--- license: apache-2.0 language: - en tags: - OneScience - Earth Science - Land Surface - CLM5 - Model Emulation - Parameter Estimation frameworks: PyTorch ---

CLM5-Emulator

# Model Introduction CLM5-Emulator uses machine learning to emulate global biophysical responses of Community Land Model version 5. Six parameters drive predictions of GPP and LHF EOF components and bounded parameter estimation. Paper: A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5 https://doi.org/10.5194/ascmo-6-223-2020 # Model Description The model was proposed by researchers at NCAR and CERFACS. It was trained with GSWP3-driven CLM5 parameter perturbation ensembles and FLUXNET-MTE observational targets. Two independent feed-forward networks emulate GPP and LHF spatial components for surrogate modeling and biophysical parameter estimation. # Use Cases | Use Case | Description | | :---: | :--- | | CLM5 emulation | Predict GPP and LHF components from six parameters. | | Parameter estimation | Search the bounded normalized parameter space. | | Spatial reconstruction | Reconstruct global responses from EOF components. | | ModelScope/OneCode execution | Validate structured data, training, inference, metrics, and visualization. | | Multi-GPU training | Start multi-process training through `torchrun`. | # Usage Instructions ```bash hf download OneScience-Group/CLM5-Emulator --local-dir ./CLM5-Emulator cd CLM5-Emulator ``` ### 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 --nproc_per_node=2 --nnodes=1 --master_addr="localhost" --master_port=29500 scripts/train.py python scripts/inference.py python scripts/result.py ``` Synthetic data retain `[B,6]` inputs, independent GPP/LHF targets, three EOF modes, and the logical 4-by-5-degree grid. Both target networks participate in backpropagation, and single-process and two-process DDP training have been verified. Inference restores the checkpoint, produces components with shape `[8,2,3]` and fields with shape `[8,2,46,72]`, and verifies finite outputs. Training results are saved to `result/checkpoints/clm5_emulator.pt`; inference and evaluation results are saved under `result/output/` and `result/evaluation/`. # Official OneScience Information | Platform | OneScience Main Repository | Skills Repository | |---|---|---| | Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills | | GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills | # Citation and License This repository is an independent engineering reproduction of the public CLM5-Emulator specifications. The original paper is licensed under CC BY 4.0; the paper, CLM5, GSWP3, and FLUXNET-MTE data remain subject to their respective licenses and terms.