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

Data sets

Data of Karhunen-Loéve (KL) ensemble generation algorithm for biogenic emissions from EURAD-IM Annika Vogel and Hendrik Elbern https://doi.org/10.5281/zenodo.4772909

Model code and software

Karhunen-Loéve (KL) Ensemble Routines of the EURAD-IM modeling system Annika Vogel and Hendrik Elbern https://doi.org/10.5281/zenodo.4468571

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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.