the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Jan 2025
Past, Present, and Future Variability of Atlantic Meridional Overturning Circulation in CMIP6 Ensembles
Abstract. The Atlantic Meridional Overturning Circulation (AMOC) is a key component of the climate system, exhibiting strong variability across daily to millennial timescales and significantly influencing global climate. Sensitive to external conditions such as freshwater input, greenhouse gas concentrations, and aerosol forcing, important variations of the AMOC can be triggered by anthropogenic emissions. This study presents a comprehensive analysis of sources of AMOC variance in state-of-the-art climate ensemble models. By decomposing the effects of scenario, model, ensemble, and time variability, along with their interactions, through an Analysis of Variance (ANOVA), we identify three distinct regimes of AMOC variability from 1850 to 2100. The first regime, spanning most of the historical period, is characterized by a relatively stable AMOC dominated by internal variability. The second regime, initiated by AMOC decline at the end of the 20th century and lasting until mid-21st century, is governed by a transient increase of time variability. Notably, the direct effect of forcing differences remains muted all along this regime, despite the start of emission-scenarios in 2015. The third regime, beginning around 2050, is marked by the emergence and rapid dominance of inter-scenario variability. Throughout the simulations, model variability remains the primary source of uncertainty, influenced by aerosol forcing response, AMOC decline magnitude, and the physical variability. A key finding of this work is the evidence that internal variability decreases simultaneously with AMOC intensity and seems proportional to emission-scenario intensity.
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RC1: 'Comment on egusphere-2025-17', Anonymous Referee #1, 30 Jan 2025
This study investigates the variability of AMOC across scenarios, models, ensembles and time in CMIP6 models. The authors applied a novel method, and this part needs other reviewers’ expertise. It is a solid study, but it is difficult to see the key messages, and the implications and discussions of the results should be improved. At least the key points highlighted in the end should be better discussion in the middle sections. Some details are needed – for example, please elaborate on the initial conditions of the model ensemble (L51-53, Table 1), and the reason for incorporating the time dimension is unclear (L71-72).
Citation: https://doi.org/10.5194/egusphere-2025-17-RC1 -
AC1: 'Reply on RC1', Arthur Coquereau, 13 Feb 2025
We thank the reviewer for the feedback. We will address each point below and provide a revised version of the manuscript at the end of the interactive discussion.
1. Key Messages
We understand the concern of the reviewer about the visibility of our key messages. We will revise the manuscript to better highlight our main findings. Specifically, we will introduce the key points from the "Summary and Discussion" directly in the “Results” section.
2. Initial Conditions
We agree that further explanation of the initial conditions of the ensemble models is relevant. We will indicate in the revised manuscript that initial conditions are derived following a predefined strategy for the CMIP6 framework, starting with different years of the multi-secular preindustrial control run (known as piControl), which is run under fixed external forcing conditions from the year 1850 (Eyring et al., 2016). This strategy allows us to sample the phase-space and estimate the different possible trajectory evolutions.
3. Time Dimension
We agree with the reviewer that the introduction lacks an important motivation for incorporating the time dimension: the simple fact that adding the time dimension allows us to study how the temporal variability of the system changes over time. It allows us to study interannual-to-decadal variability through successive 30-year climate periods and estimate the evolution of this interannual-to-decadal variability (including trends). As an example, when the AMOC declines in CMIP6, this variation is a factor of variability for AMOC. However, when time variability is not taken into account, this contribution is not visible. Historically ANOVA has been applied without incorporating the time dimension.This can probably be explained by the fact that ANOVA is often used to characterize uncertainty, and that a trend common to all scenarios, models, and members is not a factor of uncertainty, whereas it is a factor of variability. Hence incorporating the time dimension allows us to generalize ANOVA methodology for variability study. This was mentioned in the “Method” section (l. 111-112), but we will explicitly express this simple idea of taking into account temporal variability and dynamical adjustment in the introduction of the revised manuscript.
References:
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Citation: https://doi.org/10.5194/egusphere-2025-17-AC1
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AC1: 'Reply on RC1', Arthur Coquereau, 13 Feb 2025
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RC2: 'Comment on egusphere-2025-17', Anonymous Referee #2, 21 Feb 2025
- I miss an interpretation of what I see in Fig. 1. The caption explains what each curve represents, but how to interpret the differences is unclear to me and not (well) discussed in the text.
- In the same Fig. different y-axes are used, which is misleading. I would suggest to you use the same y-scale everywhere (the one from panel f), or at least the panel f scale in b,d,f and the panel e scale in a,c,e.
- I think it is misleading to talk about the “large ensembles” and the “small ensembles” in many places, suggesting that differences in AMOC variability are due to ensemble size, while it is most probably to different models being analysed and more and less models available in the small and large ensemble. I suggest to use “The models with a large ensemble” vs “the models with a smaller ensemble”, or just ensemble A and ensemble B. This especially holds to the amount of decline. The much larger decline in the small ensemble is simply due to including models with larger AMOC sensitivity while the large ensemble by chance consists of 3 models that do not weaken much. This fact has nothing to do with ensemble size. On the other hand, when discussing the interactions I accept that ensemble size can be driving factor for differences in certain periods.
- Please say what R T, S mean in the caption of Fig. 3. Figures should be stand-alone understandable without having to go bac to the text to see what they actually display.
- Showing the interactions is of interest but I completely miss a physical picture of how I should interpret these interactions and what physical process they represent. What does it mean? Please explain in the text.
- I would rephrase the conclusion between anthropogenic forcing intensity (which could be positive as well in terms of AMOC: aerosols!) and ensemble variance decrease or decrease of natural variability by saying there is a strong link between forced AMOC weakening and decrease in ….(l 318)
- Discussion on line 319-320 and further down in 3.2.2. I see what you say in your figures, but don’t have a clear picture of what SRT interaction actually means in terms of physical processes, so this part is not meaningful to me (see also point 5).
- My physical interpretation of 2.3 is that up to 2050 scenarios with stronger greenhouse forcing have smaller weakening in aerosol emissions and vice versa, making the impact on AMOC almost scenario independent, until the aerosol effect is gone, and we see the effect of greenhouse gas forcing alone.
- Discussion around line 360. I disagree with the interpretation, see point 3.
- Summary your point 2. I think what you see in phase 2 is also forced by SSP scenarios, but as said before the net forcing on AMOC is reasonably equal for the scenarios. SSP126 forces stronger AMOC weakening than SSP245 and SSP585 in the first half of this century because the aerosols are faster removed from the atmosphere in this scenario.
Citation: https://doi.org/10.5194/egusphere-2025-17-RC2
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