Transient dynamics of terrestrial carbon storage [electronic resource] : Mathematical foundation and numeric examples
- Washington, D.C. : United States. Dept. of Energy. Office of Science, 2016.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- pages 1-47 : digital, PDF file
- Additional Creators:
- Oak Ridge National Laboratory
United States. Department of Energy. Office of Science
United States. Department of Energy. Office of Scientific and Technical Information
- <p>Terrestrial ecosystems absorb roughly 30% of anthropogenic CO<sub>2</sub> emissions since preindustrial era, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling, experimental, and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under climate change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is time-dependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times. <br><br> Furthermore, this and our other studies have demonstrated that one matrix equation can exactly replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a three-dimensional (3D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. Moreover, the emulators make data assimilation computationally feasible so that both C flux- and pool-related datasets can be used to better constrain model predictions of land C sequestration. We also propose that the C storage potential be the targeted variable for research, market trading, and government negotiation for C credits.</p>
- Published through SciTech Connect.
Biogeosciences Discussions (Online) 2016 ISSN 1810-6285 AM
Yiqi Luo; Zheng Shi; Xingjie Lu; Jianyang Xia; Junyi Liang; Ying Wang; Matthew J. Smith; Lifen Jiang; Anders Ahlstrom; Benito Chen; Oleksandra Hararuk; Alan Hastings; Forrest Hoffman; Belinda Medlyn; Shuli Niu; Martin Rasmussen; Katherine Todd-Brown; Ying -Ping Wang.
- Funding Information:
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