From the journal Environmental Science: Atmospheres Peer review history

13-Apr-2024

Dear Dr Maishal:

Manuscript ID: EA-ART-03-2024-000030
TITLE: Quantifying of the Drivers and Heterogeneity of Global Total Precipitable Water

Thank you for your submission to Environmental Science: Atmospheres, published by the Royal Society of Chemistry. I sent your manuscript to reviewers and I have now received their reports which are copied below.

After careful evaluation of your manuscript and the reviewers’ reports, I will be pleased to accept your manuscript for publication after revisions.

Please revise your manuscript to fully address the reviewers’ comments. When you submit your revised manuscript please include a point by point response to the reviewers’ comments and highlight the changes you have made. Full details of the files you need to submit are listed at the end of this email.

Please submit your revised manuscript as soon as possible using this link :

*** PLEASE NOTE: This is a two-step process. After clicking on the link, you will be directed to a webpage to confirm. ***

https://mc.manuscriptcentral.com/esatmos?link_removed

(This link goes straight to your account, without the need to log in to the system. For your account security you should not share this link with others.)

Alternatively, you can login to your account (https://mc.manuscriptcentral.com/esatmos) where you will need your case-sensitive USER ID and password.

You should submit your revised manuscript as soon as possible; please note you will receive a series of automatic reminders. If your revisions will take a significant length of time, please contact me. If I do not hear from you, I may withdraw your manuscript from consideration and you will have to resubmit. Any resubmission will receive a new submission date.

The Royal Society of Chemistry requires all submitting authors to provide their ORCID iD when they submit a revised manuscript. This is quick and easy to do as part of the revised manuscript submission process. We will publish this information with the article, and you may choose to have your ORCID record updated automatically with details of the publication.

Please also encourage your co-authors to sign up for their own ORCID account and associate it with their account on our manuscript submission system. For further information see: https://www.rsc.org/journals-books-databases/journal-authors-reviewers/processes-policies/#attribution-id

Environmental Science: Atmospheres strongly encourages authors of research articles to include an ‘Author contributions’ section in their manuscript, for publication in the final article. This should appear immediately above the ‘Conflict of interest’ and ‘Acknowledgement’ sections. I strongly recommend you use CRediT (the Contributor Roles Taxonomy, https://credit.niso.org/) for standardised contribution descriptions. All authors should have agreed to their individual contributions ahead of submission and these should accurately reflect contributions to the work. Please refer to our general author guidelines https://www.rsc.org/journals-books-databases/author-and-reviewer-hub/authors-information/responsibilities/ for more information.

I look forward to receiving your revised manuscript.

Yours sincerely,
Prof. Nønne Prisle
Associate Editor, Environmental Sciences: Atmospheres

************

Reviewer 1

The proposed manuscript analyzes the spatiotemporal variability and trends in Total Precipitable Water (TPW) over the global oceans and examines the causal relationships between TPW and various climate indices using the PCMCI+ algorithm. The study provides insights into the drivers of TPW and the complex interactions between atmospheric moisture content and large-scale climate patterns. The novel application of causal discovery techniques and the global scope make this a valuable contribution to understanding TPW variability and its drivers. I recommend minor revisions before acceptance.

Main questions:

1. The causal discovery analysis using the PCMCI+ algorithm is a key strength of the paper. However, interpreting causal relationships in complex Earth systems is challenging. How confident can we be that the detected causal links represent true physical mechanisms rather than spurious correlations? What additional evidence or physical understanding would strengthen the causal interpretations?
2. The trend analysis uses simple linear regression. Given the complexity of the climate system and potential nonlinearities, would a more sophisticated trend analysis method be more appropriate? How sensitive are the reported trends to the choice of method?
3. The study focuses on causal links between TPW and climate indices. But both TPW and the indices may be influenced by common external forcings like anthropogenic greenhouse gases. To what extent can the detected causal relationships be attributed to internal variability versus external forcing?
4. The causal discovery analysis considers time lags of up to 4 months. Is this long enough to capture slower ocean-atmosphere feedback processes that may be important for TPW variability? What is the justification for the chosen maximum time lag?
5. Uncertainties in the satellite-derived TPW data and reanalysis products used are not discussed in detail. How might observational uncertainties impact the results, especially for trend detection? A more thorough treatment of uncertainties would enhance confidence in the conclusions.
6. The physical mechanisms behind some of the detected causal links are not explored in depth. For example, what processes explain the two-way causal link between TPW and the Western Pacific Index? More discussion of mechanisms would strengthen the interpretation of the causal networks.

Minor comments:

1. The abstract is quite lengthy and detailed. Consider condensing it to focus on the key objectives, methods, main findings, and implications. Some of the specific results can be omitted from the abstract.
2. The background and motivation for the study are well explained in the introduction. However, a clear statement of the research questions or hypotheses being addressed would strengthen this section.
3. The section data and methods provide a good overview of the datasets and the PCMCI+ algorithm used. A few additional details would be helpful, such as specifying the time period of analysis, clarifying how significance was determined for the trends, providing a reference or brief explanation of the k-nearest neighbor approach for CMI, etc.
4. Check that all acronyms are defined at first use (e.g., WHWP in line 370, SO and AO in line 41, SPO in line 292, defined in line 405, line 45 ENSO, etc.).
5. The causal discovery analysis (section 3.4 and 3.5) is a strength of the paper. However, some interpretation of the physical mechanisms behind the detected causal links would be valuable. What do the relationships tell us about the climate system?
6. The conclusions (lines 538-556) provide a good synthesis of the results. The implications of the work for understanding climate change impacts are important and could be expanded upon. What are the next steps for this research?
7. Line 538: "Word" should be "World"
8. Line 171: Define "PC" at first use
9. Line 483: "observe" should be "observed"
10. Line 385: Fix the typo in "Ʈmax" since you have used “T” in line 412 and ‘tau” in line 385. Also in Figure 4.

Reviewer 2

The authors need to thoroughly re-check the references cited in the manuscript.

[This text has been copied from the PDF response to reviewers and does not include any figures, images or special characters.]

RESPONSE TO THE EDITOR REFEREE COMMENTS
Referee: 1
Comments to the Author
The proposed manuscript analyzes the spatiotemporal variability and trends in Total Precipitable Water (TPW) over the global oceans and examines the causal relationships between TPW and various climate indices using the PCMCI+ algorithm. The study provides insights into the drivers of TPW and the complex interactions between atmospheric moisture content and large-scale climate patterns. The novel application of causal discovery techniques and the global scope make this a valuable contribution to understanding TPW variability and its drivers. I recommend minor revisions before acceptance.
Thank you for your valuable time, effort, and insightful comments. I have attended to all suggestions and revised the MS accordingly. Please find the revised MS, Supplementary file, and Author response file herewith. I do hope that the referee will find the revised MS more interesting and recommend a publication in the Environmental Science: Atmospheres journal very soon.
Main questions:
1. The causal discovery analysis using the PCMCI+ algorithm is a key strength of the paper. However, interpreting causal relationships in complex Earth systems is challenging. How confident can we be that the detected causal links represent true physical mechanisms rather than spurious correlations? What additional evidence or physical understanding would strengthen the causal interpretations?
The identified the challenge of interpreting causal relationships within complex Earth systems, particularly when employing computational algorithms such as PCMCI+ for causal discovery. While PCMCI+ provides a valuable framework for identifying potential causal links, distinguishing between true physical mechanisms and spurious correlations requires careful consideration and additional evidence. Primarily, it is essential to acknowledge the inherent limitations of observational data in capturing the full complexity of Earth system dynamics. Observational datasets may be subject to various sources of noise, biases, and uncertainties, which can contribute to the identification of spurious correlations. Therefore, validating the detected causal links against independent datasets or model simulations can help assess their robustness and reliability. Furthermore, incorporating physical mechanisms understanding and domain knowledge into the interpretation of causal relationships is crucial. A deep understanding of the underlying mechanisms governing the processes involved can provide valuable insights into the plausibility of the identified causal links. For example, causal relationships that align with known physical principles, such as the influence of ocean-atmosphere interactions on climate variability, are more likely to represent genuine causal mechanisms. I have already added a few key points about true causality and underlying physical mechanisms in discussion section lines 544-559.
2. The trend analysis uses simple linear regression. Given the complexity of the climate system and potential nonlinearities, would a more sophisticated trend analysis method be more appropriate? How sensitive are the reported trends to the choice of method?
In this analysis, I used linear regression and its capture trend of TPW very well. However, there have many other methods. For example Polynomial Regression in different degree thresholds. Is below

Other regression methods can give different result but better to stick linear regression. I can understand your concern about nonlinearity of TPW.
3. The study focuses on causal links between TPW and climate indices. But both TPW and the indices may be influenced by common external forcings like anthropogenic greenhouse gases. To what extent can the detected causal relationships be attributed to internal variability versus external forcing?
I appreciate the insightful comment regarding our study on the causal relationships between TPW and climate indices. Indeed, both TPW and climate indices are subject to various influences, including external forcing like anthropogenic greenhouse gases, as well as internal variability inherent within the climate system. Internal variability refers to the inherent fluctuations within the climate system that arise from complex interactions between various atmospheric and oceanic processes. These fluctuations can manifest as natural climate oscillations, such as the ENSO, NAO, or the PDO, among others. These internal mechanisms can contribute to short-term variations in TPW and climate indices, independent of external forcing. On the other hand, external forcing, particularly anthropogenic greenhouse gases, exert a long-term influence on the climate system by altering the Earth's energy balance. The increase in greenhouse gas concentrations due to human activities enhances the greenhouse effect, leading to global warming and changes in various climate parameters, including TPW and climate indices. In light of these considerations, disentangling the contributions of internal variability and external forcing to the observed causal relationships presents a significant challenge. While statistical methods can help identify correlations between TPW and climate indices, attributing these correlations to specific drivers requires careful analysis; often involving the use of sophisticated climate modeling approaches and robust uncertainty quantification techniques can break this limit.
4. The causal discovery analysis considers time lags of up to 4 months. Is this long enough to capture slower ocean-atmosphere feedback processes that may be important for TPW variability? What is the justification for the chosen maximum time lag?
Yes. Extending the time lag analysis beyond one season, perhaps to encompass multiple seasons or even a full annual cycle, could indeed capture longer-term ocean-atmosphere feedback processes that may influence TPW variability. Many ocean-atmosphere processes, such as the El Niño-Southern Oscillation (ENSO) or the Indian Ocean Dipole (IOD), exhibit strong seasonal variability and may operate on timescales longer than a single season. Some feedback mechanisms, such as ocean heat storage, release, and atmospheric circulation patterns, may operate on timescales longer than four months. Extending the time lag analysis can help uncover these slower processes.
However, due to the limitation of the analysis and the difficulty of explaining the physical mechanisms behind it, I used a maximum time lag of 4 months. There is not even a time lag if the alpha value change result drastically changes. That’s why the global TPW case used different alpha levels (0.01, 0.1, and 0.5) and a maximum time lag of up to 3 months, but different oceanic regions chose fixed alpha 0.1 and a maximum time lag of up to 4 months.
5. Uncertainties in the satellite-derived TPW data and reanalysis products used are not discussed in detail. How might observational uncertainties impact the results, especially for trend detection? A more thorough treatment of uncertainties would enhance confidence in the conclusions.
Done. Please check the revised MS line 166-169, 170-172.
6. The physical mechanisms behind some of the detected causal links are not explored in depth. For example, what processes explain the two-way causal link between TPW and the Western Pacific Index? More discussion of mechanisms would strengthen the interpretation of the causal networks.
Done. Please check the revised MS line 568-591.
Minor comments:
1. The abstract is quite lengthy and detailed. Consider condensing it to focus on the key objectives, methods, main findings, and implications. Some of the specific results can be omitted from the abstract.
Done. Please check revised MS now mush shorter than previous (250 words to 220 words). Line 33-46.
2. The background and motivation for the study are well explained in the introduction. However, a clear statement of the research questions or hypotheses being addressed would strengthen this section.
Done. Please check revised MS. Line 151-157.
3. The section data and methods provide a good overview of the datasets and the PCMCI+ algorithm used. A few additional details would be helpful, such as specifying the time period of analysis, clarifying how significance was determined for the trends, providing a reference or brief explanation of the k-nearest neighbor approach for CMI, etc.
Done. Please check the revised MS line 204-219.
4. Check that all acronyms are defined at first use (e.g., WHWP in line 370, SO and AO in line 41, SPO in line 292, defined in line 405, line 45 ENSO, etc.).
Done. Acronym first use is corrected throughout the MS and some typos as well. Please check the revised MS.
5. The causal discovery analysis (section 3.4 and 3.5) is a strength of the paper. However, some interpretation of the physical mechanisms behind the detected causal links would be valuable. What do the relationships tell us about the climate system?
Yes, it is very important to understand the connection and underneath physical mechanisms in the climate system. I have already discussed a few points in the discussion section. I have added two major points in section 3.4, lines 392-396 and 397-400. In addition, some text about physical mechanisms should be added in the discussion section lines 568-591.
6. The conclusions (lines 538-556) provide a good synthesis of the results. The implications of the work for understanding climate change impacts are important and could be expanded upon. What are the next steps for this research?
Done. Please check the revised MS lines 607-610.
7. Line 538: "Word" should be "World"
Done. Please check the revised MS line 593.
8. Line 171: Define "PC" at first use
Done. Please check the revised MS line 181.
9. Line 483: "observe" should be "observed"
Done. Please check the revised MS line 515.
10. Line 385: Fix the typo in "Ʈmax" since you have used “T” in line 412 and ‘tau” in line 385. Also in Figure 4.
Done. Indeed, its Ʈmax. Please check the revised MS line 444; it is corrected, and also corrected in supplementary figures.

Referee: 2
The authors need to thoroughly re-check the references cited in the manuscript.
Done. I have thoroughly checked the complete manuscript and its looks fine. However, I have cited few new references and algorithms of k-nearest neighbor approach for CMI in material and methods section. Please check revised MS.

18-May-2024

Dear Dr Maishal:

Manuscript ID: EA-ART-03-2024-000030.R1
TITLE: Quantifying of the Drivers and Heterogeneity of Global Total Precipitable Water

Thank you for submitting your revised manuscript to Environmental Science: Atmospheres. I am pleased to accept your manuscript for publication in its current form. I have copied any final comments from the reviewer(s) below.

You will shortly receive a separate email from us requesting you to submit a licence to publish for your article, so that we can proceed with the preparation and publication of your manuscript.

You can highlight your article and the work of your group on the back cover of Environmental Science: Atmospheres. If you are interested in this opportunity please contact the editorial office for more information.

Promote your research, accelerate its impact – find out more about our article promotion services here: https://rsc.li/promoteyourresearch.

We will publicise your paper on our Twitter account @EnvSciRSC – to aid our publicity of your work please fill out this form: https://form.jotform.com/211263048265047

How was your experience with us? Let us know your feedback by completing our short 5 minute survey: https://www.smartsurvey.co.uk/s/RSC-author-satisfaction-energyenvironment/

By publishing your article in Environmental Science: Atmospheres, you are supporting the Royal Society of Chemistry to help the chemical science community make the world a better place.

With best wishes,

Prof. Nønne Prisle
Associate Editor, Environmental Sciences: Atmospheres

Reviewer 1

Thanks for addressing my comments, and congrats on the nice work.

Sincerely,

Vagner Ferreira
Hohai University - China