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Peer-Review Record

Using the CHIRPS Dataset to Investigate Historical Changes in Precipitation Extremes in West Africa

Climate 2020, 8(7), 84; https://doi.org/10.3390/cli8070084
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Reviewer 3: Anonymous
Climate 2020, 8(7), 84; https://doi.org/10.3390/cli8070084
Received: 23 May 2020 / Revised: 22 June 2020 / Accepted: 24 June 2020 / Published: 30 June 2020

Round 1

Reviewer 1 Report

All the recommendations have been carried out properly. The draft has been deeply improved.

 

Author Response

We thank the reviewer 1 for the careful review and positive comments which helped to improve the manuscript.

Submission Date
23 May 2020
Date of this review
11 Jun 2020 06:31:17
Reply date
21 Jun 2020

 

Reviewer 2 Report

This study investigates the precipitation trends over 5 countries in northwestern Africa using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and various observational data. They found the distinct precipitation trends in terms of six Expert Team on Climate Change Detection and Indices (ETCCDI) rainfall-based indices such as total precipitation, cumulative wet days and cumulative day days, extreme days, and intensity days.

I found this paper interesting, with a good amount of information regarding precipitation trends for the past about 35 years over northwestern Africa. In particular, it is great to link precipitation patterns and trends in terms of ETCCDI, so that making its interpretation easier and clear (although the link provided in the manuscript (etccdi.pacificclimate.org/list_27_indices.sht) didn’t work out).

As mentioned in the paper, it would be great to look into the processes involving it, but this reviewer understands this may be beyond the scope of this study. It looks like the interannual (ENSO) and intraseasonal (MJO) variability doesn’t seem to affect much on this trend, but sudden change such as over Guinea Coast may be related to recent strong El Nino and La Nina pattern and other large-scale variability and recent climate change. Note the regional difference of precipitation among regions are quite large, thus a more in-depth understanding of what causes this regional difference would be important.

However, there are still a few issues in this manuscript.

1)     This reviewer thinks that the writing in line 192 and Line 199-201 are contradicting. In Figure 3, the authors said CHIRPS data overestimate precipitation for all the stations, and then CHIRPS-based precipitation data is “good” enough to be used for flood and drought monitoring in the study areas, which may make readers confusing. This reviewer finds the CHIPRS dataset useful, as shown in Fig.2, 3 and CHIRPS is pretty good to capture overall feature of rain gauge precipitation although they overestimate in the rainy season and some mismatch during the dry month (DJF) in the study region.

   Thus, it would have been better if the authors calculate how much precipitation is overestimated using CHIRPS compared to observation, during the given month and location (each rain gauge stations, in Fig.3). You may not need to generate the plot; just state in the text.

 2)     It looks like northern Africa such as Senegal and Niger have increasing precipitation (and wet days) trends while decreasing dry day trend, while somewhat neutral precipitation trend or slightly decreasing trend in wet days and neutral trend in dry days. Interestingly the pattern in Senegal and Niger seems to be similar even though one is inland and another is a coastal region.

   Furthermore, there is a lot more precipitation over Burkina Faso than Senegal and Niger. The clear difference between these three regions is locations; one is coastal and another is inland and mountain regions. Maybe it can be partially explained by topography because orography-induced precipitation could indeed play a role in the “intensity” of precipitation. Are there any terrain height difference? Orographic precipitation and Mesoscale convective system are conducive to the terrain and geography, it would be great if the authors add the terrain information in Fig. 1.

3)     This is a minor comment, but the manuscript doesn’t seem to be a final version. Lots of red marks with yellow highlighter drawn were found in many places in the text. Please make sure the version when you do a future submission.

Author Response

Please see the attachment below!

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments on “Using CHIRPS dataset to investigate historical changes in precipitation extremes in West Africa” by Didi Sacré Regis et al.

In this investigation the authors studied recent changes in precipitation for five countries in West Africa.  For this, the authors use six climate change indices and studied the indices recent trends and inter-decadal variation. This study in within the scope of the journal and the results are vary relevant for the economy of the region.

Overall, the manuscript is well structure and well written. It is evident that it has already undergone a first and in-depth review that has greatly improved its content and analysis. My only point regards the validation of the dataset which I detailed in the major comments. But this is clearly a personal and subjective opinion and all of my comments and suggestions are totally optional. I recommend it for publication.

 

Major comments

A dataset validation over five grid points (five locations) it’s hardly a dataset validation. I would call validation over 5 locations/cities but not validation of the dataset. My concerns are:

One argument is it lacks spatial representation. For instance, it was not verified the rainfall spatial gradient. A second argument relates with the representation of extreme rainfall, at country level. One of the aims of this investigation. I’m mentioning it because satellite information misrepresents extremes; it has a bias towards less intensity events and has an excess of light rainfall (as the authors well putted in Lines 155-188). Finally, it also undermines any conclusion regarding the spell’s length frequency, and intensity, since the excess of light precipitation, from the satellite data, can increase the CDD and the CWD length, the RRI and the SDII, respectively.

Minor comments:

I would suggest, reviewing the word "table “ and “figure” throughout the manuscript, sometimes appears in uppercase and sometimes in lowercase, especially when enclosure in parenthesis.

Line 139/figure 1. “The spatial mean rainfall annual variability of each country is presented in Figure1.”

Do the numbers stand for the maximum and minimum rainfall, and is this information related with the ratios presented in the figure 1? If so, I would suggest adding a line in the figure caption explaining the numbers meaning (“Numbers stand for/represent the maximum and ….”). I would like to remember the authors that the figures are self-explanatory and do not depend the text for a clear interpretation.

The colour scale is missing. I think that the colour scale for Cote d’Ivory is not the same as for the other countries.

Line 182- “The CHIRPS dataset” See my comments regarding “Validation”.

Line 183- observation->observationS

Figure 2 - Explain the grey shadow

Line 208 “2010at” -> needs extra space

Line 221 “large values produced for instance during El Niño years”. Needs reference

Line 230 “WAM” and “SST”->spell out

Line 265 Table 3 should appear before figure 4. The units are missing (slope and mean).

Figure 4,5,6 caption “… least squares, the best …“-> “The best line with the minimal distance from the data” I think there is a verb missing.

Figure 4,5,6 the slope units are missing. The authors can add an extra line to caption with that information.

Line 267 – Who’s “They”?

Line 268 “-5 and -5 days per decade respectively,…”-> “-5 days per decade”

Line 281 and Line 285 (Fig. 6) and (Figure 6).

Author Response

Please see the attachment

Author Response File: Author Response.pdf

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