Terrarium.jl: A framework for fully differentiable and GPU-accelerated land modeling to enable online downscaling in coarse-scale ESMs

Global land surface and hydrological models are crucial components of Earth System Models (ESMs). In addition to providing realistic boundary conditions for the atmosphere and ocean components, they also play a key role in understanding Earth’s changing energy imbalance and the response of the terrestrial carbon and water cycles to anthropogenic climate change. The land surface components of most ESMs typically rely on reduced-complexity parameterizations of land processes in order to efficiently resolve the transient coupling of the land surface to the atmosphere at global scales. The complexity of such models is therefore limited by the coarse spatial resolution of the atmosphere and thus they are not easily constrained by in situ and remote sensing observations of land surface parameters. As a result, offline downscaled and bias-corrected climate models and reanalysis products are often used as forcings when calibrating land surface and hydrological models at local and regional scales. We argue that this lack of online coupling in the downscaling step is one of many factors contributing to persistent biases in modern ESMs. As such, there is a need for a new generation of land models which can support more flexible coupling with the atmosphere as well as the incorporation of data-driven components. Here we present Terrarium.jl, a Julia-based land modeling framework for GPU-accelerated and automatically differentiable simulations of soil, snow, and vegetation dynamics, along with their corresponding land-atmosphere exchange fluxes. We demonstrate the value of GPU acceleration and differentiability through a series of performance benchmarks and sensitivity analyses. We further present our initial experiments in achieving stable coupling to a reduced-complexity atmosphere model, SpeedyWeather.jl, as well as a proof-of-concept for online downscaling from the scale of an intermediate-complexity ESM (~5°) to that of ERA5 (~0.25°). We discuss the main challenges encountered thus far and outline a roadmap for future development.