Constrained Carbon Partitioning: A Self‐Trained Physics‐Informed Machine Learning Model Refines GPP Estimates From Eddy Covariance Measurements

). These differences likely reflect a combination of mechanisms including light-induced respiration suppression consistent with the Kok effect, stomatal-transpiration coupling constraints, and dynamic allocation between respiration components. The flux differences vary across plant functional types (PFTs), where forested ecosystems (deciduous broadleaf, evergreen needleleaf, and mixed), savannas and grasslands show the largest negative annual GPP deviations (-10% to -18% versus nighttime partitioning), while croplands and open shrublands show moderate negative deviations (-5% to -10%). The lower GPP estimates by PFT are closer to those inferred by Keenan et al. that explicitly considers limitations on RECO from the Kok effect. We discuss implications for our understanding of ecosystem and global carbon cycle processes, as well as ways to further benefit from the full information content of eddy covariance observations by combining physics with knowledge of biological processes.