24 Nov 2022
24 Nov 2022
Status: this preprint is open for discussion.

Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing

Leighton A. Regayre1,2, Lucia Deaconu3,4, Daniel P. Grosvenor1,2,5, David Sexton2, Christopher C. Symonds5, Tom Langton3, Duncan Watson-Paris3, Jane P. Mulcahy2, Kirsty J. Pringle1,5,6, Mark Richardson5, Jill S. Johnson1,7, John Rostron2, Hamish Gordon1,8, Grenville Lister9,10, Philip Stier3, and Ken S. Carslaw1 Leighton A. Regayre et al.
  • 1Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
  • 2Met Office Hadley Centre, Exeter, Fitzroy Road, Exeter, Devon, EX1 3PB, UK
  • 3Atmospheric, Oceanic and Planetary Physics Department, University of Oxford, Oxford, OX1 3PU
  • 4Faculty of Environmental Science and Engineering, Babes-Bolyai University, Cluj, Romania, 400294
  • 5Centre for Environmental Modelling and Computation, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK
  • 6Edinburgh Parallel Computing Centre, Bayes Centre, University of Edinburgh, EH8 9BT
  • 7School of Mathematics and Statistics, University of Sheffield, Sheffield, S3 7RH, UK
  • 8Department of Chemical Engineering and Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
  • 9Department of Meteorology, University of Reading, RG6 6AH, UK
  • 10National Centre for Atmospheric Science, Reading, RG6 6AH, UK

Abstract. Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill at making climate projections. Efforts to improve the representations of physical processes in climate models, including extensive comparisons with observations, have not significantly constrained the range of possible aerosol forcing values. A far stronger constraint, in particular for the lower (most-negative) bound, can be achieved using global mean energy-balance arguments based on observed changes in historical temperature. Here, we show that structural deficiencies in a climate model, revealed as inconsistencies among observationally constrained cloud properties, limit the effectiveness of observational constraint of the uncertain physical processes. We sample uncertainty in 37 model parameters related to aerosols, clouds and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate one million model variants (different parameter settings from Gaussian Process emulators) against satellite-derived observations over several cloudy regions. We show it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 % to a range in close agreement with energy-balance constraints (around -1.3 to -0.1 W m-2). However, incorporating observations associated with model inconsistencies weakens the constraint because the inconsistencies introduce conflicting information about relationships between model parameter values and aerosol forcing. Our estimated aerosol forcing range is the maximum feasible constraint using these observations and our structurally imperfect model. Structural model developments, targeted at the inconsistencies identified here, would enable a larger set of observations to be used for constraint, which would then narrow the uncertainty further.

Leighton A. Regayre et al.

Status: open

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Leighton A. Regayre et al.

Data sets

A-CURE: Monthly mean perturbed parameter ensemble data Regayre, L.; Carslaw, K.; Deaconu, L.; Symonds, C.; Richardson, M.; Langton, T.; Watson-Parris, D.; Stier, P.

Model code and software

article_code_constraint_aerosol_ERF Regayre, L.

Leighton A. Regayre et al.


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Short summary
We show that potential structural deficiencies in a climate model can be exposed by comprehensively exploring its parametric uncertainty, and that these deficiencies limit how much the model uncertainty can be reduced through observational constraint. Combined consideration of parametric and structural uncertainties provides a future pathway towards building models that have greater physical realism and lower uncertainty.