Climate-carbon cycle modelling hierarchy

Much climate science relies on numerical modelling to both understand the processes of the Earth system and to make predictions or projections of how it may change in the future. International climate policy relies on the outcomes of these models to make decisions which will affect the lives and livelihoods of billions of people – so it is vital that they are well understood and their use is based on robust understanding of what they can (and also what they cannot) tell us.

Spatially resolved General Circulation Models (GCMs) have evolved over recent decades in both their spatial resolution (allowing finer detail to be studied) and their process complexity (including but not limited to biogeochemistry and feedbacks between climate and ecosystems). This expansion of their capability makes them more useful and relevant than ever, but they are extremely slow to run on even the worlds most powerful super computers. Conversely very simple models exist which can be run thousands (or millions) of times, but do not include the full detail of the GCMs. Finally there are models of intermediate complexity which sit between these extremes and also make valuable contributions through differing combinations of comprehensiveness and computational efficiency.

All classes of models have something to offer – it is important to understand their strengths and weakness and to choose the most suitable tool for the job. Moreover, use of these models together can be very powerful. For example IPCC reports tend to draw firstly on complex GCMs but then through thorough calibration processes propagate their information to larger numbers of scenarios using simplified climate emulators.

In this talk I will briefly outline how this mode of use of the full modelling hierarchy has developed in the field of carbon cycle feedbacks and in quantifying the remaining carbon budget – which allows detailed planning of climate mitigation policy aligned with the goals of the Paris Agreement. I will show the development of our understanding of climate-carbon cycle feedbacks from complex models and how these have been used first to determine a simple relationship between cumulative CO₂ emissions and global warming (so called TCRE: transient climate response to carbon emissions), and then how simple models have been used in conjunction with complex models to explore the processes behind this relationship and begin to allow propagation of observational constraints.

I will end by outlining emerging knowledge on the strengths and weakness of each class of model (e.g. how simple is too simple?) and identifying research gaps for moving forward.