Preprints
https://doi.org/10.5194/egusphere-2024-1595
https://doi.org/10.5194/egusphere-2024-1595
08 Jul 2024
 | 08 Jul 2024
Status: this preprint is open for discussion.

Efficacy of high-resolution satellite observations in inverse modeling of carbon monoxide emissions using TM5-4dvar (r1258)

Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis

Abstract. Carbon monoxide in the atmosphere adversely affects air quality and climate, making knowledge about its sources crucial. However, current global bottom-up emission estimates retain significant uncertainties. In this study, we attempt to reduce these uncertainties by optimizing emission estimates for the second half of the year 2018 on a global scale with a focus on the northern hemisphere through the top-down approach of inverse modeling. Specifically, we introduce observations from the TROPOspheric Monitoring Instrument (TROPOMI) into the TM5-4DVAR model. The emissions are further constrained using NOAA surface flask measurements. We conducted six experiments to investigate the impact of data use in our inversions, varying the a priori emissions and observational datasets.

Notably, the inversion driven by satellite observations alone captures flask measurements south of 55° N almost as good as the inversions that included those measurements. This indicates that our method could be suitable for near real-time inversions based purely on satellite observations. Compared to the bottom-up estimates, all experiments result in strong (by up to 75 %) broad-scale emission reductions in China and India. In part, the reduction over China can be attributed to policy changes. Additionally, the OH climatology used to simulate chemical loss appears to be underestimated in that region, which also skews the inversions towards lower emissions. Conversely, in most experiments, we find strong localized emission increments over Europe and the Sahara. These are likely artifacts caused by the model's limited capabilities to capture the surface flask measurements in those regions and are not reproduced by the satellite-only inversion.

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Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis

Status: open (until 02 Sep 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis

Data sets

Data and scripts for manuscript "Efficacy of high-resolution satellite observations in inverse modeling of carbon monoxide emissions" J. R. Nüß, N. Daskalakis, F. G. Piwowarczyk, A. Gkouvousis, O. Schneising, M. Buchwitz, M. Kanakidou, M. C. Krol, and M. Vrekoussis https://doi.org/10.5281/ZENODO.11244729

Model code and software

TM5-4DVAR inverse modeling suit with extensions for TROPOMI CO observations J. R. Nüß, N. Daskalakis, F. G. Piwowarczyk, A. Gkouvousis, O. Schneising, M. Buchwitz, M. Kanakidou, M. C. Krol, and M. Vrekoussis https://doi.org/10.5281/ZENODO.6884685

Interactive computing environment

Jupyternotebooks for regridding of satellite observations into super-observations J. R. Nüß, F. G. Piwowarczyk, and A. Hilboll https://doi.org/10.5281/ZENODO.6883805

Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis

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Short summary
We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed into a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.