A global coupled Eulerian-Lagrangian model and 1 × 1 km CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; surface flux dataset for high-resolution atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; transport simulations

Ganshin, A.; Oda, Tomohiro; Saito, Makoto; Maksyutov, Shamil; Valsala, Vinu; Andres, R. J.; Fisher, Rebecca E.; Lowry, David; Lukyanov, A.; Matsueda, Hidekazu; Nisbet, E. G.; Rigby, Matthew; Sawa, Yousuke; Toumi, Ralf; Tsuboi, Kazuhiro; Varlagin, Andrej; Zhuravlev, R.

doi:10.5194/gmd-5-231-2012

Cited by 38 publications

(42 citation statements)

References 46 publications

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“…In Sect. 4 we address several problems regarding the coupled model that have not been covered previously (Ganshin et al, 2012). In Sect.…”

Section: A Belikov Et Al: A-gelca V10: Development and Validationmentioning

confidence: 99%

“…In this paper we use a global Eulerian-Lagrangian coupled model, the principles of which are described by Ganshin et al (2012). The coupled model consists of FLEXPART (version 8.0; run in backward mode) as the Lagrangian particle dispersion model, and NIES TM (version NIES-08.1i) as the Eulerian off-line global transport model.…”

Section: Global Coupled Eulerian-lagrangian Modelmentioning

confidence: 99%

“…The first term in this formula describes the contribution of the nearby sources of the considered component; these sources are located along the trajectories inside layer h (500 m). The value of the first term is proportional to the flux in each cell along the trajectory, and to the time during which the air particle is inside this cell (Ganshin et al, 2012). The background grid values of the concentrations (calculated by the Eulerian model), which are interpolated to the final points of the backward trajectories, are transferred to the observation point and are the second term in the right-hand side of Eq.…”

Section: Global Coupled Eulerian-lagrangian Modelmentioning

confidence: 99%

“…The paper by Ganshin et al (2012) describing the development of the GELCA model provides a model testing report. The advantage of GELCA in reproducing the high-concentration spikes and short-term variations caused mainly by anthropogenic emissions is more vivid when using high-resolution (1 km × 1 km) surface fluxes compared to standard-resolution (1 • × 1 • ) fluxes.…”

Section: Assessment Of the Coupled Modelmentioning

confidence: 99%

“…We expanded the comparison undertaken by Ganshin et al (2012) to a 2-year period using an updated set of prescribed fluxes, which combines four components similar to the analysis performed by Takagi et al (2011) and Maksyutov et al (2013): (a) anthropogenic fluxes from the Open source Data Inventory of Anthropogenic CO 2 (ODIAC; Oda and Maksyutov, 2011) and the Carbon Dioxide Information (Ito, 2010;Saito et al, 2011Saito et al, , 2014; (c) oceanic fluxes predicted by a data assimilation system based on the Offline ocean Tracer Transport Model (OTTM; Valsala and Maksyutov, 2010); and (d) biomass burning emissions from the Global Fire Emissions Database (GFED) version 3.1 (van der Werf et al, 2010). Biosphere fluxes have daily time step, while the others are monthly.…”

Section: Assessment Of the Coupled Modelmentioning

confidence: 99%

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

et al. 2016

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Abstract. We present the development of the Adjoint of the Global Eulerian-Lagrangian Coupled Atmospheric (A-GELCA) model that consists of the National Institute for Environmental Studies (NIES) model as an Eulerian threedimensional transport model (TM), and FLEXPART (FLEXible PARTicle dispersion model) as the Lagrangian Particle Dispersion Model (LPDM). The forward tangent linear and adjoint components of the Eulerian model were constructed directly from the original NIES TM code using an automatic differentiation tool known as TAF (Transformation of Algorithms in Fortran; http://www.FastOpt.com), with additional manual pre-and post-processing aimed at improving transparency and clarity of the code and optimizing the performance of the computing, including MPI (Message Passing Interface). The Lagrangian component did not require any code modification, as LPDMs are self-adjoint and track a significant number of particles backward in time in order to calculate the sensitivity of the observations to the neighboring emission areas. The constructed Eulerian adjoint was coupled with the Lagrangian component at a time boundary in the global domain. The simulations presented in this work were performed using the A-GELCA model in forward and adjoint modes. The forward simulation shows that the coupled model improves reproduction of the seasonal cycle and short-term variability of CO 2 . Mean bias and standard deviation for five of the six Siberian sites considered decrease roughly by 1 ppm when using the coupled model. The adjoint of the Eulerian model was shown, through several numerical tests, to be very accurate (within machine epsilon with mismatch around to ±6 e −14 ) compared to direct forward sensitivity calculations. The developed adjoint of the coupled model combines the flux conservation and stability of an Eulerian discrete adjoint formulation with the flexibility, accuracy, and high resolution of a Lagrangian backward trajectory formulation. A-GELCA will be incorporated into a variational inversion system designed to optimize surface fluxes of greenhouse gases.

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“…In Sect. 4 we address several problems regarding the coupled model that have not been covered previously (Ganshin et al, 2012). In Sect.…”

Section: A Belikov Et Al: A-gelca V10: Development and Validationmentioning

confidence: 99%

Section: Global Coupled Eulerian-lagrangian Modelmentioning

confidence: 99%

Section: Global Coupled Eulerian-lagrangian Modelmentioning

confidence: 99%

Section: Assessment Of the Coupled Modelmentioning

confidence: 99%

Section: Assessment Of the Coupled Modelmentioning

confidence: 99%

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

et al. 2016

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An intercomparison of inverse models for estimating sources and sinks of CO₂ using GOSAT measurements

Houweling

Baker

Basu³

et al. 2015

JGR Atmospheres

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140

142

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This study presents the outcome of an inverse modeling intercomparison experiment on the use of total column CO 2 retrievals from Greenhouse Gas Observing Satellite (GOSAT) for quantifying global sources and sinks of CO 2 . Eight research groups submitted inverse modeling results for the first year of GOSAT measurements. Inversions were carried out using only GOSAT data, a combination of GOSAT and surface measurements, and using only surface measurements. As expected, the most robust flux estimates are obtained at large scales (e.g., within 20% of the annual flux at the global scale), and they quickly diverge toward the scale of the subcontinental TRANSCOM regions and beyond (to >100% of the annual flux). We focus our analysis on a shift in the CO 2 uptake over land from the Tropics toward the Northern Hemisphere Extra tropics of ∼1 PgC/yr when GOSAT data are used in the inversions. This shift is largely driven by TRANSCOM regions Europe and Northern Africa, showing, respectively, an increased uptake and release of 0.7 and 0.9 PgC/yr. Inversions using GOSAT data show a reduced gradient between midlatitudes of the Northern Hemisphere and the Tropics, consistent with the latitudinal shift in carbon uptake. However, the reduced gradients degrade the agreement with background aircraft and surface measurements. To narrow the range of inversion-derived flux, estimates will require further efforts to understand the differences not only between the retrieval schemes but also between inverse models, as their contributions to the overall uncertainty are estimated to be of similar magnitude.

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Comparing GOSAT observations of localized CO₂ enhancements by large emitters with inventory‐based estimates

Janardanan

Maksyutov

Oda

et al. 2016

Geophysical Research Letters

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We employed an atmospheric transport model to attribute column‐averaged CO2 mixing ratios (XCO2) observed by Greenhouse gases Observing SATellite (GOSAT) to emissions due to large sources such as megacities and power plants. XCO2 enhancements estimated from observations were compared to model simulations implemented at the spatial resolution of the satellite observation footprint (0.1° × 0.1°). We found that the simulated XCO2 enhancements agree with the observed over several continental regions across the globe, for example, for North America with an observation to simulation ratio of 1.05 ± 0.38 (p < 0.1), but with a larger ratio over East Asia (1.22 ± 0.32; p < 0.05). The obtained observation‐model discrepancy (22%) for East Asia is comparable to the uncertainties in Chinese emission inventories (~15%) suggested by recent reports. Our results suggest that by increasing the number of observations around emission sources, satellite instruments like GOSAT can provide a tool for detecting biases in reported emission inventories.

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A global coupled Eulerian-Lagrangian model and 1 × 1 km CO<sub>2</sub> surface flux dataset for high-resolution atmospheric CO<sub>2</sub> transport simulations

Cited by 38 publications

References 46 publications

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

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

An intercomparison of inverse models for estimating sources and sinks of CO₂ using GOSAT measurements

Comparing GOSAT observations of localized CO₂ enhancements by large emitters with inventory‐based estimates

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A global coupled Eulerian-Lagrangian model and 1 × 1 km CO&lt;sub&gt;2&lt;/sub&gt; surface flux dataset for high-resolution atmospheric CO&lt;sub&gt;2&lt;/sub&gt; transport simulations

Cited by 38 publications

References 46 publications

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

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

An intercomparison of inverse models for estimating sources and sinks of CO2 using GOSAT measurements

Comparing GOSAT observations of localized CO2 enhancements by large emitters with inventory‐based estimates

Contact Info

Product

Resources

About

A global coupled Eulerian-Lagrangian model and 1 × 1 km CO<sub>2</sub> surface flux dataset for high-resolution atmospheric CO<sub>2</sub> transport simulations

An intercomparison of inverse models for estimating sources and sinks of CO₂ using GOSAT measurements

Comparing GOSAT observations of localized CO₂ enhancements by large emitters with inventory‐based estimates