The contribution of stochastic vegetation dynamics to overall model uncertainty of the global carbon sink

The terrestrial carbon sink plays a central role in the global carbon cycle, providing a strong negative feedback on anthropogenic climate change. However, it is also one of the more uncertain elements when simulating past and future carbon dynamics, mainly due to the challenge of modeling biological and ecological complexity across scales. One possible strategy, taken by the dynamic vegetation model LPJ-GUESS, is to use stochastic processes to describe key ecological processes, whose mechanistic modeling is still challenging (e.g. tree mortality, establishment, seed dispersal and disturbance).

Such introduced randomness can propagate through the model in various ways and may result in a final model output that is probabilistic in nature as well. Internal stochasticity can thus be seen as an additional source of model uncertainty, which so far has rarely been investigated systematically.

We perform global simulations of terrestrial carbon dynamics with LPJ-GUESS and quantify the resulting stochastic uncertainty. We find that stochasticity-induced uncertainty is a relevant share of overall uncertainty, comparable in magnitude to scenario uncertainty in some instances. When introducing stochastic processes into Earth system models, the resulting additional uncertainty should therefore be something to always be aware of.