Accurate simulation of the onset of spring is crucial for predicting the photosynthetic productivity of forest ecosystems. Nonetheless, the potential of phenology simulations on predictions of forest gross primary productivity (GPP) remains poorly understood, with previous studies generally focused on predicting phenology dates themselves or on limited scales. Here, we constructed a framework through the critical process of leaf growth to couple a terrestrial biosphere model—FORCCHN2 with five widely used phenology models, including one-phase (involving heat forcing), two-phase (considering chilling demand), and multi-phase models (integrating photoperiod effects) through the critical process of leaf growth. We evaluated the GPP performance from the multiple forest biomes in 74 eddy covariance (EC) sites and compared the total trends to satellite observations across the northern hemisphere forests. The predictions of the five models could reproduce 62.41–67.24 % variations of the GPP observations in all EC sites. In the deciduous broadleaf forest sites, we found the two-phase and multi-phase coupled models performed better than the one-phase coupled model. Chilling may play an essential role in controlling photosynthetic activity in deciduous broadleaf forests. Moreover, the multi-phase coupled model integrated photoperiod effects performed best at capturing GPP changes of the whole northern hemisphere forests. The predictions also showed the GPP in the northern hemisphere ranged from 21.68 to 26.06 Pg C year, and the total GPP showed an increasing trend. This study provides local and regional available evidence for the impact of phenology on photosynthesis with climate warming. The results highlight the necessity for enhancing understanding of the phenology factors on GPP and integrating the appropriate phenology representation to improve predictions of photosynthetic productivity for forests, especially in prediction to large scales and long-term trends.

中文翻译：

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北半球森林春季物候对光合生产力的优化表征

准确模拟春季的到来对于预测森林生态系统的光合生产力至关重要。尽管如此，物候模拟在预测森林总初级生产力（GPP）方面的潜力仍然知之甚少，之前的研究通常集中于预测物候日期本身或在有限的范围内。在这里，我们通过叶子生长的关键过程构建了一个框架，将陆地生物圈模型——FORCCHN2与五种广泛使用的物候模型耦合起来，包括单相（涉及热强迫）、两相（考虑冷需求）和多相模型。通过叶子生长的关键过程的相模型（集成光周期效应）。我们评估了 74 个涡度协方差 (EC) 站点的多个森林生物群落的 GPP 性能，并将总体趋势与北半球森林的卫星观测结果进行了比较。五个模型的预测可以重现所有 EC 站点中 GPP 观测结果的 62.41-67.24% 的变化。在落叶阔叶林场地，我们发现两相和多相耦合模型比一相耦合模型表现更好。寒冷可能在控制落叶阔叶林的光合作用活动中发挥重要作用。此外，多相耦合模型综合光周期效应在捕捉整个北半球森林的GPP变化方面表现最好。预测还显示，北半球GPP在21.68至26.06 Pg C年之间，GPP总量呈增长趋势。这项研究为气候变暖时物候对光合作用的影响提供了当地和区域的可用证据。结果强调，有必要加强对 GPP 物候因素的理解，并整合适当的物候表征，以改进对森林光合生产力的预测，特别是在大尺度和长期趋势的预测方面。