Inverse modeling of European CH<sub>4</sub> emissions 2001–2006

Bergamaschi, P.; Krol, Maarten; Meirink, Jan Fokke; Dentener, Frank; Segers, Arjo; Aardenne, J. van; Monni, Suvi; Vermeulen, Alex; Schmidt, Martina; Ramonet, Michel; Yver, C.; Meinhardt, F.; Nisbet, E. G.; Fisher, Rebecca E.; O’Doherty, Simon; Dlugokencky, E. J.

doi:10.1029/2010jd014180

Cited by 147 publications

(216 citation statements)

References 42 publications

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“…However, a better approach would be to constrain the global-scale and regional-scale emissions within the same inversion framework, so that the optimized emissions on the global-scale will provide less biased boundary conditions for the regional inversion. Such an approach has been used to constrain CH 4 and N 2 O emissions over South America and Europe (Meirink et al, 2008;Bergamaschi et al, 2010;Corazza et al, 2011) with the nested TM5 model. An issue with this approach is that the adjustment in the emissions on the global scale will have to be projected through long-range transport to the nested domain.…”

Section: Optimization On the Initial And Boundary Conditionsmentioning

confidence: 99%

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

et al. 2015

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Abstract. Chemical transport models (CTMs) driven with high-resolution meteorological fields can better resolve small-scale processes, such as frontal lifting or deep convection, and thus improve the simulation and emission estimates of tropospheric trace gases. In this work, we explore the use of the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system with the nested high-resolution version of the model (0. With optimized lateral boundary conditions, regional inversion analyses can reduce the sensitivity of the CO source estimates to errors in long-range transport and in the distributions of the hydroxyl radical (OH), the main sink for CO. To further limit the potential impact of discrepancies in chemical aging of air in the free troposphere, associated with errors in OH, we use surface-level multispectral MOPITT (Measurement of Pollution in The Troposphere) CO retrievals, which have greater sensitivity to CO near the surface and reduced sensitivity in the free troposphere, compared to previous versions of the retrievals. We estimate that the annual total anthropogenic CO emission from the contiguous US 48 states was 97 Tg CO, a 14 % increase from the 85 Tg CO in the a priori. This increase is mainly due to enhanced emissions around the Great Lakes region and along the west coast, relative to the a priori. Sensitivity analyses using different OH fields and lateral boundary conditions suggest a possible error, associated with local North American OH distribution, in these emission estimates of 20 % during summer 2004, when the CO lifetime is short. This 20 % OH-related error is 50 % smaller than the OH-related error previously estimated for North American CO emissions using a global inversion analysis. We believe that reducing this OH-related error further will require integrating additional observations to provide a strong constraint on the CO distribution across the domain. Despite these limitations, our results show the potential advantages of combining high-resolution regional inversion analyses with global analyses to better quantify regional CO source estimates.

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Section: Optimization On the Initial And Boundary Conditionsmentioning

confidence: 99%

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

et al. 2015

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“…The TM5 4DVAR inverse modelling system has been used in studies of several atmospheric trace gases, including CH 4 (Meirink et al, 2008b;Bergamaschi et al, 2009Bergamaschi et al, , 2010, CO (Hooghiemstra et al, 2012a, b), CO 2 Guerlet et al, 2013) and CH 3 CCl 3 . Here it is used for estimating large-scale emissions of CH 4 , by optimizing the agreement between measured and model simulated mixing ratios.…”

Section: Tm5 4dvarmentioning

confidence: 99%

A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements

et al. 2014

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Abstract. This study investigates the use of total column CH 4 (XCH 4 ) retrievals from the SCIAMACHY satellite instrument for quantifying large-scale emissions of methane. A unique data set from SCIAMACHY is available spanning almost a decade of measurements, covering a period when the global CH 4 growth rate showed a marked transition from stable to increasing mixing ratios. The TM5 4DVAR inverse modelling system has been used to infer CH 4 emissions from a combination of satellite and surface measurements for the period 2003-2010. In contrast to earlier inverse modelling studies, the SCIAMACHY retrievals have been corrected for systematic errors using the TCCON network of ground-based Fourier transform spectrometers. The aim is to further investigate the role of bias correction of satellite data in inversions. Methods for bias correction are discussed, and the sensitivity of the optimized emissions to alternative bias correction functions is quantified. It is found that the use of SCIAMACHY retrievals in TM5 4DVAR increases the estimated inter-annual variability of large-scale fluxes by 22 % compared with the use of only surface observations. The difference in global methane emissions between 2-year periods before and after July 2006 is estimated at 27-35 Tg yr −1 . The use of SCIAMACHY retrievals causes a shift in the emissions from the extra-tropics to the tropics of 50 ± 25 Tg yr −1 . The large uncertainty in this value arises from the uncertainty in the bias correction functions. Using measurements from the HIPPO and BARCA aircraft campaigns, we show that systematic errors in the SCIAMACHY measurements are a main factor limiting the performance of the inversions. To further constrain tropical emissions of methane using current and future satellite missions, extended validation capabilities in the tropics are of critical importance.Published by Copernicus Publications on behalf of the European Geosciences Union.

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“…For the measurement error, we have used the estimates given by the data providers, which includes random and, as far as is known, systematic errors and is approximately 0.3 ppb (circa 0.1 %). For the transport model errors, we have estimated two contributions: (1) transport errors (following Rödenbeck et al, 2003) and (2) errors from a lack of subgrid-scale variability (following Bergamaschi et al, 2010), both of which were calculated using forward model simulations run with the same prior fluxes and meteorology as the inversions. The first error uses the 3-D mole fraction gradient around the grid cell where the site is located as a proxy for the transport error, and thus strong vertical and/or horizontal gradients lead to large error estimates.…”

Section: Observation Error Covariance Matrixmentioning

confidence: 99%

“…The second error uses the change in mole fraction in the grid cell integrated over the e-folding time for flushing the grid cell with the modelled wind speed. This is used as a proxy for the influence of not accounting for the homogeneous distribution of fluxes within the grid cell and their location relative to the observation site (for details, see Bergamaschi et al 2010). …”

Section: Observation Error Covariance Matrixmentioning

confidence: 99%

Nitrous oxide emissions 1999 to 2009 from a global atmospheric inversion

et al. 2014

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Abstract. N 2 O surface fluxes were estimated for 1999 to 2009 using a time-dependent Bayesian inversion technique. Observations were drawn from 5 different networks, incorporating 59 surface sites and a number of ship-based measurement series. To avoid biases in the inverted fluxes, the data were adjusted to a common scale and scale offsets were included in the optimization problem. The fluxes were calculated at the same resolution as the transport model (3.75 • longitude × 2.5 • latitude) and at monthly time resolution. Over the 11-year period, the global total N 2 O source varied from 17.5 to 20.1 Tg a −1 N. Tropical and subtropical land regions were found to consistently have the highest N 2 O emissions, in particular in South Asia (20 ± 3 % of global total), South America (13 ± 4 %) and Africa (19 ± 3 %), while emissions from temperate regions were smaller: Europe (6 ± 1 %) and North America (7 ± 2 %). A significant multi-annual trend in N 2 O emissions (0.045 Tg a −2 N) from South Asia was found and confirms inventory estimates of this trend. Considerable interannual variability in the global N 2 O source was observed (0.8 Tg a −1 N, 1 standard deviation, SD) and was largely driven by variability in tropical and subtropical soil fluxes, in particular in South America (0.3 Tg a −1 N, 1 SD) and Africa (0.3 Tg a −1 N, 1 SD). Notable variability was also found for N 2 O fluxes in the tropical and southern oceans (0.15 and 0.2 Tg a −1 N, 1 SD, respectively). Interannual variability in the N 2 O source shows some correlation with the El Niño-Southern Oscillation (ENSO), where El Niño conditions are associated with lower N 2 O fluxes from soils and from the ocean and vice versa for La Niña conditions.

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Inverse modeling of European CH₄ emissions 2001–2006

Cited by 147 publications

References 42 publications

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

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

A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements

Nitrous oxide emissions 1999 to 2009 from a global atmospheric inversion

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Inverse modeling of European CH4 emissions 2001–2006

Cited by 147 publications

References 42 publications

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

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

A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements

Nitrous oxide emissions 1999 to 2009 from a global atmospheric inversion

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Inverse modeling of European CH₄ emissions 2001–2006