Mapping the uncertainty in global CCN using emulation
In the last two IPCC assessments aerosol radiative forcings have been given the largest uncertainty range of all forcing agents assessed. This forcing range is really a diversity of simulated forcings in different models. An essential step towards reducing model uncertainty is to quantify and attrib...
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ftdoajarticles:oai:doaj.org/article:2694d02be65647c8bac99e7776f3917a 2023-05-15T18:25:55+02:00 Mapping the uncertainty in global CCN using emulation L. A. Lee K. S. Carslaw K. J. Pringle G. W. Mann 2012-10-01T00:00:00Z https://doi.org/10.5194/acp-12-9739-2012 https://doaj.org/article/2694d02be65647c8bac99e7776f3917a EN eng Copernicus Publications http://www.atmos-chem-phys.net/12/9739/2012/acp-12-9739-2012.pdf https://doaj.org/toc/1680-7316 https://doaj.org/toc/1680-7324 doi:10.5194/acp-12-9739-2012 1680-7316 1680-7324 https://doaj.org/article/2694d02be65647c8bac99e7776f3917a Atmospheric Chemistry and Physics, Vol 12, Iss 20, Pp 9739-9751 (2012) Physics QC1-999 Chemistry QD1-999 article 2012 ftdoajarticles https://doi.org/10.5194/acp-12-9739-2012 2022-12-31T01:41:52Z In the last two IPCC assessments aerosol radiative forcings have been given the largest uncertainty range of all forcing agents assessed. This forcing range is really a diversity of simulated forcings in different models. An essential step towards reducing model uncertainty is to quantify and attribute the sources of uncertainty at the process level. Here, we use statistical emulation techniques to quantify uncertainty in simulated concentrations of July-mean cloud condensation nuclei (CCN) from a complex global aerosol microphysics model. CCN was chosen because it is the aerosol property that controls cloud drop concentrations, and therefore the aerosol indirect radiative forcing effect. We use Gaussian process emulation to perform a full variance-based sensitivity analysis and quantify, for each model grid box, the uncertainty in simulated CCN that results from 8 uncertain model parameters. We produce global maps of absolute and relative CCN sensitivities to the 8 model parameter ranges and derive probability density functions for simulated CCN. The approach also allows us to include the uncertainty from interactions between these parameters, which cannot be quantified in traditional one-at-a-time sensitivity tests. The key findings from our analysis are that model CCN in polluted regions and the Southern Ocean are mostly only sensitive to uncertainties in emissions parameters but in all other regions CCN uncertainty is driven almost exclusively by uncertainties in parameters associated with model processes. For example, in marine regions between 30° S and 30° N model CCN uncertainty is driven mainly by parameters associated with cloud-processing of Aitken-sized particles whereas in polar regions uncertainties in scavenging parameters dominate. In these two regions a single parameter dominates but in other regions up to 50% of the variance can be due to interaction effects between different parameters. Our analysis provides direct quantification of the reduction in variance that would result if a parameter ... Article in Journal/Newspaper Southern Ocean Directory of Open Access Journals: DOAJ Articles Aitken ENVELOPE(-44.516,-44.516,-60.733,-60.733) Southern Ocean Atmospheric Chemistry and Physics 12 20 9739 9751 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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language |
English |
topic |
Physics QC1-999 Chemistry QD1-999 |
spellingShingle |
Physics QC1-999 Chemistry QD1-999 L. A. Lee K. S. Carslaw K. J. Pringle G. W. Mann Mapping the uncertainty in global CCN using emulation |
topic_facet |
Physics QC1-999 Chemistry QD1-999 |
description |
In the last two IPCC assessments aerosol radiative forcings have been given the largest uncertainty range of all forcing agents assessed. This forcing range is really a diversity of simulated forcings in different models. An essential step towards reducing model uncertainty is to quantify and attribute the sources of uncertainty at the process level. Here, we use statistical emulation techniques to quantify uncertainty in simulated concentrations of July-mean cloud condensation nuclei (CCN) from a complex global aerosol microphysics model. CCN was chosen because it is the aerosol property that controls cloud drop concentrations, and therefore the aerosol indirect radiative forcing effect. We use Gaussian process emulation to perform a full variance-based sensitivity analysis and quantify, for each model grid box, the uncertainty in simulated CCN that results from 8 uncertain model parameters. We produce global maps of absolute and relative CCN sensitivities to the 8 model parameter ranges and derive probability density functions for simulated CCN. The approach also allows us to include the uncertainty from interactions between these parameters, which cannot be quantified in traditional one-at-a-time sensitivity tests. The key findings from our analysis are that model CCN in polluted regions and the Southern Ocean are mostly only sensitive to uncertainties in emissions parameters but in all other regions CCN uncertainty is driven almost exclusively by uncertainties in parameters associated with model processes. For example, in marine regions between 30° S and 30° N model CCN uncertainty is driven mainly by parameters associated with cloud-processing of Aitken-sized particles whereas in polar regions uncertainties in scavenging parameters dominate. In these two regions a single parameter dominates but in other regions up to 50% of the variance can be due to interaction effects between different parameters. Our analysis provides direct quantification of the reduction in variance that would result if a parameter ... |
format |
Article in Journal/Newspaper |
author |
L. A. Lee K. S. Carslaw K. J. Pringle G. W. Mann |
author_facet |
L. A. Lee K. S. Carslaw K. J. Pringle G. W. Mann |
author_sort |
L. A. Lee |
title |
Mapping the uncertainty in global CCN using emulation |
title_short |
Mapping the uncertainty in global CCN using emulation |
title_full |
Mapping the uncertainty in global CCN using emulation |
title_fullStr |
Mapping the uncertainty in global CCN using emulation |
title_full_unstemmed |
Mapping the uncertainty in global CCN using emulation |
title_sort |
mapping the uncertainty in global ccn using emulation |
publisher |
Copernicus Publications |
publishDate |
2012 |
url |
https://doi.org/10.5194/acp-12-9739-2012 https://doaj.org/article/2694d02be65647c8bac99e7776f3917a |
long_lat |
ENVELOPE(-44.516,-44.516,-60.733,-60.733) |
geographic |
Aitken Southern Ocean |
geographic_facet |
Aitken Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_source |
Atmospheric Chemistry and Physics, Vol 12, Iss 20, Pp 9739-9751 (2012) |
op_relation |
http://www.atmos-chem-phys.net/12/9739/2012/acp-12-9739-2012.pdf https://doaj.org/toc/1680-7316 https://doaj.org/toc/1680-7324 doi:10.5194/acp-12-9739-2012 1680-7316 1680-7324 https://doaj.org/article/2694d02be65647c8bac99e7776f3917a |
op_doi |
https://doi.org/10.5194/acp-12-9739-2012 |
container_title |
Atmospheric Chemistry and Physics |
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12 |
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20 |
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9739 |
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9751 |
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