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-CH4 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 the impact of transport model errors on estimated methane fluxes.
Highlights
Methane (CH4) is the second most important anthropogenically emitted long-lived greenhouse gas in the atmosphere
Results from ten chemical transport models (CTMs) have been extracted from the TransCom-CH4 experiment: ACTM (Atmospheric Chemistry Transport Model) (Patra et al, 2009), IFS (Integrated Forecast System)
Associated with a potentially erroneous estimation of errors in the R matrix, especially concerning transport model errors, an amplification of the impact of errors can occur, leading to a larger difference in the global emissions compared to the target emissions, for NET1 compared to NET2
Summary
Methane (CH4) is the second most important anthropogenically emitted long-lived greenhouse gas in the atmosphere. Additional sinks are the destruction in dry soils (methanotrophic bacteria), the oxidation in the stratosphere (OH, O(1D)) and the oxidation by active chlorine in the marine planetary boundary layer (PBL) (Allan et al, 2007). Because methane both plays a key role in air quality issues (Fiore et al, 2002) and is 23 times more effective as a greenhouse gas than CO2 on a 100-yr horizon (Denman et al, 2007), it is pertinent to better understand and to accurately quantify the spatial and temporal patterns of methane sources and sinks. Disagreements between recent studies (Kai et al, 2011; Levin et al, 2012; Aydin et al, 2011; Simpson et al, 2012; Rigby et al, 2008; Montzka et al, 2011; Bousquet et al, 2006, 2011) explaining the weakening in the CH4 growth rate from 2000 to 2006 and its increase since 2007 reinforce the idea that methane fluxes are poorly understood, both for their longterm mean and for their inter-annual variations
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