Using statistical models to explore ensemble uncertainty in climate impact  studies: the example of air pollution in Europe

Lemaire, Vincent E. P.; Colette, Augustin; Menut, Laurent

doi:https://doi.org/10.5194/acp-16-2559-2016

Articles | Volume 16, issue 4

https://doi.org/10.5194/acp-16-2559-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/acp-16-2559-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 16, issue 4

Research article

|

02 Mar 2016

Research article |

| 02 Mar 2016

Using statistical models to explore ensemble uncertainty in climate impact studies: the example of air pollution in Europe

Vincent E. P. Lemaire, Augustin Colette, and Laurent Menut

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Final revised paper (published on 02 Mar 2016)
Supplement to the final revised paper
Preprint (discussion started on 21 Oct 2015)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC C9411: 'Using proxies to explore ensemble uncertainty in climate impact studies: the example of air pollution', Anonymous Referee #2, 17 Nov 2015
- AC C12313: 'Reply to referee 2 comments', Vincent Lemaire, 10 Feb 2016
RC C9751: 'Interactive comment on “Using proxies to explore ensemble uncertainty in climate impact studies: the example of air pollution” by V. E. P. Lemaire et al.', Anonymous Referee #1, 25 Nov 2015
- AC C12314: 'Reply to referee 1 comments', Vincent Lemaire, 10 Feb 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Vincent Lemaire on behalf of the Authors (11 Feb 2016) Author's response Manuscript

ED: Publish as is (22 Feb 2016) by Jørgen Brandt

AR by Vincent Lemaire on behalf of the Authors (23 Feb 2016)

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Short summary

Because of its sensitivity to unfavorable weather patterns, air pollution is sensitive to climate change. Its impact is typically assessed using deterministic chemistry-transport models forced by an ensemble of climate projection. Because of the high computational cost of such initiative, elaborated techniques are required to optimize the exploration of ensemble of future projections. We develop such a technique, which also allows quantifying uncertainties in climate and air quality projections.