Spatiotemporal Changes in Temperature and Precipitation in West Africa. Part I: Analysis with the CMIP6 Historical Dataset

Round 1

Reviewer 1 Report

In general, I think that this study presents a useful step forward for the community and that it builds onto the present state of knowledge. I have a couple of major concerns with the presented introduction/methodology that should be addressed. The manuscript should be returned to the authors for a round of major revisions and re-evaluated afterward. However, more details about past research/ methodology and discussion need to be clarified. My major comments and questions are as follows:

• You need to rewrite the abstract section
• You mentioned “Extreme climate and weather events lead to a wide range of impacts on the society and environment and pose serious challenges “, which is very generic. The authors should explain the impact of extreme climate on the study region with proper citations. The authors have not provided sufficient motivation for why agricultural productivity indicators are needed. The authors should explain with a couple of new paragraphs on this aspect in the introduction section. Also, you need to provide more literature reviews associated with the research gap/limitation.
• Your study area has a high dependency on local climate, topographic complexity. Can you provide detailed climatic information for the selected study areas? You should show Köppen–Geiger climatic zones on the map.
• Why did use satellite bases products such as CHIRPS, CHIRTS, and CRU instead of reanalysis precipitation (Era5) or MSWEP datasets?
• Can you provide a high impactful schematic diagram to understand your proposed methodology where the big impact of the results can be presented?
• Your error analysis results are incomplete. Can you provide a table for error analysis in terms of systematic error (absolute mean relative error) and random error (normalized root mean square error) for the evaluation results? You can follow:

Khan, R.S. et al 2021: Artificial Intelligence-Based Techniques for Rainfall Estimation Integrating Multisource Precipitation Datasets. Atmosphere 2021, 12, 1239.

Mei, et al. 2016: Evaluating satellite precipitation error propagation in runoff simulations of mountainous basins. J. Hydrometeor., 17, 1407–1423, https://doi.org/10.1175/JHM-D-15-0081.1

• Kling–Gupta efficiency (KGE) provides three distinct components representing the correlation, the bias, and a measure of relative variability in the simulated and observed values. Can you provide the Kling–Gupta efficiency metric to show the overall performances?

 

Author Response

Point 1: You need to rewrite the abstract section

Response 1: The abstract has been rewritten and updated.

Point 2: You mentioned “Extreme climate and weather events lead to a wide range of impacts on the society and environment and pose serious challenges “, which is very generic. The authors should explain the impact of extreme climate on the study region with proper citations. The authors have not provided sufficient motivation for why agricultural productivity indicators are needed. The authors should explain with a couple of new paragraphs on this aspect in the introduction section. Also, you need to provide more literature reviews associated with the research gap/limitation.

Response 2: We reorganized the introduction from page 2 line 64 to address comments from the review and also added a couple of paragraphs to bring out motivations and gaps of the study. We also revealed how other authors link the impact of rainfall variability and changes on agricultural productivity.

Point 3: Your study area has a high dependency on local climate, topographic complexity. Can you provide detailed climatic information for the selected study areas? You should show Köppen–Geiger climatic zones on the map.

Response 3: The study area has been redrawn to present both topography and climatic zone details.

Point 4: Why did use satellite bases products such as CHIRPS, CHIRTS, and CRU instead of reanalysis precipitation (Era5) or MSWEP datasets?

Response 4: For our study, we need to be sure that the dataset comes from the same project and has both variables Temperature (Maximum and minimum), and Precipitation. It is noticed that the proposed dataset (e.g.: the Multi-Source Weighted-Ensemble Precipitation (MSWEP)) by the reviewer does not have both Temperature and precipitation, but only precipitation. Additionally, [R1] assessed the performance of reanalysis (ERA5 and GLDAS) and satellite (TRMM and CHIRPS) datasets to reproduce the in-situ observations. They concluded based on the Pearson correlation coefficient, the root mean square error, and the multiplicative bias metrics that CHIRPS and ERA5 are the highest quality precipitation products over the study area. But CHIRPS performed better on the grid to point comparison. That is the reason why we did not use Era5. On the same line, [R2] a better performance of both satellite produce (CHIRPS and CRU) to simulate local observational precipitations. Several studies [R3-R6] over our study area also refer to either CRU or CHIRPS, CHIRTS to evaluate models’ performance.

Point 5: Can you provide a high impactful schematic diagram to understand your proposed methodology where the big impact of the results can be presented?

Response 5: We have tried to address this comment of the reviewer by explaining with bullets the chronology of the method applied.

Point 6: Your error analysis results are incomplete. Can you provide a table for error analysis in terms of systematic error (absolute mean relative error) and random error (normalized root mean square error) for the evaluation results? You can follow:

Response 6: The present study aims to find robust signals among all the models. The models are therefore kept with their imperfections and report as same to better quantify the trends. This allows comparing the common features and differences/uncertainties.

Then, it will be difficult to apply the methods suggested by the reviewer in a kind of analysis. Consequently, we suggested counting a certain percentage of the contribution of models (here 80% of models need to reflect the trends).

Reference

R1. Morales-Velázquez, M. I., Herrera, G. D. S., Aparicio, J., Rafieeinasab, A., & Lobato-Sánchez, R. (2021). Evaluating reanalysis and satellite-based precipitation at regional scale: A case study in southern Mexico. Atmósfera, 34(2), 189-206.

R2. Fallah A, Rakhshandehroo GR, Berg P, O S, Orth R. Evaluation of precipitation datasets against local observations in southwestern Iran. Int J Climatol. 2020;40:4102–4116. https://doi.org/10.1002/ joc.6445

R3. Akinsanola, A.A., Ogunjobi, K.O., Ajayi, V.O. et al. Comparison of five gridded precipitation products at climatological scales over West Africa. Meteorol Atmos Phys 129, 669–689 (2017). https://doi.org/10.1007/s00703-016-0493-6

R4. Diasso, U., Abiodun, B.J. Drought modes in West Africa and how well CORDEX RCMs simulate them. Theor Appl Climatol 128, 223–240 (2017). https://doi.org/10.1007/s00704-015-1705-6

R5. Quenum, G. M. L., Klutse, N. A., Dieng, D., Laux, P., Arnault, J., Kodja, J. D., & Oguntunde, P. G. (2019). Identification of potential drought areas in West Africa under climate change and variability. Earth Systems and Environment, 3(3), 429-444.

R6. Quenum, G. M. L., Klutse, N. A., Alamou, E. A., Lawin, E. A., & Oguntunde, P. G. (2020). Precipitation Variability in West Africa in the Context of Global Warming and Adaptation Recommendations. African Handbook of Climate Change Adaptation, 1-22. 

Reviewer 2 Report

Authors have done a comprehensive assessment of characteristics of extreme climate variables using different CMIP6 data using different trend analysis methods over west Africa. However, some specific comments are suggested:

1. Page 1, Line 26 The meaning of (TX90p/TX90p) and (TX10p/TN10p) is not clear please recheck and present it in a clear way.
2. Page 1, Line 28: the acronyms CDD and CWD should be written in expanded form when they first appear in the manuscript. Also, check other acronyms.
3. Page 2, Line 67: “world spaces” could be replaced by “region”.
4. Page 3, Line 111: Study Location area map can be prepared showing the topography of the region for a better understanding of the readers.
5. Page 3, Line 120: The rationale behind selecting the five sub-regions should be justified in the manuscript. Also, why did the author not select a sub-region based on agro-climatic zones is not clear.
6. Page 5, Line 163: Why Two data (CRU data and CHIRTS) sets have been selected for evaluation of temperature datasets. More clarity is expected in this section on these two datasets.
7. Page 10, Line 280: CMIP6 data may not be used without the bias correction of the data. The authors should explain the bias correction technique for rainfall data and also the evaluation parameters for each GCM model in this section.
8. Page 13, Line 333: The Legend shows a higher value of maximum temperature in blue colour whereas general convention is to show the higher temperature in red color. Authors may consider inverting the colour scheme for this map.
9. Page 21, Line 489: The degree sign in the manuscript has underlined the underline from degree symbol should be removed throughout the manuscript.
10. Page 31, Line 762: Authors may add the conclusion regarding the consequence of the changes in the extreme indices of climate variables on the agriculture and water availability in the region. This will be interesting for the readers

Author Response

1. Page 1, Line 26 The meaning of (TX90p/TX90p) and (TX10p/TN10p) is not clear. Please recheck and present it in a clear way.

Response 1: The sentence has been well corrected

2. Page 1, Line 28: the acronyms CDD and CWD should be written in expanded form when they first appear in the manuscript. Also, check other acronyms.

Response 2: All acronyms are defined before using them.

3. Page 2, Line 67: “world spaces” could be replaced by “region”.

Response 3: The “world spaces” has been corrected to “region”

4. Page 3, Line 111: Study Location area map can be prepared showing the topography of the region for a better understanding of the readers.

Response 4: The study area has been redrawn to address both topography and climatic zone details.

5. Page 3, Line 120: The rationale behind selecting the five sub-regions should be justified in the manuscript. Also, why did the author not select a sub-region based on agro-climatic zones is not clear.

Response 5: The comment has been solved.

6. Page 5, Line 163: Why Two data (CRU data and CHIRTS) sets have been selected for evaluation of temperature datasets. More clarity is expected in this section on these two datasets.

Response 6: In this section, we are just presenting the observed dataset that we used to assess the models. CRU (precipitation and temperatures) datasets have been successfully used over our study area with previous works [R1-R4]. Since we used CHIRPS (which is a precipitation dataset) the corresponding temperature variables are provided with the recent release CHIRTS [R5].

7. Page 10, Line 280: CMIP6 data may not be used without the bias correction of the data. The authors should explain the bias correction technique for rainfall data and also the evaluation parameters for each GCM model in this section.

Response 7: Model data used were not bias-corrected. Multiple GCMs were used to adequately represent the climate model uncertainty. In other words, they are selected to cover most of the uncertainty related to the climate model structure. The idea is to capture the various uncertainties each GCM model presents when compared with observation. A bias correction technique will be used when we later look at the impact of these models by removing the discrepancies for impact studies.

8. Page 13, Line 333: The Legend shows a higher value of maximum temperature in blue colour whereas general convention is to show the higher temperature in red color. Authors may consider inverting the colour scheme for this map.

Response 8: The legend of been corrected to address the reviewer's comment.

9. Page 21, Line 489: The degree sign in the manuscript has underlined the underline from degree symbol should be removed throughout the manuscript.

Response 9: The degree signs in the manuscript have been corrected.

10. Page 31, Line 762: Authors may add the conclusion regarding the consequence of the changes in the extreme indices of climate variables on the agriculture and water availability in the region. This will be interesting for the readers

Response 10: We really appreciate this aspect rose by the reviewers. But the current study does not aim to evaluate the impact of the climate indices on particular fields (water resource, agriculture, etc.). There is a plan to bring out these aspects in the ongoing work we are leading now. For this paper we submitted to your attention we focused on the assessment of trends (both spatial and temporal scales) of the indices in West Africa based on tendency analysis tools.

Reference

R1. Akinsanola, A.A., Ogunjobi, K.O., Ajayi, V.O. et al. Comparison of five gridded precipitation products at climatological scales over West Africa. Meteorol Atmos Phys 129, 669–689 (2017). https://doi.org/10.1007/s00703-016-0493-6

R2. Diasso, U., Abiodun, B.J. Drought modes in West Africa and how well CORDEX RCMs simulate them. Theor Appl Climatol 128, 223–240 (2017). https://doi.org/10.1007/s00704-015-1705-6

R3. Quenum, G. M. L., Klutse, N. A., Dieng, D., Laux, P., Arnault, J., Kodja, J. D., & Oguntunde, P. G. (2019). Identification of potential drought areas in West Africa under climate change and variability. Earth Systems and Environment, 3(3), 429-444.

R4. Quenum, G. M. L., Klutse, N. A., Alamou, E. A., Lawin, E. A., & Oguntunde, P. G. (2020). Precipitation Variability in West Africa in the Context of Global Warming and Adaptation Recommendations. African Handbook of Climate Change Adaptation, 1-22. 

R5. Funk, C. et al. A high-resolution 1983–2016 Tmax climate data record based on infrared temperatures and stations by the Climate Hazard Center. J. Clim. 32, 5639-5658 (2019)

 

Round 2

Reviewer 1 Report

The authors significantly improved the quality of the paper by addressing most of the previous comments. This research work will be very effective for the water research community. I recommend the manuscript for publication after minor changes:

• Can you explain hydrologic uncertainty? Also, explain uncertainty in terms of climate change.
• Your study area is characterized by complex topography, you should compare your results with another complex terrain with the future recommendations in the discussion section.
• Can you discuss the seasonal impact as the study area exhibited significant seasonal variation in terms of hydrology?

 

 

Author Response

We really impressed with the comment of the reviewer which will be mostly investigated in future studies we are planning to send to review. However, we tried to address it partially as we understood comments in the discussion section.

Point1: Can you explain hydrologic uncertainty? Also, explain uncertainty in terms of climate change.

Reponse1: The question is a bit confusing for us. 
We are not sure having understand clearly the concern of the reviewer when talking about hydrologic uncertainty and climate change uncertainty. We did not address these two thematics in our manuscript. But regarding the climate change uncertainty, this could be addressed when comparisons were made between the historical and the projected periods. For this aspect, we also have to investigate the contribution of natural disasters as well as the anthropogenic effects. This is not our scoop for the present study. But it opens our mind to consider a kind of aspect in some work we are leading currently.

Point2: Your study area is characterized by complex topography, you should compare your results with another complex terrain with the future recommendations in the discussion section.

Reponse2: The findings were compared with some other work performed in China which also present some complex topography like WA.

Point3: Can you discuss the seasonal impact as the study area exhibited significant seasonal variation in terms of hydrology?

Reponse3: We really appreciate the concern of the reviewer to rise more aspects about hydrology for the present study. The work could be more relevant taking into account those aspects. The present study basically throws more light on the changes of climate extremes among CMIP6 models. It does not focus on the understanding theimpact of hydrologic uncertainty and its impact on West Africa.