Noise-induced tipping of Atlantic Meridional Overturning Circulation under climate mitigation scenarios

Domain: earth_science
Published: 2025-12-13
Editorial outcome: minor_revision

Abstract
Growing attention to decarbonization to limit global warming raises an important question of whether climate mitigation efforts could effectively prevent a potential collapse of the Atlantic Meridional Overturning Circulation (AMOC), a major tipping element in the climate system. Using CO2 stabilization simulation with the Community Earth System Model version 1, we show a marked ensemble divergence in AMOC evolution under identical or comparable CO2 forcing, towards either recovery or a multi-century collapsed state. This divergence arises from the accumulation of stochastic noise near the AMOC stability threshold, characterized by persistent high-pressure anomalies over the subpolar North Atlantic. In other words, internal atmospheric variability can push the AMOC across its stability threshold and trigger collapse, aided by the basin-wide salt-advection feedback. A modest delay in implementing mitigation further increases the likelihood of crossing this threshold, amplifying the risk of collapse. This study underscores high uncertainties in future climate projections in response to climate mitigation efforts and highlights the urgent need for strong and immediate international actions.

Introduction
The Atlantic Meridional Overturning Circulation (AMOC) is a critical component of Earth’s climate system1, responsible for the global redistribution of oceanic heat, salt, and carbon. Its changes have substantial and far-reaching impacts on the global climate system, extending well beyond regional scales. Indeed, the influences of the AMOC on the climate have been observed and projected through the observations and future warming scenarios2–5 (e.g., the North Atlantic warming hole). Paleo-proxy records reveal that the geological past witnessed even more dramatic AMOC-driven climate changes, the so-called Dansgaard-Oeschger events6. Seminal earlier studies have identified the existence of multiple equilibrium states within the AMOC7 and attributed the abrupt transitions between these equilibria to triggering the recurrent global-scale climate changes in the past8–10.

These multiple lines of evidence from paleoclimate records and model-based studies highlight the multiple-equilibria nature of the AMOC, associated with various tipping points11,12. In this context, a tipping point denotes a critical threshold at which a small additional perturbation can trigger substantial and self-perpetuating changes in the system’s future state12. Such alterations ultimately drive the system into an alternative state, where positive feedbacks are strong enough to overcome the stabilizing negative feedbacks. Specifically, the basin-wide salt-advection feedback has been identified as the primary mechanism controlling the AMOC transition7,13. Physical insights into the AMOC bistability have been advanced through a hierarchy of models, ranging from conceptual box models to oceanic eddy-permitting fully coupled global climate models (GCMs), in response to changing external forcing such as freshwater input over the North Atlantic and atmospheric carbon dioxide (CO2) concentrations7,8,14–22. These modeling studies have demonstrated a clear hysteresis behavior and tipping event of the AMOC.

The past serves as a mirror to the future; so, the paleo-proxy evidence of the past AMOC tipping supported by climate modeling raises the concern that the AMOC may cross the tipping point and collapse in a future warming world as the CO2 concentrations continue to increase. While most state-of-the-art climate models project that a complete shutdown of the AMOC is unlikely within the 21st century23, there are growing arguments that the current AMOC system may be on a self-perpetuating tipping course based on statistical early warning signals24–26 and a diagnostic indicator of AMOC stability18,27–31. While there is an international consensus for decarbonization, this possibility of AMOC collapse represents an important source of uncertainty in future climate responses to anthropogenic mitigation efforts32. For example, if climate mitigation actions are implemented after the AMOC has already crossed its tipping point, it might not recover and continue to collapse despite the stabilization or reduction of CO2 concentration, potentially pushing our climate system into uncharted future states. This underscores that when and how future mitigation actions are executed should be carefully planned, taking into account the tipping point of the AMOC. Analyzing this aspect could offer invaluable insights to decision-makers, helping them to weigh the benefits and risks of various climate mitigation options. However, this area still demands further exploration to understand its implications.

To address this question, here we evaluate the effectiveness of climate mitigation efforts initiated at different times in preventing an AMOC tipping, using large ensemble simulations of CO2 stabilization with a state-of-the-art GCM. An overview of the experimental design is given in the following subsection; additional details can be found in the “Methods” section. Here we find that the AMOC can evolve toward either recovery or collapse under the same or comparable CO2 forcing. We attribute this stochastic bifurcation to the accumulation of internal atmospheric perturbations near the AMOC stability threshold, with a spatial pattern characterized by persistent high-pressure anomalies over the subpolar North Atlantic. We further find that modest delays in implementing mitigation increase the probability of an AMOC collapse.

Results
CO2 stabilization simulations using CESM1
We conducted three sets of experiments using the Community Earth System Model version 1 (CESM1). The first one, referred to as “PD”, is a present-day control simulation spanning 900 years with a constant atmospheric CO2 concentration (367 ppm). The second, denoted as “1pctCO2”, is a typical global warming simulation with a 1% per year increase in CO2 concentration over 140 years until it quadruples (black line in Fig. 1a, years 2001 to 2140). It consists of 28 ensemble members, branched from different initial conditions in the PD simulation. The final experiment, the central focus of this study, involves a climate mitigation scenario with the CO2 concentration fixed at the same level as the starting point for 200 years. This experiment is branched from the 7 different CO2 levels in the 1pctCO2 simulation (see colored horizontal parallels in Fig. 1a). Among them, the four blue-colored experiments with relatively lower CO2 levels consist of 3 ensemble members each, while the other three experiments with relatively higher CO2 levels (green, brown, and purple) have 28 ensemble members each (legends in Fig. 1a). Each experiment is labeled as “EXPbranchedyear” and this naming will be used throughout the rest of this paper. Such CO2 stabilization simulations, initiated from seven different global warming levels, allow us to explore the potential occurrence of AMOC tipping events under relaxed warming scenarios aligned with future climate mitigation actions implemented at different times.

AMOC collapse despite climate mitigation efforts
We first examine the temporal evolution of the AMOC strength defined as the maximum of the meridional ocean stream function below 500 m at 45°N in each CO2 stabilization experiment. The AMOC continues to weaken in response to the increase in atmospheric CO2 levels (black line in Fig. 1b), a consistent result among different state-of-the-art climate models23. It is naturally expected that the AMOC will be gradually stabilized or potentially begin to recover once the anthropogenic increase in CO2 concentrations ceases. Indeed, in the 4 blue-colored simulations (EXP2070, EXP2080, EXP2090, and EXP2100), the AMOC gradually strengthens after an initial weakening for decades (Fig. 1b, c). The initial weakening of the AMOC persists for a couple of decades, due to the slow adjustment of the ocean circulation, especially in the deep ocean, toward its equilibrium. This is partly due to the continued increase in global mean temperature after CO2 stabilization (Supplementary Fig. 1). As the forced CO2 concentration is higher, the initial weakening phase of the AMOC tends to last longer, suggesting that the AMOC recovery takes longer as climate mitigation measures are delayed.

The AMOC recovery in all 4 blue-colored simulations implies that the AMOC has not yet passed its bifurcation tipping point with the earlier cessation of the anthropogenic CO2 increases. However, the other 3 simulations (EXP2105, EXP2110, and EXP2115) show strikingly different outcomes. They exhibit significant disparities in the evolution of the AMOC and show a broader spread in their inter-ensemble distributions. Specifically, in the case of EXP2105 (green line in Fig. 1d), most ensemble members exhibit initial weakening and subsequent recovery of the AMOC as in the 4 blue-colored simulations (Fig. 1c). Yet, one ensemble member shows a continued weakening of the AMOC, and this negative trend implies that the AMOC is transitioning into an alternative collapsed state. Interestingly, this bifurcation of the AMOC evolution among ensemble members is much more pronounced in the EXP2110 (brown line in Fig. 1d), where the AMOC collapses in 17 ensemble members. Furthermore, with a 5-year delay in mitigation, the AMOC collapses in all 28 members of the EXP2115 (purple line in Fig. 1d).

This suggests that, in this model, the bifurcation tipping point, defined as the threshold of external forcing (CO2 concentration) for AMOC, may lie between 1097 ppm (EXP2110) and 1152 ppm (EXP2115). We caution, however, that this should not be misinterpreted as evidence that the AMOC can remain stable under such high CO2 levels, thereby implying that higher emissions would be permissible. Such a high threshold may arise from a bias in AMOC stability in our model compared with observations (Fig. 2a); therefore, in reality, the tipping point may occur at much lower CO2 concentrations. In other words, our results indicate that the AMOC tipping behavior is governed dynamically by the stability or strength of the AMOC system, rather than by CO2 level itself. Further details on AMOC stability are provided in the following section.

At the same time, from a more practical perspective, despite only 10-year difference in the initiation of each experiment between EXP2105 and EXP2115, the future outcomes of the AMOC in these two simulations are markedly different. This reveals that a relatively small delay of global warming relaxation can abruptly increase the probability of the AMOC collapse near the bifurcation point. It is noteworthy that the declining rate of the AMOC in the EXP2115 is comparable to that in the 1pctCO2 with increasing CO2 concentrations (black line vs. purple line in Fig. 1b), even though there is no longer CO2 increase. This highlights the critical role of the intrinsic dynamics of the AMOC, which persistently amplifies its initial weakening through positive salt-advection feedback, even under the stabilized external CO2 forcing.

Moreover, we conducted an additional 250 years of integration for 3 ensemble members exhibiting AMOC collapse and 3 ensemble members showing AMOC recovery in EXP2110, respectively (450 years in total; Supplementary Fig. 2). In the extended 250-year simulation, both the recovering and collapsing ensemble members reach their respective multi-century quasi-equilibrium states (~16 Sv for recovering vs ~3 Sv for collapsing members). Note that while these states persist for several centuries, it remains uncertain whether they represent truly stable equilibrium states. Given a longer integration period, the collapsed AMOC might either recover toward its original strength or weaken further, potentially transitioning into a reversed circulation state. However, from a practical standpoint, even a collapsed AMOC persisting for several centuries is sufficient to drive substantial and widespread climate impacts on human timescales. Therefore, we consider these states to be quasi-stable, as they persist for an extended period despite the inherent uncertainty regarding their ultimate fate.

To ensure the more robustness of our findings, we further analyzed the GISS-E2-1-G model, the only publicly available CMIP6 model that meets two key criteria: (1) integration of full shared socioeconomic pathway (SSP) scenarios up to the year 2500, and (2) provision of multiple ensemble members. This model provides insights into how AMOC responses diverge under different future warming scenarios (Supplementary Fig. 3). In SSP1-2.6 (green), the AMOC consistently recovers across all ensemble members (#10). In SSP2-4.5 (brown), a bifurcated response is observed, with some ensemble members showing recovery (#13) while others exhibit collapse (#2)19. Under the high-emission SSP5-8.5 scenario (purple), the AMOC universally collapses across all ensemble members (#6). The three SSP scenarios follow distinct CO2 forcing trajectories, complicating direct comparisons with our CESM1 mitigation experiments conducted at different times. However, the AMOC bifurcation response observed in the SSP2-4.5 scenario, which is comparable to the experimental protocol in this study, is particularly noteworthy. Comprehensively, these results underscore the robustness of AMOC tipping behaviors around its tipping point across different models and scenarios, further highlighting the critical importance of timely mitigation in preventing AMOC collapse.

AMOC shift towards unstable regime
To investigate the mechanism of this simulated AMOC tipping in a mitigated warming world, a simple and widely-used diagnostic indicator of the AMOC stability (denoted as ΔFov = FovS − FovN) is analyzed (“Methods”). This indicator effectively represents the sign and strength of the basin-wide salt-advection feedback of the AMOC. The FovS and FovN represent freshwater transport by the overturning circulation at the southern and northern Atlantic boundaries, respectively. The difference between them thus indicates freshwater convergence/divergence driven by the AMOC. A positive (negative) ΔFov indicates that the AMOC is in a stable (unstable) regime, characterized by a negative (positive) basin-wide salt-advection feedback in response to initial buoyancy forcing; the AMOC imports (exports) freshwater to (from) the Atlantic. Therefore, a negative ΔFov implies that an initial AMOC weakening could eventually trigger AMOC collapse through the self-amplifying salt-advection feedback. The ΔFov at the beginning of the CESM1 1pctCO2 simulation is positive (Fig. 2a), indicating a stable AMOC in the present-day climate.

With increasing CO2 concentration, the ΔFov gradually decreases (Fig. 2a). The decreasing AMOC stability indicator under greenhouse warming is also shown in available observations as well as in the CMIP6 multi-model average under the SSP5-8.5 scenario20,31. Even though the sign of ΔFov is positive until the end of the 1pctCO2 simulation (year 2140), but it approaches quite close to the neutral state (ΔFov ≈ 0). This suggests that the AMOC is gradually losing its barrier to tipping, moving into a more susceptible state to collapse. An intriguing point here is that the sign of ΔFov effectively constrains the bifurcation of the AMOC evolution shown in Fig. 1d. All runs exhibiting AMOC collapse in three CO2 stabilization simulations have ΔFov crossing zero, whereas the others do not (Fig. 2b). In other words, when the sign of ΔFov becomes negative and further decreases, the AMOC continues to weaken through the relatively stronger self-amplifying process, confirming that the primary mechanism for AMOC tipping is indeed the powerful salt-advection feedback. Considering the usefulness of the ΔFov sign change in constraining the ensemble divergence of AMOC evolution, we hereafter use the point at which ΔFov reaches zero as the threshold of AMOC tipping in our simulations and analysis, marking the critical transition beyond which the AMOC continues to weaken. It is noteworthy that the ΔFov evolution in GISS-E2-1-G under the SSP2-4.5 scenario also effectively constrains the ensemble bifurcation of AMOC evolution (Supplementary Fig. 3c), providing further support for its diagnostic value across different model and scenario.

Stochastic bifurcation of the AMOC evolution
In general, there are three possible mechanisms for AMOC tipping33,34. First, if the external forcing (e.g., freshwater input or CO2 concentration) passes a certain bifurcation point, AMOC tipping can occur where a bistable system loses one of its stable states. This mechanism is referred to as bifurcation-induced tipping (B-tipping)16,17,20. Second, stochastic noise can lead to the transition of the AMOC into its alternative state by helping it overcome tipping barriers even before the bifurcation point, a process known as noise-induced tipping (N-tipping)19,35,36. Lastly, the third mechanism, rate-induced tipping (R-tipping), occurs when the rate of change in external forcing exceeds a critical threshold that the system can adapt to, causing the AMOC to transition to an alternative state15,37.

These three mechanisms are not independent but are closely related, and their combined effects are important for understanding the tipping dynamics of the AMOC. For example, N-tipping of the AMOC is more likely to occur near the bifurcation point with a lower barrier to tipping, making the system more susceptible to collapse. This occurs as the basin of attraction flattens, reducing its curvature, thereby increasing its sensitivity to perturbations. The clear bifurcation of the AMOC evolution observed in EXP2110 and the GISS-E2-1-G model under the SSP2-4.5 scenario (recall Fig. 1d and Supplementary Fig. 3b) is a good example of this. Moreover, these simulations can be considered an example of partial tipping, which indicates the system may undergo partial tipping before reaching full tipping, as described by ref. 38.

To better understand this phenomenon, we next focus on the EXP2110 simulation. Specifically, we explore the ensemble spread in EXP2110 to understand the role of stochastic noise in determining the divergent AMOC behaviors in the EXP2110. Figure 3a shows the temporal evolution of AMOC strength in the EXP2110 as in Fig. 1d but categorized into 3 groups based on the future AMOC evolution. Three groups are named collapse (red), uncertain (gray), and recovery (blue) group, consisting of 17, 2, and 9 ensemble members, respectively. The temporal evolution of ΔFov in each ensemble member (Fig. 3b) is shown after sorting them in ascending order based on the last 50-year (years 2261–2310) average of the AMOC strength (Fig. 3a). As discussed in the previous section, all members in the collapse group cross the zero ΔFov. Furthermore, the ensemble members with a stronger AMOC weakening cross the zero ΔFov more rapidly (blue dots in Fig. 3b).

Since all the EXP2110 runs are forced by the identical external forcing, stochastic noise is responsible for the bifurcation of AMOC evolution (Fig. 3a) by controlling when and whether the ΔFov crosses the threshold. Atmospheric internal variability as a stochastic noise, characterized by a white noise spectrum, drives climate variability with a redder spectrum39,40 via exchanges of properties such as heat, freshwater, and momentum at the air-sea interface. In this study, we consider the stochastic noise by calculating the deviation of each ensemble member from the ensemble mean, under the assumption that the ensemble mean reflects the externally forced response, while the deviations represent internal variability governed by a probabilistic distribution19,41,42. By this definition, we check the stochastic noise of winter surface density flux (DF), including both heat and freshwater contributions (Fig. 3c), integrated over the Labrador-Irminger Sea (LIS, black contour in Fig. 3e), where oceanic convection is the most vigorous and closely tied to the AMOC. The negative (positive) winter DF in the LIS can directly decrease (increase) deep water formation in that region, hence the AMOC. Prior to reaching the threshold in the collapse group, the ensemble distribution of the stochastic noise of the LIS DF exhibits considerably chaotic behavior, making it challenging to discern any differences between the recovery and collapse groups (Fig. 3c).

Instead, we focus on the cumulative effect of the stochastic noise, as suggested by Hasselmann39. The accumulation of stochastic noise indicates the time integration of these random internal processes. However, it should be noted that the ensemble deviation is no longer purely random after the tipping occurs, as the AMOC-driven mean state changes begin to dominate. Therefore, we focus on the time integration of stochastic noise from the start of the simulation up to the threshold (ΔFov = 0) in each ensemble member. This approach provides a reasonable estimate of the role of internal variability in shaping AMOC trajectories near the tipping threshold. The distribution of time-cumulative noise of the winter LIS DF indeed exhibits a distinctive difference between the recovery and collapse groups (Fig. 3d). The collapse group shows an overall negative signal before the threshold, indicating a larger cumulative influx of heat and/or freshwater into the ocean, which contributes to reducing the surface density. The negative DF deviation in the collapse group persistently weakens the AMOC, pushing it towards its threshold and ultimately resulting in the AMOC collapse via the stronger salt-advection feedback.

For a more detailed analysis, we examine the spatial pattern of the cumulative stochastic noise in the collapse group. The spatial patterns shown in Fig. 3e–g represent the 17-member collapse group average of the cumulative stochastic noise, calculated until the ΔFov reaches zero at each grid point in each ensemble member. The most pronounced signal of the cumulative DF is indeed located over the LIS (Fig. 3e). This is largely dominated by the thermal contribution, although the haline component also contributes to the negative deviation of DF (Supplementary Fig. 4). Specifically, the reduced evaporation in the collapse group (Fig. 3f) is crucial for the negative DF response in terms of both thermal and haline contributions: greater freshwater input into the ocean and lesser latent heat loss into the atmosphere (Supplementary Figs. 5 and 6). Based on our results, sustained monitoring of long-term cumulative evaporation, particularly over the LIS, could serve as a valuable early warning signal for AMOC collapse.

Furthermore, we found that the less evaporation is tightly linked to the reduced wind speed (Supplementary Fig. 7a) associated with a distinctive anomalous anticyclonic circulation (Fig. 3g) that weakens the semi-permanent Icelandic Low pressure system (black contours in Fig. 3g and shadings in Supplementary Fig. 7b). The cumulative atmospheric circulation forcing may result from multiple sources of internal variability, such as the North Atlantic Oscillation43–45, teleconnections from the tropical Indian Ocean46,47 or El Niño-Southern Oscillation48–50. Disentangling the relative importance of these processes is beyond the scope of this study and could be conducted in an extended study.

Future climate uncertainty driven by AMOC tipping
Previous studies have shown that climate models simulate the far-reaching climate impacts of a substantially weakened or collapsed AMOC4,51,52. In line with their findings, our result also reveals that the AMOC tipping causes the strong cooling in the subpolar gyre region in the Atlantic mainly by the declining northward oceanic heat transport within the Atlantic. This localized cooling subsequently extends to the entire Northern Hemisphere (NH) and also in the land area in the Southern Hemisphere (SH) (Fig. 4a). The substantial NH cooling results in an overall reduction in precipitation in the NH, pushing the intertropical convergence zone to the south (Fig. 4b). Moreover, it is observed that the distinct and continuous sea level rise (SLR) in the broad subpolar North Atlantic and Arctic (Fig. 4c, d) in the collapse group in response to the AMOC decline. The anthropogenic SLR is already known as irreversible53, but curbing the increase in anthropogenic CO2 can slow down the ongoing SLR. However, our results imply that the AMOC collapse would significantly increase the irreversibility of regional SLR, particularly in the subpolar North Atlantic and Arctic regions.

These climate changes related to the AMOC collapse have been well documented in many previous studies, often based on comparisons between different external forcing scenarios (e.g., freshwater hosing on vs. off)4,51 or between different periods within a given scenario (e.g., future vs. pre-industrial)52. In contrast, the global-scale climate impacts, demonstrated in this study (Fig. 4), arise under identical or nearly identical external forcing scenarios—either from stochastic fluctuations near the AMOC tipping point (EXP2110) or from small differences in the timing of mitigation forcing (EXP2105 vs. EXP2115). This highlights that substantial uncertainty in future climate trajectories, especially across the NH (Fig. 5), can emerge not only from the magnitude of external forcing but also from internal variability and noise-induced tipping behavior. For example, using a metric, surface air temperature area-averaged over the NH, we display the distributions every 20 years in EXP2105, EXP2110, and EXP2115 (Fig. 5c–e). While their initial states are almost similar, their final destinations are quite different, indicating a highly uncertain future climate under a mitigation scenario. Our results, therefore, underscore a distinct aspect compared with earlier studies: even under comparable levels of CO2 forcing, noise-induced tipping of the AMOC can generate large and persistent spreads in regional climate outcomes. To ensure more reliable projections under the increasing likelihood of decarbonization, mitigation strategies must thus be carefully designed with consideration of the AMOC threshold and the possibility of tipping.

Methods
CESM1 CO2 stabilization simulations
We perform three sets of experiments using the CESM1, which consists of the atmosphere (Community Atmospheric Model version 5), ocean (Parallel Ocean Program version 2), ice (Community Ice Code version 4), and land models (Community Land Model version 4). The atmosphere and land models operate with ~1° resolution, with 30 vertical levels. The ocean and ice models use a nominal 1° resolution (the meridional resolution is ~1/3° near the equator), with 60 vertical levels.

1. Present-day control simulation (PD): The first experiment, referred to as “PD”, is a control simulation representative of present-day conditions. This experiment spans 900 years with a constant atmospheric CO2 concentration of 367 ppm, providing a stable baseline for comparison and serving as the initial condition for subsequent experiments.

2. Global warming simulation (1pctCO2): The second experiment, denoted as “1pctCO2”, simulates a typical global warming scenario in which atmospheric CO2 concentration increases by 1% per year over a period of 140 years until it quadruples relative to the initial value (black line in Fig. 1a, corresponding to model years 2001 to 2140). This experiment comprises 28 ensemble members, each initialized from different macro states within the PD simulation to capture variability arising from internal climate dynamics. More specifically, after discarding the first 600 years of the PD run as model spin-up, we selected initial conditions from the subsequent 300 years according to distinct phases of leading multi-decadal climate variability modes. Six ensemble members were initialized during maximum positive phases of the Atlantic Multidecadal Oscillation, and six during maximum negative phases. Similarly, eight ensemble members were drawn from maximum positive phases of the Pacific Decadal Oscillation, and eight from maximum negative phases. This macro-perturbation design ensures that the ensemble adequately samples uncertainties linked to multi-decadal variability in both the Atlantic and Pacific basins, thereby providing a diverse set of initial conditions for assessing climate evolution, including AMOC under external forcing. This setup has the advantage that all ensemble members are generated from the same climate model under identical external forcing. Consequently, the ensemble spread arises solely from differences in the initial conditions, allowing us to isolate the role of internal climate variability without conflating uncertainties arising from model structure or physics. It is noteworthy that the initial states at the time of branching do not determine the tipping behavior in our simulations, confirming that the ensemble divergence arises from the accumulation of stochastic internal variability during the integration rather than from differences in the initial conditions themselves, which is a central finding of this study.

3. CO2 stabilization simulation (EXPbranchedyear): The third and primary focus of this study is a climate mitigation experiment. In this setup, the atmospheric CO2 concentration is stabilized at specific levels for an additional 200 years. These stabilization levels are selected based on seven distinct branching points from the 1pctCO2 simulation, representing different CO2 concentrations at various stages of the warming trajectory (colored horizontal parallels in Fig. 1a). The four experiments with relatively lower CO2 stabilization levels (blue-colored in Fig. 1a) each consist of 3 ensemble members. The remaining three experiments, associated with higher CO2 stabilization levels (green, brown, and purple in Fig. 1a), each include 28 ensemble members, allowing for a robust exploration of variability under stronger forcing conditions. To facilitate clarity, each stabilization experiment is labeled as “EXPbranchedyear” based on the year of branching from the 1pctCO2 simulation. This labeling convention is consistently used throughout the paper to refer to specific experiments.

AMOC stability indicator
The freshwater transport by the AMOC at a certain latitude (ϕ) is calculated as

Fov(ϕ) = − 1/S0 ∫0D ∫W v* dx dz

where S0 = 34.8 psu is a reference salinity, v* = v − v^ is the baroclinic meridional ocean velocity, v^ is the barotropic meridional ocean velocity (section average), and hSi indicate the zonal mean salinity. The AMOC stability indicator (ΔFov) is measured as the difference of Fov across the southern and northern boundaries of the Atlantic as follows:

ΔFov = FovS − FovN

where FovS denotes the freshwater transport at the southern boundary (34°S) and FovN indicates the transport at the northern boundary, which is the sum of the transport across the three sections between Arctic and Atlantic18,55: the Canadian Arctic Archipelago (FovCAA), the Fram Strait (FovFS), and the Barents Sea Opening (FovBSO).

FovN = FovCAA + FovFS + FovBSO

Note that the calculation of Fov,CAA, Fov,FS, and Fov,BSO basically follows the Eq. (1), but with v* normal to each section. Integration and average are then performed along each section.

Surface density flux
As in previous studies56,57, the surface density flux (fdensity) at the sea surface is composed of thermal and haline contributions, and it can be expressed as

fdensity = − α Q/Cp − β SSS F / (1 − SSS)

where the first and second terms are thermal and haline contribution, respectively. α and β indicate the thermal expansion and haline contraction coefficients, respectively. Cp, Q, SSS, and F are the specific heat capacity of seawater, net surface heat flux, sea surface salinity, and net freshwater flux, respectively.

Data availability
The data used in this study are available in the Figshare database under accession code (https://doi.org/10.6084/m9.figshare.30173038.v1). Ocean reanalysis data are available from ORAS5 (https://doi.org/10.24381/cds.67e8eeb7) and ECCOv4r4 (https://ecco-group.org/products-ECCO-V4r4.htm). CMIP6 model output is freely accessible through the ESGF data portal (https://esgf-node.llnl.gov/projects/cmip6).

Code availability
The codes used in this study are available in the Figshare database under accession code (https://doi.org/10.6084/m9.figshare.30173038.v1). All data analyses and figure generation were conducted using the Python programming language, primarily with the open-source packages numpy, xarray, matplotlib, and basemap for data analysis and visualization. The basemap package for Python, used for global mapping of data, is available online at (https://matplotlib.org/basemap/).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of:\nAbrupt Amplification of Uncertainty in Atlantic Meridional Overturning Circulation Near Its Tipping Point\nThis is an important paper shedding light on various aspects of the stability of the AMOC, using model simulation\nensembles. I generally find the results convincing and well presented, and I recommend publication with minor revisions.\nIt is very interesting how stochastic variability becomes critical when the AMOC is forced to pass close to the AMOC tipping\npoint, something which was previously described only in Romanou et al. in a very limited way. The role of the spatial\nstructure of the relevant stochastic forcing was particularly interesting to me.\nThe abstract and discussion section nicely summarize the main findings and implications for mitigation. However, I would\nadvise the authors to reconsider the title. “Abrupt amplification of uncertainty” doesn’t seem to do justice to the main\nmessages of the paper, in my view.\nOtherwise I only have minor comments as below.\nLine 46: a remarkable characteristic of the AMOC known as the “tipping point”11,12\nThere is not just one tipping point. E.g., Heinrich events make the AMOC collapse, while DO events switch convection in the\nNordic Seas on and then off again.\n131: This suggests that the bifurcation tipping point, defined as the threshold of external forcing (CO2 concentration) for\nAMOC stability in this model, may lie between 1097 ppm (EXP2110) and 1152 ppm (EXP2115).\nThis threshold is highly model dependent and as shown later, might be far too high given that the model Fov has the wrong\nsign compared to observations, so I find this sentence problematic – it could be cited out of context to suggest we are quite\nsafe and can emit a lot more CO2.\nAlso, as shown later it appears that it is not directly a CO2 concentration threshold that matters, but rather an AMOC strength\nthreshold. That is inline with what Drijfhout et al. conclude for CMIP6 models (preprint:\nhttps://www.researchsquare.com/article/rs-5077014/v1): they find if AMOC reduces to below ~10 Sv the AMOC will collapse.\nThis seems to be also the case here, looking at the individual runs. In particular, Fig. 3a appears to show that the recovery\ngroup is characterized by the AMOC not dipping below a certain threshold between 9 and 10 Sv, and indeed already where\nthe recovery and collapse groups separate they seem to be sorted by AMOC strength, while the forcing is the same.\nSection: AMOC shift from stable to unstable regime\nThe usefulness of the Fov stability indicator is remarkable, as this was initially introduced by Rahmstorf (1996) to indicate\nthe difference between a bistable and monostable AMOC regime in equilibrium, and thus not necessarily to indicate a\ntipping point in transient scenarios. Given it is based (mainly) on the sign of the overturning freshwater transport at 34 South,\nfor this feedback to become relevant the advection time scale between there and the northern Atlantic needs to be\nconsidered, and a transient weakening that is fast compared to that time scale could override the usefulness of Fov. Maybe\nthe authors would like to discuss/comment on why Fov nevertheless works well here?\n419: This new reference should be added re the observed sign of Fov: Arumí‐Planas, C., S. Dong, R. Perez, M. J. Harrison,\nR. Farneti and A. Hernández‐Guerra (2024). A Multi‐Data Set Analysis of the Freshwater Transport by the Atlantic\nMeridional Overturning Circulation at Nominally 34.5°S. Journal of Geophysical Research: Oceans 129(6) doi:\n10.1029/2023jc020558.", "reviewer_2": "The paper “Abrupt amplification of uncertainty in Atlantic Meridional Overturning Circulation near its tipping point” examines\nwhether climate mitigation can prevent a collapse of the Atlantic Meridional Overturning Circulation (AMOC), a key climate\ntipping element. Using simulations with CO₂ stabilization and zero-emissions scenarios, the authors find that AMOC stability\ndiverges significantly depending on the timing of mitigation. Additionally, stochastic fluctuations near the tipping point can\npush the system across its tipping point. I liked to read the paper and overall think that the text is well written. The topic that\nis covered is also of general interest and currently one of the most extensively studied tipping elements. While I believe that\nthe manuscript has potential (due to the very clearly carried out experiments, the topical nature of the work), I see several\nimportant and substantial points of critique that, taken together, are in my view sufficient to prevent publication in Nature\nCommunications in its current form.\nI hope the authors will find my comments helpful.\nMajor points:\n1. Much of the simulations in the paper rely on the 28 ensemble members using CESM1. It is, however, unclear and not\ndescribed (e.g. in the methods) how these ensemble members were constructed and why they are a good representative of\nthe relevant uncertainties (e.g. of the climate system or more specifically of the AMOC representation in CESM).\n2. It did not become clear enough for me why the second Earth System model CESM2 was used and what makes it different\nto CESM1 so that it justifies to say that two different ESMs were used (it is still just 1 ESM with 2 versions, is it?). The authors\nwrite to “evaluate the robustness of the results” but then didn’t carry out the same results (with CESM2 as with CESM1)\nwith constant CO2-concentration but ZEC-results. Thus, it isn’t clear to me why CESM1 or 2 are not used for both experiments.\nHowever, I really liked that GISS-E2-1-G data was used to compare against.\n3. I think the title is not ideal because close to the tipping point subtle (stochastic) changes can always determine whether a\ntipping element loses stability or not – this is not really new in my view (and has also been shown in the GISS simulations\nthat the authors co-evaluate). Still, I think that noise-induced tipping is still relatively novel in ESM simulations\n4. Lastly, I am not sure that the part on AMOC impacts is novel since in the last 2-3 years, there have been several papers on\nthe AMOC impact using several Earth System Models and one of the most cited ones (van Westen et al., 2023, Science\nAdvances, doi: 10.1126/sciadv.adk1189) also uses CESM.\nHere are some further points to consider:\n1. In the abstract, it is necessary to name the Earth System Models that are used in the study.\n2. The abstract states that the timing (in the sense of this paper, the level of CO2-concentration) of mitigation actions matters\nto keep the AMOC still safe but this is the very definition of a tipping point. If a certain level of warming (or CO2\nconcentration) is crossed, the AMOC loses stability is what is essentially stated.\n3. Line 78: The authors state “two state of the art GCMs”: Which two GCMs are meant? I assume CESM1 and CESM2 (it is\nunclear to me why not just using CESM1 or 2 because they are from the same ESM family, and it is even less clear to me\nwhy slightly different experiments were carried out if the goal was to check the robustness – see my major comment 2)\n4. Line 90: Why are the chosen initial conditions a good representation of the AMOC uncertainty in CESM? I think this is a\nkey shortcoming of the study and I would recommend a methods section on that to cover the choice of the ensemble in\ndetail.\n5. L 133: Why is the AMOC tipping point defined between 1097 and 1152 ppm although already EXP2105 (1043 ppm)\nshows one ensemble member tipping? Maybe the authors could also state the respective global warming that represents\nthese levels of global warming and put this in relation to the Armstrong McKay et al. (2022, Science, doi:\n10.1126/science.abn7950) values that state the AMOC tipping point between 1.4-8.0°C. This study shows that the tipping\npoint may not be located in the upper half of the spectrum (i.e. between 5-8°C).\n6. L136-140: “This reveals that a relatively small delay in global warming relaxation …”. Of course, close to the tipping point\nit matters when exactly additional emissions are stopped. The question rather is in my view whether the observed CO2-\nconcentrations are (i) realistic to be achieved (which I am not sure of given they are beyond 1043 ppm), and (ii) if they are\nrealistic: are they really close-by and reached within 10 years in representative emission scenarios (for instance in a SSP3-\n7.0 or SSP5-8.5).\n7. L258: The AMOC is not a slow-onset tipping element as compared to the Amazon rainforest and also not compared\nagainst the monsoon systems. Since the time of the Ritchie et al. (2021, Nature, doi.org/10.1038/) paper, this has been\nrevised (see Armstrong McKay et al., 2022, Science, Tab. 1 timescales). Thus, the AMOC is either a medium or actually\nrather a fast-onset tipping element.\n8. L360-390: That’s a very nice analysis but is not really novel in my view (see, e.g. van Westen et al., 2024, Sci. Adv. Also\nusing CESM1).\n9. L390-392: Remove\n10. L402: “is as narrow as just 10 years”: This depends on the rate of additional emissions and I would appreciate a\ndiscussion of how realistic it is to increase CO2 by 1% until the late 21st century. I am not saying that this is excluded to\nhappen but it seems to me that the timespan is too short and would be longer in reality.\n11. L441: I find the description of the models CESM1 and 2 insufficient and this should be elaborated on. In particular it\nshould be discussed how the 28 different ensemble members were constructed (which initial conditions? why are they\nuseful?). Also, it is necessary to describe why two (not so) different ESMs were used for two not very similar experiments\nand why this provides additional trust in the robustness of the results."}, "author_response": "Response: We sincerely thank the reviewer for recognizing the novelty and significance of our study,\nas well as for the positive overall assessment. We also agree with the reviewer’s concern that the\nprevious title did not fully capture the main message of our work. Considering the reviewer’s suggestion\nand the central focus of our study on noise-induced AMOC tipping driven by internal climate variability,\nwe have revised the title to: “Noise-induced tipping of Atlantic Meridional Overturning Circulation\nunder climate mitigation scenarios.” Detailed responses to the specific comments are provided\nbelow.\nL46: a remarkable characteristic of the AMOC known as the “tipping point”11,12\nThere is not just one tipping point. E.g., Heinrich events make the AMOC collapse, while DO events\nswitch convection in the Nordic Seas on and then off again.\nResponse: We agree with the reviewer’s comment. Accordingly, we have revised the sentence as\nfollows:\n(P03 L47-51) “These multiple lines of evidence from paleoclimate records and model-based studies\nhighlight the multiple‐equilibria nature of the AMOC, associated with various tipping points. In this\ncontext, a tipping point denotes a critical threshold at which a small additional perturbation can trigger\nsubstantial and self-perpetuating changes in the system’s future state.”\nL131: This suggests that the bifurcation tipping point, defined as the threshold of external forcing (CO2\nconcentration) for AMOC stability in this model, may lie between 1097 ppm (EXP2110) and 1152 ppm\n(EXP2115). This threshold is highly model dependent and as shown later, might be far too high given\nthat the model Fov has the wrong sign compared to observations, so I find this sentence problematic –\nit could be cited out of context to suggest we are quite safe and can emit a lot more CO2.\nAlso, as shown later it appears that it is not directly a CO2 concentration threshold that matters, but\nrather an AMOC strength threshold. That is inline with what Drijfhout et al. conclude for CMIP6 models\n(preprint: https://www.researchsquare.com/article/rs-5077014/v1): they find if AMOC reduces to below\n~10 Sv the AMOC will collapse. This seems to be also the case here, looking at the individual runs. In\nparticular, Fig. 3a appears to show that the recovery group is characterized by the AMOC not dipping\nbelow a certain threshold between 9 and 10 Sv, and indeed already where the recovery and collapse\ngroups separate they seem to be sorted by AMOC strength, while the forcing is the same.\nResponse: We fully agree with the reviewer’s concern. Although we noted “in this model” to indicate\nthat the threshold applies only to our simulations, we acknowledge that, as the reviewer pointed out, it\ncould be cited out of context to suggest that the AMOC is quite safe and that much higher CO₂ emissions\nwould be permissible. This would be a misleading interpretation. Therefore, we have revised the text\nby adding the following clarification to prevent potential misinterpretation:\n(P07 L137-146) “This suggests that, in this model, the bifurcation tipping point, defined as the threshold\nof external forcing (CO concentration) for AMOC, may lie between 1097 ppm (𝐸𝑋𝑃 ) and 1152 ppm\n2 2110\n(𝐸𝑋𝑃 ). We caution, however, that this should not be misinterpreted as evidence that the AMOC can\n2115\nremain stable under such high CO levels, thereby implying that higher emissions would be permissible.\n(Fig. 2a), therefore in reality, the tipping point may occur at much lower CO concentrations. In other\n2\nwords, our results indicate that the AMOC tipping behavior is governed dynamically by the stability or\n2\nstrength of the AMOC system, rather than by CO level itself. Further details on AMOC stability are\n2\nprovided in the following section.”\nSection: AMOC shift from stable to unstable regime\nThe usefulness of the Fov stability indicator is remarkable, as this was initially introduced by Rahmstorf\n(1996) to indicate the difference between a bistable and monostable AMOC regime in equilibrium, and\nthus not necessarily to indicate a tipping point in transient scenarios. Given it is based (mainly) on the\nsign of the overturning freshwater transport at 34 South, for this feedback to become relevant the\nadvection time scale between there and the northern Atlantic needs to be considered, and a transient\nweakening that is fast compared to that time scale could override the usefulness of Fov. Maybe the\nauthors would like to discuss/comment on why Fov nevertheless works well here?\nResponse: We thank the reviewer for raising this important point. As the reviewer noted, the ∆𝐹\n𝑜𝑣\nindicator was originally introduced as a stability metric in equilibrium contexts (Rahmstorf, 1996;\nDijkstra, 2007), and it has often been interpreted as a predictor of future AMOC evolution perturbed by external\nforcing in such frameworks (Liu et al., 2014, 2020). We agree that in transient scenarios, the absolute\nvalue or sign of ∆𝐹 does not necessarily indicate a AMOC tipping point, since the system has not yet\n𝑜𝑣\nequilibrated and the relevant advective timescales may complicate its interpretation.\nIn our study, although the CESM1 experiments involve CO₂ stabilization (constant external forcing) after\nan initial transient increase, the model is still approaching equilibrium at the prescribed concentration\nlevel. Thus, we acknowledge that applying ∆𝐹 as an absolute threshold in this context has limitations.\n𝑜𝑣\nNevertheless, we found ∆𝐹 to be a useful diagnostic for analyzing the relative tendencies of divergent\n𝑜𝑣\nAMOC evolution. Specifically, the near-zero in ∆𝐹 aligns well with the subsequent divergence\n𝑜𝑣\nbetween recovery and collapse trajectories, even under transient forcing. At the same time, we\nrecognize that this consistency should not be taken to imply that ∆𝐹 is a definitive or universally valid\n𝑜𝑣\nindicator in transient contexts.\nIn line with the reviewer’s suggestion, we have revised the manuscript to clarify that ∆𝐹 = 0 is not\n𝑜𝑣\ninterpreted as a definitive indicator of instability in the transient simulations. Instead, we emphasize its\nrole as a diagnostic for relative shifts in AMOC stability, which provides physical insight into the\nensemble divergence in our study. To further address the reviewer’s concern, we have added a\ncautionary note in the Discussion section, stating that the absolute use of ∆𝐹 as a stability predictor\n𝑜𝑣\nin transient scenarios should not be overinterpreted and that its applicability and usefulness need to be\nbetter understood through additional modeling efforts and further theoretical developments.\nReferences\nRahmstorf, 1996, On the freshwater forcing and transport of the Atlantic thermohaline circulation,\nClimate Dynamics, https://doi.org/10.1007/s003820050144\nhttps://doi.org/10.1111/j.1600-0870.2007.00267.x\nLiu et al. 2014, Why is the AMOC monostable in coupled general circulation models?, Journal of Climate,\nhttps://doi.org/10.1175/JCLI-D-13-00264.1\nLiu et al. 2017, Overlooked possibility of a collapsed Atlantic Meridional Overturning Circulation in\nwarming climate, Science Advances, https://doi.org/10.1126/sciadv.1601666\n(P18 L378-390) “In our CESM1 ensemble experiments, the sign change of ∆𝐹 consistently coincides\n𝑜𝑣\nwith the ensemble divergence between recovery and collapse trajectories (Fig. 2b), suggesting that it\nremains a useful diagnostic of the relative tendency of AMOC evolution under transient forcing. However,\nwe note that the ∆𝐹 indicator was originally formulated in equilibrium contexts to characterize AMOC\n𝑜𝑣\nstability regimes (Rahmstorf 1996), and it may not necessarily indicate a tipping point in transient cases.\nWe therefore acknowledge that ∆𝐹 < 0 should not be overinterpreted as a definitive indicator of\n𝑜𝑣\ninstability in transient scenarios. Instead, in this study we use the ∆𝐹 to illustrate relative shifts in\n𝑜𝑣\nstability regimes and to provide dynamical context for the ensemble divergence. While the timing of sign\nchange of ∆𝐹 aligns well with the tipping behavior in our experiments (Figs. 2 and 3), its application\n𝑜𝑣\nand interpretation in transient scenarios should be regarded with caution and need to be better\nunderstood through further investigations across models and theoretical frameworks.”\nL419: This new reference should be added re the observed sign of Fov: Arumí‐Planas, C., S. Dong, R.\nPerez, M. J. Harrison, R. Farneti and A. Hernández‐Guerra (2024). A Multi‐Data Set Analysis of the\nFreshwater Transport by the Atlantic Meridional Overturning Circulation at Nominally 34.5°S. Journal of\nGeophysical Research: Oceans 129(6) doi: 10.1029/2023jc020558.\nResponse: We thank the reviewer for letting us know this important reference. We have now cited\nArumí‐Planas et al. (2024) in the revised manuscript.\nThe paper “Abrupt amplification of uncertainty in Atlantic Meridional Overturning Circulation near its\ntipping point” examines whether climate mitigation can prevent a collapse of the Atlantic Meridional\nOverturning Circulation (AMOC), a key climate tipping element. Using simulations with CO₂ stabilization\nand zero-emissions scenarios, the authors find that AMOC stability diverges significantly depending on\nthe timing of mitigation. Additionally, stochastic fluctuations near the tipping point can push the system\nacross its tipping point. I liked to read the paper and overall think that the text is well written. The topic\nthat is covered is also of general interest and currently one of the most extensively studied tipping\nelements. While I believe that the manuscript has potential (due to the very clearly carried out\nexperiments, the topical nature of the work), I see several important and substantial points of critique\nthat, taken together, are in my view sufficient to prevent publication in Nature Communications in its\ncurrent form. I hope the authors will find my comments helpful.\nResponse: We thank the reviewer for the thorough and constructive evaluation of our manuscript, as\nwell as for the positive remarks regarding the clarity of the experiments, the topical relevance of the\nstudy, and the overall presentation of the text. We fully acknowledge the reviewer’s concerns and agree\nthat several aspects of the manuscript required further clarification and strengthening. In the revised\nversion, we have carefully addressed these points and improved the manuscript. Our detailed\nresponses to the reviewer’s specific comments are provided in the sections below.\n1. Much of the simulations in the paper rely on the 28 ensemble members using CESM1. It is, however,\nunclear and not described (e.g. in the methods) how these ensemble members were constructed and\nwhy they are a good representative of the relevant uncertainties (e.g. of the climate system or more\nspecifically of the AMOC representation in CESM).\nResponse: We thank the reviewer for raising this important point. Since our analysis primarily addresses\nthe inter-ensemble spread in AMOC evolution, it is indeed essential to clarify how the 28 CESM1\nensemble members were constructed and why they provide a meaningful representation of relevant\nuncertainties. We agree that the original manuscript did not sufficiently explain this aspect.\nOur experiment comprises 28 ensemble members, each initialized from different macro states within\nthe 900-year present-day (PD) control simulation. We discarded the first 600 years of the PD run as\nspin-up and selected initial conditions from the subsequent 300 years according to distinct phases of\nleading modes of multi-decadal climate variability. Specifically, six members were initialized during\nmaximum positive phases of the Atlantic Multidecadal Oscillation (AMO), and six during maximum\nnegative phases. Similarly, eight members were drawn from maximum positive phases of the Pacific\nDecadal Oscillation (PDO), and eight from maximum negative phases.\nThis macro-perturbation design ensures that the ensemble samples a wide range of internal climate\nvariability relevant to AMOC stability, particularly the dominant multi-decadal modes in both the Atlantic\nand Pacific basins. By holding external forcing identical across all members, the ensemble spread\narises solely from differences in initial conditions, thereby allowing us to isolate uncertainties in AMOC\nevolution that originate specifically from internal variability rather than from differences in model\nstructure or physics. This approach is consistent with the philosophy of single-model large ensembles,\nwhich are widely used to quantify internal variability and its role in long-term climate projections.\nNevertheless, as the integrations progressed, future AMOC changes in response to CO forcing showed\n2\nno consistent dependence on initial conditions across ensemble members. For example, the inter-\nensemble correlation between the AMOC strength indices at the initial states of the 28 members and\nthe AMOC strength indices defined in 𝐸𝑋𝑃 (last 50-year average; used to distinguish collapse and\n2110\nrecovery groups in Fig. 3a) is only ~0.06 (p > 0.77). Likewise, the correlation with the subpolar North\nAtlantic SST index, which is often used as a proxy for AMOC strength (Boer, 2021), is also very weak\n(~0.13, p > 0.5). These results indicate that the initial AMOC and climate states at the time of branching\ndo not determine the subsequent AMOC tipping behavior in 𝐸𝑋𝑃 , further confirming that the\n2110\nensemble spread arises from the accumulation of stochastic internal variability during the integration\nrather than from differences in the initial conditions.\nreviewer’s concern and clarify why the CESM1 macro-perturbation ensemble provides an appropriate\nframework for investigating uncertainties in climate evolution including the AMOC.\nReferences\nBoer, 2021, Observation-based early-warning signals for a collapse of the Atlantic Meridional\nOverturning Circulation, Nature Climate Change, https://doi.org/10.1038/s41558-021-01097-4\n(P19 L407-427) “This experiment comprises 28 ensemble members, each initialized from different\nmacro states within the PD simulation to capture variability arising from internal climate dynamics. More\nspecifically, after discarding the first 600 years of the PD run as model spin-up, we selected initial\nconditions from the subsequent 300 years according to distinct phases of leading multi-decadal climate\nvariability modes. Six ensemble members were initialized during maximum positive phases of the\nAtlantic Multidecadal Oscillation, and six during maximum negative phases. Similarly, eight ensemble\nmembers were drawn from maximum positive phases of the Pacific Decadal Oscillation, and eight from\nmaximum negative phases. This macro-perturbation design ensures that the ensemble adequately\nsamples uncertainties linked to multi-decadal variability in both the Atlantic and Pacific basins, thereby\nproviding a diverse set of initial conditions for assessing climate evolution, including AMOC under\nexternal forcing. This setup has the advantage that all ensemble members are generated from the same\nclimate model under identical external forcing. Consequently, the ensemble spread arises solely from\ndifferences in the initial conditions, allowing us to isolate the role of internal climate variability without\nconflating uncertainties arising from model structure or physics. It is noteworthy that the initial states at\nthe time of branching do not determine the tipping behavior in our simulations, confirming that the\nensemble divergence arises from the accumulation of stochastic internal variability during the\nintegration rather than from differences in the initial conditions themselves, which is a central finding of\nthis study.”\n2. It did not become clear enough for me why the second Earth System model CESM2 was used and\nwhat makes it different to CESM1 so that it justifies to say that two different ESMs were used (it is still\njust 1 ESM with 2 versions, is it?). The authors write to “evaluate the robustness of the results” but then\ndidn’t carry out the same results (with CESM2 as with CESM1) with constant CO2-concentration but\nZEC-results. Thus, it isn’t clear to me why CESM1 or 2 are not used for both experiments. However, I\nreally liked that GISS-E2-1-G data was used to compare against.\nResponse: We thank the reviewer for this insightful comment. As noted, large-ensemble simulations\ncomparable to our 28-member CESM1 long-term CO stabilization experiment are not publicly available\n2\nfor other models, which motivated our initial use of CESM2 as a supplementary test. Our intention was\nto evaluate robustness using CESM2, which incorporates substantial physical and numerical\nimprovements over CESM1 (Danabasoglu et al., 2020). However, we acknowledge that, because the\nrobustness of the CESM1 results. Following the reviewer’s suggestion, we have therefore removed the\nCESM2 analysis from the revised manuscript and now emphasize the robustness of our findings using\nthe CESM1 ensemble in combination with the GISS-E2-1-G simulations. We are grateful to the reviewer\nfor this constructive recommendation.\nDanabasoglu et al., 2020, The Community Earth System Model Version 2 (CESM2), Journal of\nAdvances in Modeling Earth Systems, https://doi.org/10.1029/2019MS001916\n3. I think the title is not ideal because close to the tipping point subtle (stochastic) changes can always\ndetermine whether a tipping element loses stability or not – this is not really new in my view (and has\nalso been shown in the GISS simulations that the authors co-evaluate). Still, I think that noise-induced\ntipping is still relatively novel in ESM simulations\nResponse: We also agree with the reviewer’s concern that the current title does not fully capture the\nmain message of our work. Considering the reviewer’s suggestion and the central focus of our study\non noise-induced AMOC tipping driven by internal climate variability in response to mitigated warming,\nwe have revised the title to: “Noise-induced tipping of Atlantic Meridional Overturning Circulation\nunder climate mitigation scenarios.”\n4. Lastly, I am not sure that the part on AMOC impacts is novel since in the last 2-3 years, there have\nbeen several papers on the AMOC impact using several Earth System Models and one of the most\ncited ones (van Westen et al., 2023, Science Advances, doi: 10.1126/sciadv.adk1189) also uses CESM.\nResponse: We agree with the reviewer that the mean-state climate impacts of an AMOC collapse have\nbeen extensively investigated in previous studies, as also noted in our manuscript. Most of these works,\nhowever, have focused on comparisons between future warming scenarios and the present-day climate,\nor on idealized perturbation experiments such as freshwater hosing (including van Westen et al., 2024,\nwhich the reviewer mentioned). In these studies, the climate impacts of AMOC collapse are primarily\nattributed to differences in external forcing. In contrast, our study shows that similarly large-scale\nimpacts can also emerge under mitigation scenarios, even though the CO forcing is the same. This\n2\nimplies a substantial spread in climate trajectories despite comparable or identical levels of external\nforcing due to tipping behavior of AMOC (Figs. 4–5). We therefore believe that the novelty of our work\nlies in highlighting the role of internal variability and noise-induced tipping in shaping future climate\nuncertainty under mitigation, which has not been emphasized in earlier studies. We appreciate the\nreviewer’s comment, which encouraged us to clarify this distinction more explicitly in the revised\nmanuscript.\n(P15 L322-340) Impacts of the AMOC tipping on climate → Future climate uncertainty driven by\nAMOC tipping\n8\n“These climate changes related to the AMOC collapse have been well documented in many previous\nstudies, often based on comparisons between different external forcing scenarios (e.g., freshwater\nhosing on vs. off) or between different periods within a given scenario (e.g., future vs. pre-industrial). In\ncontrast, the global-scale climate impacts, demonstrated in this study (Fig. 4), arise under identical or\nnearly identical external forcing scenarios—either from stochastic fluctuations near the AMOC tipping\npoint (𝐸𝑋𝑃 ) or from small differences in the timing of mitigation forcing (𝐸𝑋𝑃 vs. 𝐸𝑋𝑃 ). This\n2110 2105 2115\nhighlights that substantial uncertainty in future climate trajectories, especially across the NH (Fig. 5),\ncan emerge not only from the magnitude of external forcing but also from internal variability and noise-\ninduced tipping behavior. For example, using a metric, surface air temperature area-averaged over the\nNH, we display the distributions every 20 years in 𝐸𝑋𝑃 , 𝐸𝑋𝑃 , and 𝐸𝑋𝑃 (Figs. 5c-e). While\n2105 2110 2115\ntheir initial states are almost similar, their final destinations are quite different, indicating a highly\nuncertain future climate under a mitigation scenario. Our results therefore, underscore a novel aspect\ncompared with earlier studies: even under comparable levels of CO₂ forcing, noise-induced tipping of\nthe AMOC can generate large and persistent spreads in regional climate outcomes. To ensure more\nreliable projections under the increasing likelihood of decarbonization, mitigation strategies must thus\nbe carefully designed with consideration of the AMOC threshold and the possibility of tipping.”\n1. In the abstract, it is necessary to name the Earth System Models that are used in the study.\nResponse: Corrected.\n(P02 L24-25) “Using CO₂ stabilization simulation with the Community Earth System Model version 1…“\n2. The abstract states that the timing (in the sense of this paper, the level of CO2-concentration) of\nmitigation actions matters to keep the AMOC still safe but this is the very definition of a tipping point. If\na certain level of warming (or CO2 concentration) is crossed, the AMOC loses stability is what is\nessentially stated.\nResponse: We thank the reviewer for this helpful comment. We agree that our original abstract phrasing\ncould be interpreted as a tautological restatement of the tipping point definition. To avoid this, we have\nrevised the abstract accordingly. The revised abstract no longer emphasizes the “timing” of mitigation\nin terms of CO₂ concentration but instead highlights our key finding: that even under identical CO₂\nstabilization forcing, the AMOC exhibits divergent evolutions toward recovery or collapse due to noise-\ninduced tipping.\n(Abstract, P02 L21-34) “Growing attention to decarbonization to limit global warming raises an important\nquestion of whether climate mitigation efforts could effectively prevent a potential collapse of the Atlantic\nMeridional Overturning Circulation (AMOC), a major tipping element in the climate system. Using CO\n2\nstabilization simulation with the Community Earth System Model version 1, we show a marked\nensemble divergence in AMOC evolution under identical or comparable CO forcing, towards either\n2\nrecovery or a multi-century collapsed state. This divergence arises from the accumulation of stochastic\nnoise near the AMOC stability threshold, characterized by persistent high-pressure anomalies over the\nsubpolar North Atlantic. In other words, internal atmospheric variability can push the AMOC across its\nstability threshold and trigger collapse, aided by the basin-wide salt-advection feedback. Even a modest\ndelay in implementing mitigation further increases the likelihood of crossing this threshold, amplifying\nthe risk of collapse. This study underscores high uncertainties in future climate projections in response\nto climate mitigation efforts and highlights the urgent need for strong and immediate international\nactions.”\n3. L78: The authors state “two state of the art GCMs”: Which two GCMs are meant? I assume CESM1\nand CESM2 (it is unclear to me why not just using CESM1 or 2 because they are from the same ESM\nfamily, and it is even less clear to me why slightly different experiments were carried out if the goal was\nto check the robustness – see my major comment 2)\nResponse: We thank the reviewer for this helpful clarification. As noted in our response to Major\nComment 2, we have removed the CESM2 analysis from the revised manuscript to avoid potential\n10\nconfusion. Accordingly, we have also revised the text to read “a state-of-the-art GCM” instead of “two\nstate-of-the-art GCMs.”\n(P04 L78-87) “To address this question, here we evaluate the effectiveness of climate mitigation efforts\ninitiated at different times in preventing an AMOC tipping, using large ensemble simulations of CO\n2\nstabilization with a state-of-the-art GCM. An overview of the experimental design is given in the following\nsubsection; additional details can be found in the Methods section. Here we find that the AMOC can\nevolve toward either recovery or collapse under the same or comparable CO forcing. We attribute this\n2\nstochastic bifurcation to the accumulation of internal atmospheric perturbations near the AMOC stability\nthreshold, with a spatial pattern characterized by persistent high-pressure anomalies over the subpolar\nNorth Atlantic. We further find that modest delays in implementing mitigation increase the probability of\nan AMOC collapse.”\n4. L90: Why are the chosen initial conditions a good representation of the AMOC uncertainty in CESM?\nI think this is a key shortcoming of the study and I would recommend a methods section on that to cover\nthe choice of the ensemble in detail.\nResponse: We thank the reviewer for raising this important point. As noted in our response to Major\nComment 1, we have added a detailed description in the Methods section of the revised manuscript\nexplaining how the 28 initial conditions were selected.\n(P19 L407-427) “This experiment comprises 28 ensemble members, each initialized from different\nmacro states within the PD simulation to capture variability arising from internal climate dynamics. More\nspecifically, after discarding the first 600 years of the PD run as model spin-up, we selected initial\nconditions from the subsequent 300 years according to distinct phases of leading multi-decadal climate\nvariability modes. Six ensemble members were initialized during maximum positive phases of the\nAtlantic Multidecadal Oscillation, and six during maximum negative phases. Similarly, eight ensemble\nmembers were drawn from maximum positive phases of the Pacific Decadal Oscillation, and eight from\nmaximum negative phases. This macro-perturbation design ensures that the ensemble adequately\nsamples uncertainties linked to multi-decadal variability in both the Atlantic and Pacific basins, thereby\nproviding a diverse set of initial conditions for assessing climate evolution, including AMOC under\nexternal forcing. This setup has the advantage that all ensemble members are generated from the same\nclimate model under identical external forcing. Consequently, the ensemble spread arises solely from\ndifferences in the initial conditions, allowing us to isolate the role of internal climate variability without\nconflating uncertainties arising from model structure or physics. It is noteworthy that the initial states at\nthe time of branching do not determine the tipping behavior in our simulations, confirming that the\nensemble divergence arises from the accumulation of stochastic internal variability during the\nintegration rather than from differences in the initial conditions themselves, which is a central finding of\nthis study.”\n11\n5. L133: 1) Why is the AMOC tipping point defined between 1097 and 1152 ppm although already\nEXP2105 (1043 ppm) shows one ensemble member tipping? 2) Maybe the authors could also state the\nrespective global warming that represents these levels of global warming and put this in relation to the\nArmstrong McKay et al. (2022, Science, doi: 10.1126/science.abn7950) values that state the AMOC\ntipping point between 1.4-8.0°C. This study shows that the tipping point may not be located in the upper\nhalf of the spectrum (i.e. between 5-8°C).\nResponse:\n1) We thank the reviewer for this important question. The AMOC collapse observed in some members\nof 𝐸𝑋𝑃 (1043 ppm) and 𝐸𝑋𝑃 (1097 ppm) corresponds to noise-induced tipping, which is the\n2110 2115\nmain focus of this study. That is, even though the bifurcation tipping point has not yet been fully crossed,\nthe system can approach the stability threshold closely enough that small stochastic perturbations\ntrigger a collapse (Ashwin et al. 2012). In contrast, in 𝐸𝑋𝑃 (1152 ppm), all 28 ensemble members\n2115\nshow an AMOC collapse, which indicates that at this forcing level the system has passed the bifurcation\ntipping point. For this reason, we interpret the bifurcation tipping point to lie between 1097 and 1152\nppm, while collapses at lower CO concentrations (1043 and 1097ppm) are attributed to noise-induced\n2\ntipping events.\nAshwin et al. 2012, Tipping points in open systems: bifurcation, noise-induced and rate-dependent\nexamples in the climate system, Phil. Trans. R. Soc. A, https://doi.org/10.1098/rsta.2011.0306\n2) We thank the reviewer for this valuable suggestion. Following the comment, we calculated the\nrespective global warming levels at 1097 ppm and 1152 ppm, which correspond to approximately 3.8–\n4.0 °C above present-day. Note that our simulations were branched from the present-day condition. If\nreferenced to preindustrial, this warming level would be about 1 °C higher. This falls within the range\nreported by Armstrong McKay et al. (2022), who estimated the AMOC tipping point to lie between 1.4–\n8.0 °C. As we noted in the manuscript, however, this threshold applies only to our model simulations.\nAlthough we stated “in this model” to indicate that the threshold is model-dependent, it could be cited\nout of context to suggest that the AMOC is quite safe and that much higher CO emissions or higher\n2\nglobal warming would be permissible. This would be a misleading interpretation.\nArmstrong McKay et al. 2022, Exceeding 1.5°C global warming could trigger multiple climate tipping\npoints, Science, https://doi.org/10.1126/science.abn7950\nInstead, we attribute such high threshold in our simulations to a bias in AMOC stability compared with\nobservations. Like many CMIP6 models, CESM1 exhibits a positive AMOC stability under the present\nclimate, whereas observational estimates already suggest neutral or even negative values (Fig. 2a).\nThis bias makes the simulated AMOC appear more stable than in reality. Consequently, the true\nbifurcation tipping point might occur at substantially lower CO levels or warming thresholds. In other\n2\n12\nwords, our results indicate that the AMOC tipping behavior is governed dynamically by the intrinsic\nstability or strength of the AMOC system, rather than by CO concentration or global warming level\n2\nalone. Therefore, we have revised the text to clarify this point in the manuscript.\n(P07 L137-146) “This suggests that, in this model, the bifurcation tipping point, defined as the threshold\nof external forcing (CO concentration) for AMOC, may lie between 1097 ppm (𝐸𝑋𝑃 ) and 1152 ppm\n2 2110\n(𝐸𝑋𝑃 ). We caution, however, that this should not be misinterpreted as evidence that the AMOC can\n2115\nremain stable under such high CO levels, thereby implying that higher emissions would be permissible.\n(Fig. 2a), therefore in reality, the tipping point may occur at much lower CO concentrations. In other\n2\nwords, our results indicate that the AMOC tipping behavior is governed dynamically by the stability or\nstrength of the AMOC system, rather than by CO level itself. Further details on AMOC stability are\n2\nprovided in the following section.”\n6. L136-140: “This reveals that a relatively small delay of global warming relaxation can abruptly\nincrease the probability of the AMOC collapse near the bifurcation point.” Of course, close to the tipping\npoint it matters when exactly additional emissions are stopped. The question rather is in my view\nwhether the observed CO2-concentrations are (i) realistic to be achieved (which I am not sure of given\nthey are beyond 1043 ppm), and (ii) if they are realistic: are they really close-by and reached within 10\nyears in representative emission scenarios (for instance in a SSP3-7.0 or SSP5-8.5).\nResponse: We thank the reviewer for this thoughtful comment. The scenario we applied, 1pctCO (1%\n2\nper year increase), is roughly comparable to the SSP5-8.5 scenario. For example, in the SSP5-8.5\nscenario, atmospheric CO concentrations are projected to reach roughly 1000–1100 ppm between\n2\n2090 and 2100. We acknowledge that such concentrations are indeed associated with the highest-\nemission, worst-case scenario, and thus the AMOC tipping point simulated in our model may appear to\noccur far in the future. However, as noted in our response to Minor Comment 5, our results demonstrate\nthat the AMOC stability is more critical than CO concentration or global warming level itself in\n2\ndetermining the tipping point. To make this clearer, we have revised the relevant section of the\nmanuscript as indicated in our response to Comment 5.\n(P07 L137-146) “This suggests that, in this model, the bifurcation tipping point, defined as the threshold\nof external forcing (CO concentration) for AMOC, may lie between 1097 ppm (𝐸𝑋𝑃 ) and 1152 ppm\n2 2110\n(𝐸𝑋𝑃 ). We caution, however, that this should not be misinterpreted as evidence that the AMOC can\n2115\nremain stable under such high CO levels, thereby implying that higher emissions would be permissible.\n(Fig. 2a), therefore in reality, the tipping point may occur at much lower CO concentrations. In other\n2\nwords, our results indicate that the AMOC tipping behavior is governed dynamically by the stability or\nstrength of the AMOC system, rather than by CO level itself. Further details on AMOC stability are\n2\nprovided in the following section.”\n13\n7. L258: The AMOC is not a slow-onset tipping element as compared to the Amazon rainforest and also\nnot compared against the monsoon systems. Since the time of the Ritchie et al. (2021, Nature,\ndoi.org/10.1038/) paper, this has been revised (see Armstrong McKay et al., 2022, Science, Tab. 1\ntimescales). Thus, the AMOC is either a medium or actually rather a fast-onset tipping element.\nResponse: We thank the reviewer for providing this important clarification, which helps us better\nunderstand the AMOC among other climate tipping elements. We fully acknowledge that, as noted by\nArmstrong McKay et al. (2022), the AMOC is more appropriately categorized as a medium-to-fast\ntipping element rather than a slow one. The section in question was based on CESM2 zero-emission\nexperiments. Since we have removed the CESM2 analysis from the revised manuscript (as noted in\nour response to Major Comment 2), this part has also been deleted accordingly.\n8. L360-390: That’s a very nice analysis but is not really novel in my view (see, e.g. van Westen et al.,\n2024, Sci. Adv. Also using CESM1).\nResponse: As also noted in our response to Major Comment #4, we agree that the mean-state climate\nimpacts of an AMOC collapse have been extensively investigated in previous studies, including van\nWesten et al. (2024). Most of these works, however, rely on comparisons across different warming\nscenarios or on idealized perturbation experiments such as freshwater hosing. In contrast, our analysis\nhighlights that similarly large-scale impacts can also emerge under mitigation scenarios through noise-\ninduced tipping behavior, leading to divergent climate trajectories even under comparable or identical\nexternal forcing. We therefore believe that the novelty of our work lies in highlighting the role of internal\nvariability and noise-induced tipping in shaping future climate uncertainty under mitigation, which has\nnot been emphasized in earlier studies. We appreciate the reviewer’s comment for encouraging us to\nbetter emphasize this novelty in the revised manuscript.\n(P15 L322-340) Impacts of the AMOC tipping on climate → Future climate uncertainty driven by\nAMOC tipping\n“These climate changes related to the AMOC collapse have been well documented in many previous\nstudies, often based on comparisons between different external forcing scenarios (e.g., freshwater\nhosing on vs. off) or between different periods within a given scenario (e.g., future vs. pre-industrial). In\ncontrast, the global-scale climate impacts, demonstrated in this study (Fig. 4), arise under identical or\nnearly identical external forcing scenarios—either from stochastic fluctuations near the AMOC tipping\npoint (𝐸𝑋𝑃 ) or from small differences in the timing of mitigation forcing (𝐸𝑋𝑃 vs. 𝐸𝑋𝑃 ). This\n2110 2105 2115\nhighlights that substantial uncertainty in future climate trajectories, especially across the NH (Fig. 5),\ncan emerge not only from the magnitude of external forcing but also from internal variability and noise-\ninduced tipping behavior. For example, using a metric, surface air temperature area-averaged over the\nNH, we display the distributions every 20 years in 𝐸𝑋𝑃 , 𝐸𝑋𝑃 , and 𝐸𝑋𝑃 (Figs. 5c-e). While\n2105 2110 2115\ntheir initial states are almost similar, their final destinations are quite different, indicating a highly\n14\nuncertain future climate under a mitigation scenario. Our results therefore, underscore a novel aspect\ncompared with earlier studies: even under comparable levels of CO₂ forcing, noise-induced tipping of\nthe AMOC can generate large and persistent spreads in regional climate outcomes. To ensure more\nreliable projections under the increasing likelihood of decarbonization, mitigation strategies must thus\nbe carefully designed with consideration of the AMOC threshold and the possibility of tipping.”\n9. L390-392: Remove\nResponse: Removed.\n10. L402: “At least in our model and idealized scenario, the critical window between AMOC recovery\nand a multi-century collapse is as narrow as just 10 years, underscoring the timely mitigation”: This\ndepends on the rate of additional emissions and I would appreciate a discussion of how realistic it is to\nincrease CO2 by 1% until the late 21st century. I am not saying that this is excluded to happen but it\nseems to me that the timespan is too short and would be longer in reality.\nResponse: We thank the reviewer for this valuable comment. As pointed out, the ~10-year timescale\napplies only to the specific 1% CO increase scenario used in our model experiments. We fully agree\n2\nthat under lower-emission-rate scenarios, the corresponding timescale would be substantially longer.\nSince citing a specific value of “10 years” could be misleading for readers, we have removed this\nsentence from the revised manuscript.\n11. L441: I find the description of the models CESM1 and 2 insufficient and this should be elaborated\non. In particular it should be discussed how the 28 different ensemble members were constructed\n(which initial conditions? why are they useful?). Also, it is necessary to describe why two (not so)\ndifferent ESMs were used for two not very similar experiments and why this provides additional trust in\nthe robustness of the results.\nResponse: We thank the reviewer for this important comment. As also noted in our response to Major\nComment 1, we agree that the original manuscript did not provide sufficient description of the models\nused or of how the 28 CESM1 ensemble members were constructed. In the revised manuscript, we\nnow provide detailed information on the CESM1 configuration, including the component models, their\nversions, and the horizontal resolution, as well as a clear description of how the initial conditions for the\n28 ensemble members were generated. We also agree with the reviewer’s concern that, although\nCESM2 produced qualitatively similar results, the use of a somewhat different experimental setup\nmeans that it does not provide a fully consistent test of robustness for our CESM1 results. Therefore,\nwe have removed the CESM2 zero-emission analysis from the revised manuscript. We are grateful to\nthe reviewer for this constructive feedback, which has helped us to improve the clarity and rigor of our\nstudy.\n15\n(P18 L394-427) “We perform three sets of experiments using the Community Earth System Model\nversion 1 (CESM1), which consists of the atmosphere (Community Atmospheric Model version 5),\nocean (Parallel Ocean Program version 2), ice (Community Ice Code version 4), and land models\n(Community Land Model version 4). The atmosphere and land models operate with approximately 1°\nresolution, with 30 vertical levels. The ocean and ice models use a nominal 1° resolution (the meridional\nresolution is ~1/3° near the equator), with 60 vertical levels. 1. Present-day control simulation (PD):\nThe first experiment, referred to as “PD,” is a control simulation representative of present-day conditions.\nThis experiment spans 900 years with a constant atmospheric CO concentration of 367 ppm, providing\n2\na stable baseline for comparison and serving as the initial condition for subsequent experiments. 2.\nGlobal warming simulation (1pctCO2): The second experiment, denoted as “1pctCO2,” simulates a\ntypical global warming scenario in which atmospheric CO concentration increases by 1% per year over\n2\na period of 140 years until it quadruples relative to the initial value (black line in Fig. 1a, corresponding\nto model years 2001 to 2140). This experiment comprises 28 ensemble members, each initialized from\ndifferent macro states within the PD simulation to capture variability arising from internal climate\ndynamics. More specifically, after discarding the first 600 years of the PD run as model spin-up, we\nselected initial conditions from the subsequent 300 years according to distinct phases of leading multi-\ndecadal climate variability modes. Six ensemble members were initialized during maximum positive\nphases of the Atlantic Multidecadal Oscillation, and six during maximum negative phases. Similarly,\neight ensemble members were drawn from maximum positive phases of the Pacific Decadal Oscillation,\nand eight from maximum negative phases. This macro-perturbation design ensures that the ensemble\nadequately samples uncertainties linked to multi-decadal variability in both the Atlantic and Pacific\nbasins, thereby providing a diverse set of initial conditions for assessing climate evolution, including\nAMOC under external forcing. This setup has the advantage that all ensemble members are generated\nfrom the same climate model under identical external forcing. Consequently, the ensemble spread\narises solely from differences in the initial conditions, allowing us to isolate the role of internal climate\nvariability without conflating uncertainties arising from model structure or physics. It is noteworthy that\nthe initial states at the time of branching do not determine the tipping behavior in our simulations,\nconfirming that the ensemble divergence arises from the accumulation of stochastic internal variability during\nthe integration rather than from differences in the initial conditions themselves, which is a central finding of\nthis study.”\n16"}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have carefully considered my comments and I think that the scientific quality of the manuscript has increased\nsubstantially. Scientifically, I am now convinced of the high quality of the paper and, as opposed to my first review with\nsubstantial improvements needed, would be happy to recommend this paper for publication.", "reviewer_1": "Review of “Abrupt Amplification of Uncertainty in Atlantic Meridional Overturning Circulation Near Its Tipping Point” by Oh et al.\nReviewer #1\nThis is an important paper shedding light on various aspects of the stability of the AMOC, using model\nsimulation ensembles. I generally find the results convincing and well presented, and I recommend\npublication with minor revisions.\nIt is very interesting how stochastic variability becomes critical when the AMOC is forced to pass close\nto the AMOC tipping point, something which was previously described only in Romanou et al. in a very\nlimited way. The role of the spatial structure of the relevant stochastic forcing was particularly interesting\nto me.\nThe abstract and discussion section nicely summarize the main findings and implications for mitigation.\nHowever, I would advise the authors to reconsider the title. “Abrupt amplification of uncertainty”\ndoesn’t seem to do justice to the main messages of the paper in my view. Otherwise, I only have\nminor comments as below."}, "author_response": ""}]

Decision letter