Generative Model-Based Downscaling of the Surface Mass Balance of the Greenland Ice Sheet

The surface mass balance (SMB) is projected to become the main driver of mass changes for the Greenland Ice Sheet (GrIS) by the end of this century. Therefore, it is crucial to have realistic projections of the SMB for future estimates of mass loss and sea-level rise.

To date, estimates of the surface mass balance are most often provided by either (i) stand-alone parameterization schemes, such as positive degree days (PDD) or energy balance approaches, (ii) direct outputs from Earth system models (ESMs), or (iii) regional climate models (RCMs) forced by boundary conditions from ESMs. Each of these approaches has its disadvantages. Stand-alone parameterization schemes are often overly simplified and unable to capture smaller-scale processes at the surface. ESMs often provide forcing fields that are too coarse compared to the resolution required for ice sheets. Meanwhile, regional climate models are expensive to run and computationally slow.

In this study, we address these issues by employing a generative model-based approach to realistically downscale the SMB directly from ESM fields to a 5 km resolution. We train a diffusion-based model on historical and future SMB fields from the regional climate model MAR. This allows us to generate high-resolution SMB fields in a fraction of the time required by a regional climate model. We condition our diffusion model on an initial estimate of the SMB derived from ESMs. Specifically, we add noise to the initial ESM estimate and subsequently de-noise the SMB field at different noise levels. By selecting the noise level during inference, we can effectively choose the spatial scale at which ESM features should be preserved.

Our approach enables fast, simple, and probabilistic downscaling of the SMB and potentially other climate fields.