Submitted as: model description paper 24 Nov 2020

Submitted as: model description paper | 24 Nov 2020

Review status: a revised version of this preprint is currently under review for the journal GMD.

FaIRv2.0.0: a generalised impulse-response model for climate uncertainty and future scenario exploration

Nicholas J. Leach1, Stuart Jenkins1, Zebedee Nicholls2,3, Christopher J. Smith4,5, John Lynch1, Michelle Cain1, Tristram Walsh1, Bill Wu1, Junichi Tsutsui6, and Myles R. Allen1,7 Nicholas J. Leach et al.
  • 1Department of Physics, Atmospheric, Oceanic, and Planetary Physics, University of Oxford, United Kingdom
  • 2Australian–German Climate and Energy College, University of Melbourne, Australia
  • 3School of Earth Sciences, University of Melbourne, Australia
  • 4School of Earth and Environment, University of Leeds, Leeds, UK
  • 5International Institute for Applied Systems Analysis, Laxenburg, Austria
  • 6Environmental Science Laboratory, Central Research Institute of Electric Power Industry, Abiko-shi, Japan
  • 7Environmental Change Institute, University of Oxford, Oxford, UK

Abstract. Here we present an update to the FaIR model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis and education. In this update we have focussed on identifying a minimum level of structural complexity in the model. The result is a set of six equations, five of which correspond to the standard Impulse Response model used for greenhouse gas (GHG) metric calculations in the IPCC's fifth assessment report, plus one additional physically-motivated additional equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. This additional equation is necessary to reproduce non-linearities in the carbon cycle apparent in both Earth System Models and observations. These six equations are transparent and sufficiently simple that the model is able to be written in standard tabular data analysis packages, such as Excel; increasing the potential user base considerably. However, we demonstrate that the equations are flexible enough to be tuned to emulate the behaviour of several key processes within more complex models from CMIP6. The model is exceptionally quick to run, making it ideal for integrating large probabilistic ensembles. We apply a constraint based on the current estimates of the global warming trend to a one million member ensemble, using the constrained ensemble to make scenario dependent projections and infer ranges for properties of the climate system. Through these analyses, we reaffirm that simple climate models (unlike more complex models) are not themselves intrinsically biased hot or cold: it is the choice of parameters and how those are selected that determines the model response, something that appears to have been misunderstood in the past. This updated FaIR model is able to reproduce the global climate system response to GHG and aerosol emissions with sufficient accuracy to be useful in a wide range of applications; and therefore could be used as a lowest common denominator model to provide consistency in different contexts. The fact that FaIR can be written down in just six equations greatly aids transparency in such contexts.

Nicholas J. Leach et al.

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Status: final response (author comments only)
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Nicholas J. Leach et al.

Nicholas J. Leach et al.


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Short summary
This paper presents an update of the FaIR simple climate model, used for estimating the impact of anthropogenic greenhouse gas and aerosol emissions on the global climate. This update aims to significantly increase the structural simplicity of the model, making it more understandable and transparent. This simplicity allows it to be implemented in a wide range of environments, including Excel. We suggest that it could be used not only in academic or corporate research, but also in education.