CMIP5 Decadal Precipitation over an Australian Catchment

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

By my point of view, the article is an excellent manuscript. The article assesses the performance of eight models (GCMs), contributed to CMIP5 decadal prediction, for monthly hindcast precipitation over the Brisbane River catchment, Australia. The finding leads the way in evaluation of models’ performance. The author articulates their finding and the the content of research is comprehensive, I appreciate the effort put into this work; however, I have several concerns that need to be addressed before the manuscript can be considered for publication.

1. The parameter to evaluate the models is not enough, how about the AIC and BIC?

2.For the evaluation for dry and wet period, is that possible to evaluate the performance of different models’ monthly variation? 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Review of the paper titled "CMIP5 Decadal Precipitation at Catchment Level" by Hossain et al. 

 

General comment:

The article has important scientific merit in the context of studies evaluating GCM projections of the current climate on an Australian catchment scale. However, several parts of the manuscript require improvement in textual quality (there are many long paragraphs that lose meaning; there are paragraphs using many repetitive words and terms). This leaves the text scientifically weak, so I recommend a careful review of the whole article. Therefore, my opinion is Major Revisions.

 

Below are several suggestions, questions and recommendations for authors to consider.

 

TITLE:

I suggest that the country (Australia) be included.

 

ABSTRACT

The text needs to improve in some parts that are meaningless or incomplete sentences.

The text contains very technical information and many acronyms. For example, categories I, II and II reflect a vague idea and do not help to understand the results.

The Abstract does not have a coherent closure. What is the relevant result and its importance for Australia's climatology and hydrology?

 

Line 10:

for different temporal and spatial

multiple instead different is better.

 

Line 11:

studies were for the temperature and temperature-based climate indices

Is the text correct?

 

INTRODUCTION

In the introduction, the authors mention the term hindcast precipitation, but they do not describe what it is and which time period they want to refer to. Please clarify this point.

 

The objective of the work is well justified for application on catchment scale. However, the authors did not explain why they chose Brisbane River as the study area. By the way, it would be very important to show the map of Australia and indicate the location of the study area and its importance in terms of the country hydrology.

 

Line 34:

to measure the models’ credibility in future prediction

The word credibility is not scientifically appropriate. The term uncertainty is the most used in the context of this type of study.

 

Lines 33 to 45:

Evaluation: this word appears six times in the text…. This first introductory paragraph is long and has repetitive ideas... authors should aim for better textual quality, without repeating words.

 

Data Processing

Lines 144-145:

This statement about the Brisbane River catchment is based on which climatological study?

 

METHODOLOGY

Line 150:

The authors used several acronyms for statistical methods, the meanings of which are described below. This information may need to be included in the initial text of this item.

 

RESULTS AND ANALYSIS

The results are very well presented and in a logical sequence with scientific text that reflects interesting findings which contribute to the theme of evaluations of CMIP5 global projections for the study area.

 

DISCUSSION

Authors should review the context of the first and second paragraphs.

The first paragraph (lines 394-406) repeats several aspects that have already been mentioned previously.

The second paragraph (lines 407-446) is too long, and the text does not seem very suitable for this discussion item.

The remaining paragraphs are coherent and appropriate in this item.

 

CONCLUSION

Line 509:

Use of the term credibility that is not scientifically appropriate. See similar comment above.

 

Lines 509-517:

In a nine-line paragraph, the authors used the word performance six times. Again, I recommend authors to aim for better textual quality, without word repetitions.

 

Shouldn't the sequence of the text of the first three conclusions have a semicolon at the end?

This is a basic grammatical error!

 

What is the relevant result and its importance to the climatology or hydrology of Australia? In what sense do the findings contribute to the area of water resources? These points are important in closing the article.

 

Final comment:

This work has the possibility of being published in 2024, with results based on CMIP5 data that were made available around 2013. The most current CMIP6 projections were made available in 2022. I think this is a relevant point that should not be omitted. What is the authors' opinion on this?

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The paper present reasonable methodology, allowing evaluate effectiveness of different models in representing observed climatological parameters. Though it is not new, that different models have different success at different scales and different regions.

In my view it can be published in Hydrology and the conclusions would be of use to others.

What I would suggest to emphasize in tabulated form in addition to the “performance matrix” – the quality of representation of actual meteorological parameters analyzed by the authors. It can be at the level of all the used models and at the level of the “MMEMs”. In most cases we need not the overall performance, but some certain characteristic, such as maximum precipitation, lowest temperature, etc. Otherwise, there are even interesting results on comparison of ensembles and individual models results, which is important for further CMIP databases usage.The paper present reasonable methodology, allowing evaluate effectiveness of different models in representing observed climatological parameters. Though it is not new, that different models have different success at different scales and different regions.

In my view it can be published in Hydrology and the conclusions would be of use to others.

What I would suggest to emphasize in tabulated form in addition to the “performance matrix” – the quality of representation of actual meteorological parameters analyzed by the authors. It can be at the level of all the used models and at the level of the “MMEMs”. In most cases we need not the overall performance, but some certain characteristic, such as maximum precipitation, lowest temperature, etc. Otherwise, there are even interesting results on comparison of ensembles and individual models results, which is important for further CMIP databases usage.

Comments on the Quality of English Language

There are some typing errors in the  text

Author Response

The authors would like to give very special thanks to the reviewers for their valuable professional comments and also for going through our manuscript. The authors have addressed all the comments one by one and revised the original manuscript accordingly.

 

General Comment: The paper present reasonable methodology, allowing evaluate effectiveness of different models in representing observed climatological parameters. Though it is not new, that different models have different success at different scales and different regions. In my view it can be published in Hydrology and the conclusions would be of use to others.

Author's Response: The authors would like to give special thanks to the reviewer for their valuable professional comments. Authors really appreciate it.

Comments on the presentation of the results:  What I would suggest to emphasize in tabulated form in addition to the “performance matrix” – the quality of representation of actual meteorological parameters analyzed by the authors. It can be at the level of all the used models and at the level of the “MMEMs”.

Authors response: Thank you for your suggestion. We have used eight models and their 10 initialization years. In addition, numerous skill tests and their different range of thresholds. To present all information together needs a very big table that cannot be accommodated in a single page. If we split the figure for individual skills and their thresholds, the number of tables will be huge and will not be looking good and the reader may feel the repetition of information.

Here, most of the results are presented using Heatmaps and we used Python programming for this kind of plotting. A single heat map enables to presentation of huge information in a figure as needed. For this reason, eight models, 10 initialization years, and a wide range of skill tests along with their different thresholds were managed to present in a single figure.  The authors understand the reviewer's suggestion, but we checked and realized that the table will not be better than the existing plotting to present all the results. Besides, the heatmap is easy to understand. Readers will understand the results at a glance and will be able to recognize the better models from the bright colours of the legends. The brighter the color better the model performance.

Comments on the models and their ensembles: In most cases we need not the overall performance, but some certain characteristic, such as maximum precipitation, lowest temperature, etc. Otherwise, there are even interesting results on comparison of ensembles and individual models results, which is important for further CMIP databases usage.

Authors’ response: In the third paragraph of the Introduction Authors mentioned that Hossain et al. compared the model performances for identifying drift and their correction. They compared the performance of individual ensembles corresponding to multi-model ensembles’ mean (MMEMs) and showed that the performance of MMEMs was better than individual ensembles. For this reason, this study considered multi-model ensembles. The text is given below. Please check the reference therein.

Numerous studies evaluated CMIP5 models over Australia [1], [8]–[11] but studies on evaluating CMIP5 decadal precipitation at catchment scale can hardly be found. After Mehrotra et al. [8], who assessed the CMIP5 decadal hindcast precipitation over different hydrological regions (0.50 ×.0.50) in Australia, recently Hossain et al. [12], [13] used the CMIP5 decadal precipitation at a further finer resolution of 0.050 ×0.050 (5km×5km) for Brisbane River catchment Australia for the first time. Hossain et al. [12], [13] compared the model performances for investigating the model drift and their subsequent correction using alternative drift correction methods for both the monthly and seasonal mean precipitation. However, they compared the model performances at a single grid point within the Brisbane River catchment. On the contrary, Mehrotra et al. [8] used only a multi-model approach but did not consider individual models finer than 0.50 spatial resolution.

Another reason for selecting ensembles’ mean was provided in the last paragraph of the original manuscript. Please check the text given below.

“Many researchers have suggested using MMEM [14]–[17] while using GCM data to reduce the model biases. The use of MMEM may enhance the model performances [2], [18] by reducing the biases to some extent but there is no information available on the ranking of GCM models and based on this, which and how many models should be considered to produce MMEM so that it could provide better outcome. This is essential for CMIP5 decadal precipitation because of its wide range in spatial and temporal variability in providing the model output ten years ahead. That is why the objective of this paper is, first, to categorize the models based on their performances at the catchment level with a spatial resolution of 0.050 and next, to identify the best combination of different models that would provide better performance.”

Ref: in the original manuscript

[12] M. M. Hossain, N. Garg, A. H. M. F. Anwar, M. Prakash, and M. Bari, “Intercomparison of drift correction alternatives for <scp>CMIP5</scp> decadal precipitation,” International Journal of Climatology, p. joc.7287, Jul. 2021, doi: 10.1002/joc.7287.

[13]    M. M. Hossain, N. Garg, A. H. M. F. Anwar, M. Prakash, and M. Bari, “Drift in CMIP5 decadal precipitation at catchment level,” Stochastic Environmental Research and Risk Assessment, vol. 8, p. 5, Dec. 2021, doi: 10.1007/s00477-021-02140-8.

Comments on the methodology: The paper present reasonable methodology, allowing evaluate effectiveness of different models in representing observed climatological parameters. Though it is not new, that different models have different success at different scales and different regions.

Authors' response: Thanks for your comments. It has been justified by the results presented in this study.

Note: To address the above comments, we have not changed anything in the original manuscript.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors were able to appropriately answer all the suggested and recommended questions. I consider that the article is now in a version with scientific merit for publication.

I have just one more point for the authors to review:

Item 2 - Study area, lines 187-190: Is the quantitative information on precipitation climatology based on any previous studies? Or did the authors calculate with their data? This point must be clarified.

Author Response

 It was calculated from the monthly observed gridded data, collected from the Australian Bureau of Meteorology. This information has been clarified in the revised manuscript. Please see the following text (as given below), which has been added to the revised manuscript.

“From the monthly observed gridded precipitation (1911-2015) over the Brisbane River catchment, it is found that the monthly precipitation varied from nil to 1360 mm with an annual average precipitation of 628 mm, and the number of upper and lower extremes are not quite small.”