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. 2025 Oct 7;15:34959. doi: 10.1038/s41598-025-18940-9

The 2023 drought in West Africa and associated vulnerability to food insecurity

Vincent Ondima Kongo 1,#, Nkongho Ayuketang Arreyndip 2,3,✉,#
PMCID: PMC12504697  PMID: 41057407

Abstract

The return of El Niño in 2023 brought record global temperatures, an increase in forest fires, floods, heatwaves, and intense and severe droughts. How the climate of 2023 affected agricultural activities in West Africa is not yet known. This paper analyses the changes in the patterns of local climate variables during the planting season and the potential consequences for agricultural productivity in West Africa. The study uses the ERA5, Normalized Difference Vegetation Index (NDVI), and Standardized Precipitation Evaporation Index (SPEI) datasets and performs a thorough spatio-temporal analysis and anomaly calculation of precipitation, air temperature, relative humidity, NDVI, drought, and soil moisture. Compared to the climatology, we observed early precipitation in March, high temperatures and humidity, low precipitation, and a loss of vegetation in the middle of the growing season (April to June). The direct influence of El Niño proved to be weak, suggesting that El Niño could be the cause, together with other factors such as global mean temperature and Atlantic circulation. Farmers in West Africa usually expect rainfall in early April to start the agricultural season. This shift in rainfall patterns can have a serious impact on agricultural activities and increase vulnerability to food insecurity in the region. Rather than relying solely on rainfall, we recommend the use of advanced irrigation techniques and the development of drought-resistant crops to combat the effects of El Niño in the future.

Keywords: El Niño 2023, Precipitation patterns, Planting season, Droughts, West Africa

Subject terms: Climate change, Hydrology

Introduction

El Niño-Southern Oscillation (ENSO) is a cyclical global phenomenon that has received much attention and media coverage in recent years due to its strong influence on shifting weather patterns1,2 and triggering extreme climate events around the world3. After three years of La Niña, which ended in March 2023, El Niño returned in 2023 with unprecedented intensity4. The year 2023 brought record-breaking temperatures, with the highest temperatures ever recorded5. There were reported cases of an increase in wildfires in the USA6 and flooding in the Horn of Africa, particularly in Kenya, killing dozens of people in Nairobi7,8. In addition, record-breaking summer heatwaves were reported in Europe9 and intense and severe droughts in South America, Southern Africa, Asia, and the Pacific10,11. Whether El Niño is the major driver of these extreme events is what is unknown at the moment. Some researchers have found that West African monsoon variability is mainly controlled by atmospheric dynamics and land–surface interactions12. At the same time, others have found that the Atlantic Multi-decadal Oscillation (AMO) and the Atlantic Meridional Mode (AMM) show a positive linear correlation to Sahel rainfall and that Sea Surface Temperature (SST) and ENSOs have a weak negative effect on Sahel rainfall13,14. Their results indicate that there are other major drivers of West African monsoon variability and extremes.

The strong influence of El Niño on rainfall patterns has been shown to have a profound impact on agricultural activities15, particularly in the Global South, where agriculture is the backbone of many economies. For example, a study by Fifer et al16 investigated the impact of El Niño on rainfall patterns in the Sahel region of West Africa. Their study found that the 2015 El Niño event led to unpredictable weather patterns and lower rainfall across the Sahel. According to16, various parts of West Africa experienced delayed rains that started late and subsequently delayed the sowing of millet, peanuts, and sorghum in central and eastern Niger, southwestern Mauritania, Chad, northwestern Senegal, and eastern Nigeria. When the rains finally came, floods occurred in some regions, destroying the early plantations. In other studies, Haile et al17 showed that ENSO-induced climate variability negatively affects the yield of cereal crops, with different effects on different crops, Iizumi et al18 conducted a study to explore the global impact of ENSO on major crops such as soybeans, corn, rice, and wheat, which found that ENSO affects the production of these major crops worldwide while Lal et al19 investigated the impact of El Niño and La Niña episodes on crop production and rainfall variability. The authors focused on how the Indian monsoon relates to the ENSO phenomenon. They found that El Niño was associated with less rainfall than usual during the monsoon season, especially from July to August. Many other studies come to similar conclusions, namely that El Niño significantly reduces rainfall and shifts rainfall periods, increases temperatures, and exacerbates drought, leading to significant crop failures and thus posing a major threat to global food security. Interestingly, La Niña years have the opposite effect and lead to better yields and higher production20–28.

No studies have been carried out so far on the devastating effects of the climate of the 2023 El Niño year in the rich, rain-fed agricultural region of West Africa and the role of El Niño in these impacts. The West African region was chosen because of the strong dependence of countries in this region on agriculture for livelihood and economic progress. This region also has very rich arable land for agricultural expansion. The aim of this study is to assess the agricultural impacts of the 2023 climate extreme events and the link El Niño has to these impacts, with a focus on the planting season. In West Africa, two seasons prevail: the rainy season usually lasts from April to October, while the dry season lasts from November to March. Farmers in West Africa usually wait for the first rains to start the agricultural season. In many West African countries in the equatorial rainforest zone (Gulf of Guinea), the predominant planting season for most crops lasts from April to July (Fig. 1), while in most Sahelian countries it lasts from May to August (Fig. 1). April and May are on average the core period of the planting season, when a lot of rainfall is needed for the plants to germinate. A shift in the rainfall pattern can significantly disrupt the agricultural schedule and lead to early or late sowing. This can affect crop growth and yields and cause serious hardship to a region already threatened by food insecurity. In the rest of the paper, Sects. 2 and 3 present the results and discussion of our findings respectively and we end with a conclusion and recommendations in Sect. 4 while the data we have used, its description, and the methods we have employed for our analysis is presented in Sect. 5.

Fig. 1.

Fig. 1

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Various crop types and their planting seasons in West Africa. The dominant planting season for most crops across many West African countries in the Gulf of Guinea runs from April to July (a, b, c, d, e, and f) while that for most Sahel countries (g, h, i, j, and k) runs from May to August. The data was taken from the United States Department of Agriculture (USDA) found at29.

Results

Spatio-temporal analysis

To investigate the impact of the 2023 extreme climate events and whether El Niño is the main driver, we calculated the sea surface temperature anomalies of 2023 with reference to the base year from 1991 to 2020. The results are presented in Fig. 2a, b, c, d, e, and f, for March, April, May, June, July, and August, respectively. This figure shows that El Niño occurred as early as March, with Niño 3.0 dominating. Niño 3.4 started to become strong in April and became much stronger in June, while Niño 4.0 dominated in August. The SST time series (Fig. 2g) shows a clear increasing trend in SST, with the anomaly bar chart (Fig. 2h) indicating that the 2023 SST was the warmest on record. These observed trends mean that there is a high probability of more intense and frequent El Niño events in the future30.

Fig. 2.

Fig. 2

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Sea Surface Temperature (SST) anomalies of 2023 compared against the 1991-2020 base year for March (a), April (b), May (c), June (d), July (e), and August (f). (g) and (h) are the time series and anomaly bar charts, respectively. This figure shows the presence of El Nino in 2023, and 2023 being the hottest SST on record. The maps were plotted using the Python package Cartopy 0.24.1 (https://pypi.org/project/Cartopy/).

For the first analysis of the effect of the 2023 climate extremes in West Africa, we examine the anomalies in surface temperatures and humidity from March to August (Fig. 3a, b, c, d, e, f and g, h, i respectively). This figure shows that during the months of April and May (Fig. 3b and c), which fall in the middle of the planting season, many countries and regions in West Africa experienced unusually high temperatures, especially in the Sahel region. From June to August, unusually high humidity prevailed in the tropical Savannah. These extreme conditions can affect the germination and growth of plants, cause the drying of crops, and provide a favorable environment for the proliferation of insect pests that attack crops31,32. On average, the Sahel region is more affected than the rainforest region throughout the planting season, leading to devastating production losses. In Fig. 4a, b, c, d, e, f and g, h, i, we compute the anomalies of the NDVI and total precipitation, respectively, over the planting and growing months. This figure shows that April and May, which fall in the heart of the planting season experienced abnormally low vegetation and precipitation compared to March. Between June and July, the western part of West Africa and the Sahel’s vegetation were negatively impacted, while the Gulf of Guinea suffered from a drop in vegetation in August. The countries most affected by vegetation losses are Nigeria, Benin, Togo, Northern Ghana, and most of the Sahel. Senegal, Liberia, and some parts of Northern Cameroon were the most impacted by a drop in precipitation.

Fig. 3.

Fig. 3

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Surface temperature (a–f) and relative humidity (g–l) anomalies in West Africa over the planting and growing months March to August, respectively. This figure shows that April and May (b and c), which fall in the heart of the planting season, experienced abnormally high temperatures in many countries and regions of West Africa. Abnormally high humidities were experienced around the tropical Savanna from June to August. The maps were plotted using the Python package Cartopy 0.24.1 (https://pypi.org/project/Cartopy/).

Fig. 4.

Fig. 4

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The Normalized difference vegetation index (NDVI) (a–f) and total precipitation (g–l) anomalies in West Africa over the planting and growing months, March to August, respectively. This figure shows that April and May (b and c), which fall in the heart of the planting season, experienced abnormally low vegetation and precipitation (h and i) compared to March (a). Between June and July (d and e), Western Sahel’s vegetation is negatively impacted, while the Gulf of Guinea suffers in August (f) from a drop in vegetation. The maps were plotted using the Python package Cartopy 0.24.1 (https://pypi.org/project/Cartopy/).

In a similar manner, we examined the droughts and soil moisture anomalies (Fig. 5). SPEI drought index anomalies show no pattern (Fig. 5a,b, c, d, e, f), but the countries most affected were Nigeria, the Ivory Coast, Ghana, Niger, the North-eastern part of Mali, and the Southern part of Cameroon. Soil moisture anomalies (Fig. 5g, h, i) were shown to be significantly low from April to June but high in July and August, especially in the tropical Savanna.

Fig. 5.

Fig. 5

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SPEI drought index (a–f) and soil moisture (g–l) anomalies in West Africa over the planting and growing months March to August, respectively. The figure shows no pattern of drought anomalies, but the planting seasons show very low soil moisture. In the drought plots, the Gulf of Guinea is the most severely impacted by droughts from March to June, while the tropical Savanna and the Sahel are impacted in July and August. The maps were plotted using the Python package Cartopy 0.24.1 (https://pypi.org/project/Cartopy/).

Time series analysis

To investigate the impact of the 2023 climate extremes on temperature, precipitation, relative humidity, and soil moisture, we analyze their seasonal cycles compared to climatology. Fig. 6a shows that temperatures were higher throughout 2023 compared to climatology. In Fig. 6c, we observe a significant decrease in the precipitation time series from March to May, while soil moisture (Fig. 6d) and relative humidity (Fig. 6b) show no significant change. In the daily time series over the months of the planting season, a significant amount of precipitation is observed in March 2023 (Fig. 6i) compared to climatology. The period from April to June (Fig. 6j, k, l) shows multiple extreme events, especially in April, with April having a lesser rainfall on average compared to climatology. No significant change is precipitation amount is observed in July (Fig. 6m), while August (Fig. 6n) shows precipitation was higher on average in 2023 compared to climatology. The anomaly bar charts show that 2023 recorded the warmest surface temperature (Fig. 6e), while the precipitation anomaly is also negative but lower than in 2022 (Fig. 6g). The year 2023 brought higher humidity (Fig. 6f) and soil moisture (Fig. 6h) compared to climatology, but the soil moisture over West Africa appears to have dropped when compared with the previous year (2022). The time series analysis shows an increasing trend in surface temperatures (Figure 6o) and a slightly decreasing trend in precipitation (Fig. 6q), soil moisture (Fig. 6r), and relative humidity (Fig. 6p) in West Africa.

Fig. 6.

Fig. 6

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Monthly temperature (a), relative humidity (b), precipitation (c), and soil moisture (d) seasonal cycles of 2023 compared to climatology (1991–2020) indicate higher temperatures in 2023 compared to climatology through the year. In (c) we observe a significant depression in precipitation time series from March to May while soil moisture appears lower in most parts of the planting season. Relative humidity (b) doesn’t show any significant change. (e), (f), (g), and (h) are the anomaly bar charts for temperature, relative humidity, precipitation, and soil moisture respectively. The anomaly bar charts show that 2023 was the hottest on record in surface temperature (e) while the precipitation anomaly (g) also shows to be negative. From (i) to (n) are the daily time series over the months of the planting season. In (i), we observe a significant amount of rainfall in March 2023 compared to climatology. Annual time series analysis shows an increasing trend in surface temperatures (o), a slightly increasing trend in precipitation (q), and a slightly decreasing trend in soil moisture (r) and relative humidity (p).

In Fig. 7, we perform a comparative study to investigate the relationship between rainfall, crop yield, and harvest area for four principal crops (Maize, Rice, Wheat, and Soybeans) aggregated over West Africa. The normalized FAOSTAT yields and area of harvest data were used. The figure shows a significant drop in Maize yield even under an increase in the area of harvest in 2023. A similar pattern is observed for Soybeans and Wheat except for Rice, which strongly depends on irrigated water for its cultivation. This analysis suggests that the 2023 extreme events might have been the cause for a major drop in maize yield across West Africa, followed by Wheat and finally Soybeans. To investigate the role of El Niño in these regional shifts in weather patterns, we plot anomalies time series of precipitation, and ENSO indices (Fig. 8) to uncover any patterns or relationships. Both time series (Fig. 8a) show a good agreement as years of El Niño (red peaks in SST) agree with negative precipitation years. At the same time, when we investigate the statistical correlation between SST in the Niño 3.4 region and local climate variables through a Pearson correlation plot, we find that Niño 3.4 SST weakly correlates with many local climate variables over West Africa (Fig. 8b). Precipitation, in particular, shows a very weak correlation with Niño 3.4 SSTs. In the same Pearson correlation map, precipitation is shown to strongly correlate to regional surface temperature, which is a subset of the global mean surface temperature. These results indicate that the Global mean surface temperature might be the major driver of droughts in West Africa. The weak effects of El Niño should not be ignored, as when coupled with other remote climate indices like the North Atlantic Oscillation, Atlantic Tripole SST, Pacific Decadal Oscillation, and Global mean surface temperature, they can cause significant damage to local climate.

Fig. 7.

Fig. 7

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Comparative plots to investigate the relationship between rainfall, crop yield, and harvest area for four principal crops (Maize, Rice, Wheat, and Soybeans) aggregated over West Africa. The normalized FAOSTAT yields and area of harvest data were used from 1991 to 2023.

Fig. 8.

Fig. 8

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Comparative diagrams to analyse the correlation of precipitation time series and ENSO indices (a) and (b) is the Pearson correlation heatmap to analyze the correlation of regional variables, especially precipitation and ENSOs. The correlations were calculated from 2010 to 2023. A correlation of 0.00 means that there is no linear relationship between the variables. Values that are very close to zero in the negative and positive direction indicate a weak negative or positive relationship. Values close to 0.5 indicate a moderate relationship, while values above 0.5 indicate a strong relationship. The time series and the correction map show a very weak relationship between the West African precipitation and the ENSOs (r=0.05).

Discussion

The geographical location of West Africa makes it highly vulnerable to the impacts of remote climate indices. Climate indices such as the global mean surface temperature, the Southern and Northern Oscillation, the Atlantic Tripole SST indices, the Pacific Decadal Oscillation, the global integrated angular momentum, the solar flux, the North Atlantic Oscillation, the tropical Pacific SST, the El Niño 3.4 index, monsoon winds etc, have all been shown to impact West African climate especially precipitation variability to varying degrees13,15,33,34. During ENSO episodes, the direct role of ENSOs in extreme local climate events is usually unclear because of the complexity of the climate system. Many researchers have based their findings on speculations that ENSOs might be the cause of regional extreme events. In this study, we have investigated the changes in local climate variables during the 2023 El Niño year compared to the 1991 to 2020 base year. We have computed and analyzed anomalies of surface temperature, precipitation, soil moisture, vegetation index, droughts, and humidity. We found that during the 2023 El Niño year, there was a significant drop in precipitation from April to June. These months coincide with the farming months in most West African countries. Moreover, we observed extreme temperatures, humidity, and the loss of vegetation during these months. Soil moisture was also impacted. A similar result was found by Wenjia et al35 when they investigated the possible causes of the Central Equatorial African (CEA) long-term drought. By analyzing multiple sources of observations and reanalysis data, they found that the long-term drought during April, May, and June over CEA may reflect the large-scale response of the atmosphere to tropical SST variations. Emmanuel36 investigated the linkages between El Niño-Southern Oscillation (ENSO) and precipitation in West African regions. Their results show that the values of the correlation between the Southern Oscillation Index (SOI) and precipitation anomalies increase northward during the wet season for both La Niña and El Niño periods. Strong links were established between SOI and precipitation anomalies, ranging between -0.45 and 0.35. Through correlation analysis, we have shown that the Niño 3.4 SST weakly correlates with precipitation and local climate variables, indicating that El Niño might not be the only driver of the observed droughts period from April to June 2023 in West Africa.

Our findings have also been proven by Henchiri et al37 who carried out a Meteorological Drought Analysis and Return Periods over North and West Africa with links to El Niño–Southern Oscillation (ENSO). Their main finding was that there is a direct connection between drought and the North Atlantic Oscillation Index (NAOI) over Morocco, Algeria, and the sub-Saharan countries, and some slight indications that drought is linked with SOI over Guinea, Ghana, Sierra Leone, Mali, Cote d’Ivoire, Burkina Faso, Niger, and Nigeria. Even with its weak impacts, there is a likelihood that under future warming, El Niño frequency might increase, which can further increase West Africa’s vulnerability to severe droughts30.

For the potential agricultural impacts of the 2023 climate extremes,16 had shown that El Niño events led to unpredictable weather patterns and lower rainfall in the Sahel. Their results also showed that delayed rains occurred in different parts of West Africa, which started late and subsequently delayed the sowing of millet, groundnut, and sorghum in central and eastern Niger, southwestern Mauritania, Chad, northwestern Senegal, and eastern Nigeria. This is consistent with the findings of this paper, which shows that the first rains occurred in March, possibly catching farmers off guard and causing them to rush into planting while drought prevailed in April, May, and June. Crops such as maize, millet, peanuts, yams, cocoyams, potatoes, and rice are at risk of being severely affected by this shift in rainfall patterns. In the Gulf of Guinea, the agricultural zone of Côte d’Ivoire, and the Niger Delta region in Nigeria, including the Western part of Cameroon, show to be affected by severe drought, while the entire Sahel region shows the highest vulnerability. These extreme weather events and conditions pose a significant threat to regional food security.

Conclusion and recommendations

Atmospheric and oceanic circulations and microphysical processes, coupled with land-atmosphere interactions, can strongly influence local weather conditions and sometimes lead to extreme events. ENSO is one such phenomenon that triggers extreme weather events around the world. These extreme events have a significant impact on agricultural activities as they lead to extremely wet or dry conditions. West Africa is one of the African regions where people depend on agriculture for their livelihoods. The year 2023 was an El Niño year, with some of the most extreme weather conditions recorded in different parts of the world. How these phenomena affected local weather conditions and what impact they might have on agriculture in West Africa remained unknown. This study investigated the changes in local weather patterns with a focus on rainfall during the agricultural season in West Africa to assess the potential impact of the 2023 El Niño on agriculture. Using several reanalysis datasets, including ERA5, SPEI drought index, and NDVI, the anomalies of climate variables such as sea surface temperatures, air temperatures, precipitation, soil moisture, drought, vegetation indices, and relative humidity were calculated compared to a 30-year base period (1991–2020). We have observed a slight shift in rainfall patterns in West Africa, bringing early rains in March and low rainfall in the normal agricultural season (April to June), when a lot of rain is expected. The Sahel countries of Niger, Mali and Burkina Faso were the worst affected, while in the coastal regions of West Africa, the rich agricultural regions of the delta region in Nigeria and Côte d’Ivoire were the worst affected by drought. Crops such as maize, wheat, soybeans, peanuts, and millet are susceptible to yield losses under these conditions and give rise to concerns about regional food security.

To limit the negative impact of future El Niño years on agricultural production in West Africa, farmers could adopt the following adaptation strategies to increase crop yields.

  • Implementation of efficient irrigation systems for use during the drought periods of the growing season can help fight El Niño impacts.

  • Adoption of drought-resistant crops. Some crops, like cassava and sorghum, can tolerate high temperatures. The technological development of crop hybrids that can tolerate extreme temperatures and drought conditions can help fight against future El Nino and other extreme climate events.

  • Early Warning Systems. Even though El Niño leads to unpredictable weather patterns, governments, policymakers, and farmers will be aware of the impact of this phenomenon on weather patterns by developing and using early warning systems. This will help farmers plan their activities better before the phenomenon’s onset, thus reducing its adverse effects.

  • Use of bio-stimulants. Bio-stimulants play a vital role in helping crops develop resistance against various ecological abiotic stress, which results from expeditious shifts in climatic conditions. They help crops fight drought and temperatures induced by climate change. This will help different crops thrive well during an El Niño year, thus promoting agricultural productivity.

  • Shifting crop species. ENSO-induced climate variability affects cropping patterns. Therefore, farmers could plant different crops in response to perceived temperature and rainfall variations. However, this will be accompanied by accurate climate prediction, which will help farmers decide which crops to plant.

  • Switching planting dates. This is relatively the easiest adaptive strategy that farmers could implement. The onset of the rainy season is vital when planning to plant crops, especially rainfed crops. Early planting could affect crops due to insufficient soil moisture affecting seed germination. On the other hand, in the case of late planting, heavy rains could wash away the planted seeds; therefore, changing the dates for planting could be crucial during an El Niño year to achieve high crop yield.

In addition, we perform statistical and time series analyses to understand the role of El Niño in these impacts. Our findings showed that El Niño had a weak effect. This indicates that other remote climate indices might also significantly contribute to the observed drought conditions. We recommend a deeper analysis to attribute local extreme events to their main underlying drivers for better adaptation and mitigation strategies. With recent advancements in Machine learning and Artificial Intelligence (AI) techniques, these tools can be used to uncover the main drivers of local weather extremes.

Data and method

ERA5 data

The dataset used for this study has been obtained from the Copernicus Climate Data Store (CDS) and Global Drought Crops Monitoring (GDCM). The ERA5 dataset is the 5th “European Centre for Medium-Range Weather Forecasts (ECMWF)” generation reanalysis of the worldwide weather and climate. Two entries were obtained from this dataset: (i) “ERA5 monthly mean data on single levels from 1940 to present” and (ii) “ERA5 hourly data on single levels from 1940 to present”. In this study, Total Precipitation, Surface Temperature, Sea Surface Temperature (SST), Soil moisture, and Dew Point Temperature for the West Africa region with coordinates Inline graphic and Inline graphic, were extracted and used. The datasets have a horizontal resolution of Inline graphic38.

Since the relative humidity parameter was not available in the ERA5 single pressure level dataset, we use the following equation39:

graphic file with name 41598_2025_18940_Article_Equ1.gif 1

where Inline graphic is the dew point temperature, Temp is temperature, Inline graphic Inline graphicC, and Inline graphic are revised Magnus coefficients recommended by40.

Descriptive statistics of the variables

Table 1 shows the descriptive statistics of the variables used at the global and regional levels. Average global air temperature ranges between Inline graphicC and Inline graphicC, while in West Africa, temperature ranges between Inline graphicC and Inline graphicC. The range of other variables is shown.

Table 1.

Global descriptive statistics of the climatic variables under study.

Variable Unit Mean Standard deviation Median Maximum Minimum
Global Sea Surface Temperature Inline graphicC 13.7876 7.9524 14.7755 36.5314 −3.8839
Air Temperature Inline graphicC 5.4763 20.9748 10.4592 42.3284 −72.3730
Relative Humidity % 74.2700 19.5109 77.3410 99.2357 3.2365
Precipitation m 0.0024 0.0027 0.0016 0.1489 0.0000
Soil Moisture Inline graphic 0.2086 0.0838 0.2082 1.000 0.0000
West Africa Air Temperature Inline graphicC 26.838 3.4543 26.4725 38.0472 12.1500
Relative Humidity % 58.1945 58.1945 73.0864 95.8451 6.5548
Precipitation m 0.0029 0.0035 0.0015 0.0511 0.0000
Soil Moisture Inline graphic 0.1568 0.0965 0.1466 0.4596 0.0200
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SPEI data

For the drought data, the SPEI (https://global-drought-crops.csic.es/#map_name=all_spei_0.5#map_position=2174) dataset was used, obtained from GDCM, consisting of global drought indices. Variables include temperature, precipitation, soil moisture, streamflow, and snowpack. These are indicators that describe drought conditions. The drought indices are calculated using numerical representations of drought severity based on climatic data, including temperature and precipitation41. The SPEI time scale is between 0.5 and 48 months from 1979 to the present. The March-August anomalies were computed for 2023 with reference to the 1991–2020 climatology.

NDVI data

The Normalized Difference Vegetation Index (NDVI) data was used to investigate the changes in vegetation over West Africa in 2023 compared to climatology. Changes in vegetation directly signify how crops can also be affected. This dataset contains a gridded daily Normalized Difference Vegetation Index (NDVI) derived from the Surface Reflectance Climate Data Record (CDR). The data record spans from 1981 to 10 days before the present using data from NOAA polar-orbiting satellites on a Inline graphic by Inline graphic global grid42. The data can be found at https://doi.org/10.7289/V5ZG6QH9.

Spatio-temporal analysis

This research employed spatio-temporal analysis techniques to analyze the temperature, precipitation, relative humidity, soil moisture, NDVI, and drought indices over West Africa. This method was used to identify different patterns and trends of the climatic variables in West Africa.

Time series plots

Time series is a specialized technique to analyze a data collection sequence over time. It’s primarily used to identify patterns, trends, data relationships, anomaly detection, and forecasting43. This study used time series analysis to identify trends and patterns of the climatic variables over a particular period. In particular, this study used a 12-month rolling mean to depict how the climatic variables have changed over time. Using the rolling mean smoothed the data, reducing noise and enhancing the visibility of underlying patterns and trends. The monthly rolling mean or weighted moving average (WMA) was computed using the formula:

graphic file with name 41598_2025_18940_Article_Equ2.gif

where:

  • WMA = Average value

  • Inline graphic = Weight assigned to each data point

  • Inline graphic = Actual value at each point

  • n = Number of periods of weighting group, i.e. 12 months

The weights were obtained using the following formula:

graphic file with name 41598_2025_18940_Article_Equ3.gif

The cosine function was used because it considers the contracting areas towards the poles. This is because, despite the grids having the same spatial resolution, they cover different earth surfaces. In particular, the grids cover large spatial areas in the tropics compared to those at the poles.

Anomaly computation

Anomalies were computed to identify deviations of the climatic variables from the climatology. These anomalies are significant as they help to distinguish anthropogenic climate change from short-term fluctuations. To compute the anomalies, the climatology period was defined (1991–2020), the climatology mean was computed, and finally, the anomaly was computed (subtracting the long-term mean from the current year (2023)). The following formula was employed:

graphic file with name 41598_2025_18940_Article_Equ4.gif

where Inline graphic is the anomaly of the year Inline graphic at a location (j, k), Inline graphic is the variable of the year Inline graphic at a location (j, k). Inline graphic is the sum of the previous years that covers a period of 30 years. N is the number of data points.

Acknowledgements

The authors would like to thank the African Institute for Mathematical Science (AIMS) South Africa Center for the material, financial, and personnel support. The authors will also want to thank Copernicus Climate Services and NOAA for making the ERA5 and NDVI datasets accessible.

Author contributions

NAA conceived the project. VOK and ANN provided the data. All authors carried out the analysis and wrote the paper.

Funding

This project is jointly funded by the European Union (Grant Agreement No. 945361) through the Marie Skłodowska-Curie Actions COFUND scheme and Next Generation EU.

Data availability

The ERA5 monthly and hourly data used in this work was downloaded from the Copernicus Climate Data Store(CDS) found at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview. The SPEI drought data can be found at https://global-drought-crops.csic.es/#map_name=all_spei_0.5#map_position=2174 while the NDVI dataset can be found at https://doi.org/10.7289/V5ZG6QH9.

Declarations

Competing interests

The authors declare no competing interests.

Disclaimer

All the analyses and plots have been generated exclusively by the authors. No graphs, plots or materials have been taken from anywhere without permission or references.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Vincent Ondima Kongo and Nkongho Ayuketang Arreyndip have contributed equally to this work.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The ERA5 monthly and hourly data used in this work was downloaded from the Copernicus Climate Data Store(CDS) found at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview. The SPEI drought data can be found at https://global-drought-crops.csic.es/#map_name=all_spei_0.5#map_position=2174 while the NDVI dataset can be found at https://doi.org/10.7289/V5ZG6QH9.

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