Advanced computing technologies to enhance modeling of the climatic impacts of large explosive eruptions

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.