the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Aug 2025
Understanding mesoscale convective processes over the Congo Basin using the Model for Prediction Across Scales-Atmosphere (MPAS-A)
Abstract. The Congo Basin in Central Africa is one of three convective centers in the tropics, characterized by a high proportion of precipitation produced by mesoscale convective systems (MCSs). However, process-level understanding of these systems and their relationship to environmental factors over the Congo Basin remains unclear, largely due to scarce in-situ observations. This study employs the Model for Prediction Across Scales–Atmosphere (MPAS-A), a global cloud-resolving model, to investigate MCSs in this region. Compared to satellite-observed brightness temperature (Tb), MPAS-A realistically simulates key MCS features, allowing a detailed comparison between two mesoscale convective complex (MCC) cases: one over the southern mountainous region (MCC-south) and the other over the northern lowland forests (MCC-north). MCC-south is larger, longer-lived, and moves a longer distance than MCC-north. Our analysis shows that MCC-south is supported by higher thermodynamic energy and more favorable vertical wind shear ahead of the system. The shear extends up to 400 km, explains up to 65 % of the Tb variance, and is well balanced by a moderately strong cold pool. In contrast, MCC-north features weaker, localized shear near the center and a stronger cold pool. The African Easterly Jet helps maintain the shear in both cases, but an overly strong jet may suppress low-level westerlies and weaken convection. These results show how latitude and topography modulate environmental influences on Congo Basin MCS developments. The findings underscore the value of global cloud-resolving models in data-sparse regions for understanding convective systems and their impacts on weather extremes and societal risks.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-3591', Anonymous Referee #1, 22 Sep 2025
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RC2: 'Comment on egusphere-2025-3591', Anonymous Referee #2, 17 Oct 2025
The manuscript presents a study on mesoscale convective systems (MCSs) over the Congo Basin in Central Africa using km-scale simulations with MPAS-A and various observations. The study focuses on convective events happening in November 2023 that lead to severe flooding in the area. With the help of a MCS tracking tool (TAMS), MCSs from observations and the simulations were identified, categorized, and tracked. Overall, the MCSs from the simulations quite nicely agree with observed MCSs in location, frequency, and track characteristics.
The study then focuses on two mesoscale convective complex (MCC) cases (MCC-south and MCC-north) and shows how wind-shear conditions are crucial for the behavior of those MCCs, with more favorable wind-shear conditions leading to a more sustained MCC for MCC-south, compared to MCC-north. Using linear regression between vertical wind shear and brightness temperature, the importance of wind shear is not only qualitatively explained, but also underlaid with quantitative numbers. The study also highlights that the magnitude and location of the African Easterly Jet also affects wind-conditions and consequently MCSs.
Overall, the manuscript is very well written and shows the value of using km-scale simulations to improve our process understanding of MCSs, especially in regions where observations might be sparse. I recommend the manuscript for publication but would like to see the following points addressed by the authors.
Major points:
All Figures
The figures are generally nicely designed and well described in the captions. However, the labels and units are often missing. While these are mentioned in the captions, I feel that the figures could be substantially improved by also adding labels and units to the axes and colorbars.Figure 2
It would be great to also have an idea about the variance and not only the mean of the climatological values. You could, for example, do this with shading or other lines for quantiles. I think this would be particularly interesting, as, from my perspective, the enhanced values of runoff and precipitation during the period of interest do not seem very impressive at first glance, considering that this was a 60 year return time event. Having some idea about the variability could probably help making it clearer. Or were there other conditions that could explain this huge flooding despite the not extremely pronounced enhanced precipitation?Figure 3 (c)
How can the violin plot show durations of > 3h for DSL if, by definition, a DSL’s duration must be <= 3h?L279-282
I have difficulties with the interpretation of this sentence and the references. Does this mean that compared to observations MPAS generally has stronger convection and moisture convergence? If so, it is not entirely clear on what this is based on to me. In two cited papers (Raghavendra et al., 2022 and Feng et al., 2023b) MPAS is not used. Is it because the dynamics and physics of WRF are similar to MPAS and thus also the characteristics in preciptation? If yes, this should be made clearer. Furthermore, I don’t necessarily see increased precipitation in Rahghavendra et al. (2022) compared to observations – maybe I missed it. I also find the word “overestimation” not ideal, considering that observations often also have some errors (as we can see in the differences in Fig. 2) and, in this case, the CMORPH dataset is used for comparison, which shows the least amount of precipitation of the three datasets for the considered period of time.L407 ff
I don’t think such strong and specific conclusions can be drawn based on the analysis (“vertical wind shear explains X% of the total variance”). While I think the results shown in Fig. 10 can quantify the importance of shear to some extent, I don’t think that a causality (“explains”) with a specific number can be concluded from linear regression results which still only show correlation. The “explains 65%” is also mentioned on line 479. Using the same argument, also the sentence on line 495 and line 32 in the abstract are a bit too strong.Minor points:
L67
As a reader, I’d find it nice to also have the other two core regions of convection mentioned here. While this might be common knowledge for researchers focusing in that area, others might be curious.L199
“< 235 K regions that contain embedded < 219 K” regionsFigure 3 (a)
I would prefer for less elevation categories or have them more dense (i.e. 300m bins) with a maximum extension. Now, there are almost no grid points > 2000m and the ones that are are so close to each other, that it is difficult to see which category they belong to. Going not all the way to 3200m with the scale would likely improve the plot (sharper gradients for lower levels) without loosing too much information about the upper levels.Table 1
I would still list the “< 219K region has area of >= 4000 km^2” in the table (there’s enough space). While this is written in the text, it would improve the value of the table on its own. Also, the shape criteria for MCC is listed but never mentioned in the text. Maybe add a sentence regarding this to the text to help the reader understand without having to check in the referenced literature.L361
I would write the whole word in the title (“African Easterly Jet”) for better readability.Citation: https://doi.org/10.5194/egusphere-2025-3591-RC2
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This study titled "Understanding mesoscale convective processes over the Congo Basin using the Model for Prediction Across Scales-Atmosphere (MPAS-A)" addresses the lack of in-situ observations by utilizing a global cloud-resolving model to investigate MCSs. I feel this paper is a proof-of-concept for future research work. The research aims to improve the process-level understanding of these systems and their relationship to environmental factors, which are responsible for a significant portion of the region's precipitation.
The study compares two specific mesoscale convective complex (MCC) cases: one over the southern mountainous region (MCC-south) and another over the northern lowland forests (MCC-north). The findings indicate that while both are influenced by the African Easterly Jet, they differ in their dynamics. The MCC-south case is larger, more persistent, and is driven by higher thermodynamic energy and a strong vertical wind shear extending up to 400 km ahead of the system. This shear is well-balanced by a moderately strong cold pool and accounts for a significant portion of the observed brightness temperature variance. In contrast, the MCC-north case is characterized by weaker, localized shear and a stronger cold pool.
In conclusion, the research demonstrates that both latitude and topography play a crucial role in modulating the environmental conditions that influence the development of MCSs in the Congo Basin. The findings highlight the efficacy of using high-resolution global models like MPAS-A to study convective systems in data-sparse regions, offering valuable insights into their impact on extreme weather and societal risks. Based on the paper's findings and the model's capabilities, MPAS-A is a powerful tool for a variety of atmospheric research experiments. Its ability to handle both high-resolution regional and lower-resolution global scales within a single framework makes it particularly versatile. In the future, I would like to see new research insights by incorporating a future climate state (e.g., initialize the model using present and future climate and understand how results differ from those derived using IMPALA data), perturbation experiments to better understand orographic, dynamic and thermodynamic processes, and diurnal cycle (e.g., Alber et al https://doi.org/10.1016/j.atmosres.2021.105869). The opportunities for high spatio-temporal process-based research work over Africa is quite limitless.
Great to see this paper finally out and my best wishes to the research team.
Minor comments: Line 97-99: Why is this paragraph italicized?
Figure 1a: Can you replace this figure with something better such as a variable resolution MPAS Voronoi mesh version (e.g., https://mpas-dev.github.io/atmosphere/atmosphere.html) .
Figure 2: The figure caption for Figure 2 can be re-worded for better clarity.