Increased interannual variability of Sahel rainfall under greenhouse warming

Domain: earth_science
Published: 2025-12-27
Editorial outcome: major_revision

Abstract
The Sahel is home to tens of millions of people in developing regions and is frequently influenced by extreme events such as drought and floods. Multi-model mean projections indicate an increase in Sahel mean rainfall under greenhouse warming, but how Sahel rainfall interannual variability and extreme seasons might change is unclear. Using the latest generation of climate models, here we find that under a high emission scenario, Sahel rainfall variability increases, featuring a higher frequency of both extreme wet and dry seasons. The increase of rainfall interannual variability is stronger in the central-eastern Sahel than in the western Sahel. The increase in variability is due to several factors, including a projected increase in El Niño-Southern Oscillation variability, which leads to a stronger impact on low-level wind and moisture flux over the Sahel resulting in more extreme seasons, and a higher mean rainfall over the Sahel favoring large anomalies. Thus, the Sahel is likely to experience more extreme wet and dry seasons relative to its warmer and wetter mean climate.

Introduction
The Sahel is located in a transition area south of the Sahara Desert and north of the wet tropical rainforests with strong interannual/seasonal rainfall variability. Both drought and flood conditions can lead to devastating impacts on local rainfed agriculture and the people dependent on it. In particular, extreme dry years superimposed on the long-term drought, as seen in 1984, can affect a large portion of the population in countries like Sudan and cause food shortages for millions of people.

Sahel rainfall is concentrated in the July-August-September (JAS) season, following the seasonal marching of the western African monsoon. Interannual (or JAS) Sahel rainfall variability is influenced by tropical sea surface temperature (SST) variability. For example, warming of the eastern Pacific during a developing El Niño event can lead to a dry Sahel by increasing the upper tropospheric temperature over the tropics and the atmospheric stability above the Sahel, and forcing easterly wind anomalies over the Sahel as a Gill-Matsuno response which transport moisture out of the Sahel. The Atlantic Niño/Niña, featuring positive (negative) SST anomalies over the equatorial Atlantic, can reduce (increase) low-level pressure gradient between North Africa and the equator and weaken (strengthen) the monsoon, leading to a dry (wet) Sahel.

The interannual relationship between tropical SST and Sahel rainfall is unstable and modulated by the background climate mean state change. While the Atlantic Niño tends to lead to dry anomalies over Sahel pre-1970s, the negative relationship between the Atlantic SST and Sahel rainfall breaks down post-1970s. Post-1970s, an Atlantic Niño event is accompanied by negative anomalies of tropical eastern Pacific SST, which offset the drying effect of the Atlantic Niño and dominate Sahel rainfall variability in recent decades. The change in the interannual tropical SST–Sahel rainfall relationship is related to the decadal background SST mean state fluctuations, which determine the location of the Intertropical Convergence Zone (ITCZ; influenced by the interhemispheric SST gradients in the Atlantic Ocean) and the amplitude of El Niño-Southern Oscillation (ENSO) (associated with Pacific SST mean state). The modulation of the tropical SST–Sahel rainfall linkage by the climate mean state in recent decades implies that the interannual variability of Sahel rainfall may change in response to future shifts in the mean state under global warming.

Previous studies of future changes in Sahel rainfall have primarily focused on the changes in climatological means and sub-seasonal timescales. Coupled Model Intercomparison Project phase 6 (CMIP6) models tend to show a wetter Sahel mean state in the 21st century compared with the 20th century under greenhouse warming, driven by mechanisms such as a faster warming in the northern hemisphere, favoring moisture convergence in the North Atlantic and the enhanced land–sea warming contrast which moves the rain band northward to the Sahel. On daily to sub-seasonal scales, greenhouse warming tends to induce more extreme rainfall days associated with mesoscale convective systems due to the strengthening of the meridional temperature gradient over the Sahel, but a reduction in the total number of rainy days.

How variability of Sahel rainfall on interannual timescales will change in a warmer and wetter mean climate under greenhouse warming is unclear. Using the latest climate models in CMIP6, here we show a robust increase in JAS Sahel rainfall variability in the 21st century under a high emission scenario, accompanied by an increase in both extreme dry and wet seasons.

Results
Increased variability of Sahel rainfall and occurrence of extreme seasons
We use 42 available CMIP6 models forced with historical forcing from 1900 to 2014 and with Shared Socioeconomic Pathways (SSP) 5-8.5 high emission scenario from 2015 to 2099, and evaluate the Sahel (11°N to 15°N, 17°W to 40°E) rainfall interannual variability change, represented by the standard deviation of JAS rainfall anomalies, from the 20th century (1900–1999) to the 21st century (2000–2099) (see “Observation and CMIP6 data” in Methods).

Most of the CMIP6 models (39 out of the total 42 models, 93%) show an increase in JAS Sahel rainfall variability from the 20th century to the 21st century but with a large spread in the magnitude of change (Fig. 1a). The multi-model mean increase is statistically significant according to a bootstrap test (see “Bootstrap test” in Methods) (Fig. 1a). There is no significant relationship between model performance in simulating present-day Sahel rainfall variability and the magnitude of the projected change (see “Sensitivity test” in Methods; Supplementary Fig. 1).

The increase in rainfall variability is underpinned by an increase in the frequency of extreme seasons. A majority of the models project not only more extreme wet seasons (defined as years with JAS rainfall anomalies larger than 1.5 standard deviations; 40 out of the 42 models, 95%) but also more extreme dry seasons (defined as years with JAS rainfall anomalies smaller than −1.5 standard deviations; 30 out of the 42 models, 71%) in the future, both strongly correlated with the increase in Sahel rainfall variability (Fig. 1b, c). The multi-model mean projects a 140% (60%) increase in the frequency of extreme wet (dry) seasons, from 4.4 (4.3) events per 100 years in the 20th century to 10.6 (6.9) events per 100 years in the 21st century. Using other thresholds for defining extreme seasons yields similar results (Supplementary Fig. 2). The overall wetting trend means that future dry seasons may be less severe in absolute terms, but strong negative rainfall anomalies (relative to the elevated future rainfall climatology) will pose substantial risks to local communities and ecosystems that have adapted to a wetter environment.

Previous studies have suggested that the response of rainfall over the western Sahel (17°W to 5°W) and central-eastern Sahel (CE, 5°W to 40°E) to greenhouse warming might be different (see “West-East Contrast” in Methods). Indeed, the interannual rainfall variability increase from the 20th century to the 21st century is more robust over the CE Sahel (all 42 models show an increase) than over the western Sahel (79% of the models show an increase). The increase in variability translates to more extreme dry and wet seasons in the CE Sahel and more extreme wet seasons in the western Sahel. The change of the frequency of extreme dry seasons in the western Sahel has no inter-model consensus (see “West-East Contrast” in Methods).

We also examine the change of Sahel rainfall variability under a SSP1-2.6 low emission scenario in 39 available CMIP6 models. A total of 32 out of the 39 models (82%) show an increased Sahel rainfall variability from the 20th century to the 21st century under the SSP1-2.6 emission scenario (Supplementary Fig. 3). The inter-model consensus is weaker compared to that under the SSP 5-8.5 emission scenario (93%), and the multi-model mean change of rainfall variability (0.07 mm/day) is smaller than that under the high emission scenario (0.15 mm/day). We then focus on the high emission scenario to explore the mechanisms driving the increase in Sahel rainfall variability.

A stronger influence from ENSO under greenhouse warming
We start by examining changes in the impact of tropical SST variability on the Sahel rainfall variability. As expected, the interannual variability of Sahel rainfall is dominated by the tropical eastern Pacific SST (ENSO) variability in observations from 1979 to 2022 (Supplementary Fig. 4a). A developing El Niño in JAS is associated with negative rainfall anomalies over the whole Sahel with the strongest anomalies located in the central Sahel (Supplementary Fig. 4b). The Pacific anomalous warming stimulates local convection and rearranges the atmosphere circulation in the tropics through a Gill-Matsuno like response and modulation of the Walker circulation, inducing low-level high pressure anomalies in the tropical north Atlantic and surface easterly wind anomalies over the Sahel (Supplementary Fig. 4c). The low-level easterly wind transports moisture out of the Sahel and causes negative rainfall anomalies.

Most of the CMIP6 models (36 out of 42, 86%) project an increase in JAS ENSO variability (represented by JAS Niño 3.4 index) in the 21st century and models with a stronger increase in ENSO variability systematically simulate a stronger increase in Sahel rainfall variability with a significant inter-model correlation of 0.53, scaled by the global SST warming rate (Fig. 2a). Even without stronger ENSO SST variability, greenhouse warming tends to amplify the impact of ENSO on Sahel rainfall. The amplification is associated with a faster eastern Pacific warming than the surrounding regions, which enhances local convection and increases the sensitivity of lower-tropospheric humidity to ENSO. When combined with increased ENSO variability, the overall Sahel rainfall response to ENSO becomes stronger under greenhouse warming. Consistently, most models (38 out of 42) simulate a stronger total response of Sahel rainfall to ENSO from the 20th century to the 21st century, represented by a regression of Sahel rainfall onto normalized Niño 3.4 index in each period; the stronger the increase in Sahel rainfall response to ENSO, the greater the increase in Sahel rainfall variability (Fig. 2b) (see “Sensitivity test” in Methods; Supplementary Fig. 5a). The stronger impact from ENSO contributes to the increased variability of rainfall in both western Sahel and CE Sahel, while the inter-model correlation of rainfall variability changes and ENSO variability changes is higher for the CE Sahel than that for the western Sahel (see “West-East Contrast” in Methods).

In a warmer climate, a developing El Niño in JAS can lead to stronger easterly wind anomaly over the Sahel and the North Atlantic, which transports moisture out of the Sahel and inhibits rainfall (Fig. 2c), either through stronger Kelvin waves propagating eastward from the Pacific or through stronger Rossby waves generated by the convection anomalies over the Indian Ocean. The low-level wind change is also associated with a weakened monsoon, preventing the northward movement of the rain belt and drying the Sahel (Fig. 2c). A total of 37 out of 42 models project an increase in 850 hPa zonal wind variability over the Sahel; a stronger increase in zonal wind variability is systematically associated with a stronger increase in Sahel rainfall variability (Fig. 2d) (see “Sensitivity test” in Methods; Supplementary Fig. 5b).

In observations, a developing El Niño event is often accompanied by an Atlantic Niño pattern in the equatorial eastern Atlantic (Supplementary Fig. 6), which typically weakens the drying effect of El Niño on Sahel rainfall by moving the ITCZ northward and strengthening the monsoon. Under greenhouse warming, Atlantic Niño/Niña variability is projected to decrease. This reduction may favor an increase in Sahel rainfall variability by diminishing the offsetting effect of Atlantic Niño/Niña on ENSO influences (see “Possible role of the reduced Atlantic Niño/Niña variability” in Methods; Supplementary Fig. 7).

A wetter mean state conducive to rainfall variability
We also examine the impact of the increase in local seasonal mean rainfall. Comparing the 21st century and 20th century, models that simulate a larger increase in JAS mean rainfall tend to project a stronger increase in JAS rainfall interannual variability over the Sahel (Fig. 3a). The inter-model correlation between the mean state change and interannual variability change of JAS Sahel rainfall is 0.53, which is significant above the 95% confidence level. The relationship between changes in rainfall interannual variability and changes in local mean state is robust across 42 models for both the western Sahel and CE Sahel regions (see “West-East Contrast” in Methods).

In terms of extreme seasons, a wetter mean state contributes to more extreme dry seasons over the Sahel in the 21st century, with an inter-model correlation of 0.46 across 42 models, significant at the 95% confidence level (Fig. 3b). A weak relationship is found between rainfall mean state change and change in extreme wet seasons (Fig. 3c). An increased climatological rainfall is conducive to more extreme dry seasons relative to that climatology, because the dry mean state in the current climate may limit substantial negative rainfall anomalies, even when a strong El Niño occurs.

The projected increase of JAS Sahel mean rainfall is suggested to be related to the SST mean state change. Here we find a close relationship between the Sahel mean rainfall change and the Atlantic SST gradient change (defined as the difference in JAS SST between averages over the 0°−30°N, 60°W−17°W, and 30°S−0°, 60°W−10°E area) (Fig. 3d). A warmer north Atlantic than the south Atlantic favors a northward movement of the ITCZ and strengthens the monsoon, which promotes more rainfall over the Sahel on long timescales and induces stronger interannual variability. In the 42 CMIP6 models, the Sahel rainfall mean state change yields a weak inter-model correlation with ENSO variability change (Supplementary Fig. 8), suggesting that it contributes to the change in Sahel rainfall variability independent from that by the ENSO variability change.

In addition to the influence of SST mean pattern change, previous studies have also highlighted the roles of uniform SST warming and the direct radiative effects of increased CO₂ in driving changes in the Sahel mean rainfall. Below, we further investigate changes in the interannual variability of Sahel rainfall under the impact of uniform SST warming and direct radiative forcing, excluding the effects of changes in SST interannual variability (such as ENSO) and SST warming patterns (differential Atlantic SST warming), using simulations from Atmospheric Model Intercomparison Project (AMIP) experiments.

Opposing impact from uniform SST warming and direct radiative effect
We compare Sahel rainfall variability in the simulations of 10 models participating in the AMIP and AMIP-p4k experiments from CMIP6 (see “Analysis of AMIP experiments” in Methods) to explore the effect of uniform SST warming on Sahel rainfall variability change. The AMIP experiments are forced with observed SSTs from 1979–2013, and in the AMIP-p4k experiments, a uniform warming of 4 K is added to observed SST variability (see Supplementary Fig. 9a for an example; see “Analysis of AMIP experiments” in Methods). In these two sets of experiments, SST properties, such as ENSO and SST mean pattern, remain the same.

Greenhouse warming tends to increase rainfall variability by increasing the specific humidity of the atmosphere and boosting rainfall event even though the circulation is unchanged. However, this can be offset by the stabilization of the atmosphere that is also caused by global warming. As expected, low level (850 hPa) specific humidity is increased from the AMIP experiment to the AMIP-p4k experiment in all 10 models (Supplementary Fig. 10a). However, no significant change of JAS Sahel rainfall variability is observed in the AMIP-p4k experiments compared with the AMIP experiments, with 5 models showing an increase while the other 5 models showing a decrease (Fig. 4a). The multi-model mean change shows a decrease that is not statistically significant (Fig. 4a).

All 10 AMIP-p4k experiments show a drier Sahel mean state compared with the AMIP experiments (Supplementary Fig. 10b), which may reduce Sahel rainfall variability. Similar to the mean state–interannual variability relationship in Fig. 3a, models with a larger decrease in mean rainfall from the AMIP experiment to the AMIP-p4k experiment tend to simulate a larger decrease in Sahel rainfall variability. Thus, a uniform SST warming with unchanged SST variability is not responsible for the projected increase in Sahel rainfall variability by CMIP6 models.

The direct radiative effect of the increased CO₂ has an opposite impact on the Sahel rainfall mean state and interannual variability change to that from uniform SST warming. Here we examine the direct radiative effect using 10 AMIP-4CO₂ experiments forced with observed SSTs but with CO₂ levels multiplied by four (see “Analysis of AMIP experiments” in Methods). The direct radiative effect of increased greenhouse gas emissions, measured as the difference between the AMIP-4CO₂ and the AMIP experiments, tends to warm North Africa (see Supplementary Fig. 9b for an example) and weaken the shallow meridional circulation, as characterized by the northward near-surface flow from the Sahel to the Sahara accompanied by 700 hPa southward dry flow from the Sahara to the Sahel. The weakened dry flow from the north enhances JAS Sahel rainfall (Supplementary Fig. 10c), which in turn leads to stronger interannual variability of Sahel rainfall in AMIP-4CO₂ experiments (Fig. 4b). Thus, the uniform SST warming and direct radiative effect tend to have opposite impacts on Sahel rainfall mean state and interannual variability.

Methods
Observation and CMIP6 data
We focus on the JAS rainfall variability change over the Sahel (17°W to 40°E, 11°N to 15°N). Monthly averaged total rainfall, SST, sea level pressure, and 850 hPa wind field data from ERA5 (fifth generation of the European Centre for Medium-Range Weather Forecasts atmospheric reanalyses) dataset are used as observations to explore the variability of Sahel rainfall and its relationship with tropical SST from 1979–2022. ERA5 data have a horizontal resolution of 0.25°×0.25°. All anomalies are calculated relative to the climatology from 1979 to 2022 after a quadratic detrending.

A total of 42 available CMIP6 models forced with historical forcing from 1900 to 2014 and with Shared Socioeconomic Pathways (SSP) 5-8.5 emission scenario from 2015 to 2099, are used to investigate changes in Sahel rainfall variability under greenhouse warming and associated mechanisms. Data of 39 models (data of the three models are unavailable) forced with SSP1-2.6 emission scenario from 2015 to 2099 are also used to explore the Sahel rainfall variability change under a low emission scenario. A total of 28 CMIP5 models forced under historical forcing to 2005 and future greenhouse gas forcing till 2099 under the Representative Concentration Pathway 8.5 (RCP85) are applied to examine the rainfall variability change. All the monthly rainfall, SST, and wind field data are interpolated to a 1°×1° horizontal resolution. Similar to observations, anomalies are calculated with reference to the climatology of the whole period (200 years from 1900 to 2099) after removing the quadratic trend.

We use a t-test to perform the significance test for the correlation coefficient.

The extreme dry and wet rainfall seasons in each model are defined as the years with JAS rainfall anomalies smaller (larger) than −1.5 (1.5) s.d. of the 200-year climate. The long-term trend has been removed, and the extreme seasons in the 20th century and the 21st century are defined relative to their corresponding climatology.

Bootstrap test
A bootstrap test method is used to examine the significance of multi-model mean change. As in Fig. 1a, we randomly resample the 42 values of Sahel rainfall variability in the 20th century to conduct 10,000 realizations of 42-sample Sahel rainfall variability, and any sample value can be repeated. The same analysis is repeated for the 21st century. The multi-model mean change of Sahel rainfall variability is significant at the 95% confidence level if the multi-model mean change is greater than the sum of the standard deviations of the 10,000 realizations in the two centuries. The error bars in the multi-model mean in Fig. 1a show the one standard deviation of the 10,000 realizations. The same method is used to test the significance of the changes in Sahel rainfall variability and mean state, and 850 hPa specific humidity in AMIP experiments.

Sensitivity test
We apply a quadratic detrending method for calculating the anomalies, which estimates and removes the least-squares quadratic trend from the time series, because the global warming trend is not linear. We also calculate anomalies using an 11-year running window to remove decadal-scale variability. In this method, anomalies for 1900–1904 and 2095–2099 are referenced to the climatology of 1900–1910 and 2089–2099, respectively. The remaining anomalies are obtained using an 11-year running climatology. Both methods yield similar results.

Without model selection, the majority of CMIP6 models indicate an increased Sahel rainfall variability due to greenhouse warming. We further assess the potential influence of present-day model performance on this projection using two metrics: the regional average of rainfall variability and the spatial pattern correlation of variability with observations over the Sahel. The results reveal that the ability of simulating present-day rainfall interannual variability has a negligible impact on its future change (Supplementary Fig. 1).

Changes from the 20th century to the 21st century are scaled by the global SST warming rate over the same period to limit the influence of the different climate sensitivities in models. Sahel rainfall variability change in two models is abnormally larger than in other models after scaling by the global warming rate (Fig. 2b, d). However, the scaling process has little qualitative impact on our results (see Supplementary Fig. 5 for examples).

West-east contrast
Rainfall over the western Sahel and CE Sahel tends to have different responses to greenhouse warming. While the climate models simulate increased rainfall over the CE Sahel, rainfall over the western Sahel shows less change in the 21st century under the high emission scenario. We compare the interannual rainfall variability change over the two regions.

Over the western Sahel, 33 out of the total 42 CMIP6 models (79%) show an increase in JAS rainfall variability from the 20th century to the 21st century. However, the multi-model mean change of the rainfall variability is not significant according to the bootstrap test. The increase of interannual variability translates into more extreme wet (>1.5 s.d.; 33 out of the total 42 CMIP6 models) seasons in the future over the western Sahel. The change of the frequency of the extreme dry seasons (<−1.5 s.d.) has no inter-model consensus, with 20 models showing an increase.

The interannual variability of rainfall over the CE Sahel in the 42 models all show an increase in the 21st century. The multi-model mean change is significant according to the bootstrap test. The increased variability leads to more extreme wet (40 out of the 42 models show an increase) and dry (33 out of the 42 models show an increase) seasons over the CE Sahel in the future.

The increased variability of ENSO under greenhouse warming leads to stronger rainfall variability in both the western Sahel and CE Sahel. The inter-model correlation between rainfall variability change and ENSO variability change from the 20th century to the 21st century among 42 models is 0.32 for the western Sahel and 0.5 for the CE Sahel, both significant above the 95% confidence level.

The interannual rainfall variability changes in the western Sahel and the CE Sahel under greenhouse warming are both related to the projected local rainfall mean state change. The inter-model correlation between rainfall variability change and local rainfall mean state change from the 20th century to the 21st century among 42 models is 0.47 for the western Sahel and 0.63 for the CE Sahel, both significantly above the 95% confidence level.

Possible role of the reduced Atlantic Niño/Niña variability
Under greenhouse warming, ENSO exerts a stronger impact on Sahel rainfall, while the influence from the Atlantic is weaker, a change somewhat similar to that observed since the 1970s. Models have shown that the Atlantic Niño/Niña by itself is an important source modulating Sahel rainfall. Under greenhouse warming, climate models simulate a reduction in Atlantic Niño/Niña variability. Models with a larger decrease in the Atlantic Niño/Niña variability (represented by the standard deviation of SST anomalies averaged over 3°S to 3°N, 20°W to 0°E) tend to simulate a greater increase in Sahel rainfall variability (Supplementary Fig. 7a).

However, we do not find a statistically significant inter-model correlation between Sahel rainfall variability and the Atlantic Niño/Niña variability in the simulated present-day climate. One possible reason for a future reduction in Atlantic Niño/Niña variability to favor an increase in Sahel rainfall variability might be that the offsetting effect of the Atlantic Niño/Niña on ENSO–Sahel rainfall connection is weaker when the Atlantic Niño/Niña variability decreases. For example, an El Niño event is often accompanied with negative SST anomalies over the equatorial eastern Atlantic, that is, an Atlantic Niña condition (Supplementary Fig. 6). Cool tropical Atlantic SSTs promote stronger temperature gradients between the tropical Atlantic and Sahara, strengthening the monsoon and pushing the ITCZ northward, favoring Sahel rainfall and offsetting the drying effect of the El Niño. In a warmer climate, reduced Atlantic Niño/Niña variability limits the offsetting effect. Compared with the 20th century, the response of the ITCZ to an Atlantic Niña is much weaker in the 21st century, featuring a southward retreat resembling its response to El Niño (comparing Supplementary Fig. 7b and Fig. 2c). In this case, an El Niño moves the ITCZ and the rainfall belt over the North Atlantic southward more easily and induces a dry condition over the Sahel. In addition, the Atlantic Niño/Niña variability change shows a weak relationship with the ENSO variability change and the Sahel rainfall mean state change (Supplementary Fig. 13), suggesting their independent roles.

Analysis of AMIP experiments
AMIP, AMIP-p4k and AMIP-4CO₂ experiments from 10 available CMIP6 models from 1979 to 2013 are used to explore the effect of greenhouse warming on Sahel rainfall variability with fixed SST variability. Monthly data are interpolated to a 1°×1° horizontal resolution. Anomalies are calculated with reference to the climatology of 1979 to 2013 after removing the quadratic trend.

AMIP experiments are based on atmospheric models forced by observed SST and sea ice concentrations. AMIP-p4k experiments have similar settings to AMIP experiments except that a uniform 4 K warming is added to the SST (see Supplementary Fig. 9a for an example). AMIP-4CO₂ experiments are forced with observed SSTs but with CO₂ levels multiplied by four (see Supplementary Fig. 9b for an example). Thus, the SST mean state pattern and SST interannual variability are the same between the three sets of experiments. The difference between AMIP and AMIP-p4k experiments can represent the influence of the uniform SST warming, and the difference between AMIP and AMIP-4CO₂ experiments can represent the influence of the direct radiative effect of CO₂.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review comments/suggestions on the manuscript entitled \"Increased interannual variability of Sahel rainfall under greenhouse warming\"\nSummary\nThe Sahel is one of the regions in the developing world that is particularly susceptible to the effects of climate change. The\nimpacts of climate change are most evident in rainfall, which has become increasingly variable in recent years. The increase\nin interannual variability of rainfall is associated with an increased frequency of extreme events such as droughts and floods.\nThis has devastating impacts on the millions of people who rely on rain-fed agriculture as their main economic activity for\nsustaining their livelihoods through income and food. To alleviate these impacts and safeguard the security and\nsustainability of economic and social development, it is crucial to understand the future changes in interannual rainfall\nvariability over the Sahel region. The authors projected an increased interannual variability of rainfall contributed by both wet\nand dry extreme seasonal events, with contrasting magnitude between central-eastern and western Sahel. Finally, they\ndiscussed the possible mechanism for such an increase in interannual rainfall variability, as derived from ENSO and mean\nrainfall.\nThe work is comprehensive and has potential interests, especially in a region where the performance evaluation of the\nmodels and prediction has been challenging. The manuscript has clear content and is well organized. It has the qualities\nthat align with the theme and weight of the journal. As such, it is commendable. But there are a couple of issues that need to\nbe addressed before further consideration, as detailed hereunder.\nMajor concerns:\n1. The main focus is the changes in interannual variability of rainfall. However, Sahel rainfall also exhibits decadal and\ninterdecadal components, as exemplified by the shift from a wet state in the 1950s and 1960s to a dry state in the 1970s and\n1980s.The likes of these changes might appear in future periods [e.g. Dieppois et al. (2013); García-Serrano et al. (2013)].\nHow did the authors deal with the decadal signals in the Sahel rainfall?\nReferences\nDieppois, Bastien, Arona Diedhiou, Alain Durand, Matthieu Fournier, Nicolas Massei, David Sebag, Yongkang Xue, and\nBernard Fontaine. 2013. “Quasi-Decadal Signals of Sahel Rainfall and West African Monsoon since the Mid-Twentieth\nCentury.” Journal of Geophysical Research Atmospheres 118(22): 12,587-12,599.\nGarcía-Serrano, J., F. J. Doblas-Reyes, R. J. Haarsma, and I. Polo. 2013. “Decadal Prediction of the Dominant West African\nMonsoon Rainfall Modes.” Journal of Geophysical Research Atmospheres 118(11): 5260–79.\n2. The authors used 42 models for computing future changes in interannual variability of rainfall. What were the selection\ncriteria for the models? How about evaluating model performance to simulate rainfall interannual variability relative to\nobservation data in the historical period, then consider only best-performing models for future changes? Although this is not\nthe central idea of the work, it is an important preparatory step for addressing the main idea of this work. These kinds of\nanalyses can be added to the supplementary information.\n3. Although the discussion of the results is satisfactory, it can be further improved by highlighting some insights from the\nprevious generation of CMIP models. If there is related work concerning simulating the interannual rainfall variability based\non previous model versions, In the discussion, the authors should consider comparing their findings with those of related\nstudies (say \"CMIP5 and 6\") over the study region and beyond and most importantly, give the possible\nimplications/applications of the findings on climate/agriculture/environment etc.\nMinor issues:\n1. P2, L23: I am a bit confused with the use of the word \"countries\". I was expecting to see the word \"regions\" because the\nsentence begin with \"The Sahel…\". Kindly check it.\n2. P2, L32: Remove the acronym \"ENSO\" because it has not been used elsewhere in the abstract.\n3. P3, L38: I don't see the need to put the word \"dry\" before \"Sahara Desert\" because it is known that the desert is a dry area.\nConsider removing it.\n4. L64, P3: Kindly provide the abbreviation for \"ENSO\" on its first appearance in the main text section.\n5. P4, L88: Add a space between degree and lat/lon direction. Check it throughout the manuscript and for other cases of\nsimilar concern.\n6. P4, L89-90: I am wondering why the change in standard deviation rainfall was taken between 2000-2099 and 1900-1999\nand not between 2015-2099 and 1900-2014? The historical period in which models are forced by historical forcings ends in\n2014, and afterward, the models are forced by SSP scenarios.\n7. P4, L91: What is quadratic detrend? What's its strength over common linear detrend methods in the context of rainfall?\nThe authors can briefly describe it in the methods section. Moreover, this last line (L90-93) describing quadratic trends may\nnot be necessary here because it is basically the repetition of what has been described in the methods section.\n8. Fig. 1a: In the caption, the authors mentioned that green circles denote the models that feature a decrease in precipitation\nvariability. But, looking at the MCM-UA-1-0 model, it shows no change in std values between the two periods. Kindly check\nit.\n9. Extended Data Fig. 2a: There is a white line at around zero longitude. The authors can sort out the issue.\n10. Extended Data Fig. 2: Which method did you use to perform the significance test for the correlations? Is a significance\ntest also performed on the wind pattern? The authors can briefly introduce the method in the methods section.\n11. P7, L172: Is it a double hyphen?\n12. P7, L180-183: A bit hard to follow this sentence. Consider revising it to enhance understanding.\n13. Fig. 4a: The std values of HadGEM3-GC31-ALL between AMIP and AMIP-p4k simulations are comparable, and yet the\nmodel is incorporated in the group of models showing increasing tendency. Could you clarify this?\n14. Fig. 4a: In the first line of the caption, 1979 to 2013? Did you mean 1979 to 2023? Also, in the fourth line from the end of\nthe caption concerning the green circles, did you mean a decrease or an increase?\n15. P19, L 466: Adjust spacing before JAS", "reviewer_2": "Review of\n“Increased interannual variability of Sahel rainfall under greenhouse warming”\nThis paper highlights the increase in interannual variability in rainfall over the Sahel, and links this increase to changes in\nENSO, the mean state and changes in the Atlantic SSTs. It also shows that uniform SST changes and radiative changes\ndue to increased carbon dioxide levels have opposite effects on rainfall variability over the Sahel.\nThe paper is robust and interesting, and is an important contribution to the literature. I recommend minor revisions.\nMajor-minor comments:\nI think the section at the beginning of the discussion and conclusions on the role of the Atlantic should be a separate section\nin the results. The discussion and conclusions I feel should not introduce new results, which this section does.\nThe conclusion that there are more extreme dry seasons is based on the mean rainfall increasing, and therefore there being\nmore seasons that are 1.5 standard deviations below the mean. This doesn’t actually mean there are more seasons e.g. with\nmean rainfall <1mm/day, or more drought seasons. I think it would be good to compare the changing frequency of extreme\ndry seasons based on some fixed or consistent thresholds, to see if there are actually more dry seasons, as this is what will\nhave a notable impact on populations. The results show more extreme wet seasons in the western Sahel but not more\nextreme dry seasons, potentially due to smaller total rainfall increases in the western Sahel. This is maybe not\nrepresentative of the “on the ground” changes that populations will face. I think this is a notable limitation that needs to be\naddressed.\nMinor comments:\nLine 70 – should “on” be “in”\nLine 78 – would be clearer as “total number of rainy days”\nFigure 1b-c (and other scatters) is the multi-model mean shown? Would be good to include?\nLine 136-140. Please rephrase – this sentence starts with talking about small changes in ENSO variability, and ends with\nincrease in ENSO variability. Could be clearer.\nLine 156 – why is 38 in words?\nLine 227 – should be “drier”\nLine 243 – this is the first time a shallow meridional circulation in North Africa is mentioned, please explain more how this\nleads to stronger variability.\nLine 246 – is the offset exact? Is one factor larger than the other? Would be good to explore this."}, "author_response": "We thank you for taking the time and effort to review our manuscript. We have\nconsidered all of your comments and suggestions.\nBelow, we provide a point-by-point response to your comments.\nMajor concerns:\n1. The main focus is the changes in interannual variability of rainfall. However, Sahel\nrainfall also exhibits decadal and interdecadal components, as exemplified by the shift\nfrom a wet state in the 1950s and 1960s to a dry state in the 1970s and 1980s.The likes\nof these changes might appear in future periods [e.g. Dieppois et al. (2013); García-\nSerrano et al. (2013)]. How did the authors deal with the decadal signals in the Sahel\nrainfall?\nReferences\nDieppois, Bastien, Arona Diedhiou, Alain Durand, Matthieu Fournier, Nicolas Massei,\nDavid Sebag, Yongkang Xue, and Bernard Fontaine. 2013. “Quasi-Decadal Signals of\nSahel Rainfall and West African Monsoon since the Mid-Twentieth Century.” Journal\nof Geophysical Research Atmospheres 118(22): 12,587-12,599.\nGarcía-Serrano, J., F. J. Doblas-Reyes, R. J. Haarsma, and I. Polo. 2013. “Decadal\nPrediction of the Dominant West African Monsoon Rainfall Modes.” Journal of\nGeophysical Research Atmospheres 118(11): 5260–79.\nWe appreciate the reviewer's suggestion regarding decadal variability and have added\nthe provided references to the manuscript. While we acknowledge the importance of\nthis low-frequency signal, the primary focus of our work is on projecting future changes\nin interannual rainfall variability, above changes in the mean state.\nIn the original version, we detrended the data and compared the changes between two\n100-year periods. This approach minimizes the influence of low-frequency decadal\nvariability while emphasizing the long-term greenhouse warming signal.\nTo test the robustness of our results, we have implemented an additional method to\nremove decadal influence. This method calculates SST anomalies using an evolving\nclimatology: a 11-year running window is used for the core period (e.g., the anomaly\nfor the year 2000 is referenced to the 1995–2005 mean); the anomalies during the 1900-\n1904 and 2095-2099 are calculated relative to the fixed climatology of 1900-1910 and\n2089-2099, respectively.\nRecalculating Sahel rainfall variability with this new anomaly basis produces results\nnearly identical to our original method (Fig. R1). The inter-model correlation of the\nprojected change in Sahel rainfall interannual variability between the two methods is\n0.99. We have stated that our results are not sensitive to the method calculating the\ninterannual variability in the Method section. Please see lines 327-333.\nLines 327-333: “We apply a quadratic detrending method for calculating the anomalies,\nwhich estimates and removes the least-squares quadratic trend from the time series,\nbecause the global warming trend is not linear. We also calculate anomalies using an\n11-year running window to remove decadal-scale variability. In this method, anomalies\nfor 1900–1904 and 2095–2099 are referenced to the climatology of 1900–1910 and\n2089–2099, respectively. The remaining anomalies are calculated using an 11-year\nrunning climatology. Both methods yield similar results”.\nFig. R1 | Changes in JAS Sahel rainfall variability using an alternative method for\ncalculating anomalies. The figure shows the change in standard deviation (mm/day)\nof JAS Sahel rainfall from the 20th century (blue) to the 21st century (red) across 42\nCMIP6 models. Error bars on the multi-model mean represent the 95% confidence level,\nderived from a bootstrap test. Green circles highlight models that simulate a decrease\nor no change in rainfall variability. Unlike main Fig. 1a, the rainfall anomalies here are\ncomputed using an 11-year running window. Specifically, anomalies for the first 5 years\n(1900–1904) and last 5 years (2095–2099) are calculated relative to the climatology of\nthe first 11 years (1900–1910) and last 11 years (2089–2099), respectively. All other\nanomalies are computed using an 11-year centered window; for example, the anomaly\nfor the year 2000 is calculated relative to the mean of the period 1995–2005.\n2. The authors used 42 models for computing future changes in interannual variability\nof rainfall. What were the selection criteria for the models? How about evaluating\nmodel performance to simulate rainfall interannual variability relative to observation\ndata in the historical period, then consider only best-performing models for future\nchanges? Although this is not the central idea of the work, it is an important preparatory\nstep for addressing the main idea of this work. These kinds of analyses can be added to\nthe supplementary information.\nThanks for the suggestion. We utilized all 42 available models with rainfall and SST\noutput, without performing any model selection, as the majority of the models\nconsistently project an increase in Sahel interannual rainfall variability.\nIn response to your suggestions regarding model evaluation, we have examined the\npotential influence of model performance on future projections. Model performance is\nassessed using two indices: the regional mean rainfall variability during the\nobservational period (1979–2022) and the pattern correlation of the spatial distribution\nof rainfall variability between observations and models over the Sahel region. The\nobserved rainfall variability over the Sahel is 0.58 mm/day, which lies within the middle\nof the model-simulated range. We find no strong relationship between simulation of\npresent-day Sahel rainfall variability and its projected future change (Fig. R2a).\nFurthermore, there is no significant relationship between the pattern correlation of\nobservation and models in terms of rainfall variability map and the Sahel rainfall\nvariability future change (Fig. R2b). These results suggest that model performance in\nsimulating the present-day climate has little influence on projections.\nIn the revised manuscript, we have added Fig. R2 as the new Extended Data Fig. 1\nand discussed the potential influence of models bias on future projections. Please see\nlines 334-340.\nLines 334-340: “Without model selection, the majority of CMIP6 models indicate an\nincreased Sahel rainfall variability due to greenhouse warming. We further assessed the\npotential influence of present-day model performance on this projection using two\nmetrics: the regional average of rainfall variability and the spatial pattern correlation of\nvariability with observations over the Sahel. The results reveal that the ability of\nsimulating present-day rainfall interannual variability has a negligible impact on its\nfuture change (Extended Data Fig. 1)”.\nFig. R2 (Extended Data Fig. 1) | Impact of present-day climate simulation in\nmodels on projected Sahel rainfall variability changes. a, Relationship across\nCMIP6 models between JAS Sahel rainfall variability (standard deviation) for the\nperiod 1979–2022 and its projected change from the 20th to the 21st century. Anomalies\nfor the 1979–2022 period are calculated relative to the 44-year climatology after\ndetrending. b, Relationship between the pattern correlation of JAS rainfall variability\nin models and observations and the projected change in Sahel rainfall variability.\nPattern correlations were calculated over the entire Sahel region. The correlation\ncoefficient and the P value are shown in red.\n3. Although the discussion of the results is satisfactory, it can be further improved by\nhighlighting some insights from the previous generation of CMIP models. If there is\nrelated work concerning simulating the interannual rainfall variability based on\nprevious model versions, In the discussion, the authors should consider comparing their\nfindings with those of related studies (say \"CMIP5 and 6\") over the study region and\nbeyond and most importantly, give the possible implications/applications of the\nfindings on climate/agriculture/environment etc.\nThank you for the suggestion. While some research, such as Akinsanola et al. (2020),\nhas evaluated future changes in Sahel rainfall interannual variability using a small\nsubset of four CMIP5 models and projected an increase under greenhouse warming,\nwe\ndidn’t find much comprehensive analysis using more CMIP5 models.\nIn response to your suggestion, we have examined the Sahel rainfall interannual\nvariability change in 28 CMIP5 models under similar scenario as in CMIP6 (Fig. R3,\nnew Extended Data Fig. 12). Our analysis shows that the multi-model mean of 28\nCMIP5 models projects an increase in rainfall interannual variability from the 20th to\nthe 21st century. However, the inter-model consensus is low; approximately 50% of\nmodels project an increase, while the other half project a decrease (Fig. R3). The more\nrobust and consistent increase found in CMIP6 models, compared to CMIP5, can be\nattributed to two key factors: a stronger projected increase in ENSO variability (Cai et\nal., 2022) and a more substantial wetting of the Sahel mean state in CMIP6\n(Monerie\net al., 2020). These are in part associated with a higher climate sensitivity in CMIP6.\nThe stronger change in CMIP6 than in CMIP5 strengthens our conclusion that increased\nENSO variability and a wetter Sahelian mean state will lead to more severe rainfall\nextremes under greenhouse warming. We have added this discussion and a\ncorresponding Extended Data fig. 12 to the revised manuscript. Please see lines: 268-\n273 and lines 281-285.\nLines 268-273: “The increase in Sahel rainfall variability under greenhouse warming is\nalso captured by some CMIP5 models30. However, the increase is much larger and with\nstronger inter-model consensus in CMIP6 models than in the CMIP5 models51\n(Extended Data Fig. 12), which might be attributed to a stronger projected increase in\nENSO variability52 and a more substantial wetting of the Sahel mean state in CMIP6\nmodels53.”\nLines 281-285: “Our results suggest that as local residents adapt to the projected\nincrease in Sahel mean rainfall, they are likely to experience more extreme dry and wet\nseasons above the evolving mean climate. Emission reductions are required to mitigate\nthe projected rise in rainfall variability and frequency of extreme seasons.”\nFig. R3 (Extended Data Fig. 12) | JAS Sahel rainfall variability change in CMIP5\nmodels. JAS Sahel rainfall variability (standard deviation) change from the 20th century\n(blue) to the 21st century (red) in 28 CMIP5 models. The Sahel rainfall anomalies have\nbeen normalized over the whole 200 years period before calculating the standard\ndeviation. The Error bars in the multi-model mean indicate the 95% confidence level\ncalculated using a bootstrap test. Green circles indicate models which simulate a\ndecrease or no change in Sahel rainfall variability.\nReferences\nAkinsanola, A. A., Zhou, W., Zhou, T. & Keenlyside, N. Amplification of synoptic to\nannual variability of West African summer monsoon rainfall under global warming. npj\nClimate and Atmospheric Science 3, 21, doi:10.1038/s41612-020-0125-1 (2020).\nCai, W. et al. Increased ENSO sea surface temperature variability under four IPCC\nemission scenarios. Nature Climate Change 12, 228-231, doi:10.1038/s41558-022-\n01282-z (2022).\nMonerie, PA., Wainwright, C.M., Sidibe, M. et al. Model uncertainties in climate change\nimpacts on Sahel precipitation in ensembles of CMIP5 and CMIP6 simulations. Clim\nDyn 55, 1385–1401 (2020). https://doi.org/10.1007/s00382-020-05332-0\nMinor issues:\n1. P2, L23: I am a bit confused with the use of the word \"countries\". I was expecting to\nsee the word \"regions\" because the sentence begin with \"The Sahel…\". Kindly check\nit.\nThank you for the suggestion. We have changed it to “regions”.\n2. P2, L32: Remove the acronym \"ENSO\" because it has not been used elsewhere in\nthe abstract.\nDeleted. Thanks.\n3. P3, L38: I don't see the need to put the word \"dry\" before \"Sahara Desert\" because it\nis known that the desert is a dry area. Consider removing it.\nYou are right. The word “dry” has been deleted. Thanks.\n4. L64, P3: Kindly provide the abbreviation for \"ENSO\" on its first appearance in the\nmain text section.\nDone. Thank you for the suggestion.\n5. P4, L88: Add a space between degree and lat/lon direction. Check it throughout the\nmanuscript and for other cases of similar concern.\nThank you for spotting this. It has been added and checked throughout the main text\nand Methods.\n6. P4, L89-90: I am wondering why the change in standard deviation rainfall was taken\nbetween 2000-2099 and 1900-1999 and not between 2015-2099 and 1900-2014? The\nhistorical period in which models are forced by historical forcings ends in 2014, and\nafterward, the models are forced by SSP scenarios.\nTo detect the impact of greenhouse warming, we compare a 100-year period in the 20th\ncentury with a 100-year period in the 21st century, ensuring a fair comparison with equal\ntime lengths—a standard methodology employed in numerous studies. Although the\nperiod 2000-2014 is part of the historical simulation, it is subject to greenhouse forcing,\nthereby still capturing the influence of anthropogenic warming.\nWe have tested the sensitivity of our results to the choice of time periods, including the\nspecific interval suggested by the reviewer, and find that our core conclusions remain\nrobust. For details, please see Fig. R4.\nFig. R4 | JAS Sahel rainfall variability change using different period. a, JAS Sahel\nrainfall variability (standard deviation, mm/day) change from the 1900-2014 (blue) to\nthe 2015-2099 (red) in 42 CMIP6 models. The Error bars in the multi-model mean\nindicate the 95% confidence level calculated using a bootstrap test. Green circles\nindicate models which simulate a decrease or no change in Sahel rainfall variability.\n7. P4, L91: What is quadratic detrend? What's its strength over common linear detrend\nmethods in the context of rainfall? The authors can briefly describe it in the methods\nsection. Moreover, this last line (L90-93) describing quadratic trends may not be\nnecessary here because it is basically the repetition of what has been described in the\nmethods section.\nA quadratic detrending method estimates and removes the least-squares quadratic trend\nfrom the time series. This approach is chosen because the greenhouse warming signal\nis not linear, as evidenced by the evolution of tropical SSTs shown in Fig. R5a. For\nrainfall evolution, quadratic detrending demonstrates a superior ability to remove long-\nterm trends compared to linear detrending (Fig. R5b).\nWe have added more information of quadratic detrending (please see lines 327-333)\nand have removed the original L90-93. Thanks for the suggestions.\nLines 327-333: “We apply a quadratic detrending method for calculating the anomalies,\nwhich estimates and removes the least-squares quadratic trend from the time series,\nbecause the global warming trend is not linear. We also calculate anomalies using an\n11-year running window to remove decadal-scale variability. In this method, anomalies\nfor 1900–1904 and 2095–2099 are referenced to the climatology of 1900–1910 and\n2089–2099, respectively. The remaining anomalies are calculated using an 11-year\nrunning climatology. Both methods yield similar results”.\nFig. R5 | Example of a quadratic trend. a, Time evolution of the multi-model mean\nJAS tropical SST (10°S–10°N) from the 42 CMIP6 models, illustrating a nonlinear\n(quadratic) trajectory. b, Time evolution of the multi-model mean JAS Sahel rainfall\n(black line), with its quadratic trend (blue line) and linear trend (red line) overlaid. The\nquadratic trend provides a more accurate representation of the long-term evolution of\nSahel rainfall compared to the linear trend.\n8. Fig. 1a: In the caption, the authors mentioned that green circles denote the models\nthat feature a decrease in precipitation variability. But, looking at the MCM-UA-1-0\nmodel, it shows no change in std values between the two periods. Kindly check it.\nWe appreciate you highlighting this point. Actually, the standard deviation change in\nMCM-UA-1-0 is negative, though small in magnitude. In Figure 1, a green circle is\nused to indicate models where the projected change is either negative or shows no\nchange, which differs from the positive change indicated by the multi-model mean. We\nhave updated figure caption to clarify this criterion.\n9. Extended Data Fig. 2a: There is a white line at around zero longitude. The authors\ncan sort out the issue.\nFixed. Thanks.\n10. Extended Data Fig. 2: Which method did you use to perform the significance test\nfor the correlations? Is a significance test also performed on the wind pattern?\nThe authors can briefly introduce the method in the methods section.\nWe have used a t-test to assess the statistical significance of the correlation analyses.\nThis information has been added to the Methods section.\nThe statistical significance was not initially indicated for the wind patterns. In the\nrevised manuscript, we have now masked wind anomalies that are not significantly\ncorrelated with ENSO. Please refer to the updated Extended Data Fig. 2 for details\n(Fig. R6).\nFig. R6 (now Extended Data Fig. 4) ∣ ENSO dominates Sahel rainfall variability in\nobservation from 1979 to 2022. a, Correlation of JAS Sahel rainfall and SST\nanomalies from 1979 to 2022 in ERA5 data. Correlations of ±0.26, ±0.3, ±0.4 are\nstatistically significant at 90%, 95%, 99% confidence levels, respectively. b, Same as\na, but for the correlation between JAS Niño3.4 index and rainfall anomalies. The red\nboxes show the area of the Sahel. c, Same as a, but for the correlation (or regression)\nbetween JAS Niño3.4 index and sea level pressure (color) and 850hPa wind (regression;\nvectors, m/s) anomalies from 1979 to 2022 in ERA5 data. Only wind anomalies having\na significant correlation (95%) with the ENSO index are shown.\n11. P7, L172: Is it a double hyphen?\nThanks for spotting the problem. Changed to single hyphen.\n12. P7, L180-183: A bit hard to follow this sentence. Consider revising it to enhance\nunderstanding.\nRevised. Please see lines 192-194.\nLines 192-194: “An increased climatological rainfall is conducive to more extreme dry\nseasons relative to that climatology, because the dry mean state in the current climate\nmay limit substantial negative rainfall anomalies, even when a strong El Niño occurs.”\n13. Fig. 4a: The std values of HadGEM3-GC31-ALL between AMIP and AMIP-p4k\nsimulations are comparable, and yet the model is incorporated in the group of models\nshowing increasing tendency. Could you clarify this?\nThank you for your comment. The green circles are used to denote models where the\nprojected change differs from the multi-model mean. In this specific case, we count\nmodels that show either no change or a positive change, in contrast to the Multi-model\nmean projection. We have clarified this criterion in the revised figure caption.\n14. Fig. 4a: In the first line of the caption, 1979 to 2013? Did you mean 1979 to 2023?\nAlso, in the fourth line from the end of the caption concerning the green circles, did you\nmean a decrease or an increase?\nWe apologize for the confusion. The CMIP6 AMIP-p4K and AMIP-4xCO2\nexperiments were both conducted over the common period of 1979–2013. We have\ncorrected the description in the main text and Methods section. Thank you for\nidentifying this issue.\nRegarding the green circles: they are used to identify models whose projected changes\ndiffer from the multi-model mean. Specifically:\nIn Figure 4a, where the multi-model mean change is negative, the green circles indicate\nmodels with a positive change or no change.\nIn Figure 4b, where the multi-model mean change is positive, the green circles indicate\nmodels with a negative change or no change.\nThis distinction has been clarified in relevant figure captions.\n15. P19, L 466: Adjust spacing before JAS\nAdjusted. Thanks.\nReviewer #2 (Remarks to the Author):\nReview of\n“Increased interannual variability of Sahel rainfall under greenhouse warming”\nThis paper highlights the increase in interannual variability in rainfall over the Sahel,\nand links this increase to changes in ENSO, the mean state and changes in the Atlantic\nSSTs. It also shows that uniform SST changes and radiative changes due to increased\ncarbon dioxide levels have opposite effects on rainfall variability over the Sahel.\nThe paper is robust and interesting, and is an important contribution to the literature. I\nrecommend minor revisions.\nThank you for your positive assessment and for recognizing the importance of our work.\nWe have carefully addressed your comments in the revised manuscript. Please see a\npoint-by-point response to your comments.\nMajor-minor comments:\nI think the section at the beginning of the discussion and conclusions on the role of the\nAtlantic should be a separate section in the results. The discussion and conclusions I\nfeel should not introduce new results, which this section does.\nThank you for the suggestion. We have moved the detailed analysis regarding the\nimpact of Atlantic Niño/Niña on Sahel rainfall variability to the Methods section. A\ndiscussion of these results has been integrated into the main text following our\ndescription of the influence from ENSO, ensuring a clearer and more logical flow to\nthe argument. Please see lines 171-178.\nLines 171-178: “In observations, a developing El Niño event is often accompanied by\nan Atlantic Niña pattern in the equatorial eastern Atlantic (Extended Data Fig. 6), which\ntypically weakens the drying effect of El Niño on Sahel rainfall by moving the ITCZ\nnorthward and strengthening the monsoon1,2. Under greenhouse warming, Atlantic\nNiño/Niña variability is projected to decrease37,38. This reduction may favor an increase\nin Sahel rainfall variability by diminishing the offsetting effect of Atlantic Niño/Niña\non ENSO influences (see “Possible role of the reduced Atlantic Niño/Niña variability”\nin Methods; Extended Data Figs. 7).”\nThe conclusion that there are more extreme dry seasons is based on the mean rainfall\nincreasing, and therefore there being more seasons that are 1.5 standard deviations\nbelow the mean. This doesn’t actually mean there are more seasons e.g. with\nmean rainfall <1mm/day, or more drought seasons. I think it would be good to compare the\nchanging frequency of extreme dry seasons based on some fixed or consistent\nthresholds, to see if there are actually more dry seasons, as this is what will have a\nnotable impact on populations. The results show more extreme wet seasons in the\nwestern Sahel but not more extreme dry seasons, potentially due to smaller total rainfall\nincreases in the western Sahel. This is maybe not representative of the “on the ground”\nchanges that populations will face. I think this is a notable limitation that needs to be\naddressed.\nThank you for the suggestion. We define extreme events in future climates relative to\nthe corresponding climatology, which evolves under greenhouse warming. In the Sahel\ncase, the future rainfall mean state is projected to increase, meaning local residents,\necosystems, agriculture, and the economy have to adapt to the wetter condition. In this\ncontext, a drought characterized by large negative rainfall anomalies (relative to the\nwetter future climatology) may not be as absolutely dry as droughts in the historical\nperiod (the extreme dry seasons are not increased if using a fixed threshold), yet could\nstill cause significant impacts. Thus, although the frequency of dry seasons defined by\na fixed threshold may not increase due to the overall wetting trend, our results indicate\nthat populations having already adapted to the new climate baseline will face more\nfrequent extremes (larger anomalies) in the future.\nWe have clarified the definition of extreme events and have added more discussion in\nthe revised manuscript. Please see lines and lines 310-313 and lines 107-111.\nLines 310-313: “The extreme dry and wet rainfall seasons in each model are defined\nas the years with JAS rainfall anomalies smaller (larger) than -1.5 (1.5) s.d. of the 200\nyears climate. The long-term trend has been removed and the extreme seasons in the\n20th century and the 21st century are defined relative to their corresponding climatology.”\nLines 107-111: “While the overall wetting trend means that future dry seasons may be\nless severe in absolute terms, strong negative rainfall anomalies (relative to the elevated\nfuture rainfall climatology) will still pose substantial risks to local communities and\necosystems that have adapted to a wetter environment.”\nMinor comments:\nLine 70 – should “on” be “in”\nChanged to “in”. Thank you for the suggestion.\nLine 78 – would be clearer as “total number of rainy days”\nChanged accordingly. Thanks.\nFigure 1b-c (and other scatters) is the multi-model mean shown? Would be good to\ninclude?\nThank you for the suggestion. We have now included the multi-model mean in all the\nscatter plots (including Main figures and Extended Data figures).\nLine 136-140. Please rephrase – this sentence starts with talking about small changes\nin ENSO variability, and ends with increase in ENSO variability. Could be clearer.\nThank you for the suggestion. We have rephrased it, Please see lines 146-152.\nLines 146-152: “Even without stronger ENSO SST variability, greenhouse warming\ntends to amplify the impact of ENSO on Sahel rainfall. The amplification is associated\nwith a faster eastern Pacific warming than the surrounding regions, which enhances\nlocal convection and increases the sensitivity of lower-tropospheric humidity to\nENSO20,36. When combined with increased ENSO variability, the overall Sahel rainfall\nresponse to ENSO becomes stronger under greenhouse warming.”\nLine 156 – why is 38 in words?\nWe used words because it's the start of a sentence. We have changed it to \"A total of 38\nout of 42 models\" in the revised manuscript.\nLine 227 – should be “drier”\nChanged. Thanks for spotting this typo.\nLine 243 – this is the first time a shallow meridional circulation in North Africa is\nmentioned, please explain more how this leads to stronger variability.\nThank you for the suggestion. We have added more details about the change of shallow\nmeridional circulation and its impact on Sahel rainfall. Please see lines 246-254.\nLines 246-254: “The direct radiative effect of increased greenhouse gas emissions,\nmeasured as the difference between the AMIP-4CO and the AMIP experiments, tends\n2\nto warm north Africa (see Extended Data Fig. 9b for an example) and weaken the\nshallow meridional circulation in North Africa44, as characterized by the northward\nnear-surface flow from the Sahel to the Sahara accompanied by 700hPa southward dry\nflow from the Sahara to the Sahel. The weakened dry flow from the north enhances JAS\nSahel rainfall (Extended Data Fig. 10c), which in turn leads to stronger interannual\nvariability of Sahel rainfall in all AMIP-4CO experiments (Fig. 4b).”\nexplore this.\nThank you for your suggestion. We initially used the term \"offset\" based on the fact that\nthe multi-model mean changes in the two sets of experiments are opposite in sign—\nspecifically, a uniform SST warming tends to decrease rainfall variability, while the\ndirect radiative effect tends to increase it.\nAlthough the magnitude of the increase in the AMIP-4xCO2 experiment is larger than\nthe decrease in the AMIP-p4K experiment, we do not find a significant inter-model\nrelationship between rainfall variability changes in AMIP-4xCO2 experiment and that\nin AMIP-p4K experiment, likely due to a limited number of models that are used. Thus,\nit may not be appropriate to conclude that the direct radiative effect has a definitively\nlarger influence than ocean warming.\nTo prevent any potential confusion, we have changed the word “offset” to “have\nopposite impacts” and rephrased the sentence accordingly. Please see the updated text\non lines 254-256.\nLines 254-256: “Thus, the uniform SST warming and direct radiative effect tend to have\nopposite impacts on Sahel rainfall mean state and interannual variability.”\nThank you again for your help."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have satisfactorily addressed all my concerns. I have no further comments and would like to recommend the\npublication of the revised manuscript."}, "author_response": ""}]

Decision letter