LDEO pCO2-Residual Method

The ocean reduces human impacts on global climate by absorbing and sequestering CO2 from the atmosphere. To quantify global, time-resolved air-sea CO2 fluxes, surface ocean pCO2 is needed. A common approach for estimating full-coverage pCO2 is to train a machine learning algorithm on sparse in situ pCO2 data and associated physical and biogeochemical observations. Though these associated variables have understood relationships to pCO2, it is often unclear how they drive pCO2 outputs. Here, we make two advances that enhance connections between physical understanding and reconstructed pCO2. First, we apply pre-processing to the pCO2 data to remove the direct effect of temperature. This enhances the biogeochemical/physical component of pCO2 in the target variable and reduces the complexity that the machine learning must disentangle. Second, we demonstrate that the resulting algorithm has physically understandable connections between input data and the output biogeochemical/physical component of pCO2. The final pCO2 reconstruction agrees modestly better with independent data than most other approaches. Uncertainties in the reconstructed pCO2 and impacts on the estimated CO2 fluxes are quantified. Uncertainty in piston velocity drives substantial flux uncertainties in some regions, but does not increase globally integrated estimates of uncertainty in CO2 fluxes from observation-based products. Our reconstructed CO2 fluxes show larger interannual variability than smoother neural network approaches, but a lesser trend since 2005.海洋通过吸收并封存大气中的二氧化碳（CO₂），以减轻人类活动对全球气候的影响。为了量化全球尺度、时间分辨的海-气CO₂通量，需要获取海洋表层二氧化碳分压（pCO₂）数据。当前用于估算全覆盖pCO₂的主流方法，是基于稀疏的原位pCO₂实测数据以及配套的物理与生物地球化学观测数据训练机器学习算法。尽管已知这些配套变量与pCO₂之间存在已被认知的关联，但通常难以厘清它们如何驱动pCO₂的输出结果。本研究取得两项进展，可强化物理机制认知与pCO₂重建结果之间的关联：其一，我们对pCO₂数据开展预处理，以消除温度的直接影响。这一操作能够增强目标变量中pCO₂的生物地球化学与物理组分占比，并降低机器学习模型所需解耦的复杂度。其二，我们证明所得算法的输入数据与pCO₂的生物地球化学/物理组分输出之间，存在可被物理机制解释的关联。最终的pCO₂重建结果与独立观测数据的匹配精度，小幅优于多数其他同类方法。本研究量化了重建pCO₂的不确定性，以及其对CO₂通量估算的影响。活塞速度（piston velocity）的不确定性会在部分区域引发显著的通量不确定性，但不会提升基于观测衍生产品的全球总CO₂通量不确定性估计值。我们重建的CO₂通量相较于采用平滑化处理的神经网络方法，呈现出更大的年际变率，但自2005年以来的增长趋势更为平缓。