Adjoint of the global Eulerian–Lagrangian coupled atmospheric transport  model (A-GELCA v1.0): development and validation

Belikov, Dmitry; Maksyutov, Shamil; Yaremchuk, A. I.; Ganshin, A.; Kaminski, Thomas W.; Blessing, Simon; Sasakawa, Motoki; Gómez-Peláez, Ángel J.; Starchenko, Alexander V.

doi:10.5194/gmd-9-749-2016

Cited by 20 publications

(35 citation statements)

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“…We used a global Eulerian-Lagrangian coupled model NIES-TM-FLEXPART-VAR (NTFVAR) that consists of the National Institute for Environmental Studies (NIES) model as a Eulerian three-dimensional transport model (TM), and FLEXPART (FLEXible PARTicle dispersion model) as the Lagrangian Particle Dispersion Model (LPDM). The forward transport model and model development were described by Ganshin et al [36] and Belikov et al [30], and the application to inverse modeling using the Kalman smoother at moderate resolution of 1 • × 1 • was demonstrated by Zhuravlev et al [37], Ishizawa et al [38], and Shirai et al [29]. Our transport model is a further modification of the model described by Belikov et al [30].…”

Section: The Transport Modelmentioning

confidence: 90%

“…The forward transport model and model development were described by Ganshin et al [36] and Belikov et al [30], and the application to inverse modeling using the Kalman smoother at moderate resolution of 1 • × 1 • was demonstrated by Zhuravlev et al [37], Ishizawa et al [38], and Shirai et al [29]. Our transport model is a further modification of the model described by Belikov et al [30]. The coupled model combines National Institute for Environmental Studies Transport Model (NIES-TM) v08.1i with a horizontal resolution of 2.5 • and 32 hybrid-isentropic vertical levels [39] and Flexpart model v. 8.0 [40], and runs in backward mode with a surface flux resolution of 0.1 • .…”

Section: The Transport Modelmentioning

confidence: 99%

“…The coupled model combines National Institute for Environmental Studies Transport Model (NIES-TM) v08.1i with a horizontal resolution of 2.5 • and 32 hybrid-isentropic vertical levels [39] and Flexpart model v. 8.0 [40], and runs in backward mode with a surface flux resolution of 0.1 • . The changes in the current version with respect to the study by Belikov et al [30] include the derivation of adjoint components using the adjoint code compiler Tapenade [41] instead of the TAF (Transformation of Algorithms in Fortran) compiler [42]. Additionally, the transport matrix indexing and sorting algorithms were revised to allow efficient memory usage for handling large matrixes of Lagrangian responses to surface fluxes required for the application of the model to satellite data.…”

Section: The Transport Modelmentioning

confidence: 99%

“…Houweling et al [27] reviewed the development of global inverse modeling of methane, and Jacob et al [28] also supported their research by presenting the advantages of increased observational data from surface stations, satellites and aircraft, and state-of-the-art computational facilities. The inverse model has become more important for allowing better quantification of methane emissions.Studies have shown an extension of regional Lagrangian inverse modelling to global scale inverse modeling based on a combination of the 3D global Eulerian models and the Lagrangian models (e.g., [29,30]). They have demonstrated an enhanced capability of the transport resolving high resolution surface emission patterns using inverse modeling schemes relying on regional and global basis flux patterns.…”

mentioning

confidence: 99%

“…Studies have shown an extension of regional Lagrangian inverse modelling to global scale inverse modeling based on a combination of the 3D global Eulerian models and the Lagrangian models (e.g., [29,30]). They have demonstrated an enhanced capability of the transport resolving high resolution surface emission patterns using inverse modeling schemes relying on regional and global basis flux patterns.…”

mentioning

confidence: 99%

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Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories

et al. 2019

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We present a global 0.1 • × 0.1 • high-resolution inverse model, NIES-TM-FLEXPART-VAR (NTFVAR), and a methane emission evaluation using the Greenhouse Gas Observing Satellite (GOSAT) satellite and ground-based observations from 2010-2012. Prior fluxes contained two variants of anthropogenic emissions, Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2 and adjusted EDGAR v4.3.2 which were scaled to match the country totals by national reports to the United Nations Framework Convention on Climate Change (UNFCCC), augmented by biomass burning emissions from Global Fire Assimilation System (GFASv1.2) and wetlands Vegetation Integrative Simulator for Trace Gases (VISIT). The ratio of the UNFCCC-adjusted global anthropogenic emissions to EDGAR is 98%. This varies by region: 200% in Russia, 84% in China, and 62% in India. By changing prior emissions from EDGAR to UNFCCC-adjusted values, the optimized total emissions increased from 36.2 to 46 Tg CH 4 yr −1 for Russia, 12.8 to 14.3 Tg CH 4 yr −1 for temperate South America, and 43.2 to 44.9 Tg CH 4 yr −1 for contiguous USA, and the values decrease from 54 to 51.3 Tg CH 4 yr −1 for China, 26.2 to 25.5 Tg CH 4 yr −1 for Europe, and by 12.4 Tg CH 4 yr −1 for India. The use of the national report to scale EDGAR emissions allows more detailed statistical data and country-specific emission factors to be gathered in place compared to those available for EDGAR inventory. This serves policy needs by evaluating the national or regional emission totals reported to the UNFCCC.is slowing down [1][2][3]. Methane (CH 4 ) plays a growing role in anthropogenic climate change with its share of more than 20% of the total greenhouse gas (GHG) concentration [4][5][6]. Reducing methane emissions would make a significant contribution to climate change mitigation on a shorter time scale, due to its relatively short lifetime compared with that of CO 2 [7,8]. The determination of national emission reduction targets and policies is directly affected by the amount of methane emissions produced by individual countries [9]. However, studies on emission estimates suggest that there are still large uncertainties in methane budgets, and more effort should be made to better quantify the methane emissions [10][11][12][13][14][15]. Still, it is challenging to verify the accuracy of methane emissions on a country scale. The national reports submitted to the United Nations Framework Convention on Climate Change (UNFCCC) by different countries might use different methods to estimate emissions, which could be different from global inventory datasets produced by the scientific community, such as the Global Carbon Project (GCP) [16], and the Emissions Database for Global Atmospheric Research (EDGAR) [17][18][19]. Complementary to these bottom-up emission estimations, top-down estimations by inverse models combined with atmospheric measurements have been widely used and have proven worthy for emission inventory evaluation (e.g., [20][21][22][23][24][25][26]). Houweling et al. [27] reviewed the devel...

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