the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Apr 2023
Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution
Sanne Muis
Sönke Dangendorf
Thomas Wahl
Julius Oelsmann
Stefanie Heinicke
Katja Frieler
Matthias Mengel
Abstract. Rising seas are a threat for human and natural systems along coastlines. The relation between global warming and sea-level rise is established, but the quantification of impacts of historical sea-level rise on a global scale is largely absent. To foster such quantification, we here present a reconstruction of historical hourly (1979–2015) and monthly (1900–2015) coastal water levels and a corresponding counterfactual without long-term trends in sea level. The dataset pair allows for impact attribution studies that quantify the contribution of sea level rise to observed changes in coastal systems following the definition of the Intergovernmental Panel on Climate Change (IPCC). Impacts are ultimately caused by water levels that are relative to the local land height, which makes the inclusion of vertical land motion a necessary step. Also, many impacts are driven by sub-daily extreme water levels. To capture these aspects, the factual data combines reconstructed geocentric sea level on a monthly time scale since 1900, vertical land motion since 1900 and hourly storm-tide variations since 1979. The inclusion of observation-based vertical land motion brings the trends of the combined dataset closer to tide gauge records in most cases, but outliers remain. Daily maximum water levels get in closer agreement with tide gauges through the inclusion of intra-annual ocean density variations. The counterfactual data is derived from the factual data through subtraction of the quadratic trend. The dataset is made available openly through the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).
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Simon Treu et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2023-112', Anonymous Referee #1, 12 May 2023
GENERAL COMMENTS
The submitted manuscript presents a compelling study on global sea level, considering various and diverse datasets. Additionally, the authors have generated and provided a counterfactual dataset that excludes the sea level trend. The work is highly interesting and relevant to the journal to which it has been submitted. I recommend its acceptance and publication following a minor review.
SPECIFIC COMMENTS
1- The introductory chapter is fine, but lines 94-99 should be relocated to the methods section.
2- It would be beneficial to provide direct links to the datasets used in this study, including CoDEC, HR, VLM, GESLA-2, and altimetry. Currently, the Data and Code Availability section only mentions codes and counterfactual data. Could you please clarify if HR is still available upon request by contacting Sonke Dangendorf? Additionally, it would be helpful to explain the process for accessing all the datasets easily.
3- Line 110 is unclear to me regarding the meaning of "adjust it for residual VLM" I would appreciate further clarification on how the global geocentric sea level was precisely obtained. Is the procedure explained in lines 175-181? Please provide more information to clarify this point.
4- Could you please provide an explanation and justification for using the GESLA-2 dataset in this study, despite the availability of GESLA-3 since November 2021? Is there any potential impact on the results due to this choice? Furthermore, there appears to be a discrepancy in the references provided for the GESLA dataset. Initially, Muis et al. 2020 is mentioned (line 128), while later, Woodworth et al. 2016 is referenced (line 160) for the same dataset. Could you clarify why two different references are used for the same dataset? Lastly, it would be beneficial to include the expanded form of the GESLA acronym, similar to how you later provide the expanded form for PSMSL.
5- Line 165: "with at least one year of observations in the interval 1993-2015" I'm still not convinced. Is this truly sufficient to allow for a valid alignment? Please provide further information on this matter.
6- Lines 183-184: This is repetitive. Please double-check and remove any duplicated statements in the Materials and Methods chapter, including line 199 and 207.
7- Line 185: Is it truly appropriate to extrapolate VLMs all the way back to 1900? Wouldn't this introduce a considerable margin of error and oversimplify the analysis? While this approach may be suitable for GIA and geological components, VLMs entail more intricate considerations. How did you address the substantial non-linear effects (both spatially and temporally) caused by underground fluid exploitation from WWII to the 1990s?
8- Line 193: "We thus expect that it depends on the location which product performs better", Could you please provide further clarification or explanation regarding the basis for this expectation?
9- Lines 208-210: This sentence appears to be quite complex. Would it be beneficial to use acronyms for each byproduct or perhaps express the information in the form of a mathematical equation?
10- The provided link (2) directs to the AVISO website homepage, which does not provide information about a specific altimetry product. Furthermore, the link (3) leads to a product that is no longer accessible on the CMEMS website. It is unclear which altimetry product has been utilized for this analysis. If a DUACS L4 product from CMEMS or C3S was employed, it should be clearly indicated throughout the text instead of referencing AVISO, as AVISO is no longer responsible for distribution.
11- Counterfactual water levels: I would appreciate further elaboration on the rationale behind choosing a quadratic fit for detrending the series. Could you provide more details on why this specific model was selected and whether it effectively removes the trend and accounts for nonlinearities? Furthermore, in line 220, it is mentioned that the effects of nonlinearity "can be expected to be minor." Was any testing conducted to assess the accuracy of trend removal after applying the quadratic fit? Lastly, in line 398, you clarify that the quadratic trend estimates do not account for the non-linear behavior discussed in Dangendorf et al. 2019 (but also Slangen et al. 2016 and Frederikse et al. 2020 should be taken into account and referenced). Given this, is your statement "we expect our trend estimation to largely exclude natural variability…" sufficiently strong and reliable?
12- Line 238: In addition to referencing "Fig. 2a" and before "Fig. 2c," it is necessary to include and briefly explain Fig. 2b.
13- Lines 308-322: It is advisable, in my opinion, to transfer a substantial portion of the information in this paragraph to the methods section. By doing so, the procedural details can be appropriately placed, and the density of this paragraph can be reduced.
14- Lines 319-321: Could you please provide more explanation as to why specifically the 18 highest values were selected? Do these values correspond to the >99th percentile? It would be beneficial to present and clarify this information in a more explicit manner.
15- Lines 355-358: It seems that this paragraph is redundant and lacks a clear purpose. It partially repeats information from the methods section and contains elements that would be more suitable for the discussion or conclusions. It might be necessary to either remove this paragraph or rephrase it to ensure its coherence and relevance within the context of the text.
16- Lines 369-375: This paragraph appears to be misplaced in the discussion section. It would be more suitable to relocate it to either the methods section or the introduction, where the issue of VLM is first introduced and explained.
17- Lines 385-389: This paragraph seems to duplicate information that has already been discussed in the methods section. It is unclear why it is repeated in this particular location.
18- Figure 1: Due to the current resolution, it is challenging to discern the three distinct symbols. To enhance clarity, it is recommended to either enlarge the map or incorporate a color code in addition to the symbology.
19- Figure 2: The meaning of the numbers on the x-axis is not apparent. Could you please specify whether they represent a progressive pure number of TGs considered? It would be beneficial to provide this clarification either in the figure itself or in the caption. The same clarification should also be provided for figures 2 and 5 in relation to the x-axis.
20- Figure 3: The use of blue, black, pink, and red colors together is not considered colorblind friendly, which can pose accessibility challenges. To ensure inclusivity, it is advisable to utilize a different color palette or employ different line styles (such as solid, dash-dotted, dotted) to differentiate the time series. It is worth noting that the HR time series already meets the criteria for colorblind-friendly representation.
TECHNICAL CORRECTIONS
- The usage of terms such as 'sea-level rise' and 'sea level rise' is inconsistent throughout the manuscript. The same inconsistency applies to terms like 'sea level change,' 'sea level variability,' and 'GESLA2.' I recommend using hyphens consistently in all of these terms. Further, there is inconsistency among 'water levels' and 'waterlevels', and 'Figure' with 'Fig.'. Please ensure the homogeneity of the terms used.
- Line 36: remove a round bracket.
- double space found at line 136, 194, 201, 346, 366
- line 180: "gauges.We" space needed.
- RSL has never been specified, do that at the first term appearance (should be line 34).
- line 268: Tanzania
Citation: https://doi.org/10.5194/essd-2023-112-RC1 -
RC2: 'Comment on essd-2023-112', Anonymous Referee #2, 05 Jun 2023
GENERAL COMMENTS
The ms shows a new analysis of global sea level using different data sets. The core of the study is the counterfactual analysis to exclude long-term trends in sea level. In my opinion, the study is interesting and valuable of publication after major revision. See comments below.
SPECIFIC COMMENTS
1 – Introduction. Lines 95-101. I suggest the Authors to provide link to data sets used in the study to facilitate readers in understanding the work done and possibly to replicate the analysis.
2 – Material and methods. Line 111. Specifies how geocentric water levels are calculated with the probabilistic VLM. This is a relevant point for the analysis and may affect results.
3 – Tide gauge data set. Line 163. I think it is risky to interpolate the monthly averages of tide records to hourly resolution. The authors should provide an example of this interpolation with relative uncertainties to make the reader understand the reliability of the method used.
Line 165. Probably “at least one year of observations in the interval 1993-2015" is not enough to get a valid alignment. Again, authors should provide an example of this interpolation with relative uncertainties.
4 – Factual water levels. Line 185-181. Which model did the author adopt to estimate the long-term (secular) effect of GIA?
Concerning vertical land motion, did the authors evaluate the effects of large earthquakes on the sea level? (e.g., Indonesia 2004; Japan, 2011, etc.).
5 – Lines 187-210. Text is hardly understandable. I suggest the authors to make it more linear also through the use of a figure with a flowchart.
6 - Line 220, the effects of nonlinearity "can be expected to be minor." What do you mean by “minor”?
7 – Long term sea level trends. Line 246-247. RMSE might have a bias, I'd suggest a statistical test comparing the distributions of the samples. The difference is just 0.23 cm: please explain if there is a real improvement from this analysis.
8 – Intra-annual variability. Line 264. I suggest doing a spectral analysis alongside fig.3 to see any anomalies.
9- Lines 319-321: how were the 18 points selected?
10 – Extreme water levels. Line 321. Since the average value has been removed, the 1% can be high. What value does the surge component correspond to?
11 – Discussion. Line 363-371. It is unclear here whether the VLM calculated from the geodetic data has been added to the GIA value. The GNSS stations record the VLM which also includes the GIA, obviously.
FIGURES
Figure 2, 4 and 5. Hardly readable. I suggest using thicker strokes and larger dots.
Figure 2 and 5. Please, explain the meaning of the numbers on the x-axis.
Figure 3. How do you explain the difference in the data for Miami?
Citation: https://doi.org/10.5194/essd-2023-112-RC2 -
AC1: 'Comment on essd-2023-112', Simon Treu, 02 Oct 2023
Dear Dr Simona Simoncelli,
We thank you and the reviewers for the time you invested in providing us with constructive comments on the manuscript. We agree with the points raised and feel that the manuscript has benefitted from the suggested changes.
We have made significant improvements to our manuscript by:
- Transitioning from GESLA-2 to GESLA-3 standards.
- Undertaking comprehensive revisions for a more detailed methodology description.
- Incorporating extensive sensitivity checks to ensure robustness.
- Refining the content to eliminate redundancy.
- Updating the "Code and data availability" section with links to all datasets used.
Please find a detailed response to each of the comments made by the reviewers attached.
We thank you for your consideration of this manuscript for publication in Environmental Research Letters.
Yours sincerely,
Simon Treu
Simon Treu et al.
Data sets
Water levels at tide gauges from: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution S. Treu, S. Muis, S. Dangendorf, T. Wahl, J. Oelsmann, S. Heinicke, K. Frieler, and M. Mengel https://doi.org/10.5281/zenodo.7771386
Model code and software
Source code of: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution Simon Treu https://doi.org/10.5281/zenodo.7771501
Simon Treu et al.
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