---
license: mit
tags:
  - climate
  - weather-forecasting
  - swin-transformer
  - flow-matching
---

# 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](https://huggingface.co/datasets/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 |