Deficiencies of Phenology Models in Simulating Spatial and Temporal Variations in Temperate Spring Leaf Phenology

Spring leaf phenology and its response to climate change have crucial effects on surface albedo, carbon balance, and the water cycle of terrestrial ecosystems. Based on long-term (period 1963-2014) in situ observations of budburst date and leaf unfolding date of more than 300 deciduous woody species from 32 sites across the temperate zone in China, we conducted model-data comparison of spatial and temporal variations for spring leaf phenology calculated using the phenology modules that were embed into 10 existing terrestrial ecosystem models. Our results suggested that ORganizing Carbon and Hydrology in Dynamic EcosystEms and Spatially Explicit Individual-Based performed the best in reproducing the spatial patterns of spring leaf phenology, but tended to underestimate the temporal variations in responding to temperature warming, showing low interannual variability (IAV) and temperature sensitivity (S-T). In contrast, the performances of Vegetation Integrated SImulator for Trace Gases were the best in modeling IAV and S-T. BIOME3, Lund-Potsdam-Jena model, Joint UK Land Environment Simulator, BioGeochemical Cycles, Community Land Model, Integrated Biosphere Simulator, and Commonwealth Scientific and Industrial Research Organisation Atmosphere Biosphere Land Exchange Model failed to reproduce both the spatial and temporal patterns. Using temperature series (1960-2100) form Coupled Model Intercomparison Project Number 6 scenarios to force the 10 phenology modules, our results highlighted large uncertainties in predicting spring leaf phenology changes with the warming climate, and more work is required to deal with the deficiencies of phenology model parameters and algorithms.

Automated summary

Spring leaf phenology plays a crucial role in surface albedo, carbon balance, and the water cycle of terrestrial ecosystems. This study compared the performance of ten terrestrial ecosystem models in reproducing the spatial and temporal variations of spring leaf phenology using in situ observations from China. The results showed that ORCHIDEE and VISIT performed the best in different aspects, while the other models failed to reproduce both the spatial and temporal patterns. The study also highlighted the uncertainties in predicting spring leaf phenology changes with the warming climate and the need for further improvement in model parameters and algorithms.