CEDAR-GPP: Global GPP dataset incorporating CO2 fertilization upscaled from eddy covariance measurements

[yanghuikang]

CEDAR-GPP is the first global GPP dataset upscaled from eddy covariance measurements, robustly incorporating both the direct and indirect CO2 fertilization effects on photosynthesis. The dataset is generated with machine-learning models and comprehensive satellite remote sensing data. CEDAR-GPP demonstrates enhanced consistency with dynamic global vegetation models (DGVMs) regarding long-term trends, compared to major satellite-based GPP datasets which neglect the direct CO2 effects. The monthly GPP data spans 1982 to 2020 at the 0.05-degree resolution, available in NetCDF format.

References:
Kang, Y., Bassiouni, M., Gaber, M., Lu, X., Keenan, T., 2024. CEDAR-GPP: A Spatiotemporally Upscaled Dataset of Gross Primary Productivity Incorporating CO2 Fertilization. https://doi.org/10.5281/zenodo.8212706

Kang, Y., Gaber, M., Bassiouni, M., Lu, X., Keenan, T., 2023. CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO2 fertilization. Earth System Science Data Discussions 1–51. https://doi.org/10.5194/essd-2023-337