ArchesClimate-SSP

Deep-learning climate emulator (Swin-Transformer-based, autoregressive) trained to reproduce IPSL-CM6A-LR and CanESM5 monthly climate states conditioned on external forcings (GHGs, aerosols, ozone, solar irradiance). Generates unseen SSP scenario trajectories cheaply, without rerunning the full Earth System Model.

Code: https://github.com/gclyne/ArchesClimate (see INFERENCE.md there for the full inference recipe).

What's in this repo

Only what's needed to instantiate and load the network — weights + the Hydra config each checkpoint was trained with:

<run_name>/
  step-step=NNNNNN.ckpt   # model weights (raw + EMA)
  config.yaml              # exact training-time Hydra config

Required companion data

This repo does not include initial conditions, forcing (boundary condition) trajectories, static fields, or normalization stats — those live in the paired dataset repo: gclyne/ArchesClimateDataset.

See INFERENCE.md in the code repo for exactly which files from that dataset repo are needed for a given checkpoint, and how to run a rollout.

Runs currently published here

Run	Steps	Notes
pf2_emafix_step40000	40,000	Pushforward-length-2 energy-score deterministic model, EMA weights