Assessing Spring Phenology Models with Photosynthesis Integration: Mechanistic Drivers of the Carbon–Frost Trade-Off

Accurate prediction of spring phenology is critical for understanding ecosystem carbon and water dynamics under changing climates. In this study, we applied a revised optimality-based model (R-OPT) that integrates a mechanistic photosynthesis framework into the existing OPT model to simulate leaf unfolding date. We evaluated R-OPT alongside three widely used models—Growing Degree Days (GDD), Chilling–Forcing Trade-off (CFT), and Optimality-based (OPT) models—across multiple Plant Functional Types (PFTs) and sites using repeated 5-fold cross-validation. Findings reveal that R-OPT consistently outperforms the other models, achieving the lowest median RMSE (13.11 days), indicating enhanced predictive accuracy and explanatory power. Although the model incurs slightly higher complexity (median AIC = 13.44), the improvement in prediction justifies the trade-off. Our results highlight the importance of incorporating plant functional traits and environmental heterogeneity in phenological modeling. PFT-specific differences, such as the lower RMSEs for evergreen forbs and deciduous broadleaf PFTs versus larger uncertainties for drought-deciduous and semi-evergreen PFTs, underscore that current models may insufficiently capture key environmental drivers, including precipitation and partial leaf retention. Latitudinal and elevational variations in trade-off parameter a, and the prominence of leaf-level carbon assimilation traits (Aleaf) as drivers of phenology, demonstrate the critical role of physiological traits in shaping PFT-specific phenological timing. These findings have significant implications for large-scale ecosystem modeling. By linking phenology directly to photosynthetic processes, R-OPT enhances predictive skill and biological interpretability, supporting improved simulations of carbon and water fluxes. Overall, R-OPT offers a mechanistically grounded and robust framework for advancing predictive understanding of spring phenology and its ecological and climate-relevant consequences.