Reddington, C.L., Carslaw, K.S., Stier, P. et al. (35 more authors) (2017) The Global Aerosol Synthesis and Science Project (GASSP): measurements and modeling to reduce uncertainty. Bulletin of the American Meteorological Society, 98 (9). pp. 1857-1877. ISSN 0003-0007
Abstract
The largest uncertainty in the historical radiative forcing of climate is caused by changes in aerosol particles due to anthropogenic activity. Sophisticated aerosol microphysics processes have been included in many climate models in an effort to reduce the uncertainty. However, the models are very challenging to evaluate and constrain because they require extensive in situ measurements of the particle size distribution, number concentration, and chemical composition that are not available from global satellite observations. The Global Aerosol Synthesis and Science Project (GASSP) aims to improve the robustness of global aerosol models by combining new methodologies for quantifying model uncertainty, to create an extensive global dataset of aerosol in situ microphysical and chemical measurements, and to develop new ways to assess the uncertainty associated with comparing sparse point measurements with low-resolution models. GASSP has assembled over 45,000 hours of measurements from ships and aircraft as well as data from over 350 ground stations. The measurements have been harmonized into a standardized format that is easily used by modelers and nonspecialist users. Available measurements are extensive, but they are biased to polluted regions of the Northern Hemisphere, leaving large pristine regions and many continental areas poorly sampled. The aerosol radiative forcing uncertainty can be reduced using a rigorous model–data synthesis approach. Nevertheless, our research highlights significant remaining challenges because of the difficulty of constraining many interwoven model uncertainties simultaneously. Although the physical realism of global aerosol models still needs to be improved, the uncertainty in aerosol radiative forcing will be reduced most effectively by systematically and rigorously constraining the models using extensive syntheses of measurements.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 American Meteorological Society. This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Advanced Manufacturing Institute (Sheffield) |
Funding Information: | Funder Grant number Natural Environment Research Council NE/J024252/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Jul 2021 11:40 |
Last Modified: | 07 Jul 2021 11:40 |
Status: | Published |
Publisher: | American Meteorological Society |
Refereed: | Yes |
Identification Number: | 10.1175/bams-d-15-00317.1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:175229 |