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The First Global Carbon Dioxide Flux Map Derived from TanSat Measurements

Abstract

Space-borne measurements of atmospheric greenhouse gas concentrations provide global observation constraints for top-down estimates of surface carbon flux. Here, the first estimates of the global distribution of carbon surface fluxes inferred from dry-air CO2 column (XCO2) measurements by the Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite (TanSat) are presented. An ensemble transform Kalman filter (ETKF) data assimilation system coupled with the GEOS-Chem global chemistry transport model is used to optimally fit model simulations with the TanSat XCO2 observations, which were retrieved using the Institute of Atmospheric Physics Carbon dioxide retrieval Algorithm for Satellite remote sensing (IAPCAS). High posterior error reduction (30%–50%) compared with a priori fluxes indicates that assimilating satellite XCO2 measurements provides highly effective constraints on global carbon flux estimation. Their impacts are also highlighted by significant spatiotemporal shifts in flux patterns over regions critical to the global carbon budget, such as tropical South America and China. An integrated global land carbon net flux of 6.71 ± 0.76 Gt C yr−1 over 12 months (May 2017–April 2018) is estimated from the TanSat XCO2 data, which is generally consistent with other inversions based on satellite data, such as the JAXA GOSAT and NASA OCO-2 XCO2 retrievals. However, discrepancies were found in some regional flux estimates, particularly over the Southern Hemisphere, where there may still be uncorrected bias between satellite measurements due to the lack of independent reference observations. The results of this study provide the groundwork for further studies using current or future TanSat XCO2 data together with other surface-based and space-borne measurements to quantify biosphere-atmosphere carbon exchange.

References

  1. Basu, S., and Coauthors, 2013: Global CO2 fluxes estimated from GOSAT retrievals of total column CO2. Atmospheric Chemistry and Physics, 13, 8695–8717, https://doi.org/10.5194/acp-13-8695-2013.

    Article  Google Scholar 

  2. Buchwitz, M., O. Schneising, J. P. Burrows, H. Bovensmann, M. Reuter, and J. Notholt, 2007: First direct observation of the atmospheric CO2 year-to-year increase from space. Atmospheric Chemistry and Physics, 7, 4249–4256, https://doi.org/10.5194/acp-7-4249-2007.

    Article  Google Scholar 

  3. Chen, C., and Coauthors, 2019: China and India lead in greening of the world through land-use management. Nature Sustainability, 2, 122–129, https://doi.org/10.1038/s41893-019-0220-7.

    Article  Google Scholar 

  4. Chevallier, F., M. Remaud, C. W. O’Dell, D. Baker, P. Peylin, and A. Cozic, 2019: Objective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions. Atmos. Chem. Phys., 19, 14233–14251, https://doi.org/10.5194/acp-19-14233-2019.

    Article  Google Scholar 

  5. Crisp, D., and Coauthors, 2017: The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products. Atmospheric Measurement Techniques, 10, 59–81, https://doi.org/10.5194/amt-10-59-2017.

    Article  Google Scholar 

  6. Crowell, S., and Coauthors, 2019: The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network. Atmospheric Chemistry and Physics, 19, 9797–9831, https://doi.org/10.5194/acp-19-9797-2019.

    Article  Google Scholar 

  7. Deng, F., and Coauthors, 2014: Inferring regional sources and sinks of atmospheric CO2 from GOSAT XCO2 data. Atmospheric Chemistry and Physics, 14, 3703–3727, https://doi.org/10.5194/acp-14-3703-2014.

    Article  Google Scholar 

  8. Feng, L., P. I. Palmer, H. Bösch, and S. Dance, 2009: Estimating surface CO2 fluxes from space-borne CO2 dry air mole fraction observations using an ensemble Kalman Filter. Atmospheric Chemistry and Physics, 9, 2619–2633, https://doi.org/10.5194/acp-9-2619-2009.

    Article  Google Scholar 

  9. Feng, L., P. I. Palmer, Y. Yang, R. M. Yantosca, S. R. Kawa, J.-D. Paris, H. Matsueda, and T. Machida, 2011: Evaluating a 3-D transport model of atmospheric CO2 using ground-based, aircraft, and space-borne data. Atmospheric Chemistry and Physics, 11, 2789–2803, https://doi.org/10.5194/acp-11-2789-2011.

    Article  Google Scholar 

  10. Feng, L., P. I. Palmer, R. J. Parker, N. M. Deutscher, D. G. Feist, R. Kivi, I. Morino, and R. Sussmann, 2016: Estimates of European uptake of CO2 inferred from GOSAT XCO2 retrievals: Sensitivity to measurement bias inside and outside Europe. Atmos. Chem. Phys., 16, 1289–1302, https://doi.org/10.5194/acp-16-1289-2016.

    Article  Google Scholar 

  11. Feng, L., and Coauthors, 2017: Consistent regional fluxes of CH4 and CO2 inferred from GOSAT proxy XCH4: XCO2 retrievals, 2010–2014. Atmospheric Chemistry and Physics, 17, 4781–4797, https://doi.org/10.5194/acp-17-4781-2017.

    Article  Google Scholar 

  12. Gurney, K. R., and Coauthors, 2002: Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models. Nature, 415, 626–630, https://doi.org/10.1038/415626a.

    Article  Google Scholar 

  13. Houweling, S., and Coauthors, 2015: An intercomparison of inverse models for estimating sources and sinks of CO2 using GOSAT measurements. J. Geophys. Res., 120, 5253–5266, https://doi.org/10.1002/2014JD022962.

    Article  Google Scholar 

  14. Jiang, F., and Coauthors, 2016: A comprehensive estimate of recent carbon sinks in China using both top-down and bottom-up approaches. Sci. Rep., 6, 22130, https://doi.org/10.1038/srep22130.

    Article  Google Scholar 

  15. Keppel-Aleks, G., P. O. Wennberg, and T. Schneider, 2011: Sources of variations in total column carbon dioxide. Atmospheric Chemistry and Physics, 11, 3581–3593, https://doi.org/10.5194/acp-11-3581-2011.

    Article  Google Scholar 

  16. Kuhlmann, G., G. Broquet, J. Marshall, V. Clément, A. Löscher, Y. Meijer, and D. Brunner, 2019: Detectability of CO2 emission plumes of cities and power plants with the Copernicus Anthropogenic CO2 Monitoring (CO2M) mission. Atmospheric Measurement Techniques, 12, 6695–6719, https://doi.org/10.5194/amt-12-6695-2019.

    Article  Google Scholar 

  17. Kuze, A., H. Suto, M. Nakajima, and T. Hamazaki, 2009: Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring. Appl. Opt., 48, 6716–6733, https://doi.org/10.1364/AO.48.006716.

    Article  Google Scholar 

  18. Liu, Y., D. X. Yang, and Z. N. Cai, 2013: A retrieval algorithm for TanSat XCO2 observation: Retrieval experiments using GOSAT data. Chinese Science Bulletin, 58, 1520–1523, https://doi.org/10.1007/s11434-013-5680-y.

    Article  Google Scholar 

  19. Liu, Y., and D. X. Yang, 2016: Advancements in theory of GHG observation from space. Science Bulletin, 61(5), 349–352, https://doi.org/10.1007/s11434-016-1022-1.

    Article  Google Scholar 

  20. Liu, Y., and Coauthors, 2018: The TanSat mission: Preliminary global observations. Science Bulletin, 63(18), 1200–1207, https://doi.org/10.1016/j.scib.2018.08.004.

    Article  Google Scholar 

  21. Maksyutov, S., and Coauthors, 2013: Regional CO2 flux estimates for 2009–2010 based on GOSAT and ground-based CO2 observations. Atmospheric Chemistry and Physics, 13, 9351–9373, https://doi.org/10.5194/acp-13-9351-2013.

    Article  Google Scholar 

  22. Oda, T., and S. Maksyutov, 2011: A very high-resolution (1 km×1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights. Atmospheric Chemistry and Physics, 11, 543–556, https://doi.org/10.5194/acp-11-543-2011.

    Article  Google Scholar 

  23. Olsen, S. C., 2004: Differences between surface and column atmospheric CO2 and implications for carbon cycle research. J. Geophys. Res., 109, D02301, https://doi.org/10.1029/2003JD003968.

    Google Scholar 

  24. Palmer, P., L. Feng, and H. Boesch, 2011: Spatial resolution of tropical terrestrial CO2 fluxes inferred using space-borne column CO2 sampled in different earth orbits: The role of spatial error correlations. Atmospheric Measurement Techniques, 4(9), 1995–2006, https://doi.org/10.5194/amt-4-1995-2011.

    Article  Google Scholar 

  25. Palmer, P. I., L. Feng, D. Baker, F. Chevallier, H. Bösch, and P. Somkuti, 2019: Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal. Nature Communications, 10, 3344, https://doi.org/10.1038/s41467-019-11097-w.

    Article  Google Scholar 

  26. Peters, W., and Coauthors, 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), 18 925–18 930, https://doi.org/10.1073/pnas.0708986104.

    Article  Google Scholar 

  27. Peylin, P., D. Baker, J. Sarmiento, P. Ciais, and P. Bousquet, 2002: Influence of transport uncertainty on annual mean and seasonal inversions of atmospheric CO2 data. J. Geophys. Res., 107(D19), 4385, https://doi.org/10.1029/2001JD000857.

    Article  Google Scholar 

  28. Peylin, P., and Coauthors, 2013: Global atmospheric carbon budget: Results from an ensemble of atmospheric CO2 inversions. Biogeosciences, 10, 6699–6720, https://doi.org/10.5194/bg-10-6699-2013.

    Article  Google Scholar 

  29. Ran, Y., and X. Li, 2019: TanSat: A new star in global carbon monitoring from China. Scientific Bulletin, 64(5), 284–285, https://doi.org/10.1016/j.scib.2019.01.019.

    Google Scholar 

  30. Reuter, M., and Coauthors, 2017: How much CO2 is taken up by the European terrestrial biosphere? Bull. Amer. Meteor. Soc., 98, 665–671, https://doi.org/10.1175/BAMS-D-15-00310.1.

    Article  Google Scholar 

  31. Saeki, T., and Coauthors, 2013: Inverse modeling of CO2 fluxes using GOSAT data and multi-year ground-based observations. SOLA, 9, 45–50, https://doi.org/10.2151/sola.2013-011.

    Article  Google Scholar 

  32. Scholes, R. J., P. M. S. Monteiro, C. L. Sabine, and J. G. Canadell, 2009: Systematic long-term observations of the global carbon cycle. Trends in Ecology & Evolution, 24, 427–430, https://doi.org/10.1016/j.tree.2009.03.006.

    Article  Google Scholar 

  33. Takahashi, T., and Coauthors, 2009: Corrigendum to “Climatological mean and decadal change in surface ocean PCO2, and net sea-air CO2 flux over the global oceans” [Deep Sea Res. II 56 (2009) 554–577]. Deep Sea Research Part I: Oceanographic Research Papers, 56, 2075–2076, https://doi.org/10.1016/j.dsr.2009.07.007.

    Article  Google Scholar 

  34. van der Werf, G. R., and Coauthors, 2010: Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmospheric Chemistry and Physics, 10, 11 707–11 735, https://doi.org/10.5194/acp-10-11707-2010.

    Article  Google Scholar 

  35. Wang, H., F. Jiang, J. Wang, W. Ju, and J. M. Chen, 2019: Terrestrial ecosystem carbon flux estimated using GOSAT and OCO-2 XCO2 retrievals. Atmos. Chem. Phys., 19, 12067–12082, https://doi.org/10.5194/acp-19-12067-2019.

    Article  Google Scholar 

  36. Wang, J., and Coauthors, 2020: Large Chinese land carbon sink estimated from atmospheric carbon dioxide data. Nature, 586, 720–723, https://doi.org/10.1038/s41586-020-2849-9.

    Article  Google Scholar 

  37. Yang, D. X., Y. Liu, Z. N. Cai, J. B. Deng, J. Wang, and X. Chen, 2015: An advanced carbon dioxide retrieval algorithm for satellite measurements and its application to GOSAT observations. Science Bulletin, 60(23), 2063–2066, https://doi.org/10.1007/s11434-015-0953-2.

    Article  Google Scholar 

  38. Yang, D. X., Y. Liu, Z. N. Cai, X. Chen, L. Yao, and D. R. Lu, 2018: First global carbon dioxide maps produced from TanSat measurements. Advances in Atmospheric Sciences, 35, 621–623, https://doi.org/10.1007/s00376-018-7312-6.

    Article  Google Scholar 

  39. Yang, D. X., and Coauthors, 2020: Toward high precision XCO2 retrievals from TanSat observations: Retrieval improvement and validation against TCCON measurements. J. Geophys. Res., 125, e2020JD032794, https://doi.org/10.1029/2020JD032794.

    Google Scholar 

  40. Yang, D. X., and Coauthors, 2021: A new TanSat XCO2 global product towards climate studies. Advances in Atmospheric Sciences, 38(1), 8–11, https://doi.org/10.1007/s00376-020-0297-y.

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Key R&D Program of China (Grant No. 2016YFA0600203), the Key Research Program of the Chinese Academy of Sciences (ZDRW-ZS-2019-1), the National Key R&D Program of China (Grant No. 2017YFB0504000), and the Youth Program of the National Natural Science Foundation of China (Grant No. 41905029). Liang FENG is supported by the UK NERC National Centre for Earth Observation (NCEO). The TanSat L1B data service is provided by IRCSD and CASA (131211KYSB20180002). We also thank the FENGYUN Satellite Data Center of the National Satellite Meteorological Center, who provided the TanSat L1B data service. The authors thank the TanSat mission and highly appreciate the support from everyone involved.

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Correspondence to Jing Wang.

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Yang, D., Liu, Y., Feng, L. et al. The First Global Carbon Dioxide Flux Map Derived from TanSat Measurements. Adv. Atmos. Sci. 38, 1433–1443 (2021). https://doi.org/10.1007/s00376-021-1179-7

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Key words

  • TanSat
  • carbon flux
  • CO2
  • flux inversion