Climate change is the greatest global challenge of the 21st century. Models that reliably forecast future climates associated with different policy scenarios are urgently needed. This project has developed a new model for the key source of uncertainty in earth system models: the terrestrial carbon climate feedback, using a methodology to account for all known sources of uncertainty and enable robust estimates of the confidence that can be placed in predictions and objective model refinement.
Currently one of the largest single sources of uncertainty in global climate models is the future of carbon stored in vegetation*. In recent years vegetation (mostly forests) has soaked up 25% of human CO2 emissions, but models disagree drastically about the future of this ‘carbon sink’: will vegetation soak up ever larger amounts of carbon, acting as an ever-stronger brake on climate change? Or will vegetation become a significant carbon source, accelerating climate change which in turn would demand much more radical policy action to reduce CO2 emissions now? These alternative scenarios have been predicted by conteporary DGVMs but unfortunately technical obstacles make it practically impossible to compare existing models and identify objectively what the most likely future scenario is.
We aim to solve this problem, and the wider problem of developing predictive models of complex natural systems in such a way as to be able to place a robust measure of confidence in their predictions.
So far we have produced the first fully data-constrained global terrestrial carbon model. All parameters and component processes are probibalistically data-constrained. This allows us to assign a robust measure of confidence to model predictions but also enables us to assess confidence in all model components, objectively target where refinements are most needed, and assess the costs and benefits of model refinements.
- 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.
- 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, in Biogeosciences Discussions, 19 October 2015.
- Matthew J. Smith, Derek P. Tittensor, Vassily Lyutsarev, and Eugene Murphy, Inferred support for disturbance-recovery hypothesis of North Atlantic phytoplankton blooms, in Journal of Geophysical Research - Oceans, AGU Publishing, 6 October 2015.
- Johannes Meyerholt, Soenke Zaehle, and Matthew J. Smith, Different representations of biological nitrogen fixation cause major variation in projected terrestrial biosphere responses to elevated levels of atmospheric CO2 , AGU, 6 October 2015.
- William R. Weider, Steven D. Allison, Eric A. Davidson, Katerina Georgiou, Oleksandra Hararuk, Yujie He, Francesca Hopkins, Matthew J. Smith, Benjamin Sulman, Katherine Todd-Brown, Ying-Ping Wang, Jianyang Xia, and Xiaofeng Xu, Explicitly representing soil microbial processes in Earth system models, in Global Biogeochemical Cycles, AGU Publications, 1 October 2015.
- 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, in Biogeosciences Discussions, 7 September 2015.
- Matthew J. Smith, Stephen Emmott, Drew W. Purves, Lucas N. Joppa, and Vassily Lyutsarev, Joined-up Planetary Information, in the Cloud and on Devices, 15 December 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.
- Oleksandra Hararuk, Matthew J. Smith, and Yiqi Luo, Microbial models with data-driven parameters predict stronger soil carbon responses to climate change, in Global Change Biology, Wiley, 1 December 2014.
- Yiqi Luo, Trevor F. Keenan, and Matthew J. Smith, Predictability of the terrestrial carbon cycle, in Global Change Biology, Wiley, October 2014.
- Paul I. Palmer and Matthew J. Smith, Model human adaptation to climate change, in Nature, 28 August 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, in Biogeosciences, European Geosciences Union, 7 April 2014.
- Maria Bruna, Jonathan Chapman, and Matthew J Smith, Model reduction for slow-fast stochastic systems with metastable behaviour, in Journal of Chemical Physics, American Institute of Physics, April 2014.
- Matthew J. Smith, Paul I. Palmer, Drew W. Purves, Mark C. Vanderwel, Vassily Lyutsarev, Ben Calderhead, Lucas N. Joppa, Christopher M. Bishop, and Stephen Emmott, Changing how Earth System Modelling is done to provide more useful information for decision making, science and society., in Bulletin of the American Meteorological Society, American Meteorological Society, February 2014.
- S. Caldararu, D. W. Purves, and P.I. Palmer, Phenology as a strategy for carbon optimality: a global model, in Biogeosciences, vol. 11, 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, in Biogeosciences Discussions, European Geosciences Union, December 2013.
- Matthew R Evans, Mike Bithell, Stephen J. Cornell, Sasha R. X. Dall, Sandra Diaz, Stephen Emmott, Bruno Ernande, Volker Grimm, David J. Hodgson, Simon L. Lewis, Georgina M. Mace, Michael Morecroft, Aristides Moustakas, Eugene Murphy, Tim Newbold, K. J. Norris, Owen Petchey, Matthew J. Smith, Justin M. J. Travis, and Tim G. Benton, Predictive systems ecology, in Proceedings of the Royal Society B, Royal Society, 22 November 2013.
- Isabel M D Rosa, Drew Purves, Carlos Souza Jr, and Robert M Ewers, Predictive Modelling of Contagious Deforestation in the Brazilian Amazon, in PLOS One, PLoS, October 2013.
- Sadia E Ahmed, Carlos M Souza, J Riberio, and Rob M Ewers, Temporal patterns of road network development in the Brazilian Amazon, in Regional Environmental Change, vol. 13, pp. 927-937, Springer, October 2013.
- Michael Harfoot, Derek P. Tittensor, Tim Newbold, Greg McInerny, Matthew J. Smith, and Jorn P.W. Scharlemann, Integrated assessment models for ecologists: the present and the future, in Global Ecology and Biogeography, Wiley, June 2013.
- 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, in Biogeosciences, vol. 10, pp. 583-606, European Geosciences Union, 29 January 2013.
- Drew W Purves, Jorn P W Scharlemann, Mike Harfoot, Tim Newbold, Derek Tittensor, Jon Hutton, and Stephen Emmott, Time to Model All Life on Earth, in Nature, vol. 493, no. 7432, pp. 295-297, Nature Publishing Group, 17 January 2013.
- 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, in Global Change Biology, Wiley, January 2013.
- 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, in Biogeosciences Discussions, vol. 9, pp. 13439-13496, European Geosciences Union, 4 October 2012.