OceanSODA-ETHZ: A global gridded dataset of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification (v2023) (NCEI Accession 0220059)

This dataset contains a global gridded dataset of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification (v2023). The full marine carbonate system is calculated from machine learning estimates of Total Alkalinity (TA) and the fugacity of carbon dioxide (fCO2). The surface-ocean fCO2 presented here is the ensemble mean of 16 two-step clustering-regression machine learning estimates. The ensemble is a combination of eight clustering instances and two regression methods. For the clustering, we use K-means clustering (21 clusters) repeated with different initiations, resulting in slightly different clusters. Two machine learning regression methods are applied to each of these clustering instances. These machine learning methods are feed-forward neural-network (FFNN), and gradient boosted machine using decision trees (GBDT). The average of the ensemble members is used as the final estimate. Further, the standard deviation of the ensemble members is an analog of the uncertainty. The same two-step clustering-regression approach is used to estimate TA. The final estimate is the mean of 16 ensemble members. Eight of the ensemble members estimate standard TA while the other half estimate salinity normalized TA (S0 ≈ 34.0). Each ensemble member has 12 clusters. Support vector regression (SVR) is used as the regression method. Again, the standard deviation of the ensemble members is an analog of the uncertainty. Total alkalinity and pCO2 are then used to solve for the remaining parameters of the marine carbonate system using the PyCO2SYS software. The temperature and salinity products used in this calculation are provided in the file. Phosphate and silicate from the interpolated World Ocean Atlas (2018) product were used. We use the following total scale for pH. The product extends from the start of 1982 to the end of 2022.

本数据集为面向海洋酸化季节至年代际研究的全球网格化海洋表层碳酸盐系统数据集（v2023版）。完整的海洋碳酸盐系统由总碱度（Total Alkalinity, TA）与二氧化碳逸度（fugacity of carbon dioxide, fCO₂）的机器学习估算值计算得到。本研究提供的表层海洋fCO₂为16个两步聚类-回归机器学习估算结果的集合平均。该集合由8个聚类实例与2种回归方法组合而成：聚类环节采用K均值聚类（设定21个簇）并以不同初始条件重复运行，由此得到存在细微差异的聚类结果；针对每个聚类实例，均应用2种机器学习回归方法——前馈神经网络（feed-forward neural-network, FFNN）与基于决策树的梯度提升机（gradient boosted machine, GBDT）。以所有集合成员的平均值作为最终估算结果，而集合成员的标准差可作为不确定性的近似表征。总碱度的估算采用相同的两步聚类-回归方法，最终估算结果同样为16个集合成员的平均值。其中8个集合成员估算标准总碱度，剩余8个则估算盐度归一化总碱度（S₀≈34.0）。每个集合成员包含12个簇，回归方法采用支持向量回归（support vector regression, SVR），其集合成员的标准差同样可作为不确定性的近似表征。随后借助PyCO2SYS软件，利用总碱度与pCO₂求解海洋碳酸盐系统的其余参数。本计算所使用的温度与盐度数据已随数据集文件一并提供。计算所需的磷酸盐与硅酸盐数据来自插值后的《世界海洋图集2018》（World Ocean Atlas 2018）产品。本数据集采用总标度计算pH值。数据集的时间覆盖范围为1982年初至2022年末。

搜集汇总

背景与挑战

背景概述

OceanSODA-ETHZ是一个全球网格化的表层海洋碳酸盐系统数据集，专门用于海洋酸化的季节到年代际研究，覆盖时间从1982年至2024年，空间范围覆盖全球海洋。数据集基于机器学习集成方法估算总碱度和二氧化碳分压，并计算其他关键参数如pH和溶解无机碳，同时提供不确定性估计，适用于气候变化和海洋化学分析。