We present the first digital twin framework that operationalizes the production of multi-decadal, global climate projections at kilometre-scale resolution, developed within the European Union’s Destination Earth initiative. Using three coupled Earth system models and selected impact-sector applications, we have built end-to-end workflows for both regular and on-demand climate projections on two EuroHPC supercomputers, LUMI and MareNostrum5. These workflows produced the first-ever multi-decadal simulations at 5 km resolution across all major Earth system components, using the same output parameters and grid, and achieving a production throughput of 0.6 simulated years per day and a climate data portfolio of 6.6 petabytes. We demonstrate the scalability of two of these Earth system models across both CPU and GPU-based systems at global resolutions up to 1 km, across atmosphere, ocean, land, and sea-ice, and report record-breaking full-machine performance on LUMI and MareNostrum5 of up to 97 simulated days per day at 1 km resolution.

Quantitative photoacoustic tomography aims at estimating optical parameters from photoacoustic images that are formed utilizing the photoacoustic effect caused by the absorption of an externally introduced light pulse. This optical parameter estimation is an ill-posed inverse problem, and thus it is sensitive to measurement and modeling errors. In this work, we propose a novel way to solve the inverse problem of quantitative photoacoustic tomography based on the perturbation Monte Carlo method. Monte Carlo method for light propagation is a stochastic approach for simulating photon trajectories in a medium with scattering particles. It is widely accepted as an accurate method to simulate light propagation in tissues. Furthermore, it is numerically robust and easy to implement. Perturbation Monte Carlo maintains this robustness and enables forming gradients for the solution of the inverse problem. We validate the method and apply it in the framework of Bayesian inverse problems. The simulations show that the perturbation Monte Carlo method can be used to estimate spatial distributions of both absorption and scattering parameters simultaneously. These estimates are qualitatively good and quantitatively accurate also in parameter scales that are realistic for biological tissues.