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Detecting the Responses of CO₂ Column Abundances to Anthropogenic Emissions from Satellite Observations of GOSAT and OCO-2

Sheng, Mengya and Lei, Liping and Zeng, Zhao-Cheng and Rao, Weiqiang and Zhang, Shaoqing (2021) Detecting the Responses of CO₂ Column Abundances to Anthropogenic Emissions from Satellite Observations of GOSAT and OCO-2. Remote Sensing, 13 (17). Art. No. 3524. ISSN 2072-4292. doi:10.3390/rs13173524.

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The continuing increase in atmospheric CO₂ concentration caused by anthropogenic CO₂ emissions significantly contributes to climate change driven by global warming. Satellite measurements of long-term CO₂ data with global coverage improve our understanding of global carbon cycles. However, the sensitivity of the space-borne measurements to anthropogenic emissions on a regional scale is less explored because of data sparsity in space and time caused by impacts from geophysical factors such as aerosols and clouds. Here, we used global land mapping column averaged dry-air mole fractions of CO₂ (XCO₂) data (Mapping-XCO₂), generated from a spatio-temporal geostatistical method using GOSAT and OCO-2 observations from April 2009 to December 2020, to investigate the responses of XCO₂ to anthropogenic emissions at both global and regional scales. Our results show that the long-term trend of global XCO₂ growth rate from Mapping-XCO₂, which is consistent with that from ground observations, shows interannual variations caused by the El Niño Southern Oscillation (ENSO). The spatial distributions of XCO₂ anomalies, derived from removing background from the Mapping-XCO₂ data, reveal XCO₂ enhancements of about 1.5–3.5 ppm due to anthropogenic emissions and seasonal biomass burning in the wintertime. Furthermore, a clustering analysis applied to seasonal XCO₂ clearly reveals the spatial patterns of atmospheric transport and terrestrial biosphere CO₂ fluxes, which help better understand and analyze regional XCO₂ changes that are associated with atmospheric transport. To quantify regional anomalies of CO₂ emissions, we selected three representative urban agglomerations as our study areas, including the Beijing-Tian-Hebei region (BTH), the Yangtze River Delta urban agglomerations (YRD), and the high-density urban areas in the eastern USA (EUSA). The results show that the XCO₂ anomalies in winter well capture the several-ppm enhancement due to anthropogenic CO₂ emissions. For BTH, YRD, and EUSA, regional positive anomalies of 2.47 ± 0.37 ppm, 2.20 ± 0.36 ppm, and 1.38 ± 0.33 ppm, respectively, can be detected during winter months from 2009 to 2020. These anomalies are slightly higher than model simulations from CarbonTracker-CO₂. In addition, we compared the variations in regional XCO₂ anomalies and NO₂ columns during the lockdown of the COVID-19 pandemic from January to March 2020. Interestingly, the results demonstrate that the variations of XCO₂ anomalies have a positive correlation with the decline of NO₂ columns during this period. These correlations, moreover, are associated with the features of emitting sources. These results suggest that we can use simultaneously observed NO₂, because of its high detectivity and co-emission with CO₂, to assist the analysis and verification of CO₂ emissions in future studies.

Item Type:Article
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URLURL TypeDescription
Sheng, Mengya0000-0001-8261-621X
Zeng, Zhao-Cheng0000-0002-0008-6508
Alternate Title:Detecting the Responses of CO2 Column Abundances to Anthropogenic Emissions from Satellite Observations of GOSAT and OCO-2
Additional Information:© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( 4.0/). Received: 25 July 2021; Accepted: 3 September 2021; Published: 5 September 2021. We are grateful for the ACOS-GOSAT v9r data and OCO-2 v10r data, which were provided by the ACOS/OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology and obtained from the ACOS/OCO-2 data archive maintained at the NASA Goddard Earth Science Data and Information Services Center. CarbonTracker CT2019B results were provided by NOAA ESRL, Boulder, Colorado, USA from the website at (assessed on 13 May 2021). We thank NOAA ESRL and PSL for providing potential temperature data and ENSO indices. We thank the European Space Agency (ESA) and Google Earth Engine for providing Sentinel-S5P NO2 products, CGER-NIES for providing ODIAC data at assessed on 29 November 2020 (Data DOI: doi:10.17595/20170411.001), and the World Data Centre for Greenhouse Gases (WDCGG) for providing global atmospheric CO₂ data. We also acknowledge the Land Processes Distributed Active Archive Center (LP DAAC) at the National Aeronautics and Space Administration (NASA) for sharing land cover type and NDVI data derived from MODIS. This research was funded by the National Key Research and Development Program of China (Grant No. 2020YFA0607503), the Key Program of the Chinese Academy of Sciences (Grant No. ZDRW-ZS-2019-1), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19080303). Author Contributions: Conceptualization, M.S., Z.-C.Z. and L.L.; Data curation, M.S. and S.Z.; Formal analysis, M.S., Z.-C.Z. and L.L.; Methodology, M.S. and L.L.; Software, M.S. and W.R. All authors have read and agreed to the published version of the manuscript. The authors declare no conflict of interest.
Funding AgencyGrant Number
National Key Research and Development Program of China2020YFA0607503
Chinese Academy of SciencesZDRW-ZS-2019-1-3
Chinese Academy of SciencesXDA19080303
Subject Keywords:Mapping XCO2; anthropogenic emission; GOSAT; OCO-2; NO2 columns
Issue or Number:17
Record Number:CaltechAUTHORS:20211006-145325354
Persistent URL:
Official Citation:Sheng, M.; Lei, L.; Zeng, Z.-C.; Rao, W.; Zhang, S. Detecting the Responses of CO2 Column Abundances to Anthropogenic Emissions from Satellite Observations of GOSAT and OCO-2. Remote Sens. 2021, 13, 3524.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111234
Deposited By: Tony Diaz
Deposited On:06 Oct 2021 16:52
Last Modified:06 Oct 2021 16:52

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