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Article

Trends in the Occurrence of Compound Extremes of Temperature and Precipitation in Côte d’Ivoire

by
Elisée Yapo Akobé
1,*,
Adama Diawara
1,2,
Fidèle Yoroba
1,2,
Benjamin K. Kouassi
1,2,
Assi Louis Martial Yapo
2,3,
Ibrahima Diba
4,
Kouakou Kouadio
1,2,
Dro T. Tiémoko
2,5,
Dianikoura Ibrahim Koné
1 and
Arona Diedhiou
6,7
1
Laboratory of Sciences Matter, Environment and Solar Energy (LASMES), University Félix Houphouet-Boigny, UFR SSMT, Abidjan 22 BP 582, Côte d’Ivoire
2
Geophysical Station of Lamto (GSL), N’douci BP 31, Côte d’Ivoire
3
Department of Sciences and Technology, University Alassane Ouattara, Bouaké 01 BP V 108, Côte d’Ivoire
4
Laboratoire d’Océanographie, des Sciences de l’Environnement et du Climat (LOSEC), Université Assane SECK de Ziguinchor, Ziguinchor BP 523, Senegal
5
Laboratory of Fundamental and Applied Physics, University Nangui Abrogoua, Abidjan 02 BP 801, Côte d’Ivoire
6
African Centre of Excellence on Climate Change, Biodiversity and Sustainable Agriculture (ACE CCBAD), University Félix Houphouet-Boigny, Abidjan 22 BP 582, Côte d’Ivoire
7
Institute of Environmental Geosciences, Université Grenoble Alpes, IRD, CNRS, Grenoble INP, IGE, 38000 Grenoble, France
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(1), 3; https://doi.org/10.3390/atmos16010003
Submission received: 29 August 2024 / Revised: 5 November 2024 / Accepted: 12 November 2024 / Published: 24 December 2024
(This article belongs to the Section Meteorology)
Browse Figures
Versions Notes

Abstract

:
The aim of this study is to characterize the compound extremes of rainfall and temperature in Côte d’Ivoire. For this purpose, we analyzed the outputs of fourteen (14) climate models from the CORDEX-Africa project. Results show an increase (approximately 4.3 °C) in the surface temperature and a decrease (5.90%) of the mean rainfall in the near (2036–2065) and far futures (2071–2100) over Côte d’Ivoire during the January–February–March (JFM) period. The analysis of the compound extremes of the wet/warm type highlights an increase in the frequency of this climatic hazard in the northern and central parts of the country during the January–March (JFM) season in the near and far futures. The dry/warm mode will increase in the central and southern parts of the country in the near future and in the whole country in the far future. These increases in compound extremes could lead to an increase in droughts and natural disasters across the country and could have a negative impact on socio-economic activities, such as transportation and agricultural production. This work could provide decision support for political decision-makers in formulating future public policies for managing agricultural production, food security, and natural disasters.

1. Introduction

Climate change is a phenomenon that we are all witnessing because of its harmful consequences, which are already clearly visible.
The resurgence of extreme events, such as floods, droughts, and frequent heatwaves, accompanied by bushfires (in Africa, Europe, America, etc.) is a visible consequence of climate change. These events have unprecedented negative effects on both people and the environment. For example, the 2003 heatwave resulted in 15,000 deaths in France [1,2,3], and 7000 in Russia [4,5]. Similarly, many fires have occurred in 2021 in Europe [6] (San-Miguel-Ayanz et al., 2022). These fires have caused major loss of life and massive destruction of flora and fauna.
Furthermore, in West Africa in general and in Côte d’Ivoire in particular, several authors have conducted studies on climate events based on individual variables, i.e., using only temperature or precipitation to show extreme events, and their trends and impacts [6,7,8,9,10,11]. However, other studies have shown that these events can occur simultaneously or successively [12,13], forming climatic hazards known as compound extreme events. These compounds are “precipitation–thermal” events. Their occurrence is linked to both precipitation and temperature exceeding a threshold.
In other words, these phenomena are concomitantly linked to temperature and precipitation values outside the norm and/or that are undesirable.
In addition, compound weather, climate events, or a combination of several factors and/or hazards contribute to societal and environmental risks [14,15]. The co-occurrence of extremely high temperatures and low or high precipitation can intensify negative effects on ecosystems and society, causing worse impacts on food production and security compared to the impacts of extreme individual variables [16,17,18,19].
However, West Africa in general and Côte d’Ivoire in particular are regions of Africa where the main activity of the population is rain-fed agriculture [20,21]. Côte d’Ivoire’s economy is based mainly on coffee and cocoa, which are perennial tropical plants. The yields of coffee and cocoa are strongly linked to climatic parameters, such as temperature, precipitation, and wind speed [22].
Compound extreme weather events are better understood and documented in Europe [23,24,25,26], Asia [15,27,28,29,30,31,32,33] and America [34,35,36,37,38,39], where several authors have demonstrated their impacts on humans and the environment. In Africa, these phenomena have been little studied. Indeed, the lack of documentation and impoverishment of West African states make this region highly vulnerable to climate change, which is becoming a key factor in the increase in compound extremes. Thus, the conditions under which extreme climatic events occur and how they are characterized depend on the region and some factors, such as a favorable initial state, the presence of large-scale drivers, positive local feedback, and stochastic processes (noise) [40]. In Côte d’Ivoire, studies related to compound events have not yet been conducted. Thus, the need to characterize these events is essential in West Africa, where the economy strongly depends on agriculture and weather conditions. Moreover, in the beginning, climate change scenarios were carried out with global climate models (GCMs) [41] (IPCC, 2013). However, these GCMs face enormous difficulties in representing the climate at the regional scale because of their low horizontal resolution (200 to 300 km). These GCMs do not take into account certain surface processes such as topography, or heterogeneities of the Earth’s surface. To remedy this, regional climate models (RCMs) are increasingly being used to dynamically disaggregate GCMs [42,43,44] (Giorgi et al., 2014; Laprise et al., 2013; Camara et al., 2013). The main objective of this work is to study the evolution of compound extreme events of precipitation and temperature in Côte d’Ivoire during the January to March (JFM), April to June (AMJ), July to September (JAS), and October to December (OND) seasons for present and future climates using fourteen (14) regional climate models involved in CORDEX-Africa phase 2 simulations. The first category of CORDEX-Africa simulations (present time) consists of evaluating the performance of RCMs to reproduce past climates. Studies conducted in this context (e.g., [42,45,46,47] Gbobaniyi et al., 2014; Kim et al., 2014; Nikulin et al., 2012; Camara et al., 2013) have shown that these CORDEX RCMs represent well enough the spatial distribution of rainfall and temperature over Africa, and particularly in West Africa, and also the ensemble mean of the models outperforms regional climate models taken individually.
The remainder of this paper is structured as follows: Section 2 describes the study area, the CORDEX-Africa simulations, the methods used, and the definition of the compound extremes. Section 3 presents the results and discussion, followed by a summary and a conclusion.

2. Materials and Methods

2.1. The Study Area

This study focuses on Côte d’Ivoire, a country located in West Africa on the edge of the Gulf of Guinea (9° W–2.5° W; 4° N–11° N), with a surface area of 322,462 km2 (refer to Figure 1). Furthermore, Côte d’Ivoire consists of three climatic zones, including the littoral, central, and northern zones, characterized by a specific rainfall regime [48]. The littoral zone and the center have a bimodal precipitation regime (two rainy seasons with peaks observed in June and October), while the north presents a single rainy season (July–September) with a peak occurring in August.

2.2. Models and Data

This study used a set of fourteen (14) simulations carried out by eight regional climate models (RCMs) forced by seven global climate models (GCMs) from the second phase of the “COordinated Regional climate Downscaling EXperiment” (CORDEX-Africa) project [49]. Seven (GCMs) derived from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The CORDEX project involves the coordination of numerical climate simulations, implemented by several research centers. It provides reliable climate change scenarios at a high resolution (~50 km), enabling climate impact studies and associated uncertainties for future climate projections using RCMs [50,51,52,53]. The CORDEX project is based on two types of simulations, with two objectives:
-
To provide a framework for evaluating and comparing the performance of RCMs (evaluation framework).
-
To design a set of experiments to produce climate projections for use in impact and adaptation studies (climate projection phase).
The use of all of these high-resolution RCMs is essential to account for the complex topography, the strong variation in vegetation cover, and the variability of rainfall at the mesoscale over West Africa [46,47,48]. It is also an advantage to use RCMs as they provide more detailed information on the climate at the sub-regional and local levels [54,55].
The CORDEX simulations used in this study are from the second phase (climate projection phase), including daily precipitation and minimum and maximum temperatures at a horizontal resolution of 0.44° (~50 km). These simulations cover the African continent and use a dynamic downscaling technique, in which RCMs are forced by Global Climate Models (GCMs) (Table 1). The RCMs’ outputs are available from this website: https://www.cordex.org/output.html (accessed on 31 March 2018) These models are described in details by [47].
The historical simulations cover the period 1950–2005, and the projections span the 2006–2100 period under the RCP4.5 and RCP8.5 forcing scenarios [56,57]. This study is focused on the worst RCP8.5 scenario.

2.3. Methods

A compound extreme event can be defined as two or more extreme events occurring simultaneously or successively, or as combinations of events that are not themselves extreme but lead to a negative event or impact when combined [21,58,59].
According to Leonard [18], a compound event is an extreme that depends on several variables or a statically dependent event.
Clearly, if we consider the variables (precipitation and temperature), we can distinguish several types of compound extremes depending on the choice of percentiles (for example, the 10th, 25th, 75th, 90th, and 95th percentiles) as in [60,61,62].
Based on previous definitions and the choice of the 10th and 90th percentiles [29,63,64], we characterized the dry/warm and wet/warm compound modes in Côte d’Ivoire, annually, during the January–March (JFM), April–June (AMJ), July–September (JAS), and October–December (OND) seasons.
This subdivision of the seasons is commonly used in all climatological studies in West Africa. It is based on the northward and then southward migration of the Intertropical Convergence Zone (ITCZ), which also reflects latitudinal variation in the rain belt. From January to July, the ITCZ migrates northwards, reaching its maximum position in August. Then, from September to December, the ITCZ descends southwards towards the Gulf of Guinea. Because of its geographical position, Côte d’Ivoire experiences alternating dry and rainy seasons. The JFM (January, February, and March) is a dry season with high temperatures and less rain. AMJ (April–May–June) marks the start of the main rainy season, with higher temperatures in April and May, especially in the south of the country. JAS (July–August–September) is the rainy season in the north and center, with maximum rainfall in the north and moderate temperatures. JAS is the short dry season in the south. OND (October–November–December) marks the end of the rainy season in the north and the start of the short rainy season in the center and south.
For the ensemble mean of the models and for different periods (historical, near future, and far future), we first calculated separately the dry days (number of days with daily mean rainfall different from zero and below the 10th percentile of daily mean rainfall), the warm days (number of days with daily temperature above the 90th percentile of daily maximum temperature) and the wet days (number of days with daily mean rainfall above the 90th percentile of daily mean rainfall). The dry/warm metric corresponds to the total number of days where a dry day coincides with the warm day. The wet/warm metric corresponds to the total number of days where a wet day coincides with the warm day. Therefore, the considered compound events (dry/warm and wet/warm) are defined as being when two extremes occur simultaneously (dry and warm or wet and warm).

3. Results and Discussions

3.1. Annual Cycles of Mean Temperature and Precipitation over Different Areas of Côte d’Ivoire

Côte d’Ivoire, located in West Africa, particularly in the Guinea Gulf region, has an equatorial, a tropical savannah, and a transition to a tropical monsoon climate in the coastal, central, and northern regions of the country, respectively. The annual cycles of the mean temperature and rainfall under the RCP8.5 scenario are shown in Figure 2. The annual cycle of the mean temperature varied according to the different climatic zones.
In the south, the seasonal cycle of the daily mean temperature shows a significant peak with 28.94 °C in February, while in the center and the north, there are two peaks in April (28.96 °C) and in November (26.69 °C) for the center and in April (28.69 °C) and in November (26.42 °C) for the north, under RCP8.5. The annual cycles of the mean temperature over the climatic zones of Côte d’Ivoire are in line with the findings of [56].
The seasonal cycle of the mean precipitation exhibited a significant peak over the three sub-regions of Côte d’Ivoire (Figure 2b,d,f). In the northern region, the rainfall regime is characterized by a peak in August. This result is consistent with the findings of [48], who used in situ observation data from 22 stations in Côte d’Ivoire over the period 1964–1997. The peaks observed in July for the south and August for the central regions highlight the unimodal nature of the rainfall regime. Similarly, using CPC and ARC2 observational data, the results of [56] also demonstrated a bimodal rainfall regime in the south and center of Côte d’Ivoire. Consequently, the ensemble mean of the models fails to reproduce the bimodal feature of the annual cycle of the mean precipitation in the south and center of Côte d’Ivoire, mainly because of the physical parameterizations of the models. Numerous studies [52,61,64,65] (Ceccherini et al., 2017; Sylla et al., 2016; Giorgi et al., 2014; Boko et al., 2007) highlighted that the predictions of models based on greenhouse gas emissions scenarios showed that global warming continues to increase. However, these temperature changes are not uniform.

3.2. Changes in Precipitation and Temperature

Changes in rainfall and temperature are partly linked to climate change due to anthropogenic effects of human activities. These changes affect the mean rainfall and maximum, minimum, and mean temperatures in the southern, central, and northern climatic zones of Côte d’Ivoire.
Figure 3 shows the changes in mean rainfall during the January–March (JFM) season for the historical period 1976–2005 (Hist, blue), the near future 2036–2065 (NF, green), and the far future 2071–2100 (FF, red) over the southern, central, and northern climatic zones of Côte d’Ivoire for the ensemble mean of the CORDEX-Africa simulations. The results show that the rainfall will decrease in the south and the center of the country in the near- and far futures. Figure 4a–c shows the projections of the maximum, minimum, and mean temperatures over the JFM summer period for the three (03) periods (historical, near and far futures) in the southern, central, and northern climate zones of Côte d’Ivoire, respectively. The analysis shows that the minimum, maximum, and mean temperatures will increase in the south, north, and south of Côte d’Ivoire in the near and far futures. This could lead to a high frequency of extremes events, such as diurnal heatwaves and a dry/warm mode, and an increase in future energy consumption. Indeed, the climate change scenarios used in West Africa showed that the greatest temperature rises are recorded in semi-arid areas such as the Sahara and the Sahel, and the lowest in the lower latitudes, especially in the Guinean zone [66].

3.3. Compound Extremes of Precipitation and Temperature

3.3.1. Present and Future Climates

The dry/warm (DW) and wet/warm (WW) precipitation and temperature extremes are shown in Figure 5 and Figure 6, respectively. These hazards are presented for two modes of variability: their inter-seasonal trends (JFM, AMJ, JAS, and OND) and their sub-regional trends over the different climate zones of Côte d’Ivoire (littoral (south), center, and north) (Figure 6). The analysis of the total number of compound dry/warm extremes per year over the periods 1976–2005 (historical), 2036–2065 (near future), and 2071–2100 (far future) shows that the ensemble mean of the models simulates a high occurrence of compound dry/warm and wet/warm events. The dry/warm mode will decrease in the future for all seasons.
Furthermore, human health and crop yields could be adversely affected by the dry/warm mode because high temperatures pose a serious threat to human health and plants [17,21].
Figure 6 shows the total number of dry/warm and wet/warm compounds for the JFM, AMJ, JAS, and OND seasons over the historical (1976–2005) and future periods in the south, center, and north of Côte d’Ivoire.
In AMJ and JAS, the compound dry/warm type (DW) will decrease in the three sub-regions of the country. The wet/warm type (WW) will increase in DJF and OND. In the AMJ and JAS seasons, DW mode decreased in the three sub-regions in the near and far futures. The WW mode will increase in the south, center and north of the country in the near and far futures. During the OND season, DW mode showed a downward trend over all climatic zones, while WW mode showed an upward trend in the future.
Furthermore, heavy rainfall associated with high temperatures (or slight rainfall associated with abnormal temperatures) can simultaneously intensify thermal amplitude, which has a negative impact on living organisms and their growth. This could also have a major impact on agriculture, particularly during plant flowering.

3.3.2. Spatial Changes in Compound Extremes for the JFM Season

Previous study conducted by [62] Diba et al. (2022) showed good performance by the CMIP5 models in representing the compound extremes of dry/warm and wet/warm modes over West Africa at the seasonal and intraseasonal timescales in spite of the presence of some biases. Changes in the occurrence of compound dry/warm extremes in the near (2036–2065) and far (2071–2100) future periods are shown in Figure 7. The ensemble mean of the models projected an increase in dry/warm compound extremes in the southern and central parts of Côte d’Ivoire under the RCP8.5 scenario. The wet/warm mode will increase in the northern and central parts of the country. The minimum values of changes in the dry/warm mode in the northern part of the country could be explained by the advent of the harmattan phenomenon, which intensified during JFM in the northern part of Côte d’Ivoire. This means that it is cooler in the north than south of Côte d’Ivoire. Thus, the influence of harmattan attenuates the heat at this time of year and reduces its occurrence in the north of the country. Similarly, it shows a south–north gradient, with maxima located in the north, and mainly from the center to the north (Figure 7). The ensemble mean of the models projected a high occurrence of compound extremes of DW and WW modes in the study area, which is likely to change significantly in the future. In the north of Côte d’Ivoire, most of the rainfall is accompanied by strong sunshine. This could explain the high occurrence of these hazards.
These authors assessed the variability in global land and crop areas affected by compound dry and warm events. The authors of [15,18,27,30] assessed the variability of compound dry/warm events in global land and crop areas. Their results have projected an increase in compound dry/warm events over the entire spatial extent of global land areas during June–August (JJA) and December–February (DJF).
Following [67], the intensification of the dependence between precipitation and temperature favors an increase in the dependence on the compound dry/warm mode. The authors of [68,69] have also shown that increasing temperature is the main factor in the variation in compound dry/warm events in Europe. This study could contribute to increasing the resilience of agriculture and human health to the effects of extreme climate events.
The increase in WW could have a major impact on agriculture and food security in Côte d’Ivoire. The author of [16] showed that compound events had a negative impact on agriculture (wheat and maize production) in China.
Under the rcp4.5 scenario [additional material], the dry/warm mode shows little change in the south and an increase in frequency in the north in the far future under the rcp4.5 scenario. The wet/warm mode is more intense in the far future than in the near future. Compared with rcp8.5, the rcp4.5 scenario shows higher percentages of changes than those simulated in the rcp8.5 scenario, but in absolute terms, the number of compound events is lower in rcp4.5 than in rcp8.5. This analysis shows that whatever the scenario considered, the occurrence of compound events will increase in the near and distant future, but much less in number in rcp4.5 than in rcp8.5.

4. Conclusions

This study contributes to the study of climatic events, and represents, in particular, an initiative to characterize the occurrence of compound extreme events (i.e., dry/warm (DW) and wet/warm (WW) modes) in Côte d’Ivoire under future climates.
This study, carried out with fourteen (14) CORDEX-Africa simulations, has made it possible to characterize compound events (DW and WW) as well as their future evolutions under the greenhouse gas emission scenario RCP8.5 over Côte d’Ivoire. As for results, in the southern part of the country, the seasonal cycle of the daily mean temperature shows a significant peak, with 28.94 °C in February, while in the center and the northern parts, there are two peaks in April (28.96 °C) and in November (26.69 °C) for the center and in April (28.69 °C) and in November (26.42 °C) for the North.
The overall average of the models showed a decrease in precipitation and an increase in temperature (maximum, minimum, and average) from the south to the north of the country in the near and far future periods.
The analysis of historical CORDEX simulations data showed a significant increase in the occurrence of compound events in the past climate.
According to the multi-model ensemble, the frequency of compound events will increase in the near future (2036–2065) and far future (2071–2100) in JFM. This increase will affect human health and agriculture sectors in the future. While, in the AMJ, JAS and OND seasons, DW events will decrease (~−0.076 for NF and ~−0.399 for FF), while WW events will increase (~0.605 for NF and ~0.620 for FF). The increase in WW events under the greenhouse gas emission scenario RCP8.5 could have a major impact on the agriculture of the local population in Côte d’Ivoire. Results provide strong evidence that temperature variations in West Africa are linked to the hydrologic cycle.
This increase in compound events will have a negative impact on agriculture, and therefore on the economy of Côte d’Ivoire. Irrigation could therefore be an aid to adaptation strategies to protect the agricultural sector.
The results of this study could also help to increase the resilience of certain human activities in West Africa to climate change, especially in Côte d’Ivoire, a country where agriculture is the main activity and source of income for most of the population.
In short, it is undeniable that this work needs to be taken further in order to improve the state of studies of the impact of compound events on human activities such as agriculture in West Africa in the context of climate change.

Author Contributions

Conceptualization, E.Y.A.; Methodology, E.Y.A., A.D. (Adama Diawara) and A.D. (Arona Diedhiou); Software, D.T.T. and D.I.K.; Validation, A.D. (Arona Diedhiou); Formal analysis, F.Y., B.K.K. and K.K.; Data curation, A.L.M.Y.; Writing—original draft, E.Y.A., A.D. (Adama Diawara) and A.D. (Arona Diedhiou); Writing—review & editing, A.L.M.Y. and I.D.; Supervision, A.D. (Adama Diawara) and A.D. (Arona Diedhiou). All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Institut de Recherche pour le Développement (IRD), France (UMR IGE Imputation, grant no. 252RA5).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data from these simulations are available free of charge on request by e-mail to the corresponding authors.

Acknowledgments

We would like to thank the CORDEX-AFRICA coordinators and the Centre national de calcul de Côte d’Ivoire (CNCCI) at the Université Félix Houphouet Boigny (UFHB) for providing the computing power used to carry out the simulations in this work. The authors also thank the Institute of Research for Development (IRD, France) and the Laboratoire Mixte International NEXUS (LMI-NEXUS) (Abidjan, Côte d’Ivoire). We confirm that all the people mentioned above have consented to the acknowledgements.

Conflicts of Interest

The authors declare no potential conflicts of interest.

References

  1. Soubeyroux, J.M.; Schneider, M.; Ouzeau, G. Recensement des vagues de chaleur en france a contexte de changement climatique. In Proceedings of the XXVIIIe Colloque de l’Association Internationale de Climatologie, Liège, Belgium, 1–4 July 2015; pp. 615–620. [Google Scholar]
  2. Wagner, V.; Aymeric, U.; Calmet, C.; Mathild, P. Évolution des vagues de chaleur et de la mortalité associée en france, 2004–2014. Bull. Épidémiologique Hebd. 2018, 16, 320–325. [Google Scholar]
  3. Poumadere, M.; Mays, C.; Le Mer, S.; Blong, R. The 2003 heat wave in France: Dangerous climate change here and now. Risk Anal. 2005, 25, 1483–1494. [Google Scholar] [CrossRef] [PubMed]
  4. Barriopedro, D. The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe. Science 2011, 332, 220–224. [Google Scholar] [CrossRef] [PubMed]
  5. Matsueda, M. Predictability of Euro—Russian blocking in summer of 2010. Geophys. Res. Lett. 2011, 38, 1–6. [Google Scholar] [CrossRef]
  6. San-Miguel-Ayanz, J.; Durrant, T.; Boca, R.; Maianti, P.; Liberta, G.; Artes Vivancos, T.; Jacome Felix Oom, D.; Branco, A.; De Rigo, D.; Ferrari, D.; et al. Forest Fires in Europe, Middle East and North Africa 2021, EUR 31269 EN; Publications Office of the European Union: Luxembourg, 2022; ISBN 978-92-76-58616-6. [Google Scholar] [CrossRef]
  7. Kouassi, A.M.; Kouamé, K.F.; Koffi, Y.B.; Dje, K.B.; Paturel, J.E.; Oulare, S. Analyse de la variabilité climatique et de ses influences sur les régimes pluviométriques saisonniers en Afrique de l’Ouest: Cas du bassin versant du N’zi (Bandama) en Côte d’Ivoire. Cybergeo Eur. J. Geogr. 2010. [Google Scholar] [CrossRef]
  8. Sarr, B.; Kafando, L. Identification des risques climatiques de la culture du maïs au Burkina Faso. Int. J. Biol. Chem. Sci. 2011, 5, 1659–1675. [Google Scholar] [CrossRef]
  9. Ringard, J.; Dieppois, B.; Rome, S.; Diedhiou, A.; Pellarin, T.; Konaré, A.; Diawara, A.; Konaté, D.; Dje, B.K.; Katiellou, G.L.; et al. The intensification of thermal extremes in west Africa. Glob. Planet. Chang. 2016, 139, 66–77. [Google Scholar] [CrossRef]
  10. Kabe, A.D.; Tellro, N.; Dadoum, M. Variabilité pluviométrique et activités socio-économiques des pasteurs et agropasteurs de la partie Tchadienne du bassin du Lac-Tchad à Bol Résumé. Afr. Sci. 2019, 15, 67–79. [Google Scholar]
  11. Yapo, A.L.M.; Diawara, A.; Yoroba, F.; Kouassi, B.K.; Sylla, M.B.; Kouadio, K.; Odoulami, R.C.; Tiémoko, D.T. Twenty-First Century Projected Changes in Extreme Temperature over Côte d’Ivoire (West Africa). Int. J. Geophys. 2019, 2019, 5610328. [Google Scholar] [CrossRef]
  12. Herold, J.; Eamus, D.; Liu, G.; Liu, H.; Yin, Y.; Basconcillo, J.; Moon, I.; Wang, B. Projected changes in the frequency of climate extremes over southeast Australia Projected changes in the frequency of climate extremes over southeast Australia. Environ. Res. Commun. 2021, 3, 011001. [Google Scholar] [CrossRef]
  13. Gissing, A.; Timms, M.; Browning, S.; Crompton, R. Compound natural disasters in Australia: A historical analysis. Environ. Hazards 2022, 21, 159–173. [Google Scholar] [CrossRef]
  14. Ridder, N.N.; Pitman, A.J.; Ukkola, A.M. Do CMIP6 Climate Models Simulate Global or Regional Compound Events Skillfully? Geophys. Res. Lett. 2021, 48, e2020GL091152. [Google Scholar] [CrossRef]
  15. Zscheischler, J.; Westra, S.; Van Den Hurk BJ, J.M.; Seneviratne, S.I.; Ward, P.J.; Pitman, A.; Aghakouchak, A.; Bresch, D.N.; Leonard, M.; Wahl, T.; et al. Future climate risk from compound events. Nat. Clim. Chang. 2018, 8, 469–477. [Google Scholar] [CrossRef]
  16. Lu, Y.; Hu, H.; Li, C.; Tian, F. Increasing compound events of extreme hot and dry days during growing seasons of wheat and maize in China. Sci. Rep. 2018, 8, 16700. [Google Scholar] [CrossRef]
  17. Overpeck, J.T. The challenge of hot drought. Nature 2013, 503, 350–351. [Google Scholar] [CrossRef]
  18. Leonard, M.; Westra, S.; Phatak, A.; Lambert, M.F.; van den Hurk, B.; Mcinnes, K.; Risbey, J.; Stafford-Smith, M. A compound event framework for understanding extreme impacts. Wiley Interdiscip. Rev. Clim. Chang. 2014, 5, 113–128. [Google Scholar] [CrossRef]
  19. Mazdiyasni, O.; AghaKouchak, A. Substantial increase in concurrent droughts and heatwaves in the United States. Proc. Natl. Acad. Sci. USA 2015, 112, 11484–11489. [Google Scholar] [CrossRef]
  20. Hao, Z. Compound events and associated impacts in China. iScience 2022, 25, 104689. [Google Scholar] [CrossRef]
  21. Dinku, T.; Ceccato, P.; Grover-Kopec, E.; Lemma, M.; Connor, S.J.; Ropelewski, C.F. Validation of satellite rainfall products over East Africa’s complex topography. Int. J. Remote Sens. 2007, 28, 1503–1526. [Google Scholar] [CrossRef]
  22. Diba, I.; Camara, M.; Sarr, A.B.; Basse, J. Caractérisation des extrêmes composés de précipitation et de température au Sénégal: Climat présent et futur. Afr. Sci. 2021, 18, 12–30. [Google Scholar]
  23. Yoroba, F.; Kouassi, B.K.; Diawara, A.; Yapo, L.A.M.; Kouadio, K.; Tiemoko, D.T.; Kouadio, Y.K.; Koné, I.D.; Assamoi, P. Evaluation of Rainfall and Temperature Conditions for a Perennial Crop in Tropical Wetland: A Case Study of Cocoa in Côte d’Ivoire. Adv. Meteorol. 2019, 2019, 9405939. [Google Scholar] [CrossRef]
  24. Morán-Tejeda, E.; López-Moreno, J.I.; Beniston, M. The changing roles of temperature and precipitation on snowpack variability in Switzerland as a function of altitude. Geophys. Res. Lett. 2013, 40, 2131–2136. [Google Scholar] [CrossRef]
  25. Ionita, M.; Caldarescu, D.E.; Nagavciuc, V. Compound Hot and Dry Events in Europe: Variability and Large-Scale Drivers. Front. Clim. 2021, 3, 688991. [Google Scholar] [CrossRef]
  26. Otero, N.; Martius, O.; Allen, S.; Bloomfield, H.; Schaefli, B. Characterizing renewable energy compound events across Europe using a logistic regression-based approach. Meteorol. Appl. 2022, 29, e2089. [Google Scholar] [CrossRef]
  27. Vyshkvarkova, E.; Sukhonos, O. Compound Extremes of Air Temperature and Precipitation in Eastern Europe. Climate 2022, 10, 133. [Google Scholar] [CrossRef]
  28. Sharma, S.; Mujumdar, P. Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India. Sci. Rep. 2017, 7, 15582. [Google Scholar] [CrossRef]
  29. Zhou, S.; Zhang, Y.; Park Williams, A.; Gentine, P. Projected increases in intensity, frequency, and terrestrial carbon costs of compound drought and aridity events. Sci. Adv. 2019, 5, eaau5740. [Google Scholar] [CrossRef]
  30. Wu, X.; Hao, Z.; Hao, F.; Li, C.; Zhang, X. Spatial and Temporal Variations of Compound Droughts and Hot Extremes in China. Atmosphere 2019, 10, 95. [Google Scholar] [CrossRef]
  31. Wu, X.; Hao, Z.; Tang, Q.; Singh, V.P.; Zhang, X.; Hao, F. Projected increase in compound dry and hot events over global land areas. Int. J. Climatol. 2020, 41, 393–403. [Google Scholar] [CrossRef]
  32. Wu, J.; Chen, Y.; Liao, Z.; Gao, X.; Zhai, P.; Hu, Y. Increasing risk from landfalling tropical cyclone- heatwave compound events to coastal and inland OPEN ACCESS Increasing risk from landfalling tropical cyclone-heatwave compound events to coastal and inland China. Environ. Res. Lett. 2022, 17, 105007. [Google Scholar] [CrossRef]
  33. Tang, Z.; Yang, T.; Lin, X.; Li, X.; Cao, R.L.W. Future changes in the risk of compound hot and dry events over China estimated with two large ensembles. PLoS ONE 2022, 17, e0264980. [Google Scholar] [CrossRef] [PubMed]
  34. Wang, C.; Li, Z.; Chen, Y.; Li, Y.; Liu, X.; Hou, Y.; Wang, X.; Kulaixi, Z.; Sun, F. Increased Compound Droughts and Heatwaves in a Double Pack in Central Asia. Remote Sens. 2022, 14, 2959. [Google Scholar] [CrossRef]
  35. Collazo, S.; Barrucand, M.; Rusticucci, M. Hot and dry compound events in South America: Present climate and future projections, and their association with the Pacific Ocean. Nat. Hazards 2023, 119, 299–323. [Google Scholar] [CrossRef]
  36. Alizadeh, M.R.; Adamowski, J.; Nikoo, M.R.; AghaKouchak, A.; Dennison, P.; Sadegh, M. A century of observations reveals increasing likelihood of continental-scale compound dry-hot extremes. Sci. Adv. 2020, 6, eaaz4571. [Google Scholar] [CrossRef] [PubMed]
  37. Brazda, S.; Šraj, M. Classification of Floods in Europe and North America with Focus on Compound Events. ISPRS Int. J. Geo-Inf. 2022, 11, 580. [Google Scholar] [CrossRef]
  38. Olmo, M.E.; Weber, T.; Teichmann, C.; Bettolli, M.L. Compound Events in South America Using the CORDEX-CORE Ensemble: Current Climate Conditions and Future Projections in a Global Warming Scenario. J. Geophys. Res. Atmos. 2022, 127, e2022JD037708. [Google Scholar] [CrossRef]
  39. Zscheischler, J.; Lehner, F. Attributing Compound Events to Anthropogenic Climate Change. Bull. Am. Meteorol. Soc. 2022, 103, E936–E953. [Google Scholar] [CrossRef]
  40. Sillmann, J.; Thorarinsdottir, T.; Keenlyside, N.; Schaller, N.; Alexander, L.V.; Hegerl, G.; Seneviratne, S.I.; Vautard, R.; Zhang, X.; Zwiers, F.W. Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities. Weather Clim. Extrem. 2017, 18, 65–74. [Google Scholar] [CrossRef]
  41. IPCC. Climate Change. The Physical Science Basis. In Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel o; Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; IPCC: Geneva, Switzerland, 2013. [Google Scholar]
  42. Camara, M.; Diedhiou, A.; Sow, B.A.; Diallo, M.D.; Diatta, S.; Mbaye, I.; Diallo, I. Analyse de la pluie simulée par les modèles climatiques régionaux de CORDEX en Afrique de l’Ouest. Sécheresse 2013, 24, 14–28. [Google Scholar]
  43. Laprise, R.; Hernández-Díaz, L.; Tete, K.; Sushama, L.; Šeparović, L.; Martynov, A.; Winger, K.V.M. Climate projections over CORDEX Africa domain using the fifth-generation Canadian Regional Climate Model (CRCM5). Clim. Dyn. 2013, 41, 3219–3246. [Google Scholar] [CrossRef]
  44. Giorgi, F.; Coppola, E.; Raffaele, F.; Diro, G.T.; Fuentes-Franco, R.; Giuliani, G.; Mamgain, A.; Llopart, M.P.; Mariotti, L.T.C. Changes in extremes and hydroclimatic regimes in the CREMA ensemble projections. Clim. Chang. 2014, 125, 39–51. [Google Scholar] [CrossRef]
  45. Gbobaniyi, E.; Sarr, A.; Sylla, M.B.; Diallo, I.; Lennard, C.; Dosio, A.; Dhiédiou, A.; Kamga, A.; Klutse, N.A.; Hewitson, B.N.G. Climatology, annual cycle and interannual variability of precipitation and temperature in CORDEX simulations over West Africa. Int. J. Climatol. 2014, 34, 2241–2257. [Google Scholar] [CrossRef]
  46. Kim, J.; Waliser, D.E.; Mattmann, C.A.; Goodale, C.E.; Hart, A.F.; Zimdars, P.A.; Crichton, D.J.; Jones, C.; Nikulin, G.; Hewitson, B.J.C. Evaluation of the CORDEX-Africa multi-RCM hindcast: Systematic model errors. Clim. Dyn. 2014, 42, 1189–11202. [Google Scholar] [CrossRef]
  47. Nikulin, G.; Meteorological, S.; Asrar, G.; Northwest, P.; Cerezo-mota, R. Precipitation Climatology in an Ensemble of CORDEX-Africa Regional Climate Simulations. J. Clim. 2012, 25, 6057–6078. [Google Scholar] [CrossRef]
  48. Kouadio, Y.; Ali, K.; Zahiri, E.-P.; Assamoi, A. Etude de la prédictibilité de la pluviométrie en Côte d’Ivoire durant la période de Juillet à Septembre. Rev. Ivoirienne Sci. Technol. 2007, 10, 117–134. [Google Scholar]
  49. Giorgi, F.; Gutowski, W.J. Regional Dynamical Downscaling and the CORDEX Initiative. Annu. Rev. Environ. Resour. 2015, 40, 467–490. [Google Scholar] [CrossRef]
  50. Giorgi, F.; Jones, C.; Asrar, G.R. Addressing climate information needs at the regional level: The CORDEX framework. World Meteorol. Organ. Bull. 2009, 58, 175. [Google Scholar]
  51. Giorgi, F.; Gutowski, W.J. Coordinated Experiments for Projections of Regional Climate Change. Curr. Clim. Chang. Rep. 2016, 2, 202–210. [Google Scholar] [CrossRef]
  52. Teichmann, C.; Eggert, B.; Elizalde, A.; Haensler, A.; Jacob, D.; Kumar, P.; Moseley, C.; Pfeifer, S.; Rechid, D.; Remedio, A.R.; et al. How does a regional climate model modify the projected climate change signal of the driving GCM: A study over different CORDEX regions using REMO. Atmosphere 2013, 4, 214–236. [Google Scholar] [CrossRef]
  53. Dosio, A.; Panitz, H.J.; Schubert-Frisius, M.; Lüthi, D. Dynamical downscaling of CMIP5 global circulation models over CORDEX-Africa with COSMO-CLM: Evaluation over the present climate and analysis of the added value. Clim. Dyn. 2015, 44, 2637–2661. [Google Scholar] [CrossRef]
  54. Sylla, M.B.; Giorgi, F.; Pal, J.S.; Gibba, P.; Kebe, I.; Nikiema, M. Projected changes in the annual cycle of high-intensity precipitation events over West Africa for the late twenty-first century. J. Clim. 2015, 28, 6475–6488. [Google Scholar] [CrossRef]
  55. Sylla, M.B.; Elguindi, N.; Giorgi, F.; Wisser, D. Projected robust shift of climate zones over West Africa in response to anthropogenic climate change for the late 21st century. Clim. Chang. 2016, 134, 241–253. [Google Scholar] [CrossRef]
  56. Yapo, A.L.M. Impacts du Changement Climatique sur la Fréquence et l’Intensité des Evènements Extrêmes en Côte d’Ivoire. Ph.D. Thesis, University Félix Houphouet-Boigny, Abidjan, Côte d’Ivoire, 2021. [Google Scholar]
  57. Moss, R.H.; Edmonds, J.A.; Hibbard, K.A.; Manning, M.R.; Rose, S.K.; van Vuuren, D.P.; Carter, T.R.; Emori, S.; Kainuma, M.; Kram, T.; et al. The next generation of scenarios for climate change research and assessment. Nature 2010, 463, 747–756. [Google Scholar] [CrossRef] [PubMed]
  58. van Vuuren, D.P.; Edmonds, J.; Kainuma, M.; Riahi, K.; Thomson, A.; Hibbard, K.; Hurtt, G.C.; Kram, T.; Krey, V.; Lamarque, J.F.; et al. The representative concentration pathways: An overview. Clim. Chang. 2011, 109, 5–31. [Google Scholar] [CrossRef]
  59. Schuhen, N.; Sillmann, J.; Cattiaux, J. Defining compound extreme events on objective spatiotemporal scales. In Proceedings of the 25th EGU General Assembly, Vienna, Austria, 23–28 April 2023; p. EGU-13562. [Google Scholar] [CrossRef]
  60. Beniston, M.; Diaz, H.F. The 2003 heat wave as an example of summers in a greenhouse climate? Observations and climate model simulations for Basel, Switzerland. Glob. Planet. Chang. 2004, 44, 73–81. [Google Scholar] [CrossRef]
  61. Hao, Z.; Aghakouchak, A.; Phillips, T.J. Changes in concurrent monthly precipitation and temperature extremes. Environ. Res. Lett. 2013, 8, 034014. [Google Scholar] [CrossRef]
  62. Diba, I.; Diedhiou, A.; Famien, A.M.; Camara, M.; Fotso-Nguemo, T.C. Changes in compound extremes of rainfall and temperature over West Africa using CMIP5 simulations. Environ. Res. Commun. 2022, 4, 105003. [Google Scholar] [CrossRef]
  63. Hao, Z.; Singh, V.P.; Hao, F. Compound extremes in hydroclimatology: A review. Water 2018, 10, 16–21. [Google Scholar] [CrossRef]
  64. Ceccherini, G.; Russo, S.; Ameztoy, I.; Marchese, A.F.C.-M.C. Heat waves in Africa 1981–2015, observations and reanalysis. Nat. Hazards Earth Syst. Sci. 2017, 17, 115–125. [Google Scholar] [CrossRef]
  65. Boko, M.; Niang, I.; Nyong, A.; Vogel, C.; Githeko, A.; Medany, M.; OsmanElasha, B.; Tabo, R.; Yanda, P. Africa Climate Change: Impacts, adaptation and vulnerability. In Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J., Hanson, C.E., Eds.; Cambridge University Press: Cambridge, UK, 2007; pp. 433–467. [Google Scholar]
  66. Hulme, M.; Doherty, R.; Ngara, T.; New, M.L.D. African climate change: 1900–2100. Clim. Res. 2001, 17, 145–168. [Google Scholar] [CrossRef]
  67. Zscheischler, J.; Seneviratne, S.I. Dependence of drivers affects risks associated with compound events. Sci. Adv. 2017, 3, e1700263. [Google Scholar] [CrossRef] [PubMed]
  68. Sarhadi, A.; Ausín, M.C.; Wiper, M.P.; Touma, D.; Diffenbaugh, N.S. Multidimensional risk in a nonstationary climate: Joint probability of increasingly severe warm and dry conditions. Sci. Adv. 2018, 4, eaau3487. [Google Scholar] [CrossRef] [PubMed]
  69. Manning, C.; Widmann, M.; Bevacqua, E.; Van Loon, A.F.; Maraun, D.; Vrac, M. Increased probability of compound long-duration dry and hot events in Europe during summer (1950–2013). Environ. Res. Lett. 2019, 14, 094006. [Google Scholar] [CrossRef]
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Figure 1. Topography of the study area.
Figure 1. Topography of the study area.
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Figure 2. Annual cycle of the mean temperature (a,c,e) and mean precipitation (b,d,f) over the historical period 1976–2005 (black), the near future period 2036–2065 (blue) and the far future period 2071–2100 (red) for the different zones (south, center, and north) under the RCP8.5 scenario.
Figure 2. Annual cycle of the mean temperature (a,c,e) and mean precipitation (b,d,f) over the historical period 1976–2005 (black), the near future period 2036–2065 (blue) and the far future period 2071–2100 (red) for the different zones (south, center, and north) under the RCP8.5 scenario.
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Figure 3. Projection of the mean precipitation (mm/day) during the JFM season (January to March) for the historical period 1986–2005 (HIST in blue), the near future 2036–2065 (NF in green), and the far future 2071–2100 (FF in red) in the south, center and north of Côte d’Ivoire for the ensemble mean of all models.
Figure 3. Projection of the mean precipitation (mm/day) during the JFM season (January to March) for the historical period 1986–2005 (HIST in blue), the near future 2036–2065 (NF in green), and the far future 2071–2100 (FF in red) in the south, center and north of Côte d’Ivoire for the ensemble mean of all models.
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Figure 4. Projections of the maximum (a), minimum (b), and mean (c) temperatures (°C) during the JFM season (January–March) for the historical period 1976–2005 (His in blue), the near future 2036–2065 (NF in green), and the far future 2071–2100 (FF in red) in the south, center, and north of Côte d’Ivoire for the ensemble mean of all models.
Figure 4. Projections of the maximum (a), minimum (b), and mean (c) temperatures (°C) during the JFM season (January–March) for the historical period 1976–2005 (His in blue), the near future 2036–2065 (NF in green), and the far future 2071–2100 (FF in red) in the south, center, and north of Côte d’Ivoire for the ensemble mean of all models.
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Figure 5. Inter-seasonal boxplots of the total number of compound dry/warm (left) and wet/warm (right) modes during the JFM, AMJ, JAS, and OND seasons over the historical period 1976–2005 and future periods (2036–2065 and 2071–2100) for the ensemble mean of the CORDEX-Africa models.
Figure 5. Inter-seasonal boxplots of the total number of compound dry/warm (left) and wet/warm (right) modes during the JFM, AMJ, JAS, and OND seasons over the historical period 1976–2005 and future periods (2036–2065 and 2071–2100) for the ensemble mean of the CORDEX-Africa models.
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Figure 6. Inter-seasonal and sub-regional boxplots of the total number of compound dry/warm (left) and wet/warm (right) modes during the FM, AMJ, JAS, and OND seasons over the historical period 1976–2005 and future periods (2036–2065 and 2071–2100) for the ensemble mean of the CORDEX-Africa models.
Figure 6. Inter-seasonal and sub-regional boxplots of the total number of compound dry/warm (left) and wet/warm (right) modes during the FM, AMJ, JAS, and OND seasons over the historical period 1976–2005 and future periods (2036–2065 and 2071–2100) for the ensemble mean of the CORDEX-Africa models.
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Figure 7. Changes in the occurrence (%) of compound extreme dry/warm (a,c) and wet/warm (b,d) modes during January–March (JFM) season in the near (2036–2065) and far (2071–2100) futures under RCP8.5 scenario.
Figure 7. Changes in the occurrence (%) of compound extreme dry/warm (a,c) and wet/warm (b,d) modes during January–March (JFM) season in the near (2036–2065) and far (2071–2100) futures under RCP8.5 scenario.
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Table 1. List of the 14 CORDEX-Africa phase 2 simulations used in this study.
Table 1. List of the 14 CORDEX-Africa phase 2 simulations used in this study.
RCMsGCMsRCPsStatus
CanESM28.5ESGF
CNRM-CM58.5
SMHI-RCA4ES-EARTH-r128.5
IPSL-CM5A-MR8.5
MPI-ESM-LR8.5
ES-EARTH-r128.5
CLMcom-CCLM4-8-17HadGEM2-ES8.5ESGF
CNRM-CM58.5
DMI-HIRHAM5EC-EARTH-r38.5ESGF
KNMI-RACMO22EEC-EARTH-r18.5ESGF
CCCma-CanRCM4CanESM28.5CCCMA ftp
MPI-CSC-REMO2009MPI-ESM-LR8.5RCM group
CNRM-ALADIN52CNRM-CM58.5RCM group (not all vars)
BCCR-WRF331NorESM1-M8.5RCM group
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Akobé, E.Y.; Diawara, A.; Yoroba, F.; Kouassi, B.K.; Yapo, A.L.M.; Diba, I.; Kouadio, K.; Tiémoko, D.T.; Koné, D.I.; Diedhiou, A. Trends in the Occurrence of Compound Extremes of Temperature and Precipitation in Côte d’Ivoire. Atmosphere 2025, 16, 3. https://doi.org/10.3390/atmos16010003

AMA Style

Akobé EY, Diawara A, Yoroba F, Kouassi BK, Yapo ALM, Diba I, Kouadio K, Tiémoko DT, Koné DI, Diedhiou A. Trends in the Occurrence of Compound Extremes of Temperature and Precipitation in Côte d’Ivoire. Atmosphere. 2025; 16(1):3. https://doi.org/10.3390/atmos16010003

Chicago/Turabian Style

Akobé, Elisée Yapo, Adama Diawara, Fidèle Yoroba, Benjamin K. Kouassi, Assi Louis Martial Yapo, Ibrahima Diba, Kouakou Kouadio, Dro T. Tiémoko, Dianikoura Ibrahim Koné, and Arona Diedhiou. 2025. "Trends in the Occurrence of Compound Extremes of Temperature and Precipitation in Côte d’Ivoire" Atmosphere 16, no. 1: 3. https://doi.org/10.3390/atmos16010003

APA Style

Akobé, E. Y., Diawara, A., Yoroba, F., Kouassi, B. K., Yapo, A. L. M., Diba, I., Kouadio, K., Tiémoko, D. T., Koné, D. I., & Diedhiou, A. (2025). Trends in the Occurrence of Compound Extremes of Temperature and Precipitation in Côte d’Ivoire. Atmosphere, 16(1), 3. https://doi.org/10.3390/atmos16010003

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