Predictability of the terrestrial carbon cycle
Abstract
Abstract Terrestrial ecosystems sequester roughly 30% of anthropogenic carbon emission. However this estimate has not been directly deduced from studies of terrestrial ecosystems themselves, but inferred from atmospheric and oceanic data. This raises a question: to what extent is the terrestrial carbon cycle intrinsically predictable? In this paper, we investigated fundamental properties of the terrestrial carbon cycle, examined its intrinsic predictability, and proposed a suite of future research directions to improve empirical understanding and model predictive ability. Specifically, we isolated endogenous internal processes of the terrestrial carbon cycle from exogenous forcing variables. The internal processes share five fundamental properties (i.e., compartmentalization, carbon input through photosynthesis, partitioning among pools, donor pool‐dominant transfers, and the first‐order decay) among all types of ecosystems on the Earth. The five properties together result in an emergent constraint on predictability of various carbon cycle components in response to five classes of exogenous forcing. Future observational and experimental research should be focused on those less predictive components while modeling research needs to improve model predictive ability for those highly predictive components. We argue that an understanding of predictability should provide guidance on future observational, experimental and modeling research.
- Authors:
-
- Department of Microbiology and Plant Biology University of Oklahoma Norman OK USA, Center for Earth System Science Tsinghua University Beijing 100084 China
- Department of Biological Sciences Macquarie University Sydney Australia, Computational Science Laboratory Microsoft Research Cambridge UK
- Computational Science Laboratory Microsoft Research Cambridge UK
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1400674
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Global Change Biology
- Additional Journal Information:
- Journal Name: Global Change Biology Journal Volume: 21 Journal Issue: 5; Journal ID: ISSN 1354-1013
- Publisher:
- Wiley-Blackwell
- Country of Publication:
- United Kingdom
- Language:
- English
Citation Formats
Luo, Yiqi, Keenan, Trevor F., and Smith, Matthew. Predictability of the terrestrial carbon cycle. United Kingdom: N. p., 2014.
Web. doi:10.1111/gcb.12766.
Luo, Yiqi, Keenan, Trevor F., & Smith, Matthew. Predictability of the terrestrial carbon cycle. United Kingdom. https://doi.org/10.1111/gcb.12766
Luo, Yiqi, Keenan, Trevor F., and Smith, Matthew. Wed .
"Predictability of the terrestrial carbon cycle". United Kingdom. https://doi.org/10.1111/gcb.12766.
@article{osti_1400674,
title = {Predictability of the terrestrial carbon cycle},
author = {Luo, Yiqi and Keenan, Trevor F. and Smith, Matthew},
abstractNote = {Abstract Terrestrial ecosystems sequester roughly 30% of anthropogenic carbon emission. However this estimate has not been directly deduced from studies of terrestrial ecosystems themselves, but inferred from atmospheric and oceanic data. This raises a question: to what extent is the terrestrial carbon cycle intrinsically predictable? In this paper, we investigated fundamental properties of the terrestrial carbon cycle, examined its intrinsic predictability, and proposed a suite of future research directions to improve empirical understanding and model predictive ability. Specifically, we isolated endogenous internal processes of the terrestrial carbon cycle from exogenous forcing variables. The internal processes share five fundamental properties (i.e., compartmentalization, carbon input through photosynthesis, partitioning among pools, donor pool‐dominant transfers, and the first‐order decay) among all types of ecosystems on the Earth. The five properties together result in an emergent constraint on predictability of various carbon cycle components in response to five classes of exogenous forcing. Future observational and experimental research should be focused on those less predictive components while modeling research needs to improve model predictive ability for those highly predictive components. We argue that an understanding of predictability should provide guidance on future observational, experimental and modeling research.},
doi = {10.1111/gcb.12766},
journal = {Global Change Biology},
number = 5,
volume = 21,
place = {United Kingdom},
year = {Wed Dec 03 00:00:00 EST 2014},
month = {Wed Dec 03 00:00:00 EST 2014}
}
https://doi.org/10.1111/gcb.12766
Web of Science
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