Abstract
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 −0.55 ppm 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.
Highlights
Carbon dioxide (CO2) is a major atmospheric greenhouse gas in terms of radiative forcing, with its concentration having increased significantly over the past century (Dlugokencky and Tans, 2015)
We present a modified version of the Stochastic Time-Inverted Lagrangian Transport (STILT) model, “XSTILT”, for extracting urban XCO2 signals from NASA’s
We introduce a new background determination that combines Orbiting Carbon Observatory 2 (OCO-2) observations and the STILT-based atmospheric transport, and we account for errors in our background estimates
Summary
Carbon dioxide (CO2) is a major atmospheric greenhouse gas in terms of radiative forcing, with its concentration having increased significantly over the past century (Dlugokencky and Tans, 2015). Previous studies have demonstrated the potential for detecting and deriving urban CO2 emission signals from satellite CO2 observations, in the form of XCO2 enhancements above the background, without making use of much atmospheric transport information (Hakkarainen et al, 2016; Kort et al, 2012; Schneising et al, 2013; Silva and Arellano, 2017; Silva et al, 2013). A negligible amount to ∼ 20 % of the modeled enhancements are reported as the error impact due to the STILT particle number (released from a fixed level), depending on adopted particle numbers, examined species, and their components/sources (Zhao et al, 2009; Gerbig et al, 2003; Mallia et al, 2015) When it comes to representing an atmospheric column using particle ensembles, many studies describe their setups for receptors/particles without detailed explanations of why the setups were chosen or the error impact on modeling XCO2 due to model configurations. We examine several satellite overpasses and focus on a small spatial domain adjacent to Riyadh for each overpass
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