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the Creative Commons Attribution 4.0 License.
| 29 Aug 2025
Multidecadal behavior of the North Atlantic Oscillation during the last millennium
Abstract. The North Atlantic Oscillation (NAO) is a major source of atmospheric variability in the Northern Hemisphere, affecting temperature, precipitation, and storm tracks across North America and Eurasia. Understanding NAO variability on multidecadal to centennial timescales requires paleo-reconstructions, but previously published reconstructions disagree on the magnitude of low-frequency NAO variability over the last millennium. Paleoclimate proxies for the oxygen and hydrogen isotope composition of meteoric waters have thus far been under-utilized in published NAO reconstructions. Here, we present a new reconstruction of the NAO over the last millennium using the Iso2k database, a collection of globally distributed water isotope-based paleoclimate proxy records. In contrast to recent NAO reconstructions, we find significant multidecadal to centennial scale variability. Critically, however, the strength of the low-frequency signal has not been consistent throughout the last millennium. Isotope-enabled model simulations did not reproduce the low-frequency signal in the NAO reconstructions and thus it may be necessary to account for low-frequency variability when projecting the impacts of the NAO on temperature and precipitation under future climate scenarios.
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RC1: 'Comment on egusphere-2025-4121', Anonymous Referee #1, 24 Sep 2025
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AC1: 'Reply on RC1', Andrew Flaim, 22 Nov 2025
We thank the referee for the thoughtful and constructive comments. The perspective on the definition of the NAO index relative to the reconstruction product was especially helpful, as were the points about ice core age uncertainty. We carefully considered all suggestions, carrying out additional analyses and revising several figures to ensure we could confidently incorporate the new discussion points into a revised manuscript. We describe the changes in more detail below with the main referee comments reproduced in bold and our responses in plain text.
[T]he water isotopes are clearly telling us something useful about NAO variability and its impacts over the NH, but the reconstruction from them ought not be considered an unbiased expression of the NAO alone. Address the issue I have brought up regarding what they are actually reconstructing: the NAO or NAO impacts and how that might affect the frequency domain properties of their result.
The referee raises an interesting point. In fact none of the NAO reconstructions that we compare ours with are actually reconstructions of the NAO atmospheric pressure index itself, but rather reconstructions of NAO-related impacts. We will add discussion to the manuscript clarifying this point. We also partially address this in response to a comment made by referee #2 in which they suggest extracting all the proxy sites from the CESM isotope output and comparing to the spectrum of the proxy reconstruction. This method makes a more explicit comparison between our reconstruction and the atmospheric impacts of the NAO in the model. We will update Figure 7 in the revised manuscript to reflect these changes.
At least mention the issue of accumulated dating errors in ice cores and how that might affect the power spectrum and wavelet spectrum interpretations. I realize this cannot be quantified here, but it really should be noted as something to consider.
We agree with this suggestion and will add text discussing the accumulating age errors and their impact on the spectral features of the reconstruction. Referee #2 also raised several comments regarding ice core age models and uncertainty which we have taken into consideration. We have performed additional analysis focused on the impacts of ice cores with greater age uncertainty and describe the results in the response to referee #2. Those results will also be included as supplementary figures in the revised manuscript.
Citation: https://doi.org/10.5194/egusphere-2025-4121-AC1
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AC1: 'Reply on RC1', Andrew Flaim, 22 Nov 2025
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RC2: 'Reviewer on egusphere-2025-4121', Jesper Sjolte, 10 Oct 2025
Review Flaim et al. CP
The study of Flaim et al. seeks to reconstruct the NAO over the past millennium using water stable isotopes from geological archives. Using a regression-based method including randomization to test dependency on proxy data, the authors produce an ensemble reconstruction. Flaim et al. then correlate the ensemble reconstruction to other reconstructions and examine the dependency of different types of proxy data. The main science question is focused on the multidecal-centennial variance of the NAO reconstruction, which is found not to be reproduced by a millennium-length isotope enabled simulation.
General comments
Having worked for many years on the topic of climate and atmospheric circulation reconstructions using records of water stable isotope records, I welcome new research in this area. With that said I don’t think the premises and treatment of previous work is entirely correct in the study by Flaim et al. Also, I have concerns about some of the proxy records incorporated used in the study due to large uncertainties in the age-scales, which appears not to be taken into account by the authors. Furthermore, the seasonality in proxy data has been researched extensively, but it is not explained how this plays a role in this study. Finally, the study of the multidecal-centennial NAO signal is limited to the NAO record of Flaim et al. and one model run, while I would have expected a comparison with published NAO reconstructions. In summary, I think the study by Flaim et al. needs extensive reworking to be publishable.
My advice is to develop the section on the multidecal-centennial NAO signal and the dependency on choice of proxy records, combined with a comparison with published NAO records and an extended discussion on where the multidecal-centennial signal comes from in proxy-based NAO reconstructions.
Below I provide the details of my main concerns with this study. I do not provide detailed comments on the text.
Age uncertainties:
- I consider the time scale of speleothems, ice cores from Svalbard and Alpine sites too uncertain to be directly incorporated in this type of reconstruction without taking into account age-scale uncertainties. For example, the Svalbard age-depth scale is based on a flow model with a few volcanic horizons for reference (Isaksson et al., 2001). This should inflate the uncertainty envelope back in time considerably.
- For Greenland ice core time-scale pre ~1200 CE an error in time scale has been uncovered after publication and therefore in the inclusion in iso2K database. See e.g., Adolphi & Muscheler (2015). The options are to correct the raw ice data with new dating or shorten reconstruction (I too the latter approach in my work).
Seasonality:
- Proxy data: what is the seasonality of the proxydata used in the reconstruction?
- It appears that all proxydata are assigned to calendar year? (L155). I think it doesn’t make sense to use the approach of Putman & Bowen for proxy data.
- See publication on seasonality, circulation patterns and climate variability: Vinther et al. (2010) and Sjolte et al. (2020).
- The use of the calendar year for Greenland ice core data causes loss of signal.
Impact of changing number of records trough time:
- Type of proxies partly investigated (Fig S5) but not the influence of varying the number of proxy records. What is the performance of the reconstruction using only spanning the full timeframe? This would indicate what the skill is in the earlier part of the reconstruction.
- How does Figure 3 look like for the period prior to the validation period?
Multi-decadal to centennial variability:
- The authors discuss the multi-decadal to centennial variability of other NAO reconstructions, but there is no comparison of different NAO reconstructions. Since you make use of wavelet analysis, cross-wavelet power and wavelet coherence would be appropriate.
- If the focus is multi-decadal to centennial variability the time window for the validation is very short, and I supposed done on annual data with no filtering?
- How does Figure 3 look like for the period prior to the validation period using a decadal filter (e.g., a Gauss filter)?
- It is well-known that models underestimate multi-decadal to centennial variability, in particularly on regional scales (Laepple et al., 2023). Instead of just repeating this, you could investigate where the model underestimates the isotope variability by extracting all the proxy sites from the model output and comparing to the spectrum of the isotope records.
Reference to previous work on the topic and claim that isotope records are underutilized: L12-13 and L102-104:
- I think this claim is not correct. Even if we disregard my own work on the topic this ignores the use of ice core isotope records in previous work by Cook et al. (2002), Ortega et al. (2015), Michel et al. (2020) etc. some also referred to by the Flaim et al.
- I think part of the perceived underutilization could come from some records not being used, as people (myself included) refrain from using these records due to large dating uncertainties (see point on age uncertainties above).
- My work on this topic can be found in (Sjolte et al., 2011, 2018, 2020, 2023, 2025; Tao et al., 2023). I hope you will find this relevant for your study. My reconstructions are available for download. Follow links in publications.
References
Elisabeth Isaksson, Mark Hermanson, Sheila Hicks, Makoto Igarashi, Kokichi Kamiyama, John Moore, Hideaki Motoyama, Derek Muir, Veijo Pohjola, Rein Vaikmäe, Roderik S.W van de Wal, Okitsugu Watanabe, Ice cores from Svalbard––useful archives of past climate and pollution history, Physics and Chemistry of the Earth, Parts A/B/C, Volume 28, Issues 28–32, 2003, Pages 1217-1228, ISSN 1474-7065, https://doi.org/10.1016/j.pce.2003.08.053.(https://www.sciencedirect.com/science/article/pii/S147470650300202X)
Laepple, T., Ziegler, E., Weitzel, N. et al. Regional but not global temperature variability underestimated by climate models at supradecadal timescales. Nat. Geosci. 16, 958–966 (2023). https://doi.org/10.1038/s41561-023-01299-9
Sjolte, J., G. Hoffmann, S. J. Johnsen, B. M. Vinther, V. Masson‐Delmotte, and C. Sturm, Modeling the water isotopes in Greenland precipitation 1959–2001 with the meso‐scale model REMO‐iso, J. Geophys. Res., 116, D18105, doi:10.1029/2010JD015287 2011.
Sjolte, J., Sturm, C., Adolphi, F., Vinther, B. M., Werner, M., Lohmann, G., and Muscheler, R.: Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, Clim. Past, 14, 1179-1194, https://doi.org/10.5194/cp-14-1179-2018, 2018.
Sjolte, J., Adolphi, F., Vinther, B. M., Muscheler, R., Sturm, C., Werner, M., and Lohmann, G.: Seasonal reconstructions coupling ice core data and an isotope-enabled climate model – methodological implications of seasonality, climate modes and selection of proxy data, Clim. Past, 16, 1737–1758, https://doi.org/10.5194/cp-16-1737-2020, 2020.
Sjolte, J., Tao, Q., & Muscheler, R. (2023). Updated gridded reconstruction of sea level pressure, temperature, and precipitation during winter in the North Atlantic region covering 1241-1970 CE [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8328301
Sjolte, J. and Tao, Q.: Climate field reconstructions for the North Atlantic region of annual, seasonal and monthly resolution spanning CE 1241–1970, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-2911, 2025.
Tao, Q. , Sjolte, J. , & Muscheler, R. (2023). Persistent model biases in the spatial variability of winter North Atlantic atmospheric circulation. Geophysical Research Letters, 50, e2023GL105231. https://doi.org/10.1029/2023GL105231
Vinther, B.M., Jones, P.D., Briffa, K.R., Clausen, H.B., Andersen, K.K., Dahl-Jensen, D. and Johnsen, S.J., 2010: Climatic signals in multiple highly resolved stable isotope records from Greenland, Quaternary Science Reviews 29: 522-538.
Citation: https://doi.org/10.5194/egusphere-2025-4121-RC2 -
AC2: 'Reply on RC2', Andrew Flaim, 22 Nov 2025
We thank the referee for the useful points raised in this review. We appreciated the suggestions and took them into careful consideration, including performing some new analyses and modifying existing figures to incorporate these new discussion points into a modified manuscript. We believe the changes, which we describe below, will greatly improve the manuscript. We have reproduced the referee’s main comments in bold, with our responses in plain text.
Age uncertainties:
- I consider the time scale of speleothems, ice cores from Svalbard and Alpine sites too uncertain to be directly incorporated in this type of reconstruction without taking into account age-scale uncertainties. For example, the Svalbard age-depth scale is based on a flow model with a few volcanic horizons for reference (Isaksson et al., 2001). This should inflate the uncertainty envelope back in time considerably.
- For Greenland ice core time-scale pre ~1200 CE an error in time scale has been uncovered after publication and therefore in the inclusion in iso2K database. See e.g., Adolphi & Muscheler (2015). The options are to correct the raw ice data with new dating or shorten reconstruction (I too[k] the latter approach in my work).
Thank you for these comments. We plan to add text to the discussion regarding the potential impacts of age model uncertainty in the proxy data. Our methodological approach relies on the inclusion of a diverse set of proxy types, which is one of the key strengths of large-scale data synthesis efforts. In particular, inclusion of diverse proxy systems permits the use of sites from a range of geographical areas (rather than being biased towards, for example, locations with glaciers and ice sheets). To the referee’s point, we recognize that a downside of this approach is that it makes the reconstruction susceptible to diverse uncertainties associated with multiple proxy types. By design, we restricted our analysis to well-dated records with annual resolution in order to avoid the larger pitfalls of age uncertainty inherent to radiocarbon chronologies and other approaches. The inclusion of the speleothems and flow-modeled ice core records was therefore an explicit choice, and that choice is consistent with many other published studies which also include such records (e.g. Mann et al., (2007), PAGES 2013, PAGES 2019, Falster et al., (2023), etc.). So while other reconstructions may take a different approach of applying more extreme data exclusion protocols and work with a very small number of proxy records, the purpose of this paper is to use the wider-net approach, which is a new and strong contribution to the field.
The diverse set of proxy data in the Iso2k database has the added benefit of limiting the weighting of any single record, so the uncertainty envelope of the resulting reconstruction is not particularly sensitive to the uncertainties of a given age relationship. The spectral biases inherent to a given proxy type or age model are less impactful because their influence is mediated by the information provided by other data, and the common signal can be more effectively identified (Ortega et al., 2015). That, in combination with the relatively well constrained age-depth relationships even in proxies that are not explicitly layer-counted, gives us confidence when incorporating these proxies into the reconstruction. For these reasons we feel the inclusion of speleothems and flow modeled ice core records is well justified.
The referee does raise an interesting point here about the sensitivity of the reconstruction to the age model uncertainty inherent to the records in question. In response to this comment, we tested that sensitivity by removing the non-layer-counted records, both one at a time and all at once, then re-computing the reconstructions, and re-performing the spectral analysis. Of the 94 records in the reconstruction, there were 7 non-layer-counted records included. Removing any one of these does not notably change the power spectrum of the reconstruction, suggesting the uncertainty inherent in those records is not a substantial component of the low-frequency signal in the reconstruction. Removing all 7 of the records does slightly reduce the power of the multidecadal peak in the MTM such that the median of the ensemble falls below the 95% confidence level based on an AR(1) null. The range of the ensemble still shows substantial power above that threshold, however, and the wavelet results also show extensive significant multi-decadal power across the last millennium. It is likely that the reduced multidecadal peak is mainly a function of network size, i.e. removing 7 of the records in the reconstruction.
To that end, in response to this comment as well as Referee #1, we updated Figure 6b to propagate the reconstruction ensemble uncertainty into the MTM analysis. This allows us to evaluate whether the random removal of a portion (15% each time; L163) of the dataset would change the spectral results. Effectively, propagating this uncertainty addresses the issue of whether removing 7+ records could change the results of the reconstruction. Results showed that, for the peak in multidecadal power, some of the ensemble members also fell below the AR(1) 95% confidence level. While this analysis does not change the major results and points of our paper, it does suggest that changes in the strength of multidecadal variability are partially sensitive to the size, coverage, or spatial distribution of the records included in the reconstruction, including the 87 layer-counted records with very little age uncertainty. We therefore consider ensemble size and composition to be an important discussion point that should be added to the paper. As such, we will update Figure 6b in the revised manuscript showing these results, as well as add a supplementary figure showing the impact of removing the non-layer-counted records from the reconstruction. We will also include a supplementary table in the revised manuscript highlighting the non-layer-counted records and describing their age modeling and uncertainty.
We found the information about the Greenland ice core timescale very interesting and appreciate the referee bringing it to our attention. We investigated how this issue may impact the reconstruction, and in doing so we determined that the uncertainty contributed to the reconstruction by the proposed update to the GICC05 timescale is minimal, amounting to about +/- 5 years of age uncertainty in the individual Greenland ice cores between 1000–1200 CE (Sinnl et al., 2022). We will add mention of this uncertainty to the revised manuscript. During the process of revising the manuscript we plan to reach out to the Iso2k ice core team for advice on the chronology of the Greenland records and how those uncertainties are incorporated into the database. We have not seen a solution to this issue yet in other data synthesis products that use the Greenland ice cores, but we will make suggestions to the Iso2k team when the database is updated. We appreciate the referee sharing his knowledge on this topic.
Seasonality:
- Proxy data: what is the seasonality of the proxydata used in the reconstruction?
- It appears that all proxydata are assigned to calendar year? (L155). I think it doesn’t make sense to use the approach of Putman & Bowen for proxy data.
- See publication on seasonality, circulation patterns and climate variability: Vinther et al. (2010) and Sjolte et al. (2020).
- The use of the calendar year for Greenland ice core data causes loss of signal.
The question of seasonal sensitivities is an important one and the referee is right that information is lost when integrating the few subannually-resolved records (3 out of 94 records) to an annual timescale. In our approach, we made the decision to treat all proxy records similarly, so we chose to remain agnostic to the seasonal sensitivities assigned to individual proxy records (either in their original publications or during integration into the Iso2k database, though we note such information is not always available). We rely instead on the annual signal, which is something that can be obtained from all of the records. This approach has good precedent in the literature (e.g., Falster et al., (2023), Ortega et al., (2015), PAGES 2019). Our method does account for seasonality of the NAO itself in that it tests the correlation of each proxy record with the DJF NAO instrumental index and weights the proxy data according to the resulting coefficient. This process inherently incorporates the seasonal sensitivity of the annual data to the winter NAO, with the reconstruction product representing the aggregate isotopic response in that season. We will ensure that the treatment of each record as an annual signal is incorporated into the Discussion of a revised manuscript.
Regarding the seasonality of the Greenland ice cores: We appreciated this referee’s expertise on this front. During the manuscript revision process we will consult with the Iso2k ice core team to ensure that the metadata for the Greenland ice cores cited in Vinther 2010 is correct, in addition to discussing the age model concerns raised above.
Impact of changing number of records t[h]rough time:
- Type of proxies partly investigated (Fig S5) but not the influence of varying the number of proxy records. What is the performance of the reconstruction using only spanning the full timeframe? This would indicate what the skill is in the earlier part of the reconstruction.
- How does Figure 3 look like for the period prior to the validation period?
We did in fact investigate the influence of the variable number of proxy records through time; we found that proxy record availability had minimal impact for a given validation/calibration interval (L126-128). Varying the number of proxies is also implicitly tested using the regional subsets, as well as the noise-padded regional subsets. However this comment is well taken in that additional clarification is needed, so to that end we have generated a new supplementary figure which explicitly quantifies the change in the reconstruction validation metric as the number of available records declines with time. This figure will be included in the supplementary material of the revised manuscript. The updated figure we plan to add that incorporates ensemble size uncertainty into the spectral analysis (see response to the above comment about the non-layer-counted records) will also help to address this concern.
We note that changing the calibration and validation windows does, in essence, influence the number of available proxy records (because not all have complete coverage during the 20th century). Based on this referee comment, we will add additional text to the manuscript to discuss the selection of the calibration/validation windows, which we had extensively tested prior to writing the manuscript but had not included much information about in the current draft. As expected, alternative validation intervals impact the performance in accordance with the change in available data in the calibration interval. For example, calibrating on the earlier portion of the instrumental interval (e.g. 1823–1923 CE) and validating on the later portion (1824–1999 CE) causes the calibration procedure to miss large portions of the shorter records that inform the reconstruction. Therefore we do not feel it is really possible to assess pre-validation interval stats the way the referee is asking, but we agree that additional details are needed on the calibration/validation decisions and we will add those to the text accordingly.
Multi-decadal to centennial variability:
- The authors discuss the multi-decadal to centennial variability of other NAO reconstructions, but there is no comparison of different NAO reconstructions. Since you make use of wavelet analysis, cross-wavelet power and wavelet coherence would be appropriate.
This is a great suggestion. In the revised version of the manuscript we will explore time series spectral and wavelet analyses of other reconstructions.
- If the focus is multi-decadal to centennial variability the time window for the validation is very short, and I supposed done on annual data with no filtering?
Yes, the validation was performed against the DJF instrumental NAO with no filtering.
- How does Figure 3 look like for the period prior to the validation period using a decadal filter (e.g., a Gauss filter)?
We weren’t certain what was being suggested here. We interpreted this question to suggest performing the reconstruction after withholding validation data, applying a decadal low-pass filter to the result and the instrumental index, and then testing the correlation between the low-pass filtered reconstruction and instrumental index in the validation interval. If that is correct, we don’t believe filtering the validation data would give an accurate measure of reconstruction performance at low frequencies for two reasons. First, the degrees of freedom for the overlapping data become quite small because, as discussed in the validation period response above, the availability of validation data is limited. Second, the non-stationarity of low-frequency variability in our reconstruction is a key component of our results (Section 4.3), and although previous studies have suggested minimal low-frequency variability in the instrumental NAO index (e.g. Cook et al., 2019) we believe this may not be fully representative of the spectral characteristics of the atmospheric impacts of the NAO over the last millennium. Still, we acknowledge the implied critique here, which is that the low-pass filtered validation scores may be low because low frequency variability in the instrumental NAO index is weak. We are working on some low-pass validation tests and we will consider including them as a supplementary figure in the revised manuscript.
- It is well-known that models underestimate multi-decadal to centennial variability, in particularly on regional scales (Laepple et al., 2023). Instead of just repeating this, you could investigate where the model underestimates the isotope variability by extracting all the proxy sites from the model output and comparing to the spectrum of the isotope records.
This was a very interesting suggestion and we look forward to incorporating it into the revised version of the manuscript. We will update our spectral analysis of the NAO in CESM by subsampling the CESM precipitation isotope field according to Iso2k proxy locations. We will use this to perform a CPS reconstruction calibrated against the CESM NAO timeseries. Preliminary results from this analysis show that the spectral characteristics of the resulting “reconstruction” are very similar to the CESM NAO, but show slightly stronger decadal variability.
Reference to previous work on the topic and claim that isotope records are underutilized: L12-13 and L102-104:
- I think this claim is not correct. Even if we disregard my own work on the topic this ignores the use of ice core isotope records in previous work by Cook et al. (2002), Ortega et al. (2015), Michel et al. (2020) etc. some also referred to by the Flaim et al.
- I think part of the perceived underutilization could come from some records not being used, as people (myself included) refrain from using these records due to large dating uncertainties (see point on age uncertainties above).
- My work on this topic can be found in (Sjolte et al., 2011, 2018, 2020, 2023, 2025; Tao et al., 2023). I hope you will find this relevant for your study. My reconstructions are available for download. Follow links in publications.
We appreciate the referee’s work on this topic and recognize that some ice cores have been used in previous NAO reconstructions, including the referee’s. However we disagree that this means that isotope proxy records have been extensively utilized overall. Even well-dated, layer-counted archives such as tree ring cellulose δ18O have been largely left out of NAO reconstructions.The wood cellulose isotope records included in our reconstruction are very well dated and underutilized in previously published NAO reconstructions. Additionally, many of the records included in Ortega et al., (2015) and Michel et al., (2020) are speleothems and lake sediments, suggesting the age uncertainty that the referee is highlighting here is not a primary constraint on the use of isotope records in the literature. Finally, only 7 of the 94 isotope records included in our reconstruction are not annually layer counted (i.e. would fall into the category which the referee describes as having large dating uncertainties).
Of the reconstructions in Figure 3, Cook et al. (2002) uses 365 tree ring records and a small but undefined number of ice core records from only two sites in Greenland. Of Ortega et al. (2015)’s 48 records, 5 were Greenland ice core δ18O and one was speleothem δ18O. Michel et al. (2020) use annually resolved records from the Pages2k temperature database and Ortega et al. (2015), but they do not clearly document their final filtered dataset, making it hard to determine how many isotope records were actually included. Although Pages2k contains some isotope-bearing sites like the Greenland ice cores, using the Pages2k database is not equivalent to reconstructing the NAO from isotope data, as the Pages2k database by definition only includes records deemed temperature-sensitive; therefore overlap with our isotope-based reconstruction is limited.
Citation: https://doi.org/10.5194/egusphere-2025-4121-AC2
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This paper seeks to tackle the debate about how the NAO varies at different timescales over the past millennium using a large network of water isotope records. The use of water isotope records for this purpose is new and this paper deserves to be published after due consideration of the issues I bring up.
I think the problem with the debate about high vs. low-frequency variability in the NAO is in part a matter of definition. If we restrict ourselves to the original definition of the NAO by Hurrell, van Loon, Jones, and others, the NAO is simply an atmospheric pressure difference index in the North Atlantic, with little inter-decadal or longer variability indicated as far back as 1824 in CRU instrumental pressure data and back to 1781 in a high-quality extension of the NAO index by Phil Jones. This is clearly indicated by their “flat” (no slope) power spectra shown in Fig. 5b of the Cook et al. (2019) paper. Thus, it should come as no surprise that the Cook et al. (2019) winter NAO index reconstruction, after due consideration taken to match the overall slope of the instrumental data power spectra, should have a “flat” power spectrum as well, as opposed to the substantial “redness” in some other NAO reconstructions (e.g. Ortega et al., 2015).
For this reason, I argue that the NAO reconstruction being presented in this paper (and in Ortega as well), with its substantially greater decadal-to-centennial variability, is not a reconstruction of the NAO atmospheric pressure index itself, but a reconstruction that includes NAO impacts from long-range, persistent, effects on atmospheric circulation and regional climate. The authors know this because it is explicitly stated by them on lines 64-66 of the paper (“Water isotopes provide information about the NAO on broader spatial and temporal scales … because they integrate on basin-wide to hemispheric scales …”). See also lines 79-81. As such, what is presented here is distinctly different from the reconstruction of the NAO index itself. This difference in definition was also reflected in the title of the Hurrell and van Loon (1997) NAO paper “Decadal variations in climate ASSOCIATED with the North Atlantic Oscillation” [my emphasis added]. So the water isotopes are clearly telling us something useful about NAO variability and its impacts over the NH, but the reconstruction from them ought not be considered an unbiased expression of the NAO alone.
Considerable discussion is made about the somewhat intermittent multi-decadal variability indicated in the wavelet spectra. While there is little doubt that this could reflect true variations in the strength of natural forcing at these timescales, no mention is made of the likelihood that the dating of some of the annual water isotope records is very likely to degrade back in time. (Certainly for speleothems, which are never precisely dated, and should only be expected to reflect lower frequency variability.) This is known to be a problem with ice core records in general and these make up the bulk of the longer records used in the NAO reconstruction (see Figs. 1 and 2b). The result will almost certainly be a loss of high-frequency signal and a relative increase of lower-frequency power in the composite reconstruction as the dating errors accumulate and the composite consequently smooths back in time. This is actually suggested in Fig. 2a by the visible reduction in the amplitude of variability before 1700 CE. I also note that the wavelet spectrum of the NAO reconstruction based on glacier ice only (Fig. S5a) shows an almost total loss of power a periods <5 years before 1790 whereas the reconstruction based on better dated wood cellulose records maintain it better. This could reflect a gradual loss of dating fidelity back in time in the ice cores. This said, in no way I am suggesting that the loss of precise dating invalidates the usefulness of the reconstruction at lower frequencies, but it should be acknowledged as another likely contributor to the changing pattern of variability seen in the wavelet spectra.
Given these concerns, in order to recommend publication I would need the authors to: