LDEO-HPD

Available for 1959–2022, monthly, 1x1 degree, near-global coverage

Description

The LDEO Hybrid Physics Data (LDEO-HPD) pCO2 product estimates the temporal evolution of surface ocean fCO2 and air-sea CO2 exchange, utilizing the strengths of observations and global ocean biogeochemical models (GOBMs).

GOBMs are internally consistent, mechanistic representations of the ocean circulation and carbon cycle, and have long been the standard for making spatio-temporally resolved estimates of air-sea CO2 fluxes. However, there is often a bias between the modeled fCO2 and available surface ocean measurements (Fay & McKinley 2021). The LDEO-HPD approach trains an eXtreme Gradient Boosting (XGB) algorithm to learn a non-linear relationship between model-data fCO2 mismatch and observed predictor variables (SST, SSS, chlorophyll concentration, mixed layer depth). The GOBM fCO2 is then corrected with the predicted model-data misfit to estimate real-world fCO2 for the observation period (1982 through present). (Gloege et al. 2022)

The largest component of the GOBM corrections are climatological, indicating that spatial seasonal climatological patterns in the models do not often match up with climatological variations in the observations. Smaller corrections at other timescales suggest either that these are well captured by the GOBMs or the data are insufficient to constrain them. The dominance of climatological corrections allows for the extension of the LDEO-HPD fCO2 product backwards in time. A climatology of model-observation misfits for the best-observed period (2000-present) is applied to the GOBMs for 1959-1981, and an interannually varying correction is used for 1982 onward. (Bennington et al 2022).

Since 2022, the LDEO-HPD has been included in the annual release of the Global Carbon Budget

References

Bennington, V., Gloege, L., & McKinley, G. A. (2022). Variability in the global ocean carbon sink from 1959 to 2020 by correcting models with observations. Geophysical Research Letters, 49, e2022GL098632. https://doi. org/10.1029/2022GL098632

Fay, A. R., & McKinley, G. A. (2021). Observed regional fluxes to constrain modeled estimates of the ocean carbon sink. Geophysical Research Letters, 48, e2021GL095325. https://doi. org/10.1029/2021GL095325

Gloege, L., Yan, M., Zheng, T., & McKinley, G. A. (2022). Improved quantification of ocean carbon uptake by using machine learning to merge global models and pCO2 data. Journal of Advances in Modeling Earth Systems, 14, e2021MS002620. https://doi. org/10.1029/2021MS002620