Characterizing model errors in chemical transport modeling of methane: using GOSAT XCH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; data with weak-constraint four-dimensional variational data assimilation

Stanevich, Ilya; Jones, Dylan B. A.; Strong, Kimberly; Keller, Martin; Henze, D. K.; Parker, Robert J.; Wunch, Debra; Notholt, Justus; Petri, Christof; Warneke, Thorsten; Sussmann, Ralf; Schneider, Mat­thias; Hase, Frank; Kivi, Rigel; Deutscher, Nicholas M.; Velazco, Voltaire A.; Walker, Kaley A.; Deng, Feng

doi:10.5194/acp-21-9545-2021

Cited by 14 publications

(36 citation statements)

References 84 publications

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“…We discuss later in Section 6 the spatial distribution of the analysis increment and error variance reduction. Note that our analysis evaluation with TCCON stations shows a comparable result to the weak-constraint 4D-Var in the global GEOS-Chem data assimilation employed by Stanevich et al (2021)-Table 1 [15].…”

Section: Evaluation Against Independent Observationssupporting

confidence: 77%

“…In practice, observation thinning (i.e., reducing the spatial density of the observations) or inflating the observation error variance are two standard procedures to deal with spatially correlated observation errors [34,35]. Procedures based on variance inflation were employed to maintain a better consistency between the model and GOSAT [10] using the residual error method of Heald et al (2004) [36] or between GOSAT and independent observations [15]. However, in this study, we use observation thinning to alleviate the error correlation between observations.…”

Section: Estimation Of Correlation Lengths and Observation Error Variancementioning

confidence: 99%

“…TCCON is a ground-based FTS network that provides a time series of CH 4 columnaveraged abundance worldwide. TCCON data has been used to compare with model simulations and satellite data primarily for validation purposes [15,[47][48][49][50][51][52]. We used the GGG2014 version of TCCON XCH 4 data from seven sites (red circles in Figure 7), including Park falls [53], Orleans [54], Lamont [55], Bremen [56], Sodankyla [57], Izana [58], and Bialystok [59], available at https://tccondata.org/2014 (accessed on 14 November 2021).…”

Section: Evaluation Against Independent Observationsmentioning

confidence: 99%

“…Several studies provide in-depth investigations of the possible causes of methane model bias in a global atmospheric model (e.g., GEOS-Chem), such as stratospheric bias [10,15,37,52,68], weakening of vertical transport due to a coarse model resolution [14], OH burden in the chemistry model [6,9,65,69]. Besides those, H-CMAQ could suffer from bias due to inaccurate initial conditions, insufficient lower top of the model boundary conditions, and interhemispheric exchange of methane.…”

Section: Evaluation Against Independent Observationsmentioning

confidence: 99%

“…Hence, it implies that the only source of error arises from inaccurate emissions. In a comparable study, Stanevich et al (2019 and [14,15] accounts for model transport error under a weak-constraint 4D-Var inversion, which partly addresses the uncertainties due to lateral boundary inflow and the initial concentration. However, those uncertainties may not be associated with optimal error statistics, which influence the quality of the analysis.…”

Section: Introductionmentioning

confidence: 99%

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Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics

et al. 2022

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We applied the parametric variance Kalman filter (PvKF) data assimilation designed in Part I of this two-part paper to GOSAT methane observations with the hemispheric version of CMAQ to obtain the methane field (i.e., optimized analysis) with its error variance. Although the Kalman filter computes error covariances, the optimality depends on how these covariances reflect the true error statistics. To achieve more accurate representation, we optimize the global variance parameters, including correlation length scales and observation errors, based on a cross-validation cost function. The model and the initial error are then estimated according to the normalized variance matching diagnostic, also to maintain a stable analysis error variance over time. The assimilation results in April 2010 are validated against independent surface and aircraft observations. The statistics of the comparison of the model and analysis show a meaningful improvement against all four types of available observations. Having the advantage of continuous assimilation, we showed that the analysis also aims at pursuing the temporal variation of independent measurements, as opposed to the model. Finally, the performance of the PvKF assimilation in capturing the spatial structure of bias and uncertainty reduction across the Northern Hemisphere is examined, indicating the capability of analysis in addressing those biases originated, whether from inaccurate emissions or modelling error.

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Section: Evaluation Against Independent Observationssupporting

confidence: 77%

Section: Estimation Of Correlation Lengths and Observation Error Variancementioning

confidence: 99%

Section: Evaluation Against Independent Observationsmentioning

confidence: 99%

Section: Evaluation Against Independent Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics

et al. 2022

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Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations

Jacob

Zhang

et al. 2022

Preprint

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Atmospheric methane mixing ratio rose by 15 ppbv between 2019 and 2020, the fastest growth rate on record. We conduct a global inverse analysis of 2019-2020 GOSAT satellite observations of atmospheric methane to analyze the combination of sources and sinks driving this surge. The atmospheric methane growth rate increased by 31 Tg a-1 from 2019 to 2020, representing a 36 Tg a-1 forcing on the methane budget away from steady state. 86% of the forcing in the base inversion is from increasing emissions (82 ± 18% in the 9-member inversion ensemble), and 14% is from decrease in tropospheric OH. Half of the increase in emissions is from Africa (15 Tg a-1) and appears to be driven by wetland inundation. There is also a large relative increase in emissions from Canada and Alaska (4.8 Tg a-1 , 24%) that could be driven by temperature sensitivity of boreal wetland emissions.

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Decadal Methane Emission Trend Inferred from Proxy GOSAT XCH4 Retrievals: Impacts of Transport Model Spatial Resolution

et al. 2022

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In recent studies, proxy XCH4 retrievals from the Japanese Greenhouse gases Observing SATellite (GOSAT) have been used to constrain top-down estimation of CH4 emissions. Still, the resulting interannual variations often show significant discrepancies over some of the most important CH4 source regions, such as China and Tropical South America, by causes yet to be determined. This study compares monthly CH4 flux estimates from two parallel assimilations of GOSAT XCH4 retrievals from 2010 to 2019 based on the same Ensemble Kalman Filter (EnKF) framework but with the global chemistry transport model (GEOS-Chem v12.5) being run at two different spatial resolutions of 4° × 5° (R4, lon × lat) and 2° × 2.5° (R2, lon × lat) to investigate the effects of resolution-related model errors on the derived long-term global and regional CH4 emission trends. We found that the mean annual global methane emission for the 2010s is 573.04 Tg yr−1 for the inversion using the R4 model, which becomes about 4.4 Tg yr−1 less (568.63 Tg yr−1) when a finer R2 model is used, though both are well within the ensemble range of the 22 top-down results (2008–17) included in the current Global Carbon Project (from 550 Tg yr−1 to 594 Tg yr−1). Compared to the R2 model, the inversion based on the R4 tends to overestimate tropical emissions (by 13.3 Tg yr which is accompanied by a general underestimation (by 8.9 Tg yr−1) in the extratropics. Such a dipole reflects differences in tropical-mid-latitude air exchange in relation to the model’s convective and advective schemes at different resolutions. The two inversions show a rather consistent long-term CH4 emission trend at the global scale and over most of the continents, suggesting that the observed rapid increase in atmospheric methane can largely be attributed to the emission growth from North Africa (1.79 Tg yr−2 for R4 and 1.29 Tg yr−2 for R2) and South America Temperate (1.08 Tg yr−2 for R4 and 1.21 Tg yr−2 for R2) during the first half of the 2010s, and from Eurasia Boreal (1.46 Tg yr−2 for R4 and 1.63 Tg yr−2 for R2) and Tropical South America (1.72 Tg yr−2 for R4 and 1.43 Tg yr−2 for R2) over 2015–19. In the meantime, emissions in Europe have shown a consistent decrease over the past decade. However, the growth rates by the two parallel inversions show significant discrepancies over Eurasia Temperate, South America Temperate, and South Africa, which are also the places where recent GOSAT inversions usually disagree with one other.

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Characterizing model errors in chemical transport modeling of methane: using GOSAT XCH<sub>4</sub> data with weak-constraint four-dimensional variational data assimilation

Cited by 14 publications

References 84 publications

Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics

Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics

Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations

Decadal Methane Emission Trend Inferred from Proxy GOSAT XCH4 Retrievals: Impacts of Transport Model Spatial Resolution

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