the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Jun 2024
Testing the reliability of global surface temperature reconstructions of the last glacial cycle
Abstract. Reconstructing past variations of the global mean surface temperature is used to characterise the Earth system response to perturbations as well as validate Earth system simulations. Reconstructing GMST beyond the instrumental period relies on algorithms aggregating local proxy temperature records. Here, we propose to establish standards for the evaluation of the performance of such reconstruction algorithms. Our framework relies on pseudo-proxy experiments. That is, we test the ability of the algorithm to reconstruct a simulated GMST, using artificially generated proxy data created from the same simulation. We apply the framework to an adapted version of the GMST reconstruction algorithm used in Snyder (2016), and the synthesis of marine proxy records for temperature of the last 130 kyr from Jonkers et al. (2020). We use an ensemble of 4 transient simulations of the last glacial cycle or the last 25 kyr for the pseudo-proxy experiments.
We find the algorithm to be able to reconstruct timescales longer than 4 kyr over the last 25 kyr. However, beyond 40 kyr BP, age uncertainty limits the algorithm capability to timescales longer than 15 kyr. The main sources of uncertainty are a factor, that rescales near global mean sea surface temperatures to GMST, the proxy measurement, the specific set of record locations, and potential seasonal bias. Increasing the number of records significantly reduces all sources of uncertainty but the scaling. We also show that a trade-off exists between the inclusion of a large number of records, which reduces the uncertainty on long time scales, and of only records with low age uncertainty, high accumulation rate, and high resolution, which improves the reconstruction of the short timescales.
Finally, the method and the quantitative results presented here can serve as a basis for future evaluations of reconstructions. We also suggest future avenues to improve reconstruction algorithms and discuss the key limitations arising from the proxy data properties.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1387', Bryan C. Lougheed, 17 Jun 2024
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AC1: 'Reply on RC1', Jean-Philippe Baudouin, 11 Jul 2024
Preliminary reply to the comments of the first reviewer.
Before replying in detail to the comments by Bryan C. Lougheed, we would like to clarify a few essential points that our study may have failed to clearly convey.
First, we do not compare the reconstruction data published in Snyder 2016 with synthetic, model-based GMST reconstructions. Instead, the goal of the study is to evaluate the methodology itself, at the base of the reconstruction. We consider therefore only one reconstruction methodology (also called algorithm in the study). The methodology used in our study includes minor changes from the one used by Snyder 2016, as explained in Section 3.1.2. This adaptation of the algorithm has a negligible influence on our results.
The core of our analysis is based on pseudo-proxy experiments. For this, synthetic timeseries (i.e. pseudo-proxy records) are created from climate model data. These timeseries are specific to a climate simulation. The reconstruction algorithm is applied to the synthetic timeseries to produce an estimate of the simulated GMST. This estimation is then compared with the GMST of the simulation, which we can directly compute from the raw model output. In this pseudo-proxy framework, the true GMST is known (it is given by the GMST of the climate simulation output), and we can quantify how different our estimation is from it. We draw our results from this comparison: the estimated GMST can deviate from the simulated GMST due to the perturbations that were added during the construction of the synthetic pseudo-proxy timeseries (e.g., random noise due to measurement uncertainty, or smoothing to represent bioturbation), or due to the way the reconstruction algorithm estimates the GMST from the synthetic timeseries. Some of these results can, with caution, be extrapolated to the real reconstruction published in Snyder 2016 as well as other studies using a similar methodology to reconstruct the GMST (e.g., Friedrich & Timmermann, 2018).
We understand that the use of a slightly different reconstruction algorithm and a new SST proxy database could confuse readers. They can indeed ask: why not use the same database and algorithm? This would indeed enable a stricto sensu evaluation of the reconstruction algorithm published in Snyder 2016. It is however not possible to compute the synthetic model-based timeseries with the database published by Snyder 2016 the same way as for the PalMod database. The data published by Snyder 2016 misses depths and age ensembles, which are needed to account for bioturbation (from accumulation rates) and age uncertainties in the pseudo-proxy records. In particular, simply sampling an age value given the age of a measurement and the uncertainty of the age (Snyder supposed a uniform 5kyr standard deviation) is not an option. This would lead to a break of the stratigraphic principle, stating that ages should always increase with depth. As we show that bioturbation and age uncertainty are two major processes influencing the accuracy of the reconstructed GMST, we cannot use the Snyder database to perform our pseudo-proxy experiments.
We hope this short reply can help readers to better understand our study. We will respond in more detail to all comments by Bryan C. Lougheed in a subsequent reply. We will also consider a more careful framing of our study in a future version of our manuscript to reduce the risk of readers misunderstanding the purpose and methodology of our paper.
Citation: https://doi.org/10.5194/egusphere-2024-1387-AC1 -
RC2: 'Reply on AC1', Bryan C. Lougheed, 12 Jul 2024
Dear authors,
Thank you for your reply in the discussion forum, which has helped me and will also help other readers/reviewers.
It has become clearer to me now that your paper is indeed not data/model comparison, but seeks to calculate GMST from a climate model in two different ways:
(1) GMST directly from a climate model
(2) From the same climate model, create synthetic sediment core records at known core locations, and calculate GMST from those synthetic core records using Snyder GMST.
Subsequently, you compare the two GMSTs that you have calculated using the above two methods, whereby the second method mimics the current state-of-the-art.
If I understand correctly, you only use real-world data to provide realistic age-depth information (i.e. sedimentation rates) for the synthetic core records, which makes sense.
I did not get the above at first reading of the paper. Some revisions leading to a more concise and shorter introduction text, as well as the use of clear and consistent terms would help readers similar to myself better understand the experimental design. Perhaps it would also be useful to include a flowchart as a figure? I have attached an example under the big blue paperclip icon. Hopefully it is a correct summary of the experimental design!
Kind regards,
Bryan
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AC3: 'Reply on RC2', Jean-Philippe Baudouin, 25 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1387/egusphere-2024-1387-AC3-supplement.pdf
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AC3: 'Reply on RC2', Jean-Philippe Baudouin, 25 Sep 2024
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RC2: 'Reply on AC1', Bryan C. Lougheed, 12 Jul 2024
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AC1: 'Reply on RC1', Jean-Philippe Baudouin, 11 Jul 2024
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RC3: 'Comment on egusphere-2024-1387', Kaustubh Thirumalai, 08 Aug 2024
Review of Baudouin et al. 2024
Kaustubh Thirumalai University of Arizona
Baudouin et al. employ pseudo-proxy experiments to test the sensitivity of global mean surface temperature (GMST) reconstructions to paleoclimate proxy uncertainty using output from several transient climate simulations over the last glacial cycle. They utilize the sedproxy forward model to distort temperature timeseries from the simulations in quasi-realistic ways to mimic the processes involved in the preservation and extraction of proxy signals from paleoclimate sensors. Focusing on a recent and comprehensive collation of sea-surface temperature (SST) records over the past 130 kyrs (Jonkers et al. 2020—J20), the authors perform several statistical resampling experiments on the simulated SSTs, parsed by various metadata parameters within the J20 dataset (number of records, proxy type, location, etc.) Their main aim is to assess the sensitivity of the uncertainty in reconstructing GMSTs over the past 130 kyr and the role of each of the investigated proxy metadata parameters. Broadly, the authors conclude that whereas orbital-scale variability of GMST is accurately retrievable over this time interval, centennial-to-millennial scale GMST reconstructions are “unreliable,” primarily due to age-model uncertainty and the lack of detailed site-based sedimentological parameters. Overall, I enjoyed reading this manuscript and found their analysis and discussion convincing. I believe that this work presents an advance to our sub-discipline. Below, I detail some major and minor comments/suggestions to improve the scope of the authors’ work and potentially increase the accessibility of their manuscript.
Major Points
- Bioturbation mixing depth parameterization: The authors conclude that “Our results show also the existence of a trade-off between the inclusion of a large number of records, which overall reduces the uncertainty, and of only the highest quality records (low age uncertainty, high accumulation rate, high resolution), which improves the reconstruction of the short timescale” where the latter refers to reconstructions being relatively free from bioturbation- and age-model-related smoothening to sufficiently preserve short-timescale (or multicentennial-to-millennial) climatic signals. The manuscript (Table 1 and Figure 2 caption) suggests that the authors used a constant (presumably?) sediment mixed-layer depth (Table 1 only indicates ‘bioturbation’ and would benefit from more explicit details about what is being parameterized) of 10 cm. In my opinion, and according to my cursory assessment of global bioturbation rates (I realize that Dolman & Laepple, 2018 argue otherwise—but I’d like to see the numbers) of the data presented in Teal et al. (2010)—10 cm is far too high for average global mixing depths, particularly given J20’s bias towards tropical and near-coastal proxy locations. I would like to see how a value of, for example, 5 cm would perform for the Full PP and related experiments. I understand that the authors have attempted to parse the sensitivity of ‘age uncertainty’ versus ‘bioturbation’ and other associated parameters in their analysis, but this does not address the entire hierarchy of choices with a lower bioturbation rate. If feasible, I’d recommend that the authors perform such an experiment (Full PP with reduced bioturbation rate) and check whether more high-frequency information is retained in the associated spectra.
- Clarity regarding the use+utility of J20: Unless I am mistaken (which is highly likely), the authors do not actually use or show the GMST calculated (using any algorithm) from the reconstructions collated in J20. They merely use the metadata of proxy parameters within J20 as a framework for their pseudo-proxy experiments. The clarity of the manuscript would be improved if the authors were more upfront about this aspect in their abstract and introduction. On the other hand, this also leads me to question this omission as potential comparisons between reconstructed (from data), simulated, and resampled GMST calculations would be highly interesting. However, I recognize that this may be beyond the scope of this work—accordingly, I feel that the authors should preface this aspect and consider using the term metadata in their abstract and text. I think that the title of the manuscript should include/reflect ‘sensitivity/uncertainty experiments’ and/or ‘pseudo-proxy experiments’ because in its current form, without comparing simulated and actually reconstructed GMST, I do not think the authors can claim to test the ‘reliability the reliability of global surface temperature reconstructions of the last glacial cycle’. Rather, they are testing the reliability of the methodologies used to create global mean reconstructions… hence I feel that a title revision is needed.
- Inclusion of ‘Full PP at random locations’: It appears that the authors do not show any results from this experiment, yet it plays an important role in their analysis (see, e.g., Lines 335–340: “In addition, location resampling and latitude band configurations, which aim to account for it, are not large enough to cover the bias. Yet, the pseudo-proxy experiments using random proxy locations can reproduce the simulated MSST.”) I recommend that the authors make a plot showing results from this experiment and to be more quantitative/precise regarding the ability or lack thereof of these simulations to reproduce simulated mean SSTs.
- Attribution of a specific set of J20 locations as a significant bias: Based on the last point, the authors state that “Therefore, the bias is caused by the specific set of locations in the J20 dataset: there is an over-representation of regions with strong LGM cooling”. Whereas this assessment may be accurate, I do not find the regions that the authors identify to be a convincing explanation (e.g., NW Atlantic/Kurushio extension)—instead, it seems to me that this is a bias related to the relative proximity of core locations to continents—where land-based cooling strongly impacts these sites as opposed to open-ocean marine conditions. Is it possible for the authors to combine inferences from the ‘Full PP at random locations’ or another sensitivity experiment to test this possibility?
Minor Suggestions
- Line 81: “…needed to develop our evaluation standards.” Please rephrase.
- Lines 91–93: Are there only 7 (112–105) unspecified datasets? Figure 1 says otherwise—please clarify.
- Lines 162–163: Please consider adding more information to contextualize why the following steps are being performed. This would be a great spot to clarify the involvement/extent of usage (or lack thereof) of the actual records within J20.
- Lines 236–237: 0.26 K and 0.23 K seem to be exceedingly low values for analytical uncertainty. Does this take into account sampling uncertainty (see e.g. Thirumalai et al. 2013) which is the uncertainty that foraminiferal shells (with lifespans of a ~month) would have grown at different points of time within the sampling interval, and thus will have uncertainty in reconstructing the ‘interval mean’? If sedproxy takes this into account, it would be good to mention this aspect.
- Lines 245–246: Have you considered performing a depth-sensitivity test? i.e. instead of the uppermost grid location, what about the integration of the top 50 m—which is a more realistic scenario for the proxy integration of temperature signals for the chosen sensors. Perhaps this also might explain the cool bias?
- General comment on Figures: Where there are many lines depicted in figures, it is very difficult to parse the colors of each line (especially on the spectral plots) and to associate them with the legend. I would strongly consider using a different backdrop color or thicker lines with a different subplot layout to more cleary delineate results from various experiments.
- Discussion and Lines 545–555: The authors should consider discussing the values of scaling utilized in Clark et al. (2024) and how they fit within this portion of the discussion.
References
Teal, L. R., Bulling, M. T., Parker, E. R., & Solan, M. (2010). Global patterns of bioturbation intensity and mixed depth of marine soft sediments. Aquatic Biology, 2(3), 207–218. https://doi.org/10.3354/ab00052
Thirumalai, K., Partin, J. W., Jackson, C. S., & Quinn, T. M. (2013). Statistical constraints on El Niño Southern Oscillation reconstructions using individual foraminifera: A sensitivity analysis. Paleoceanography, 28(3), 401–412. https://doi.org/10.1002/palo.20037
Citation: https://doi.org/10.5194/egusphere-2024-1387-RC3 -
AC2: 'Reply on RC3', Jean-Philippe Baudouin, 25 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1387/egusphere-2024-1387-AC2-supplement.pdf
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1387', Bryan C. Lougheed, 17 Jun 2024
-
AC1: 'Reply on RC1', Jean-Philippe Baudouin, 11 Jul 2024
Preliminary reply to the comments of the first reviewer.
Before replying in detail to the comments by Bryan C. Lougheed, we would like to clarify a few essential points that our study may have failed to clearly convey.
First, we do not compare the reconstruction data published in Snyder 2016 with synthetic, model-based GMST reconstructions. Instead, the goal of the study is to evaluate the methodology itself, at the base of the reconstruction. We consider therefore only one reconstruction methodology (also called algorithm in the study). The methodology used in our study includes minor changes from the one used by Snyder 2016, as explained in Section 3.1.2. This adaptation of the algorithm has a negligible influence on our results.
The core of our analysis is based on pseudo-proxy experiments. For this, synthetic timeseries (i.e. pseudo-proxy records) are created from climate model data. These timeseries are specific to a climate simulation. The reconstruction algorithm is applied to the synthetic timeseries to produce an estimate of the simulated GMST. This estimation is then compared with the GMST of the simulation, which we can directly compute from the raw model output. In this pseudo-proxy framework, the true GMST is known (it is given by the GMST of the climate simulation output), and we can quantify how different our estimation is from it. We draw our results from this comparison: the estimated GMST can deviate from the simulated GMST due to the perturbations that were added during the construction of the synthetic pseudo-proxy timeseries (e.g., random noise due to measurement uncertainty, or smoothing to represent bioturbation), or due to the way the reconstruction algorithm estimates the GMST from the synthetic timeseries. Some of these results can, with caution, be extrapolated to the real reconstruction published in Snyder 2016 as well as other studies using a similar methodology to reconstruct the GMST (e.g., Friedrich & Timmermann, 2018).
We understand that the use of a slightly different reconstruction algorithm and a new SST proxy database could confuse readers. They can indeed ask: why not use the same database and algorithm? This would indeed enable a stricto sensu evaluation of the reconstruction algorithm published in Snyder 2016. It is however not possible to compute the synthetic model-based timeseries with the database published by Snyder 2016 the same way as for the PalMod database. The data published by Snyder 2016 misses depths and age ensembles, which are needed to account for bioturbation (from accumulation rates) and age uncertainties in the pseudo-proxy records. In particular, simply sampling an age value given the age of a measurement and the uncertainty of the age (Snyder supposed a uniform 5kyr standard deviation) is not an option. This would lead to a break of the stratigraphic principle, stating that ages should always increase with depth. As we show that bioturbation and age uncertainty are two major processes influencing the accuracy of the reconstructed GMST, we cannot use the Snyder database to perform our pseudo-proxy experiments.
We hope this short reply can help readers to better understand our study. We will respond in more detail to all comments by Bryan C. Lougheed in a subsequent reply. We will also consider a more careful framing of our study in a future version of our manuscript to reduce the risk of readers misunderstanding the purpose and methodology of our paper.
Citation: https://doi.org/10.5194/egusphere-2024-1387-AC1 -
RC2: 'Reply on AC1', Bryan C. Lougheed, 12 Jul 2024
Dear authors,
Thank you for your reply in the discussion forum, which has helped me and will also help other readers/reviewers.
It has become clearer to me now that your paper is indeed not data/model comparison, but seeks to calculate GMST from a climate model in two different ways:
(1) GMST directly from a climate model
(2) From the same climate model, create synthetic sediment core records at known core locations, and calculate GMST from those synthetic core records using Snyder GMST.
Subsequently, you compare the two GMSTs that you have calculated using the above two methods, whereby the second method mimics the current state-of-the-art.
If I understand correctly, you only use real-world data to provide realistic age-depth information (i.e. sedimentation rates) for the synthetic core records, which makes sense.
I did not get the above at first reading of the paper. Some revisions leading to a more concise and shorter introduction text, as well as the use of clear and consistent terms would help readers similar to myself better understand the experimental design. Perhaps it would also be useful to include a flowchart as a figure? I have attached an example under the big blue paperclip icon. Hopefully it is a correct summary of the experimental design!
Kind regards,
Bryan
-
AC3: 'Reply on RC2', Jean-Philippe Baudouin, 25 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1387/egusphere-2024-1387-AC3-supplement.pdf
-
AC3: 'Reply on RC2', Jean-Philippe Baudouin, 25 Sep 2024
-
RC2: 'Reply on AC1', Bryan C. Lougheed, 12 Jul 2024
-
AC1: 'Reply on RC1', Jean-Philippe Baudouin, 11 Jul 2024
-
RC3: 'Comment on egusphere-2024-1387', Kaustubh Thirumalai, 08 Aug 2024
Review of Baudouin et al. 2024
Kaustubh Thirumalai University of Arizona
Baudouin et al. employ pseudo-proxy experiments to test the sensitivity of global mean surface temperature (GMST) reconstructions to paleoclimate proxy uncertainty using output from several transient climate simulations over the last glacial cycle. They utilize the sedproxy forward model to distort temperature timeseries from the simulations in quasi-realistic ways to mimic the processes involved in the preservation and extraction of proxy signals from paleoclimate sensors. Focusing on a recent and comprehensive collation of sea-surface temperature (SST) records over the past 130 kyrs (Jonkers et al. 2020—J20), the authors perform several statistical resampling experiments on the simulated SSTs, parsed by various metadata parameters within the J20 dataset (number of records, proxy type, location, etc.) Their main aim is to assess the sensitivity of the uncertainty in reconstructing GMSTs over the past 130 kyr and the role of each of the investigated proxy metadata parameters. Broadly, the authors conclude that whereas orbital-scale variability of GMST is accurately retrievable over this time interval, centennial-to-millennial scale GMST reconstructions are “unreliable,” primarily due to age-model uncertainty and the lack of detailed site-based sedimentological parameters. Overall, I enjoyed reading this manuscript and found their analysis and discussion convincing. I believe that this work presents an advance to our sub-discipline. Below, I detail some major and minor comments/suggestions to improve the scope of the authors’ work and potentially increase the accessibility of their manuscript.
Major Points
- Bioturbation mixing depth parameterization: The authors conclude that “Our results show also the existence of a trade-off between the inclusion of a large number of records, which overall reduces the uncertainty, and of only the highest quality records (low age uncertainty, high accumulation rate, high resolution), which improves the reconstruction of the short timescale” where the latter refers to reconstructions being relatively free from bioturbation- and age-model-related smoothening to sufficiently preserve short-timescale (or multicentennial-to-millennial) climatic signals. The manuscript (Table 1 and Figure 2 caption) suggests that the authors used a constant (presumably?) sediment mixed-layer depth (Table 1 only indicates ‘bioturbation’ and would benefit from more explicit details about what is being parameterized) of 10 cm. In my opinion, and according to my cursory assessment of global bioturbation rates (I realize that Dolman & Laepple, 2018 argue otherwise—but I’d like to see the numbers) of the data presented in Teal et al. (2010)—10 cm is far too high for average global mixing depths, particularly given J20’s bias towards tropical and near-coastal proxy locations. I would like to see how a value of, for example, 5 cm would perform for the Full PP and related experiments. I understand that the authors have attempted to parse the sensitivity of ‘age uncertainty’ versus ‘bioturbation’ and other associated parameters in their analysis, but this does not address the entire hierarchy of choices with a lower bioturbation rate. If feasible, I’d recommend that the authors perform such an experiment (Full PP with reduced bioturbation rate) and check whether more high-frequency information is retained in the associated spectra.
- Clarity regarding the use+utility of J20: Unless I am mistaken (which is highly likely), the authors do not actually use or show the GMST calculated (using any algorithm) from the reconstructions collated in J20. They merely use the metadata of proxy parameters within J20 as a framework for their pseudo-proxy experiments. The clarity of the manuscript would be improved if the authors were more upfront about this aspect in their abstract and introduction. On the other hand, this also leads me to question this omission as potential comparisons between reconstructed (from data), simulated, and resampled GMST calculations would be highly interesting. However, I recognize that this may be beyond the scope of this work—accordingly, I feel that the authors should preface this aspect and consider using the term metadata in their abstract and text. I think that the title of the manuscript should include/reflect ‘sensitivity/uncertainty experiments’ and/or ‘pseudo-proxy experiments’ because in its current form, without comparing simulated and actually reconstructed GMST, I do not think the authors can claim to test the ‘reliability the reliability of global surface temperature reconstructions of the last glacial cycle’. Rather, they are testing the reliability of the methodologies used to create global mean reconstructions… hence I feel that a title revision is needed.
- Inclusion of ‘Full PP at random locations’: It appears that the authors do not show any results from this experiment, yet it plays an important role in their analysis (see, e.g., Lines 335–340: “In addition, location resampling and latitude band configurations, which aim to account for it, are not large enough to cover the bias. Yet, the pseudo-proxy experiments using random proxy locations can reproduce the simulated MSST.”) I recommend that the authors make a plot showing results from this experiment and to be more quantitative/precise regarding the ability or lack thereof of these simulations to reproduce simulated mean SSTs.
- Attribution of a specific set of J20 locations as a significant bias: Based on the last point, the authors state that “Therefore, the bias is caused by the specific set of locations in the J20 dataset: there is an over-representation of regions with strong LGM cooling”. Whereas this assessment may be accurate, I do not find the regions that the authors identify to be a convincing explanation (e.g., NW Atlantic/Kurushio extension)—instead, it seems to me that this is a bias related to the relative proximity of core locations to continents—where land-based cooling strongly impacts these sites as opposed to open-ocean marine conditions. Is it possible for the authors to combine inferences from the ‘Full PP at random locations’ or another sensitivity experiment to test this possibility?
Minor Suggestions
- Line 81: “…needed to develop our evaluation standards.” Please rephrase.
- Lines 91–93: Are there only 7 (112–105) unspecified datasets? Figure 1 says otherwise—please clarify.
- Lines 162–163: Please consider adding more information to contextualize why the following steps are being performed. This would be a great spot to clarify the involvement/extent of usage (or lack thereof) of the actual records within J20.
- Lines 236–237: 0.26 K and 0.23 K seem to be exceedingly low values for analytical uncertainty. Does this take into account sampling uncertainty (see e.g. Thirumalai et al. 2013) which is the uncertainty that foraminiferal shells (with lifespans of a ~month) would have grown at different points of time within the sampling interval, and thus will have uncertainty in reconstructing the ‘interval mean’? If sedproxy takes this into account, it would be good to mention this aspect.
- Lines 245–246: Have you considered performing a depth-sensitivity test? i.e. instead of the uppermost grid location, what about the integration of the top 50 m—which is a more realistic scenario for the proxy integration of temperature signals for the chosen sensors. Perhaps this also might explain the cool bias?
- General comment on Figures: Where there are many lines depicted in figures, it is very difficult to parse the colors of each line (especially on the spectral plots) and to associate them with the legend. I would strongly consider using a different backdrop color or thicker lines with a different subplot layout to more cleary delineate results from various experiments.
- Discussion and Lines 545–555: The authors should consider discussing the values of scaling utilized in Clark et al. (2024) and how they fit within this portion of the discussion.
References
Teal, L. R., Bulling, M. T., Parker, E. R., & Solan, M. (2010). Global patterns of bioturbation intensity and mixed depth of marine soft sediments. Aquatic Biology, 2(3), 207–218. https://doi.org/10.3354/ab00052
Thirumalai, K., Partin, J. W., Jackson, C. S., & Quinn, T. M. (2013). Statistical constraints on El Niño Southern Oscillation reconstructions using individual foraminifera: A sensitivity analysis. Paleoceanography, 28(3), 401–412. https://doi.org/10.1002/palo.20037
Citation: https://doi.org/10.5194/egusphere-2024-1387-RC3 -
AC2: 'Reply on RC3', Jean-Philippe Baudouin, 25 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1387/egusphere-2024-1387-AC2-supplement.pdf
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Jean-Philippe Baudouin
Nils Weitzel
Maximilian May
Lukas Jonkers
Andrew M. Dolman
Kira Rehfeld
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1909 KB) - Metadata XML