CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO 2 fertilization

作者：Yanghui Kang, Maoya Bassiouni, Max Gaber, Xinchen Lu, Trevor F. Keenan · 发表于：Earth system science data · 年份：2025 · DOI：10.5194/essd-17-3009-2025 · 被引用次数：10 · 研究领域：Atmospheric and Environmental Gas Dynamics、Plant Water Relations and Carbon Dynamics、Climate variability and models

Abstract. Gross primary productivity (GPP) is the largest carbon flux in the Earth system, playing a crucial role in removing atmospheric carbon dioxide and providing carbohydrates 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 machine-learning-upscaled GPP product that incorporates the direct CO2 fertilization effect on photosynthesis. Our product is comprised of monthly GPP estimates and their uncertainty at 0.05° 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 predict monthly GPP (R2 ∼ 0.72), the mean seasonal cycles (R2 ∼ 0.77), and spatial variabilities (R2 ∼ 0.63) based on cross-validation at flux sites. After incorporating the direct CO2 effects, the predicted long-term GPP trend across global flux towers substantially increases from 3.1 to 4.5–5.4 gC m−2 yr−1, which aligns more closely with the 7....