the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Simulated rainfall extremes over southern Africa over the 20th and 21st centuries
Birgit Hünicke
Abstract. In Southern Africa, precipitation is a crucial variable linked to agriculture and water supply. In addition, extreme precipitation causes devastating flooding, and heavy rainfall events are a significant threat to the population in this region. We analyse here the spatial patterns of extreme precipitation and its projected changes in the future. We also investigate whether the Agulhas Current, a major regional oceanic current system, influences those events. For this purpose, we analyse simulations with the regional atmospheric model CCLM covering the last decades and the 21st century. The simulations are driven by atmospheric reanalysis and by two global simulations. The regional simulations display the strongest precipitation over Madagascar, the Mozambique channel, and the adjacent mainland. Extreme rainfall events are most intense over the mountainous regions of Madagascar and Drakensberg and the African Great Lakes. In general, extremes are stronger in the Summer Rainfall Zone than in the Winter Rainfall Zone.
Extremes are projected to become more intense over the South African coast in the future. For the KwaZulu-Natal Province, the heaviest rainfall event in the future is twice as strong as the strongest extreme simulated in the historical period and the recently observed disastrous extreme event in April 2022. The impact of the Agulhas Current System on strong rainfall events over the South African coast does not clearly appear in the simulations.
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Nele Tim et al.
Status: open (until 04 Oct 2023)
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RC1: 'Comment on nhess-2023-147', Anonymous Referee #1, 28 Aug 2023
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The manuscript by Tim et al. analyzes simulations of a regional climate model (CCLM) to assess the evolution of extreme rainfall. I don't have a very favorable opinion of this manuscript, which seems to just present the results of a single model and doesn't propose a regional synthesis linked to previous work. The format needs to be reviewed, with many paragraphs with a single sentence, which shows a lack of organization in the arguments presented. The two main problems are 1/ the total lack of comparison with observed rainfall observations, climate simulations are only compared with reanalyses, and 2/ the results seem contradictory compared to previous studies, which are based on a much larger number of simulations. Thus, I question the relevance of the whole study. The work that would be required to improve the manuscript seems very extensive and beyond the scope of a major revision.
Page 1, line 24, perhaps relevant here to add more references that deal with changes in extreme rainfall over the same region:
Abiodun, B.J., Abba Omar, S., Lennard, C. and Jack, C. (2016), Using regional climate models to simulate extreme rainfall events in the Western Cape, South Africa. Int. J. Climatol., 36: 689-705. https://doi.org/10.1002/joc.4376
Engelbrecht, C.J., Engelbrecht, F.A. and Dyson, L.L. (2013), High-resolution model-projected changes in mid-tropospheric closed-lows and extreme rainfall events over southern Africa. Int. J. Climatol., 33: 173-187. https://doi.org/10.1002/joc.3420
Mason, S.J., Waylen, P.R., Mimmack, G.M. et al. Changes in Extreme Rainfall Events in South Africa. Climatic Change 41, 249–257 (1999). https://doi.org/10.1023/A:1005450924499
Page 2 line 35, I feel like this section could be a bit improved, to better describe the synoptic influences on extreme rainfall in this area
Manhique, A.J., Reason, C.J.C., Silinto, B. et al. Extreme rainfall and floods in southern Africa in January 2013 and associated circulation patterns. Nat Hazards 77, 679–691 (2015). https://doi.org/10.1007/s11069-015-1616-y
Reading the rather short introduction, I’m left with a question about the novelty of this work. The introduction is quick and does not stress enough what is known/what is unknown about changes in extreme rainfall in Southern Africa. An overview should be provided about 1/ studies analyzing long term trends in extreme rainfall and 2/ the climate change scenarios already available in this region. At the end of the introduction, it should be clearly explained what is the novelty of the present study.
Section 2.1 data, I don’t understand the rationale of using the JRA-55 reanalysis for the hindcast simulations and the ERA5 reanalysis “for validation purpose”. Is the ERA5 reanalysis considered as observations here? The hindcast runs should be compared to observed rainfall.
Page 3, line 65, percentiles are computed for each grid cells, but for the Cape and Natal regions the grid cells are first averaged. It does not make sense to me. This methodological choice should be explained.
Page 3, line 85 “Thus, the spatial distribution of precipitation extremes seems to be generally realistically simulated by CCLM”. So you compare a reanalysis (JRA55) with another reanalysis (ERA5) and you think if they agree it means simulated precipitation is realistic. Simulations should be evaluated by observed rainfall, or perhaps with merged products like CHIRPS that uses raingauges data and satellite data. There is absolutely no mention in the manuscript that ERA5 could be a reliable source of information for extreme rainfall in that area.
Page 4, line 95: are these values realistic compared to actual observations?
Figure 3: please add in the caption the time periods when the trends have been computed.
Page 6, line 119: “The highest grid-cell-scale percentile is found in the future” this is not clear.
Figure 4: “Threshold of the 99th percentile of precipitation..” this is not clear. Do you mean “value” instead of “threshold” ?
Page 8, line 145: I question the relevance of proving the largest event over such a large domain (also mentioned before in the manuscript). The value of 1 pixel for 1 day over long term simulations has absolutely no meaning in terms of future trends, it would me more relevant to provide an average of changes in percentiles, or the magnitude of the trend, for the whole domain than only providing one single value.
Page 8, line 162: “number of extremes per year” ; how are extreme defined ? values above the 99th percentile?
Page 9, line 183: please list here the other potential drivers
Sections 3.4 and 3.5 are hard to follow. Why a focus on these regions, it is not explained. Another methodology is presented here (2-day sums etc) that should rather be in the method section. Figure 6 is not called in the text. It refers to the “coastline” so I guess the Cape region. So if this is correct, the results are wrong since it can be clearly seen in figure 6 left panel that the y-axis is higher for future (0-600) than for historical (0-400), so precipitation extremes are increasing but the opposite can be read in section 3.4.
Page 16, line 265, please provide a more recent citation than Trenberth 2003 about this statement on the evolution of extreme rainfall
Page 17, line 275: please explain what could be the reason why CMIP5 model project an increase and your study a decrease. Pohl et al. 2015 use 15 models when in the present work only one is used. Therefore there is much more confidence in the robustness of the results of Pohl et al. 2017.
Citation: https://doi.org/10.5194/nhess-2023-147-RC1 -
RC2: 'Comment on nhess-2023-147', Anonymous Referee #2, 07 Sep 2023
reply
Review of “Simulated rainfall extremes over southern Africa over the 20th and 21st centuries” submitted to Natural Hazards and Earth System Sciences (Manuscript ID: nhess-2023-147). This paper proposes to analyze the spatial patterns of extreme precipitation and its projected changes in the future in a regional climate model (RCM), and how it could be linked to the Agulhas Current. This is an interesting topic, especially if the paper could help understand how future changes in extreme rainfall are affected by larger-scale circulation features, such as the Agulhas Current. However, this aspect is not much developed in the present study.
The paper mainly focuses on analyzing regional trends in extreme rainfall using a single realization from a single RCM at 16km. However, considering how different the results could be in different RCMs and/or in the same RCM driven with different GCMs, I wonder how much trust we give attribute to this type of analysis. It would be good if the author could highlight a bit more the novelty of their approach, compared to previous work using CORDEX. How does this RCM compare with CORDEX models in the region?
Similarly, in the present manuscript, one RCM is compared to ERA5. However, ERA5 also has biases, it would be good to see how the RCM compared with observed station data and/or satellite-derived datasets (CHIRPS, PERSIAN, TRMM).
Finally, I’m also concerned with the absence of statistical testing, which makes the entire discussion/results a bit subjective. The authors compare different statistics in different datasets and over different time periods, but the discussion/results are never supported by statistical tests. For instance, in Figure 1 (but the same apply to most figure), can we not have a plot of the difference between ERA5 and the RCM? Can we not include a t-test to evaluate the significance of the difference between the 99th percentiles? Can we not include a Mann-Kendall test to assess the trend significance?
Citation: https://doi.org/10.5194/nhess-2023-147-RC2
Nele Tim et al.
Nele Tim et al.
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