WITHDRAWN Quantifying Uncertainty in Terrestrial Ecosystem Carbon Cycle: A Particle Filter Approach

Music City Center 

Uptake of carbon by terrestrial ecosystems is expected to be a major role player in future climate projections, and therefore it is integral that our forecasts are informed and quantify as many sources of uncertainty as possible. To incorporate these sources of uncertainty, a typical approach is to use process-based ecosystem models as the latent process in a statistical state space model. The inference for these models is challenging: ecosystem process models are over-parameterized and have little data available to constrain parameters and estimate latent states. Furthermore, for multivariate dynamical systems with few observations, particle filter approximations to the marginal log-likelihood contain Monte Carlo variance that cannot be reduced by increasing the number of particles. As a result, classical techniques such as particle Markov Chain Monte Carlo and iterated filtering become infeasible. In this talk, we discuss frameworks for biophysically realistic parameterizations of state space models, parameter estimation, model selection, and uncertainty quantification in the presence of irreducible Monte Carlo error through the applied lens of carbon cycle forecasting.

Particle filter

Uncertainty quantification

Monte Carlo error