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SOCOML
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SOCOML : Southern Ocean CO2 Machine Learning products
We present a comprehensive, quality-controlled reconstruction of key carbonate system parameters in the Southern Ocean interior—by leveraging machine learning techniques (ESPER_NN) and integrating all available Argo float profiles with ship-based survey data. The resulting datasets are gridded at 1°×1° horizontal resolution and 84 vertical pressure levels (0-5,600 dbar), and are provided as distinct climatological products. Each gridded product is accompanied by uncertainty estimates. The climatological products covers nearly the whole Southern Ocean based on direct measurements instead of applying interpolating mapping methods, thereby providing a more robust result. Model performance is assessed through cross-comparison of Argo and shipboard measurements.
| Authors | Affiliation |
|---|---|
| Yingxu Wu | (Polar and Marine Research Institute, Jimei University, China) |
| Di Qi | (Polar and Marine Research Institute, Jimei University, China) |
| Wanqin Zhong | (Polar and Marine Research Institute, Jimei University, China) |
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