Vegetation phenology regulates many ecosystem processes and is an indicator of the biological responses to climate change. It is important to model the timing of leaf senescence accurately, since the canopy duration and carbon assimilation are strongly determined by the timings of leaf senescence. However, the existing phenology models are unlikely to accurately predict the end of the growing season (EGS) on large scales, resulting in the misrepresentation of the seasonality and interannual variability of biosphere–atmosphere feedbacks and interactions in coupled global climate models. In this paper, we presented a novel large-scale temperature dominated model integrated with the physiological adaptation of plants to the local temperature to assess the spatial pattern and interannual variability of the EGS. Our model was validated in all temperate vegetation types over the Northern Hemisphere. The results indicated that our model showed better performance in representing the spatial and interannual variability of leaf senescence, compared with the original phenology model in the Integrated Biosphere Simulator (IBIS). Our model explained approximately 63% of the EGS variations, whereas the original model explained much lower variations (coefficient of determination R2 = 0.01–0.18). In addition, the differences between the EGS reproduced by our model and the MODIS EGS at 71.3% of the pixels were within 10 days. For the original model, it is only 26.1%. We also found that the temperature threshold (TcritTm) of grassland was lower than that of woody species in the same latitudinal zone.
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, PR China;Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, PR China;Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, PR China;Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, PR China;Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, PR China;Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, PR China;Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
Recommended Citation:
Yang Fu,Zeyu Zheng,Haibo Shi,et al. A Novel Large-Scale Temperature Dominated Model for Predicting the End of the Growing Season[J]. PLOS ONE,2016-01-01,11(11)