CaltechAUTHORS
  A Caltech Library Service

Regional uncertainty of GOSAT XCO_2 retrievals in China: quantification and attribution

Bie, Nian and Lei, Liping and Zeng, ZhaoCheng and Cai, Bofeng and Yang, Shaoyuan and He, Zhonghua and Wu, Changjiang and Nassar, Ray (2018) Regional uncertainty of GOSAT XCO_2 retrievals in China: quantification and attribution. Atmospheric Measurement Techniques, 11 (3). pp. 1251-1272. ISSN 1867-8548. https://resolver.caltech.edu/CaltechAUTHORS:20180321-091524917

[img] PDF - Published Version
Creative Commons Attribution.

4Mb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20180321-091524917

Abstract

The regional uncertainty of the column-averaged dry air mole fraction of CO_2 (XCO_2) retrieved using different algorithms from the Greenhouse gases Observing SATellite (GOSAT) and its attribution are still not well understood. This paper investigates the regional performance of XCO_2 within a latitude band of 37–42° N segmented into 8 cells in a grid of 5° from west to east (80–120° E) in China, where typical land surface types and geographic conditions exist. The former includes desert, grassland and built-up areas mixed with cropland; and the latter includes anthropogenic emissions that change from small to large from west to east, including those from the megacity of Beijing. For these specific cells, we evaluate the regional uncertainty of GOSAT XCO_2 retrievals by quantifying and attributing the consistency of XCO_2 retrievals from four algorithms (ACOS, NIES, OCFP and SRFP) by intercomparison. These retrievals are then specifically compared with simulated XCO_2 from the high-resolution nested model in East Asia of the Goddard Earth Observing System 3-D chemical transport model (GEOS-Chem). We also introduce the anthropogenic CO_2 emissions data generated from the investigation of surface emitting point sources that was conducted by the Ministry of Environmental Protection of China to GEOS-Chem simulations of XCO_2 over the Chinese mainland. The results indicate that (1) regionally, the four algorithms demonstrate smaller absolute biases of 0.7–1.1 ppm in eastern cells, which are covered by built-up areas mixed with cropland with intensive anthropogenic emissions, than those in the western desert cells (1.0–1.6 ppm) with a high-brightness surface from the pairwise comparison results of XCO_2 retrievals. (2) Compared with XCO_2 simulated by GEOS-Chem (GEOS-XCO_2), the XCO_2 values from ACOS and SRFP have better agreement, while values from OCFP are the least consistent with GEOS-XCO_2. (3) Viewing attributions of XCO_2 in the spatio-temporal pattern, ACOS and SRFP demonstrate similar patterns, while OCFP is largely different from the others. In conclusion, the discrepancy in the four algorithms is the smallest in eastern cells in the study area, where the megacity of Beijing is located and where there are strong anthropogenic CO_2 emissions, which implies that XCO_2 from satellite observations could be reliably applied in the assessment of atmospheric CO_2 enhancements induced by anthropogenic CO_2 emissions. The large inconsistency among the four algorithms presented in western deserts which displays a high albedo and dust aerosols, moreover, demonstrates that further improvement is still necessary in such regions, even though many algorithms have endeavored to minimize the effects of aerosols scattering and surface albedo.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.5194/amt-11-1251-2018DOIArticle
https://www.atmos-meas-tech.net/11/1251/2018PublisherArticle
ORCID:
AuthorORCID
Nassar, Ray0000-0001-6282-1611
Additional Information:© 2018 Author(s). This work is distributed under the Creative Commons Attribution 4.0 License. Published by Copernicus Publications on behalf of the European Geosciences Union. Received: 14 Jul 2017 – Discussion started: 10 Oct 2017 - Revised: 09 Jan 2018 – Accepted: 31 Jan 2018 – Published: 05 Mar 2018. This research was supported by the National Research Program on Global Changes and Adaptation: “Big data on global changes: data sharing platform and recognition” (grant no. 2016YFA0600303, 2016YFA0600304). We are grateful for: NIES products from NIES GOSAT Project; albedo data from Beijing Normal University; XCO2 data from the TCCON data archive, operated by the California Institute of Technology; and support from the GEOS-Chem team. ACOS V3.5 and ACOS V7.3 were produced by the ACOS=OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology, and obtained from the JPL website, http://CO2.jpl.nasa.gov. We are grateful for aerosol data from Aeronautics and Space Administration (NASA). The satellite XCO2 products OCFP and SRFP were obtained from the ESA project GHG-CCI website (http://www.esa-ghg-cci.org/) and the data providers Univ. Leicester (OCFP product) and SRON and KIT (SRFP) granted permission for the use of the data for peer-reviewed publications. The authors declare that they have no conflict of interest.
Funders:
Funding AgencyGrant Number
National Research Program on Global Changes and Adaptation2016YFA0600303
National Research Program on Global Changes and Adaptation2016YFA0600304
Issue or Number:3
Record Number:CaltechAUTHORS:20180321-091524917
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180321-091524917
Official Citation:Bie, N., Lei, L., Zeng, Z., Cai, B., Yang, S., He, Z., Wu, C., and Nassar, R.: Regional uncertainty of GOSAT XCO2 retrievals in China: quantification and attribution, Atmos. Meas. Tech., 11, 1251-1272, https://doi.org/10.5194/amt-11-1251-2018, 2018
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:85391
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:26 Mar 2018 21:58
Last Modified:09 Mar 2020 13:19

Repository Staff Only: item control page