Ensembles of Global Climate Model Variants Designed for the Quantification and Constraint of Uncertainty in Aerosols and Their Radiative Forcing. (22nd November 2019)

• Record Type: Journal Article
• Authors: Yoshioka, M.; Regayre, L. A.; Pringle, K. J.; Johnson, J. S.; Mann, G. W.; Partridge, D. G.; Sexton, D. M. H.; Lister, G. M. S.; Schutgens, N.; Stier, P.; Kipling, Z.; Bellouin, N.; Browse, J.; Booth, B. B. B.; Johnson, C. E.; Johnson, B.; Mollard, J. D. P.; Lee, L.; Carslaw, K. S.
• Is Part Of: Journal of advances in modeling earth systems. Volume 11:Number 11(2019)
• Page Start: 3728
• Page End: 3754
• Publication Date: 2019-11-22
• Subjects: aerosols -- radiative forcing -- ERF -- perturbed parameter ensemble -- emulators -- uncertainty; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2019MS001628
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: 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 Met Office Hadley Centre Global Environment Model‐U.K. Chemistry and Aerosol 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 preindustrial and present‐day atmospheres. The uncertainty in aerosol radiative forcing in our ensembles is comparable to the range of estimates from multimodel intercomparison projects. The mean aerosol effective radiative forcing is −1.45 W/m 2 (credible interval of −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 Intergovernmental Panel on Climate Change Fifth 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 multimodel differences. Plain Language Summary: Atmospheric aerosol particles such as dust, pollutants, and smoke interfere with light from the Sun and modify the properties of clouds and thereby affect Earth's climate. However, the effect that aerosols have on climate is one of the major causes of uncertainty in global climate model simulations. We performed a large number of climate model simulations (called an ensemble), with many parts of the model slightly varied, in order to understand the complex behavior of the model and to explore the causes of uncertainty in model outputs. This paper describes the designs of two climate model ensembles and provides the first analyses of the causes of model uncertainty. The first ensemble was designed to comprehensively understand the behavior of aerosols in the atmosphere, while the other includes more general uncertainties in atmospheric processes that can affect aerosols. Each ensemble consists of around 200 simulations. The ranges of the aerosol climate effect in our ensembles are comparable to the ranges of previous estimates from studies that analyzed multiple climate models. These ensembles can be used to reduce uncertainty in how aerosols affect climate by comparing with satellite and ground‐based measurements. Key Points: Two ensembles of atmospheric simulations were performed perturbing aerosol and physical parameters under different model setups Thousands of Gaussian process emulators sampling parameter spaces enabled statistical analyses of model's parametric uncertainty. Fully explored parametric uncertainty of aerosol radiative forcing in a model was found to be comparable to that in multimodel studies