Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections

Abstract

Autumn leaf phenology marks the end of the growing season, during which trees assimilate atmospheric CO2. Since autumn leaf phenology responds to climatic conditions, climate change affects the length of the growing season. Thus, autumn leaf phenology is often modelled to assess possible climate change effects on future CO2 mitigating capacities and species compositions of forests. Projected trends have been mainly discussed with regards to model performance and climate change scenarios. However, there has been no systematic and thorough evaluation of how performance and projections are affected by the calibration approach. Here, we analyzed >2.3 million performances and 39 million projections across 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. Calibration and validation were based on >45 000 observations for beech, oak, and larch from 500 Central European sites each. Phenology models had the largest influence on model performance. The best performing models were (1) driven by daily temperature, day length, and partly by seasonal temperature or spring leaf phenology and (2) calibrated with the Generalized Simulated Annealing algorithm (3) based on systematically balanced or stratified samples. Assuming an advancing spring phenology, projected autumn phenology shifts between 13 and +20 days by 2080–2099, resulting in a lengthening of the growing season by 7–40 days. Climate scenarios and sites explained more than 80 % of the variance in these shifts and thus had eight to 22 times the influence of phenology models. Warmer climate scenarios and better performing models predominantly extended the growing season more than cooler scenarios and poorer models. Our results justify inferences from comparisons of process-oriented phenology models to phenology-driving processes and we advocate species-specific models for such analyses and subsequent projections. For sound calibration, we recommend a combination of cross-validations and independent tests, using randomly selected sites from stratified bins based on mean annual temperature and average autumn phenology, respectively. Poor performance and little influence of phenology models on autumn phenology projections suggest that the models are overlooking relevant drivers. While the uncertain projections indicate an extension of the growing season, further studies are needed to develop models that adequately consider the relevant processes for autumn phenology.