The growing seasons of global forest ecosystems from 1850 to 2100 estimated with a probabilistic temperature-based model

Abstract. Global climate warming has significantly altered forest phenology in the past decades, with measurable shifts in the timing and duration of the growing season (GS). These changes are expected to intensify in the future, potentially affecting both ecosystem productivity and land-atmosphere interactions. Accurately representing GS dynamics is therefore essential for assessing ecosystem vulnerability and improving the representation of vegetation processes in Earth system models. Here, we introduce GS-P, a probabilistic, temperature-based model developed within a machine-learning framework to estimate the start and end of the growing season (SGS and EGS) for global forest ecosystems over the period 1850–2100.

Results show stable GS timing until the 1970s, followed by significant shifts characterized by earlier SGS and later EGS, leading to a global extension of the GS. Under future climate scenarios, GS duration is projected to increase by approximately one month under low-emission conditions and up to two months under high-emission scenarios, with stronger responses in the Northern Hemisphere. Compared to alternative models, GS-P achieves comparable or improved predictive accuracy while exhibiting greater extrapolation capabilities and providing explicit uncertainty estimates. Furthermore, the model effectively represents key ecological features, such as stronger temperature control and greater spatial heterogeneity in spring than autumn phenology, and detection of regions where temperature alone provides limited explanatory power, suggesting a stronger role of additional drivers. Additionally, GS-P enables the identification of regions characterized by transitional states and high prediction uncertainty, potentially reflecting climate–ecosystem disequilibrium and enhanced ecosystem vulnerability. This model provides a flexible and interpretable framework for simulating GS dynamics at the global scale, offering improved constraints for carbon cycle modelling and supporting the assessment of ecosystem responses to future climate change.