Multi-centennial internal variability in the North Atlantic could drive additional warming over Europe

Domain: neuroscience
Published: 2026-02-11
Editorial outcome: major_revision

Abstract
Europe has experienced abnormal warming over the recent decades. Model-based studies highlight that multi-centennial internal variability of the North Atlantic can strongly affect European temperatures. However, the limited availability of high-resolution proxy records has hindered observational assessment of the existence and amplitude of such variability in the real climate system. Here, we compile annual-to-decadal proxy-based Holocene reconstructions, instrumental observations, and climate model simulations to demonstrate the existence of this multi-centennial variability mode and quantify its amplitude. We show that this mode is closely tied to the internal variability of the Atlantic Meridional Overturning Circulation. Its temporal evolution explains part of the observed 20th century variability, and a shift towards a positive phase in the late 1990s can explain the recent amplified warming over Europe. When its amplitude is constrained by observations, this internal variability may enhance anthropogenic warming in Northern Europe by up to 30% over 2000–2035.

Introduction
During the 20th century, European summer temperature depicts a warming trend during the first 40 years1, followed by a cooling trend until the late 1970s2. Since the 1980s, Europe’s temperature has risen rapidly, becoming the fastest-warming continent on Earth, as highlighted in the European State of the Climate 2023 report from Copernicus Climate Change Service (C3S) and the World Meteorological Organization (WMO). However, most of the state-of-the-art General Circulation Models (GCM) simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) tend to underestimate the warming and heat extremes in Europe compared to recent observations3–6. As shown in Fig. 1a, b, recent trends in annual temperature over Europe are higher than the global average, and the observed temperature anomalies (compared to 1851–1900) clearly surpass the CMIP6 ensemble mean since the beginning of the last century (Fig. 1a, c). This is consistent with previous studies showing that CMIP6 GCM simulations tend to underestimate the observed warming over France7. Recent regional studies associated this fact either with poor representation in CMIP6 models of the recent observed trends in atmospheric circulation in western Europe8, or with prescribing constant aerosol forcing in EURO-CORDEX models over Europe9, or with a poor estimate of long-term aerosol changes in EURO-CORDEX simulations for western-central Europe10. Here, we highlight that larger warming over Europe as compared to the CMIP6 ensemble mean is also true in winter and is even larger than in summer (Fig. 1d).

The role of internal climate variability in this greater warming over Europe has been poorly assessed so far. In this context, our study focuses on internal climate variability and its potential role in the pronounced European warming in recent decades. We focus on multi-centennial climate fluctuations, where research remains limited: the existence, magnitude, and mechanisms of such low-frequency variability are still poorly constrained, while there is still no clear consensus on the existence of such a low-frequency variability and its underlying drivers11–13. Using high-resolution proxy records and model simulations, we aim to quantify this slow variability and evaluate its contribution to regional warming.

Climate is actually influenced by internal variability, which stems from the complex, chaotic interactions among the various components of the climate system14. Internal variability interacts with the effects of anthropogenically-forced climate change, caused most notably by the increasing emissions of greenhouse gases since the Industrial Revolution. Although the global warming observed over the past century is primarily attributed to the increase in greenhouse gas concentration, historical and future simulations include a large uncertainty, especially at the continental scale15. For instance, it has been shown16 that over France, 50% of the uncertainty in the warming level by 2050 is due to internal climate variability, 38% to the uncertainties in the climate models used, and the remaining 12% to the greenhouse gas emissions scenarios chosen by human societies. The latter, therefore, represents quite a small fraction of uncertainty within this time horizon. By 2100, these proportions move to 8, 22, and 70% for internal variability, climate models, and greenhouse gas emissions, respectively. At smaller spatial scales, where climate tends to be more variable, the contribution of internal variability to the uncertainty by 2100 can be dominant17. Understanding the magnitude and underlying mechanisms of internal variability is therefore essential for improving regional climate projections17.

In addition to the large uncertainty related to internal climate variability, recent modelling results have highlighted that atmospheric circulation might not sufficiently respond to changes in its boundary conditions, such as the ocean and sea ice (e.g. ref. 18). This important finding suggests that ocean variability could have a greater impact on the atmosphere than currently simulated by existing climate models. The variability over land related to the ocean could be therefore underestimated by climate models19, as a result of the so-called signal-to-noise paradox20. The Atlantic Meridional Overturning Circulation (AMOC), a key driver of heat transport in the North Atlantic, has shown internal centennial variability in some climate models that mirrors continental temperature shifts. This variability is reflected in the Atlantic Multi-decadal Variability (AMV), the dominant pattern of North Atlantic temperature fluctuations, highlighting a strong link between ocean circulation and regional climate patterns over Europe21,22. For example, Europe experiences widespread cooling when the AMOC is reduced23. Based on climate models simulations, ref. 13 suggested, that part of the global anthropogenic warming may have been obscured by a weakening of the AMOC during the second part of the 20th century24, which could be mainly associated with an internal multi-centennial variability mode. However, this variability was highly model-dependent, raising questions about its existence in the real world.

Given the short period of instrumental temperature data and the influence of the anthropogenic forcing during this period, it is not possible to provide an answer to these questions without longer observations of the past. To understand climate variability on multi-centennial timescales, long and high-resolution temperature reconstructions from palaeoclimate proxies and climate models are required. For this purpose, we focus here on the present interglacial period, the Holocene, over the last 6000 years. While other interglacial periods might also be relevant, the Holocene is well-documented in terms of temperature proxy data25, allowing for a more accurate reconstruction of past climates and providing a detailed backdrop for understanding low-frequency climate fluctuations. Additionally, advancements in climate modelling have allowed more refined models providing transient simulations over a large part of the Holocene26–28. While most proxy-model comparisons of palaeoclimate variability have been limited to the last millennium29, a too short period to estimate multi-centennial variability properly, a few recent studies have begun to use longer transient simulations11,30. Building on these advancements, we compare here various transient simulations, as well as available high-resolution (seasonal to decadal) palaeoclimate reconstructions of surface atmospheric temperature mainly over Europe25,31, covering the last 6000 years, and a recent proxy-based reanalysis covering the Holocene32 (see Methods). The aim of this study is to: (i) identify a common multi-centennial signal of natural climate variability across different palaeoclimate information sources and its origin; and (ii) deduce its potential impact on recent and near-future temperature trends in Europe.

Results
Multi-centennial climate variability: a consistent signal across models, reconstructions, and Holocene reanalysis

Figure 2 compares the spectral density, significantly different from a red noise33, of temperature variations since the mid-Holocene, in proxy-based reconstructions (individual sites in Europe and Greenland; Fig. 2a and see Methods), transient climate models simulations and Holocene reanalysis data32 (over Europe; Fig. 2b), and simulated AMOC (Fig. 2c). The spectral analysis of low-pass filtered temperature data (see Methods) reveals significant periodicities (above the 99% significance level from the red noise) at the multi-centennial scales (~100–200 years) in most direct proxy reconstructions from diverse European sites, including the UK, the southwestern Norwegian continental margin, Sweden, and Finland. Comparable significant periodicities are also observed at a site in Greenland, although they can exceed 200 years, consistent with the findings of ref. 34. Similar spectral analyses have been conducted in transient simulations and in the Holocene reanalysis over Europe (Fig. 2b), in its northern (10°W–50°E, 55°N–70°N), central (10°W–50°E, 45°N–55°N) and southern sectors (10°W–50°E, 35°N–45°N) (Supplementary Figs. 1–3) showing similar peaks at multi-centennial timescales. This confirms that the multi-centennial-scale periodicity has a spatial signature that extends far beyond the individual sites where reconstructions are available and can be found over most of Europe. The consistency of this pattern across various models and European regions, as confirmed by reconstructions, suggests that processes at the multi-centennial scale might be a key driver of long-term climate variability. There are also smaller peaks in the spectral density, indicating multi-decadal variability at frequencies around 70 years, which can be related to the AMV35 mode.

Wavelet analysis (see Methods) reveals that most proxy records (Supplementary Fig. 4) exhibit multi-centennial-scale variability (~70–300 years), though its occurrence and intensity vary across records and time periods. Some proxies, such as the one from Lapland (tree-ring width), display more persistent variability, whereas others, like the one from Nautajärvi (lake sediment) and Diss Mere (lake sediment), show more intermittent signals. Wavelet analysis in the Holocene reanalysis and transient simulations over Europe (Supplementary Fig. 5) reveals a similar multi-centennial variability (~70–300 years). Furthermore, significant coherent spectral power is concentrated at multi-decadal to centennial timescales (~60–200 years) across most proxy pairs (Supplementary Fig. 6). At longer periods (>200 years), coherence occurs intermittently and varies among proxy pairs. The reanalysis (Supplementary Fig. 7) and the IPSL-CM6A-LR transient simulations (Supplementary Fig. 8) show patterns broadly consistent with those observed in the proxy records. These results indicate coherent multi-decadal to centennial variability across sites and suggest that the proxy records are primarily linked through common variability at these timescales, supporting the interpretation that they respond to large-scale climate phenomenon during specific Holocene intervals. While the simulations reproduce the timing and dominant spatial scales of common variability reasonably well, the amplitude of coherent variability is generally weaker in the reanalysis than in both the model simulations and the proxy records. When quantified in temperature units, IPSL-CM6A-LR exhibits larger amplitudes than the reanalysis across all regions, with model-to-reanalysis standard deviation ratios of ~4–6 (Supplementary Table S2). In contrast, comparisons between IPSL-CM6A-LR simulations and proxy records yield lower ratios, mostly between ~1 and 4, indicating a more moderate model overestimation relative to proxies. This implies that IPSL-CM6A-LR provides an upper-end estimate of low-frequency temperature variability, rather than a conservative one. Indeed, the Holocene reanalysis itself also shows reduced variability compared to those proxy records (Supplementary Table S2). The reanalysis likely underestimates real variability owing to limitations in spatial coverage, seasonal proxy sensitivities, temporal resolution, covariance-based infilling32, and low signal-to-noise ratios in proxy records36. All those limitations might lead to a damping in the amplitude of the variations in the reanalysis.

Former studies13,37 have suggested that the multi-centennial temperature variability was linked to a low-frequency mode of the AMOC in climate models. Mehling et al.38, for instance, analysed AMOC variability in pre-industrial control simulations (piControl) from nine CMIP6 models and found that six models exhibited a statistically significant mode of multi-centennial variability. Similar multi-centennial variability was also evident in the piControl of the IPSL-CM6A-LR13 and EC-Earth39 climate models, indicating that this mode might be primarily driven by internal dynamics rather than external forcing over the Holocene. Here, we also identify a multi-centennial AMOC variability (~100–200 years) in the transient model simulations since the mid-Holocene (Fig. 2c), with periodicities (Supplementary Fig. 9) similar to those found in European temperature reconstructions (Supplementary Fig. 4) and transient simulations over Europe (Supplementary Fig. 5). Moreover, a wavelet coherence analysis of transient simulations reveals that the AMOC and European temperature are in phase (Supplementary Fig. 10), exhibiting persistent coherence in the multi-centennial variability mode during the last six thousand years. These results reinforce the hypothesis that multi-centennial variability is an inherent feature of the coupled ocean-atmosphere system driven primarily by the AMOC. While there is no consensus yet on a unique forcing mechanism for the internal multi‑centennial AMOC variability, several plausible mechanisms have been proposed in the literature. Those mechanisms are mainly implying either large salinity changes that accumulates in the Arctic or the propagation of salinity anomalies from the southern part of the Atlantic. Indeed, in a pre‑industrial control simulation from the IPSL‑CM6‑LR climate model, a pronounced multi‑centennial AMOC variability emerges, driven by a delayed accumulation and release of freshwater in the Arctic, which

Methods

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript focuses on internal climate variability and highlights the significant impact of multi-centennial variability in the North Atlantic (AMOC) on European temperatures. It argues that up to 50% of the uncertainty in climate model projections of future warming is attributable to internal variability, which remains poorly understood. The manuscript further emphasizes that understanding the magnitude and underlying mechanisms of this variability is essential for improving the accuracy of regional climate predictions. The authors also flag an issue with atmospheric circulation, which might not sufficiently respond to changes in its boundary conditions.\n\nThe manuscript is interesting and well written. The topic is relevant for a broad community and noteworthy.\nHowever, I have a main concern regarding the use of the proxy records (I am not a climate modeler, so I would recommend that someone with such a background evaluate the methods).\n\nThe authors focus on proxy records from the Holocene, where a great amount of data already exists. However, the selected sites/records are not from regions where the discrepancy between the data and the model is the largest—I wonder why? What was the rationale for selecting these sites? I think this should be mentioned. Why are they mainly covering Northern Europe? Will the conclusions based on this dataset be unbiased, given that it does not represent a broader spatial coverage?\n\nThis leads to another issue: why compare different proxies? My main concern is that the differences in the records may be related to the different methods/proxies applied (e.g., tree rings vs. lake deposits), and not necessarily to the frequency mode of AMOC. How do these two systems respond to temperature change? What is their sensitivity to detecting changes in temperature? Will both tree rings and lake deposits be able to detect the same frequencies of temperature changes? This is quite crucial, but the authors seem to ignore that every proxy has its limitations and uncertainties. Without addressing these issues at the proxy level, any further conclusions are speculative. If there is indeed so much data available from the Holocene (line 128), why not focus on the same archive and spread the sites across Europe?\n\nAnd my last point: what is the temporal resolution of these records?\n\nMinor points:\nLine 71: “where research remains limited,” – what authors means by this?\nLine 75: why capital letters here “Time Evolution of Global and European surface temperature Anomalies.”\nLine 113: I think that while introduction of AMOC is OK here, I would suggest to introduce AMV since this the pattern of temperature fluctuations which is in focus of this paper.\nLines 122-139 – it is not really clear which proxy data authors are using (mention either instrumental data or proxy data). Earlier they mention e.g. temperatures (line 115) and refer also to a paper (ref.26) on temperatures. I think that clarification will make that section less vague.", "reviewer_2": "This study assesses the role of (multi-)centennial climate variability on recent and near-future temperature trends over Europe. It is shown that temperature variability on these timescales linked to the AMOC is quite common in paleoclimate proxies and transient climate model simulations. Then, one member from a single climate model ensemble, which appears to match European temperatures over the instrumental period, is used to analyze which phase of low-frequency variability was realized in the real world. The authors conclude that this particular realization of internal variability amplifies recent and near-future European temperature trends.\n\nWhile this study sets out with an interesting and ambitious research question, it is not convincing that the methods used (especially the use of one simulation of only one climate model) yield robust results. The paleoclimate model/data comparison also misses some steps to show that there is indeed a robust mode of multi-centennial variability during the Holocene. It might be possible to address these shortcomings, but this might change the conclusions. Therefore, I recommend resubmitting this manuscript only after substantial revisions.\n\nAdditionally, it remains unclear how the approach and results here differ from the previous model/data comparison of multi-centennial variability by Askjaer et al. 2022 (ref. 9) as well as from the analysis by Bonnet et al. 2021 (ref. 11), who used the same single-model ensemble for a very similar research question regarding near-term temperature projections.\n\nMajor comments\nUse of only one climate model.\nThe main findings of this paper depend sensitively on the realism of simulated multi-centennial variability in a single climate model, which is known to have a quite strong multi-centennial (AMOC) variability. In CMIP6, more than a dozen models now provide (large) ensemble simulations of the historical period (cf. Maher et al. 2024 and https://www.cesm.ucar.edu/community-projects/mmlea/v1) and the simulated amplitudes of (low-frequency) variability differ widely.\n\nBecause there are no (paleoclimate) constraints that would show that the IPSL model is the only one with realistic internal variability, the analysis of recent/near-future changes should include historical ensembles of as many models as possible (or as many as possible which meet some paleoclimate constraints) – only then, the impact of internal variability can be robustly assessed.\n\nIn addition, I don’t think that choosing a single ensemble member that (visually?) matches the observations best is an adequate way of constraining the ensemble – a certain degree of uncertainty should be accounted for, for example by using a weighted mean of several ensemble members according to their RMSE with observations.\n\nModel-data comparison.\nFrom my understanding, this comparison should mainly lend credibility to the multi-centennial mode of variability in the IPSL model which is later used to assess temperature trends. However, I am not convinced that it does so entirely since\n(1) the spectral analysis of individual proxies currently does not show a coherent “mode of variability” (L343). For this, the phasing of the variability between different records would need to be verified (the European records should be more or less in phase, also with the AMOC reconstruction)\n(2) the dominant period in the IPSL model (around 200 years from Fig. 2b and Jiang et al. 2021, ref. 36) is not very well-represented in the proxy data;\n(3) as far as I can see, the amplitude of multi-centennial variability in the IPSL model (and other models) is not quantitatively evaluated, although this is promised in the abstract (L34);\n(4) proxy uncertainties are not accounted for, but periodicity, amplitudes and significance of individual peaks might be sensitive to them.\n\nMinor comments\nFig. 1: The mismatch of observed vs simulated (European) temperatures could also be explained by an overestimation of modelled temperatures over the early period (1850-1900). For example, in Fig. 1a, the offset between the multi-model mean and observations looks quite constant between around 1920 and 2000. A more fair comparison between models and observations should probably use a more recent reference period, especially since observations over the early period are sparse and therefore the observational data are more uncertain than over more recent periods. The period 1900-1997 is already used later in Fig. 3.\n\nBesides, Figure 1 in the IPCC AR6 Summary for Policymakers (https://www.ipcc.ch/report/ar6/wg1/figures/summary-for-policymakers/figure-spm-1/), which uses the same 1850-1900 reference period, shows a very good match between the multi-model mean and observations for global mean temperature. Could you explain how this difference to your results arises?\n\nL202: It is claimed that “two independent reconstructions” show the influence of the AMOC on European surface temperatures. But I am not sure how independent these two reconstructions really are, since the AMOC reconstruction appears to be derived from SST proxies, so a correlation with reconstructed SST fields is expected by construction.\n\nL217: A small side note, but I am surprised that (a part of) the recent warming is attributed here to changes in aerosol radiative forcing, while the more obvious explanation is increasing CO2 emissions. From a quick search in ref. 42, I could not find anything about (global) temperature effects, so it is probably better to remove this statement.\n\nL226: Comparison with instrumental records: are these really independent from the paleoclimate reconstructions? Haven’t these temperature proxies been calibrated to instrumental temperature data?\n\nFig. 3: When low-pass filtering a <200-year time series with a 60-year filter, it becomes quite important how the “edges” (the first and last 60 years) of the time series are treated. Which method (e.g., zero padding) was used for that, and how did you make sure this did not induce any unwanted artifacts? See also the comment for L302 below.\n\nL258: It might also be that the IPSL model underestimates aerosol radiative forcing, which was also mentioned (and not rejected) as a possible explanation by Bonnet et al. 2021.\n\nL290: It is more common to remove the forced signal from observations by using the multi-model mean (e.g., Kravtsov & Spannagle 2008) instead of a single-model mean.\n\nL302: Comparing Tables 1 and S3, the forced temperature trend over Northern Europe is about 2 times smaller over 2025-2035 compared to 2000-2035, which is strange and results in the very large number of 172% for 2025-2035. What is the explanation for this weakening while CO2 emissions in SSP2-4.5 are still rising? Are you sure that this is not an artifact of filtering (see comment for Fig. 3), or were trends estimated from the unfiltered time series?\n\nL326: Why should the internal variability in the model match the full signal (i.e., forced response + internal variability) of the AMOC reconstruction?\n\nL333: This has also been discussed in other studies, e.g., Lee et al. 2024, who linked it to a prolonged positive NAO phase.\n\nL382: It is probably worth commenting a bit more on the “notable loss of variance”. What is the reason, and why are the other records not affected? Is it certain that this is not a real signal, for example due to a state-dependence of variability?\n\nL470: It’s unclear why data are low-pass filtered prior to spectral or wavelet analysis – for a “gain in readability” it would be enough to cut off the frequency axis above high frequencies you are not interested in, but use data at their native resolution for the computations.\n\nL476: According to the title of the cited paper, shouldn’t spectral analysis with this program also work with unevenly spaced time series? If yes, that would probably be preferable to prevent potential interpolation artifacts.", "reviewer_3": "Paper review for \"Multi-centennial internal variability in the North Atlantic drives additional warming over Europe\" by A.Al-Yaari, D. Swingedouw, P. Braconnot, L. Boyall, P. Lincoln, O. Marti, T. Caley, T. Extier, and C. Martin-Puertas.\n\nWhat are the noteworthy results?\nThe noteworthy result is that internal climate variability within the AMOC has to be considered and can has a strengthen effect on the antrhopogenic influence on climate warming.\n\nWill the work be of significance to the field and related fields?\nYes, I think it is of significance.\n\nHow does it compare to the established literature?\nDue to that climate modelling is not my expertice I cannot really assess this topic.\n\nDoes the work support the conclusions and claims, or is additional evidence needed?\nYes, the work supports the conclusion and claims.\n\nAre there any flaws in the data analysis, interpretation and conclusions?\nNot to my opinion.\n\nIs the methodology sound? Does the work meet the expected standards in your field?\nAs said before, I am not an expert in climate modelling, therefore I cannot assess it. However, from the point of view of a person working with palaeoclimate reconstructions, this point could be extendent and more records can be included in the study. This is my major point of critism.\n\nIs there enough detail provided in the methods for the work to be reproduced?\nAlso here, as I said before, Im am not an expert in climate modelling and therefore, cannot assess this question.\n\nI made further comments and suggestions in the pdf files of the manuscript, which I attached."}, "author_response": "Point-by-point response to reviewer’s comments on manuscript entitled ‘Multi-centennial internal variability in the North Atlantic drives additional warming over Europe’\nReviewer #1 (Remarks to the Author):\nThe manuscript focuses on internal climate variability and highlights the significant impact of multi-centennial variability in the North Atlantic (AMOC) on European temperatures. It argues that up to 50% of the uncertainty in climate model projections of future warming is attributable to internal variability, which remains poorly understood. The manuscript further emphasizes that understanding the magnitude and underlying mechanisms of this variability is essential for improving the accuracy of regional climate predictions. The authors also flag an issue with atmospheric circulation, which might not sufficiently respond to changes in its boundary conditions.\nThe manuscript is interesting and well written. The topic is relevant for a broad community and noteworthy.\nWe sincerely thank Reviewer #1 for the constructive and encouraging comments on our manuscript. We have carefully incorporated all the suggested revisions into the updated version.\nWe greatly appreciate your recognition that the topic of our study is both noteworthy and relevant to a broad scientific community.\nComment 1.1 However, I have a main concern regarding the use of the proxy records (I am not a climate modeler, so I would recommend that someone with such a background evaluate the methods).\nThe authors focus on proxy records from the Holocene, where a great amount of data already exists. However, the selected sites/records are not from regions where the discrepancy between the data and the model is the largest—I wonder why? What was the rationale for selecting these sites? I think this should be mentioned. Why are they mainly covering Northern Europe? Will the conclusions based on this dataset be unbiased, given that it does not represent a broader spatial coverage?\nAuthors’ response: Thank you for raising this important point regarding the rationale behind our site selection. We appreciate the opportunity to clarify our methodology and to provide additional insights into how the proxy sites were selected. While we acknowledge that numerous Holocene proxy records exist, many do not meet the chronological precision or temporal resolution required for inclusion in this study. This constraint represented the main limitation during data selection. Our primary objective is to assess the impact of AMOC-driven multi-centennial variability on European temperatures, which requires both proxy data and model simulations with at least decadal variability. Indeed, to be able to robustly (from a statistical point of view) identify multi-centennial, this kind of resolution (~ 10 years) is necessary to have a fine enough estimate of the variability at centennial time scale (cf.1,2). This criterion was therefore the one we followed to select the proxy records used.\nAccordingly, we compiled quantitative temperature reconstructions (see response to the following comment for further details) from the Temperature 12k database¹ and from recently developed Bayesian-based reconstructions derived from varved European lake sediments produced by our research group3 (SCUBIDO reconstructions hereafter). We agree with the reviewer that the spatial coverage of these records is limited and mainly concentrated in northern Europe. However, for the specific objectives of this study, this limitation is not critical, as the selected proxies cover the region where AMOC-related climate variability exerts its strongest influence on European temperatures (Fig. S8).\nIn response to the reviewer’s valuable suggestion, we have clarified this rationale in the revised manuscript [Proxy records Section]:\nLines 425-442: “The temperature proxy-based reconstructions used in this study are sourced from the \"Temperature 12k Database\" (Temp12k) compiled by Kaufman et al. 4 and recently developed Bayesian-based temperature reconstructions from varved European lake sediments produced by our research group3 (SCUBIDO reconstructions hereafter). The criteria for selecting the proxy records included in this study were based on the quality of their chronologies and the temporal resolution of the proxy reconstructions over the past 6,000 years across Europe (10°W–50°E, 35°N–70°N). We selected records with chronologies derived from annual layer counts (e.g., tree-rings and varved sediments) and quantitative temperature reconstructions with annual to decadal resolution, which is necessary to have a fine enough estimate of the variability at centennial time scale1,2. Applying these criteria to the Temperature 12k database and SCUBIDO reconstructions, we identified five temperature-sensitive proxy records (listed in Table 2). In the case of the Bayesian-based reconstruction, the quantification of the proxy data in degree Celsius is based on a calibration period of instrumental climate data and overlapping X-Ray Fluorescence data (i.e., chemical element composition of the sediments) to learn about the direct relationship between each geochemical element and the covariant response between the elements and climate. The understanding of these relationships is then applied down core to transform the semi-quantitative proxy data into a posterior distribution of palaeoclimate with quantified uncertainties3”.\nTable 2: The distribution of the different types of archives represented by the proxy data.\nName Temporal Archive / Proxy Climate Age Lat Lon Ref\nresolution variable (BP)\n(yrs)\nLapland 1 Wood / tree-ring Summer -55-7450 69 25 5\n(Norway) width temperature\nTorneträsk 1 Wood/ tree-ring Summer -47-7356 68 20 6\n(Sweden) width temperature\nDiss Mere 1 Lake Annual -18-10286 52.37 1.1 3\n(UK) Sediment / XRF mean\ndata temperature\nNautajärvi 1 Lake Annual -70-9828 61.8 24.68 3\n(Finland) Sediment / XRF mean\ndata temperature\nP1003 12.2362 Marine Summer -48.236- 63.76 5.25 7\n(Norway) Sediment / temperature 7880.835\nd18O of\nplanktonic\nforaminifera\nComment 1.2 This leads to another issue: why compare different proxies? My main concern is that the differences in the records may be related to the different methods/proxies applied (e.g., tree rings vs. lake deposits), and not necessarily to the frequency mode of AMOC. How do these two systems respond to temperature change? What is their sensitivity to detecting changes in temperature? Will both tree rings and lake deposits be able to detect the same frequencies of temperature changes? This is quite crucial, but the authors seem to ignore that every proxy has its limitations and uncertainties. Without addressing these issues at the proxy level, any further conclusions are speculative. If there is indeed so much data available from the Holocene (line 128), why not focus on the same archive and spread the sites across Europe?\nAuthors’ response: We fully agree with the reviewer that proxy records inherently contain uncertainties related to both the characteristics of the archive and the nature of the proxy itself. However, in this study we focus on quantitative temperature reconstructions derived from proxy calibration against instrumental data, rather than qualitative reconstructions. For the Temp12k tree-ring records, temperature reconstructions are based on regression models calibrated to instrumental temperature data. The SCUBIDO reconstructions are generated using a more advanced Bayesian inference framework, also grounded in instrumental calibration 3.\nConsequently, the datasets used in this study provide temperature time series expressed in degree Celsius, which allows for direct comparison among records, regardless of archive type or proxy. In fact, combining different archive types (tree rings, marine sediment, and lake sediments) can be advantageous, as it helps to mitigate potential biases (e.g., growing-season versus annual mean temperature signals) and capture complementary aspects of past temperature variability across multiple timescales. The selected records therefore represent an optimal compromise between temporal length and resolution, enabling detection of both high-frequency (decadal) and low-frequency (multi-centennial) variability associated with AMOC dynamics.\nRegarding the reviewer’s suggestion to rely on a single proxy type distributed across Europe, as mentioned in our response to Comment 1.1, doing so would have further limited the number of available records suitable for inclusion in this study.\nComment 1.3 And my last point: what is the temporal resolution of these records?\nAuthors’ response: The temporal resolution of each proxy record is provided in Table 2 of the Methods section. The records from Lapland, Torneträsk, Diss Mere, and Nautajärvi have annual resolution, while the P1003 record has a resolution of 12.2 years. We have now reported more clearly the temporal resolution of the selected records and why having annual to decadal resolution is crucial for this study (see our response to Comment 1.1).\nMinor points\nComment 1.4 Line 71: “where research remains limited,” – what authors means by this?\nAuthors’ response: By “where research remains limited,” we mean that multi-centennial climate fluctuations in the North Atlantic and their impact on European temperatures have been relatively poorly studied. While there is considerable work on shorter-term variability (annual to decadal) and on forced climate trends, the existence, magnitude, and mechanisms of low-frequency, multi-centennial internal variability are still not well constrained, and there is no clear consensus on how these slow fluctuations influence European warming. Our study specifically addresses this gap by analysing high-resolution proxy records and model simulations to quantify and better understand multi-centennial variability and its role in amplifying regional warming.\nWe have revised the manuscript accordingly:\nLines 73-78: “We focus on multi-centennial climate fluctuations, where research remains limited: the existence, magnitude, and mechanisms of such low-frequency variability are still poorly constrained, while there is still no clear consensus on the existence of such a low frequency variability and its underlying drivers8–10. Using high-resolution proxy records and model simulations, we aim to quantify this slow variability and evaluate its contribution to regional warming.”\nComment 1.5 Line 75: why capital letters here “Time Evolution of Global and European surface temperature Anomalies.”\nAuthors’ response: In the new submission, we corrected these typos as follows: “Time evolution of global and European surface temperature anomalies”.\nComment 1.6 Line 113: I think that while introduction of AMOC is OK here, I would suggest to introduce AMV since this the pattern of temperature fluctuations which is in focus of this paper.\nAuthors’ response: We fully agree with the reviewer comment and in the new submission we modified the text as per suggestion:\nLines 120-125: “The Atlantic Meridional Overturning Circulation (AMOC), a key driver of heat transport in the North Atlantic, has shown internal centennial variability in some climate models that mirrors continental temperature shifts. This variability is reflected in the Atlantic Multidecadal Variability (AMV), the dominant pattern of North Atlantic temperature fluctuations, highlighting a strong link between ocean circulation and regional climate patterns over Europe11,12.”\nComment 1.7 Lines 122-139 – it is not really clear which proxy data authors are using (mention either instrumental data or proxy data). Earlier they mention e.g. temperatures (line 115) and refer also to a paper (ref.26) on temperatures. I think that clarification will make that section less vague.\nAuthors’ response: We revised the mentioned passage with the clarification about temperature integrated, and we have highlighted the additions in bold as per suggestion.\nLines 131-149: “Given the short period of instrumental temperature data and the influence of the anthropogenic forcing during this period, it is not possible to provide an answer to these questions without longer observations of the past. To understand climate variability on multi-centennial timescales, long and high-resolution temperature reconstructions from paleoclimate proxies and climate models are required. For this purpose, we focus here on the present interglacial period, the Holocene, over the last 6,000 years. While other interglacial periods might be also relevant, the Holocene is well-documented in terms of temperature proxy data 1, allowing for a more accurate reconstruction of past climates and providing a detailed backdrop for understanding low-frequency fluctuations. Additionally, advancements in climate modelling have allowed more refined models providing transient simulations over large part of the Holocene 8–10. While most proxy-model comparisons of paleoclimate variability have been limited to the last millennium 11 – a too short period to estimate multi-centennial variability properly – a few recent studies have begun to use longer transient simulations 3,12. Building on these advancements, we compare here various transient simulations, as well as newly available high-resolution (decadal to seasonal) temperature palaeoclimate reconstructions over Europe 1,2 and a recent proxy-based reanalysis covering the Holocene 13 (see Methods) to: (i) identify a common multi-centennial signal of natural climate variability across different palaeoclimate information sources and its origin; and (ii) deduce its potential impact on recent and near-future temperature trends in Europe.”\nReviewer #2 (Remarks to the Author)\nThis study assesses the role of (multcentennial climate variability on recent and near-future temperature trends over Europe. It is shown that temperature variability on these timescales linked to the AMOC is quite common in paleoclimate proxies and transient climate model simulations. Then, one member from a single climate model ensemble, which appears to match European temperatures over the instrumental period, is used to analyze which phase of low-frequency variability was realized in the real world. The authors conclude that this particular realization of internal variability amplifies recent and near-future European temperature trends.\nComment 2.1 While this study sets out with an interesting and ambitious research question, it is not convincing that the methods used (especially the use of one simulation of only one climate model) yield robust results. The paleoclimate model/data comparison also misses some steps to show that there is indeed a robust mode of multi-centennial variability during the Holocene. It might be possible to address these shortcomings, but this might change the conclusions. Therefore, I recommend resubmitting this manuscript only after substantial revisions.\nAdditionally, it remains unclear how the approach and results here differ from the previous model/data comparison of multi-centennial variability by Askjaer et al. 2022 (ref. 9) as well as from the analysis by Bonnet et al. 2021 (ref. 11), who used the same single-model ensemble for a very similar research question regarding near-term temperature projections.\nAuthors’ response: We sincerely thank Reviewer #2 for the constructive and encouraging comments on our manuscript. We would like to clarify how our study differs from Askjaer et al.8 and Bonnet et al.10. While both of these studies addressed multi-centennial variability, they did so from different perspectives and at the global scale. In contrast, our work focuses specifically on Europe, a region where the impacts of variability and warming are especially strong.\nCompared to Askjaer et al.8, our study incorporates newly available high-resolution (annual) proxy-based temperature reconstructions3 and Holocene reanalysis13 that were not included in their global-scale compilation. This enables us to constrain multi-centennial variability at the European scale in much greater temporal detail, providing insights directly relevant to recent and near-term warming trends.\nRelative to Bonnet et al.10, our approach is also distinct. Their study focused on the role of AMOC-related variability in global temperature evolution and near-term projections, without considering paleoclimate evidence or regional impacts. In contrast, our work uses an interdisciplinary model–data comparison, combining paleoclimate reconstructions, observations, transient, historical, and future simulations to demonstrate the existence of a multi-centennial variability mode and its amplifying role on European warming. Moreover, Bonnet et al.10 did not explicitly analyse or discuss European warming, whereas our study shows how the current phase of multi-centennial AMOC-driven variability is crucial to understanding the magnitude of observed and projected warming in Europe.\nIn summary, our contribution bridges the gap between paleoclimate and model-based perspectives at the European scale, using newly available proxy records and an observational constraint framework. This provides an independent line of evidence that multi-centennial internal variability is amplifying the recent and near-future European warming signal, something that neither Askjaer et al.8 nor Bonnet et al.10 explicitly addressed.\nWe included into the conclusion of the revised manuscript the following:\nLines 416-423: “In this context, our contribution is to bridge the gap between paleoclimate and model-based perspectives at the European scale, using newly available high-resolution proxy records and an observational-constraint framework. This provides an independent line of evidence that multi-centennial internal variability is amplifying the recent and near-future European warming signal. The existence and current phase of this variability mode are therefore crucial and should be considered more systematically to provide more reliable information for future climate projections, particularly when discussing adaptation strategies for coming multi-decadal timescale.”\nMajor comments\nComment 2.2 Use of only one climate model. The main findings of this paper depend sensitively on the realism of simulated multi-centennial variability in a single climate model, which is known to have a quite strong multi-centennial (AMOC) variability. In CMIP6, more than a dozen models now provide (large) ensemble simulations of the historical period (cf. Maher et al. 2024 and https://www.cesm.ucar.edu/community-projects/mmlea/v1) and the simulated amplitudes of (low-frequency) variability differ widely. Because there are no (paleoclimate) constraints that would show that the IPSL model is the only one with realistic internal variability, the analysis of recent/near-future changes should include historical ensembles of as many models as possible (or as many as possible which meet some paleoclimate constraints) – only then, the impact of internal variability can be robustly assessed.\nAuthors’ response: We thank the reviewer for highlighting the availability and importance of the CMIP6 Multi-Model Large Ensemble Archive, version 2 (MMLEAV2; https://www.cesm.ucar.edu/community-projects/mmlea/v1)14, which provides an unprecedented resource for assessing internal and forced variability across models over the historical era and beyond. We fully agree that the rationale for focusing only on IPSL-CM6A-LR in the original submission was not sufficiently clear. IPSL-CM6A-LR was initially chosen because our analysis emphasizes multi-centennial variability, a feature that is existing in this model.\nIn response to the reviewer’s valuable suggestion, and to place the IPSL-CM6A-LR results within the broader CMIP6 context, we have extended our analysis to include additional models from MMLEA-V2 that provide complete historical simulations starting in 1850 and offer a sufficient number of ensemble members. Specifically, we analysed seven models: CanESM5, CESM2, CNRM-CM6-1, E3SM-2-1, IPSL-CM6A-LR, MIROC6, and MPI-ESM1-2-LR. Since Holocene simulation was not available for those models, we applied the same spectral analysis to the piControl simulations of these models (Figure R1) over Northern Europe (10°W–50°E, 55°N–70°N), along with the reanalysis of Erb et al.13 (mid-Holocene to present). This figure reveals that IPSL-CM6A-LR indeed exhibits pronounced centennial variability, with distinct peaks around 150 and 200 years, in close agreement with the reanalysis.\nFigure R1. Normalized spectral density (spectral analysis output scaled by the standard deviation of each dataset for direct comparison within a single panel), above the 90% significant red noise threshold for temperature variability for Holocene reanalysis13 and CMIP6 picontrol simulations over Northern Europe (10°W–50°E, 55°N–70°N).\nThe CNRM-CM6-1 model also exhibits strong centennial-scale variability, but apparently stronger than that inferred from the Holocene Reanalysis. The other models (CanESM5, CESM2, E3SM-2-1, MIROC6, and MPI-ESM1-2-LR) are less consistent with the reanalysis. Assuming that most of the variability at centennial timescales during the Holocene primarily reflects internal climate variability, a reasonable hypothesis given that the amplitude of solar forcing was likely very small 15,16, the piControl simulations from IPSL-CM6A-LR and CNRM-CM6-1 provide the most appropriate comparisons to the variability highlighted in the Holocene Reanalysis. Consequently, since the main objective of this study is to assess the impact of such multi-centennial internal variability on the recent period, the large ensembles from the other models may be less relevant for this purpose.\nFurthermore, when examining the CNRM large ensemble, which is only available for the historical period (1850–2014; Fig. R2), the best ensemble member (i.e., the member showing the highest correlation with observations) also reveals an internally generated (dotted line) temporal evolution characterized by early 20th-century warming, mid-century cooling, and late 20th-century warming, consistent with the results from the IPSL-CM6A-LR model. However, it is noteworthy that the amplitude of the multi-centennial variability in CNRM-CM6-1 appears excessively strong, consistent with its spectral characteristics when compared to the Holocene Reanalysis\nFigure R2. 60-year low pass filtered temperature anomalies relative to 1900-1997 CNRM-CM6-1 members (grey), Hadley centre (HadCRUT5), and the Berkeley Earth Surface Temperatures (Berkeley) over Northern Europe (10°W–50°E, 55°N–70°N). The black line represents the forced response (the ensemble mean of all CNRM-CM6-1 members). The model member in blue is the one that align more closely with the Berkeley observation-based estimates. The dotted line stands for the internal variability from the blue member (cf. Methods).\nThese additional analyses demonstrate that IPSL-CM6A-LR is not unique in exhibiting multi-centennial variability. We do not claim that this model is the best overall; however, it might be among the best models, that propose large ensemble, to address the question of multi-centennial variability. While we acknowledge the diversity in variability amplitudes across CMIP6 models and the associated uncertainties, this expanded comparison, thanks to the reviewer’s insightful comment, further supports our conclusions and places the IPSL-CM6A-LR results in a broader multi-model context.\nWe integrate all these additional analysis into the revised manuscript as follows:\nLines 242-258: “To further evaluate this hypothesis and better quantify the potential role of internal variability in recent changes over Europe, we considered the CMIP6 Multi-Model Large Ensemble Archive, version 2 (MMLEA-V2 14), which provides an unprecedented resource for assessing internal and forced variability across models over the historical era and beyond. Among the available ensembles, we analysed seven models that offer complete historical simulations since 1850 and a sufficient number of members: CanESM5, CESM2, CNRM-CM6-1, E3SM-2-1, IPSL-CM6A-LR, MIROC6, and MPI-ESM1-2-LR. Spectral analyses of the piControl simulations over Northern Europe (10°W–50°E, 55°N–70°N) show that IPSL-CM6A-LR exhibits pronounced centennial-scale variability, with peaks around 150 and 200 years, consistent with the reanalysis of Erb et al.13 (Supplementary Fig. S10). Although IPSL-CM6A-LR is not unique in exhibiting multi-centennial variability, it remains among the few models that closely reproduce reanalysis-based spectral features. For this reason, we use IPSL-CM6A-LR as the main model in this study to investigate the interplay between forced and internal variability at the multi-centennial time scale. Specifically, we used the IPSL-CM6A-LR large ensemble of extended historical simulations (IPSL-EHS), which consists of 32 members spanning the period 1850–2059, all sharing the same external forcings but differing in their initial conditions (see Methods).”\nWe also included in the revised manuscript:\nLines 342-346 : CNRM-CM6-1 also exhibits centennial-scale variability (Supplementary Fig. S10); however, its amplitude appears overestimated. Its best ensemble member closely reproduces the observed 20th century temperature evolution over Northern Europe, similarly to IPSL-CM6A-LR (Fig. 3b), including early-century warming, mid-century cooling, and renewed warming after the 1980s (Supplementary Fig. S15).\nComment 2.3 In addition, I don’t think that choosing a single ensemble member that (visually?) matches the observations best is an adequate way of constraining the ensemble – a certain degree of uncertainty should be accounted for, for example by using a weighted mean of several ensemble members according to their RMSE with observations.\nAuthors’ response: We apologize for the lack of clarity in the submitted version. Member 8 was not selected based on visual inspection; rather, it ranked highest in terms of both correlation (R) and RMSE across the sites Lapland and Torneträsk, as well as over Northern Europe, as shown in the Supplementary Information (Figs. S10 and S11) of the original submission (11 and 12 in the revised Supplementary Information).\nFollowing the reviewer’s suggestion, we revised Figure 3b in the main article by replacing the single-member analysis with weighted averages of the top members ranked by correlation with the Berkeley Earth observations, namely, Members 8, 29, and 9 (Figure R3). Weights were assigned inversely to the rank, such that the best-correlated member received the largest weight.\nThe resulting weighted means (using the top 1, 2, and 3 members) also show a steeper temperature increase over Northern Europe in recent decades compared to the forced response (black line in Figure R3). Importantly, the projected temperatures for the coming decade remain higher than the forced response in all cases, underscoring the potential contribution of internal variability to the pronounced recent warming and the possible amplification of future warming trends in this region.\nWe also estimated the amplification of forced warming due to internal variability accounting for weighted means of the top-ranked ensemble members. Accounting for weighted means of the three top-ranked ensemble members (see Supplementary Fig. S11) reduces the estimated contribution of internal variability over Northern Europe from approximately 53 % to 42 % when using the two highest-ranked members, and to 40 % when including the three highest-ranked members, indicating some sensitivity to the selection of ensemble members. However, incorporating additional members into the mean does not necessarily provide a more realistic representation of the real world. Since each ensemble member represents a physically plausible realization of the climate system, averaging across multiple members tends to reduce internal variability as compared to the externally forced signal.\nWe included this figure in the Supplementary Information and referred to it in the revised manuscript as follows.:\nLines 326-334: “Accounting for weighted means of the three top-ranked ensemble members (see Supplementary Fig. S11) reduces the estimated contribution of internal variability over Northern Europe from approximately 53 % to 42 % when using the two highest-ranked members, and to 40 % when including the three highest-ranked members, indicating some sensitivity to the selection of ensemble members (see Supplementary Fig S14). However, incorporating additional members into the mean does not necessarily provide a more realistic representation of the real world. Since each ensemble member represents a physically plausible realization of the climate system, averaging across multiple members tends to reduce internal variability as compared to the externally forced signal.”\nFigure R3. a 60-year low pass filtered temperature anomalies relative to 1900-1997 over Northern Europe (longitude: -10° to 50°E, latitude: 55° to 70°E). The black line represents the forced response (the ensemble mean of all IPSL-EHS members). Temperature anomalies from “Members mean” show weighted means of the top 1, 2, and 3 members ranked by correlation with observations. Weights are inversely proportional to rank (the best member receives the highest weight). The dotted lines stand for the internal variabilities.\nModel-data comparison. Comment 2.4 From my understanding, this comparison should mainly lend credibility to the multi-centennial mode of variability in the IPSL model which is later used to assess temperature trends. However, I am not convinced that it does so entirely since\n(1) the spectral analysis of individual proxies currently does not show a coherent “mode of variability” (L343). For this, the phasing of the variability between different records would need to be verified (the European records should be more or less in phase, also with the AMOC reconstruction)\nAuthors’ response: We appreciate the reviewer’s insightful comment regarding the phasing between regional proxy records. However, previous detailed studies7,17 have shown that in-phase variability across European proxies should not necessarily be expected. In fact, Sejrup et al.7 explicitly examined this issue using the same P1003 and Torneträsk records included in our study. Their spectral and multivariate analyses demonstrated that the dominant multi-centennial modes of variability vary in phase and amplitude through time and space. Bradley and Jones18 and Wanner et al.17 have also pointed out that some of the decadal to multi-centennial variability is not in phase between different archives/locations. This reflects the complex nature of North Atlantic climate variability, which arises from multiple, interacting mechanisms leading to leads and lags in the spatial response across Europe. It mirrors realistic regional differences in the propagation of large-scale ocean–atmosphere signals such as those associated with the AMOC and related teleconnections. These signals can influence different parts of Europe and the North Atlantic with variable timing and magnitude, depending on the structure of the events themselves and shifting background states.\nComment 2.5 (2) the dominant period in the IPSL model (around 200 years from Fig. 2b and Jiang et al. 2021, ref. 36) is not very well-represented in the proxy data;\nAuthors’ response: While the dominant multi-centennial mode in the IPSL model is centred around ~200 years, our spectral analyses reveal other statistically significant periodicities (at the 99% confidence level) between 100 and 160 years that align well with those observed in the proxy records (Fig. 2b).\nMoreover, this centennial-scale variability is not strictly stationary through time. Wavelet power spectra of reconstructed (Fig. S4) and simulated (Fig. S5) European temperatures show that the dominant frequencies vary in both strength and period, with intermittent amplification of variability within the 100–200-year band. These results confirm that the centennial variability captured by the IPSL model is temporally modulated rather than characterized by a fixed spectral peak, and that its expression in proxy records is consistent with this non-stationary behaviour.\nComment 2.6 (3) as far as I can see, the amplitude of multi-centennial variability in the IPSL model (and other models) is not quantitatively evaluated, although this is promised in the abstract (L34);\nAuthors’ response: In the original submission, the question of amplitude was addressed in Table 1, but this information was certainly not clearly enough explained and highlighted.\nThus, following the reviewer’s valuable suggestion, we have reworked the entire section “Constrained internal variability vs. externally forced signal” to provide a clearer description of the quantitative evaluation of the amplitude of multi-centennial internal variability in the IPSL model and its comparison with observations. The revised section now explicitly defines and assesses the amplitude of internal variability using standard deviations, as detailed below.\n“From the IPSL-EHS ensemble, we define the forced (or externally driven) response of temperature or the AMOC as the ensemble mean of the corresponding variable. For each member, the internal (or internally driven) variability is obtained as the deviation of that member from the ensemble mean. To quantitatively evaluate the amplitude of multi-centennial internal variability, we compute the standard deviation (σ) of the internal component (Member – Forced) over the common period. This σ value represents the amplitude of the internal variability in each model member. The same metric is calculated for the observational datasets (e.g., Berkeley Earth) after subtracting the forced component estimated from the ensemble mean.\nThe ratio of standard deviations (SD ratio) between the observational and modelled internal variability is calculated as:\n𝜎 (𝑂𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛−𝐹𝑜𝑟𝑐𝑒𝑑 (𝑒𝑛𝑠𝑒𝑚𝑏𝑙𝑒 𝑚𝑒𝑎𝑛))\n𝐴𝑚𝑝𝑙𝑖𝑡𝑢𝑑𝑒 𝑟𝑎𝑡𝑖𝑜 = ( )\n𝜎 (𝑀𝑒𝑚𝑏𝑒𝑟 8− 𝐹𝑜𝑟𝑐𝑒𝑑 (𝑒𝑛𝑠𝑒𝑚𝑏𝑙𝑒 𝑚𝑒𝑎𝑛)\nThe resulting SD ratios are 1.00 for Lapland, 0.96 for Torneträsk, 0.90 for Northern Europe, 0.87 for Europe, and 0.99 for the global scale, respectively. The internal rate of temperature increase constrained by observations (R , ), which accounts for potential overestimation or\nint constrained\nunderestimation of the centennial-scale internal variability amplitude in the model compared to observations, is then calculated as:\nR , = R x SD ratio\nint constrained int, Member 8\nFinally, the contribution of internal variability to forced warming is expressed as:\n𝑅\n𝑖𝑛𝑡,𝑐𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑒𝑑\n𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒 𝑖𝑛 𝑓𝑜𝑟𝑐𝑒𝑑 𝑤𝑎𝑟𝑚𝑖𝑛𝑔 𝑑𝑢𝑒 𝑡𝑜 𝑖𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = ( ) 𝑥 100\n𝑅\n𝑓𝑜𝑟𝑐𝑒𝑑\nwhere:\nR is the internal rate of temperature increase from member 8;\nint, Member 8\n𝑅 is the forced rate of temperature increase (multi-member ensemble mean); and\n𝑓𝑜𝑟𝑐𝑒𝑑\nR , is the internal rate of temperature increase constrained by observations.”\nint constrained\nWe also included this information in Table 1:\nTable 1: Rates of temperature increase (derived from a linear regression using annual mean time series) between years 2000 and 2035 across different regions, including Lapland (Norway), Torneträsk (Sweden), Northern Europe (10°W–50°E, 55°N–70°N), Europe (10°W–50°E, 35°N–70°N), and the global scale, based on IPSL-EHS simulations. The internal rate of increase constrained by observations is obtained by rescaling the internal variability from Member 8 (Member 8 – Forced) using the amplitude (standard deviation) ratio between the observed internal signal (Berkeley Earth) and Member 8, as described in the Methods. This is designed to try to account for potential slight overestimation of the amplitude of the centennial variability in the model as compared to observations. The contribution of internal variability to forced warming (%) has been calculated as the ratio between the rate of increase from the constrained (see Methods) internal variability and the rate of increase from the forced signal. An asterisk (*) indicates a slope that is significantly different from zero (p-value < 0.01).\nRate of temp. increase Lapland Torneträsk Northern Europe Global\nover 2000-2035 Europe\n(°C/decade)\nForced (multi-member 0.54* 0.51* 0.57* 0.48* 0.44*\nensemble mean)\nInternal (member 8-forced) 0.20 0.18 0.33* 0.28* 0.13*\nAmplitude ratio (Observed 1 0.96 0.90 0.87 0.99\ninternal / Model internal)\nInternal variability 0.20 0.17 0.30 0.24 0.12\nconstrained by observed\namplitude\nIncrease in the forced 37% 33% 53% 50% 27%\nwarming due to internal\nvariability\nComment 2.7 (4) proxy uncertainties are not accounted for, but periodicity, amplitudes and significance of individual peaks might be sensitive to them.\nAuthors’ response: We thank the reviewer for this helpful comment. We believe that the main sources of uncertainty in the selected proxy records are related to (i) potential seasonal biases, (ii) calibration to temperature, and (iii) age uncertainties. While the first two might not affect spectral analysis, we believe that the age uncertainty is not expected to be a significant issue, as the proxy records are annually resolved and based on annually constrained chronologies (tree rings and varved sediments). The reported age uncertainty primarily arises from cumulative counting errors and from uncertainties associated with other dating methods (e.g., radiocarbon or tephrochronology), when annual-layer chronologies are anchored to such dates. This uncertainty mainly affects the absolute age of a record or the dating of a specific event (e.g., a climate event occurring at 4.2 ± 0.3 kyr BP). Counting errors are typically quantified as the standard deviation among independent counts of annual layers (for example, in varve chronologies, at least three separate counts are commonly performed; see Ojala et al.19. Because both the tree-ring and varve records are annually resolved, we are also able to estimate counting errors for the duration of specific intervals, which are independent of cumulative counting errors (e.g., the Maunder Minimum lasted 70 ± 2 years).\nThe annual resolution of these chronologies allows us to perform time series analyses without temporal uncertainty affecting the periodicities themselves (e.g., tree-ring width = one data point per year). However, age uncertainty may slightly influence the absolute placement of these periodicities along the timescale. The only exception is the marine record included in this study, which has a temporal resolution of approximately 12 years according to its chronology. While this may introduce an uncertainty of ±12 years (on average)7 in the identified periodicities, such an error is negligible when considering variability at multi-centennial timescales.\nWe have now included in the proxy records Section (Methods) details on the age uncertainties associated with their chronologies.\nLines 458-473: “In this study, age uncertainty is not expected to be a significant issue, as the proxy records are annually resolved and based on annually constrained chronologies (tree-rings and varved sediments). The reported age uncertainty primarily arises from cumulative counting errors and from uncertainties associated with other dating methods (e.g., radiocarbon or tephrochronology), when annual-layer chronologies are anchored to such dates. This uncertainty mainly affects the absolute age of a record or the dating of a specific event (e.g., a climate event occurring at 4.2 ± 0.3 kyr BP). Counting errors are typically quantified as the standard deviation among independent counts of annual layers (for example, in varve chronologies, at least three separate counts are commonly performed3. The annual resolution of these chronologies allows us to perform time series analyses without temporal uncertainty affecting the periodicities themselves. However, age uncertainty may slightly influence the absolute placement of these periodicities along the timescale. The only exception is the marine record included in this study, which has a temporal resolution of approximately 12 years according to its chronology. While this may introduce an uncertainty of ±12 years (on average)7 in the identified periodicities, such an error is negligible when considering variability at multi-centennial timescales.”\nMinor comments\nComment 2.8 Fig. 1: The mismatch of observed vs simulated (European) temperatures could also be explained by an overestimation of modelled temperatures over the early period (1850-1900). For example, in Fig. 1a, the offset between the multi-model mean and observations looks quite constant between around 1920 and 2000. A more fair comparison between models and observations should probably use a more recent reference period, especially since observations over the early period are sparse and therefore the observational data are more uncertain than over more recent periods. The period 1900-1997 is already used later in Fig. 3.\nAuthors’ response: We thank the reviewer for this suggestion and acknowledge that observational uncertainties are larger during the early period. We used three observational datasets to try to account for this uncertainty. We argue that we would prefer to retain the 1850–1900 reference period, as this is the reference used in the IPCC assessments to define “pre-industrial” conditions and serves as the baseline for assessing on-going and future warming. This choice is widely accepted within the climate science community, as it ensures consistency with international policy targets such as the Paris Agreement and facilitates direct comparison with synthesis from the IPCC reports. In Figure 3, we chose 1900-1997 as it is the common period between the different datasets shown in the latter figure. Still, in order to address the reviewer’s comment, we reproduced Figure 1b using alternative periods, such as 1900–1930, as used by20 over France, who also found that CMIP6 models underestimate observed temperatures, especially within DCENT observations which is the most recent reconstruction and includes important updates related to various improvements in observed datasets. Consistently, our results confirm that CMIP6 models still underestimate the observations over Europe relative to the reference period, as shown in Figure R4.\nFigure R4. Time evolution of European surface temperature anomalies (raw in dotted line and 31-year running mean in solid line) relative to the 1900–1930 baseline showing the spread (standard deviation) of CMIP6 GCM simulations (grey), the CMIP6 GCM ensemble mean (black), Berkeley Earth Surface Temperatures (Berkeley21, in red), the Dynamically Consistent ENsemble of Temperature (DCENT22, in dark red), and HadCRUT5 observations (HadCRUT523, in orange).\nComment 2.9 Besides, Figure 1 in the IPCC AR6 Summary for Policymakers (https://www.ipcc.ch/report/ar6/wg1/figures/summary-for-policymakers/figure-spm-1/), which uses the same 1850-1900 reference period, shows a very good match between the multi-model mean and observations for global mean temperature. Could you explain how this difference to your results arises?\nAuthors’ response: We thank the reviewer for this insightful comment. Figure 1a in our paper is consistent with the IPCC AR6 representation. The apparent difference arises mainly from the inclusion of the DCENT reconstruction in our analysis, which is not shown in the IPCC figure. The DCENT dataset suggests a slightly stronger warming compared to other existing observational estimates24, which explains the observed discrepancy.\nComment 2.10 L202: It is claimed that “two independent reconstructions” show the influence of the AMOC on European surface temperatures. But I am not sure how independent these two reconstructions really are, since the AMOC reconstruction appears to be derived from SST proxies, so a correlation with reconstructed SST fields is expected by construction.\nAuthors’ response: We thank the reviewer for this comment. We agree that the AMOC reconstruction25 is indeed based on North Atlantic SST proxy records. However, in the case of the Holocene temperature reanalysis13, the European surface temperature field is constrained primarily by terrestrial proxies (e.g., tree rings, pollen, lake sediments), rather than marine SST proxies, which are not all included in the reanalysis. Therefore, while the two reconstructions are not entirely independent, they are derived from distinct proxy domains and methodologies (multiproxy SST compilation versus data assimilation of land-based proxies), which we believe provides complementary evidence for the linkage between AMOC variability and European temperature.\nWe have revised the manuscript to clarify this point and lower the fact that they are fully independent.\nRevised text in the manuscript:\nLines 207-211: “Within available reconstructions over the Holocene, the linkage between AMOC variability and European temperature is supported by significant correlations between AMOC strength and surface temperature (Supplementary Fig. S8) from two reconstructions: temperature from a Holocene reanalysis 13 and an AMOC index25 based on multiproxy North Atlantic SST records26, which are not all used in the reanalysis.”\nComment 2.11 L217: A small side note, but I am surprised that (a part of) the recent warming is attributed here to changes in aerosol radiative forcing, while the more obvious explanation is increasing CO2 emissions. From a quick search in ref. 42, I could not find anything about (global) temperature effects, so it is probably better to remove this statement.\nAuthors’ response: This statement is omitted as per suggestion.\nComment 2.12 L226: Comparison with instrumental records: are these really independent from the paleoclimate reconstructions? Haven’t these temperature proxies been calibrated to instrumental temperature data?\nAuthors’ response: We thank the reviewer for this important remark. Indeed, the paleoclimate reconstructions are not entirely independent from the instrumental records, as the proxies are calibrated against instrumental temperature data to establish the proxy–temperature relationship. However, this calibration does not affect the multi-centennial variability that we focus on in this study. Our use of paleoclimate data is intended as an extension of the instrumental record into the past, providing a continuous perspective on temperature evolution.\nTo clarify our intention, we have modified the wording in the manuscript from:\n“Among the temperature proxies used in this study, only two Torneträsk in Sweden and Lapland in Norway (Fig. 3a, Supplementary Fig. S9) with annual high resolution extend within the recent period (1900–2005), making them particularly valuable for comparison with instrumental records (see Methods). These reconstructions closely mirror the observed trends. Additionally, the reconstructed temperature signal (red line in Fig. 3a) extends beyond 1900, offering paleoclimate evidence of earlier temperature fluctuations.\nTo:\nLines 232-237: “Among the temperature proxies used in this study, the tree-ring width chronologies that include the Lapland record from Norway5 and the Torneträsk record from Sweden 6, which provide annually resolved records extending into the recent period (1900–2005). These records capture instrumental-era variability and extend further back in time, offering paleoclimate evidence of earlier temperature fluctuations (Fig. 3a, Supplementary Fig. S9; see Methods).”\nComment 2.13 Fig. 3: When low-pass filtering a <200-year time series with a 60-year filter, it becomes quite important how the “edges” (the first and last 60 years) of the time series are treated. Which method (e.g., zero padding) was used for that, and how did you make sure this did not induce any unwanted artifacts? See also the comment for L302 below.\nAuthors’ response: For the low-pass filtering, we applied a Butterworth filter that internally handles the treatment of the series edges. Specifically, it subtracts the mean of the input series and then pads the data at both ends by reflection to a length of twice the maximum period before applying the filter. This reflection padding is designed to minimize edge effects and avoid artifacts that would arise from simpler approaches such as zero-padding. After filtering, the function removes the padding and adds back the series mean. Thus, no explicit zero-padding was applied.\nThe use of reflection padding in combination with the forward–backward filtfilt algorithm ensures that phase distortions are removed and that unwanted artifacts at the edges are minimized.\nWe have now included this into the Method section in the revised manuscript:\nLines 555-562: “For this study, a low-pass Butterworth filter is applied, following the approach of ref. 27, as it provides an optimal balance between signal attenuation and phase response. It internally handles the treatment of the series edges by subtracting the mean of the input series and then padding the data at both ends by reflection to a length of twice the maximum period before applying the filter via filtfilt. This reflection padding is designed to minimize edge effects and avoid artifacts that would arise from simpler approaches such as zero-padding. After filtering, the function removes the padding and adds back the series mean.”\nComment 2.14 L258: It might also be that the IPSL model underestimates aerosol radiative forcing, which was also mentioned (and not rejected) as a possible explanation by Bonnet et al. 2021.\nAuthors’ response: Yes, this is a valid possibility. However, several studies (e.g., Menary et al.28) have suggested that aerosol forcing may be overestimated in CMIP6 models. To account for this potential caveat, we have revised the original text:\n“This forced signal lacks the amplitude of a number of the low frequency fluctuations from the proxy and instrumental records and does not fully align with either the reconstruction or the observations (Fig. 3). This indicates that those centennial variations cannot be only explained by external forcing from, e.g., the aerosols, but rather by internal variability.”\nWith the following revised text integrating reviewer’s comment:\nLines 276-283: “This forced signal lacks the amplitude of several low-frequency fluctuations evident in the proxy and instrumental records and does not fully align with either the reconstruction or the observations (Fig. 3). While it is possible that the IPSL model underestimates aerosol radiative forcing⁵, Menary et al.28 instead suggest that CMIP6 models, in general, may overestimate aerosol forcing. Therefore, we argue that this discrepancy likely indicates that the observed centennial-scale variations cannot be explained solely by external forcing, but instead reflect a substantial contribution from internal variability.”\nComment 2.15 L290: It is more common to remove the forced signal from observations by using the multi-model mean (e.g., Kravtsov & Spannagle 2008) instead of a single-model mean.\nAuthors’ response: We acknowledge the reviewer’s point. However, our study specifically aims to investigate the interplay between forced and internal variability within the IPSL-EHS model framework and to compare it consistently with proxy-based and observational reconstructions. For this reason, we define the forced signal as the IPSL-EHS ensemble mean rather than the multi-model mean. Using a single-model ensemble mean is a standard approach when the objective is to characterize the internal variability of a given model.\nTo address the reviewer’s concern, we have also repeated the analysis using the CMIP6 multi-model mean to remove the forced signal from the observations over Northern Europe (see Figure R5). The results are consistent with those obtained using the IPSL-EHS ensemble mean, confirming that internal variability contributes additional warming over Northern Europe.\nWe have clarified this rationale in the revised manuscript.\nLines 346-351: “Furthermore, the forced signal is defined as the IPSL-EHS ensemble mean, rather than the multi-model mean29, in order to ensure consistency with our focus on internal variability within the IPSL model framework. Nevertheless, we repeated the analysis using the CMIP6 multi-model mean and found that the results are consistent (see supplementary Fig S16), further confirming that internal variability enhances warming over Northern Europe.”\nFigure R5. (a) Temperature anomalies relative to 1900-1997 from Hadley centre (HadCRUT5), the Berkeley Earth Surface Temperatures (BEST), and the Dynamically Consistent ENsemble of Temperature (DCENT) over Northern Europe. (b) Same as a but with 60-year low pass filter. The black line represents the forced response (the ensemble mean of CMIP6 models). The dotted line stands for the internal variability (the difference between Berkeley and CMIP6).\nComment 2.16 L302: Comparing Tables 1 and S3, the forced temperature trend over Northern Europe is about 2 times smaller over 2025-2035 compared to 2000-2035, which is strange and results in the very large number of 172% for 2025-2035. What is the explanation for this weakening while CO2 emissions in SSP2-4.5 are still rising? Are you sure that this is not an artifact of filtering (see comment for Fig. 3), or were trends estimated from the unfiltered time series?\nAuthors’ response: We confirm that the trends were estimated from the unfiltered time series, so the result is not an artifact of filtering. The very high percentage value for 2025–2035 arises from the fact that this is a short time window, over which the externally forced signal is relatively weak compared to internal variability.\nComment 2.17 L326: Why should the internal variability in the model match the full signal (i.e., forced response + internal variability) of the AMOC reconstruction?\nAuthors’ response: The reviewer is fully right. In order to clarify this, we add this into the revised manuscript:\nLines 376-390: “Since 1900, observed and modelled changes in AMOC strength have shown periods of strengthening, which are largely attributable to the system's intrinsic internal variability rather than solely to persistent external anthropogenic forcings30,31. The apparent agreement does not imply that internal variability alone explains the reconstruction, but rather that the amplitude and phase of the internal component in the model can strongly project onto the total (forced + internal) signal seen in observations. Indeed, Lee et al.32 show that the total AMOC variations over 1960–2020 cannot be explained by external forcing alone, as the residual internal variability contributes substantially, at times dominating the total response. For example, they found that half of the AMOC increase from 1960 to 1990 and much of the continued increase until 2000 were driven by internal variability, which also partly offsets the externally forced weakening after 2000. This highlights that reconstructions of the full AMOC signal inevitably reflect a combination of both forced and internal components, and the fact that our modelled internal variability aligns with the reconstructed AMOC underscores the large role of the internal component in shaping the observed record.”\nComment 2.18 L333: This has also been discussed in other studies, e.g., Lee et al. 2024, who linked it to a prolonged positive NAO phase.\nAuthors’ response: Thanks for pointing out this interesting study, which we now include in the new submission as follows:\nLines 397-400: “This interpretation is consistent with other studies, such as33, who linked the apparent stability of recent AMOC observations to the influence of a prolonged positive phase of the NAO, itself reflecting internal multi-decadal variability in the North Atlantic system.”\nComment 2.19 L382: It is probably worth commenting a bit more on the “notable loss of variance”. What is the reason, and why are the other records not affected? Is it certain that this is not a real signal, for example due to a state-dependence of variability?\nAuthors’ response: In the revised manuscript, we have now added a possible reason to explain the loss of variance in the revised manuscript:\nLines 451-455: “The loss of variance may arise from several factors, including inherent proxy limitations, as proxies often respond nonlinearly to environmental variables. The loss of variance, in Diss Mere, for instance, is due to the transition from annually laminated sediments to homogenous sediments with a higher sedimentation rate3.”\nComment 2.20 L470: It’s unclear why data are low-pass filtered prior to spectral or wavelet analysis – for a “gain in readability” it would be enough to cut off the frequency axis above high frequencies you are not interested in, but use data at their native resolution for the computations.\nAuthors’ response: To estimate the red-noise spectra, we used the REDFIT program34, which applies the Lomb–Scargle periodogram and is specifically designed to handle unevenly spaced data such as paleoclimate records. REDFIT fits the time series to a simulated first-order autoregressive [AR(1)] process, which serves as the null hypothesis. However, because we use high-resolution (annual) time series spanning the Holocene, the raw data contain substantial high-frequency noise. This can cause the AR(1) model to fail for some series, preventing a valid fit. To address this, we applied a low-pass Butterworth filter to remove short-term fluctuations, thereby allowing REDFIT to reliably estimate the AR(1) process and avoid fitting errors. Since our study focuses on multi-centennial variability, filtering out the high-frequency component is both appropriate and necessary. The same procedure was applied uniformly to all proxy records and model simulations to ensure consistency during the pre-processing stage.\nComment 2.21 L476: According to the title of the cited paper, shouldn’t spectral analysis with this program also work with unevenly spaced time series? If yes, that would probably be preferable to prevent potential interpolation artifacts.\nAuthors’ response: Please see our reply to comment 2.13 and comment 2.20.\nReviewer #3 (Remarks to the Author)\nPaper review for \"Multi-centennial internal variability in the North Atlantic drives additional warming over Europe\" by A.Al-Yaari, D. Swingedouw, P. Braconnot, L. Boyall, P. Lincoln, O. Marti, T. Caley, T. Extier, and C. Martin-Puertas.\nWhat are the noteworthy results?\nThe noteworthy result is that internal climate variability within the AMOC has to be considered and can has a strengthen effect on the antrhopogenic influence on climate warming.\nWill the work be of significance to the field and related fields?\nYes, I think it is of significance.\nHow does it compare to the established literature?\nDue to that climate modelling is not my expertice I cannot really assess this topic.\nDoes the work support the conclusions and claims, or is additional evidence needed?\nYes, the work supports the conclusion and claims.\nAre there any flaws in the data analysis, interpretation and conclusions?\nNot to my opinion.\nWe sincerely thank Reviewer #3 for the constructive and encouraging comments provided below on our manuscript. We appreciate the recognition that our results highlight the important role of internal climate variability within the AMOC in strengthening the anthropogenic influence on climate warming, and considering the work to be of significance for the field. We are also grateful for the positive assessment that our data analysis, interpretation, and conclusions are sound and well supported. We thank the reviewer for the detailed observation of the main and supplementary figures and for the valuable suggestions that have greatly improved the quality and clarity of the figures.\nComment 3.1 Is the methodology sound? Does the work meet the expected standards in your field?\nAs said before, I am not an expert in climate modelling, therefore I cannot assess it. However, from the point of view of a person working with palaeoclimate reconstructions, this point could be extendent and more records can be included in the study. This is my major point of critism.\nAuthors’ response: Thank you for raising this important point regarding the rationale behind our site selection. We appreciate the opportunity to clarify our methodology and to provide additional insights into how the proxy sites were selected. While we acknowledge that numerous Holocene proxy records exist, many do not meet the chronological precision or temporal resolution required for inclusion in this study. This constraint represented the main limitation during data selection. Our primary objective is to assess the impact of AMOC-driven multi-centennial variability on European temperatures, which requires both proxy data and model simulations with at least decadal resolution. Indeed, to be able to robustly (from a statistical point of view) identify multi-centennial, this kind of resolution (~ 10 years) is necessary to have a fine enough estimate of the variability at centennial time scale (cf.1,2). This criterion was therefore the one we followed to select the proxy records used.\nAccordingly, we compiled quantitative temperature reconstructions from the Temperature 12k database¹ and from recently developed Bayesian-based reconstructions derived from varved European lake sediments produced by our research group3 (SCUBIDO reconstructions hereafter).\nWe agree with the reviewer that the spatial coverage of these records is limited and mainly concentrated in northern Europe. However, for the specific objectives of this study, this limitation is not critical, as the selected proxies cover the region where AMOC-related climate variability exerts its strongest influence on European temperatures (Fig. S8).\nIn response to the reviewer’s valuable suggestion, we have clarified this rationale in the revised manuscript[Proxy records Section]:\nLines 425-442: “The temperature proxy-based reconstructions used in this study are sourced from the \"Temperature 12k Database\" (Temp12k) compiled by Kaufman et al. 4 and recently developed Bayesian-based temperature reconstructions from varved European lake sediments produced by our research group3 (SCUBIDO reconstructions hereafter). The criteria for selecting the proxy records included in this study were based on the quality of their chronologies and the temporal resolution of the proxy reconstructions over the past 6,000 years across Europe (10°W–50°E, 35°N–70°N). We selected records with chronologies derived from annual layer counts (e.g., tree-rings and varved sediments) and quantitative temperature reconstructions with annual to decadal resolution, which is necessary to have a fine enough estimate of the variability at centennial time scale1,2. Applying these criteria to the Temperature 12k database and SCUBIDO reconstructions, we identified five temperature-sensitive proxy records (listed in Table 2). In the case of the Bayesian-based reconstruction, the quantification of the proxy data in degree Celsius is based on a calibration period of instrumental climate data and overlapping X-Ray Fluorescence data (i.e., chemical element composition of the sediments) to learn about the direct relationship between each geochemical element and the covariant response between the elements and climate. The understanding of these relationships is then applied down core to transform the semi-quantitative proxy data into a posterior distribution of palaeoclimate with quantified uncertainties3”.\nTable 2: The distribution of the different types of archives represented by the proxy data.\nName Temporal Archive / Proxy Climate Age Lat Lon Ref\nresolution variable (BP)\n(yrs)\nLapland 1 Wood / tree-ring Summer -55-7450 69 25 5\n(Norway) width temperature\nTorneträsk 1 Wood/ tree-ring Summer -47-7356 68 20 6\n(Sweden) width temperature\nDiss Mere 1 Lake Annual -18-10286 52.37 1.1 3\n(UK) Sediment / XRF mean\ndata temperature\nNautajärvi 1 Lake Annual -70-9828 61.8 24.68 3\n(Finland) Sediment / XRF mean\ndata temperature\nP1003 12.2362 Marine Summer -48.236- 63.76 5.25 7\n(Norway) Sediment / temperature 7880.835\nd18O of\nplanktonic\nforaminifera\nI made further comments and suggestions in the pdf files of the manuscript, which I attached.\nComment 3.2 L8 : Please give all addresses in a consistent form. For example:\nInstitute, University, Postal code and city, country\nAuthors’ response: Done\nComment 3.3 L35: remove newly\nAuthors’ response: Done\nComment 3.4 L49: replace pelao-reconstructions with palaeoclimate-reconstructions\nAuthors’ response: Done\nComment 3.5 L64:western, Why just Western Europe? Before, you talked about whole Europe.\nAuthors’ response: We thank the reviewer for this important point. This is because Ref. 7 (now 26 in the revised manuscript) is focused on Western Europe, since this is where the signal concerning changes in the warm extremes is the largest.\nTo clarify this point, we added into the manuscript:\nLines 62-68: “This is consistent with previous studies showing that CMIP6 GCM simulations tend to underestimate the observed warming over France20. Recent regional studies, using Coordinated Regional Climate Downscaling Experiment for Europe (EURO-CORDEX), of summer temperature evolution associated this fact with either poor representation in models of the recent observed trends in atmospheric circulation in western Europe35, or to prescribing constant aerosol forcing models over Europe36, or to a poor estimate of long-term aerosol changes in western-central Europe37.”\nComment 3.6 L65: see comment above. Why Western Europe?\nAuthors’ response: We answered this comment in the former one.\nComment 3.7 Figure 1 : The Berkley curve and triangles in d are hard to see. Please use another colour, which has more contrast to the others. (a) No parentheses around “global”. Replace Fig. with Figure 1. a with (a) (b) (c) (d), rolling with running,CMIP6 with CMIP6 GCM, remove historical + SSP2-4.5, 1993-2022 or 2020? Replace and with “with” in and HadCRUT5 data and the CMIP6 simulations for the period 1993–2020\nAuthors’ response: The figure was improved following these suggestions.\nComment 3.8 L102: replace respectively with, respectively\nAuthors’ response: Done\nComment 3.9 L119: 20th, th in superscript\nAuthors’ response: Done\nComment 3.10 L125: replace time series with reconstructions\nAuthors’ response: Done\nComment 3.11 L128: This contradicts the statement above, with only little amount of proxy records from Europe.\nAuthors’ response: The statement regarding the Holocene being 'well-documented' refers to the global abundance of proxy archives. However, as detailed in our methodology, the selection criteria for this study (specifically requiring high-resolution records) significantly limited the number of European records that could be included.\nComment 3.12 L135: remove newly\nAuthors’ response: Done.\nComment 3.13 L153:Figs.S1-3 space is missing in front of Fig\nAuthors’ response: Corrected.\nComment 3.14 L157: You include four climate reconstructions from northern Europe, one from central Europe, but there is none from southern Europe. Therefore, I think it is hard to say that the models are confirmed by the reconstructions. Are there more climate reconstructions from central and southern Europe you can include into this study? There are for example long reconstructions from tree-rings from the European Alps.\nAuthors’ response: We thank the reviewer for drawing attention to the availability of tree-ring stable isotope records from the European Alps. However, a recent study38 demonstrated that these records are predominantly sensitive to evapotranspiration processes, rendering them more suitable for reconstructing drought stress rather than temperature. Consequently, no temperature reconstruction has been derived from the European Alps tree-ring stable isotopes.\nComment 3.15 L164: replace tree ring with tree-ring\nAuthors’ response: Corrected throughout the paper.\nComment 3.16 L166: replace Sediment with sediment\nAuthors’ response: Done.\nComment 3.17 Figure 2 : Write the axes labelling in black and not dark grey. Please make a bit more space between the y-axis numbers and the labelling. Please change the panel labelling from big, bold letters (a, b, c) to smaller and put them in parentheses as you did in figure 1. The colours of the Nautajarvi and Tornetrask are hard to differentiate from each other. Please choose another colour for one of them. Make a, b, c in bold (in the caption) as you did in Figure 1.\nAuthors’ response: Figure 2 has been updated as per suggestion.\nComment 3.18 L184: replace et al with ae al.\nAuthors’ response: Done.\nComment 3.19 L188: Are these the same models as mentioned in the figures? The labels are not exactly the same. Please check in the complete manuscript that the different models are always labelled in the exact same way. Otherwise, it is hard for the reader to follow.\nAuthors’ response: We have corrected this so that all terms are now consistent.\nComment 3.20 L210: Space line missing (before the new section)\nAuthors’ response: Done.\nComment 3.21 L215: I would not really say that it is a cooling period. It is more a period of stable temperatures or with a less steep increase in temperatures.\nAuthors’ response: We followed the interpretation commonly presented in the literature (e.g., Ref. 39). Accordingly, we have modified the text to read as follows:\nLines 225-227: “There is a cooling between 1940 and 1980, which is often attributed to the dominant influence of anthropogenic aerosols during this period39.”\nComment 3.22 L225: There are more temperature reconstructions over Europe based on tree-ring width which should be included here. For example, from the European Alps.\nAuthors’ response: Please see our reply to comment 3.14.\nComment 3.23 L229: It is not so clear what the red curve is based on. Please add here some more information.\nAuthors’ response: We have now added “the temperature reconstruction from the Torneträsk tree-ring width chronology”.\nComment 3.24 Figure 3 : Make the panel labelling consistent with the other figures: (a) and in bold. Are these all temperature anomalies? Please clarify this in the y-axis labelling. And then it has not to be mentioned in the panel headings. C. AMOC at 50 N to 50 °N. Please write the x-axis labelling (Numbers) in black. “Figure 3” in bold.\nAuthors’ response: Figure 3 has been improved as per suggestion. All the figures are now consistent.\nComment 3.25 Table 1 : Europe is missing in the figure caption\nAuthors’ response: We have now added “Europe (10°W–50°E, 35°N–70°N),”\nComment 3.26 L286: Europe is missing here\nAuthors’ response: We have now added “Europe (10°W–50°E, 35°N–70°N),”\nComment 3.27 L295-296: Please list them in the same order as in the table.\nAuthors’ response: Done.\nComment 3.28 L310: replace on-going to ongoing\nAuthors’ response: Done.\nComment 3.29 L325: In which figure? Please add this.\nAuthors’ response: We have now added “,in Fig. 3c,”.\nComment 3.30 L331-340: 21st (st in superscript)\nAuthors’ response: Done.\nComment 3.31 L332-339: 20th (th in superscript)\nAuthors’ response: Done.\nComment 3.32 Comment 3.31 L342: There are five palaeoclimate reconstructions in this study with which the models were compared. This is not a large number, and also the tree-ring based reconstructions are not going back to the mid-Holocene.\nAuthors’ response: “Large” was omitted.\nComment 3.33 L359: These are just two records. There are more high-resolution lake records in Europe.\nAuthors’ response: Please see our reply to Comment 3.13.\nComment 3.34 L361: Which three records you selected here. Which climate archive and from where they are? Please add some more information here.\nAuthors’ response: We added into the manuscript:\nLines 232-237: “Among the temperature proxies used in this study, the tree-ring width chronologies that include the Lapland record from Norway5 and the Torneträsk record from Sweden 6, which provide annually resolved records extending into the recent period (1900–2005). These records capture instrumental-era variability and extend further back in time, offering paleoclimate evidence of earlier temperature fluctuations (Fig. 3a, Supplementary Fig. S9; see Methods).”\nComment 3.35 L379: Then you should use the models for summer temperature to compare with the proxy data.\nAuthors’ response: A direct comparison is complicated because some models only provide annual output, while the Holocene reconstructions from Erb et al.13 are on a decadal scale.\nComment 3.36 Table 2 : Give the names of the records in a consistent way as before in the manuscript. Age (Bp) Also here, make the column wider, that the numbers could be read in a not confusing way. Is this record really just starting around 1900? I think you mean -55.\nAuthors’ response: Corrected from 55 to -55 and the table is now wider.\nComment 3.37 D180 From what? Foraminifera?\nAuthors’ response: Yes, the d18O value of planktonic foraminifera (added).\nComment 3.38 24.86 Should be written in one line. Make the column wider.\nAuthors’ response: Columns are now wider.\nComment 3.39 Table 3 : Please give the different climate models in the same order as in Fig. 2. Please also do this throughout the entire manuscript and all figures. This makes it easier for the reader to follow, especially for a reader who is not an expert in climate models.\nAuthors’ response: Done.\nComment 3.40 In the Spatial resolution column: Please replace every comma with a point.\nAuthors’ response: Done.\nComment 3.41 What do you mean with length?\nAuthors’ response: Length was replaced by Time span.\nComment 3.42 L435: What does large mean here? Can you give the time span?\nAuthors’ response: A large part of the Holocene was replaced by 2000-10000 BP.\nComment 3.43 L478: 99th (th superscript)\nAuthors’ response: Done.\nComment 3.44 L540: Space missing in front the\nAuthors’ response: Done.\nComment 3.45 TableS2 Europe is missing here. Please add also with longitude and latitude.\nAuthors’ response: Done.\nComment 3.46 Table S3 Are there Asterisks missing in this and the next line? I think 0.55 is significant.\nAuthors’ response: We confirm that there is no Asterisk given the short period.\nComment 3.47 Figure S1 change from yr to yrs\nAuthors’ response: Done.\nComment 3.48 Figure S1 Please arrange the different simulations in all figures and tables in the same order. This makes it easier for the reader to follow.\nAuthors’ response: Done.\nComment 3.49 Figures S1 In the figure caption it is 99% and also in the text of the main manuscript.\nAuthors’ response: This was corrected in the figure.\nComment 3.50 Figure S2 See comments to Figure S1.\nAuthors’ response: This was corrected in the figure.\nComment 3.51 Figure S3 See comments to Figure S1.\nAuthors’ response: This was corrected in the figure.\nComment 3.52 Figure S4 Please lable the panels as in the text of the main manuscript e.g., Lapland (Norway) and so on. This is too small to read. Furthermore, this scale is just needed once for this figure and therefore, all panels can be a bit bigger. Age BP is missing as x-axis lable. Please list the reconstructions in the order of the figure from top left to bottom right.\nAuthors’ response: Thank you so much for this detailed observation. The figure has been improved accordingly.\nComment 3.53 Figure S5 See comment to Figure S4. Please check the order of the simmulations. See comment above.\nAuthors’ response: Done.\nComment 3.54 Figure S6 See comments to Figure S4 and S5.\nAuthors’ response: Done.\nComment 3.55 Figure S7 See comments to Figures S4, S5, and S6.\nAuthors’ response: Done.\nComment 3.56 Figure S8 Can you please just make these maps for Europe as Figure 1c. This makes it more convenient for the reader. “rolling” you mean running mean here?\nAuthors’ response: We thank the reviewer for this helpful suggestion. Our intention in presenting the maps on a global scale was to highlight the strong influence of the AMOC over Europe in comparison to other regions of the world. “rolling” was replaced by running mean.\nComment 3.57 Figure S9 Due to that this is just one panel \"a\" can be deleted. Delet °C here.\nAuthors’ response: a and °C are deleted.\nComment 3.58 Figure S10 I do not really understand what was correlated with what in the three rows. Perhaps something is missing in the figure caption. Please clarify this.\nAuthors’ response: The caption has been improved as follows:\n“Correlation between 60-year low-pass–filtered temperature anomalies (relative to 1851–2021) from the Berkeley Earth Surface Temperature dataset and (i) HADCRUT5 observations, and (ii) all extended historical simulations from IPSL-CM6A-LR ensemble members, over the period 1851–2021, for Lapland, Torneträsk, and Northern Europe.”\nComment 3.59 Figure S11. See comments to Figure S10\nAuthors’ response: The caption has been improved as follows:\n“RMSE (Root Mean Squared Error) between 60-year low-pass–filtered temperature anomalies (relative to 1851–2021) from the Berkeley Earth Surface Temperature dataset and (i) HADCRUT5 observations, and (ii) all extended historical simulations from IPSL-CM6A-LR ensemble members, over the period 1851–2021, for Lapland, Torneträsk, and Northern Europe.”\nComment 3.60 Figure S12. Oder the legend in the way that the relevant colours for panel a and b can be differentiated. Y axis In °C? Please add this. Remove (Berkeley).\nAuthors’ response: The legend is now ordered in the way that the relevant colours for panel a and b can be differentiated. °C is now added to the figure.\nComment 3.61 Figure S13 See comment above. Please lable the different reconstructions as in the main text and in a consistend way. Please make the lable Age (BP) consitent for all figures.\nAuthors’ response: The reconstructions are now labelled consistently with the main text and across all figures. The x-axis title has been set to “Age (BP)” throughout the paper.\nComment 3.62 Figure S14 See comments above.\nAuthors’ response: Done.\nReferences\n1. Grisart, A. et al. Sub-millennial climate variability from high-resolution water isotopes in the EPICA Dome C ice core. Climate of the Past 18, 2289–2301 (2022).\n2. Münch, T. & Laepple, T. What climate signal is contained in decadal- to centennial-scale isotope variations from Antarctic ice cores? Climate of the Past 14, 2053–2070 (2018).\n3. Boyall, L. et al. SCUBIDO: a Bayesian modelling approach to reconstruct palaeoclimate from multivariate lake sediment data. Climate of the Past 21, 1465–1480 (2025).\n4. Kaufman, D. et al. A global database of Holocene paleotemperature records. Sci Data 7, 115 (2020).\n5. Helama, S. et al. Summer temperature variations in Lapland during the Medieval Warm Period and the Little Ice Age relative to natural instability of thermohaline circulation on multi-decadal and multi-centennial scales. Journal of Quaternary Science 24, 450–456 (2009).\n6. Grudd, H. et al. A 7400-year tree-ring chronology in northern Swedish Lapland: natural climatic variability expressed on annual to millennial timescales. The Holocene 12, 657–665 (2002).\n7. Sejrup, H. P., Haflidason, H. & Andrews, J. T. A Holocene North Atlantic SST record and regional climate variability. Quaternary Science Reviews 30, 3181–3195 (2011).\n8. Askjær, T. G. et al. Multi-centennial Holocene climate variability in proxy records and transient model simulations. Quaternary Science Reviews 296, 107801 (2022).\n9. Bae, S. W., Lee, K. E., Ko, T. W., Kim, R. A. & Park, Y.-G. Holocene centennial variability in sea surface temperature and linkage with solar irradiance. Sci Rep 12, 15046 (2022).\n10. Bonnet, R. et al. Increased risk of near term global warming due to a recent AMOC weakening. Nat Commun 12, 6108 (2021).\n11. Delworth, T. L., Zhang, R. & Mann, M. E. Decadal to Centennial Variability of the Atlantic from Observations and Models. in Ocean Circulation: Mechanisms and Impacts—Past and Future Changes of Meridional Overturning 131–148 (American Geophysical Union (AGU), 2007). doi:10.1029/173GM10.\n12. Delworth, T. L. & Zeng, F. The Impact of the North Atlantic Oscillation on Climate through Its Influence on the Atlantic Meridional Overturning Circulation.\nhttps://doi.org/10.1175/JCLI-D-15-0396.1 (2016) doi:10.1175/JCLI-D-15-0396.1.\n13. Erb, M. P. et al. Reconstructing Holocene temperatures in time and space using paleoclimate data assimilation. Climate of the Past 18, 2599–2629 (2022).\n14. Maher, N. et al. The updated Multi-Model Large Ensemble Archive and the Climate Variability Diagnostics Package: New tools for the study of climate variability and change. EGUsphere 1–28 (2024) doi:10.5194/egusphere-2024-3684.\n15. Vieira, L. E. A., Solanki, S. K., Krivova, N. A. & Usoskin, I. Evolution of the solar irradiance during the Holocene. A&A 531, A6 (2011).\n16. Gray, L. J. et al. Solar Influences on Climate. Reviews of Geophysics 48, (2010).\n17. Wanner, H. et al. Mid- to Late Holocene climate change: an overview. Quaternary Science Reviews 27, 1791–1828 (2008).\n18. Climate since AD 1500. (Routledge, London, 2003). doi:10.4324/9780203430996.\n19. Ojala, A. E. K., Francus, P., Zolitschka, B., Besonen, M. & Lamoureux, S. F. Characteristics of sedimentary varve chronologies – A review. Quaternary Science Reviews 43, 45–60 (2012).\n20. Ribes, A. et al. An updated assessment of past and future warming over France based on a regional observational constraint. Earth System Dynamics 13, 1397–1415 (2022).\n21. Rohde, R. A. & Hausfather, Z. The Berkeley Earth Land/Ocean Temperature Record. Earth System Science Data 12, 3469–3479 (2020).\n22. Chan, D., Gebbie, G., Huybers, P. & Kent, E. C. A Dynamically Consistent ENsemble of Temperature at the Earth surface since 1850 from the DCENT dataset. Sci Data 11, 953 (2024).\n23. Morice, C. P. et al. An Updated Assessment of Near-Surface Temperature Change From 1850: The HadCRUT5 Data Set. Journal of Geophysical Research: Atmospheres 126, e2019JD032361 (2021).\n24. Chan, D., Gebbie, G. & Huybers, P. Larger and Steadier Warming since 1850 from Harmonized Land and Ocean Temperature Records. https://eartharxiv.org/repository/view/7584/ (2024).\n25. Ayache, M., Swingedouw, D., Mary, Y., Eynaud, F. & Colin, C. Multi-centennial variability of the AMOC over the Holocene: A new reconstruction based on multiple proxy-derived SST records. Global and Planetary Change 170, 172–189 (2018).\n26. Eynaud, F. et al. Compiling multiproxy quantitative hydrographic data from Holocene marine archives in the North Atlantic: A way to decipher oceanic and climatic dynamics and natural modes? Global and Planetary Change 170, 48–61 (2018).\n27. Schurer, A. P., Hegerl, G. C., Mann, M. E., Tett, S. F. B. & Phipps, S. J. Separating Forced from Chaotic Climate Variability over the Past Millennium. https://doi.org/10.1175/JCLI-D-12-00826.1 (2013) doi:10.1175/JCLI-D-12-00826.1.\n28. Menary, M. B. et al. Aerosol-Forced AMOC Changes in CMIP6 Historical Simulations. Geophysical Research Letters 47, e2020GL088166 (2020).\n29. Kravtsov, S., Gavrilov, A., Buyanova, M., Loskutov, E. & Feigin, A. Forced signal and predictability in a prototype climate model: Implications for fingerprinting based detection in the presence of multidecadal natural variability. Chaos 32, 123130 (2022).\n30. Latif, M., Sun, J., Visbeck, M. & Hadi Bordbar, M. Natural variability has dominated Atlantic Meridional Overturning Circulation since 1900. Nat. Clim. Chang. 12, 455–460 (2022).\n31. Fang, S.-W., Khodri, M., Timmreck, C., Zanchettin, D. & Jungclaus, J. Disentangling Internal and External Contributions to Atlantic Multidecadal Variability Over the Past Millennium. Geophysical Research Letters 48, e2021GL095990 (2021).\n32. Lee, S.-K. et al. A pause in the weakening of the Atlantic meridional overturning circulation since the early 2010s. Nat Commun 15, 10642 (2024).\n33. Lee, S.-K. et al. A pause in the weakening of the Atlantic meridional overturning circulation since the early 2010s. Nat Commun 15, 10642 (2024).\n34. Schulz, M. & Mudelsee, M. REDFIT: estimating red-noise spectra directly from unevenly spaced paleoclimatic time series. Computers & Geosciences 28, 421–426 (2002).\n35. Vautard, R. et al. Heat extremes in Western Europe increasing faster than simulated due to atmospheric circulation trends. Nat Commun 14, 1–9 (2023).\n36. Lorenz, R., Stalhandske, Z. & Fischer, E. M. Detection of a Climate Change Signal in Extreme Heat, Heat Stress, and Cold in Europe From Observations. Geophysical Research Letters 46, 8363–8374 (2019).\n37. Schumacher, D. L. et al. Exacerbated summer European warming not captured by climate models neglecting long-term aerosol changes. Commun Earth Environ 5, 1–14 (2024).\n38. Arosio, T. et al. Tree-ring stable isotopes from the European Alps reveal long-term summer drying over the Holocene. Science Advances 11, eadr4161 (2025).\n39. Undorf, S., Bollasina, M. A. & Hegerl, G. C. Impacts of the 1900–74 Increase in Anthropogenic Aerosol Emissions from North America and Europe on Eurasian Summer Climate. Journal of Climate 31, 8381–8399 (2018)."}, {"round": 2, "reviewer_comments": {"reviewer_2": "I thank the authors for their thorough replies and revisions. I still have some concerns about the robustness of the model and proxy results, but they should not preclude publication. However, it would be good to include some additional analysis and slightly adapt the study’s overall framing.\n\nRemaining concerns\n1. Proxy record selection. In the previous round of reviews, two reviewers were concerned about the limited number of proxy records. The authors referred to Münch & Laepple (2018) and Grisart et al. (2022) to argue that “this kind of resolution (~ 10 years) is necessary”. I might have overread something, but I could not find any statement in these studies that would preclude the use of records at slightly lower resolution (say, 10 to 20 years) to assess (multi-)centennial variability. Furthermore, both studies seem to be specifically about ice cores.\n\nOne option would be to demonstrate more rigorously here why a specific minimum resolution would be needed regardless of proxy type. However, it might be easier to relax the resolution threshold a little and include some more records in the analysis, which would improve the robustness of the model-data comparison.\n\nEven if the strict temporal resolution criteria are kept, the study currently does not include any ice records from the Temp12k database, e.g., from Greenland. I don’t see why they shouldn’t be included in an analysis of AMOC-related variability – even though these sites are not technically located in Europe, their temperatures should be expected to co-vary considerably with European temperatures at centennial timescales.\n\n2. Phase relationship. The authors raise an interesting point with the Sejrup et al. (2011) study, in that it “demonstrated that the dominant multi-centennial modes of variability vary in phase and amplitude through time and space. (…) This reflects the complex nature of North Atlantic climate variability, which arises from multiple, interacting mechanisms leading to leads and lags in the spatial response across Europe”.\n\nModels’ modes of variability at multidecadal and longer timescales often yield highly coherent temperature signals over and near the North Atlantic. For a fair model-data comparison, especially regarding the link to the AMOC, it would therefore be good to compare the spectral coherence of the proxy records at (multi-)centennial timescales to the coherence in the model. This is not necessarily the same as records being in phase (my previous comment might have been confusing in this regard); rather, that they have a coherent variability, potentially with a regional phase difference (e.g., related to shifts in deep convection).\n\n3. Model-data comparison. From my understanding, arguments for the plausibility of the multi-centennial variability in the IPSL model are now mainly based on two approaches: comparison of temperature spectra with the Holocene reanalysis and the amplitude of modelled vs observed internal variability during the instrumental period. I still have some concerns regarding both approaches.\n\nRegarding the observed internal variability, I respectfully disagree with the authors’ reply that it is appropriate to use the IPSL ensemble mean for this calculation. (Of course it is appropriate to subtract the IPSL ensemble mean to calculate the model’s own internal variability.) As mentioned in the previous review, the CMIP6 multi-model mean already used in Fig. R5 (or, even better, the residual of the multi-model mean regressed onto observations, https://www.science.org/doi/full/10.1126/science.1257856) is a more appropriate proxy of the real-world forced response, from which the observed internal variability is calculated. I don’t expect this to change the results substantially, but it will probably change the percentages in Table 1.\n\nComparison with the Holocene reanalysis is so far mostly based on significant frequencies in the power spectra (Fig. 2), which match at least qualitatively. However, the amplitudes appear quite different, even with normalization but especially without. For example, judging from the color scale in Fig. S5, the spectral power of (multi-)centennial variability in the IPSL model seems about twice as large as in the reanalysis. How does this translate to an amplitude in °C? If there are indeed significant discrepancies in the amplitude, this should be acknowledged more clearly in the manuscript. Along these lines, Mehling et al. (2024) suggested that the amplitude of centennial AMOC variability in some NEMO-based models (including the IPSL model) might be overestimated due to a sea ice bias in the Labrador Sea.\n\nFinally, the comparison of the models with the Holocene reanalysis would be more convincing if the spatial patterns of the centennial temperature variability were also compared, which is again relevant for the assumed link to the AMOC.\n\nTo summarize, I am still not convinced that there is enough evidence that the multi-centennial variability in the IPSL model is entirely realistic (or, on the other hand, that the variability in the other models is unrealistic, so that they shouldn’t be considered for the analysis of temperature trends). However, conclusions such as “this mode of variability is amplifying the warming induced by human activity in Northern Europe by 53% over the 2000-2035 period” (from the abstract) rely on the realism of variability in the IPSL model.\n\nI think that this dilemma can be solved relatively easily by slightly adapting the overall framing: emphasizing the importance of multi-centennial (AMOC) variability for present-day European temperature trends while also emphasizing that discrepancies likely remain between modelled and observed/reconstructed variability, and acknowledging that centennial variability remains hard to evaluate (although this study has taken an important step in the right direction).", "reviewer_3": "Paper review of the revised version for \"Multi-centennial internal variability in the North Atlantic drives additional warming over Europe\" by A.Al-Yaari, D. Swingedouw, P. Braconnot, L. Boyall, P. Lincoln, O. Marti, T. Caley, T. Extier, and C. Martin-Puertas.\n\nWhat are the noteworthy results?\nThe noteworthy result is that internal climate variability within the AMOC has to be considered and can has a strengthen effect on the antrhopogenic influence on climate warming.\n\nWill the work be of significance to the field and related fields?\nYes, I think it is of significance.\n\nHow does it compare to the established literature?\nDue to that climate modelling is not my expertice I cannot really assess this topic.\n\nDoes the work support the conclusions and claims, or is additional evidence needed?\nYes, the work supports the conclusion and claims.\n\nAre there any flaws in the data analysis, interpretation and conclusions?\nNot to my opinion.\n\nThe revised version of this manuscript is very good and only some formal corrections should be done.\n\nI only have one point/question:\nWhat are the forcings for the internal multi-centennial variability of the AMOC? If there are some hypothesis to this, this should be added to the manuscript.\n\nI made my comments in the attached PDF files."}, "author_response": "Point-by-point response to the reviewers’ comments on the manuscript entitled ‘Multi-centennial internal variability in the North Atlantic drives additional warming over Europe’\nReviewer #1 (Remarks to the Author):\nI thank the authors for their thorough replies and revisions. I still have some concerns about the robustness of the model and proxy results, but they should not preclude publication. However, it would be good to include some additional analysis and slightly adapt the study’s overall framing.t induce any unwanted artifacts? See also the comment for L302 below.\nWe sincerely thank the reviewer for this additional feedback. Addressing the points raised has helped us to clearly position our contribution and ensure that the readers can better understand the scope and reliability of our results. We appreciate those thoughtful comments and believe the revisions have strengthened the manuscript.\nRemaining concerns\nComment 2.1 1. Proxy record selection. In the previous round of reviews, two reviewers were concerned about the limited number of proxy records. The authors referred to Münch & Laepple (2018) and Grisart et al. (2022) to argue that “this kind of resolution (~ 10 years) is necessary”. I might have overread something, but I could not find any statement in these studies that would preclude the use of records at slightly lower resolution (say, 10 to 20 years) to assess (multcentennial variability. Furthermore, both studies seem to be specifically about ice cores. One option would be to demonstrate more rigorously here why a specific minimum resolution would be needed regardless of proxy type. However, it might be easier to relax the resolution threshold a little and include some more records in the analysis, which would improve the robustness of the model-data comparison. Even if the strict temporal resolution criteria are kept, the study currently does not include any ice records from the Temp12k database, e.g., from Greenland. I don’t see why they shouldn’t be included in an analysis of AMOC-related variability – even though these sites are not technically located in Europe, their temperatures should be expected to co-vary considerably with European temperatures at centennial timescales.\nAuthors’ response: We fully agree with the reviewer and we modified the manuscript to avoid inappropriate references to the papers of Münch & Laepple (2018) and Grisart et al. (2022). We have now relaxed our criteria to a minimum of 20 years of temporal resolution, and we expanded the spatial domain from (10°W–50°E, 35°N–70°N) to (40°W–50°E, 35°N–75°N) in order to include the Greenland ice core records. As a result, we obtained additional temperature 1 records, namely the GISP2 (Greenland Ice Sheet Project) ice core record. Figure 2 and Table 2 have been updated accordingly. The spectrum of this record is commented into the text, as follows:\nL158-160: “Comparable significant periodicities are also observed at a site in Greenland, although they can exceed 200 years, consistent with the findings of Ref 37.”\nL469: “We selected records based on annual layer counts (e.g., tree rings, varved sediments) and quantitative temperature reconstructions with resolutions ranging from yearly to ~20 years. Applying these criteria to the Temperature 12k database and SCUBIDO reconstructions, we identified six temperature-sensitive proxy records (listed in Table 2).”\nFigure 2. Spectral density analysis of temperature proxy records, Holocene reanalysis data, and transient model simulations since the mid-Holocene. (a) Normalized spectral density (spectral analysis output scaled by the standard deviation of each dataset for direct 2 comparison within a single panel), above the 99% significant red noise threshold for temperature variability from annually to decadal-resolved proxy records (see Fig.1c for the geographical locations) including Diss Mere (UK), Lapland (Norway), Nautajärvi (Finland), P1003 (Norway), and Torneträsk (Sweden). (b) Same as (a) but for Holocene reanalysis 2 and climate model transient simulations over Europe (see Fig. 1c for the defined region). (c) Same as (a) but for the Atlantic Meridional Overturning Circulation (AMOC) based on transient model simulations and computed at 26°N (see Methods).\nComment 2.2 2. Phase relationship. The authors raise an interesting point with the Sejrup et al. (2011) study, in that it “demonstrated that the dominant multi-centennial modes of variability vary in phase and amplitude through time and space. (…) This reflects the complex nature of North Atlantic climate variability, which arises from multiple, interacting mechanisms leading to leads and lags in the spatial response across Europe”.\nModels’ modes of variability at multidecadal and longer timescales often yield highly coherent temperature signals over and near the North Atlantic. For a fair model-data comparison, especially regarding the link to the AMOC, it would therefore be good to compare the spectral coherence of the proxy records at (multcentennial timescales to the coherence in the model. This is not necessarily the same as records being in phase (my previous comment might have been confusing in this regard); rather, that they have a coherent variability, potentially with a regional phase difference (e.g., related to shifts in deep convection).\nAuthors’ response: To address the reviewer’s suggestion concerning power-coherence relationships, we computed the cross-wavelet transform (XWT), which can be interpreted as a wavelet-based covariance measuring the shared power between two time series. XWT identifies time-frequency regions where both series exhibit large-amplitude oscillations simultaneously, thereby quantifying common variability rather than phase similarity alone. We applied this analysis to all proxy pairs (Fig. S6), as well as to the corresponding gridpoint time series extracted from the Holocene reanalysis (Fig. S7) and from the transient simulations (Fig. S8; IPSL-CM6A-LR). Significant shared variability across proxy pairs is primarily concentrated at multi-decadal to centennial timescales (∼60–200 years), while longer-period of variability is less consistently shared among sites. Cross-wavelet analyses of both the Holocene reanalysis and IPSL-CM6A-LR transient simulations show broadly similar time-frequency patterns, with common power also focused at these timescales.\nIn the manuscript, we included the following text:\nL179: “Furthermore, significant coherent spectral power is concentrated at multidecadal to centennial timescales (approximately 60-200 years) across most proxy pairs (Supplementary Fig. S6). At longer periods (>200 years), coherence occurs intermittently and varies among 3 proxy pairs. The reanalysis (Supplementary Fig. S7) and the IPSL-CM6A-LR transient simulations (Supplementary Fig. S8) show patterns broadly consistent with those observed in the proxy records. These results indicate coherent multi-decadal to centennial variability across sites and suggest that the proxy records are primarily linked through common variability at these timescales, supporting the interpretation that they respond to large-scale climate phenomenon during specific Holocene intervals. While the simulations reproduce the timing and dominant spatial scales of common variability reasonably well, the amplitude of coherent variability is generally weaker in the reanalysis than in both the model simulations and the proxy records.”\nRegarding the observed internal variability, I respectfully disagree with the authors’ reply that it is appropriate to use the IPSL ensemble mean for this calculation. (Of course it is appropriate to subtract the IPSL ensemble mean to calculate the model’s own internal variability.) As mentioned in the previous review, the CMIP6 multi-model mean already used in Fig. R5 (or, even better, the residual of the multi-model mean regressed onto observations, https://www.science.org/doi/full/10.1126/science.1257856) is a more appropriate proxy of the real-world forced response, from which the observed internal variability is calculated. I don’t expect this to change the results substantially, but it will probably change the percentages in Table 1.\nAuthors’ response: We thank the reviewer for this useful suggestion. We agree that using CMIP6 ensemble mean is a more standard estimate of forced signal when using observations. We also agree that evaluating the realism of the amplitude of the multi-centennial variability simulated by climate models remains challenging. Following the reviewer’s recommendation, we have now computed the forced signal over 2000-2035 using the CMIP6 multi-model mean, rather than the IPSL large ensemble mean, and we have updated Table 1 accordingly.\nWe integrated the new analysis into Table 1 and the related text as follows:\nL158-160: “Table 1 presents warming trends over 2000-2035 (°C dec⁻¹) for Lapland (Norway), Torneträsk (Sweden), Northern Europe, Europe, and the global scale, separating the externally forced signal (estimated by the CMIP6 ensemble) from the internal variability. This variability estimated as the difference between Member 8 and the IPSL-EHS large ensemble mean and between observations and CMIP6 ensemble mean). The estimate of the forced signal is actually different here because within the IPSL model “world”, a good estimation of the forced signal is based on ensemble mean from this model (Fig. 3), while within the real world, a potentially better estimate of forced signal is the ensemble mean of all the latest model simulations available from CMIP6. The amplitude of the internal variability is constrained by observations through the scaling this IPSL-based internal variability onto the observed internal variability derived from the Berkeley Earth dataset (estimated as the time series of Berkeley Earth temperatures removed from the forced signal, defined as the CMIP6 ensemble (Supplementary Fig. S18). Over 2000-2035, internal variability contributes to regional warming, with magnitudes ranging from 0.05 to 0.14 °C dec⁻¹ over the regions considered in Table 1. The largest contributions occur over Lapland (0.14 °C dec⁻¹) and Northern Europe (0.11 °C dec⁻¹), highlighting the strong regional imprint of internal variability. When expressed as a percentage increase relative to the forced warming, the contribution of internal variability varies across 8 regions and estimate of external forcing. When using the estimate of the forced signal from CMIP659, internal variability amplifies the forced warming by 15-37%, with the strongest amplification found over Lapland (37%) and Northern Europe (30%). The influence of internal variability over Northern Europe is already evident over 2000-2024 (Supplementary Table S3) and is projected to reach approximately 41% of the forced signal over 2025-2035 (Supplementary Table S4). At the global scale over land, internal variability contributes more modestly (15% over the period 2000-2035), demonstrating that the amplifying effect of internal variability on warming is considerably stronger over Europe than at the global scale.\nNew Table 1: Rates of temperature increase (in °C dec⁻¹ and estimated by the slope of a linear regression) over the period 2000-2035 at Lapland (Norway), Torneträsk (Sweden), Northern Europe (10°W-50°E, 55°N-70°N), Europe (10°W-50°E, 35°N”-70°N), and the global scale over land. Forced-CMIP6 (CMIP6 multi-model ensemble mean) represents the externally forced signal estimated from CMIP6 ensemble mean. Observationally-constrained internal variability is the difference between Member 8 internal variability rate over 2000-2035 signal constrained by the “amplitude ratio”, estimated as the ratio between the standard deviation of the observed internal signal (Berkeley Earth minus CMIP6 ensemble mean) variability and the Member 8 internal variability both computed over the 1930 - 2023 period (see Methods). This amplitude ratio is an estimate of the over-estimation of the IPSL internal variability compared to observations. The percentage of the increase of the forced warming by the internal variability is calculated as the ratio between the rates of warming over 2000-2035 of the constrained internal variability divided by the Forced-CMIP6 signal and then multiplied by 100. Slopes significantly different from zero (p < 0.01) are indicated with an asterisk (*).\nRate of temperature change Lapland Torneträsk Northern Europe Global\nover 2000-2035 (°C/decade) Europe\nForced-CMIP6 0.38* 0.36* 0.37* 0.35* 0.33*\nAmplitude ratio computed 0.70 0.52 0.32 0.25 0.36\nover 1930-2023\nObservationally-constrained 0.14 0.10 0.11 0.07 0.05\ninternal variability\nIncreased warming due to 37% 28% 30% 20% 15%\ninternal variability\nFinally, the comparison of the models with the Holocene reanalysis would be more convincing if the spatial patterns of the centennial temperature variability were also compared, which is again relevant for the assumed link to the AMOC.\nAuthors’ response: We thank the reviewer for this important and constructive comment. To quantitatively assess the amplitude differences suggested by the spectral comparison, we computed the standard deviation (°C) of the low-pass filtered (60-year) temperature time series from the Holocene reanalysis, IPSL-CM6 simulations, and available proxy records (Supplementary Table S4). This analysis shows that the amplitude of temperature variability in the IPSL-CM6 simulations is larger than in the Holocene reanalysis. Specifically, for the period 6000-0 BP, the overestimation of the (mainly internal) variability of the IPSL-CM6 simulations to that of the Holocene reanalysis ranges from approximately 4 to 6. In contrast, when proxy records are used instead of the reanalysis, the corresponding ratios are generally close to unity, ranging from ~1 to 3 across sites. These results indicate that while the model simulations tend to overestimate low-frequency temperature variability relative to proxy records by a factor of approximately 1-3, the Holocene reanalysis exhibits reduced variability compared to both models and proxies. This suggests that the reanalysis likely dampens the amplitude of low-frequency variability recorded in proxy data at their respective locations, leading to an underrepresentation of centennial-scale variability.\nWe now explicitly acknowledge this discrepancy between IPSL-CM6 variability and proxy records and reanalysis in the revised manuscript and clarify its implications for interpreting model-reanalysis-proxy comparisons.\nL187-199: “While the simulations reproduce the timing and dominant spatial scales of common variability reasonably well, the amplitude of coherent variability is generally weaker in the reanalysis than in both the model simulations and the proxy records. When quantified in temperature units, IPSL-CM6A-LR exhibits larger amplitudes than the reanalysis across all regions, with model-to-reanalysis standard deviation ratios of approximately 4-6 (Supplementary Table S2). In contrast, comparisons between IPSL-CM6A-LR simulations and proxy records yield lower ratios, between ~1 to 3, indicating a more moderate model overestimation when compared to the proxy records. Indeed, the Holocene reanalysis itself also shows reduced variability compared to those proxy records (Supplementary Table S2). The 10 reanalysis likely underestimates real variability owing to limitations in spatial coverage, seasonal proxy sensitivities, temporal resolution, covariance-based infilling35, and low signal-to-noise ratios in proxy records39. All those limitations might lead to a damping in the amplitude of the variations in the reanalysis.”\nTable S2: Ratios of standard deviations of the low-pass filtered (60-year) temperature time series from IPSL-CM6A-LR simulations relative to the Holocene reanalysis and proxy records for the period 6000-0 BP. This table therefore quantifies differences in the amplitude of centennial-scale temperature variability (°C) across models, reanalysis, and proxies.\nData Diss Nautajarvi Tornetrask Lapland Northern Europe\nMere Europe\n(Finland) (Norway) (Sweden)\n(UK)\nIPSL-CM6A- 4.18 5.89 5.08 5.62 6.01 5.8\nLR / reanalysis\nIPSL-CM6A- 1.73 3,65 1 1.06 - -\nLR / proxies\nReanalysis/\n0.41 0.62 0.20 0.19 - -\nProxies\nComment 2.3c To summarize, I am still not convinced that there is enough evidence that the multi-centennial variability in the IPSL model is entirely realistic (or, on the other hand, that the variability in the other models is unrealistic, so that they shouldn’t be considered for the analysis of temperature trends). However, conclusions such as “this mode of variability is amplifying the warming induced by human activity in Northern Europe by 53% over the 2000-2035 period” (from the abstract) rely on the realism of variability in the IPSL model.\nI think that this dilemma can be solved relatively easily by slightly adapting the overall framing: emphasizing the importance of multi-centennial (AMOC) variability for present-day European temperature trends while also emphasizing that discrepancies likely remain between modelled and observed/reconstructed variability, and acknowledging that centennial variability remains hard to evaluate (although this study has taken an important step in the right direction).\nAuthors’ response: We thank the reviewer for this thoughtful and constructive comment. As outlined in our responses to the former comments, the additional analyses and revisions in the manuscript now directly address the reviewer’s concerns regarding the realism of multi-centennial variability and its role in amplifying recent warming trends. Following the reviewer’s suggestion, we have adapted the overall framing of the manuscript to emphasize both the importance of multi-centennial (AMOC-related) variability for present-day European temperature trends and the fact that discrepancies likely remain between modelled and reconstructed variability. In particular, we have now considered CMIP6 and updated related text, as mentioned above. We also revised the abstract to avoid reliance on a single model estimate and instead bracket the amplification effect using two independent estimates of the forced signal.\nIn the abstract, we replaced the statement:\n“According to an observational-constraint approach, this mode of variability is amplifying the warming induced by human activity in Northern Europe by 53% over the 2000-2035 period”\nBy:\nL43-74: “Applying observational constraints, internal variability may amplify anthropogenic warming in Northern Europe by approximately 30% during 2000-2035. While it remains difficult to estimate the amplitude of both forced and internal signals, this study provides useful estimates of this amplification using both instrumental and proxy records.”\nPaper review of the revised version for \"Multi-centennial internal variability in the North Atlantic drives additional warming over Europe\" by A.Al-Yaari, D. Swingedouw, P. Braconnot, L.Boyall, P. Lincoln, O. Marti, T. Caley, T. Extier, and C. Martin-Puertas.\nWhat are the noteworthy results?\nThe noteworthy result is that internal climate variability within the AMOC has to be considered and can has a strengthen effect on the antrhopogenic influence on climate warming.\nWill the work be of significance to the field and related fields?\nYes, I think it is of significance.\nHow does it compare to the established literature?\nDue to that climate modelling is not my expertice I cannot really assess this topic.\nDoes the work support the conclusions and claims, or is additional evidence needed?\nYes, the work supports the conclusion and claims.\nAre there any flaws in the data analysis, interpretation and conclusions?\nNot to my opinion.\nThe revised version of this manuscript is very good and only some formal corrections should be done.\nI only have one point/question:\nComment 3.1 What are the forcings for the internal multi-centennial variability of the AMOC? If there are some hypothesis to this, this should be added to the manuscript.\nAuthors’ response: We thank the reviewer for this important point. While there is no consensus yet on a unique forcing mechanism for the internal multi‑centennial AMOC variability, several plausible mechanisms have been proposed in the literature. Those mechanisms are mainly implying either large salinity changes that accumulates in the Arctic or propagation of salinity from the southern part of the Atlantic. We have cited these proposed mechanisms in the revised manuscript as follows:\nL228-252: “While there is no consensus yet on a unique forcing mechanism for the internal multi‑centennial AMOC variability, several plausible mechanisms have been proposed in the literature. Those mechanisms are mainly implying either large salinity changes that accumulates in the Arctic or the propagation of salinity anomalies from the southern part of the Atlantic. Indeed, in a pre‑industrial control simulation from the IPSL‑CM6‑LR climate model, a pronounced multi‑centennial AMOC variability emerges, driven by a delayed accumulation and release of freshwater in the Arctic, which modulates surface and subsurface salinity and density, ultimately affecting deep‑water formation in the Labrador and Nordic Seas42. A recent multi‑model study confirms that Arctic-North Atlantic freshwater exchanges and sea‑ice/ocean interactions as a plausible driver across different models for the centennial-scale AMOC 13 variability43. Similarly, in EC‑Earth3 piControl simulation, it has been shown that salinity anomalies from the Arctic, modulated by sea‑ice dynamics, accumulate there and are episodically released into the North Atlantic, altering water‑column stability and deep‑water formation, thereby driving AMOC oscillations on ~100-200 yr timescales44. Another study45 using an Earth System of intermediate complexity, also highlights the existence of multi-centennial variability of the AMOC, which they also relate to a linkage between the North Atlantic and the Arctic through changes in Arctic freshwater fluxes (precipitation minus evaporation). Using also EC-Earth, it has been proposed that the salinity‑advection feedbacks for regions south of the subpolar gyre is also contributing to this variability. The mechanism proposed argue that perturbations in salinity gradients between subtropical and subpolar regions can be advected and later damped by mixing/convection, leading to a self‑sustained oscillatory mode46. Such a mechanism highlighting a propagation of salinity anomalies from the Southern Ocean along the Atlantic until its northern part was also proposed by Ref 47 and constitute an alternative or complementary mechanism that can explain multi-centennial variability in the North Atlantic.”\nI made my comments in the attached PDF files.\nComment 3.2 The adresses are still not in a consisten way. Some have postalcodes some not. Please check again.\nAuthors’ response: Done\nComment 3.3 Figure 1 Oder the different records as in the figure caption or vice versa in the figure caption as in the figure. Please replace Multi Model mean with CMIP6 GMC ensemple mean. That makes it clearer for the reader. Should fit into the frame of the figure. Write two lines. Here as well you should use the same order as in panel a and also adapt the figure caption to this. Would delete seasons here.\nAuthors’ response: Done\nComment 3.4 L145 Covering the last 6000 years Should be added here to make this clear.\nA uthors’ response: Done\nComment 3.5 L157 Space too much.\nA uthors’ response: Done\nComment 3.6 L163 A space is missing.\nA uthors’ response: Done\nComment 3.7 Figure 2 Please make the order of the records here as in the supplemental figures to be consistent.L185 A space is missing..L187 Please make the order of the records here consistent with Figure S4.\nAuthors’ response: Done\nAuthors’ response: Done\nComment 3.9 L239 Would be nice to add the total length of the record here.\nA uthors’ response: Done\nComment 3.10 Figure 3 Please check for this figure and figures S9, S13, S14, S15, S16 that they have a consistent labelling of the y-axes. Here for example it is just written anomaly but it should be temperature anomaly. Furthermore, check all these figures for consisten way of labelling and so on. Make them all in the same design. Make the y-axis at panel b as in panel a until -2. L256 a in parantheses. L266 Small c.L266 a in parantheses;\nTable 1 L311 add Norway Sweden.\nAuthors’ response: Done\nComment 3.11 L423 Is there a hypothesis what is the forcing of this multi-centannial internal AMOC variability? If yes this would be an aspect to add and interesting for the reader.\nA uthors’ response: Done\nComment 3.12 Table 1 L311 add Norway Sweden.\nA uthors’ response: Done\nComment 3.13 L474 replace archives with proxy records\nA uthors’ response: Done\nComment 3.14 Table 2 Please make this column “climate varaible” a bit wider that the e of temperature is not in the next line. Please give two digets here as well (lat of diss mere). Age columne As well here. Delet the digets. They make an impression of a very high age precision, which is not the case. Just give years. I think 12 as resolution is enough. These four digets make an impression of a very high precision, which is not the case if also the dating uncertainty is 12 years. Please delet the four digets.d180 Write the d as greek letter and the 18 in super script.\nAuthors’ response: Done\nComment 3.15 L481 remove . The six transient model simulations used in the analysis are\nA uthors’ response: Done\nComment 3.16 L482 listed in Table 3\nA uthors’ response: Done\nComment 3.17 L483 you mean You mean gases,or?\nComment 3.18 Table 3 You mean ka?Due to consistency give it in years as well.\nA uthors’ response: Done\nComment 3.20 L524 add years after 10000\nA uthors’ response: Done\nComment 3.21 L637 Please also add here, where this person is located.\nA uthors’ response: Done\nComment 3.22 Adapt adresses to main manuscript.\nA uthors’ response: Done\nComment 3.23 Table S1 : Make the font in the heading row bold to be consistent with all the other tables.\nA uthors’ response: Done\nComment 3.24 Table S2 and S3 add Norway and Sweden here, to be consistent with the main manuscript.\nA uthors’ response: Done\nComment 3.25 Norhern Europe, Central Europe, and Southern Europe could be bigger in Figs S1-S3\nAAut hboitr ms’o rrees psopnascee: Dbeontwe een the headings of the panels and the pictures in Figs S4-S7\nComment 3.26 Fig S8 A space too much, replace main by manuscript\nA bit more space between the headings of the panels and the pictures in Figs S4-S7\nA uthors’ response: Done\nComment 3.27 add Norway and Sweden in the captions of Figs S11 and S12\nAuthors’ response: Done\nComment 3.28 Fig S13 In the figure caption (a) and so on should be bold as in the main manuskript.\nA uthors’ response: Done\nComment 3.29 Fig S15 and S16 remove a and Font here a bit smaller for the figure legend. In the figure caption (a) and so on should be bold\nA uthors’ response: Done\nx-axis represents the age before present (in years), while the y-axis indicates temperature in degrees Celsius (°C). Each panel corresponds to a specific site with its name labelled at the top.\nA uthors’ response: Done\nComment 3.31 FigS18 The reanalysis panel should be at the top of this figure to be consistent with the other figures.\nAuthors’ response: Done\n17"}, {"round": 3, "reviewer_comments": {"reviewer_2": "I thank the authors for their – once again – thorough and constructive replies and find the manuscript much improved in terms of robustness. I only have a few last minor comments, after which I would recommend the paper for publication. This will be a nice addition to the literature on impacts of low-frequency climate variability.\n- Given the many caveats of model–data comparison now also discussed in the manuscript, I think that it would be justified to be slightly more cautious in the title, e.g., by changing “drives additional warming” to “can” or “could” drive additional warming.\n- The Greenland record is still missing from Figs. S4, S6, S7, S8, S19 and Table S2.\n- The new discussion about the amplitude of the multi-centennial variability (L188-L200) currently focuses on the underestimation of the amplitude in the reanalysis. While this is interesting to note, in my view, the main point here should be the evaluation of the amplitude of the IPSL model variability. Given that its ratio compared to all proxy records is also >=1 (Table S2), it would be good to add a statement that the model variability could be overestimated and that, in any case, it appears to be an upper-end estimate.\n- This should then also be reflected in the abstract at L43: “by approximately 30%” -> “by up to 30%”"}, "author_response": "Point-by-point response to reviewer’s comments on manuscript entitled ‘Multi-centennial internal variability in the North Atlantic could drive additional warming over Europe’\nReviewer #2 (Remarks to the Author)\nI thank the authors for their – once again – thorough and constructive replies and find the manuscript much improved in terms of robustness. I only have a few last minor comments, after which I would recommend the paper for publication. This will be a nice addition to the literature on impacts of low-frequency climate variability.\nWe sincerely thank you for the additional feedback. Addressing these points has helped us more clearly position our contribution and ensure that readers can better understand the scope and reliability of our results. We appreciate your thoughtful comments and believe the revisions have strengthened the manuscript.\nComment 2.1 - Given the many caveats of model–data comparison now also discussed in the manuscript, I think that it would be justified to be slightly more cautious in the title, e.g., by changing “drives additional warming” to “can” or “could” drive additional warming.\nAuthors’ response: The titles is modified as per suggestion, as follows:\n“Multi-centennial internal variability in the North Atlantic could drive additional warming over Europe”\nComment 2.2 - The Greenland record is still missing from Figs. S4, S6, S7, S8, S19 and Table S2.\nAuthors’ response: We thank the reviewer for pointing this out. The Greenland record is included only in Figure 2, where it is used to assess whether the spectral density of temperature variability is comparable to that of other proxy records. It is intentionally not included in those figures because it lies outside the geographical domain considered in this study, which focuses on Europe (10°W–50°E, 35°N–70°N), and is therefore not relevant for the corresponding regional comparisons\nComment 2.3 - The new discussion about the amplitude of the multi-centennial variability (L188-L200) currently focuses on the underestimation of the amplitude in the reanalysis. While this is interesting to note, in my view, the main point here should be the evaluation of the amplitude of the IPSL model variability. Given that its ratio compared to all proxy records is also >=1 (Table S2), it would be good to add a statement that the model variability could be overestimated and that, in any case, it appears to be an upper-end estimate.\nAuthors’ response: We thank the reviewer for this insightful comment. We agree that the key outcome of this comparison is not only the underestimation of variability in the reanalysis, but also the implication that IPSL-CM6A-LR likely represents an upper-end estimate of multi-centennial variability. We have revised the discussion to more explicitly emphasize this point, as follows:\n“While the simulations reproduce the timing and dominant spatial scales of common variability reasonably well, the amplitude of coherent variability is generally weaker in the reanalysis than in both the model simulations and the proxy records. When quantified in temperature units, IPSL-CM6A-LR exhibits larger amplitudes than the reanalysis across all regions, with model-to-reanalysis standard deviation ratios of approximately 4-6 (Supplementary Table S2). In contrast, comparisons between IPSL-CM6A-LR simulations and proxy records yield lower ratios, mostly between ~1 and 3, indicating a more moderate model overestimation relative to proxies. This implies that IPSL-CM6A-LR provides an upper-end estimate of low-frequency temperature variability, rather than a conservative one.”\nComment 2.4 - This should then also be reflected in the abstract at L43: “by approximately 30%” -> “by up to 30%”\nAuthors’ response: In the revised manuscript, we have replaced “by approximately 30%” by “by up to 30%”."}]

Decision letter