A regional high-resolution carbon flux inversion of North America for 2004

Schuh, A. E.; Denning, Scott; Corbin, K. D.; Baker, Ian; Uliasz, M.; Parazoo, Nicholas C.; Andrews, A. E.; Worthy, Doug

doi:10.5194/bg-7-1625-2010

Cited by 117 publications

(124 citation statements)

References 44 publications

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“…This leads to long time aggregations for inversions, e.g. over one week [Schuh et al, 2010;Lauvaux et al, 2011]. In this study, the mean 15-day fluxes are to be inverted.…”

Section: Methodsmentioning

confidence: 99%

Optimal representation of source-sink fluxes for mesoscale carbon dioxide inversion with synthetic data

Bocquet

Lauvaux

et al. 2011

J. Geophys. Res.

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[1] The inversion of CO 2 surface fluxes from atmospheric concentration measurements involves discretizing the flux domain in time and space. The resolution choice is usually guided by technical considerations despite its impact on the solution to the inversion problem. In our previous studies, a Bayesian formalism has recently been introduced to describe the discretization of the parameter space over a large dictionary of adaptive multiscale grids. In this paper, we exploit this new framework to construct optimal space-time representations of carbon fluxes for mesoscale inversions. Inversions are performed using synthetic continuous hourly CO 2 concentration data in the context of the Ring 2 experiment in support of the North American Carbon Program Mid Continent Intensive (MCI). Compared with the regular grid at finest scale, optimal representations can have similar inversion performance with far fewer grid cells. These optimal representations are obtained by maximizing the number of degrees of freedom for the signal (DFS) that measures the information gain from observations to resolve the unknown fluxes. Consequently information from observations can be better propagated within the domain through these optimal representations. For the Ring 2 network of eight towers, in most cases, the DFS value is relatively small compared to the number of observations d (DFS/d < 20%). In this multiscale setting, scale-dependent aggregation errors are identified and explicitly formulated for more reliable inversions. It is recommended that the aggregation errors should be taken into account, especially when the correlations in the errors of a priori fluxes are physically unrealistic. The optimal multiscale grids allow to adaptively mitigate the aggregation errors.

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“…This leads to long time aggregations for inversions, e.g. over one week [Schuh et al, 2010;Lauvaux et al, 2011]. In this study, the mean 15-day fluxes are to be inverted.…”

Section: Methodsmentioning

confidence: 99%

Optimal representation of source-sink fluxes for mesoscale carbon dioxide inversion with synthetic data

Bocquet

Lauvaux

et al. 2011

J. Geophys. Res.

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“…Carbon cycle models now operate at resolutions much finer than US states, and their reliance on gridded inventories for a priori estimates of the spatial distribution of emissions (7)(8)(9) means that raw emissions data available at coarse spatial scales must be "downscaled" to match model grids. Increasing the spatial resolution of emission inventories has been shown to change modeled terrestrial carbon flux estimates by more than 50% (8).…”

mentioning

confidence: 99%

“…Increasing the spatial resolution of emission inventories has been shown to change modeled terrestrial carbon flux estimates by more than 50% (8). The notion that population density is a robust predictor of CO 2 emissions underpins most gridded global emissions estimates (10)(11)(12)(13)(14).…”

mentioning

confidence: 99%

Cities, traffic, and CO ₂ : A multidecadal assessment of trends, drivers, and scaling relationships

Gately

Hutyra

Wing

2015

Proc. Natl. Acad. Sci. U.S.A.

130

101

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Emissions of CO 2 from road vehicles were 1.57 billion metric tons in 2012, accounting for 28% of US fossil fuel CO 2 emissions, but the spatial distributions of these emissions are highly uncertain. We develop a new emissions inventory, the Database of Road Transportation Emissions (DARTE), which estimates CO 2 emitted by US road transport at a resolution of 1 km annually for 1980-2012. DARTE reveals that urban areas are responsible for 80% of on-road emissions growth since 1980 and for 63% of total 2012 emissions. We observe nonlinearities between CO 2 emissions and population density at broad spatial/temporal scales, with total on-road CO 2 increasing nonlinearly with population density, rapidly up to 1,650 persons per square kilometer and slowly thereafter. Per capita emissions decline as density rises, but at markedly varying rates depending on existing densities. We make use of DARTE's bottom-up construction to highlight the biases associated with the common practice of using population as a linear proxy for disaggregating national-or state-scale emissions. Comparing DARTE with existing downscaled inventories, we find biases of 100% or more in the spatial distribution of urban and rural emissions, largely driven by mismatches between inventory downscaling proxies and the actual spatial patterns of vehicle activity at urban scales. Given cities' dual importance as sources of CO 2 and an emerging nexus of climate mitigation initiatives, high-resolution estimates such as DARTE are critical both for accurately quantifying surface carbon fluxes and for verifying the effectiveness of emissions mitigation efforts at urban scales.emissions | carbon dioxide | urban | transportation | on-road

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“…A number of existing studies use this model to simulate atmospheric CO 2 mixing ratios (e.g., Law et al, 2008;Gurney et al, 2009;Baker et al, 2010;Schuh et al, 2010;Shiga et al, 2013;ASCENDS Ad Hoc Science Definition Team, 2015 et al, 2015). Several of these studies specifically use PCTM to model CO 2 in the context of satellite missions (e.g., Baker et al, 2010;ASCENDS Ad Hoc Science Definition Team, 2015;Hammerling et al, 2015).…”

Section: Atmospheric Transport Modelmentioning

confidence: 99%

Characterizing biospheric carbon balance using CO<sub>2</sub> observations from the OCO-2 satellite

Miller¹,

Michalak²,

Yadav³

et al. 2017

Preprint

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Abstract.NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite launched in summer of 2014. Its observations could allow scientists to constrain CO 2 fluxes across regions or continents that were previously difficult to monitor. This study explores an initial step toward that goal; we evaluate the extent to which current OCO-2 observations can detect patterns in biospheric CO 2 fluxes and constrain monthly CO 2 budgets. Our goal is to guide top-down, inverse modeling studies and identify areas 5 for future improvement. We find that uncertainties and biases in the individual OCO-2 observations are comparable to the atmospheric signal from biospheric fluxes, particularly during northern hemisphere winter when biospheric fluxes are small. A series of top-down experiments indicate how these errors affect our ability to constrain monthly biospheric CO 2 budgets. We are able to constrain budgets for between two and four global regions using OCO-2 observations, depending on the month, and we can constrain CO 2 budgets at the regional level (i.e., smaller than seven global biomes) in only a handful of cases (16% of 10 all regions and months). The potential of the OCO-2 observations, however, is greater than these results might imply. A set of synthetic data experiments suggests that observation or retrieval errors have a salient effect. Advances in retrieval algorithms and to a lesser extent atmospheric transport modeling will improve the results. In the interim, top-down studies that use current satellite observations are best-equipped to constrain the biospheric carbon balance across only continental or hemispheric regions.

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A regional high-resolution carbon flux inversion of North America for 2004

Cited by 117 publications

References 44 publications

Optimal representation of source-sink fluxes for mesoscale carbon dioxide inversion with synthetic data

Optimal representation of source-sink fluxes for mesoscale carbon dioxide inversion with synthetic data

Cities, traffic, and CO ₂ : A multidecadal assessment of trends, drivers, and scaling relationships

Characterizing biospheric carbon balance using CO<sub>2</sub> observations from the OCO-2 satellite

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A regional high-resolution carbon flux inversion of North America for 2004

Cited by 117 publications

References 44 publications

Optimal representation of source-sink fluxes for mesoscale carbon dioxide inversion with synthetic data

Optimal representation of source-sink fluxes for mesoscale carbon dioxide inversion with synthetic data

Cities, traffic, and CO 2 : A multidecadal assessment of trends, drivers, and scaling relationships

Characterizing biospheric carbon balance using CO&lt;sub&gt;2&lt;/sub&gt; observations from the OCO-2 satellite

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Cities, traffic, and CO ₂ : A multidecadal assessment of trends, drivers, and scaling relationships

Characterizing biospheric carbon balance using CO<sub>2</sub> observations from the OCO-2 satellite