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The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties

M. J. Smith, D. W. Purves, M. C. Vanderwel, V. Lyutsarev, and S. Emmott


The feedback between climate and the terrestrial carbon cycle will be a key determinant of the dynamics of the Earth System (the thin layer that contains and supports life) over the coming decades and centuries. However, Earth System Model projections of the terrestrial carbon-balance vary widely over these timescales. This is largely due to differences in their terrestrial carbon cycle models. A major goal in biogeosciences is therefore to improve understanding of the terrestrial carbon cycle to enable better constrained projections. Utilising empirical data to constrain and assess component processes in terrestrial carbon cycle models will be essential to achieving this goal. We used a new model construction method to data-constrain all parameters of all component processes within a global terrestrial carbon model, employing as data constraints a collection of 12 empirical data sets characterising global patterns of carbon stocks and flows. Our goals were to assess the climate dependencies inferred for all component processes, assess whether these were consistent with current knowledge and understanding, assess the importance of different data sets and the model structure for inferring those dependencies, assess the predictive accuracy of the model and ultimately to identify a methodology by which alternative component models could be compared within the same framework in the future. Although formulated as differential equations describing carbon fluxes through plant and soil pools, the model was fitted assuming the carbon pools were in states of dynamic equilibrium (input rates equal output rates). Thus, the parameterised model is of the equilibrium terrestrial carbon cycle. All but 2 of the 12 component processes to the model were inferred to have strong climate dependencies, although it was not possible to data-constrain all parameters, indicating some potentially redundant details. Similar climate dependencies were obtained for most processes, whether inferred individually from their corresponding data sets or using the full terrestrial carbon model and all available data sets, indicating a strong overall consistency in the information provided by different data sets under the assumed model formulation. A notable exception was plant mortality, in which qualitatively different climate dependencies were inferred depending on the model formulation and data sets used, highlighting this component as the major structural uncertainty in the model. All but two component processes predicted empirical data better than a null model in which no climate dependency was assumed. Equilibrium plant carbon was predicted especially well (explaining around 70% of the variation in the withheld evaluation data). We discuss the advantages of our approach in relation to advancing our understanding of the carbon cycle and enabling Earth System Models to make better constrained projections.


Publication typeArticle
Published inBiogeosciences
PublisherEuropean Geosciences Union

Newer versions

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Oleksandra Hararuk, Matthew J. Smith, and Yiqi Luo. Microbial models with data-driven parameters predict stronger soil carbon responses to climate change, Global Change Biology, Wiley, 1 December 2014.

Katherine E Todd-Brown, Yiqi Luo, James Tremper Randerson, Stephen D. Allison, and Matthew J. Smith. Understanding the Dynamics of Soil Carbon in CMIP5 Models, 15 December 2014.

S. Caldararu, D. W. Purves, and M. J. Smith. The effect of using the plant functional type paradigm on a data-constrained global phenology model, Biogeosciences Discussions, 19 October 2015.

Yiqi Luo, Zheng Shi, Lifen Jiang, Jianyang Xia, Ying Wang, Manoj Kc, Junyi Liang, Xingjie Lu, Shuli Niu, Anders Ahlstrom, Oleksandra Hararuk, Alan Hastings, Forrest Hoffman, Belinda E. Medlyn, Martin Rasmussen, Matthew J. Smith, Kathe E. Todd-Brown, and Yingping Wang. Terrestrial carbon storage dynamics: Chasing a moving target, American Geophysical Union, 5 November 2015.

Oleksandra Hararuk and Matthew J. Smith. Importance of modelling microbial dynamics in soil: evaluation of conventional and microbial soil models informed by observations across various plant functional types, 15 December 2014.

Y. P. Wang, J. Jiang, B. Chen-Charpentier, F. B. Agusto, B. Hastings, F. Hoffman, M. Rasmussen, M. J. Smith, K. Todd-Brown, Y. Wang, X. Xu, and Y. Q. Luo. Responses of two nonlinear microbial models to warming or increased carbon input, Biogeosciences Discussions, 7 September 2015.

Previous versions

Matthew J. Smith, Mark C. Vanderwel, Vassily Lyutsarev, Stephen Emmott, and Drew W. Purves. The climate dependence of the terrestrial carbon cycle; including parameter and structural uncertainties, Biogeosciences Discussions, European Geosciences Union, 4 October 2012.

Mark C Vanderwel, David A Coomes, and Drew W Purves. Quantifying variation in forest disturbance, and its effects on aboveground biomass dynamics, across the eastern United States, Global Change Biology, Wiley, January 2013.

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