Description
The common approach for estimating full-coverage fCO2 is to train a machine learning algorithm on sparse in situ fCO2 data and associated physical and biogeochemical observations. While these associated variables have understood relationships to fCO2, it is often unclear how they mechanistically drive fCO2 around the world. The LDEO fCO2-Residual method takes the basic approach and adds advances that enhance connections between physical understanding and reconstructed fCO2. The novel approach used here includes applying pre-processing to the fCO2 data to remove the direct effect of temperature, a relationship well-documented in literature and lab experiments (Takahashi et al. 2002). This enhances the biogeochemical/physical component of fCO2 in the target variable (now fCO2-Residual) and reduces the complexity that the machine learning must disentangle. The resulting algorithm has physically understandable connections between input data and the output biogeochemical/physical component of fCO2 (Bennington et al. 2022).
References
Bennington, V., Galjanic, T., & McKinley, G. A. (2022). Explicit physical knowledge in machine learning for ocean carbon flux reconstruction: The pCO2-Residual method. Journal of Advances in Modeling Earth Systems, 14, e2021MS002960. https://doi. org/10.1029/2021MS002960
Takahashi, T., Sutherland, S. C., Sweeney, C., Poisson, A., Metzl, N., Tilbrook, B., et al. (2002). Global sea–air CO2 flux based on climatological surface ocean pCO2, and seasonal biological and temperature effects. Deep Sea Research Part II: Topical Studies in Oceanography, 49(9), 1601–1622. https://doi.org/10.1016/S0967-0645(02)00003-6
