Yanghui Kang, Max Gaber, Maoya Bassiouni, Xinchen Lu, Trevor Keenan

Gross primary productivity (GPP) is the largest carbon flux in the Earth system, playing a crucial role in removing atmospheric carbon dioxide and providing the sugars and starches needed for ecosystem metabolism. Despite the importance of GPP, however, existing estimates present significant uncertainties and discrepancies. A key issue is the underrepresentation of the CO2 fertilization effect, a major factor contributing to the increased terrestrial carbon sink over recent decades. This omission could potentially bias our understanding of ecosystem responses to climate change.
Here, we introduce CEDAR-GPP, the first global upscaled GPP product that incorporates the direct CO2 fertilization effect on photosynthesis. Our product is comprised of monthly GPP estimates and their uncertainty at 0.05º resolution from 1982 to 2020, generated using a comprehensive set of eddy covariance measurements, multi-source satellite observations, climate variables, and machine learning models. Importantly, we used both theoretical and data-driven approaches to incorporate the direct CO2 effects. Our machine learning models effectively predicted monthly GPP (R2 ~ 0.74), the mean seasonal cycles (R2 ~ 0.79), and spatial variabilities (R2 ~ 0.67). Incorporation of the direct CO2 effects substantially improved the models’ ability to estimate long-term GPP trends across global flux sites. While the global patterns of annual mean GPP, seasonality, and interannual variability generally aligned with existing satellite-based products, CEDAR-GPP demonstrated higher long-term trends globally after incorporating CO2 fertilization, particularly in the tropics, reflecting a strong temperature control on direct CO2 effects. CEDAR-GPP offers a comprehensive representation of GPP temporal and spatial dynamics, providing valuable insights into ecosystem-climate interactions.