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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 12
Geosci. Model Dev., 11, 4843-4871, 2018
https://doi.org/10.5194/gmd-11-4843-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 11, 4843-4871, 2018
https://doi.org/10.5194/gmd-11-4843-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 04 Dec 2018

Development and technical paper | 04 Dec 2018

A Lagrangian approach towards extracting signals of urban CO2 emissions from satellite observations of atmospheric column CO2 (XCO2): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”)

Dien Wu1, John C. Lin1, Benjamin Fasoli1, Tomohiro Oda2, Xinxin Ye3, Thomas Lauvaux3, Emily G. Yang4, and Eric A. Kort4 Dien Wu et al.
  • 1Department of Atmospheric Sciences, University of Utah, Salt Lake City, USA
  • 2Goddard Earth Sciences Technology and Research, Universities Space Research Association, Columbia, Maryland/Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
  • 3Department of Meteorology and Atmospheric Science, Pennsylvania State University, USA
  • 4Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, USA

Abstract. Urban regions are responsible for emitting significant amounts of fossil fuel carbon dioxide (FFCO2), and emissions at the finer, city scales are more uncertain than those aggregated at the global scale. Carbon-observing satellites may provide independent top-down emission evaluations and compensate for the sparseness of surface CO2 observing networks in urban areas. Although some previous studies have attempted to derive urban CO2 signals from satellite column-averaged CO2 data (XCO2) using simple statistical measures, less work has been carried out to link upwind emission sources to downwind atmospheric columns using atmospheric models. In addition to Eulerian atmospheric models that have been customized for emission estimates over specific cities, the Lagrangian modeling approach – in particular, the Lagrangian particle dispersion model (LPDM) approach – has the potential to efficiently determine the sensitivity of downwind concentration changes to upwind sources. However, when applying LPDMs to interpret satellite XCO2, several issues have yet to be addressed, including quantifying uncertainties in urban XCO2 signals due to receptor configurations and errors in atmospheric transport and background XCO2.

In this study, we present a modified version of the Stochastic Time-Inverted Lagrangian Transport (STILT) model, X-STILT, for extracting urban XCO2 signals from NASA's Orbiting Carbon Observatory 2 (OCO-2) XCO2 data. X-STILT incorporates satellite profiles and provides comprehensive uncertainty estimates of urban XCO2 enhancements on a per sounding basis. Several methods to initialize receptor/particle setups and determine background XCO2 are presented and discussed via sensitivity analyses and comparisons. To illustrate X-STILT's utilities and applications, we examined five OCO-2 overpasses over Riyadh, Saudi Arabia, during a 2-year time period and performed a simple scaling factor-based inverse analysis. As a result, the model is able to reproduce most observed XCO2 enhancements. Error estimates show that the 68% confidence limit of XCO2 uncertainties due to transport (horizontal wind plus vertical mixing) and emission uncertainties contribute to  ∼ 33% and  ∼ 20% of the mean latitudinally integrated urban signals, respectively, over the five overpasses, using meteorological fields from the Global Data Assimilation System (GDAS). In addition, a sizeable mean difference of −0.55ppm in background derived from a previous study employing simple statistics (regional daily median) leads to a  ∼ 39% higher mean observed urban signal and a larger posterior scaling factor. Based on our signal estimates and associated error impacts, we foresee X-STILT serving as a tool for interpreting column measurements, estimating urban enhancement signals, and carrying out inverse modeling to improve quantification of urban emissions.

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Short summary
Urban CO2 enhancement signals can be derived using satellite column CO2 concentrations and atmospheric transport models. However, uncertainties due to model configurations, atmospheric transport, and defined background values can potentially impact the derived urban signals. In this paper, we present a modified Lagrangian model framework that extracts urban CO2 signals from satellite observations and determines potential error impacts.
Urban CO2 enhancement signals can be derived using satellite column CO2 concentrations and...
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