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Large Chinese land carbon sink estimated from atmospheric carbon dioxide data

Nature volume 586, pages 720–723 (2020)Cite this article

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Matters Arising to this article was published on 16 March 2022

Matters Arising to this article was published on 16 March 2022

A Publisher Correction to this article was published on 24 November 2020

This article has been updated

Abstract

Limiting the rise in global mean temperatures relies on reducing carbon dioxide (CO2) emissions and on the removal of CO2 by land carbon sinks. China is currently the single largest emitter of CO2, responsible for approximately 27 per cent (2.67 petagrams of carbon per year) of global fossil fuel emissions in 20171. Understanding of Chinese land biosphere fluxes has been hampered by sparse data coverage2,3,4, which has resulted in a wide range of a posteriori estimates of flux. Here we present recently available data on the atmospheric mole fraction of CO2, measured from six sites across China during 2009 to 2016. Using these data, we estimate a mean Chinese land biosphere sink of −1.11 ± 0.38 petagrams of carbon per year during 2010 to 2016, equivalent to about 45 per cent of our estimate of annual Chinese anthropogenic emissions over that period. Our estimate reflects a previously underestimated land carbon sink over southwest China (Yunnan, Guizhou and Guangxi provinces) throughout the year, and over northeast China (especially Heilongjiang and Jilin provinces) during summer months. These provinces have established a pattern of rapid afforestation of progressively larger regions5,6, with provincial forest areas increasing by between 0.04 million and 0.44 million hectares per year over the past 10 to 15 years. These large-scale changes reflect the expansion of fast-growing plantation forests that contribute to timber exports and the domestic production of paper7. Space-borne observations of vegetation greenness show a large increase with time over this study period, supporting the timing and increase in the land carbon sink over these afforestation regions.

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Fig. 1: Chinese terrestrial biosphere CO2 fluxes.
Fig. 2: Terrestrial biosphere CO2 fluxes.
Fig. 3: Satellite observations relating to vegetation growth.

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Data availability

GRACE data are available from http://grace.jpl.nasa.gov. ABC data are available from https://www.wenfo.org/wald/global-biomass. The NDVI, EVI and LAI data were retrieved from the online Data Pool, courtesy of the NASA Land Processes Distributed Active Archive Center, USGS/Earth Resources Observation and Science Center (https://lpdaac.usgs.gov/data_access/data_pool). CO2 mole fraction data from the Chinese sites used in this study are available at https://doi.org/10.17632/w3bwmr6rfg.1 on http://data.mendeley.com. Requests for further information about these data should be directed to S.F. Source data are provided with this paper.

Code availability

We acknowledge the Python Software Foundation: Python Language Reference, version 3.7.7; available at http://www.python.org. We also acknowledge Matplotlib (v3.1.3, 10.5281/zenodo.3984190)52. The community-led GEOS-Chem model of atmospheric chemistry and transport is maintained centrally by Harvard University (http://acmg.seas.harvard.edu/geos/) and is available on request. The ensemble Kalman filter code is publicly available as PyOSSE (https://www.nceo.ac.uk/data-tools/atmospheric-tools/).

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Acknowledgements

J.W. acknowledges support by the Strategic Priority Research Program of Chinese Academy of Sciences (grant no. XDA17010100) and funding from the UK Natural Environmental Research Council (NERC) National Centre for Earth Observation (NCEO). L.F., P.I.P. and H.B. are supported by the NERC NCEO (grant no. NE/R016518/1). H.B. acknowledges funding from the ESA CCI and C3S projects. P.I.P. also acknowledges funding from a Royal Society Wolfson Research Merit Award. Y.L. is supported by the international partnership programme of the Chinese Academy of Sciences (grant no. 134111KYSB20170010). S.F. is supported by the National Key Research and Development Program of China (grant no. 2017YFC0209700). D.Y. is supported by the National Key Research and Development Program of China (grant no. 2016YFA0600203). C.W.O. was supported by subcontracts from the OCO-2 project at the NASA Jet Propulsion Laboratory. J.W., L.F., P.I.P., Y.L., H.B. and D.Y. acknowledge funding from the ESA-MOST Dragon 3 Cooperation Programme. We gratefully acknowledge all CO2 ground observations contributors to the obspack_co2_1_GLOBALVIEWplus_v4.1_2018-10-29 and to NOAA ESRL for maintaining this database. We also acknowledge data provided by Hong Kong Observatory, the Japan–Russia Siberia Tall Tower Inland Observation Network and the World Data Centre for Greenhouse Gases. We also thank P. Somkuti for GOSAT SIF data and the broader GOSAT team who provided the L1 data.

Author information

Authors and Affiliations

  1. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Jing Wang, Yi Liu & Dongxu Yang

  2. University of Chinese Academy of Sciences, Beijing, China

    Jing Wang, Yi Liu & Dongxu Yang

  3. National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK

    Jing Wang, Liang Feng & Paul I. Palmer

  4. School of GeoSciences, University of Edinburgh, Edinburgh, UK

    Jing Wang, Liang Feng & Paul I. Palmer

  5. College of Environment, Zhejiang University of Technology, Hangzhou, China

    Shuangxi Fang

  6. Meteorological Observation Centre, China Meteorological Administration, Beijing, China

    Shuangxi Fang & Lixin Liu

  7. National Centre for Earth Observation, University of Leicester, Leicester, UK

    Hartmut Bösch

  8. Colorado State University, Fort Collins, CO, USA

    Christopher W. O’Dell

  9. Academy of Forest Inventory and Planning, State Forestry Administration, Beijing, China

    Xiaoping Tang & ChaoZong Xia

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Contributions

Y.L., P.I.P. and L.F. designed the research. S.F. and L.L. processed and evaluated the observed data. J.W. and L.F. performed the atmospheric inversion analysis and data analysis. X.T. and C.X. provided the national forest inventory data. P.I.P. and J.W. led the writing of the paper, with contributions from L.F., Y.L., S.F., H.B., C.W.O., X.T., D.Y., L.L. and C.X.

Corresponding authors

Correspondence to Paul I. Palmer, Yi Liu or Shuangxi Fang.

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The authors declare no competing interests.

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Peer review information Nature thanks Sourish Basu, Julia Marshall, Peter Rayner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 In situ atmospheric CO2 measurements across East Asia.

a, Map showing the location of atmospheric CO2 measurement sites over East Asia used in our numerical experiments. Coloured dots represent individual measurement sites referred to by letter codes (Supplementary Tables 1, 2). Blue dots represent NOAA ObsPack stations, the cyan dot represents the HKG station from WDCGG, brown dots represent Siberia stations and orange dots represent CMA regional background stations. b–g, CO2 mole fraction observations (in parts per million, ppm) at the six sites across China from 2009 to 2017 used in this study. Discrete (weekly) flask air samples, denoted by blue dots, and continuous (hourly) observations, denoted by black dots, are collected and analysed by the China Meteorological Administration. CRDS, cavity ring-down spectrometer.

Source data

Extended Data Fig. 2 Monthly subcontinental Chinese CO2 flux estimates.

a, Map showing the subcontinental geographical regions over China where we report a posteriori CO2 fluxes. Colours denote the proportion of each region that falls within mainland China. b–i, Monthly regional a priori and a posteriori biosphere CO2 fluxes (Pg C month−1) over China during 2010–2016. A posteriori fluxes are inferred from data used in the SR-1 and SR-2 inversions (see main text). Vertical bars and the orange envelope denote a priori and a posteriori uncertainties. NWC, northwest China; NC, northern China; NE, northeast China; TP, Tibetan Plateau; CC, central China; EC, eastern China; SW, southwest China; SE, southeast China, as shown in a.

Source data

Extended Data Fig. 3 Mean spatial distribution of a priori and a posteriori land biosphere CO2 fluxes from May to September, inferred from in situ and satellite observations of CO2.

a, Our a priori fluxes. b, c, The a posteriori fluxes corresponding to inversions SR-1 and SR-2 (see main text) that use in situ data. d, e, A posteriori fluxes inferred from column observations of CO2 from GOSAT and from NASA’s OCO-2, respectively. Flux estimates reported represent a temporal mean from 2010 to 2015, except for e, which is only for 2015.

Extended Data Fig. 4 Seasonal distribution and magnitude of satellite retrievals of column CO2 from December 2014 to November 2015.

a–d, Data from GOSAT (v7.3 ACOS). e–h, Data from OCO-2 (v8r). See Methods section ‘Data’ for further information.

Extended Data Fig. 5 A priori and a posteriori monthly biosphere CO2 fluxes over China inferred from the GOSAT CO2 column data and in situ data from 2010 to 2016.

We also report a posteriori fluxes inferred from OCO-2 CO2 column from September 2014 to December 2016. Vertical bars and shaded envelopes denote a posteriori uncertainties. Higher annual fluxes inferred from GOSAT and OCO-2 are due mainly to higher a posteriori fluxes during winter months when data coverage is sparse (Extended Data Fig. 4) and the fluxes are more influenced by a priori values.

Source data

Extended Data Fig. 6 Changes in forest area, forest stocks and forest coverage over six key forested Chinese provinces and over the whole of China, 1973–2013.

a–f, Values for Heilongjiang, Jilin, Liaoning, Yunnan, Guizhou and Guangxi provinces. g, Values for China. The x-axis labels refer to the National Forest Inventory of China’s State Forestry Administration: 1st (1973–1976), 2nd (1977–1981), 3rd (1984–1988), 4th (1989–1993), 5th (1994–1998), 6th (1999–2003), 7th (2004–2008) and 8th (2009–2013).

Source data

Extended Data Fig. 7 Forest cover change over China during the period 2002–2012.

a, How land cover changed from 2002 to 2012: 0 (1) denotes non-forest (forest) in both 2002 and 2012, 2 denotes conversion from non-forest to forest between 2002 and 2012, and 3 denotes conversion from forest to non-forest between 2002 and 2012. b, The forest percentage change per grid box from 2002 to 2012. Data are presented on a 0.05° × 0.05° spatial grid.

Extended Data Fig. 8 Multi-year mean of satellite observations of vegetation indices.

a, NDVI. b, EVI. c, LAI. d, Solar induced fluorescence (SIF). e, Net photosynthesis (PSN). f, Gross primary production (GPP). g, Above-ground biomass carbon (ABC). See Supplementary Information for details. The mean is over 2010–2012, inclusive, for ABC, and over 2010–2016 for the other data.

Extended Data Fig. 9 Forest area and stock of five forest-stand age groups over six key forested provinces and over the whole of China.

a–f, Forest area for Heilongjiang, Jilin, Liaoning, Yunnan, Guizhou and Guangxi provinces. g, Forest area for China. h–m, Forest stock for Heilongjiang, Jilin, Liaoning, Yunnan, Guizhou and Guangxi provinces. n, Forest stock for China. Forest stands are divided into five age groups: young, mid-aged, near-mature, mature and overmature. Data are taken from the 8th NFI.

Source data

Extended Data Table 1 Summary statistics calculated from the 6th to 8th (and 9th where available) National Forest Inventory of China’s State Forestry Administration for China and six Chinese Provinces
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Materials, including Supplementary Methods, Supplementary Notes, Supplementary Figures 114, Supplementary Tables 1–7, and Supplementary References.

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Wang, J., Feng, L., Palmer, P.I. et al. Large Chinese land carbon sink estimated from atmospheric carbon dioxide data. Nature 586, 720–723 (2020). https://doi.org/10.1038/s41586-020-2849-9

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