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Title: Transient dynamics of terrestrial carbon storage: Mathematical foundation and numeric examples

Terrestrial ecosystems absorb roughly 30% of anthropogenic CO2 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 ofmore » net C pool changes in a network of pools with different residence times. 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.« less
 [1] ;  [2] ;  [3] ;  [4] ;  [2] ;  [2] ;  [5] ;  [2] ;  [6] ;  [7] ;  [8] ;  [9] ;  [10] ;  [11] ;  [12] ;  [13] ;  [14] ;  [3]
  1. Univ. of Oklahoma, Norman, OK (United States); Tsinghua Univ., Beijing (China)
  2. Univ. of Oklahoma, Norman, OK (United States)
  3. CSIRO Oceans and Atmosphere, Aspendale, VIC (Australia)
  4. East China Normal Univ., Shanghai (China)
  5. Microsoft Research, Cambridge (United Kingdom)
  6. Stanford Univ., Stanford, CA (United States); Lund Univ., Lund (Sweden)
  7. Univ. of Texas, Arlington, TX (United States)
  8. Canadian Forest Service, Victoria, BC (Canada)
  9. Univ. of California, Davis, CA (United States)
  10. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  11. Western Sydney Univ., Penrith, NSW (Australia)
  12. Chinese Academy of Sciences (CAS), Beijing (China)
  13. Imperial College, London (United Kingdom)
  14. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
OSTI Identifier:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Biogeosciences Discussions (Online)
Additional Journal Information:
Journal Name: Biogeosciences Discussions (Online); Journal Volume: 2016; Journal ID: ISSN 1810-6285
European Geosciences Union
Research Org:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States