Submitted as: development and technical paper 23 Feb 2021

Submitted as: development and technical paper | 23 Feb 2021

Review status: a revised version of this preprint is currently under review for the journal GMD.

Efficient ensemble generation for uncertain correlated parameters in atmospheric chemical models

Annika Vogel1,2,3 and Hendrik Elbern1,2 Annika Vogel and Hendrik Elbern
  • 1Institute for Energy and Climate Research - Troposphere (IEK-8), Forschungszentrum Jülich, Germany
  • 2Rhenish Institute for Environmental Research at the University of Cologne, Germany
  • 3Institute of Geophysics and Meteorology, University of Cologne, Germany

Abstract. Atmospheric chemical forecasts highly rely on various model parameters, which are often insufficiently known, as emission rates and deposition velocities. However, a reliable estimation of resulting uncertainties by an ensemble of forecasts is impaired by the high-dimensionality of the system. This study presents a novel approach to efficiently perturb atmospheric-chemical model parameters according to their leading coupled uncertainties. The algorithm is based on the idea that the forecast model acts as a dynamical system inducing multi-variational correlations of model uncertainties. The specific algorithm presented in this study is designed for parameters which depend on local environmental conditions and consists of three major steps: (1) an efficient assessment of various sources of model uncertainties spanned by independent sensitivities, (2) an efficient extraction of leading coupled uncertainties using eigenmode decomposition, and (3) an efficient generation of perturbations for high-dimensional parameter fields by the Karhunen-Loéve expansion. Due to their perceived simulation challenge the method has been applied to biogenic emissions of five trace gases, considering state-dependent sensitivities to local atmospheric and terrestrial conditions. Rapidly decreasing eigenvalues state high spatial- and cross-correlations of regional biogenic emissions, which are represented by a low number of dominating components. Consequently, leading uncertainties can be covered by low number of perturbations enabling ensemble sizes of the order of 10 members. This demonstrates the suitability of the algorithm for efficient ensemble generation for high-dimensional atmospheric chemical parameters.

Annika Vogel and Hendrik Elbern

Status: final response (author comments only)

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

Annika Vogel and Hendrik Elbern

Model code and software

Karhunen-Loéve (KL) Ensemble Routines of the EURAD-IM modeling system Annika Vogel and Hendrik Elbern

Annika Vogel and Hendrik Elbern


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Short summary
While atmospheric chemical forecasts rely on uncertain model parameters, their huge dimension hampers 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.