This study utilizes monthly climate outputs from the CESM-LME, developed by the National Center for Atmospheric Research (NCAR), in which volcanic forcing is applied as a physically constrained perturbation to isolate externally forced aridity signals from internal climate variability. CESM-LME has been widely used to investigate volcanic impacts on climate, including large-scale hydroclimate and atmospheric circulation responses ([29]; [37]; [46]), providing a robust foundation for analysing long-term climate responses to volcanic eruptions. The CESM-LME consists of fully coupled Earth system simulations spanning 850-2005 CE, employing CESM version 1.1 with the Community Atmospheric Model version 5 (CAM5) at a resolution of 1.9˚ × 2.5˚ ([29]).

We specifically analyze the ensemble-mean volcanic-forcing simulations, which enables robust separation of externally forced climate signals from internal variability. To characterize the hydrological response, we employ the aridity index (AI) together with total column soil moisture, defined as the vertically integrated soil water content across all modelled soil layers, and surface runoff, representing total runoff generated at the land surface and excluding subsurface drainage ([24]; [21]). These variables provide complementary and physically consistent measures of land-surface hydrological responses to volcanic forcing.

Volcanic eruptions were classified as Northern Hemisphere (NH), Southern Hemisphere (SH), or Tropical events (Table 1) based on the hemispheric ratio of stratospheric sulfate aerosol loading, following [37]. Only eruptions with peak total stratospheric sulfate aerosol injections exceeding the long-term mean forcing were retained, ensuring that the analysis focuses on climatically relevant events. To further enhance robustness, we restricted the selection to eruptions whose peak aerosol forcing occurred in the same season, thereby minimizing the influence of seasonal phase differences on the composite signals ([19]; [27]; [43]).

We applied the Superposed Epoch Analysis (SEA) technique to isolate volcanic signals in the Aridity Index (AI), soil moisture, and surface runoff ([11]). Seasonal anomalies for each eruption year were calculated relative to a 5-year pre-eruption climatological baseline, separately for June-July-August (JJA), September-October-November (SON), and December-January-February (DJF). Statistical significance was evaluated using a Monte Carlo significance test, in which composite anomalies were compared against distributions generated from randomly resampled non-eruption years ([1]), providing a robust, non-parametric assessment of the volcanic signal relative to internal climate variability.

Table 1. List of selected volcanic events.

In this study, to quantify hydroclimatic responses to volcanic eruptions across Africa, we employ AI, which is widely used to represent the balance between moisture supply and atmospheric demand and, consequently, to characterize hydroclimatic dryness ([13]; [22]; [23]). AI is computed as the ratio of precipitation (P) to PET ([26]):

PET is estimated using the FAO-56 Penman-Monteith equation ([3]), the standard method for quantifying atmospheric evaporative demand. The formulation integrates net radiation, air temperature, humidity, and wind speed to represent the combined radiative and aerodynamic controls on evapotranspiration and can be expressed as

where T m e a n , Δ, γ, R_n, ω, and G denote monthly mean near-surface air temperature (˚C), slope of the saturation vapor pressure curve (kPa C⁻¹), psychrometric constant (kPa ˚C⁻¹), net radiation, and soil heat flux density, respectively. U₂ is 2 m wind speed (m s⁻¹) derived from the 10 m wind speed using the logarithmic wind profile conversion. e_s is the saturation vapor pressure (kPa), and e_a is the actual vapor pressure (kPa), computed from relative humidity. The term (R_n −G) represents the available energy for evapotranspiration. All required variables are directly available from the CESM-LME output, ensuring consistency between PET estimates and the simulated surface energy and moisture budgets.

To assess the relative roles of precipitation and PET in driving AI changes over Arica, AI anomalies were decomposed into the contributions from precipitation and PET following the approach of [10]:

where F (ΔP) and G (ΔPET) represent the respective contributions of precipitation and PET anomalies to changes in AI, and ΔAI, ΔP, and ΔPET denote the changes in AI, P, and PET relative to their pre-eruption climatological means.

To elucidate the atmospheric and land-surface processes driving divergent AI responses to volcanic eruptions across Africa, we employed a moisture budget framework. This approach quantifies the contributions of moisture transport, convergence, and surface fluxes to hydroclimatic variability. Following [4], we conducted a vertically integrated seasonal mean moisture budget analysis, expressed as

where P , E , q , V h , ω , and δ denote precipitation, evaporation, specific humidity, horizontal wind vector, vertical pressure velocity, and the residual term, respectively, and 〈 ⋅ 〉 denotes tropospheric mass integration. The residual term ( δ ) represents unresolved processes including data inconsistencies, subgrid-scale transport, numerical discretization errors, and imperfect budget closure.

Assuming ω ≈ 0 at the surface and tropopause, and neglecting moisture storage on seasonal timescales, the budget reduces to

Consequently, anomalous precipitation can be decomposed into its contributing components as

We explicitly quantified the magnitude of the residual term ( δ ) for all regions, seasons, and eruption categories considered. The residual was found to be small relative to the dominant moisture budget terms, precipitation, evaporation, and moisture flux convergence. In most cases, δ accounts for less than 5% - 10% of the total moisture tendency, indicating that the moisture budget closes satisfactorily at seasonal timescales.

The anomalous moisture budget terms were further decomposed into thermodynamic, dynamic, and nonlinear contributions as

(9)

where overbars denote climatological means, primes denote anomalies, and NL represents nonlinear residual terms. The small magnitude of δ confirms that the diagnosed thermodynamic and dynamic components robustly explain the precipitation and AI responses to volcanic forcing and that the primary conclusions drawn from the moisture budget analysis are not sensitive to residual imbalances.

Previous studies have examined the hydroclimatic impacts of volcanic eruptions, primarily focusing on precipitation responses on a global basis ([15]; [14]; [47]; [38]; [44]) and, over Africa, mainly on the Sahel and West Africa ([12]; [16]). These studies have shown that volcanic aerosol forcing perturbs large-scale circulation and monsoon rainfall. However, the hydroclimatic implications of these changes have largely been interpreted through precipitation alone. As a result, the combined influence of volcanic-driven changes in moisture supply and atmospheric evaporative demand, which together control hydroclimatic stress, remains poorly constrained at the continental scale over Africa. In addition, most work has concentrated on individual seasons or single eruption events, leaving seasonal and spatial patterns of aridity largely unresolved. Extending these studies, the present analysis quantifies African hydroclimatic responses to NH, SH, and tropical volcanic eruptions from an aridity perspective. Using AI anomalies alongside soil moisture and surface runoff, we examine the spatial and seasonal variability of AI following volcanic forcing and investigate the underlying mechanisms driving these responses. All percentage changes reported below represent anomalies relative to the 5-year pre-eruption seasonal climatology (JJA, SON, DJF), normalized by that baseline.

African AI exhibits a strong seasonal sensitivity to volcanic forcing that is closely tied to the climatological position of the seasonal rainfall band and its hemispheric displacement following NH, SH, and tropical eruption events (Figure 1). For NH eruptions, the primary drying zone is displaced northward, aligning with the rainfall belt over the Sahel and West Africa, while regions south of the band experience compensating wetting. At the continental scale, this manifests as moderate wetting of about 6.2% during JJA, slight drying of approximately 3.7% in SON, and pronounced wetting in DJF, indicating a substantial redistribution of AI across the continent. In contrast, SH eruptions shift the dominant drying zone southward, with persistent, continent-wide drying throughout the year, strongest in SON, with continued reductions of approximately 5.2% in JJA and 3.5% in DJF. Tropical eruptions generate the weakest response, with modest drying in JJA and SON and only marginal wetting in DJF.

To diagnose the regional contributions to the continental-scale signals, Africa was subdivided into five hydroclimatic regions based on fixed latitude-longitude boundaries: North Africa (20˚N - 30˚N, 15˚W - 35˚E), the Sahel and West Africa (5˚N - 20˚N, 15˚W - 25˚E), Central Africa (5˚S - 5˚N, 10˚E - 30˚E), the Horn of Africa (5˚N - 15˚N, 35˚E - 50˚E), and Southern Africa (30˚S - 15˚S, 15˚E - 35˚E). Analysis of regional AI anomalies shows that the continental mean conceals substantial spatial heterogeneity in the hydroclimatic response to volcanic forcing (Figure 1(a)-(i)). Following NH eruptions, regional AI anomalies exhibit a pronounced meridional dipole that is tightly aligned with the climatological position of the African rainy season (Figure 1(a)-(c)). This pattern reflects a northward displacement of the primary drying zone following NH eruptions, consistent with the seasonal migration of the African rainfall belt.

Figure 1.Seasonal AI anomalies following Northern Hemisphere (NH), Southern Hemisphere (SH), and Tropical (TR) volcanic eruptions during (a, d, g) JJA, (b, e, h) SON, and (c, f, i) DJF. Stippling denotes changes significant at the 90% confidence level.

During JJA, drying dominates the Sahel, West Africa, and Central Africa, while North Africa and Southern Africa become anomalously wetter, forming a pronounced meridional dipole aligned with the climatological rainy-season band. In SON, drying intensifies and expands across much of the continent, with reductions of 20.2% in the Sahel and West Africa, 23.2% in the Horn of Africa, and 21.9% in North Africa, while Southern Africa exhibits strong wetting of approximately 27.0%. By DJF, enhanced wetting over North Africa contrasts sharply with persistent drying over the Sahel and the Horn of Africa, reflecting a southward displacement of AI anomalies following the seasonal migration of the climatological rainfall belt.

Figure 2. Seasonal soil moisture anomalies following Northern Hemisphere (NH), Southern Hemisphere (SH), and Tropical (TR) volcanic eruptions during (a, d, g) JJA, (b, e, h) SON, and (c, f, i) DJF. Stippling denotes changes significant at the 90% confidence level.

Following SH eruption events, negative AI anomalies are concentrated south of the equator and within the climatological rainy-season band during JJA and SON, indicating a southward displacement of the dominant drying zone relative to NH eruptions (Figure 1(d)-(f)). Central Africa experiences the strongest deficits in JJA and SON, with drying extending into Southern Africa as the seasonal rainfall band migrates southward. In SON, this drying intensifies and expands, with AI reductions of up to 15.9%. Although localized wetting appears over the Horn of Africa in DJF, negative AI anomalies remain the dominant continental signal.

Tropical eruptions produce weaker but broadly coherent drying across the Sahel, Central Africa, and the Horn of Africa during JJA and SON, with reductions ranging from approximately 10% to 19%. In DJF, strong wetting emerges over North Africa, while drying persists elsewhere. Regions exhibiting statistically significant anomalies largely coincide with climatologically sensitive hydroclimatic zones, highlighting the pronounced spatial heterogeneity of African hydroclimatic responses to volcanic forcing.

Figure 3.Seasonal surface runoff anomalies following Northern Hemisphere (NH), Southern Hemisphere (SH), and Tropical (TR) volcanic eruptions during (a, d, g) JJA, (b, e, h) SON, and (c, f, i) DJF. Stippling denotes changes significant at the 90% confidence level.

We extend the analysis to soil moisture and surface runoff (Figures 2-3) to assess terrestrial hydrological responses to volcanic eruptions. Soil moisture refers to total column soil moisture integrated across all modelled soil layers, and runoff represents total surface runoff generated at the land surface. Soil moisture and runoff deficits are strongest where seasonal rainfall normally dominates land-atmosphere coupling, reinforcing the role of rainfall-band displacement in shaping volcanic hydroclimatic impacts. Soil moisture anomalies (Figure 2) largely mirror AI patterns (Figure 1), although they exhibit more localized spatial structures. Following NH eruption events, JJA and SON show widespread deficits across Northern and Central Africa, particularly in the Sahel and West Africa, reflecting suppressed monsoonal rainfall. In contrast, Southern Africa experiences an increase of up to 1.2%. During DJF, drying shifts southward into Central and East Africa, with modest moistening in Northern Africa. SH eruptions induce more uniform soil moisture reductions across Central and Southern Africa, with limited DJF wet anomalies in the Sahel and Horn of Africa. Tropical eruptions produce weaker and more diffuse soil moisture anomalies across the Sahel, Central, and Southern Africa.

Surface runoff responds coherently (Figure 3): NH eruptions reduce runoff in the West African monsoon and equatorial regions and increase it in Southern Africa, while SH eruptions drive widespread reductions of up to 1.6% with only localized wet patches. Tropical eruptions generate modest, diffuse reductions. Statistically significant anomalies closely align with soil moisture and AI patterns, emphasizing the tight coupling between volcanic forcing, land-surface water availability, and runoff.

In this study, the analysis further examines the relative contributions of precipitation and PET to AI changes. By isolating the individual effects of ΔP and ΔPET, we assess how each component shapes AI variability following volcanic eruption events. Across Africa, all eruption events (Figure 4) show that reductions in precipitation dominate the AI response, while ΔPET contributes only weakly and often acts to partially offset ΔP-driven anomalies. For NH eruption events (Figure 4(a)-(c) and Figure 4(j)-(l)), JJA-SON AI anomalies are driven by widespread precipitation deficits of roughly 50% across the Sahel and Northern Africa, yielding statistically robust AI reductions of approximately 36% to 48%. During DJF, these responses weaken and become more spatially fragmented.

The corresponding ΔPET anomalies are small and generally positive, reflecting volcanically induced reductions in net radiation that suppress evaporative demand, and therefore provide only a minor compensating effect, confirming that precipitation deficits are the primary driver of AI changes following NH events. In contrast, SH eruption events (Figure 4(d)-(f) and Figure 4(m)-(o)) generate a more spatially coherent signal over equatorial and Southern Africa, where precipitation declines of approximately 48% lead to AI decreases of 31% to 38%. In this case, ΔPET reductions play a relatively stronger but still secondary role, particularly during SON, consistent with eruption-induced radiative cooling suppressing atmospheric evaporative demand, although ΔP remains the dominant contributor in most regions.

Tropical eruption events (Figure 4(g)-(i) and Figure 4(p)-(r)) produce the largest and most spatially extensive ΔP-driven drying, with precipitation declines near 49% corresponding to AI reductions of 38% to 41%, particularly during JJA and SON, while ΔPET changes remain weak and spatially uniform, acting mainly to modestly modulate rather than control the precipitation-driven AI response. Overall, this decomposition demonstrates that African AI anomalies following volcanic eruption events are overwhelmingly precipitation-controlled, with ΔPET exerting a secondary influence that varies seasonally and hemispherically, underscoring pronounced asymmetries in the hydroclimatic response.

Figure 4. Contributions of ΔP (a -i) and ΔPET (j - r) to post-volcanic AI anomalies for Northern Hemisphere (a - c, j - l), Southern Hemisphere (d - f, m - o), and Tropical (g - i, p - r) eruptions. Stippling denotes changes significant at the 90% confidence level.

Furthermore, to understand the AI responses, it is essential to examine the underlying physical drivers. Volcanic eruptions alter the atmospheric energy balance and circulation patterns, affecting moisture transport, vertical and horizontal advection, and surface energy fluxes. Figures 5-6 highlight how these processes, particularly moisture flux convergence and wind anomalies, shape the spatial and seasonal distribution of AI responses across Africa.

Figure 5.Seasonal moisture budget decomposition of precipitation changes over Africa following (a) Northern Hemisphere, (b) Southern Hemisphere, and (c) Tropical volcanic eruptions.

The moisture budget decomposition (Figure 5) shows that circulation-driven advection dominates precipitation changes, with evaporation playing a secondary role. The residual term (δ) is consistently small across all regions and seasons, accounting for less than 5% - 10% of the total moisture tendency, confirming that the moisture budget closes satisfactorily and that the diagnosed dynamical and thermodynamic contributions robustly explain the observed AI responses.

NH eruptions produce negative precipitation anomalies in JJA and SON, primarily driven by enhanced horizontal and vertical dynamical advection, while DJF anomalies are weaker and more spatially fragmented. SH eruptions yield the strongest responses, especially in SON, where pronounced dynamical advection drives large negative precipitation anomalies, with modest compensation from thermodynamic effects and evaporation. Tropical eruptions exhibit peak responses in SON, dominated by circulation-induced moisture export from climatological rainfall regions.

Figure 6.Seasonal anomalies of vertically integrated moisture flux divergence (shaded) and 850-hPa wind anomalies (vectors) following Northern Hemisphere (NH), Southern Hemisphere (SH), and Tropical (TR) volcanic eruptions during JJA, SON, and DJF. Stippling denotes changes significant at the 90% confidence level.

While stronger eruptions associated with higher stratospheric aerosol loading generally induce larger radiative cooling and circulation perturbations, the composite analysis employed here emphasizes the robust, large-scale AI response common across eruption events. As a result, intensity-dependent variations are partly smoothed in the composite signal. Consequently, AI anomalies are governed primarily by circulation reorganization rather than eruption magnitude alone, although individual high-intensity eruptions may locally produce stronger hydroclimatic responses than suggested by the composite mean.

These moisture budget decomposition results are supported by vertically integrated moisture flux divergence and 850-hPa wind anomalies (Figure 6), which show that regions with reduced AI (Figure 1) align closely with areas of enhanced moisture divergence and anomalous winds directing moisture away from Central and Southern Africa. The small magnitude of the residual term further confirms that these circulation-driven moisture transport anomalies fully account for the dominant precipitation and AI responses rather than unresolved or numerical effects.

Across all eruption categories, statistically significant AI anomalies coincide with coherent circulation changes, confirming that large-scale wind-driven moisture transport, rather than local surface fluxes, primarily controls volcanic-induced drying over the continent. Together, these circulation anomalies indicate a reorganization of large-scale moisture transport, diverting moisture away from the climatological rainfall belts and thereby explaining the observed hemispheric shifts and seasonal asymmetries in volcanic-induced African aridity.