- 1Institute for Marine and Atmospheric research Utrecht, Department of Physics, Utrecht University, Utrecht, the Netherlands
- 2Center for Complex Systems Studies, Utrecht University, Utrecht, the Netherlands
- 1Institute for Marine and Atmospheric research Utrecht, Department of Physics, Utrecht University, Utrecht, the Netherlands
- 2Center for Complex Systems Studies, Utrecht University, Utrecht, the Netherlands
Abstract. Proxy records show large variability of atmospheric pCO2 on different time scales. Most often such variations are attributed to a forced response of the carbon cycle to changes in external conditions. Here, we address the problem of internally generated variations in pCO2 due to pure carbon-cycle dynamics. We focus on the effect of the strength of Atlantic Meridional Overturning Circulation (AMOC) on such internal variability. Using the Simple Carbon Project Model v1.0 (SCP-M), which we have extended to represent a suite of nonlinear carbon-cycle feedbacks, we efficiently explore the multi-dimensional parameter space to address the AMOC – pCO2 relationship. We find that climatic boundary conditions, and the representation of biological production in the model are most important for this relationship. When climate sensitivity in our model is increased, we find intrinsic oscillations due to Hopf bifurcations with multi-millennial periods. The mechanism behind these oscillations is clarified and related to the coupling of atmospheric pCO2 and the alkalinity cycle, via the river influx and the sediment outflux. This mechanism is thought to be relevant for explaining atmospheric pCO2 variability during glacial cycles.
Daan Boot et al.
Status: final response (author comments only)
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RC1: 'Review of esd-2021-42', Anonymous Referee #1, 10 Aug 2021
Boot et al assess the impact on pCO2 of a few oceanic feedbacks using a simple box model. A few parametrizations are added to the box model to represent missing feedbacks (change in temperature due to change in pCO2, changes in biological production as a function of changes in circulation and biological efficiency, rain ratio, and river input). Parameters values are assessed following the AUTO software program. It is suggested that internal pCO2 oscillations can arise due to these feedbacks when the AMOC is ~15Sv. In principle, such an exploration of parameter space is useful. In addition, since the implemented feedbacks are usually included in more complex models, this set up could help highlight the impact on pCO2 of these feedbacks. However, I find the paper hard to follow and more importantly I have some concerns with some of the assumptions taken to define the feedbacks in the internal oscillation scheme.
- The major issue relates to the parametrization added that gives rise to the internal oscillation. A part of this oscillation involves changes in the riverine flux of alkalinity as a function of pCO2 and the other is linked to an increase in temperature due to an increase in ocean alkalinity within 1000 years. What are the reasons behind these parametrizations? I understand that weathering is modulated by pCO2. However, I thought that this was a slow process, and I don’t think that a change in atm. CO2 should directly lead to a proportional change in alkalinity river influx (within 1000 years). Maybe the oscillations you highight are relevant for longer timescales, i.e. glacial/interglacial changes in pCO2. I suggest to carefully read the litterature on changes in weathering during G-IG cycles. I can’t find a reason for an increase in ocean alkalinity leading to an increase in temperature though (green box at t=0 to blue box at t=T/4 in fig. 6).
- The paper is hard to follow. A combination of 13*7 experiments are performed. They are labelled with 1 or 2 letters per feedback and numbers for experiments, making it difficult to recall what we are looking at. If more explicit labels were used in Figures 3 and 4, it would help. In addition, there is very little justification/discussion of the different experiments, leading to confusion. The parametrization of the rain ratio feedback is not common. I thought that the largest impact on rain ratio would come from changes in silicifiers, and thus silicate and/or iron concentration in the ocean. L. 278, the authors state that “for low rain ratios, we only have a constant dissolution”, which confuses me, as I don’t see a link between dissolution and rain ratio in the methods.
- Discussion and implication of the results
The study scans a large range of parameters yielding pCO2 values of 70-300 ppm, but without really trying to assess physical plausability. For example, in Figure 4, multipliers 0.1-10 are included in the parametrizations, but without much justification. What can the authors deduce from their results? What are the probable ranges?
The discussion needs to put the results back in context and discuss them in light of previous experiments. In the Introduction, the authors cite previous studies that simulated the impact of AMOC changes on the carbon cycle with Earth system models (in which most of the feedbacks explored were included). Can your results help understand better these previous simulations?
Minor points:
L. 41: I am not sure that “not well understood” is appropriate, since a lot of studies have highlighted the impact of AMOC on pCO2 and the reverse as highlighted in the 2 following paragraphs. It is however a complex interaction.
L 272: Please amend: “Fig. 4a, b is yellow..”
L. 295: What is the meaning of “we continue in the piston velocity”?
- AC1: 'Reply on RC1', Daan Boot, 27 Oct 2021
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RC2: 'Comment on esd-2021-42', Anonymous Referee #2, 16 Sep 2021
Boot et al used a box model SCP-M to study the effects of AMOC on atmospheric CO2 under two climatic boundary conditions: preindustrial and LGM. They included seven additional feedbacks into the model and implemented 13 experiments under each climatic scenario by considering different combinations of these feedbacks. They also used AUTO software to scan the parameter space and detect bifurcation. It is suggested that the pCO2-AMOC relationship relies most on the biological processes and climatic boundary conditions. The study is useful to understand the impact of each internal feedback on atmospheric pCO2 and provide insights on atmospheric pCO2 variability during glacial cycles. However, the paper is not very well written and hard to follow.
In the Inroduction section, are there any papers using 3D OGCM to simulate the atmospheric pCO2-AMOC strength relationship under PI and LGM? If so, these papers need to be properly cited.
Also, I have some major criticisms about the experiment configuration and robustness of the conclusions.
- I didn’t see any experiments to test the plausibility of the box model to address the AMOC-pCO2 relationship problem. I would suggest that you set up two more experiments fully including all the feedbacks you mentioned in Table 2 and check if the atmospheric pCO2 is reasonable under two scenarios.
- In general, I think all the experiments should be set up with other feedbacks properly included to make the case more realistic. For example, when studying the role of biological feedback (x-0 and x-1 in Table2), the x-0 could be set up with all λ = 1, x-1 then should be only with λBI=0, etc.
- In lines 266-270, the three parameters are selected as control parameters: the rain ratio, the biological production and the piston velocity. Please explain the reasons for picking these parameters. Also, the multiplier changes from 0.1 to 10 without reasonable explanations. I would suggest using more realistic ranges.
Comments/concerns about specific feedbacks/parameters are below.
- In equation (2), the authors chose 0.54oC/(Wm-2) to compute the temperature change. As this parameter is important in equation (12) to control the AMOC strength, what is the sensitivity of this parameter to coupling AMOC-carbon cycle?
- AC2: 'Reply on RC2', Daan Boot, 27 Oct 2021
Daan Boot et al.
Data sets
SCPM-AUTO Boot, D., Von der Heydt, A. S., and Dijkstra, H. A. https://doi.org/10.5281/zenodo.4972553
Model code and software
SCPM-AUTO Boot, D., Von der Heydt, A. S., and Dijkstra, H. A. https://doi.org/10.5281/zenodo.4972553
Daan Boot et al.
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