Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling

Abstract

Abstract.A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH 4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes.In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr −1 at the global scale, representing 5 % of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr −1 in North America to 7 Tg yr −1 in Boreal Eurasia (from 23 to 48 %, respectively). At the model grid-scale, the spread of inverse estimates can reach 150 % of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems.Future inversions should include more accurately prescribed observation covariances matrices in order to limit Published by Copernicus Publications on behalf of the European Geosciences Union. R. Locatelli et al.: Transport model errors in methane inversionsthe impact of transport model errors on estimated methane fluxes.

Regional data assimilation of multi-spectral MOPITT observations of CO over North America

“…Model errors in long-range transport, vertical convective transport, diffusion, and chemistry (e.g. Arellano Jr. et al, 2006;Fortems-Cheiney et al, 2011;Locatelli et al, 2013;Worden et al, 2013;Jiang et al, 2011Jiang et al, , 2013Jiang et al, , 2015 all adversely impact the inverse modeling of CO and other trace constituents (such as methane), and mitigating these errors in global models is challenging.…”

Sensitivity of the recent methane budget to LMDz sub-grid-scale physical parameterizations

“…Systematic errors in CTMs also have significant impacts on inverse estimates. In Locatelli et al (2013), it was shown that transport model errors are responsible for an uncertainty of 27 Tg CH 4 year −1 in the estimations of methane fluxes by inverse modeling at the global scale. Moreover, Locatelli et al (2015) showed that stratosphere-troposphere exchanges are systematically too fast in the version of LMDz (Laboratoire de Météorolo-gie Dynamique model with Zooming capability) using a low vertical resolution (19 levels), which could largely impact the estimation of gas fluxes, like N 2 O, whose stratospheric mixing ratios influence tropospheric mixing ratios.…”

“…Moreover, Locatelli et al (2015) showed that stratosphere-troposphere exchanges are systematically too fast in the version of LMDz (Laboratoire de Météorolo-gie Dynamique model with Zooming capability) using a low vertical resolution (19 levels), which could largely impact the estimation of gas fluxes, like N 2 O, whose stratospheric mixing ratios influence tropospheric mixing ratios. Furthermore, following Patra et al (2011), Locatelli et al (2013) showed that a bad representation of the interhemispheric exchange in an ensemble of state-of-the-art CTMs can explain most of the discrepancies in the global methane fluxes derived by inverse modeling using these different CTMs.…”

Diagnostic methods for atmospheric inversions of long-lived greenhouse gases

“…The model-data mismatch may be compared between an inversion that uses the "true" transport to calculate the sensitivity matrix versus that of an inversion that uses a different transport model (e.g., Chevallier et al, 2010;Houweling et al, 2010;Berchet et al, 2015;Locatelli et al, 2013). Assuming that the difference in performance between these two transport models is comparable to the difference between transport models used in real-data inversions, the inversion with inconsistent transport can be compared to the inversion with consistent transport to determine how much the inconsistencies in transport affect the inversion.…”

“…Posterior flux errors and error covariances can be used to assess the impact of modeling simplifications or data limitations on the accuracy and precision of flux estimation (e.g., Berchet et al, 2015;Gourdji et al, 2010). OSSEs can also be used to understand sources of bias through a simple differencing of posterior and "true" fluxes (e.g., Locatelli et al, 2013;Thompson et al, 2011;Basu et al, 2016;Bloom et al, 2016). Similar tests can be run to determine the effects of observational biases and mistuning of error statistics on the accuracy of posterior estimates (e.g., Baker et al, 2010).…”