Articles | Volume 20, issue 15
https://doi.org/10.5194/acp-20-9491-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-9491-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Aug 2020
Research article |
| 13 Aug 2020
| 13 Aug 2020
Robust observational constraint of uncertain aerosol processes and emissions in a climate model and the effect on aerosol radiative forcing
Jill S. Johnson
CORRESPONDING AUTHOR
Institute for Climate and Atmospheric Science, School of Earth and
Environment, University of Leeds, Leeds, UK
Leighton A. Regayre
Institute for Climate and Atmospheric Science, School of Earth and
Environment, University of Leeds, Leeds, UK
Masaru Yoshioka
Institute for Climate and Atmospheric Science, School of Earth and
Environment, University of Leeds, Leeds, UK
Kirsty J. Pringle
Institute for Climate and Atmospheric Science, School of Earth and
Environment, University of Leeds, Leeds, UK
Steven T. Turnock
Met Office Hadley Centre, Exeter, UK
Jo Browse
Centre for Geography and Environmental Science, University of Exeter,
Penryn, UK
David M. H. Sexton
Met Office Hadley Centre, Exeter, UK
John W. Rostron
Met Office Hadley Centre, Exeter, UK
Nick A. J. Schutgens
Earth Sciences, Faculty of Science, Vrije Universiteit Amsterdam,
Amsterdam, the Netherlands
Daniel G. Partridge
College for Engineering, Mathematics, and Physical Science, University
of Exeter, Exeter, UK
Dantong Liu
Centre for Atmospheric Sciences, School of Earth and Environmental
Sciences, University of Manchester, Manchester, UK
now at: Department of Atmospheric Sciences, School of Earth
Sciences, Zhejiang University, Hangzhou, Zhejiang, China
James D. Allan
Centre for Atmospheric Sciences, School of Earth and Environmental
Sciences, University of Manchester, Manchester, UK
National Centre for Atmospheric Science, University of Manchester,
Manchester, UK
Centre for Atmospheric Sciences, School of Earth and Environmental
Sciences, University of Manchester, Manchester, UK
Aijun Ding
Joint International Research Laboratory of Atmospheric and Earth
System Sciences (JirLATEST), School of Atmospheric Sciences, Nanjing
University, Nanjing, China
David D. Cohen
Centre for Accelerator Science, ANSTO, New Illawarra Rd, Lucas
Heights, NSW, Australia
Armand Atanacio
Centre for Accelerator Science, ANSTO, New Illawarra Rd, Lucas
Heights, NSW, Australia
Ville Vakkari
Finnish Meteorological Institute, Helsinki, Finland
Atmospheric Chemistry Research Group, Chemical Resource
Beneficiation, North-West University, Potchefstroom, South Africa
Eija Asmi
Finnish Meteorological Institute, Helsinki, Finland
Ken S. Carslaw
Institute for Climate and Atmospheric Science, School of Earth and
Environment, University of Leeds, Leeds, UK
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- Impact of Dilution Ratio and Burning Conditions on the Number Size Distribution and Size-Dependent Mixing State of Primary Particles from Domestic Solid Fuel Burning S. Zheng et al. 10.1021/acs.estlett.2c00319
- Projections of Future Air Quality Are Uncertain. But Which Source of Uncertainty Is Most Important? R. Doherty et al. 10.1029/2022JD037948
- Toward machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections F. Hourdin et al. 10.1126/sciadv.adf2758
- A Novel Network‐Based Approach to Determining Measurement Representation Error for Model Evaluation of Aerosol Microphysical Properties E. Asher et al. 10.1029/2021JD035485
- The Emissions Model Intercomparison Project (Emissions-MIP): quantifying model sensitivity to emission characteristics H. Ahsan et al. 10.5194/acp-23-14779-2023
- Optical properties and seasonal distribution of aerosol layers observed by lidar over Jinhua, southeast China S. Yu et al. 10.1016/j.atmosenv.2021.118456
- A Mechanistic Sea Spray Generation Function Based on the Sea State and the Physics of Bubble Bursting L. Deike et al. 10.1029/2022AV000750
- Statistical constraints on climate model parameters using a scalable cloud-based inference framework J. Carzon et al. 10.1017/eds.2023.12
- A large reduction of direct aerosol cooling over Poland in the last decades K. Markowicz et al. 10.1002/joc.7488
- Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing L. Regayre et al. 10.5194/acp-23-8749-2023
- Free-tropospheric aerosols contribute to large aerosol optical depth at a remote, inland location with insignificant anthropogenic emissions A. Ajay et al. 10.1016/j.jastp.2023.106160
- Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al. 10.5194/gmd-17-2387-2024
- Harmonized and high-quality datasets of aerosol optical depth at a US continental site, 1997–2018 E. Kassianov et al. 10.1038/s41597-021-00866-2
- The value of remote marine aerosol measurements for constraining radiative forcing uncertainty L. Regayre et al. 10.5194/acp-20-10063-2020
- Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations J. Mulcahy et al. 10.5194/gmd-13-6383-2020
18 citations as recorded by crossref.
- Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator D. Watson-Parris et al. 10.5194/gmd-14-7659-2021
- Simulating organic aerosol in Delhi with WRF-Chem using the volatility-basis-set approach: exploring model uncertainty with a Gaussian process emulator E. Reyes-Villegas et al. 10.5194/acp-23-5763-2023
- Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES E. Baker et al. 10.5194/gmd-15-1913-2022
- Impact of Dilution Ratio and Burning Conditions on the Number Size Distribution and Size-Dependent Mixing State of Primary Particles from Domestic Solid Fuel Burning S. Zheng et al. 10.1021/acs.estlett.2c00319
- Projections of Future Air Quality Are Uncertain. But Which Source of Uncertainty Is Most Important? R. Doherty et al. 10.1029/2022JD037948
- Toward machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections F. Hourdin et al. 10.1126/sciadv.adf2758
- A Novel Network‐Based Approach to Determining Measurement Representation Error for Model Evaluation of Aerosol Microphysical Properties E. Asher et al. 10.1029/2021JD035485
- The Emissions Model Intercomparison Project (Emissions-MIP): quantifying model sensitivity to emission characteristics H. Ahsan et al. 10.5194/acp-23-14779-2023
- Optical properties and seasonal distribution of aerosol layers observed by lidar over Jinhua, southeast China S. Yu et al. 10.1016/j.atmosenv.2021.118456
- A Mechanistic Sea Spray Generation Function Based on the Sea State and the Physics of Bubble Bursting L. Deike et al. 10.1029/2022AV000750
- Statistical constraints on climate model parameters using a scalable cloud-based inference framework J. Carzon et al. 10.1017/eds.2023.12
- A large reduction of direct aerosol cooling over Poland in the last decades K. Markowicz et al. 10.1002/joc.7488
- Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing L. Regayre et al. 10.5194/acp-23-8749-2023
- Free-tropospheric aerosols contribute to large aerosol optical depth at a remote, inland location with insignificant anthropogenic emissions A. Ajay et al. 10.1016/j.jastp.2023.106160
- Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al. 10.5194/gmd-17-2387-2024
- Harmonized and high-quality datasets of aerosol optical depth at a US continental site, 1997–2018 E. Kassianov et al. 10.1038/s41597-021-00866-2
- The value of remote marine aerosol measurements for constraining radiative forcing uncertainty L. Regayre et al. 10.5194/acp-20-10063-2020
- Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations J. Mulcahy et al. 10.5194/gmd-13-6383-2020
Latest update: 25 Apr 2024
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, sulfate and organic mass concentrations are compared to 1 million
variantsof 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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