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Detection of Spatiotemporal Extreme Changes in Atmospheric CO_2 Concentration Based on Satellite Observations

He, Zhonghua and Lei, Liping and Welp, Lisa R. and Zeng, Zhao-Cheng and Bie, Nian and Yang, Shaoyuan and Liu, Liangyun (2018) Detection of Spatiotemporal Extreme Changes in Atmospheric CO_2 Concentration Based on Satellite Observations. Remote Sensing, 10 (6). Art. No. 839. ISSN 2072-4292. https://resolver.caltech.edu/CaltechAUTHORS:20180726-123133508

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Abstract

Atmospheric CO_2 concentrations are sensitive to the effects of climate extremes on carbon sources and sinks of the land biosphere. Therefore, extreme changes of atmospheric CO_2 can be used to identify anomalous sources and sinks of carbon. In this study, we develop a spatiotemporal extreme change detection method for atmospheric CO_2 concentrations using column-averaged CO_2 dry air mole fraction (XCO_2) retrieved from the Greenhouse gases Observing SATellite (GOSAT) from 1 June 2009 to 31 May 2016. For extreme events identified, we attributed the main drivers using surface environmental parameters, including surface skin temperature, self-calibrating Palmer drought severity index, burned area, and gross primary production (GPP). We also tested the sensitivity of XCO_2 response to changing surface CO_2 fluxes using model simulations and Goddard Earth Observing System (GEOS)-Chem atmospheric transport. Several extreme high XCO_2 events are detected around mid-2010 over Eurasia and in early 2016 in the tropics. The magnitudes of extreme XCO_2 increases are around 1.5–1.8 ppm in the Northern Hemisphere and 1.2–1.4 ppm in Southern Hemisphere. The spatiotemporal pattern of detected high XCO_2 events are similar to patterns of local surface environmental parameter extremes. The extreme high XCO_2 events often occurred during periods of increased temperature, severe drought, increased wildfire or reduced GPP. Our sensitivity tests show that the magnitude of detectable anomalies varies with location, for example 25% or larger anomalies in local CO_2 emission fluxes are detectable in tropical forest, whereas anomalies must be half again as large in mid-latitudes (~37.5%). In conclusion, we present a method for extreme high XCO_2 detection, and large changes in land CO_2 fluxes. This provides another tool to monitor large-scale changes in the land carbon sink and potential feedbacks on the climate system.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3390/rs10060839DOIArticle
ORCID:
AuthorORCID
Welp, Lisa R.0000-0001-7125-0478
Zeng, Zhao-Cheng0000-0002-0008-6508
Liu, Liangyun0000-0002-7987-037X
Alternate Title:Detection of Spatiotemporal Extreme Changes in Atmospheric CO2 Concentration Based on Satellite Observations
Additional Information:© 2018 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(http://creativecommons.org/licenses/by/4.0/). This research was supported by the National Key Research and Development Program of China (2017YFA0603001) and (2016YFA0600303). University of Chinese Academy of Sciences (UCAS) Joint PhD Program is acknowledged for the PhD scholarship awarded to the first author. We acknowledge The ACOS-GOSAT v7.3 data were produced 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 (GES DISC). We also acknowledge the GOSAT Project for acquiring the spectra. CarbonTracker CT2016 results are provided by NOAA ESRL, Boulder, Colorado, USA from the website at http://carbontracker.noaa.gov. Terra MODIS GPP/NPP Product MOD17A2 was downloaded from the University of Montana. And ARIS/Aqua surface skin temperature from AIRS Science Team was downloaded from GES DISC. The self-calibrating Palmer Drought Severity Index (scPDSI) was achieved from Climate Research Unit (CRU: http://www.cru.uea.ac.uk/). And burned area data was achieved from Global Fire Emissions Database, Version 4.0 (GFED4). Author Contributions: L.L., Z.H., and Z.-C.Z. conceived and designed the experiments; Z.H. performed the experiments; L.L., Z.H., and L.W. analyzed the data; N.B., S.Y., and Z.-C.Z. contributed analysis tools; Z.H., L.L., and L.W. wrote the paper. The authors declare no conflict of interest.
Funders:
Funding AgencyGrant Number
National Key Research and Development Program of China2017YFA0603001
National Key Research and Development Program of China2016YFA0600303
University of Chinese Academy of SciencesUNSPECIFIED
Subject Keywords:XCO2; GOSAT; extreme events; spatiotemporal; biosphere-atmosphere interaction; atmospheric transport
Issue or Number:6
Record Number:CaltechAUTHORS:20180726-123133508
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180726-123133508
Official Citation:He, Z.; Lei, L.; Welp, L.R.; Zeng, Z.-C.; Bie, N.; Yang, S.; Liu, L. Detection of Spatiotemporal Extreme Changes in Atmospheric CO2 Concentration Based on Satellite Observations. Remote Sens. 2018, 10, 839
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:88301
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:26 Jul 2018 20:03
Last Modified:26 Feb 2020 23:03

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