---
license: apache-2.0
language:
- en
tags:
- OneScience
- Earth Science
- Earth System Model
- Coupled Climate
- Long Simulation
- U-Net
frameworks: PyTorch
---

<p align="center"><strong><span style="font-size: 30px;">DLESyM</span></strong></p>

# Model Introduction

DLESyM asynchronously couples deep-learning atmosphere and ocean modules for long free-running climate simulations and diagnostic precipitation.

Paper: A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate  
https://arxiv.org/abs/2409.16247

# Model Description

The model was proposed by an atmospheric-science and machine-learning research team. It was trained with 1983–2017 ERA5 fields, ISCCP OLR, and SST. Coupled DLWP, DLOM, and precipitation modules support current-climate simulation and internal-variability analysis.

# Use Cases

| Use Case | Description |
| :---: | :--- |
| Long climate simulation | Run stable atmosphere-ocean rollouts. |
| Climate variability | Analyze ENSO, monsoons, and annular modes. |
| Precipitation diagnosis | Diagnose accumulated precipitation from atmospheric states. |
| ModelScope/OneCode execution | Validate data, training, inference, climate metrics, and visualization. |
| Multi-GPU training | Start multi-process training through `torchrun`. |

# Usage Instructions

Use a GPU or DCU when available; CPU supports the default smoke configuration.

```bash
hf download OneScience-Group/DLESyM --local-dir ./DLESyM
cd DLESyM
```

### Environment Dependencies

**Hardware Requirements**

- A GPU or DCU is recommended.
- A CPU can be used for connectivity validation with the default small-sample configuration.
- DCU users should install DTK 25.04.2 or a compatible OneScience-recommended version first.

**DCU Environment**

```bash
# Activate DTK and Conda first
conda create -n onescience311 python=3.11 -y
conda activate onescience311
pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
```

**GPU Environment**

```bash
# Activate Conda first
conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
conda activate onescience311
pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
```

```bash
python scripts/fake_data.py
python scripts/train.py
torchrun --standalone --nproc_per_node=2 scripts/train.py
python scripts/inference.py
python scripts/result.py
```
Training jointly optimizes atmosphere, ocean, and precipitation modules. Inference runs four coupled cycles and evaluation reports finite drift diagnostics.
## Trained Weights
No weights are bundled under `weight/`. The authors provide configurations and weights at https://github.com/AtmosSci-DLESM/DLESyM.
# Citation and License
This repository is an independent engineering reproduction of the public DLESyM specifications.

The original preprint, official code, model weights, and related data remain subject to their respective licenses and terms.