Evaluating the self-initialization procedure for large-scale ecosystem models

Self-initialization routines generate starting values for large-scale ecosystem model applications which are needed to model transient behaviour. In this paper we evaluate the self-initialization procedure of a large-scale BGC-model for biological realism by comparing model predictions with observations from the central European virgin forest reserve Rothwald, a category I IUCN wilderness area. Results indicate that standard self-initialization towards a 'steady state' produces biased and inconsistent predictions resulting in systematically overestimated C and N pools vs. observations. We investigate the detected inconsistent predictions and use results to improve the self-initialization routine by developing a dynamic mortality model which addresses natural forest dynamics with higher mortality rates during senescence and regeneration vs. lower mortality rates during the period of optimum forest growth between regeneration and senescence. Running self-initialization with this new dynamic mortality model resulted in consistent and unbiased model predictions compared with field observations.