Global methane emission estimates for 2000–2012 from CarbonTracker Europe-CH<sub>4</sub> v1.0

Abstract

Abstract. We present a global distribution of surface methane (CH4) emission estimates for 2000–2012 derived using the CarbonTracker Europe-CH4 (CTE-CH4) data assimilation system. In CTE-CH4, anthropogenic and biospheric CH4 emissions are simultaneously estimated based on constraints of global atmospheric in situ CH4 observations. The system was configured to either estimate only anthropogenic or biospheric sources per region, or to estimate both categories simultaneously. The latter increased the number of optimizable parameters from 62 to 78. In addition, the differences between two numerical schemes available to perform turbulent vertical mixing in the atmospheric transport model TM5 were examined. Together, the system configurations encompass important axes of uncertainty in inversions and allow us to examine the robustness of the flux estimates. The posterior emission estimates are further evaluated by comparing simulated atmospheric CH4 to surface in situ observations, vertical profiles of CH4 made by aircraft, remotely sensed dry-air total column-averaged mole fraction (XCH4) from the Total Carbon Column Observing Network (TCCON), and XCH4 from the Greenhouse gases Observing Satellite (GOSAT). The evaluation with non-assimilated observations shows that posterior XCH4 is better matched with the retrievals when the vertical mixing scheme with faster interhemispheric exchange is used. Estimated posterior mean total global emissions during 2000–2012 are 516 ± 51 Tg CH4 yr−1, with an increase of 18 Tg CH4 yr−1 from 2000–2006 to 2007–2012. The increase is mainly driven by an increase in emissions from South American temperate, Asian temperate and Asian tropical TransCom regions. In addition, the increase is hardly sensitive to different model configurations ( < 2 Tg CH4 yr−1 difference), and much smaller than suggested by EDGAR v4.2 FT2010 inventory (33 Tg CH4 yr−1), which was used for prior anthropogenic emission estimates. The result is in good agreement with other published estimates from inverse modelling studies (16–20 Tg CH4 yr−1). However, this study could not conclusively separate a small trend in biospheric emissions (−5 to +6.9 Tg CH4 yr−1) from the much larger trend in anthropogenic emissions (15–27 Tg CH4 yr−1). Finally, we find that the global and North American CH4 balance could be closed over this time period without the previously suggested need to strongly increase anthropogenic CH4 emissions in the United States. With further developments, especially on the treatment of the atmospheric CH4 sink, we expect the data assimilation system presented here will be able to contribute to the ongoing interpretation of changes in this important greenhouse gas budget.

The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: implementation and global carbon balance 2001–2015

“…CTDAS is used at the Finnish Meteorological Institute (FMI) for the development of CarbonTracker Europe Methane (CTE-CH 4 ) and is used to perform global methane inversions (Tsuruta et al, 2015(Tsuruta et al, , 2017 CTDAS has also been used for the optimization of transport properties of the underlying TM5 model using observations of SF 6 (van der Veen, 2013). Previous studies demonstrated that many models, including TM5, poorly simulate the SF 6 gradients between the Northern Hemisphere (NH) and Southern Hemisphere (SH), which is mainly controlled by transport across the intertropical convergence zone (ITCZ).…”

The CarbonTracker Data Assimilation System for CO<sub>2</sub> and <i>δ</i><sup>13</sup>C (CTDAS-C13 v1.0): retrieving information on land–atmosphere exchange processes

“…All these flux terms respond to changes in local temperature, precipitation, nutrient availability, and other key environmental variables (Friedlingstein et al, 2006). Extreme climate events such as droughts can decrease GPP and increase TER and fire disturbances to a point where regional NEE is turned into a temporary carbon source (Ciais et al, 2005;Gatti et al, 2014;van der Laan-Luijkx et al, 2015). These dynamic responses (and positive feedbacks whereby increased CO 2 may lead to more droughts) are now an integral part of climate models that include fully coupled carbon cycling (Booth et al, 2012;Dai , 2012).…”

“…In so-called double-deconvolution methods this particular trait is used to untangle the global land carbon budget from the ocean carbon budget (Keeling et al, 1989;Tans et al, 1993;Ciais et al, 1995). More recently, the 13 C isotope was used to study the diurnal cycle of GPP and TER (Wehr et al, 2016) and was used as a tracer of water use efficiency to study long-term responses to CO 2 increases in tree rings (van der Sleen et al, 2015), and attempts are underway to do the same based on atmospheric records. On regional scales, variations in the ratio of 13 CO 2 / 12 CO 2 (typically reported as δ 13 C in ‰ relative to the VPDB reference ratio) reflect changes in discrimination processes associated with photosynthetic uptake of carbon by plants (e.g., Farquhar et al, 1989;Fung et al, 1997;Scholze et al, 2003;Rayner et al, 2008).…”

“…The CarbonTracker Data Assimilation System (CTDAS) has been developed to estimate global net ocean and terrestrial carbon exchange fluxes, with a focus on North America and Europe (Peters et al, , 2007(Peters et al, , 2010van der Laan-Luijkx et al, 2017). This application uses the ensemble Kalman filter (EnKF) as a Bayesian minimization approach for the estimation of weekly ocean and terrestrial carbon fluxes on a 1 • × 1 • horizontal grid to improve the agreement between modeled and measured atmospheric CO 2 .…”

“…This application uses the ensemble Kalman filter (EnKF) as a Bayesian minimization approach for the estimation of weekly ocean and terrestrial carbon fluxes on a 1 • × 1 • horizontal grid to improve the agreement between modeled and measured atmospheric CO 2 . The versatile object-oriented design of CTDAS allows flexible implementation of different components of the data assimilation system (van der Laan-Luijkx et al, 2017). Such modifications include but are not limited to (1) the configuration of the state vector; (2) the expansion of the monitoring network, such as for the Amazon (van der Laan-Luijkx et al, 2015) and China ; (3) the use of Lagrangian atmospheric transport (He et al, in preparation); and (4) to monitor other tracer gases like methane (Bruhwiler et al, 2014;Tsuruta et al, 2016).…”