Datasets for mloz: A Highly Efficient Machine Learning-Based Ozone Parameterization for Climate Sensitivity Simulations
收藏Zenodo2026-03-18 更新2026-05-26 收录
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https://zenodo.org/doi/10.5281/zenodo.19056391
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资源简介：
Datasets for reproducing JAMES paper: mloz: A Highly Efficient Machine Learning-Based Ozone Parameterization for Climate Sensitivity Simulations
NetCDF files started with fullchem*: A coarse-grained (60°×10°) version of the training datasets (from UKESM full chemistry simulations), downsampled due to storage constraints.
NetCDF files started with coef* : full-resolution mloz coefficients
NetCDF files started with Scaler* : full-resolution scalings for temperature or ozone used for normalization.
NetCDF files started with mloz*: Coarse-grained (60°×10°) online ozone prediction from mloz in UKESM or ICON.
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Zenodo创建时间：
2026-03-17
