Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Global patterns and drivers of tropical aboveground carbon changes

Nature Climate Change volume 14, pages 1064–1070 (2024)Cite this article

Subjects

Abstract

Tropical terrestrial ecosystems play an important role in modulating the global carbon balance. However, the complex dynamics and factors controlling tropical aboveground live biomass carbon (AGC) are not fully understood. Here, using remotely sensed observations, we find a moderate net AGC sink of 0.21 ± 0.06 PgC yr−1 throughout the global tropics from 2010 to 2020. This arises from a gross loss of −1.79 PgC yr−1 offset by a marked gain of 2.01 ± 0.06 PgC yr−1. Fire emissions in non-forested African shrubland/savanna biomes, coupled with post-fire carbon recovery, substantially dominated the interannual variability of tropical AGC. Fire radiative power was identified as the primary determinant of the spatial variability in AGC gains, with soil moisture also playing a crucial role in shaping trends. We highlight the dominant roles of anthropogenic and hydroclimatic determinants in orchestrating tropical land carbon dynamics and advocate for land management to conserve indispensable ecosystem services worldwide.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Spatial and temporal patterns of AGC losses during 2010–2020 over the tropics.
Fig. 2: Spatial and temporal patterns of AGC changes during 2010–2020 over the tropics.
Fig. 3: Factors influencing spatial variability and trends in AGC gains during 2010–2020 over the tropics.

Similar content being viewed by others

Data availability

The L-VOD data are available at the SMOS-IC website (https://ib.remote-sensing.inrae.fr/). The TMF data are available at https://forobs.jrc.ec.europa.eu/TMF. The van Wees fire emission data were downloaded from ref. 29 (https://zenodo.org/records/7229675). The GFED5 burned area data are available via Zenodo at https://doi.org/10.5281/zenodo.7668423 (ref. 59). The ESA-CCI AGB data are available at https://climate.esa.int/en/odp/#/project/biomass. The Zarin AGB data are available at https://data.globalforestwatch.org/datasets/3e8736c8866b458687e00d40c9f00bce_0/about. The Avitabile AGB data are available at http://lucid.wur.nl/datasets/high-carbon-ecosystems. The Baccini AGB data are available at https://developers.google.com/earth-engine/datasets/catalog/WHRC_biomass_tropical. The two Jet Propulsion Laboratory AGB maps are available at https://gfw2-data.s3.amazonaws.com/forest_cover/zip/tropical_forest_carbon_stocks.tif.aux.zip and https://zenodo.org/records/7583611(ref. 48), respectively. The atmospheric CGR data are available at https://gml.noaa.gov/. The links to the predictors for BRT models are provided in the Supplementary Information. The baseline map for map figures was obtained from GADM (https://gadm.org/). The generated maps of AGC losses and gains are available online at https://data.tpdc.ac.cn/zh-hans/data/97a05aa3-5d4f-4e44-99d1-e474166e62a6.

Code availability

The scripts used to generate all the results are MATLAB (2022B). The code is available from the corresponding author on request.

References

  1. Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): global datasets of forest above–ground biomass for the years 2010, 2017, 2018, 2019 and 2020, v4. CEDA Archive https://doi.org/10.5285/af60720c1e404a9e9d2c145d2b2ead4e (2023).

  2. Mitchard, E. T. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).

    Article  CAS  Google Scholar 

  3. Pan, Y. et al. A large and persistent carbon sink in the World’s forests, 1990–2007. Science 333, 988–993 (2011).

    Article  CAS  Google Scholar 

  4. Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change 11, 234–240 (2021).

    Article  Google Scholar 

  5. Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).

    Article  CAS  Google Scholar 

  6. Harris, N. L. et al. Baseline map of carbon emissions from deforestation in tropical regions. Science 336, 1573–1576 (2012).

    Article  CAS  Google Scholar 

  7. Arneth, A. et al. Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed. Nat. Geosci. 10, 79–84 (2017).

    Article  CAS  Google Scholar 

  8. Rammig, A. & David, M. L. The declining tropical carbon sink. Nat. Clim. Change 11, 727–728 (2021).

    Article  Google Scholar 

  9. Baccini, A. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).

    Article  CAS  Google Scholar 

  10. Heinrich, V. H. et al. The carbon sink of secondary and degraded humid tropical forests. Nature 615, 436–442 (2023).

    Article  CAS  Google Scholar 

  11. Feng, Y. et al. Doubling of annual forest carbon loss over the tropics during the early twenty-first century. Nat. Sustain. 5, 444–451 (2022).

    Article  Google Scholar 

  12. Fawcett, D. et al. Declining Amazon biomass due to deforestation and subsequent degradation losses exceeding gains. Glob. Change Biol. 29, 1106–1118 (2022).

    Article  Google Scholar 

  13. Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).

    Article  Google Scholar 

  14. Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).

    Article  CAS  Google Scholar 

  15. Tucker, C. et al. Sub-continental-scale carbon stocks of individual trees in African drylands. Nature 615, 80–86 (2023).

    Article  CAS  Google Scholar 

  16. Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants 5, 944–951 (2019).

    Article  CAS  Google Scholar 

  17. Ramo, R. et al. African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data. Proc. Natl Acad. Sci. USA 118, e2011160118 (2021).

    Article  CAS  Google Scholar 

  18. Zeng, Z. et al. Highland cropland expansion and forest loss in Southeast Asia in the twenty-first century. Nat. Geosci. 11, 556–562 (2018).

    Article  CAS  Google Scholar 

  19. Wigneron, J. P. et al. Tropical forests did not recover from the strong 2015–2016 El Nino event. Sci. Adv. 6, eaay4603 (2020).

    Article  CAS  Google Scholar 

  20. Yang, H. et al. Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Nino. Proc. Natl Acad. Sci. USA 119, e2101388119 (2022).

    Article  CAS  Google Scholar 

  21. Yang, H. et al. Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates. Glob. Change Biol. 26, 3997–4012 (2020).

    Article  Google Scholar 

  22. Baccini, A. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).

    Article  CAS  Google Scholar 

  23. Zarin, D. J. et al. Can carbon emissions from tropical deforestation drop by 50% in 5 years? Glob. Change Biol. 22, 1336–1347 (2016).

    Article  Google Scholar 

  24. Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change 11, 442–448 (2021).

    Article  Google Scholar 

  25. Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368, 869–874 (2020).

    Article  CAS  Google Scholar 

  26. Ruehr, S., et al. Evidence and attribution of the enhanced land carbon sink. Nat. Rev. Earth Environ. 4, 518–534 (2023).

  27. Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).

    Article  Google Scholar 

  28. Feng, Y. et al. Upward expansion and acceleration of forest clearance in the mountains of Southeast Asia. Nat. Sustain. 4, 892–899 (2021).

    Article  Google Scholar 

  29. van Wees, D. et al. Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED). Geosci. Model Dev. 15, 8411–8437 (2022).

    Article  Google Scholar 

  30. Silva Junior, C. H. et al. Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Sci. Adv. 6, eaax8360 (2020).

    Article  Google Scholar 

  31. Curtis, P. G. et al. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).

    Article  CAS  Google Scholar 

  32. Chaplin–Kramer, R. et al. Degradation in carbon stocks near tropical forest edges. Nat. Commun. 6, 10158 (2015).

    Article  Google Scholar 

  33. Zhu, L. et al. Comparable biophysical and biogeochemical feedbacks on warming from tropical moist forest degradation. Nat. Geosci. 16, 244–249 (2023).

    Article  CAS  Google Scholar 

  34. Dalagnol, R. et al. Mapping tropical forest degradation with deep learning and Planet NICFI data. Remote Sens. Environ. 298, 113798 (2023).

    Article  Google Scholar 

  35. Assis, T. O. et al. CO2 emissions from forest degradation in Brazilian Amazon. Environ. Res. Lett. 15, 104035 (2020).

    Article  CAS  Google Scholar 

  36. Rosan, T. M. et al. Synthesis of the land carbon fluxes of the Amazon region between 2010 and 2020. Commun. Earth Environ. 5, 46 (2024).

    Article  Google Scholar 

  37. Lapola, D. M. et al. The drivers and impacts of Amazon forest degradation. Science 379, eabp8622 (2023).

    Article  CAS  Google Scholar 

  38. Yang, H. et al. Global increase in biomass carbon stock dominated by growth of northern young forests over past decade. Nat. Geosci. 16, 886–892 (2023).

    Article  CAS  Google Scholar 

  39. Kelly, L. T. et al. Fire and biodiversity in the Anthropocene. Science 370, eabb0355 (2020).

    Article  CAS  Google Scholar 

  40. Yang, J. et al. Spatial and temporal patterns of global burned area in response to anthropogenic and environmental factors: reconstructing global fire history for the 20th and early 21st centuries. J. Geophys. Res. Biogeosci. 119, 249–263 (2014).

    Article  Google Scholar 

  41. Jiao, W. et al. Observed increasing water constraint on vegetation growth over the last three decades. Nat. Commun. 12, 3777 (2021).

    Article  CAS  Google Scholar 

  42. Liu, L. et al. Soil moisture dominates dryness stress on ecosystem production globally. Nat. Commun. 11, 4892 (2020).

    Article  CAS  Google Scholar 

  43. Wigneron, J. P. et al. SMOS-IC data record of soil moisture and L-VOD: historical development, applications and perspectives. Remote Sens. Environ. 254, 112238 (2021).

    Article  Google Scholar 

  44. Wigneron, J. P. et al. Global carbon balance of the forest: satellite-based L-VOD results over the last decade. Front. Remote Sens. 5, 1338618 (2024).

    Article  Google Scholar 

  45. Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5, 470–474 (2015).

    Article  Google Scholar 

  46. Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).

    Article  Google Scholar 

  47. Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).

    Article  CAS  Google Scholar 

  48. Saatchi, S. S. Mapping global live woody vegetation biomass at optimum spatial resolutions. Zenodo https://doi.org/10.5281/zenodo.7583611 (2023).

  49. Ometto, J. P. et al. A biomass map of the Brazilian Amazon from multisource remote sensing. Sci. Data 10, 668 (2023).

    Article  Google Scholar 

  50. Liu, L. et al. Increasingly negative tropical water-interannual CO2 growth rate coupling. Nature 618, 755–760 (2023).

    Article  CAS  Google Scholar 

  51. Chen, Y. et al. Multi-decadal trends and variability in burned area from the 5th version of the Global Fire Emissions Database (GFED5). Earth Syst. Sci. Data Discuss. 2023, 1–52 (2023).

    CAS  Google Scholar 

  52. PRODES Legal Amazon Deforestation Monitoring System (INPE, 2018); www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes

  53. Friedl, M. A. et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).

    Article  Google Scholar 

  54. Brinck, K. et al. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat. Commun. 8, 14855 (2017).

    Article  CAS  Google Scholar 

  55. Senf, C. et al. The response of canopy height diversity to natural disturbances in two temperate forest landscapes. Landsc. Ecol. 35, 2101–2112 (2020).

    Article  Google Scholar 

  56. Silva Junior, C. H. et al. Benchmark maps of 33 years of secondary forest age for Brazil. Sci. Data 7, 269 (2020).

    Article  Google Scholar 

  57. Elith, J. et al. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).

    Article  CAS  Google Scholar 

  58. Zheng, C. et al. A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1–km grid resolution. Sci. Data 10, 139 (2023).

    Article  Google Scholar 

  59. Chen, Y. Global fire emissions database (GFED5) burned area. Zenodo https://zenodo.org/doi/10.5281/zenodo.7668423 (2023).

  60. Achard, F. et al. Determination of tropical deforestation rates and related carbon losses from 1990 to 2010. Glob. Change Biol. 20, 2540–2554 (2014).

    Article  Google Scholar 

  61. Tyukavina, A. et al. Congo Basin forest loss dominated by increasing smallholder clearing. Sci. Adv. 4, eaat2993 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

This research has been funded by the European Space Agency Climate Change Initiative (ESA-CCI) Biomass project (ESA ESRIN/ 4000123662) and RECCAP2 project 1190 (ESA ESRIN/ 4000123002/18/I-NB). J.-P.W. acknowledges support from the CNES (Centre National d’Etudes Spatiales) TOSCA programme. P.C. and J.-P.W. acknowledge support from the One Forest Vision programme of the French Ministry of Research. This study was also supported by the CALIPSO (Carbon Losses in Plants, Soils and Ocean) funded through the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures programme.

Author information

Authors and Affiliations

  1. Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China

    Yu Feng, Jie Wu & Zhenzhong Zeng

  2. Laboratoire des Sciences du Climat et de l’Environnement, UMR 1572 CEA-CNRS-UVSQ, Gif-sur-Yvette, France

    Yu Feng, Philippe Ciais, Yidi Xu & Arthur Nicolaus Fendrich

  3. INRAE, Bordeaux Sciences Agro, UMR 1391 ISPA, Villenave-d’Ornon, France

    Jean-Pierre Wigneron & Xiaojun Li

  4. Faculty of Fisheries Technology and Aquatic Resources, Mae Jo University, Chiang Mai, Thailand

    Alan D. Ziegler

  5. Water Resources Research Center, University of Hawaii, Honolulu, HI, USA

    Alan D. Ziegler

  6. BeZero Carbon, London, UK

    Dave van Wees

  7. School of Earth and Environment, University of Leeds, Leeds, UK

    Dominick V. Spracklen

  8. Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK

    Stephen Sitch

  9. Department of Geoscience and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

    Martin Brandt

  10. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

    Wei Li

  11. School of Geographical Sciences, Southwest University, Chongqing, China

    Lei Fan

  12. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Zhenzhong Zeng

Authors
  1. Yu Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Philippe Ciais
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean-Pierre Wigneron
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yidi Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alan D. Ziegler
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dave van Wees
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Arthur Nicolaus Fendrich
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Dominick V. Spracklen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Stephen Sitch
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Martin Brandt
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Wei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Lei Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Xiaojun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jie Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Zhenzhong Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.C., J.-P.W. and Y.F. designed the research. Y.F. performed the analysis. X.L. and J.-P.W. prepared the raw SMOS-IC L-VOD data. D.v.W prepared the fire emission product. Y.F. and P.C. wrote the draft. All authors contributed to the interpretation of the results and the writing of the paper.

Corresponding author

Correspondence to Yu Feng.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Roger Auch, Rico Fischer, Graciela Tejada and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Table 1 Tropical aboveground biomass carbon loss from existing studies and this study
Full size table

Extended Data Fig. 1 Extent of the study region over the tropics.

a, availability of valid L-VOD observations. b, availability of TMF products. c, final study domain. The tropics are defined as the regions between 23.5°N and 23.5°S but excluding Australia, following our previous studies11. The final study domain covers the tropical regions covered by valid L-VOD observations and TMF products.

Extended Data Fig. 2 L-VOD-based AGC changes for each loss and gain process.

AGC gross losses comprise three parts, including fire emissions, deforestation and forest degradation through non-fire means. Fire emissions, encompassing both forest and non-forest areas, were estimated utilizing the van Wees fire emission product29. Non-fire deforestation and forest degradation were quantified using the Tropical Moist Forests (TMF) dataset. AGC residuals were determined by subtracting AGC losses from AGC net changes. These residuals comprise AGC changes originating from old forests and non-fire shrub/savannah loss that are not detected by TMF, and vegetation regrowth/gain, with vegetation regrowth/gain emerging as the predominant part. Consequently, the AGC residuals were deemed a proxy for AGC gross gains.

Extended Data Fig. 3 Changes in aboveground live biomass carbon (AGC) relative to 2010 from 2011–2020 over the tropics (a) and three tropical continents of America (b), Africa (c) and Asia (d).

The solid lines indicate the mean of AGC changes and shaded areas represent ±1 s.d. among the 18 estimates (see Methods).

Extended Data Fig. 4 Bivariate map of aboveground live biomass carbon (AGC) gains vs. the four most influential factors of FRP (a), Ft (b), young forest regrowth (c), and Rs (d) for spatial variability modeling.

The unit for the y-axis of the legend (AGC gains) is MgC ha−1 yr−1.

Extended Data Fig. 5 Continental partial dependence plots of the top four variables in explaining the spatial patterns and trends in aboveground live biomass carbon (AGC) gains.

a–d, the top four variables in explaining the spatial patterns of AGC gains. The unit for the y-axis of AGC gains is MgC ha−1 yr−1. e–h, the top four variables in explaining the trends in AGC gains. δ denotes the trends in time series. The unit for the y-axis of δAGC gains is MgC ha−1 yr−2. Dash lines are the 30 individual estimates and solid lines are the median (see Methods).

Extended Data Fig. 6 Bivariate map of trends in aboveground biomass carbon (δAGC) gains vs. the four most influential factors of δSM (a), δRs (b), Rs (c), and livestock density (d) for δAGC gains modeling.

The unit for the y-axis of the legend (AGC gains trend, that is, δAGC) is MgC ha−1 yr−2. δ denotes the trends in time series.

Supplementary information

Supplementary Information

Supplementary Table 1 and Figs. 1–12.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, Y., Ciais, P., Wigneron, JP. et al. Global patterns and drivers of tropical aboveground carbon changes. Nat. Clim. Chang. 14, 1064–1070 (2024). https://doi.org/10.1038/s41558-024-02115-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-024-02115-x

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology