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.
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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
12 May 2021
A Correction to this paper has been published: https://doi.org/10.1038/s41558-021-01069-8
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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.
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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.
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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.
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Supplementary text (four sections), Figs. 1–10, Tables 1–8 and Refs. 1–64.
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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
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DOI: https://doi.org/10.1038/s41558-021-01027-4
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