Apply differentiable Earth system model components as instruments for gradient-based science.
Learn from data at scale: learn parameters, structures, and ML closures
Embed machine learning in physics: train learned components while coupled
Ask scientific questions in new ways: sensitivity, attribution, causality
tutorial/ contains Differentiable Earth System Modeling with JAX, a Jupyter Book for scientists new to differentiable code — from your first jax.grad on a radiative-balance formula, through adjoint sensitivity maps, 4D-Var, and online-trained ML closures on a 1-D energy balance model, to the hard parts: gradients through chaos, checkpointing, non-smooth physics, and stiff-solver adjoints. All chapters are executed notebooks that run on a laptop.
Read it online at https://leap-stc.github.io/diff-earth/ (published by the deploy-book workflow; enable Settings → Pages → Source: GitHub Actions once). To build it locally:
conda env create -f tutorial/environment.yml
conda activate diff-earth-tutorial
jupyter-book build tutorial/Inspired by the M2LInES L96 demo book.