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Autumn phenological variation of temperate and northern ecosystems plays a major but poorly defined role in the global carbon cycle. Constructing robust process-based autumn phenology models is key for improving current ecosystem models to accurately simulate ecosystem productivity and carbon sequestration. In this study, we developed a new process-based autumn phenology model. The model was fitted and validated using 67 leaf coloration and brown-down time series collected for 27 woody and herbaceous species at 18 stations on the Qinghai–Tibetan Plateau from 1981 to 2012. Then, we used the model to analyze the spatial and interspecific differentiation of driving factors of leaf senescence. Moreover, we compared fitting and validation precisions of the new model and previously published models. Results show that leaf senescence processes were triggered by photoperiod shortening in 61.2% of time series but by daily minimum temperature decrease in 38.8% of time series. Photoperiod control of the leaf senescence start occurs predominantly at stations with shorter annual maximum daylength (88.9%), while daily minimum temperature control of the leaf senescence start appears mainly at stations with longer annual maximum daylength (71.0%), especially for the native species. The new model reveals coupling effects of shortened photoperiod and decreased daily minimum temperature on leaf senescence processes. Comparing with the representative existing process-based autumn phenology models, the new model is a more general model and more robust in fitting and predicting leaf coloration and brown-down dates. Our study has provided a new insight on the understanding of leaf senescence mechanisms in winter deciduous trees and herbaceous plants, and significantly improved the ability to predict climate change impacts on vegetation growth and carbon balance in the sensitive biogeographical region of global climate change.

Autumn phenology remains a relatively neglected aspect in climate change research, which hinders an accurate assessment of the global carbon cycle and its sensitivity to climate change. Leaf coloration, a key indicator of the growing season end, is thought to be triggered mainly by high or low temperature and drought. However, how the control of leaf coloration is split between temperature and drought is not known for many species. Moreover, whether growing season and autumn temperatures interact in influencing the timing of leaf coloration is not clear. Here, we revealed major climate drivers of leaf coloration dates and their interactions using 154 phenological datasets for four winter deciduous tree species at 89 stations, and the corresponding daily mean/minimum air temperature and precipitation data across China's temperate zone from 1981 to 2012. Results show that temperature is more decisive than drought in causing leaf coloration, and the growing season mean temperature plays a more important role than the autumn mean minimum temperature. Higher growing season temperature and lower autumn minimum temperature would induce earlier leaf coloration date. Moreover, the mean temperature over the growing season correlates positively with the autumn minimum temperature. This implies that growing season mean temperature may offset the requirement of autumn minimum temperature in triggering leaf coloration. Our findings deepen the understanding of leaf coloration mechanisms in winter deciduous trees and suggest that leaf life-span control depended on growing season mean temperature and autumn low temperature control and their interaction are major environmental cues. In the context of climate change, whether leaf coloration date advances or is delayed may depend on intensity of the offset effect of growing season temperature on autumn low temperature.

Using woody plant phenological data in the Beijing Botanical Garden from 1979 to 2013, we revealed three levels of phenology rhythms and examined their coherence with temperature rhythms. First, the sequential and correlative rhythm shows that occurrence dates of various phenological events obey a certain time sequence within a year and synchronously advance or postpone among years. The positive correlation between spring phenophase dates is much stronger than that between autumn phenophase dates and attenuates as the time interval between two spring phenophases increases. This phenological rhythm can be explained by positive correlation between above 0 °C mean temperatures corresponding to different phenophase dates. Second, the circannual rhythm indicates that recurrence interval of a phenophase in the same species in two adjacent years is about 365 days, which can be explained by the 365-day recurrence interval in the first and last dates of threshold temperatures. Moreover, an earlier phenophase date in the current year may lead to a later phenophase date in the next year through extending recurrence interval. Thus, the plant phenology sequential and correlative rhythm and circannual rhythm are interacted, which mirrors the interaction between seasonal variation and annual periodicity of temperature. Finally, the multi-year rhythm implies that phenophase dates display quasi-periodicity more than 1 year. The same 12-year periodicity in phenophase and threshold temperature dates confirmed temperature controls of the phenology multi-year rhythm. Our findings provide new perspectives for examining phenological response to climate change and developing comprehensive phenology models considering temporal coherence of phenological and climatic rhythmicity.