Preprints
https://doi.org/10.5194/egusphere-2024-1426
https://doi.org/10.5194/egusphere-2024-1426
23 May 2024
 | 23 May 2024
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Flow-dependent observation errors for GHG inversions in an ensemble Kalman smoother

Michael Steiner, Luca Cantarello, Stephan Henne, and Dominik Brunner

Abstract. Atmospheric inverse modeling is the process of estimating emissions from atmospheric observations by minimizing a cost function, which includes a term describing the difference between simulated and observed concentrations. The minimization of this difference is typically limited by uncertainties in the atmospheric transport model rather than by uncertainties in the observations. In this study, we showcase how a temporally varying, flow-dependent atmospheric transport uncertainty can enhance the accuracy of emission estimation through idealized experiments using the CTDAS-ICON-ART ensemble Kalman smoother system. We use the estimation of European CH4 emissions from the in-situ measurement network as an example, but we also demonstrate the additional benefits for trace gases with more localized sources, such as SF6. The uncertainty in flow-dependent transport is determined using meteorological ensemble simulations that are perturbed by physics and driven at the boundaries by an analysis ensemble from a global meteorology and CH4 simulation. The impact of a direct representation of temporally varying transport uncertainties in atmospheric inversions is then investigated in an observation system simulation experiment framework in various setups and for different flux signals. We show that the uncertainty in the transport model varies significantly in space and time, and it is generally highest during nighttime. We apply inversions using only afternoon observations as is common practice, but also explore the option of assimilating hourly data irrespective of the hour of day using a filter based on transport uncertainty and taking into account the temporal covariances. Our findings indicate that incorporating flow-dependent uncertainties in inversion techniques leads to more precise estimates of GHG emissions. Differences between estimated and true emissions could be reduced by 9 % to 82 % more effectively, with generally larger improvements for the SF6 inversion problem and for the more challenging setup with small flux signals.

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Michael Steiner, Luca Cantarello, Stephan Henne, and Dominik Brunner

Status: open (until 12 Jul 2024)

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Michael Steiner, Luca Cantarello, Stephan Henne, and Dominik Brunner
Michael Steiner, Luca Cantarello, Stephan Henne, and Dominik Brunner

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Short summary
Atmospheric GHG inversions have great potential to independently check reported bottom-up emissions, however they are still subject to large uncertainties. It is therefore paramount to address and reduce the largest source of uncertainty stemming from the representation of atmospheric transport in the models. In this study, we show that the use of a temporally varying, flow-dependent atmospheric transport uncertainty can enhance the accuracy of emission estimation through idealized experiment.