Matthew J. Smith, Mark C. Vanderwel, Vassily Lyutsarev, Stephen Emmott, and Drew W. Purves
4 October 2012
The feedback between climate and the terrestrial carbon cycle will be a key determinant of the dynamics of the Earth System 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 carbon cycle models. A major goal in biogeosciences is therefore to improve understanding of the terrestrial carbon cycle to enable better constrained projections. Essential to achieving this goal will be assessing the empirical support for alternative models of component processes, identifying key uncertainties and inconsistencies, and ultimately identifying the models that are most consistent with empirical evidence. To begin meeting these requirements we data-constrained all parameters of all component processes within a global terrestrial carbon model. Our goals were to assess the climate dependencies obtained for different component processes when all parameters have been inferred from empirical data, 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 to identify a methodology by which alternative component models could be compared within the same framework in 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 make better constrained projections.
|Published in||Biogeosciences Discussions|
|Publisher||European Geosciences Union|
© Author(s) 2012. This work is distributed under the Creative Commons Attribution 3.0 License.
M. J. Smith, D. W. Purves, M. C. Vanderwel, V. Lyutsarev, and S. Emmott. The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties, Biogeosciences, European Geosciences Union, 29 January 2013.
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.
Y.P.Wang, B.C.Chen, W.R.Weider, Y.Q.Luo, B.E.Medlyn, M.Rasmussen, M.J.Smith, F.B.Agusto, and F.Hoffman. Oscillatory behaviour of two nonlinear microbial models of soil carbon decomposition, Biogeosciences Discussions, European Geosciences Union, December 2013.
Yiqi Luo, Trevor F. Keenan, and Matthew J. Smith. Predictability of the terrestrial carbon cycle, Global Change Biology, Wiley, October 2014.
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.
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.
Y.P. Wang, B.C. Chen, W.R. Weider, M. Leite, B.E. Medlyn, M. Rasmussen, M.J. Smith, F.B. Augusto, F. Hoffman, and Y.Q. Luo. Oscillatory behavior of two nonlinear microbial models of soil carbon decomposition, Biogeosciences, European Geosciences Union, 7 April 2014.