Explainable Machine Learning Confirms the Global Terrestrial CO₂ Fertilization Effect From Space

The carbon dioxide (CO2) fertilization effect has captured worldwide attention, owing to its tremendous potential to challenge existing predictions of future climate. However, quantifying the CO2 fertilization effect (CFE) has proven to be challenging, given that it is closely entangled with other ecological and environmental processes. Recent years have witnessed significant advances with breakthroughs using theoretical methods to infer the CFE from eddy covariance tower measurements. Building on earlier findings, this study presents an innovative approach that utilizes explainable machine-learning techniques—describing the partial dependence of the response variable to each explanatory variable—to quantify the global CFE from remote-sensing platforms with an averaged $R ^{2}$ of 0.85. This study provides the first data-driven evidence of the global CFE and confirms the potential for extrapolation to the globe. The findings suggest that: 1) the employment of satellite vegetation proxies contributed to more than 50% of the fitting of gross primary productivity (GPP) and 2) the manifestation of the CO2 fertilization impact demonstrated heterogeneity among various types of ecosystems, and in some cases, an adverse effect was detected in broadleaf forests. Our results have significant implications for the preservation and protection of terrestrial ecosystems, particularly for a carbon-neutral future. This study, therefore, provides a valuable contribution to the growing body of knowledge in this area and highlights the potential of innovative analytical techniques to address complex ecological challenges.