Brian Groenke
I am a postdoctoral researcher at the Potsdam Institute for Climate Impact Research in Potsdam, Germany. My primary research interests are in applications of differentiable and probabilistic programming, uncertainty quantification, and scientific machine learning to geophysical modeling of Earth systems.
In my PhD, I worked on probabilistic inverse modeling of subsurface heat transfer in terrestrial permafrost. Prior to that, I worked on the application generative deep learning to statistical downscaling of climate and weather variables from coarse scale model outputs.
My industry background consists primarily of software engineering, both front-end and back-end development, with a wide range of frameworks and languages.
Sessions
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. Unlike atmosphere and ocean models, however, land models lack a fluid dynamical core and rely heavily on empirical parameterizations to represent many key processes. As such, there is a continued need for a new generation of land models which can facilitate 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 highlight how Julia’s key features enable unprecedented modularity in the model design and seamless GPU parallelization through KernelAbstractions.jl. We further demonstrate the value of GPU acceleration and differentiability through a series of performance benchmarks and sensitivity analyses. We also detail our initial experiments in achieving stable coupling to a reduced-complexity atmosphere model, SpeedyWeather.jl.
Climate models rely on parameterizations that are traditionally tuned manually. We present a differentiable calibration framework using Enzyme.jl to compute exact reverse mode gradients of energy-balance diagnostics in SpeedyWeather.jl. By batching single- timestep gradients across chaotic dynamics, we enable systematic, reproducible optimization of shortwave radiation parameters, establishing an extensible workflow for objective calibration in Earth system models.