the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Jul 2023
The perfect storm? Concurrent climate extremes in East Africa
Abstract. Concurrent extreme climate events exacerbate adverse impacts on humans, the economy, and the environment relative to extremes occurring in isolation. While changes in the frequency of individual extreme events have been researched extensively, changes in their interactions, dependence and joint occurrence have received far less attention, particularly in the East African region. Here, we analyse the joint occurrence of pairs of the following extremes over East Africa: river floods, droughts, heatwaves, crop failures, wildfires and tropical cyclones. We use bias-adjusted impact simulations under past and future climate conditions from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). We find an increase in the area affected by pairs of these extreme events, with the strongest increases for joint heatwaves & wildfires (+940 % by the end of the century under RCP6.0 relative to present day), followed by river floods & heatwaves (+900 %) and river floods & wildfires (+250 %). The projected increase in joint occurrences typically outweighs historical increases even under an aggressive mitigation scenario (RCP2.6). We illustrate that the changes in the joint occurrences are often driven by increases in the probability of one of the events within the pairs, for instance heatwaves. The most affected locations in the East Africa region by these concurrent events are areas close to the River Nile and parts of the Congo basin. Our results overall highlight that concurrent extremes will become the norm rather than the exception in East Africa, even under low-end warming scenarios.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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RC1: 'Comment on egusphere-2023-1712', Anonymous Referee #1, 29 Aug 2023
The paper aims at understanding concurrent climate extremes over the eastern parts of Africa using three different emission scenarios. The results show that, concurrent climate extremes are likely to increase with high magnitude over the Nile and Congo basin that are currently the wet regions over East Africa. Heat waves are wildfires are likely to dominate the region by the end of the 21st century as projected by all the emission scenarios.
The paper is coherent, the methods deployed are relevant and the paper conceptualization is well thought of.
Hence, I recommend the paper to be accepted for publication in EGU with minor correction.
Minor Correction.
The authors should enhance labelling of Lat and Lon in all the spatial maps since they are not currently clear.
Citation: https://doi.org/10.5194/egusphere-2023-1712-RC1 -
AC1: 'Reply on RC1', Derrick Muheki, 06 Sep 2023
Thank you for your feedback. We will ensure to revise the labelling of the Latitudes and Longitudes in the spatial maps to ensure that they are clear.
Citation: https://doi.org/10.5194/egusphere-2023-1712-AC1
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AC1: 'Reply on RC1', Derrick Muheki, 06 Sep 2023
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RC2: 'Comment on egusphere-2023-1712', Anonymous Referee #2, 10 Sep 2023
This paper analyzed the frequency and spatial extent of 15 types of concurrent extreme events in East Africa, highlighted the concurrent extremes will become the norm in the future.However. It remains some issues to be discussed before it is considered for publication.
1.The pair of concurrent extreme events defined in this paper represent two extreme events occurred within the same location in the same year, no matter if it occurred once or several times. The physics meaning of this definition is unclear. Usually the concurrent extreme events are defined based on daily data, i.e., a pair of extreme events occurring on the same day. The definition in this paper seems to be too crude to obtain physically-meaningful knowledge.
2.From fig.1, among the 15 pairs of extremes, all the concurrent extremes including heatwaves show the strongest increase. It this related to the apparent global warming trend? And all the future years will become extreme years of heatwaves?
3. The method used in this paper to search drivers of concurrent extremes seems to be too shallow. So, the results look very obvious, i.e., the global warming is the most important driver. The paper lacks an analysis of the dynamic process that cause to concurrent extreme events.Citation: https://doi.org/10.5194/egusphere-2023-1712-RC2 -
AC2: 'Reply on RC2', Derrick Muheki, 07 Oct 2023
We thank the reviewer for their time and valuable suggestions to improve the manuscript. Here below, we respond to each individual comment and propose modifications to the manuscript (in italics) to accommodate the concerns raised.
Reviewer comment 1: The pair of concurrent extreme events defined in this paper represent two extreme events occurred within the same location in the same year, no matter if it occurred once or several times. The physics meaning of this definition is unclear. Usually the concurrent extreme events are defined based on daily data, i.e., a pair of extreme events occurring on the same day. The definition in this paper seems to be too crude to obtain physically-meaningful knowledge.
Response: We thank the reviewer for this comment. There are three distinct motivations for our methodological choice to consider annual time steps in this study. The first concerns data availability: the available dataset from ISIMIP 2b only provides annual occurrences of the six categories of climate extreme events (as mentioned in one of the caveats in lines 79-80 of the manuscript). Unfortunately, no multi-hazard future data is available at daily resolution. Due to this limitation of the dataset, we define concurrent events as two events occurring in the same location, during the same time step (here being the same year). A second reason is that some of the climate extremes we consider play out over longer time scales: for example, droughts may last several months to even years, wildfires may rage an entire summer season, and crop failures may result from extreme conditions during the entire growing season. A third motivation is that the impacts of compound extremes may be larger than those for individual events even in the case where the concurrence is not on a daily timescale. These are sometimes termed temporally compounding extremes (e.g. Zscheischler et al., 2020), although for simplicity we opted not to make this terminological distinction in our study. For example, vegetation impacts of drought events can be aggravated by droughts in consecutive growing seasons (e.g. Bastos et al., 2021). Similarly, societal vulnerability to floods is modulated by the occurrence of successive flood episodes (Chacowry et al., 2018). We therefore argue that there is some relevance to considering concurrent extremes on yearly timescales.
We nonetheless agree that being able to discriminate the timescale of concurrence of the extreme events we study would allow a more nuanced analysis, as illustrated in lines 23-30. Similarly, we agree with the values of knowing whether a given extreme has occurred once or several times in a given year. We propose to clarify these messages throughout the revised text in Sect. 2 lines 78-87, as follows:- The dataset we use comes with a number of caveats. A minor caveat is that it does not contain crop failure projections under RCP8.5. More importantly, the data represents the occurrence of an extreme event category as a single event within a grid cell per year, no matter if it occurred once or several times within the same location in the same year. Finally, an extreme event such as a wildfire, river flood or tropical cyclone can only partly cover a given grid cell, whereas other extreme events (heatwaves, droughts and crop failures) are assigned by default to the entire grid cell. Thus, for the former three extremes, we consider that a grid cell is entirely affected when more than 0.5% of the 0.5°x0.5° grid cell area is simulated to be affected by the extreme event. Whilst these are limitations of the dataset, we have three distinct motivations to use it throughout our analysis: (i) the dataset is amongst the most detailed and complete of its kind, and provides information on the occurrence of extreme events within the study region over a very long time period (from 1861 until 2099); (ii) some of the climate extremes we consider play out over longer time scales, for example droughts may last several months to even years, wildfires may rage an entire summer season, and crop failures may result from extreme conditions during the entire growing season; (iii) the impacts of compound extremes may be larger than those for individual events even in the case where the concurrence is not on a daily timescale. These are sometimes termed temporally compounding extremes (e.g., Zscheischler et al. 2020b). For example, vegetation impacts of drought events can be aggravated by droughts in consecutive growing seasons (e.g., Bastos et al. 2021). Similarly, societal vulnerability to floods is modulated by the occurrence of successive flood episodes (Chacowry et al., 2018). We therefore use it as the backbone for this study.
- Bastos, A., Orth, R., Reichstein, M., Ciais, P., Viovy, N., Zaehle, S., Anthoni, P., Arneth, A., Gentine, P., Joetzjer, E., Lienert, S., Loughran, T., McGuire, P. C., O, S., Pongratz, J., and Sitch, S.: Vulnerability of European ecosystems to two compound dry and hot summers in 2018 and 2019, Earth Syst. Dynam., 12, 1015–1035, https://doi.org/10.5194/esd-12-1015-2021, 2021.
- Chacowry, A., McEwen, L. J., & Lynch, K. (2018). Recovery and resilience of communities in flood risk zones in a small island developing state: A case study from a suburban settlement of Port Louis, Mauritius. International Journal of Disaster Risk Reduction, 28, 826-838.
Reviewer comment 2: From fig.1, among the 15 pairs of extremes, all the concurrent extremes including heatwaves show the strongest increase. It this related to the apparent global warming trend? And all the future years will become extreme years of heatwaves?
Response: Yes, the increases shown in Fig.1 are due to the global warming trend. However, while our analysis shows that frequency of heatwaves will increase drastically, not all future years are projected as extreme heatwave years for every location (grid) in East Africa, even under the high warming scenarios (Illustration 1 in attached .zip). As reported by many studies, the frequency and magnitude of heatwaves is projected to increase in many regions of the world as a result of global warming (e.g., Russo et al. 2014, 2015, Thiery et al. 2021, IPCC 2021). This is generally confirmed in our analysis over East Africa (Table 2), whereby the mean percentage area affected by heatwaves is projected to increase by the end of the century under all the three future climate scenarios, with larger increases in the warmer scenarios. Furthermore, the increase in probability and spatial extent of concurrent extremes including heatwaves (in the pairs) is projected to increase, with increases in heatwaves identified as a main driver of these events. This is illustrated in lines 280 -287 of this manuscript. While we indeed find that the change in heatwave occurrence provides a strong contribution to the change in occurrence of concurrent extremes, this is far from our only result/conclusion. Indeed, we also highlight river floods and wildfires as a pair of extremes whose concurrence will increase sharply (Fig. 5), and we analyse other pairs of concurrent extremes not involving heatwaves (Appendix D). We propose to add the following text in the method (Sect. 3.3 Line 118) to clarify that the changes in the concurrent extreme occurrence are projected as a result of global warming.:- Considering that the processed impact model simulations account only for climate-induced changes in the extremes (as defined by Lange et al. (2020)), and not changes due to land-use change, we therefore only analyse the climate change-driven changes in concurrent extremes.
Reviewer comment 3: The method used in this paper to search drivers of concurrent extremes seems to be too shallow. So, the results look very obvious, i.e., the global warming is the most important driver. The paper lacks an analysis of the dynamic process that cause to concurrent extreme events
Response: Considering that the six extreme events considered each have different meteorological and physical drivers, and that we utilise extreme event data from processed impact model simulations, diving into the meteorological and physical drivers of the concurrent extreme events presents near-insurmountable challenges. We nonetheless believe that our analysis investigating the changes in the probability of occurrence of concurrent events under future climate scenarios in comparison to early-industrial conditions. This is because: (i) The models only account for climate-induced changes in the hazards and not changes due to land-use change (e.g., deforestation fires) (Lange et al., 2020). Therefore, we can only look at climate change/global warming as the driver of the changes in extreme event occurrence, and in turn the changes in concurrent extremes. (ii) We, furthermore, go deeper in our analysis by splitting the change in concurrent extreme event occurrence into: changes in only one variable per pair, and the changes in the (coupling) dependence between extreme events in a pair, as illustrated in Section 3.3. This provides a good basis to formulate some physical hypotheses on the drivers of the changes in concurrent extreme events as illustrated in Sect. 5.3.
In the revised paper, we aim to improve the communication of the method that we use to identify the drivers of changes in the concurrent extreme occurrences, as well as clarifying in Sect. 5.3 the physical hypotheses to the changes in the concurrent extremes occurrence. -- We propose to add the following text in the methods section (Sect. 3.3, Line 118-122) to clarify this point:
Considering that the processed impact model simulations account only for climate-induced changes in the extremes (as defined by Lange et al. (2020)), and not changes due to land-use change, we therefore only analyse the climate change-driven changes in concurrent extremes. At a given location, from a statistical perspective, the probability of concurrent extreme events can be affected by the effect of climate change on: (i) the probability of the individual extreme events and/or (ii) the dependence between the events (Bevacqua et al., 2020; Zscheischler et al., 2020b). To gain insights into the drivers of the changes, we compute the change in the probability of concurrent extreme events when assuming: (i) changes in the probability of extreme events in one variable only; and (ii) changes in the coupling between the variables only (Bevacqua et al., 2020). - In the discussions (Sect 5.3), we also propose to add the following text to illustrate possible dynamical causes of change in river floods.
According to Niang et al. (2014) & Seneviratne et al. (2021), the East African region is also projected to experience increased intense precipitation by the end of the century (with high confidence) under RCP8.5 scenario. This could be linked to the projected changes in events such as positive Indian Ocean Dipole and El Niño Southern Oscillation events, which play a role in increase in precipitation in parts of the East African region, and have already under present day conditions increased in frequency in relation to the pre-industrial period (medium confidence) (Seneviratne et al. 2021; Souverijns et al. 2021). However, in relation to river floods occurrence, IPCC reports that there is low confidence in the end-of-century projections of flood intensities and frequency due to inadequate data (Arias et al., 2021). Nonetheless, Alfieri et al. (2017) still projects that global warming will increase the frequency of river floods in the Nile and Congo basins, thereby greatly affecting DRC and Sudan (Alfieri et al., 2017; IPCC, 2022). The significant increase in the frequency of concurrent river floods & wildfires in the region by the end of the century can therefore be explained by the expected increase in frequency of river floods, which are the main driver of co-occurrence within this pair (Fig. 2a-c and 5a-c). While, as stated above, we find that heatwaves are a major driver for the increase in their joint occurrence with river floods by the end of the century under RCP8.5, increases in river floods themselves also shape these co-occurrences (Fig. 5d-e). - Souverijns, N., Thiery, W., Demuzere M., and Van Lipzig N. P. M: Drivers of future changes in East African precipitation. Environ. Res. Lett. 11, 2016.
-
AC2: 'Reply on RC2', Derrick Muheki, 07 Oct 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1712', Anonymous Referee #1, 29 Aug 2023
The paper aims at understanding concurrent climate extremes over the eastern parts of Africa using three different emission scenarios. The results show that, concurrent climate extremes are likely to increase with high magnitude over the Nile and Congo basin that are currently the wet regions over East Africa. Heat waves are wildfires are likely to dominate the region by the end of the 21st century as projected by all the emission scenarios.
The paper is coherent, the methods deployed are relevant and the paper conceptualization is well thought of.
Hence, I recommend the paper to be accepted for publication in EGU with minor correction.
Minor Correction.
The authors should enhance labelling of Lat and Lon in all the spatial maps since they are not currently clear.
Citation: https://doi.org/10.5194/egusphere-2023-1712-RC1 -
AC1: 'Reply on RC1', Derrick Muheki, 06 Sep 2023
Thank you for your feedback. We will ensure to revise the labelling of the Latitudes and Longitudes in the spatial maps to ensure that they are clear.
Citation: https://doi.org/10.5194/egusphere-2023-1712-AC1
-
AC1: 'Reply on RC1', Derrick Muheki, 06 Sep 2023
-
RC2: 'Comment on egusphere-2023-1712', Anonymous Referee #2, 10 Sep 2023
This paper analyzed the frequency and spatial extent of 15 types of concurrent extreme events in East Africa, highlighted the concurrent extremes will become the norm in the future.However. It remains some issues to be discussed before it is considered for publication.
1.The pair of concurrent extreme events defined in this paper represent two extreme events occurred within the same location in the same year, no matter if it occurred once or several times. The physics meaning of this definition is unclear. Usually the concurrent extreme events are defined based on daily data, i.e., a pair of extreme events occurring on the same day. The definition in this paper seems to be too crude to obtain physically-meaningful knowledge.
2.From fig.1, among the 15 pairs of extremes, all the concurrent extremes including heatwaves show the strongest increase. It this related to the apparent global warming trend? And all the future years will become extreme years of heatwaves?
3. The method used in this paper to search drivers of concurrent extremes seems to be too shallow. So, the results look very obvious, i.e., the global warming is the most important driver. The paper lacks an analysis of the dynamic process that cause to concurrent extreme events.Citation: https://doi.org/10.5194/egusphere-2023-1712-RC2 -
AC2: 'Reply on RC2', Derrick Muheki, 07 Oct 2023
We thank the reviewer for their time and valuable suggestions to improve the manuscript. Here below, we respond to each individual comment and propose modifications to the manuscript (in italics) to accommodate the concerns raised.
Reviewer comment 1: The pair of concurrent extreme events defined in this paper represent two extreme events occurred within the same location in the same year, no matter if it occurred once or several times. The physics meaning of this definition is unclear. Usually the concurrent extreme events are defined based on daily data, i.e., a pair of extreme events occurring on the same day. The definition in this paper seems to be too crude to obtain physically-meaningful knowledge.
Response: We thank the reviewer for this comment. There are three distinct motivations for our methodological choice to consider annual time steps in this study. The first concerns data availability: the available dataset from ISIMIP 2b only provides annual occurrences of the six categories of climate extreme events (as mentioned in one of the caveats in lines 79-80 of the manuscript). Unfortunately, no multi-hazard future data is available at daily resolution. Due to this limitation of the dataset, we define concurrent events as two events occurring in the same location, during the same time step (here being the same year). A second reason is that some of the climate extremes we consider play out over longer time scales: for example, droughts may last several months to even years, wildfires may rage an entire summer season, and crop failures may result from extreme conditions during the entire growing season. A third motivation is that the impacts of compound extremes may be larger than those for individual events even in the case where the concurrence is not on a daily timescale. These are sometimes termed temporally compounding extremes (e.g. Zscheischler et al., 2020), although for simplicity we opted not to make this terminological distinction in our study. For example, vegetation impacts of drought events can be aggravated by droughts in consecutive growing seasons (e.g. Bastos et al., 2021). Similarly, societal vulnerability to floods is modulated by the occurrence of successive flood episodes (Chacowry et al., 2018). We therefore argue that there is some relevance to considering concurrent extremes on yearly timescales.
We nonetheless agree that being able to discriminate the timescale of concurrence of the extreme events we study would allow a more nuanced analysis, as illustrated in lines 23-30. Similarly, we agree with the values of knowing whether a given extreme has occurred once or several times in a given year. We propose to clarify these messages throughout the revised text in Sect. 2 lines 78-87, as follows:- The dataset we use comes with a number of caveats. A minor caveat is that it does not contain crop failure projections under RCP8.5. More importantly, the data represents the occurrence of an extreme event category as a single event within a grid cell per year, no matter if it occurred once or several times within the same location in the same year. Finally, an extreme event such as a wildfire, river flood or tropical cyclone can only partly cover a given grid cell, whereas other extreme events (heatwaves, droughts and crop failures) are assigned by default to the entire grid cell. Thus, for the former three extremes, we consider that a grid cell is entirely affected when more than 0.5% of the 0.5°x0.5° grid cell area is simulated to be affected by the extreme event. Whilst these are limitations of the dataset, we have three distinct motivations to use it throughout our analysis: (i) the dataset is amongst the most detailed and complete of its kind, and provides information on the occurrence of extreme events within the study region over a very long time period (from 1861 until 2099); (ii) some of the climate extremes we consider play out over longer time scales, for example droughts may last several months to even years, wildfires may rage an entire summer season, and crop failures may result from extreme conditions during the entire growing season; (iii) the impacts of compound extremes may be larger than those for individual events even in the case where the concurrence is not on a daily timescale. These are sometimes termed temporally compounding extremes (e.g., Zscheischler et al. 2020b). For example, vegetation impacts of drought events can be aggravated by droughts in consecutive growing seasons (e.g., Bastos et al. 2021). Similarly, societal vulnerability to floods is modulated by the occurrence of successive flood episodes (Chacowry et al., 2018). We therefore use it as the backbone for this study.
- Bastos, A., Orth, R., Reichstein, M., Ciais, P., Viovy, N., Zaehle, S., Anthoni, P., Arneth, A., Gentine, P., Joetzjer, E., Lienert, S., Loughran, T., McGuire, P. C., O, S., Pongratz, J., and Sitch, S.: Vulnerability of European ecosystems to two compound dry and hot summers in 2018 and 2019, Earth Syst. Dynam., 12, 1015–1035, https://doi.org/10.5194/esd-12-1015-2021, 2021.
- Chacowry, A., McEwen, L. J., & Lynch, K. (2018). Recovery and resilience of communities in flood risk zones in a small island developing state: A case study from a suburban settlement of Port Louis, Mauritius. International Journal of Disaster Risk Reduction, 28, 826-838.
Reviewer comment 2: From fig.1, among the 15 pairs of extremes, all the concurrent extremes including heatwaves show the strongest increase. It this related to the apparent global warming trend? And all the future years will become extreme years of heatwaves?
Response: Yes, the increases shown in Fig.1 are due to the global warming trend. However, while our analysis shows that frequency of heatwaves will increase drastically, not all future years are projected as extreme heatwave years for every location (grid) in East Africa, even under the high warming scenarios (Illustration 1 in attached .zip). As reported by many studies, the frequency and magnitude of heatwaves is projected to increase in many regions of the world as a result of global warming (e.g., Russo et al. 2014, 2015, Thiery et al. 2021, IPCC 2021). This is generally confirmed in our analysis over East Africa (Table 2), whereby the mean percentage area affected by heatwaves is projected to increase by the end of the century under all the three future climate scenarios, with larger increases in the warmer scenarios. Furthermore, the increase in probability and spatial extent of concurrent extremes including heatwaves (in the pairs) is projected to increase, with increases in heatwaves identified as a main driver of these events. This is illustrated in lines 280 -287 of this manuscript. While we indeed find that the change in heatwave occurrence provides a strong contribution to the change in occurrence of concurrent extremes, this is far from our only result/conclusion. Indeed, we also highlight river floods and wildfires as a pair of extremes whose concurrence will increase sharply (Fig. 5), and we analyse other pairs of concurrent extremes not involving heatwaves (Appendix D). We propose to add the following text in the method (Sect. 3.3 Line 118) to clarify that the changes in the concurrent extreme occurrence are projected as a result of global warming.:- Considering that the processed impact model simulations account only for climate-induced changes in the extremes (as defined by Lange et al. (2020)), and not changes due to land-use change, we therefore only analyse the climate change-driven changes in concurrent extremes.
Reviewer comment 3: The method used in this paper to search drivers of concurrent extremes seems to be too shallow. So, the results look very obvious, i.e., the global warming is the most important driver. The paper lacks an analysis of the dynamic process that cause to concurrent extreme events
Response: Considering that the six extreme events considered each have different meteorological and physical drivers, and that we utilise extreme event data from processed impact model simulations, diving into the meteorological and physical drivers of the concurrent extreme events presents near-insurmountable challenges. We nonetheless believe that our analysis investigating the changes in the probability of occurrence of concurrent events under future climate scenarios in comparison to early-industrial conditions. This is because: (i) The models only account for climate-induced changes in the hazards and not changes due to land-use change (e.g., deforestation fires) (Lange et al., 2020). Therefore, we can only look at climate change/global warming as the driver of the changes in extreme event occurrence, and in turn the changes in concurrent extremes. (ii) We, furthermore, go deeper in our analysis by splitting the change in concurrent extreme event occurrence into: changes in only one variable per pair, and the changes in the (coupling) dependence between extreme events in a pair, as illustrated in Section 3.3. This provides a good basis to formulate some physical hypotheses on the drivers of the changes in concurrent extreme events as illustrated in Sect. 5.3.
In the revised paper, we aim to improve the communication of the method that we use to identify the drivers of changes in the concurrent extreme occurrences, as well as clarifying in Sect. 5.3 the physical hypotheses to the changes in the concurrent extremes occurrence. -- We propose to add the following text in the methods section (Sect. 3.3, Line 118-122) to clarify this point:
Considering that the processed impact model simulations account only for climate-induced changes in the extremes (as defined by Lange et al. (2020)), and not changes due to land-use change, we therefore only analyse the climate change-driven changes in concurrent extremes. At a given location, from a statistical perspective, the probability of concurrent extreme events can be affected by the effect of climate change on: (i) the probability of the individual extreme events and/or (ii) the dependence between the events (Bevacqua et al., 2020; Zscheischler et al., 2020b). To gain insights into the drivers of the changes, we compute the change in the probability of concurrent extreme events when assuming: (i) changes in the probability of extreme events in one variable only; and (ii) changes in the coupling between the variables only (Bevacqua et al., 2020). - In the discussions (Sect 5.3), we also propose to add the following text to illustrate possible dynamical causes of change in river floods.
According to Niang et al. (2014) & Seneviratne et al. (2021), the East African region is also projected to experience increased intense precipitation by the end of the century (with high confidence) under RCP8.5 scenario. This could be linked to the projected changes in events such as positive Indian Ocean Dipole and El Niño Southern Oscillation events, which play a role in increase in precipitation in parts of the East African region, and have already under present day conditions increased in frequency in relation to the pre-industrial period (medium confidence) (Seneviratne et al. 2021; Souverijns et al. 2021). However, in relation to river floods occurrence, IPCC reports that there is low confidence in the end-of-century projections of flood intensities and frequency due to inadequate data (Arias et al., 2021). Nonetheless, Alfieri et al. (2017) still projects that global warming will increase the frequency of river floods in the Nile and Congo basins, thereby greatly affecting DRC and Sudan (Alfieri et al., 2017; IPCC, 2022). The significant increase in the frequency of concurrent river floods & wildfires in the region by the end of the century can therefore be explained by the expected increase in frequency of river floods, which are the main driver of co-occurrence within this pair (Fig. 2a-c and 5a-c). While, as stated above, we find that heatwaves are a major driver for the increase in their joint occurrence with river floods by the end of the century under RCP8.5, increases in river floods themselves also shape these co-occurrences (Fig. 5d-e). - Souverijns, N., Thiery, W., Demuzere M., and Van Lipzig N. P. M: Drivers of future changes in East African precipitation. Environ. Res. Lett. 11, 2016.
-
AC2: 'Reply on RC2', Derrick Muheki, 07 Oct 2023
Peer review completion
Journal article(s) based on this preprint
Data sets
Postprocessed ISIMIP2b simulation output Lange et al. (2020 Earth's Future) and Thiery et al. (2021 Science) https://doi.org/10.5281/zenodo.5497633
Model code and software
concurrent_climate_extremes_in_east_africa Derrick Muheki, Axel A. J. Deijns, Emanuele Bevacqua, Gabriele Messori, Jakob Zscheischler, Wim Thiery https://github.com/VUB-HYDR/concurrent_climate_extremes_in_east_africa
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Cited
1 citations as recorded by crossref.
Axel Antonius Johannes Deijns
Emanuele Bevacqua
Gabriele Messori
Jakob Zscheischler
Wim Thiery
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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