Volume 40 Issue 4
Jun.  2021
Turn off MathJax
Article Contents
Siqi Zhang, Yan Bai, Xianqiang He, Haiqing Huang, Qiangkun Zhu, Fang Gong. Comparisons of OCO-2 satellite derived XCO2 with in situ and modeled data over global ocean[J]. Acta Oceanologica Sinica, 2021, 40(4): 136-142. doi: 10.1007/s13131-021-1844-9
Citation: Siqi Zhang, Yan Bai, Xianqiang He, Haiqing Huang, Qiangkun Zhu, Fang Gong. Comparisons of OCO-2 satellite derived XCO2 with in situ and modeled data over global ocean[J]. Acta Oceanologica Sinica, 2021, 40(4): 136-142. doi: 10.1007/s13131-021-1844-9

Comparisons of OCO-2 satellite derived XCO2 with in situ and modeled data over global ocean

doi: 10.1007/s13131-021-1844-9
Funds:  The National Key Research and Development Programme of China under contract No. 2017YFA0603004; the Fund of Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) (Zhanjiang Bay Laboratory) under contract No. ZJW-2019-08; the National Natural Science Foundation of China under contract Nos 41825014, 41676172 and 41676170; the Global Change and Air-Sea Interaction Project of China under contract Nos GASI-02-SCS-YGST2-01, GASI-02-PACYGST2-01 and GASI-02-IND-YGST2-01.
More Information
  • Corresponding author: E-mail: zhuqiankun@sio.org.cn)
  • Received Date: 2020-09-07
  • Accepted Date: 2020-12-29
  • Available Online: 2021-05-07
  • Publish Date: 2021-06-03
  • Atmospheric CO2 is one of key parameters to estimate air-sea CO2 flux. The Orbiting Carbon Observatory-2 (OCO-2) satellite has observed the column-averaged dry-air mole fractions of global atmospheric carbon dioxide (XCO2) since 2014. In this study, the OCO-2 XCO2 products were compared between in-situ data from the Total Carbon Column Network (TCCON) and Global Monitoring Division (GMD), and modeling data from CarbonTracker2019 over global ocean and land. Results showed that the OCO-2 XCO2 data are consistent with the TCCON and GMD in situ XCO2 data, with mean absolute biases of 0.25×10−6 and 0.67×10−6, respectively. Moreover, the OCO-2 XCO2 data are also consistent with the CarbonTracker2019 modeling XCO2 data, with mean absolute biases of 0.78×10−6 over ocean and 1.02×10−6 over land. The results indicated the high accuracy of the OCO-2 XCO2 product over global ocean which could be applied to estimate the air-sea CO2 flux.
  • loading
  • [1]
    Bai Yan, Cai Weijun, He Xianqiang, et al. 2015. A mechanistic semi-analytical method for remotely sensing sea surface pCO2 in river-dominated coastal oceans: A case study from the East China Sea. Journal of Geophysical Research: Oceans, 120(3): 2331–2349. doi: 10.1002/2014JC010632
    [2]
    Blumenstock T, Hase F, Schneider M, et al. 2014. TCCON data from Izana (ES), Release GGG2014R0. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [3]
    Boesch H, Baker D, Connor B, et al. 2011. Global characterization of CO2 column retrievals from shortwave-infrared satellite observations of the Orbiting Carbon Observatory-2 mission. Remote Sensing, 3(2): 270–304. doi: 10.3390/rs3020270
    [4]
    Boesch H, Brown L, Castano R, et al. 2015. Orbiting Carbon Observatory (OCO)-2. https://docserver.gesdisc.eosdis.nasa.gov/public/project/OCO/OCO2_L2_ATBD.V6.pdf [2018-01-21/2018-09-16]
    [5]
    Bousquet P, Ciais P, Miller J, et al. 2006. Contribution of anthropogenic and natural sources to atmospheric methane variability. Nature, 443(7110): 439–443. doi: 10.1038/nature05132
    [6]
    Bovensmann H, Burrows J P, Buchwitz M, et al. 1999. SCIAMACHY: Mission objectives and measurement modes. Journal of the Atmospheric Sciences, 56(2): 127–150. doi: 10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2
    [7]
    Buchwitz M, Schneising O, Burrows J P, et al. 2007. First direct observation of the atmospheric CO2 year-to-year increase from space. Atmospheric Chemistry and Physics, 7(16): 4249–4256. doi: 10.5194/acp-7-4249-2007
    [8]
    Cogan A J, Boesch H, Parker R J, et al. 2012. Atmospheric carbon dioxide retrieved from the Greenhouse gases observing satellite (GOSAT): Comparison with ground-based TCCON observations and GEOS-Chem model calculations. Journal of Geophysical Research: Atmospheres, 117(D21): D21301
    [9]
    Connor B J, Boesch H, Toon G, et al. 2008. Orbiting Carbon Observatory: Inverse method and prospective error analysis. Journal of Geophysical Research: Atmospheres, 113(D5): D05305
    [10]
    Crisp D. 2015. Measuring atmospheric carbon dioxide from space with the orbiting carbon observatory-2(OCO-2). In: Proceedings of SPIE 9607, Earth Observing Systems XX. San Diego, United States: SPIE, 960702
    [11]
    De Mazière M, Sha M K, Desmet F, et al. 2017. TCCON data from Réunion Island (RE), Release GGG2014. R1. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [12]
    Dlugokencky E, Mund J W, Crotwell A M, et al. 2019. Atmospheric Carbon Dioxide Dry Air Mole Fractions from the NOAA ESRL Carbon Cycle Cooperative Global Air Sampling Network, 1968–2018. Boulder, USA: NOAA ESRL Global Monitoring Division
    [13]
    Eldering A, O'Dell C W, Wennberg P O, et al. 2017. The Orbiting Carbon Observatory 2: First 18 months of science data products. Atmospheric Measurement Techniques, 10(2): 549–563. doi: 10.5194/amt-10-549-2017
    [14]
    Forkel M, Carvalhais N, Rödenbeck C, et al. 2016. Enhanced seasonal CO2 exchange caused by amplified plant productivity in northern ecosystems. Science, 351(6274): 696–699. doi: 10.1126/science.aac4971
    [15]
    Graven H D, Keeling R F, Piper S C, et al. 2013. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science, 341(6150): 1085–1089. doi: 10.1126/science.1239207
    [16]
    Gray J M, Frolking S, Kort E A, et al. 2014. Direct human influence on atmospheric CO2 seasonality from increased cropland productivity. Nature, 515(7527): 398–401. doi: 10.1038/nature13957
    [17]
    Griffith D W T, Deutscher N M, Velazco V A, et al. 2014a. TCCON data from Darwin (AU), Release GGG2014R0. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [18]
    Griffith D W T, Velazco V A, Deutscher N M, et al. 2014b. TCCON data from Wollongong (AU), Release GGG2014R0. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [19]
    Grise K M, Polvani L M. 2017. Understanding the time scales of the tropospheric circulation response to abrupt CO2 forcing in the Southern Hemisphere: seasonality and the role of the stratosphere. Journal of Climate, 30(21): 8497–8515. doi: 10.1175/JCLI-D-16-0849.1
    [20]
    Hase F, Blumenstock T, Dohe S, et al. 2017. TCCON data from Karlsruhe (DE), Release GGG2014R1. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [21]
    Intergovernmental Panel on Climate Change. 2007. Climate Change 2007: the Physical Science Basis: Working Group I Contribution to the Fourth Assessment Report of the IPCC. Cambridge: Cambridge University Press, 847–940
    [22]
    IPCC. 2013. Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press
    [23]
    Iraci L T, Podolske J, Hillyard P W, et al. 2016a. TCCON data from Edwards (US), Release GGG2014R1, Pasadena, California. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [24]
    Iraci L T, Podolske J, Hillyard P W, et al. 2016b. TCCON data from Indianapolis (US), Release GGG2014R1, Pasadena, California. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [25]
    Jacobson A R, Schuldt K N, Miller J B, et al. 2020. CarbonTracker CT2019. NOAA Earth System Research Laboratory, Global Monitoring Division. https://gml.noaa.gov/ccgg/carbontracker/CT2019/CT2019_doc.php [2018-04-19]
    [26]
    Jenkinson D S, Adams D E, Wild A. 1991. Model estimates of CO2 emissions from soil in response to global warming. Nature, 351(6324): 304–306. doi: 10.1038/351304a0
    [27]
    Joiner J, Yoshida Y, Vasilkov A, et al. 2011. First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences, 8(3): 637–651. doi: 10.5194/bg-8-637-2011
    [28]
    Kawakami S, Ohyama H, Arai K, et al. 2014. TCCON data from Saga (JP), Release GGG2014R0. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [29]
    Kivi R, Heikkinen P, Kyr. 2014. TCCON data from Sodankyla (FI), Release GGG2014R0. 637–651. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [30]
    Liang A L, Gong W, Han G, et al. 2017. Comparison of satellite-observed XCO2 from GOSAT, OCO-2, and ground-based TCCON. Remote Sensing, 9(10): 1033. doi: 10.3390/rs9101033
    [31]
    Maki T, Ikegami M, Fujita T, et al. 2010. New technique to analyse global distributions of CO2 concentrations and fluxes from non-processed observational data. Tellus B, 62(5): 797–809. doi: 10.1111/j.1600-0889.2010.00488.x
    [32]
    Northcott D, Sevadjian J, Sancho-Gallegos D A, et al. 2019. Impacts of urban carbon dioxide emissions on sea-air flux and ocean acidification in nearshore waters. PLoS One, 14(3): e0214403. doi: 10.1371/journal.pone.0214403
    [33]
    Notholt J, Petri C, Warneke T, et al. 2014. TCCON data from Bremen (DE), Release GGG2014R0. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [34]
    Notholt J, Warneke T, Petri C, et al. 2017. TCCON data from Ny Ålesund, Spitsbergen (NO), Release GGG2014. R0. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [35]
    Peters W, Jacobson A R, Sweeney C, et al. 2007. An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker. Proceedings of the National Academy of Sciences of the United States of America, 104(48): 18925–18930. doi: 10.1073/pnas.0708986104
    [36]
    Sherlock V, Connor B J, Robinson J, et al. 2014a. TCCON data from Lauder (NZ), 120HR, Release GGG2014R0. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [37]
    Sherlock V, Connor B J, Robinson J, et al. 2014b. TCCON data from Lauder (NZ), 125HR, Release GGG2014R0. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [38]
    Song Xuelian, Bai Yan, Cai Weijun, et al. 2016. Remote sensing of sea surface pCO2 in the bering sea in summer based on a Mechanistic Semi-Analytical Algorithm (MeSAA). Remote Sensing, 8(7): 558. doi: 10.3390/rs8070558
    [39]
    Strong K, Mendonca J, Weave D, et al. 2017. TCCON data from Eureka (CA), Release GGG2014R1. https://data.caltech.edu/records/293 [2017-09-13/2018-04-19]
    [40]
    Williams I N, Riley W J, Torn M S, et al. 2014. Biases in regional carbon budgets from covariation of surface fluxes and weather in transport model inversions. Atmospheric Chemistry and Physics, 14(3): 1571–1585. doi: 10.5194/acp-14-1571-2014
    [41]
    Wunch D, Toon G C, Blavier J F L, et al. 2011. The total carbon column observing network. Philosophical Transactions. Series A: Mathematical, Physical, and Engineering Sciences, 369(1943): 2087–2112
    [42]
    Wunch D, Wennberg P O, Osterman G, et al. 2017. Comparisons of the orbiting carbon observatory-2(OCO-2) XCO2 measurements with TCCON. Atmospheric Measurement Techniques, 10: 2209–2238. doi: 10.5194/amt-10-2209-2017
    [43]
    Yokota T, Yoshida Y, Eguchi N, et al. 2009. Global concentrations of CO2 and CH4 retrieved from GOSAT: First preliminary results. Sola, 5: 160–163. doi: 10.2151/sola.2009-041
    [44]
    Zeng Ning, Zhao Fang, Collatz G J, et al. 2014. Agricultural green revolution as a driver of increasing atmospheric CO2 seasonal amplitude. Nature, 515(7527): 394–397. doi: 10.1038/nature13893
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)

    Article Metrics

    Article views (559) PDF downloads(14) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return