Abstract. Better characterization of the optical properties of aerosol particles are an essential step to improve atmospheric models and satellite remote sensing, reduce uncertainties in predicting particulate transport, and estimate aerosol forcing and climate change. Even natural aerosols such as mineral dust or particles from volcanic eruptions require better characterization in order to define the background conditions from which anthropogenic perturbations emerge. We present a detailed laboratorial study where the spectral optical properties of the ash from the April–May (2010) Eyjafjallajökull volcanic eruption were derived over a broad spectral range, from ultra-violet (UV) to near-infrared (NIR) wavelengths. Samples of the volcanic ash taken on the ground in the vicinity of the volcano were sieved, re-suspended, and collected on filters to separate particle sizes into fine and mixed (coarse and fine) modes. We derived the spectral mass absorption efficiency α_abs [m²g⁻¹] for fine and mixed modes particles in the wavelength range from 300 to 2500 nm from measurements of optical reflectance. We retrieved the imaginary part of the complex refractive index Im(m) from α_abs, using Mie–Lorenz and T-matrix theories and considering the size distribution of particles obtained by scanning electron microscopy (SEM), and the grain density of the volcanic ash measured as ρ = 2.16 ± 0.13 g cm⁻³. Im(m) was found to vary from 0.001 to 0.005 in the measured wavelength range. The dependence of the retrieval on the shape considered for the particles were found to be small and within the uncertainties estimated in our calculation. Fine and mixed modes were also analyzed by X-ray fluorescence, exhibiting distinct elemental composition supporting the optical differences we found between the modes. This is a comprehensive and consistent characterization of spectral absorption and imaginary refractive index, density, size, shape and elemental composition of volcanic ash, which will help constrain assumptions of ash particles in models and remote sensing, thereby narrowing uncertainties in representing these particles both for short-term regional forecasts and long-term climate change.