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.

Amazonia as a carbon source linked to deforestation and climate change

Subjects

Abstract

Amazonia hosts the Earth’s largest tropical forests and has been shown to be an important carbon sink over recent decades1,2,3. This carbon sink seems to be in decline, however, as a result of factors such as deforestation and climate change1,2,3. Here we investigate Amazonia’s carbon budget and the main drivers responsible for its change into a carbon source. We performed 590 aircraft vertical profiling measurements of lower-tropospheric concentrations of carbon dioxide and carbon monoxide at four sites in Amazonia from 2010 to 20184. We find that total carbon emissions are greater in eastern Amazonia than in the western part, mostly as a result of spatial differences in carbon-monoxide-derived fire emissions. Southeastern Amazonia, in particular, acts as a net carbon source (total carbon flux minus fire emissions) to the atmosphere. Over the past 40 years, eastern Amazonia has been subjected to more deforestation, warming and moisture stress than the western part, especially during the dry season, with the southeast experiencing the strongest trends5,6,7,8,9. We explore the effect of climate change and deforestation trends on carbon emissions at our study sites, and find that the intensification of the dry season and an increase in deforestation seem to promote ecosystem stress, increase in fire occurrence, and higher carbon emissions in the eastern Amazon. This is in line with recent studies that indicate an increase in tree mortality and a reduction in photosynthesis as a result of climatic changes across Amazonia1,10.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Regions of influence.
Fig. 2: Annual mean VPs.
Fig. 3: Annual carbon fluxes.
Fig. 4: 40-year precipitation and temperature trends.
Fig. 5: Spatial results overview.

Data availability

The CO2 VP data that support the findings of this study are available from PANGAEA Data Archiving, at https://doi.org/10.1594/PANGAEA.926834Source data are provided with this paper.

References

  1. 1.

    Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).

    ADS  CAS  Google Scholar 

  2. 2.

    Phillips, O. L. & Brienen, R. J. W. Carbon uptake by mature Amazon forests has mitigated Amazon nations’ carbon emissions. Carbon Balance Manag. 12, 1 (2017).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).

    ADS  CAS  Google Scholar 

  4. 4.

    Gatti, L. V. et al. Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements. Nature 506, 76–80 (2014).

    ADS  CAS  Google Scholar 

  5. 5.

    Barkhordarian, A., Saatchi, S. S., Behrangi, A., Loikith, P. C. & Mechoso, C. R. A recent systematic increase in vapor pressure deficit over tropical South America. Sci. Rep. 9, 15331 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Leite-Filho, A. T., de Sousa Pontes, V. Y. & Costa, M. H. Effects of deforestation on the onset of the rainy season and the duration of dry spells in southern Amazonia. J. Geophys. Res. Atmos. 124, 5268–5281 (2019).

    ADS  Google Scholar 

  7. 7.

    Fu, R. et al. Increased dry-season length over southern Amazonia in recent decades and its implication for future climate projection. Proc. Natl Acad. Sci. USA 110, 18110–18115 (2013).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Tan, P. H., Chou, C. & Tu, J. Y. Mechanisms of global warming impacts on robustness of tropical precipitation asymmetry. J. Clim. 21, 5585–5602 (2008).

    ADS  Google Scholar 

  9. 9.

    Spracklen, D. V., Arnold, S. R. & Taylor, C. M. Observations of increased tropical rainfall preceded by air passage over forests. Nature 489, 282–285 (2012); corrigendum 494, 390 (2013).

    ADS  CAS  Google Scholar 

  10. 10.

    Doughty, C. E. et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519, 78–82 (2015).

    ADS  CAS  Google Scholar 

  11. 11.

    Malhi, Y. et al. The regional variation of aboveground live biomass in old-growth Amazonian forests. Glob. Change Biol. 12, 1107–1138 (2006).

    ADS  Google Scholar 

  12. 12.

    Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A. & Totterdell, I. J. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408, 184–187 (2000); erratum 408, 750 (2000).

    ADS  CAS  Google Scholar 

  13. 13.

    Aragão, L. E. O. C. et al. 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun. 9, 536 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Staal, A. et al. Forest-rainfall cascades buffer against drought across the Amazon. Nat. Clim. Chang. 8, 539–543 (2018).

    ADS  Google Scholar 

  15. 15.

    Costa, M. H. & Foley, J. A. Trends in the hydrologic cycle of the Amazon Basin. J. Geophys. Res. Atmos. 104, 14189–14198 (1999).

    ADS  Google Scholar 

  16. 16.

    Aragão, L. E. O. C. The rainforest’s water pump. Nature 489, 217–218 (2012).

    ADS  Google Scholar 

  17. 17.

    Proyecto MapBiomas Amazonía. Colección [2.0] de los mapas anuales de cobertura y uso del suelo. Mapbiomas_Amazonia http://amazonia.mapbiomas.org/mapas-de-la-coleccion (2020).

  18. 18.

    Baker, J. C. A. & Spracklen, D. V. Climate benefits of intact Amazon forests and the biophysical consequences of disturbance. Front. For. Glob. Change 2, 47 (2019).

  19. 19.

    Almeida, C. T., Oliveira-Júnior, J. F., Delgado, R. C., Cubo, P. & Ramos, M. C. Spatiotemporal rainfall and temperature trends throughout the Brazilian Legal Amazon, 1973–2013. Int. J. Climatol. 37, 2013–2026 (2017).

    Google Scholar 

  20. 20.

    Marengo, J. A. et al. Changes in climate and land use over the Amazon region: current and future variability and trends. Front. Earth Sci. 6, 228 (2018).

  21. 21.

    Costa, M. H. & Pires, G. F. Effects of Amazon and Central Brazil deforestation scenarios on the duration of the dry season in the arc of deforestation. Int. J. Climatol. 30, 1970–1979 (2010).

    Google Scholar 

  22. 22.

    Nobre, C. A. et al. Land-use and climate change risks in the amazon and the need of a novel sustainable development paradigm. Proc. Natl Acad. Sci. USA 113, 10759–10768 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Silva, C. V. J. et al. Estimating the multi-decadal carbon deficit of burned Amazonian forests. Environ. Res. Lett. 15, 114023 (2020).

    ADS  CAS  Google Scholar 

  24. 24.

    van der Werf, G. R. et al. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. 10, 11707–11735 (2010).

    ADS  Google Scholar 

  25. 25.

    NASA/GISS. Global Climate Change, Global Temperature https://climate.nasa.gov/vital-signs/global-temperature/ (accessed 6 March 2020).

  26. 26.

    Maeda, E. E. et al. Evapotranspiration seasonality across the Amazon Basin. Earth Syst. Dyn. 8, 439–454 (2017).

    ADS  Google Scholar 

  27. 27.

    Haghtalab, N., Moore, N., Heerspink, B. P. & Hyndman, D. W. Evaluating spatial patterns in precipitation trends across the Amazon basin driven by land cover and global scale forcings. Theor. Appl. Climatol. 140, 411–427 (2020).

    ADS  Google Scholar 

  28. 28.

    Leite-Filho, A. T., Costa, M. H. & Fu, R. The southern Amazon rainy season: the role of deforestation and its interactions with large-scale mechanisms. Int. J. Climatol. 40, 2328–2341 (2020).

    Google Scholar 

  29. 29.

    Esquivel-Muelbert, A. et al. Compositional response of Amazon forests to climate change. Glob. Change Biol. 25, 39–56 (2019).

    ADS  Google Scholar 

  30. 30.

    Liu, J. et al. Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño. Science 358, eaam5690 (2017).

    Google Scholar 

  31. 31.

    Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351, 600–604 (2016).

    ADS  CAS  Google Scholar 

  32. 32.

    INPE. Amazon Deforestation Monitoring Project (PRODES) http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes (2019).

  33. 33.

    Eva, H. D. et al. A Proposal for Defining the Geographical Boundaries of Amazonia; Synthesis of the Results from an Expert Consultation Workshop Organized by the European Commission in Collaboration with the Amazon Cooperation Treaty Organization. Report No. 21808-EN (European Commission, 2005).

  34. 34.

    Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).

    Google Scholar 

  35. 35.

    Tans, P. P., Bakwin, P. S. & Guenter, D. W. A feasible Global Carbon Cycle Observing System: a plan to decipher today’s carbon cycle based on observations. Glob. Change Biol. 2, 309–318 (1996).

    ADS  Google Scholar 

  36. 36.

    Marani, L. et al. Estimation methods of greenhouse gases fluxes and the human influence in the CO2 removal capability of the Amazon Forest. Rev. Virtual Química 12, 5 (2020).

    Google Scholar 

  37. 37.

    Miller, J. B. et al. Airborne measurements indicate large methane emissions from the eastern Amazon basin. Geophys. Res. Lett. 34, L10809 (2007).

    ADS  Google Scholar 

  38. 38.

    Gatti, L. V. et al. Vertical profiles of CO2 above eastern Amazonia suggest a net carbon flux to the atmosphere and balanced biosphere between 2000 and 2009. Tellus B 62, 581–594 (2010).

    ADS  Google Scholar 

  39. 39.

    Basso, L. S. et al. Seasonality and interannual variability of CH4 fluxes from the eastern Amazon Basin inferred from atmospheric mole fraction profiles. J. Geophys. Res. Atmos. 121, 168–184 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    D’Amelio, M. T. S., Gatti, L. V., Miller, J. B. & Tans, P. Regional N2O fluxes in Amazonia derived from aircraft vertical profiles. Atmos. Chem. Phys. 9, 8785–8797 (2009).

    ADS  Google Scholar 

  41. 41.

    Gatti Domingues, L. et al. A new background method for greenhouse gases flux calculation based in back-trajectories over the Amazon. Atmosphere 11, 734 (2020).

    ADS  Google Scholar 

  42. 42.

    Draxler, R. R. HYSPLIT 4 User’s Guide. Technical Memorandum ERL ARL-230 (NOAA, 1999); https://www.arl.noaa.gov/wp_arl/wp-content/uploads/documents/reports/arl-230.pdf

  43. 43.

    Cassol, H. L. G. et al. Determination of region of influence obtained by aircraft vertical profiles using the density of trajectories from the HYSPLIT model. Atmosphere 11, 1073 (2020).

    ADS  Google Scholar 

  44. 44.

    Stavrakou, T. et al. How consistent are top-down hydrocarbon emissions based on formaldehyde observations from GOME-2 and OMI? Atmos. Chem. Phys. 15, 11861–11884 (2015).

    ADS  CAS  Google Scholar 

  45. 45.

    Stein, A. F. et al. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96, 2059–2077 (2015).

    ADS  Google Scholar 

  46. 46.

    Berrisford, P. et al. Atmospheric conservation properties in ERA-Interim. Q. J. R. Meteorol. Soc. 137, 1381–1399 (2011).

    ADS  Google Scholar 

  47. 47.

    Adler, R. et al. The Global Precipitation Climatology Project (GPCP) monthly analysis (New Version 2.3) and a review of 2017 global precipitation. Atmosphere 9, 138 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Huffman, G. J. et al. Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeorol. 2, 36–50 (2001).

    ADS  Google Scholar 

  49. 49.

    Santos, S. R. Q., Sansigolo, C. A., Neves, T. T. A. T. & Santos, A. P. Variabilidade sazonal da precipitação na Amazônia: Validação da série de precipitação mensal do GPCC. Rev. Bras. Geogr. Física 10, 1721–1729 (2017).

    Google Scholar 

  50. 50.

    Landerer, F. JPL TELLUS GRACE Level-3 Monthly LAND Water-Equivalent-Thickness Surface-Mass Anomaly Release 6.0 in netCDF/ASCII/GeoTIFF Formats https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC_L3_JPL_RL06_LND (2019).

  51. 51.

    Landerer, F. W. & Swenson, S. C. Accuracy of scaled GRACE terrestrial water storage estimates. Wat. Resour. Res. 48, W04531 (2012).

    ADS  Google Scholar 

  52. 52.

    Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L. & Justice, C. O. The Collection 6 MODIS burned area mapping algorithm and product. Remote Sens. Environ. 217, 72–85 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Vermote, E. F., El Saleous, N. Z. & Justice, C. O. Atmospheric correction of MODIS data in the visible to middle infrared: first results. Remote Sens. Environ. 83, 97–111 (2002).

    ADS  Google Scholar 

  54. 54.

    Justice, C. et al. An overview of MODIS Land data processing and product status. Remote Sens. Environ. 83, 3–15 (2002).

    ADS  Google Scholar 

  55. 55.

    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).

    ADS  Google Scholar 

  56. 56.

    Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).

    ADS  Google Scholar 

  57. 57.

    Dalagnol, R., Wagner, F. H., Galvão, L. S., Oliveira, L. E. & Aragao, C. The MANVI Product: MODIS (MAIAC) Nadir-Solar Adjusted Vegetation Indices (EVI and NDVI) for South America https://zenodo.org/record/3159488#.YLeQtH4o_IU (2019).

  58. 58.

    de Almeida, C. A. et al. High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data. Acta Amazon. 46, 291–302 (2016).

    Google Scholar 

  59. 59.

    Jiang, N. & Riley, M. L. Exploring the utility of the random forest method for forecasting ozone pollution in SYDNEY. Int. J. Environ. Sustain. Dev. 1, 245–254 (2015).

    Google Scholar 

  60. 60.

    Stekhoven, D. J. & Buhlmann, P. MissForest–non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112–118 (2012).

    CAS  Google Scholar 

  61. 61.

    Junninen, H., Niska, H., Tuppurainen, K., Ruuskanen, J. & Kolehmainen, M. Methods for imputation of missing values in air quality data sets. Atmos. Environ. 38, 2895–2907 (2004).

    ADS  CAS  Google Scholar 

  62. 62.

    R Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org (R Foundation for Statistical Computing, 2018).

  63. 63.

    Stohl, A., Forster, C., Frank, A., Seibert, P. & Wotawa, G. The Lagrangian particle dispersion model FLEXPART version 6.2. Atmos. Chem. Phys. 5, 2461–2474 (2005).

    ADS  CAS  Google Scholar 

  64. 64.

    Freitas, S. R. et al. The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) – Part 1: model description and evaluation. Atmos. Chem. Phys. 9, 2843–2861 (2009).

    ADS  CAS  Google Scholar 

Download references

Acknowledgements

We thank S. Denning and E. Mitchard for valuable reviews. This work was funded by many projects supporting the long-term measurements: State of Sao Paulo Science Foundation – FAPESP (16/02018-2, 11/51841-0, 08/58120-3, 18/14006-4, 18/14423-4, 18/18493-7, 19/21789-8, 11/17914-0), UK Environmental Research Council (NERC) AMAZONICA project (NE/F005806/1), NASA grants (11-CMS11-0025, NRMJ1000-17-00431), European Research Council (ERC) under Horizon 2020 (649087), 7FP EU (283080), MCTI/CNPq (2013), CNPq (134878/2009-4, 310130/2017-4, 305054/2016-3, 314416/2020-0). We thank the staff at NOAA/GML who provided advice and technical support for air sampling and measurements in Brazil, and the pilots and technical team at aircraft sites who collected the air samples. We thank J. F. Mueller for providing modelled biogenic CO fluxes.

Author information

Affiliations

Authors

Contributions

L.V.G., M.G. and J.B.M. conceived the basin-wide measurement programme and approach; L.V.G. wrote the paper; all co-authors participated in scientific meetings to interpret the data, and commented on and reviewed the manuscript; L.G.D., A.H.S., L.S.B., H.L.G.C., G.T., L.M. and L.V.G. contributed to the region-of-influence study; L.V.G., H.L.G.C., E.A., L.S.B., S.M.C. and J.B.M. contributed to the climate data weighted analysis; L.G.D., C.S.C.C., S.M.C. and R.A.L.N. contributed to the greenhouse gas concentration analysis; G.T. provided deforestation analyses; J.B.M. and L.V.G. contributed to the estimation of biogenic CO.

Corresponding author

Correspondence to Luciana V. Gatti.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Scott Denning, Edward Mitchard and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data figures and tables

Extended Data Fig. 1 VPs, time series and annual mean CO2 concentrations.

a, Time series of mean VPs of the CO2 mole fractions of the flasks below 1.5 km a.s.l. (red circles) and above 3.8 km a.s.l. (blue circles) for sites SAN, ALF, RBA and TAB_TEF (590 VPs) and the background sites RPB, ASC and CPT. b, Annual mean VPs for the four sites (annual mean per height; see Methods). c, Annual mean ΔVP (see Methods) for each site and year. d, Annual mean differences between mean CO2 mole fractions below 1.5 km a.s.l. and means above 3.8 km a.sl. for each site and year (see Methods). e, Partial column annual means plotted against annual mean fluxes, by site.

Source data

Extended Data Fig. 2 Regions of influence.

a, Mean quarterly regions of influence for the ALF, SAN, RBA, TEF and TAB sites, averaged between 2010 and 2018, calculated using the density of back-trajectories (see Methods). b, Deforestation inside quarterly regions of influence and the Amazon mask (purple line) using data from PRODES32 (see Methods). c, Annual mean regions of influence (trajectory densities) averaged between 2010 and 2018.

Extended Data Fig. 3 Seasonal carbon flux and driver variables.

Average monthly means of potential flux driver variables at sites TAB_TEF, SAN, RBA and ALF in 2010–2018. Grey bands denote the standard deviation of the monthly mean.

Extended Data Fig. 4 Time series of carbon flux and driver variables.

As in Extended Data Fig. 3, but showing the full time series of monthly means from 2010 to 2018 for SAN, ALF, TAB_TEF and RBA. Grey bands as in Extended Data Fig. 3, showing the 2010–2018 standard deviation for each month.

Extended Data Fig. 5 ALF NBE drivers.

a, ALF annual mean NBE (NBE = total C flux − fire C flux, in g C m−2 d−1) from 2010 to 2018. Error bars are uncertainties related to the background, travel time trajectories, emission ratios CO/CO2 and natural CO flux (see Methods). b, Annual mean FCNBE, annual mean temperature and GRACE (equivalent water thickness) satellite soil water storage anomalies.

Extended Data Fig. 6 Amazon carbon fluxes per region.

a, Separation of three regions inside the Amazon Mask (7,256,362 km2, purple line). Region 1: area of combined regions of influence for SAN and ALF; region 2: area of combined region of influence for RBA and TAB (2010–2012) and RBA and TEF (2013–2018), excluding region 1; region 3: the remaining area outside regions 1 and 2 and inside the purple line. b, Annual mean fluxes for regions 1, 2 and 3 (total, blue line; fire, red line; NBE, green line).

Extended Data Fig. 7 Mean temperature and precipitation in Amazonia over the past 40 years.

a, Monthly mean temperature in Amazonia in 1979–2018, calculated using ERA Interim (ECMWF) monthly means (see Methods). Grey points are monthly mean temperatures from 1979 to 2018. Blue and red circles show decadal monthly mean temperatures for 1979–1988 and 2009–2018, respectively. Error bars denote one standard deviation for the decade. b, Blue circles are annual mean temperatures; green circles show mean temperatures for January, February and March; red circles show mean temperatures for August, September and October. c, As in a, but for precipitation calculated using GPCP version 2.3 (see Methods). d, Blue circles are annual total precipitation; green circles are total precipitation for January, February and March; and red circles are total precipitation for August, September and October.

Extended Data Fig. 8 Seasonal temperature and precipitation over the past 40 years.

Monthly precipitation (GPCP v2.3) and monthly mean temperature (ERA-Interim) for TAB, SAN, RBA, ALF and TEF, calculated using spatial weightings from 2010–2018 quarterly regions of influence (Extended Data Fig. 2a) inside the Amazon mask. Symbols are as in Extended Data Fig. 7.

Extended Data Table 1 Analysis of temperature and precipitation data obtained over the past 40 years
Extended Data Table 2 Summary of the main results for ALF, SAN, RBA and TAB_TEF

Supplementary information

Supplementary Information

This file contains 8 Supplementary Figures and 2 Supplementary Tables – see the guide at the beginning of the file for details.

Peer Review File

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gatti, L.V., Basso, L.S., Miller, J.B. et al. Amazonia as a carbon source linked to deforestation and climate change. Nature 595, 388–393 (2021). https://doi.org/10.1038/s41586-021-03629-6

Download citation

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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