Comparing GOSAT observations of localized CO₂ enhancements by large emitters with inventory‐based estimates

Abstract

We employed an atmospheric transport model to attribute column‐averaged CO2 mixing ratios (XCO2) observed by Greenhouse gases Observing SATellite (GOSAT) to emissions due to large sources such as megacities and power plants. XCO2 enhancements estimated from observations were compared to model simulations implemented at the spatial resolution of the satellite observation footprint (0.1° × 0.1°). We found that the simulated XCO2 enhancements agree with the observed over several continental regions across the globe, for example, for North America with an observation to simulation ratio of 1.05 ± 0.38 (p < 0.1), but with a larger ratio over East Asia (1.22 ± 0.32; p < 0.05). The obtained observation‐model discrepancy (22%) for East Asia is comparable to the uncertainties in Chinese emission inventories (~15%) suggested by recent reports. Our results suggest that by increasing the number of observations around emission sources, satellite instruments like GOSAT can provide a tool for detecting biases in reported emission inventories.

Assessment of Anthropogenic Methane Emissions over Large Regions Based on GOSAT Observations and High Resolution Transport Modeling

“…Our results show large number of simulated and observed enhancements in the range of 10 to 20 ppb globally. A linear relationship between observed and simulated enhancements can be reliably established in the range of 1-20 ppb (more than the~12.6 ppb random error of GOSAT XCH 4 [23]), while, for GOSAT XCO 2 , the range is 0-1 ppm (as discussed in Janardanan et al, 2016 [26]), which is half of the single scan random error of 2 ppm XCO 2 . Thus, compared to CO 2 , detectable CH 4 abundance due to anthropogenic sources are more robust.…”

“…The method is similar to estimating anthropogenic emission signature in GOSAT XCO 2 due to Large Point Sources proposed by Janardanan et al [26]. This study utilizes a Lagrangian Particle Dispersion Model, FLEXPART [34,35] with EDGAR anthropogenic methane emission inventory (spatial resolution 0.1 × 0.1 • ) to simulate (see Section 3.1) XCH 4 abundance (∆XCH 4,sim ) caused by local anthropogenic emissions at all GOSAT satellite observation locations with valid retrieval data.…”

“…Considering the sparsity of the ground-based observation networks and the necessity for wide spatial and temporal coverage, satellite observations such as from GOSAT can be an additional or alternative tool for estimation and monitoring of anthropogenic greenhouse gas emissions (e.g., [25][26][27][28][29]) by emission hotspots such as megacities and power plants and other intensive sources such as biomass burning [21,30]. Therefore, there is an emerging interest in the use of space-based observation of greenhouse gases for estimation and verification of their emissions.…”

On the impact of granularity of space-based urban CO2 emissions in urban atmospheric inversions: A case study for Indianapolis, IN

“…The inverse methodology produced 1-km resolution adjustments to the first guess (Hestia) modifying the total emissions by about 20%, a statistically significant change reflecting possible discrepancies between the two methods including the presence of additional sources beyond anthropogenic emitters (e.g., soil respiration - Gurney et al, 2016). The study also illustrated the impact of assimilating coarser resolution prior emissions taken from the Open-source Data Inventory for Anthropogenic CO 2 (ODIAC) global 1 × 1 km fossil fuel emissions dataset (Oda and Maksyutov, 2011;Oda et al, 2016, data available from http://db.cger. nies.go.jp/dataset/ODIAC/) and its impact on the spatial structures of the emission corrections.…”

“…The use of atmospheric data to verify EI's has been encouraged by several studies (e.g., Nisbet and Weiss, 2010;Pacala et al, 2010) and supported by the analysis of various types of instrumentation (e.g., Kort et al, 2012, Janardanan et al, 2016 for satellite CO 2 data; Basu et al, 2016 for C 14 radiocarbon data). Recently, an inversion analysis from the Indianapolis Flux experiment (INFLUX) project, as the first of its kind, has achieved a top-down emission estimate for a single city and demonstrated the use of atmospheric CO 2 tower data to constrain urban emissions .…”