Articles | Volume 14, issue 9
Geosci. Model Dev., 14, 5583–5605, 2021
https://doi.org/10.5194/gmd-14-5583-2021
Geosci. Model Dev., 14, 5583–5605, 2021
https://doi.org/10.5194/gmd-14-5583-2021

Development and technical paper 10 Sep 2021

Development and technical paper | 10 Sep 2021

Efficient ensemble generation for uncertain correlated parameters in atmospheric chemical models: a case study for biogenic emissions from EURAD-IM version 5

Annika Vogel and Hendrik Elbern

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-26', Astrid Kerkweg, 29 Mar 2021
    • AC1: 'Reply on CEC1', Annika Vogel, 19 May 2021
  • RC1: 'Comment on gmd-2021-26', Anonymous Referee #1, 06 Apr 2021
  • RC2: 'Comment on gmd-2021-26', Anonymous Referee #2, 12 Apr 2021
  • AC2: 'Reply to Reviewer1 and Reviewer2 (gmd-2021-26)', Annika Vogel, 19 May 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Annika Vogel on behalf of the Authors (19 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (01 Jun 2021) by Andrea Stenke
RR by Anonymous Referee #2 (08 Jul 2021)
ED: Publish as is (23 Jul 2021) by Andrea Stenke
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Short summary
While atmospheric chemical forecasts rely on uncertain model parameters, their huge dimensions hamper an efficient uncertainty estimation. This study presents a novel approach to efficiently sample these uncertainties by extracting dominant dependencies and correlations. Applying the algorithm to biogenic emissions, their uncertainties can be estimated from a low number of dominant components. This states the capability of an efficient treatment of parameter uncertainties in atmospheric models.