Southern Ocean CO2 Machine Learning products (SOCOML)

We present a comprehensive, quality-controlled reconstruction of key carbonate system parameters in the Southern Ocean interior—including total alkalinity (TA), dissolved inorganic carbon (DIC), pH (total scale), nitrate (NO3), phosphate (PO4), silicate (SiO4), anthropogenic carbon (Cₐₙₜ), and aragonite saturation (Ωₐᵣ)—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: the Float Grid (using all Argo float profiles) and the All-Data Grid (integrating all available Argo and ship-based observations). The Float Grid is further separated into the Non-O₂-Float Grid (limited to Core Argo floats) and O₂-Float Grid (limited to oxygen-measured Biogeochemical Argo floats). Each gridded product is accompanied by uncertainty estimates. The climatological products covers nearly the whole Sothern 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.本研究针对南大洋内部的关键碳酸盐系统参数，构建了一套经过质量控制的综合重建数据集，涵盖总碱度（total alkalinity，TA）、溶解无机碳（dissolved inorganic carbon，DIC）、总标度pH（pH，total scale）、硝酸盐（nitrate，NO₃）、磷酸盐（phosphate，PO₄）、硅酸盐（silicate，SiO₄）、人为碳（anthropogenic carbon，Cₐₙₜ）以及文石饱和度（aragonite saturation，Ωₐᵣ）。本数据集通过采用机器学习方法（ESPER_NN），整合所有可用的Argo浮标剖面数据与船基调查数据完成构建。所生成的数据集以1°×1°的水平分辨率与84个垂直压力层级（0~5600分巴）进行网格化处理，并以两种独立的气候态产品形式发布：浮标网格（Float Grid，采用所有Argo浮标剖面数据）与全数据网格（All-Data Grid，整合所有可用Argo浮标与船基观测数据）。浮标网格进一步划分为非氧气浮标网格（Non-O₂-Float Grid，仅限核心Argo浮标）与含氧量浮标网格（O₂-Float Grid，仅限带溶解氧测量的生物地球化学Argo浮标）。每一款网格化产品均附带不确定性估计结果。本系列气候态产品基于直接观测数据而非插值制图方法构建，几乎覆盖整个南大洋，因此结果更为稳健可靠。模型性能通过Argo浮标与船载测量数据的交叉比对进行评估。