Impact of the 25-70 Day Intraseasonal Oscillation on Extreme Rainfall distribution over Central Africa

doi:10.21203/rs.3.rs-5147526/v1

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Impact of the 25-70 Day Intraseasonal Oscillation on Extreme Rainfall distribution over Central Africa

https://doi.org/10.21203/rs.3.rs-5147526/v1

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This paper investigates the relationship between the intraseasonal oscillation (ISO) and rainfall patterns in Central Africa during the March-April-May (MAM) season. Using CHIRPS and TAMSAT precipitation data from 1983 to 2019, we analyzed the inter-annual variability of ISO spatial structure and its impact on rainfall and extreme rainfall indices. Empirical Orthogonal Function (EOF) analysis classified years into positive (10 years), negative (10 years), mixed (6 years), and neutral (11 years) ISO types. Composite rainfall anomalies were constructed based on these classifications. Results revealed significant inter-annual rainfall variability, with distinct spatial patterns associated with positive and negative ISO years. A significant spatial correlation (over 0.4) was found between ISO variations and rainfall, particularly in the eastern region. Analysis of the impact rate of ISO years showed a more nuanced distribution in CHIRPS data compared to TAMSAT. Extreme rainfall indices, calculated using ETCCDI methods, exhibited spatial disparities, with dry zones in the north and south contrasting with wetter coastal areas and Lake Victoria. Composite extreme rainfall index anomalies based on positive and negative ISO years demonstrated varying influences depending on the region and index. Positive ISO years generally saw a decrease in consecutive dry days (CDD) and an increase in consecutive wet days (CWD), extreme rainfall intensity (RR1, RR20, R95ptot, SDII) along the Atlantic coast and northwestern Ethiopia. Neutral ISO years often displayed opposite trends to mixed years, except for the RR1 index. Understanding these relationships is crucial for water resource management in Central Africa, enabling better forecasting and mitigation of extreme rainfall events.

Atmospheric Sciences

Intraseasonnal oscillation

Central Africa

Rainfall

Extreme indice

interannual variations

The rainy season, while complex, is of paramount importance to the populations of Central Africa, particularly those who rely heavily on rainfed agriculture and livestock rearing. It plays a critical role in the economy and the availability of water resources, which are highly dependent on the rainfall regime (Gitau et al., 2011). Indeed, the spatial and temporal distribution of precipitation affects agricultural productivity and food security (Verdin et al., 2005; Hastenrath et al., 2007). It also impacts natural resources (Conway et al., 2005) and even the health of populations (Epstein, 1999; Paaijmans et al., 2010; Mbouna et al., 2022). Any disruption to this rainfall regime, whether on an intra-seasonal or inter-annual scale, in the form of extreme events such as droughts or floods, can have severe consequences for livelihoods and regional stability (Tanessong et al., 2017; Almer et al., 2017). Moreover, the vulnerability of Central Africa, in particular, to the recurrence of extreme weather and climate events such as floods, bushfires, heatwaves, and water stress, which is increasing in trend, has been the subject of numerous findings (e.g: Pokam et al., 2018; Sonkoué et al., 2018; Weber et al., 2018; Nonki et al., 2019; Weber et al., 2020; Diba et al., 2021; Njouenwet et al., 2021; Ayugi et al., 2022; Fotso Kamga et al., 2023). Thus, a better understanding of factors that influence the distribution and timing of extreme rainfall events is essential for improved risk management of climate change and for increasing community resilience.

Intra-seasonal oscillations (ISOs), also known as Madden-Julian Oscillations (MJOs), are large-scale, coherent patterns of atmospheric variability that modulate precipitation, wind, and temperature across the tropics. They are characterized by periods of enhanced and suppressed convection, typically lasting 25 to 70 days, that propagate eastward across the tropics (Madden and Julian, 1994, Vincent et al., 1991 Janicot and Sultan 2001; Janicot and Mounier 2004, Maloney and Hartmann 2008). In Central Africa, the 25–70 day intra-seasonal oscillation is an important mode of climate variability. Its significant influence on the spatial distribution of rainfall across the region has attracted considerable scientific attention and has led to extensive research efforts. The climate of Central Africa has been understudied for several decades, resulting in a limited understanding of the meteorological and climatological processes that govern the region (Nicholson and Grist, 2003; Todd and Washington, 2004; Balas et al., 2007; A. Farnsworth et al., 2011). This knowledge gap is further highlighted by the reliance on rainfall proxy data in much of the existing research (Sandjon et al., 2012, 2013a, 2013b; Tchakoutio and A. Nzekou, 2016; Sandjon et al., 2020, 2021; Hirst and Hastenrath, 1984; Todd and Washington, 2007). However, the complex and varied topography of Central Africa plays a critical role in shaping rainfall distribution (Spinaye, 2012; Cook and Vizy, 2013; Gitau et al., 2012; A. Farnsworth et al., 2011; Sandjon et al., 2012).

Central Africa's rainfall patterns are significantly influenced by the Madden-Julian Oscillation (MJO), a prominent atmospheric phenomenon affecting global precipitation. While traditional studies relied on global MJO indices (Pohl and Camberlin 2006; Pohl et al. 2007; Berhane et al. 2014), these may miss crucial regional variations. To address this limitation, recent studies have developed regional MJO indices specifically tailored to Central Africa (Tazalyka and Jury 2008; Sandjon et al., 2012, 2013a, 2013b, 2020, 2021a, 2021b; Tchakoutio and Nzekou, 2016). These regional studies have provided valuable insights into the characteristics of the intraseasonal oscillation (ISO) in CA and its influence on rainfall patterns. Sandjon et al. (2012) used outgoing longwave radiation (OLR) data to identify significant variations in the regional MJO signal strength, particularly pronounced in the eastern region. Their analysis revealed a dominant ISO signal within the 20–80 day window, with peak power observed during December-April. Interestingly, consistent spatial patterns emerged across three decades, with strong activity concentrated over Northern Congo, Southern Ethiopia, and Southwestern Tanzania (Sandjon et al., 2013a, 2013b).

Further research has explored the seasonal and interannual variations of the MJO in CA. Tchakoutio and Nzekou (2016) found the MJO signal to be generally weak throughout the year except for a peak period from October to May. Sandjon et al. (2021a) identified a strong seasonal variability, with weak activity during JJA and SON seasons contrasting with strong activity during MAM and DJF. They also observed a seasonal variation in the impact of the MJO's amplitude on rainfall distribution across different regions of Central Africa. The El Niño Southern Oscillation (ENSO) also modulates MJO activity in CA. Sandjon et al. (2020) found a stronger association between MJO and rainfall in Eastern Central Africa (ECA) during La Niña and certain ENSO-neutral years. Conversely, El Niño years exhibit weaker or absent MJO activity. Wamba et al. (2023) investigated the impact of strong and weak intraseasonal events on rainfall patterns in CA during the MAM season. Their analysis focused on how these events modulate rainfall distribution and intensity through dynamic and thermodynamic mechanisms. Furthermore, they examined the spatial structure of rainfall variability during these events. Their findings revealed distinct spatial patterns. Strong events exhibited either a uniform distribution of rainfall anomalies across the entire region, or a dipole distribution with contrasting anomalies in the western and eastern regions. In contrast, weak events displayed chaotic variability without a dominant spatial pattern. Despite these advancements, a critical question remains: How does the spatial structure of the 25-70-day intraseasonal oscillation vary interannually in this region? And how does this interannual variability impact the distribution of rainfall during the MAM season?

Previous studies on intraseasonal rainfall variability in CA have primarily focused on the spatiotemporal analysis of the oscillation's characteristics and its modulation by ENSO. However, the impact on rainfall, particularly the spatial distribution of extreme rainfall indices, remains largely unexplored. Yet, the economies of countries in the region are regularly confronted with catastrophic floods and abnormal droughts, highlighting the urgency of better understanding the factors influencing these extreme events. In light of these gaps, this study aims to analyze for the first time the impact of interannual variations in the spatial structure of the 25-70-day intraseasonal oscillation on the distribution of extreme rainfall indices in CA. We will also examine the impact of these interannual variations on the distribution of rainfall during the MAM season and characterize the spatial distribution of extreme rainfall indices across the region. By addressing these questions, this study will contribute to a better understanding of the mechanisms underlying rainfall extremes in Central Africa and provide valuable insights for water resource management in the region. The findings will also inform early warning systems and disaster preparedness strategies, thereby mitigating the socioeconomic impacts of extreme rainfall events.

The following section, 'Data and Methods', describes in detail the data sets and methods used in the analysis. Section 3, 'Results and Discussion', presents and analyzes the main findings. Finally, Section 4, 'Conclusions', summarizes the main findings and implications of the research.

1) Data

The present study employed daily precipitation data, from the Tropical Applications of Meteorology using SATellite Data (TAMSAT) rainfall estimate (Maidment et al., 2014; Tarnavsky et al., 2014) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) (Funck et al., 2015), spanning from 1983 to 2019 during the MAM season. The aim was to characterize, in the two datasets, the interannual variations in the spatial structure of intraseasonal precipitation variability and to analyze its impact on the spatial distribution of mean seasonal rainfall. The analysis of the spatial distribution of extreme precipitation indices was based exclusively on CHIRPS data.

CHIRPS is a global precipitation dataset comprising data from over 30 years. The integration of satellite imagery at a 0.05°x0.05° resolution with in-situ station data enables the generation of gridded precipitation time series, facilitating the analysis of trends and the monitoring of seasonal droughts. Since February 2015, the CHIRPS version 2.0 dataset has been publicly accessible for download (https://www.chc.ucsb.edu/data ).

The spatial and temporal resolutions of CHIRPSv2 enable the study of hydroclimate, extreme precipitation, and droughts, particularly in regions with scarce historical data, such as the Caribbean and Africa (Schneider et al., 2013). CHIRPSv2 employs a smart interpolation approach (Willmott et al., 1995a, b) using anomalies derived from high-resolution data.

2) METHODS

Climatological anomalies were determined for all precipitation data during the March-April-May (MAM) season. First, the monthly mean precipitation was calculated for each month of the study period. Secondly, for each day, the corresponding monthly mean was subtracted from the daily precipitation value. This operation eliminates the seasonal trend and focuses on anomalies relative to the monthly mean. To reduce synoptic scale variations, a 5-day moving average was applied to the previously calculated anomalies. This method of anomaly calculation was also used by Wamba et al. (2023).

To extract the 25–70 day intraseasonal oscillation window from the previously calculated rainfall anomalies, the Lanczos bandpass filter (Lanczos 1956; Duchon 1972) was used. This filter has been employed in several previous and recent studies (e.g., Liebmann et al., 1999; Carvalho et al., 2002; Foltz and McPhaden, 2004; Souza and Ambrizzi, 2006; Sandjon et al., 2012, 2013a, b, 2020; Tchakoutio and A. Nzekou 2016; Wamba et al., 2023). The resulting filtered anomaly represents the intraseasonal oscillation of rainfall during the MAM season in Central Africa.

The spatial structure of the 25–70 day ISO wave was obtained by analyzing the Empirical Orthogonal Function (EOF). This technique, widely used since its introduction in climate science by Obukhov (1947, 1960), Fukuoka (1951), and Lorenz (1956), allows for dimensionality reduction of a dataset while preserving as much information as possible. It involves a multivariate process that transforms correlated data into uncorrelated variables, thereby reducing variability. Studies by Halldor and Venegas (1997) and Wilks (2011) have demonstrated that EOF is a preferred method for analyzing atmospheric field data, identifying the spatial structure of variability and its temporal evolution. Thus, for a matrix of a scalar atmospheric field M(t,x) (precipitation in our study), where ‘t’ is the time coordinate and ‘x’ the space coordinate, the covariance matrix is given by:

$$\:R={M}^{T}\left(t,x\right).M\left(t,x\right)$$

‘T’ indicates that the matrix M is the transpose matrix.

$\:$ We then solve the eigenvalue and eigenvector problem in the following equation:

$$\:R=V.A.{V}^{T}$$

Where ‘V’ is the eigenvector matrix, ‘A’ is the diagonal eigenvalues matrix.

For each eigenvalue $\:{{\prime\:}\lambda\:}_{i}{\prime\:}$, of the diagonal matrix ‘A’, we find the corresponding eigenvector $\:{{\prime\:}v}_{i}{\prime\:}$. These eigenvectors are the EOFs we are finding.

EOF1 corresponds to the first largest eigenvalue, EOF2 to the second largest, and so on. For this study, we focused on the first two EOFs. Each eigenvalue represents the proportion of variance explained by its corresponding EOF within the 'R' matrix. This proportion is calculated by dividing the eigenvalue by the trace of the 'A' matrix.

The resulting eigenvectors are mutually orthogonal and describe the spatial pattern of the oscillation. The temporal evolution of the EOFs is determined by projecting the 'M' matrix onto the eigenvectors to obtain the principal components, by following the relation:

$$\:\underset{\_}{{a}_{j}}=M.\underset{\_}{{v}_{j}}$$

Consequently, we can reconstruct the original wave using the retained eigenvectors and eigenvalues through the following equation:

$$\:M\left(t,x\right)={\sum\:}_{AllEOFs}^{}{a}_{j}\left(t\right).{v}_{j}\left(x\right)$$

The analysis of the wave reconstructed from the first two principal components and the corresponding EOFs allows us to characterize the year to year variability of the ISO’s spatial structure and to assess its influence on rainfall distribution and extreme rainfall indices over Central Africa. The combination of climatological anomalies, Lanczos filtering, and EOF analysis enabled the extraction and analysis of the 25–70 day intraseasonal oscillation of precipitation in Central Africa during the MAM season. This rigorous methodological approach provides a solid foundation for understanding the spatial and temporal patterns of this oscillation and its influence on precipitation variability in the region.

However, the extreme precipitation indices analyzed in this study are those defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), as described by Frich et al. (2002) and Zhang et al. (2011). These indices have been used in numerous studies to assess extreme precipitation events (Vondou, D.A. and Haensler, A. 2017; Pokam et al., 2018; Diedhiou et al., 2018; Sonkoue et al., 2019; Fotso-Nguemo et al., 2019; Diatta et al., 2020; Cavalcante et al., 2020; Vondou et al., 2021; Ojara et al., 2021; Ayugi et al., 2022; Tamoffo et al., 2023; Rao et al., 2024; Zakariahou et al., 2024). Six indices were calculated during the MAM season at each grid point, namely: the number of consecutive dry days (CDD), the maximum number of consecutive wet days (CWD), the number of rainy days (RR1), the total annual rainfall from days exceeding the 95th percentile (R95ptot), the number of very heavy precipitation events (RR20), and the simple daily intensity index (SDII). Vondou et al. (2021) analyzed the trend and interannual variability of these six indices over Cameroon using precipitation data from CHIRPS. Their analyses reveal a decrease in precipitation towards the north of the country, with maximum values near the Atlantic coast. Similar spatial patterns were observed for most extreme precipitation indices, except for a gradient reversal for the CDD index.

I. The year-to-year variation of ISO spatial structure over CA

To understand the interannual variations of ISO spatial structure during the MAM season in CA, we first filtered the rainfall anomaly from CHIRPS and TAMSAT observations, between 25 and 70 days, then we applied principal component analysis (EOF) to the filtered data to isolate the dominant spatial structures from the variability. It appears that EOF 1 presenting a unique model over the entire region between 10°S − 10°N and 10°E − 45°E, expresses 8.6% of the total variance, compared to 7.9% expressed by the 'EOF 2, which presents a dipolar structure from West to East. We then reconstructed the filtered data using these two main components. This reconstructed data thus represents the intraseasonal variability of rainfall during the MAM season in CA, from 1983 to 2019. Figures 1 and 2 show the seasonal average of the spatial structure of the ISO wave in CA, year by year, from 1983 to 2002 (Fig. 1) then from 2003 to 2019 (Fig. 2).

Over the entire study period, four dominant structures are observed: a single model of positive anomaly over almost the entire region, another of negative anomaly, a mixed West - East dipole model with the positive (negative) anomaly at the West (East) and vice versa, and a model which presents an almost zero anomaly over most of the domain.

By analyzing these varied structures, ten (10) years have been identified and classified as ISO positive: 1984, 1987, 1989, 1991, 1994, 1996, 1999, 2005, 2008, and 2014 (all exhibiting a positive anomaly). Similarly, ten (10) years have been categorized as ISO negative: 1990, 1992, 1993, 1997, 1998, 2003, 2004, 2010, 2015, and 2017 (each exhibiting a negative anomaly). Six (6) years have been identified as ISO mixte: 1983, 1985, 2000, 2007, 2009, and 2018 (exhibiting mixed bipolarity). Finally, eleven (11) years have been classified as ISO neutral: 1986, 1988, 1995, 2001, 2002, 2006, 2011, 2012, 2013, 2016, and 2019 (showing almost no anomalies). All of these years are listed in Table 1 below. Table 2 presents the same data, but obtained from TAMSAT observations (figures are not shown). These findings align with those of Sandjon et al. (2020), who employed monthly Outgoing Longwave Radiation (OLR) to determine the spatial structure of the ISO and identified two primary spatial structures characterizing its variability. These authors also demonstrated that the ISO index exhibits strong interannual variability. Similarly, Sandjon et al. (2021) highlighted the substantial interannual variability of the annual mean period of ISO, with years of both very long and very short periods, which also coincides with these results.

Table 1 below summarize those years :

Table 1

Characteristic years of the ISO 25–70 days in AC
Positive years	Negative years	Mixte years	Neutral years
1984, 1987, 1989, 1991, 1994, 1996, 1999, 2005, 2008, 2014	1990, 1992, 1993, 1997, 1998, 2003, 2004, 2010, 2015, 2017	1983, 1985, 2000, 2007, 2009, 2018	1986, 1988, 1995, 2001, 2002, 2006, 2011, 2012, 2013, 2016, 2019

II. Changes in Central African seasonal rainfall distribution during ISO years

The influence of interannual variations in the spatial structure of the intraseasonal oscillation (ISO) on MAM season rainfall in Central Africa is clearly illustrated by Fig. 3. This figure presents the spatial patterns of daily mean rainfall anomalies averaged across each ISO characteristic year. Positive ISO years (Fig. 3a) are characterized by a significant variation in the distribution of rainfall anomalies over the region. Positive anomalies dominate the entire Atlantic coast, covering the North-East of Angola, Congo Brazzaville, Gabon, the great southern Cameroon and Nigeria. These anomalies resume in the west of the Central African Republic and spread to the southern part of South Sudan and reach their peak in the western Ethiopia. Another peak of the positive anomaly is found between the Mozambique part of the study area and southern Tanzania. However, the negative anomalies are distributed between the South of the Congo Basin, the Northern part of Cameroon, and the peak is reached in Northern Tanzania, Burundi, Rwanda, Uganda and Kenya.

During negative ISO years (Fig. 3b), the spatial distribution of observed rainfall anomalies is almost the opposite of that during positive ISO years. Indeed, we can notice that most of the regions previously dominated by positive anomalies are affected by negative rainfall anomalies, and vice versa. Positive anomalies indicate abnormally high rainfall while negative ones indicate abnormally low rainfall over the region. The analysis of rainfall anomalies from the two observational data sources, TAMSAT (left column) and CHIRPS (right column), reveals a similar distribution, with a more pronounced intensity in the case of TAMSAT.

Figure 3c shows the rainfall anomaly during mixed ISO years. We can observe (in the right column) an almost regular alternation between positive and negative anomalies, mainly, depending on the latitude, from one region of the study area to another. The peak of positive anomalies is predominant in the eastern part of the domain, in the south-western part of Ethiopia, between the western part of Kenya and the eastern part of Uganda, and on the Indian coast between 2°S and 2°N. At the same time, the regions between 2°S -12°N and 0°E − 30°E, southern Tanzania, Kenya and the northern part of Ethiopia are dominated by negative rainfall anomalies. In contrast to the previously observed distribution, TAMSAT exhibits a quasi-dipolar west-east configuration. This distribution is characterized by an abnormal decrease in rainfall in the west (between 12°S and 12°N, 0°E and 30°E, and along the Atlantic coast), represented by negative rainfall anomalies. Conversely, the eastern part of the region (between 30°E and 50°E) experiences an abnormal increase in rainfall, indicated by positive rainfall anomalies.

The distribution of these anomalies can be associated with the very varied topography and the low-level and upper-tropospheric circulation, which modulates the convective activity in the region (Wamba et al., 2023). Sandjon et al. (2020) and Sandjon et al., (2021) further suggest that the characteristics of circulation over the oceanic portions of the region are responsible for modulating atmospheric convective anomalies on intraseasonal timescales.

To further elucidate the connection between interannual variations in the spatial structure of the ISO and rainfall distribution across the region, a spatial analysis is conducted on the correlation between the daily mean spatial structure of 25-70-day ISO and daily mean rainfall anomalies in the MAM season.

The result is depicted in Fig. 4 below, for (a) all years, (b) positive ISO years, (c) negative ISO years and (d) mixed ISO years. In the left column of the analysis, we used TAMSAT observational data. In the right column, the CHIRPS observational product. Correlations below 0.1 are not represented in either column. Black dots indicate locations where the correlation is significant at the 95% confidence level according to the two-tailed Student's t-test.

The TAMSAT observations (Fig. 4a) reveal a sparse spatial distribution of correlations greater than 0.1, with the majority of these occurring in the eastern part of the region between 30°E and 50°E and 10°S and 10°N. Here, a correlation peak reaches 0.4. By contrast, in the CHIRPS data, these correlations are observed throughout most of the region between 10°S and 10°N and 0°E and 50°E. However, there is an apparent absence of correlations in Gabon, Equatorial Guinea, parts of Cameroon between 2°N and 5°N and along the eastern border of the Congo. The correlation peak is also concentrated in this eastern part, exhibiting a more restricted spatial extent.

The correlation map in Fig. 4b shows the results for years of positive ISO, with a notable change in the spatial distribution of the strongest correlations. Initially, the core was located between central Kenya and northern Tanzania (left-hand column), and has since shrunk considerably. In contrast, in the right-hand column, the core has shifted to central Tanzania and has widened its spatial extent (Fig. 4b), when compared with the left-hand column and with Fig. 4a on the right.

The spatial coverage of correlations greater than 0.1 has widened in Figs. 5c and 5d for the left-hand column and in Fig. 5c for the right-hand column. Center of the strongest correlations remained relatively unchanged in the left-hand column (Fig. 5c); however, in the right-hand side, the highest correlation was struggling to reach 0.3. The strongest correlations (approximately 0.4) are evident in Fig. 5d in both columns and are distributed over an expanded spatial area, located to the east of the study area. In the right-hand column of Fig. 5d we can observe a concentration of significant correlations in and around Lake Turkana and in the north-eastern region of Tanzania.

Consequently, it was found that there were notable discrepancies between TAMSAT and CHIRPS data in terms of their capacity to illustrate the spatial correlation between rainfall anomalies and the 25-70-day intraseasonal oscillation (ISO) in Central Africa. CHIRPS demonstrated a more extensive spatial scope, particularly in instances where values exceeded 0.1. Furthermore, it appeared that CHIRPS was better able to capture the nuances of the spatial distribution of the aforementioned correlations than TAMSAT. This indicates that the CHIRPS data offer a more accurate representation of the relationship between ISO and rainfall patterns in the region.

Furthermore, the individual contribution of each ISO characteristic year to the total rainfall over the study period was evaluated using the impact rate, as defined in Sandjon et al., (2021a). The results, expressed as percentages, are shown in Fig. 5. This figure presents the spatial distribution of the precipitation impact rate of positive (a), negative (b) and mixed (c) ISO years. The rate is calculated by dividing the total daily precipitation for each ISO year by the accumulated precipitation over the entire study period (37 years), using TAMSAT (left column) and CHIRPS (right column) data.

The impact rate of precipitation exhibits pronounced spatial variations across both TAMSAT and CHIRPS observations.

In TAMSAT (panel a)), the maximum rates reach approximately 40% in regions between 11°N − 15°N and 13°E − 40°E. Along the Atlantic coast, parts of the Congo Basin, and eastern regions (primarily Mozambique, Tanzania, Somalia, and Ethiopia), impact rates range from 26–30%. It is notable that some isolated areas with very low rates (around 16%) exist in Kenya, the far northern part of Cameroon, and between 11°S − 15°S and 20°E − 40°E.

CHIRPS observations display a similar spatial distribution, although with broader ranges in both area and value. Maximum rates reach approximately 52% and extend across 10°N − 15°N, 0°E − 40°E. Conversely, the lowest values, around 24%, are found over Kenya and Zambia. The remaining regions exhibit rates between 28% and 30%, with some areas reaching up to 36%.

Figure 5 demonstrates a clear reversal in impact rates between positive and negative ISO years in both TAMSAT and CHIRPS observations. The regions exhibiting the highest impact rates during positive ISO years (Fig. 5a) exhibit the lowest rates during negative ISO years (Fig. 5b). This pattern is observed throughout the entire region, with values typically ranging from 22–36% (TAMSAT) and 26–40% (CHIRPS). It is noteworthy that CHIRPS observations exhibit a wider distribution of values around 30%.

Finally, Fig. 5c illustrates a notable decline in impact rates in comparison to previous years for both datasets. This decline is particularly pronounced in the TAMSAT data, where rates plummet to a range of 12–16% across most of the region. CHIRPS observations also demonstrate a decrease, although the values exhibited are slightly higher, ranging from 16–20%. These values are dominant within the region.

This analysis of precipitation impact across the study period revealed significant spatial variations in both TAMSAT and CHIRPS data. However, CHIRPS data provided a more nuanced distribution due to its wider range of values, particularly around the impact rate of 35%. Consequently, we will rely on CHIRPS observations in the remainder of our study to analyze the impact of interannual variations in ISO 25-70-day on the distribution of extreme rainfall indices.

III. Impact of the interannual variation of ISO structure on extreme rainfall indexes distribution over CA.

1) Spatial patterns of extreme rainfall indices over Central Africa

In order to gain an accurate understanding of the impact of the high interannual variability of the ISO 25–70 days on rainfall indices in Central Africa, it is first necessary to analyze the spatial distribution of these indices in the region over the entire study period.

Figure 6 illustrates the spatial characteristic of six rainfall extreme indexes over Central Africa during the single MAM season, spanning the period from 1983 to 2019.

The analysis of the number of consecutive dry days (CDD) reveals significant spatial variability across the region, with values ranging from 10 to 90 days (Fig. 6a). The lowest number of dry days is observed in the Congo Basin, which extends from 5°S to 8°N and 0°E to 30°E, with an average of approximately 10 days. This value gradually increases towards the north, between 8°N and 15°N, reaching a maximum of around 90 days between 10°N and 15°N. A similar trend is observed towards the south, between 5°S and 15°S, with a maximum of around 50 days in the Zambian portion of the study area. The majority of regions in the eastern part of the study area, including Tanzania, Kenya, Somalia, and Ethiopia, experience approximately 30 dry days. Finally, the number of consecutive dry days (CDD) across the region exhibits a pronounced north-south gradient, with the Congo Basin experiencing the fewest dry days and drier conditions increasing towards the edges of the region.

Figure 6b illustrates the distribution of consecutive wet days (CWD) across the region under consideration, revealing a notable prevalence of the highest values (between 20 and 24 days) in the Atlantic coastal regions of Cameroon, along the eastern border of the DRC, and around Lake Victoria. Nevertheless, the majority of the region experiences a number of consecutive wet days between 10 and 14. It is noteworthy that the regions identified as the driest in Fig. 6a exhibit a relatively low number of consecutive wet days in Fig. 6b.

For the number of days with daily precipitation sum exceeding 20 mm (RR20 ), the highest values (12–14) are observed over Equatorial Guinea, Gabon, the Southern part of Tanzania, Lake Malawi, Lake Victoria, the Madagascar part of the region and a few spots over Ethiopia (Fig. 6c). Conversely, regions between 5°N-15°N and 0°E-30°E, Northern Ethiopia, Djibouti, Somalia and some spots over the Congo Basin experience the fewest (less than 4) number of these very heavy precipitation days. The remaining areas typically see around 6 days with such high rainfall.

The seasonal R95ptot index (Fig. 6d), which represents the cumulative precipitation over the 95% wettest days, exhibits marked spatial variability, ranging from 25 to 375 mm. The lowest values, around 25 mm, prevail in regions above 12°N, in eastern Africa, around Lake Turkana, and surrounding areas. Values of around 50 mm are found in the latitudinal band between 8°N and 12°N, crossing the region to the western border of Ethiopia. These values are also observed in northern and eastern Ethiopia and Kenya, as well as in southeastern Angola and up to Malawi. The modal value of the R95ptot index, which occupies most of the region, including the Congo Basin and Uganda, is around 75 mm. Values between 100 and 150 mm characterize Gabon, Equatorial Guinea, western Cameroon, Congo-Brazzaville, Rwanda, Burundi, Lake Victoria, and surrounding areas. Maximum intensity is reached over Lake Malawi, southeastern Tanzania, and along the Indian Ocean coast between Kenya and Tanzania. This spatial distribution shows a strong similarity to that observed for the seasonal RR20 index, which represents the number of days with precipitation exceeding 20 mm. This concordance suggests that regions prone to intense rainfall events are also prone to such events over longer periods.

The number of days with at least 1 mm of rain, RR1, shown in Fig. 6e, varies between 5 and 75 days. The spatial distribution of the RR1 index shows moderate variability, with a notable zone extending between 8°S and 8°N, and 0°E and 30°E, where the highest index values (between 45 and 75 days) are concentrated, also observed in Uganda and around Lake Victoria. The rest of the study area has lower RR1 values, ranging from 5 to 40 days. This distribution shows some similarity to that of the CDD index, but in the opposite direction (regions with high RR1 values generally correspond to low CDD values and vice versa). This inverse relationship suggests a link between rainfall patterns and dry periods in the study area.

In Fig. 6f, the spatial distribution of the SDII intensity index varies between 4 and 50 mm across the entire region. The lowest intensity, between 4 and 8 mm, dominates the areas located between 6°N and 15°N, and 0°E and 30°E. However, the majority of the region extending between 15°S and 6°N, and 0°E and 35°E, is characterized by an intensity between 10 and 16 mm. The highest index value, between 20 and 36 mm, although not very widespread, covers Lake Malawi, a few regions in Tanzania, Kenya, and Ethiopia, as well as the Indian Ocean coast between Tanzania and Kenya. The spatial characteristics of these indices in the western part of the region show similarities to those described by Diata et al. (2020), who used CHIRPS data to analyze the spatial variability of extreme precipitation in West Africa from 1982 to 2016. These authors observed that maxima of extreme precipitation are a characteristic of orographic regions and the South Sahel.

2) Influence of ISO characteristic years on the extreme rainfall index

In this section, we use composite anomaly analysis to investigate the influence of interannual variations in the spatial structure of the 25–70 day intraseasonal oscillation (ISO) on the distribution of extreme precipitation indices in Central Africa. The composite anomaly is defined as the difference between the seasonal mean of the extreme index (CDD, CWD, RR1, R95ptot, RR20 and SDII) during the ISO years considered (positive, negative, mixed or neutral ISO years) and that of all years of the study period. Figure 7 shows the spatial distribution of these composite anomalies for positive ISO years in Central Africa. Positive anomaly values indicate a tendency for the index to increase during the ISO year considered, while a negative anomaly indicates a decreasing trend.

The CDD index (Number of consecutive dry days) exhibits dominant negative anomalies over the entire region (Fig. 7a). The lowest values (around − 2 days) are observed over the Congo Basin. This is almost identical to the distribution in Figure. 6a. The influence on the CWD index (Number of consecutive wet days) (Fig. 7b) varies considerably from one region to another. Positive anomalies (between + 2 and + 5 days) dominate the Atlantic coast between 15°S and 4°N, Lake Tanganyika and its surroundings, and northwestern Ethiopia; while negative anomalies (between − 2 and − 5 days) are observed in the mountains of western Cameroon, Zambia, and Kenya. This spatial distribution is similar to that of the RR1 index in Fig. 7e, where the positive anomaly exceeds + 6 days over the entire Atlantic coast and northwestern Ethiopia. The Central African Republic, the Democratic Republic of the Congo, and South Sudan are the regions characterized by moderate positive anomalies between + 2 and + 4 days.

The negative anomaly is concentrated in the − 2 to -6 day range over the mountain ranges of East Africa, Kenya, and Zambia. For the RR20 index (Fig. 7c), it is marked by pockets of positive and negative anomalies across the region, ranging between − 2 and + 2 days. Kenya, northwestern Tanzania, Rwanda, and Lake Victoria record the strongest negative anomalies, around − 2 days. However, Mozambique, the Democratic Republic of the Congo, western Cameroon, and Gabon record the strongest positive anomalies (+ 2 days). The spatial distribution of the R95ptot index in Fig. 8d is similar to that of the RR1 index, with precipitation anomalies ranging from − 24 to 24 mm. As for the SDII index, its intensity varies between − 3 and + 3 mm, mainly in the eastern part of the region, between Ethiopia and Mozambique, and along the Indian Ocean coasts (which are dominated by positive anomalies). In contrast, Kenya, Tanzania, Lake Victoria and Rwanda record negative anomalies for the SDII index.

Figure 8 reproduces the structure of Fig. 7, with the main difference that the composite anomaly is calculated using negative ISO years. Consequently, the spatial distribution of the index anomalies is the opposite of that observed in Fig. 7. For all indices, regions characterized by positive anomalies in positive ISO years show negative anomalies in negative ISO years and vice versa.

This section analyzes the extreme index anomalies in Central Africa during mixed ISO years.

Figure 9a shows the CDD index anomalies. The central Congo Basin (between 5°S and 5°N latitude and 0°E and 30°E longitude) exhibits a negligible influence, with an anomaly close to 0. However, the northwestern region above 5°N is dominated by significant positive anomalies ranging from + 9 to + 15 days. In contrast, the southern domain (between 5°S and 15°S latitude and 12°E and 30°E longitude) experiences negative anomalies, with a peak of -15 days observed over Zambia. The eastern part of the region also experiences a weak influence, with anomalies predominantly ranging from − 6 to + 6 days.

Analysis of the CWD and RR20 indices (Figs. 9b and 9c) reveals a moderate influence. Anomaly values across most of the domain are generally low, with some exceptions. Gabon, southeastern Tanzania, and Lake Victoria exhibit an abnormal decrease in the RR20 index, while Kenya, Ethiopia, and specific points within the Congo Basin show an abnormal increase.

The R95ptot index (refer to the entire Central African region) exhibits an anomaly ranging from − 24 to 24 mm (Figs. 9d). This anomaly varies geographically. Lake Malawi, southeastern Tanzania, and Gabon show a significant decrease in the index, while Congo-Brazzaville, Equatorial Guinea, central DRC, Uganda, Kenya, Zambia, and Madagascar are characterized by an abnormal increase.

The distribution of the RR1 index anomaly (Figs. 9e) is more organized than that of the R95ptot index. Regions between 2°N and 15°N and 0°E and 30°E are dominated by a negative anomaly concentrated between − 2 and − 6 days. This trend is also observed in central Angola, extending to Lake Tanganyika and crossing southern DRC. In contrast, the Atlantic coast between northwestern Angola and Gabon, central DRC (west to east), Zambia, and the entire region between 30°E and 45°E and 15°S and 10°N exhibit a positive RR1 index anomaly ranging from + 2 to + 6 days.

The influence of mixed ISO years on the SDII index in Figs. 9f is very weak across most of the region. However, the Indian Ocean coastal zones (between 35°E and 40°E and 15°S and 3°N) are distinguished by a significant decrease in the index (anomaly range from − 1 to -3 mm). In contrast, part of Ethiopia shows an increase in the SDII index (anomalies range from + 1 to + 3 mm).

The spatial distribution of the anomalies of extreme rainfall indices during neutral ISO years presents a pattern opposite to that of mixed ISO years, with the exception of the RR1 index (Fig. 10). Indeed, Fig. 10e shows an increase in the number of rainy days over all areas with high topography, notably the mountains of western Cameroon, Benin, Togo, the mountain ranges of East Africa, and the lakes of the same region, while the opposite trend is observed in western Ethiopia.

The aim of this study was to investigate the influence of interannual variations in the spatial structure of the ISO wave (25–70 day intraseasonal variability) on the distribution of rainfall and extreme rainfall indices over Central Africa during the rainy season (MAM). Daily rainfall data from TAMSAT and CHIRPS were used for a period of 37 years (1983–2019) covering the entire region (between longitudes 0°E and 50°E and latitudes 15°S and 15°N). The methodology used combines the calculation of climatological anomalies, Lanczos temporal filtering and the analysis of EOFs to extract the spatial structure of the 25–70 day ISO wave. The year-by-year analysis of the wave allowed the identification of four types of years based on the spatial structure of the ISO wave: positive, negative, mixed and neutral years.

Composite analysis reveals that positive ISO years are characterized by increased rainfall in Atlantic coastal areas, parts of Nigeria, and East Africa, while negative ISO years exhibit an inverse pattern with decreased rainfall in these regions. Mixed ISO years display a more complex distribution with alternating wet and dry zones. Correlation analysis between ISO and rainfall anomalies indicates a better representation of the ISO-precipitation relationship with CHIRPS compared to TAMSAT, particularly in the eastern part of the study region. Precipitation impact rates vary significantly across regions and years, with maxima reaching 40–52% and minima around 16–24%. A clear inversion in impact rates is observed between positive and negative years. Additionally, the spatial distribution of extreme rainfall indices exhibits substantial spatial variability, with areas of extreme dryness or wetness. Links between indices are evident, such as an inverse relationship between the number of consecutive dry days and the number of rainy days.

Furthermore, a complex and spatially heterogeneous pattern is observed in the influence of ISO year types on extreme precipitation indices in Central Africa. Positive ISO years generally lead to a decrease in the number of dry days, while other indices show more contrasting trends, with increases in some areas and decreases in others. Negative ISO years show trends opposite to those of positive ISO years, while mixed ISO years are characterized by an increase in the number of dry days in the northern part of the region and a decrease in the south. Anomalies of other indices are more moderate, with increases in some areas and decreases in others. Finally, neutral ISO years show an opposite trend to mixed ISO years for most indices, except for the number of rainy days (RR1), which increases in mountainous areas.

Overall, this study sheds light on the complex relationship between ISO waves and Central African rainfall patterns, with important implications for water resource management, agriculture and disaster risk reduction. Future research should focus on integrating these findings into regional climate models to improve seasonal precipitation forecasts and extreme event predictions based on ISO year types. In addition, it is crucial to investigate the physical mechanisms linking ISO variations to regional precipitation patterns and extreme precipitation events. Finally, studying the socio-economic consequences of ISO year types on water availability, agricultural productivity and drought/flood risks in Central Africa will provide a more comprehensive understanding for developing effective adaptation strategies.

Acknowlegments:

Derbetini A. Vondou is supported by the Henriette Herz Scouting Programme of Humboldt Research Fellowships. We gratefully appreciate the efforts of the International Joint Laboratory Dynamics of Terrestrial Ecosystems in Central Africa (IJL DYCOCA/LMI DYCOFAC) initiative during the realization of this work.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Funding

The authors declare that no funds, grants or other support were received during the preparation of this manuscript.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Claudin Wamba Tchinda, Alain Tchakoutio Sandjon and Derbetini A. Vondou. The first draft of the manuscript was written by Claudin Wamba Tchinda and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data availability:

CHIRPS dataset is available at https://data.chc.ucsb.edu/products/

TAMSAT dataset is available athttps://www.tamsat.org.uk/data/

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Impact of the 25-70 Day Intraseasonal Oscillation on Extreme Rainfall distribution over Central Africa

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1) Spatial patterns of extreme rainfall indices over Central Africa

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