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Discussion papers
https://doi.org/10.5194/acp-2019-834
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-834
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 21 Nov 2019

Submitted as: research article | 21 Nov 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Robust observational constraint of uncertain aerosol processes and emissions in a climate model and the effect on aerosol radiative forcing

Jill S. Johnson1, Leighton A. Regayre1, Masaru Yoshioka1, Kirsty J. Pringle1, Steven T. Turnock2, Jo Browse3, David M. H. Sexton2, John W. Rostron2, Nick A. J. Schutgens4, Daniel G. Partridge5, Dantong Liu6,a, James D. Allan6,7, Hugh Coe6, Aijun Ding8, David D. Cohen9, Armand Atanacio9, Ville Vakkari10,11, Eija Asmi10, and Ken S. Carslaw1 Jill S. Johnson et al.
  • 1Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK
  • 2Met Office Hadley Centre, Exeter, UK
  • 3Centre for Geography and Environmental Science, Universityof Exeter, Penryn, UK
  • 4Earth Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
  • 5College for Engineering, Mathematics, and Physical Science, University of Exeter, Exeter, UK
  • 6Centre for Atmospheric Sciences, School of Earthand Environmental Sciences, University of Manchester, Manchester, UK
  • 7National Centre for Atmospheric Science, University of Manchester, Manchester, UK
  • 8Joint International Research Laboratory of Atmospheric and Earth System Sciences (JirLATEST), School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
  • 9Centre for Accelerator Science, ANSTO, New Illawarra Rd, Lucas Heights, NSW, 2232, Australia
  • 10Finnish Meteorological Institute, Helsinki, FI-00101, Finland
  • 11Unit for Environmental Sciences and Management, North-West University, Potchefstroom, ZA-2520, South Africa
  • anow at: Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, Zhejiang, China

Abstract. The effect of observational constraint on the ranges of uncertain physical and chemical process parameters was explored in a global aerosol–climate model. The study uses 1 million variants of the HadGEM3-UKCA climate model that sample 26 sources of uncertainty, together with over 9000 monthly aggregated grid-box measurements of aerosol optical depth, PM2.5, particle number concentrations, sulphate and organic mass concentrations. Despite many compensating effects in the model, the procedure constrains the probability distributions of parameters related to secondary organic aerosol, anthropogenic SO2 emissions, residential emissions, sea spray emissions, dry deposition rates of SO2 and aerosols, new particle formation, cloud droplet pH and the diameter of primary combustion particles. Observational constraint rules out nearly 98 % of the model variants. On constraint, the ± 1σ (standard deviation) range of global annual mean direct radiative forcing, RFari, is reduced by 33 % to −0.14 to −0.26 W m−2, and the 95 % credible interval (CI) is reduced by 34 % to −0.1 to −0.32 W m−2. For the global annual mean aerosol–cloud radiative forcing, RFaci, the ± 1σ range is reduced by 7 % to −1.66 to −2.48 W m−2, and the 95 % CI by 6 % to −1.28 to −2.88 W m−2. The tightness of the constraint is limited by parameter cancellation effects (model equifinality) as well as the large and poorly defined representativeness error associated with comparing point measurements with a global model. The constraint could also be narrowed if model structural errors that prevent simultaneous agreement with different measurement types in multiple locations and seasons could be improved. For example, constraints using either sulphate or PM2.5 measurements individually result in RFari ± 1σ ranges that only just overlap, which shows that emergent constraints based on one measurement type may be over-confident.

Jill S. Johnson et al.
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Status: open (until 16 Jan 2020)
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Jill S. Johnson et al.
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Short summary
We use over 9000 monthly aggregated grid-box measurements of aerosol to constrain the uncertainty in the HadGEM3-UKCA climate model. Measurements of AOD, PM2.5, particle number concentrations, sulphate and organic mass concentrations are compared to 1 million variants of the model using an implausibility metric. Despite many compensating effects in the model, the procedure constrains the probability distributions of many parameters and direct radiative forcing uncertainty is reduced by 34 %.
We use over 9000 monthly aggregated grid-box measurements of aerosol to constrain the...
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