The Earth’s climate system is highly complex and responds to several forcing mechanisms, both natural and anthropogenic, with significant short- and long-term societal impacts. Among natural forcings, large explosive volcanic eruptions represent the dominant driver of abrupt cooling episodes over the past two millennia. However, the limited number of well-documented events and the substantial uncertainties in eruption source parameters, initial conditions, and aerosol forcing make the quantitative assessment of volcano–climate interactions particularly challenging. Addressing these limitations requires the integration of large and heterogeneous datasets, from satellite observations and in situ measurements to historical and paleoclimate archives, within modeling tools capable of capturing the nonlinear dynamics of the climate system. Advanced computing technologies, such as Artificial Intelligence (AI), High-Performance Computing (HPC), and emerging Quantum Computing (QC), offer new opportunities to overcome these constraints. AI and hybrid Machine Learning–physics approaches can emulate computationally expensive model components, improve the representation of aerosol–radiation processes, and accelerate sensitivity analyses, while HPC and QC can reduce the cost of large ensemble simulations and discover hidden patterns. Here, we highlight how these methodologies can enhance the study of volcano-climate interactions, improving model performance, enabling a more efficient exploration of uncertainties, and refining predictions of the climatic impacts of major explosive eruptions.
How to cite: Amato, E., Basile, L., Zago, V., and Del Negro, C.: Advanced computing technologies to enhance modeling of the climatic impacts of large explosive eruptions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-432, https://doi.org/10.5194/egusphere-egu26-432, 2026.
Volcanic eruptions are major natural drivers of climate variability and influence atmospheric composition, radiative balance, and regional climate systems. While large explosive eruptions dominate global climatic signals, smaller and more frequent basaltic events can produce regional-scale perturbations, especially where topography and geographic isolation modulate the circulation and residence time of volcanic pollutants. Hawai‘i represents a uniquely suitable natural laboratory for this purpose: its combination of steep relief, persistent trade winds, and insular setting creates conditions in which volcanic SO₂, aerosols, and wind-advected tephra are easy to detect and quantify.
The 2024–2025 Kīlauea lava fountaining episodes offer an opportunity to investigate how moderate explosive basaltic eruptions affect atmospheric composition and short-term regional climate dynamics. We combine multisensor satellite observations (GOES, TROPOMI, Sentinel-2 MSI) with artificial intelligence algorithms to retrieve key eruptive parameters, including Volcanic Radiative Power (VRP), Time-Averaged Discharge Rate (TADR), erupted volume, cloud height, SO₂ mass, and ash dispersion. These satellite-derived measurements are contextualized with local environmental data, including temperature and precipitation, to explore potential short-term atmospheric impacts.
Ground-based observations provide additional constraints, including high-resolution video analysis capturing eruptive precursors, fountain height, plume rise, and ash fall patterns. Video sequences are processed with artificial intelligence-based algorithms to extract time-resolved metrics of eruptive dynamics, generating robust datasets that complement and calibrate satellite measurements.
Here, we present the preliminary dataset and initial observations from this integrated monitoring. The approach yields a high-resolution assessment of lava fountain dynamics, associated gas and aerosol emissions, and localized atmospheric impacts, highlighting the potential of combining satellite data, video analysis, and machine learning-driven processing to improve the monitoring and understanding of volcanic processes in topographically complex island environments. The results lay the groundwork for more comprehensive analyses of future eruptive dynamics and the influence of lava fountaining eruptions on local and regional atmospheric conditions.
How to cite: Malaguti, A. B., Houghton, B. F., Corradino, C., La Spina, A., Di Bella, G. S., Pasqualon, N. G., Cariello, S., Torrisi, F., and Del Negro, C.: Preliminary Insights into Kīlauea 2024–2025 Lava Fountains from Satellite Observations, Machine Learning Approaches, and Ground-Based Validation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-904, https://doi.org/10.5194/egusphere-egu26-904, 2026.
Volcanic eruptions are significant sources of atmospheric pollutants, releasing vast quantities of silicate ash and gases, particularly sulfur dioxide (SO2), into the atmosphere. These emissions form complex volcanic clouds that pose immediate risks to aviation safety and public health and have profound long-term environmental implications. When SO2 is injected into the stratosphere, it converts into sulfate aerosols that scatter solar radiation, altering the Earth's radiative balance and influencing global climate patterns.
Consequently, the continuous monitoring of volcanic activity is essential for both immediate hazard mitigation and long-term climate studies. To achieve a comprehensive global vision of volcanic activity and ensure the rapid detection of eruptive events, geostationary Earth orbit (GEO) satellites are indispensable. Unlike polar-orbiting platforms, GEO satellites provide high temporal resolution, enabling near real-time tracking of volcanic cloud dispersion. This work highlights the synergistic potential of geostationary satellites, utilizing the Advanced Baseline Imager (ABI) on the GOES series, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the new Flexible Combined Imager (FCI) on Meteosat, and the Advanced Himawari Imager (AHI) on Himawari. Machine Learning (ML) approaches are employed to process these diverse satellite datasets, extracting high-dimensional spectral and spatial features to robustly monitor volcanic clouds across various input imagery. The use of geostationary satellite data and ML approaches ensures global coverage and fast response capabilities, allowing for precise monitoring of volcanic cloud evolution worldwide.
How to cite: Torrisi, F., Corradino, C., Cariello, S., Di Bella, G. S., Malaguti, A. B., Zago, V., and Del Negro, C.: Global monitoring of volcanic clouds: A Synergistic Approach Using GOES, Meteosat, and Himawari Geostationary Satellites, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1130, https://doi.org/10.5194/egusphere-egu26-1130, 2026.
Magmatic intrusions into sedimentary rocks can mobilise carbon-based greenhouse gases, through the interaction between magma and organic-rich sediments. These processes have been linked to rapid climate changes in the past. However, we lack appropriate modern analogues for these systems, as most volcanic systems emit very little methane. In this study, we present the first confirmed satellite detection of methane emissions associated with a volcanic system, resulting from interactions between magma and sedimentary rocks. In early 2025, Fentale Volcano, Ethiopia, released >38.2 ± 3.9 kilo tonnes of methane, with 90% emitted within one month. Peak methane emission rates reached 157 ± 41 tonnes per hour, comparable to major industrial blowouts and orders of magnitude higher than typical volcanic systems emission rates, making this, to date, the largest observed natural point-source methane emission. The emissions followed the intrusion of ~1 km3 of magma into a 50 km long dyke that did not erupt. The release of methane and carbon dioxide coincided with localised ground subsidence, thermal anomalies and a persistent low-lying plume within Fentale’s caldera. We infer that the intrusion disrupted an impermeable cap, allowing for the sudden mobilisation of previously trapped gases. This single outburst, while small relative to annual emissions from natural and anthropogenic sources, demonstrates that some volcanoes can release methane episodically. These observations highlight the importance of satellite monitoring for detecting transient volcanic degassing and provide new insights into the mechanisms by which magmatic intrusions release carbon-based greenhouse gases.
How to cite: Dualeh, E. W., Biggs, J., Sharma, S., Stix, J., Hutchison, W., Etiope, G., Jervis, D., Way, L., Dogniaux, M., Maasakkers, J. D., Aveni, S., Cusworth, D., Ireland, B., Grandin, R., Girard, M., Ramier, A., Zheng, W., Davis, T., Howell, K., and Coppola, D. and the collaborators: Satellite detection of methane outburst from an East African volcano, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13212, 2026.
Volcanic sulfur dioxide (SO2) is a primary indicator of magmatic degassing and eruptive activity and is routinely monitored using satellite observations in the Thermal Infrared (TIR) spectral range, including data from sensors such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Quantification of volcanic SO2 from TIR measurements typically relies on radiative transfer models, such as MODTRAN, which are computationally expensive and limit their applicability in near-real-time monitoring scenarios.
In this study, we present a Neural Network-based surrogate modeling approach designed to emulate a physically based TIR radiative transfer model for volcanic SO2 quantification from ASTER-like observations. Input features include TIR brightness temperatures, viewing geometry, and plume altitude.
The surrogate model is trained on a large synthetic dataset generated using MODTRAN simulations spanning a wide range of atmospheric, surface, and plume conditions, considering various eruptive scenarios. Validation results show that the surrogate accurately reproduces the MODTRAN-simulated radiances and the corresponding SO2 column estimates, with errors well below the uncertainty associated with satellite noise and model assumptions.
By reducing computational costs by several orders of magnitude, the proposed surrogate enables efficient inversion of volcanic SO2 from ASTER TIR satellite data while preserving the physical consistency of the original radiative transfer model. This approach is particularly suited for operational volcanic monitoring, ensemble retrievals, and uncertainty propagation in SO2 quantification.
How to cite: Corradino, C., Realmuto, V. J., Ramsey, M. S., and Thompson, J. O.: A TIR-based surrogate model emulating radiative transfer for volcanic SO2 quantification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20664, 2026.
