A Unified Data-Driven Framework for High-Resolution Land Surface Boundary Conditions

Accurately simulating the terrestrial carbon cycle remains a major challenge in climate science, due in part to uncertainties in how slow-varying land-surface boundaries and fast-varying biophysical states are represented and coupled in Earth-system models.  We introduce a unified data-driven framework designed to generate high-resolution (1 km) historical reconstructions and future projections of Land Use (LU), Land Cover (LC), and Leaf Area Index (LAI) for real-time coupling with digital twin platforms, such as those deployed in the Destination Earth framework.

Moving beyond sequential downscaling, this framework treats the generation of boundary conditions as a cohesive multi-task learning problem. We benchmark two distinct modeling strategies: (1) Architectures trained from scratch, where we compare the performance of convolutional baselines (U-Net) against attention-based Vision Transformers (ViT) in capturing spatial heterogeneity; and (2) Foundation Model (FM) Adaptation, where we leverage state-of-the-art Earth FMs (such as  TerraMind and Prithvi) as backbones. Within this second strategy, we evaluate the trade-offs between full fine-tuning, parameter-efficient techniques using adapters, and models trained from scratch.

By integrating static geophysical features with high-frequency climate reanalysis (ERA5) and atmospheric CO2​ concentrations, the framework ensures that vegetation dynamics remain phenologically consistent with environmental forcing. We assess these approaches based on their computational efficiency, generalization across sparse data regimes, and physical consistency between categorical (LU/LC) and continuous (LAI) variables. The final output is a suite of open-source interoperable emulators designed to act as dynamic, on-demand boundary condition generators.