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Title: Transient dynamics of terrestrial carbon storage: Mathematical foundation and its applications

Terrestrial ecosystems have absorbed roughly 30% of anthropogenic CO 2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global 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, which is 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.more » The influence happens through redistribution of net C pool changes in a network of pools with different residence times. Moreover, this and our other studies have demonstrated that one matrix equation can 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 (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical 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. Altogether, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.« less
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  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. McGill Univ., Montreal, QC (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:
Report Number(s):
Journal ID: ISSN 1726-4189
Grant/Contract Number:
AC05-76RL01830; AC05-00OR22725
Accepted Manuscript
Journal Name:
Biogeosciences (Online)
Additional Journal Information:
Journal Name: Biogeosciences (Online); Journal Volume: 14; Journal Issue: 1; Journal ID: ISSN 1726-4189
European Geosciences Union
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
54 ENVIRONMENTAL SCIENCES; carbon cycle; disequilibrium; equilibrium; model intercomparison; traceability analysis
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1376544