Exploring the Origin of the Two-Week Predictability Limit: A Revisit of Lorenz’s Predictability Studies in the 1960s

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript addresses an important aspect of atmospheric predictability, specifically focusing on the two-week predictability limit. The introduction effectively outlines the historical context and significance of this limit in atmospheric science. The paper is well-structured, with a clear flow from historical context to current research findings and future implications. Each section transitions smoothly, contributing to an overall coherent narrative. The paper provides a thorough review of methodologies used in the 1960s to establish the predictability limit. It also revisits these methodologies with modern perspectives, which is a strength. However, a more detailed explanation of the contemporary methods and models used could enhance the reader's understanding. The paper is a valuable contribution to the field of atmospheric predictability research. With some revisions to enhance clarity, detail, and practical insights, it has the potential to significantly impact both theoretical and applied aspects of weather prediction.

My comments are listed below

Major Comments:

1. The abstract could be improved by clearly stating the main findings and their implications for future research.
2. The review of Lorenz's pioneering work is comprehensive. Including a comparison table or timeline of key developments in predictability research since the 1960s could provide additional clarity.
3. It would be helpful to include more recent studies that either support or challenge these historical findings regarding Doubling Time and Predictability Limit. Visual aids such as charts or graphs summarizing these correlations could enhance comprehension.
4. The reevaluation of approaches by Lorenz and Charney et al. is well-done. This section could be strengthened by discussing how modern computational power and data assimilation techniques have improved or altered these initial findings. Including case studies or specific examples where extended predictability has been achieved would provide practical insights.
5. The suggestions for future research are relevant and well-founded. Highlighting specific research questions or hypotheses that arise from this review could provide more concrete guidance for future work.
6. The potential of AI-powered approaches is mentioned. A deeper exploration of how AI and machine learning are currently being integrated into predictability studies would be beneficial.
7. Emphasizing the practical applications of extended-range predictions in various domains (e.g., agriculture, water resources) could underscore the broader impact of this research.

Minor Comments

 

1. The manuscript is well-written, with few grammatical errors. However, some sentences are complex and could be simplified for better readability.
2. Ensure that all figures and tables are clearly labeled and referenced in the text. Figure panel numbers are missing. If possible, improve the figure quality.
3. It might be useful to include more recent studies on AI and machine learning applications in atmospheric science.

Comments on the Quality of English Language

1. The manuscript is well-written, with few grammatical errors. However, some sentences are complex and could be simplified for better readability.

Author Response

Please see the attachment. 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This review article provides a review and overview of research on atmospheric predictability. The article is well structured and detailed, providing a comprehensive overview of the historical research and current status of atmospheric predictability. However, there are some areas for improvement in the article:

1. p5: When numerical experiments were carried out on different gcm , three models were used to estimate the system growth rate with different results, analyze why the results were different, the conditions under which the three models were used, and their accuracy.

2. p14: ' regarding the validity of Figure 5, Figure 2 of the L69e provided the following caution ' is prone to misunderstanding. Is Figure 5 the second in L69e ?

3. p15: It is mentioned that the calculation results of the L69e model need to pay attention to the impact of its assumptions. These assumptions can be explained more clearly and in detail in the discussion, and their impact on the results can be discussed.

Overall, this review article provides important information about atmospheric predictability research. I can recommend for publication it.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

none.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I have no further comments.