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.
- 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
- 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
- Emily R Lines, David A Coomes, and Drew Purves, Influences of Forest Structure, Climate and Species Composition on Tree Mortality across the Eastern US, in PLoS-One, vol. 5, no. 10, PLoS, October 2010
- Drew W Purves and Stephen W Pacala, Predictive Models of Forest Dynamics, in Science, vol. 320, no. 5882, pp. 1452-1453, 13 June 2008