Publications associated with Climate Processes

Ensembles of global climate model variants designed for the quantification and constraint of uncertainty in aerosols and their radiative forcing

Journal of Advances in Modeling Earth Systems American Geophysical Union 11 (2019) 3728-3754

M Yoshioka, LA Regayre, KJ Pringle, JS Johnson, GW Mann, DG Partridge, DMH Sexton, GMS Lister, N Schutgens, P Stier, Z Kipling, N Bellouin, J Browse, BBB Booth, CE Johnson, B Johnson, JDP Mollard, L Lee, KS Carslaw

Tropospheric aerosol radiative forcing has persisted for many years as one of the major causes of uncertainty in global climate model simulations. To sample the range of plausible aerosol and atmospheric states and perform robust statistical analyses of the radiative forcing, it is important to account for the combined effects of many sources of model uncertainty, which is rarely done due to the high computational cost. This paper describes the designs of two ensembles of the HadGEM-UKCA global climate model and provides the first analyses of the uncertainties in aerosol radiative forcing and their causes. The first ensemble was designed to comprehensively sample uncertainty in the aerosol state, while the other samples additional uncertainties in the physical model related to clouds, humidity and radiation, thereby allowing an analysis of uncertainty in the aerosol effective radiative forcing. Each ensemble consists of around 200 simulations of the pre-industrial and present-day atmospheres. The uncertainty in aerosol radiative forcing in our ensembles is comparable to the range of estimates from multi-model intercomparison projects. The mean aerosol effective radiative forcing is –1.45 W m–2 (credible interval –2.07 to –0.81 W m–2), which encompasses but is more negative than the –1.17 W m–2 in the 2013 Atmospheric Chemistry and Climate Model Intercomparison Project and –0.90 W m–2 in the IPCC 5th 47 Assessment Report. The ensembles can be used to reduce aerosol radiative forcing uncertainty by challenging them with multiple measurements as well as to isolate potential causes of multi-model differences.

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