Articles | Volume 14, issue 9
Geosci. Model Dev., 14, 5583–5605, 2021
Geosci. Model Dev., 14, 5583–5605, 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


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
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.