Understanding and Modelling the Global Carbon Cycle

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.

Global predictions of plant carbonUncertainty in predictions of plant carbon

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.

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Stephen Emmott
Stephen Emmott

Vassily Lyutsarev
Vassily Lyutsarev

Drew Purves
Drew Purves

Matthew Smith
Matthew Smith