Abstract
Land ecosystems offer an effective nature-based solution to climate
change mitigation by absorbing approximately 30% of anthropogenically
emitted carbon. This estimated absorption is primarily based on
constraints from atmospheric and oceanic measurements while
quantification from direct studies of the land carbon cycle themselves
displays great uncertainty. The latter hinders prediction of the future
fate of the land carbon sink. This talk will present a matrix approach,
which will be shown to unify land carbon cycle models, help diagnose
model performance with new analytics, accelerate computational
efficiency for spin-up, enable data assimilation with complex models,
and guide carbon cycle research with a new theoretical framework. The
unified framework can be used to evaluate relative importance of various
processes, identify sources of uncertainty in model predictions, and
improve accuracy of quantification of land carbon sequestration.