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:

Disturbance suppresses the aboveground carbon sink in North American boreal forests

An Author Correction to this article was published on 12 May 2021

This article has been updated

Abstract

Climate change is altering vegetation and disturbance dynamics in boreal ecosystems. However, the aggregate impact of these changes on boreal carbon budgets is not well understood. Here we combined multiple satellite datasets to estimate annual stocks and changes in aboveground biomass (AGB) across boreal northwestern North America. From 1984 to 2014, the 2.82 × 106 km2 study region gained 434 ± 176 Tg of AGB. Fires resulted in losses of 789 ± 48 Tg, which were mostly compensated by post-fire recovery of 642 ± 86 Tg. Timber harvests contributed to losses of 74 ± 5 Tg, which were partly offset by post-harvest recovery of 32 ± 9 Tg. Earth system models overestimated AGB accumulation by a factor of 3 (+1,519 ± 171 Tg), which suggests that these models overestimate the terrestrial carbon sink in boreal ecosystems and highlights the need to improve representation of fire and other disturbance processes in these models.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Fig. 1: Spatial distribution of predicted AGB across the ABoVE Core Study Domain.
Fig. 2: Comparisons of ecoregion-wide and domain-wide AGB estimates across different available datasets.
Fig. 3: Cumulative changes in domain-wide AGB by disturbance type during the Landsat observation period (1984–2014).
Fig. 4: Increasing trends in AGB loss and recovery from fires from 1940 to 2014.
Fig. 5: Comparisons of AGB stocks and change from this study (ABoVE) and AGB values estimated for the ABoVE Core Study Domain in 27 Earth system models from the CMIP6 multimodel ensemble historical run.

Similar content being viewed by others

Data availability

The AGB data produced in this study are archived and freely available at the NASA ORNL Active Archive Center (DAAC). The DOI is https://doi.org/10.3334/ORNLDAAC/1808. The other data analysed come from the original sources. The ESA GlobBiomass dataset is available at https://doi.org/10.5285/bedc59f37c9545c981a839eb552e4084. The NACP boreal AGB dataset is available at https://doi.org/10.3334/ORNLDAAC/1273. The Spawn and Gibbs49 global AGB dataset is available at https://doi.org/10.3334/ORNLDAAC/1763. The CFS disturbance cause time series and AGB data for Canada are available at https://opendata.nfis.org/mapserver/nfis-change_eng.html. The Canadian and Alaska fire polygon data come from https://cwfis.cfs.nrcan.gc.ca/datamart/datarequest/nfdbpoly and https://fire.ak.blm.gov/predsvcs/maps.php, respectively. The EPA ecoregion boundaries are available at https://www.epa.gov/eco-research/ecoregions. The Daymet climate data are available at https://daymet.ornl.gov/getdata. The global ASTER elevation data are available at https://asterweb.jpl.nasa.gov/gdem.asp. The model outputs from CMIP6 are available at https://esgf-node.llnl.gov/search/cmip6/.

Code availability

The code used in the analyses described in this study is available from the corresponding author upon reasonable request. The plots in this manuscript were generated using the R package ggplot2 (ref. 68).

Change history

References

  1. Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).

    Article  CAS  Google Scholar 

  2. Lindroth, A., Grelle, A. & Morén, A.-S. Long-term measurements of boreal forest carbon balance reveal large temperature sensitivity. Glob. Change Biol. 4, 443–450 (1998).

    Article  Google Scholar 

  3. Kasischke, E. S., Christensen, N. Jr & Stocks, B. J. Fire, global warming, and the carbon balance of boreal forests. Ecol. Appl. 5, 437–451 (1995).

    Article  Google Scholar 

  4. Graven, H. D. et al. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science 341, 1085–1089 (2013).

    Article  CAS  Google Scholar 

  5. Welp, L. R. et al. Increasing summer net CO2 uptake in high northern ecosystems inferred from atmospheric inversions and comparisons to remote-sensing NDVI. Atmos. Chem. Phys. 16, 9047–9066 (2016).

    Article  CAS  Google Scholar 

  6. Myneni, R. B., Keeling, C., Tucker, C. J., Asrar, G. & Nemani, R. R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).

    Article  CAS  Google Scholar 

  7. Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).

    Article  CAS  Google Scholar 

  8. Girardin, M. P. et al. No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO2 fertilization. Proc. Natl Acad. Sci. USA 113, E8406–E8414 (2016).

    Article  CAS  Google Scholar 

  9. Giguère-Croteau, C. et al. North America’s oldest boreal trees are more efficient water users due to increased [CO2], but do not grow faster. Proc. Natl Acad. Sci. USA 116, 2749–2754 (2019).

    Article  Google Scholar 

  10. Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).

    Article  CAS  Google Scholar 

  11. Kasischke, E. S. & Turetsky, M. R. Recent changes in the fire regime across the North American boreal region—spatial and temporal patterns of burning across Canada and Alaska. Geophys. Res. Lett. 33, L09703 (2006).

    Google Scholar 

  12. White, J. C., Wulder, M. A., Hermosilla, T., Coops, N. C. & Hobart, G. W. A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sens. Environ. 194, 303–321 (2017).

    Article  Google Scholar 

  13. Kurz, W. A. et al. Mountain pine beetle and forest carbon feedback to climate change. Nature 452, 987–990 (2008).

    Article  CAS  Google Scholar 

  14. Ma, Z. et al. Regional drought-induced reduction in the biomass carbon sink of Canada’s boreal forests. Proc. Natl Acad. Sci. USA 109, 2423–2427 (2012).

    Article  CAS  Google Scholar 

  15. Wang, J. A. et al. Extensive land cover change across Arctic–boreal northwestern North America from disturbance and climate forcing. Glob. Change Biol. 26, 807–822 (2020).

    Article  Google Scholar 

  16. Johnstone, J. F., Hollingsworth, T. N., Chapin, F. S. & Mack, M. C. Changes in fire regime break the legacy lock on successional trajectories in Alaskan boreal forest. Glob. Change Biol. 16, 1281–1295 (2010).

    Article  Google Scholar 

  17. Wang, J. A. & Friedl, M. A. The role of land cover change in Arctic–boreal greening and browning trends. Environ. Res. Lett. 14, 125007 (2019).

    Article  Google Scholar 

  18. Beck, P. S. & Goetz, S. J. Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: ecological variability and regional differences. Environ. Res. Lett. 6, 045501 (2011).

    Article  Google Scholar 

  19. de Groot, W. J., Flannigan, M. D. & Cantin, A. S. Climate change impacts on future boreal fire regimes. For. Ecol. Manage. 294, 35–44 (2013).

    Article  Google Scholar 

  20. Bond-Lamberty, B., Peckham, S. D., Ahl, D. E. & Gower, S. T. Fire as the dominant driver of central Canadian boreal forest carbon balance. Nature 450, 89–92 (2007).

    Article  CAS  Google Scholar 

  21. Bradshaw, C. J. & Warkentin, I. G. Global estimates of boreal forest carbon stocks and flux. Glob. Planet. Change 128, 24–30 (2015).

    Article  Google Scholar 

  22. Goulden, M. L. et al. Patterns of NPP, GPP, respiration, and NEP during boreal forest succession. Glob. Change Biol. 17, 855–871 (2011).

    Article  Google Scholar 

  23. Pugh, T. A. M., Arneth, A., Kautz, M., Poulter, B. & Smith, B. Important role of forest disturbances in the global biomass turnover and carbon sinks. Nat. Geosci. 12, 730–735 (2019).

    Article  CAS  Google Scholar 

  24. Zimov, S. et al. Contribution of disturbance to increasing seasonal amplitude of atmospheric CO2. Science 284, 1973–1976 (1999).

    Article  CAS  Google Scholar 

  25. Sedano, F. & Randerson, J. T. Multi-scale influence of vapor pressure deficit on fire ignition and spread in boreal forest ecosystems. Biogeosciences 11, 3739–3755 (2014).

    Article  Google Scholar 

  26. Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572, 520–523 (2019).

    Article  CAS  Google Scholar 

  27. Matasci, G. et al. Three decades of forest structural dynamics over Canada’s forested ecosystems using Landsat time-series and lidar plots. Remote Sens. Environ. 216, 697–714 (2018).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  29. Wulder, M. A., Hermosilla, T., White, J. C. & Coops, N. C. Biomass status and dynamics over Canada’s forests: disentangling disturbed area from associated aboveground biomass consequences. Environ. Res. Lett. 15, 094093 (2020).

    Article  CAS  Google Scholar 

  30. Margolis, H. A. et al. Combining satellite lidar, airborne lidar, and ground plots to estimate the amount and distribution of aboveground biomass in the boreal forest of North America. Can. J. For. Res. 45, 838–855 (2015).

    Article  Google Scholar 

  31. Neigh, C. S. et al. Taking stock of circumboreal forest carbon with ground measurements, airborne and spaceborne LiDAR. Remote Sens. Environ. 137, 274–287 (2013).

    Article  Google Scholar 

  32. Fisher, J. B. et al. Missing pieces to modeling the Arctic–boreal puzzle. Environ. Res. Lett. 13, 020202 (2018).

    Article  Google Scholar 

  33. Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020).

    Article  CAS  Google Scholar 

  34. Kurz, W. A. et al. Carbon in Canada’s boreal forest—a synthesis. Environ. Rev. 21, 260–292 (2013).

    Article  CAS  Google Scholar 

  35. Price, D., Peng, C., Apps, M. & Halliwell, D. Simulating effects of climate change on boreal ecosystem carbon pools in central Canada. J. Biogeogr. 26, 1237–1248 (1999).

    Article  Google Scholar 

  36. Stocks, B. et al. Large forest fires in Canada, 1959–1997. J. Geophys. Res. Atmos. 107, FFR-5 (2002).

    Google Scholar 

  37. Kasischke, E. S., Williams, D. & Barry, D. Analysis of the patterns of large fires in the boreal forest region of Alaska. Int. J. Wildland Fire 11, 131–144 (2002).

    Article  Google Scholar 

  38. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  39. Fredeen, A. L., Waughtal, J. D. & Pypker, T. G. When do replanted sub-boreal clearcuts become net sinks for CO2? For. Ecol. Manage. 239, 210–216 (2007).

    Article  Google Scholar 

  40. Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 201407302 (2014).

    Google Scholar 

  41. Wenzel, S., Cox, P. M., Eyring, V. & Friedlingstein, P. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 538, 499–501 (2016).

    Article  Google Scholar 

  42. Natali, S. M. et al. Large loss of CO2 in winter observed across the northern permafrost region. Nat. Clim. Change 9, 852–857 (2019).

    Article  CAS  Google Scholar 

  43. Gedalof, Z. & Berg, A. A. Tree ring evidence for limited direct CO2 fertilization of forests over the 20th century: limited CO2 fertilization of forests. Glob. Biogeochem. Cycles 24, GB3027 (2010).

    Article  Google Scholar 

  44. Kolby Smith, W. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306–310 (2016).

    Article  CAS  Google Scholar 

  45. Duncanson, L. et al. Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California. Remote Sens. Environ. 242, 111779 (2020).

    Article  Google Scholar 

  46. Helbig, M., Pappas, C. & Sonnentag, O. Permafrost thaw and wildfire: equally important drivers of boreal tree cover changes in the Taiga Plains, Canada. Geophys. Res. Lett. 43, 1598–1606 (2016).

    Article  Google Scholar 

  47. Carpino, O. A., Berg, A. A., Quinton, W. L. & Adams, J. R. Climate change and permafrost thaw-induced boreal forest loss in northwestern Canada. Environ. Res. Lett. 13, 084018 (2018).

    Article  Google Scholar 

  48. Margolis, H., Sun, G., Montesano, P. M. & Nelson, R. F. NACP LiDAR-Based Biomass Estimates, Boreal Forest Biome, North America, 2005–2006 (ORNL DAAC, 2015); https://doi.org/10.3334/ORNLDAAC/1273

  49. Spawn, S. A. & Gibbs, H. K. Global Aboveground and Belowground Biomass Carbon Density Maps for the Year 2010 (ORNL DAAC, 2020); https://doi.org/10.3334/ORNLDAAC/1763

  50. Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global Datasets of Forest Above-ground Biomass for the Year 2017 Version 1 (Centre for Environmental Data Analysis, 2019); https://doi.org/10.5285/bedc59f37c9545c981a839eb552e4084

  51. Omernik, J. M. & Griffith, G. E. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environ. Manage. 54, 1249–1266 (2014).

    Article  Google Scholar 

  52. Wulder, M. A. et al. Monitoring Canada’s forests. Part 1: completion of the EOSD land cover project. Can. J. Remote Sens. 34, 549–562 (2008).

    Article  Google Scholar 

  53. Jin, S., Yang, L., Zhu, Z. & Homer, C. A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sens. Environ. 195, 44–55 (2017).

    Article  Google Scholar 

  54. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    Article  CAS  Google Scholar 

  55. Roy, D. P., Boschetti, L., Justice, C. & Ju, J. The collection 5 MODIS burned area product—global evaluation by comparison with the MODIS active fire product. Remote Sens. Environ. 112, 3690–3707 (2008).

    Article  Google Scholar 

  56. Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).

    Article  Google Scholar 

  57. R Core Team R: A Language and Environment for Statistical Computing Version 3.6.0 (R Foundation for Statistical Computing, 2019).

  58. Greenwell, B., Boehmke, B., Cunningham, J. & GMB Developers. gbm: Generalized Boosted Regression Models Version 2.1.5. R package (2019).

  59. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  60. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Clim. 37, 4302–4315 (2017).

    Article  Google Scholar 

  61. Wang, J. A., Sulla-Menashe, D., Woodcock, C. E., Sonnentag, O. & Friedl, M. A. ABoVE: Annual Land Cover in the ABoVE Core Domain from Landsat, 1984–2014 (ORNL DAAC, 2019); https://doi.org/10.3334/ORNLDAAC/1691

  62. Canadian National Fire Database—Agency Fire Data (Canadian Forest Service, 2002); https://cwfis.cfs.nrcan.gc.ca/ha/nfdb

  63. Alaskan Large Fire Database (Alaska Interagency Coordination Center, 2002); https://fire.ak.blm.gov/predsvcs/maps.php

  64. Thornton, M. M. et al. Daymet: Monthly Climate Summaries on a 1-km Grid for North America Version 3 (ORNL DAAC, 2018); https://doi.org/10.3334/ornldaac/1345

  65. Lumley, T. leaps: Regression Subset Selection Version 3.0. R package (2017).

  66. Mallows, C. L. Some comments on Cp. Technometrics 42, 87–94 (2000).

    Google Scholar 

  67. Li, Z., Kurz, W. A., Apps, M. J. & Beukema, S. J. Belowground biomass dynamics in the Carbon Budget Model of the Canadian Forest Sector: recent improvements and implications for the estimation of NPP and NEP. Can. J. For. Res. 33, 126–136 (2003).

    Article  Google Scholar 

  68. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

Download references

Acknowledgements

J.A.W. was supported by a National Science Foundation Graduate Research Fellowship under grant no. DGE-1247312. M.A.F. and J.A.W. acknowledge support from NASA’s Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Program, grant no. 80NSSC18K0994, and from NASA ABoVE grant no. NNX15AU63A. A.B. and M.F. acknowledge support from NASA’s Carbon Monitoring System (CMS) programme, grant no. NNX16AP24G. M.F. also acknowledges support from NASA grant no. NNH17ZDA001N-NIP. J.T.R. acknowledges support from NASA’s CMS and Interdisciplinary Science (IDS) research programmes, the US Department of Energy Office of Science BER RUBISCO Science Focus Area and the University of California Lab Fees programme. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. This study was part of ABoVE. Resources supporting this work were provided by the NASA High-End Computing Program through the NASA Center for Climate Simulation at Goddard Space Flight Center. We thank the Canadian National Forest Inventory in the Northwest Territories and the Government of Alberta Forestry Division for providing data from permanent sampling plots. We also thank C. Woodcock, L. Hutyra, O. Sonnentag and M. Goulden for reading and providing suggestions on early drafts of this work.

Author information

Authors and Affiliations

Authors

Contributions

J.A.W., M.A.F. and A.B. conceived the work. J.A.W., M.A.F., A.B. and J.T.R. designed the study. J.A.W. and M.F. prepared the AGB dataset. J.A.W. performed the data analysis and prepared the figures and tables. All authors contributed to the preparation of the manuscript text.

Corresponding author

Correspondence to Jonathan A. Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Guillermo Castilla, Jeremy Lichstein and Meelis Seedre for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Comparison of Landsat-based estimates of AGB and field-estimated AGB from permanent sampling plots.

a, Spatial distribution of permanent sampling plot data, which the Northwest Territories’ portion of the Canadian National Forest Inventory (n = 80) and the Alberta Forestry Division (n = 897). b, Scatter plot demonstrating the correlation between field-estimated AGB and Landsat-based AGB. Dashed line indicates 1:1 line. c) Errors between Landsat-estimated and field-estimated AGB values as a function of 50 Mg ha−1 AGB intervals. Numbers above the boxplots indicate the number of samples in each AGB interval. Boxes indicate the interquartile range, the lines indicate the 10th and 90th percentile range, heavy horizontal lines indicate the median, and points indicate outliers. To correct for geolocation errors and differences in spatial scale, AGB values were smoothed using a 3 × 3 Landsat pixel moving window (for example 90 m) before they were extracted.

Extended Data Fig. 2 Maps of example landscape-scale change in biomass due to fire (top row) and due to harvest (bottom row).

Insets indicate general area shown within study domain. Top row is an area in the Yukon Territories that experienced frequent fires (fire perimeters indicated with black lines). Biomass is shown for a) the year 1984 b) the year 2014 and c) the difference between 2014 and 1984. Bottom row is an area in British Columbia that experienced extensive logging, showing the areas for d) the year 1984 e) the year 2014 and f) the difference between 2014 and 1984.

Extended Data Fig. 3 Map of the spatial distribution of disturbances occurring within the ABoVE domain.

Areas outside of harvest (prior to 1985) and fire (prior to 1940) have an unknown disturbance history (‘Not Disturbed or Other Disturbance’).

Extended Data Fig. 4 Ecoregion-specific trajectories of post-disturbance recovery of AGB from fire or harvest.

Colors indicate disturbance type. Points indicate the mean AGB for each disturbance and age class. Error bars indicate two standard errors (95% confidence interval) about each mean. In some ecoregions, there are few fires in the early part of the dataset, resulting in large uncertainties at long times since disturbance. To minimize uncertainty from the sparse observations, we report here just the first 45 years of data.

Extended Data Fig. 5 Cohort-based analysis of post-disturbance recovery.

AGB values were sampled from pixels with a stand age of 15 years across all years and ecoregions in the domain for both fires and harvest. Points indicate means and bars indicate two standard errors for each ecoregion-year-disturbance type group (Combined across all ecoregions, Fire N = 82,582; Harvest N = 67,074). Dotted lines indicate linear regressions predicting 15-year post-disturbance AGB as a function of year.

Supplementary information

Supplementary Information

Supplementary text (four sections), Figs. 1–10, Tables 1–8 and Refs. 1–64.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J.A., Baccini, A., Farina, M. et al. Disturbance suppresses the aboveground carbon sink in North American boreal forests. Nat. Clim. Chang. 11, 435–441 (2021). https://doi.org/10.1038/s41558-021-01027-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-021-01027-4

This article is cited by

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