Forests have considerable potential to mitigate anthropogenic climate change through carbon sequestration, as well as provide society with substantial co-benefits. However, climate change risks may fundamentally compromise the permanence of forest carbon storage. Here, we conduct a multi-method synthesis of contiguous US forest aboveground carbon storage potential at both regional and species levels through a fusion of historical and future climate projections, extensive forest inventory plots datasets, machine learning/niche models, and mechanistic land surface model ensemble outputs. We find diverging signs and magnitudes of projected future forest aboveground carbon storage potential across contrasting approaches, ranging from an average total gain of 6.7 Pg C to a loss of 0.9 Pg C, in a moderate-emissions scenario. The Great Lakes region and the northeastern United States showed consistent signs of carbon gains across approaches and future scenarios. Substantial risks of carbon losses were found in regions where forest carbon offset projects are currently located. This multi-method assessment highlights the current striking uncertainty in US forest carbon storage potential estimates and provides a critical foundation to guide forest conservation, restoration and nature-based climate solutions.
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CMIP6 data outputs are publicly available from the CMIP6 data portal (https://esgf-node.llnl.gov/search/cmip6/). Future wildfire and climate stress predictions are from a previous study (https://doi.org/10.5281/zenodo.4741333). Raw FIA data were downloaded from the FIA Data Mart in CSV format on 6 August 2020. TerraClimate is downloaded from https://www.climatologylab.org/terraclimate.html. All raw data generated in this paper are available at https://doi.org/10.6084/m9.figshare.20069408. Source data are provided with this paper.
All analyses and figure preparation are performed in R programming language. The source codes to reproduce our analysis are available at https://doi.org/10.6084/m9.figshare.20069408. The growth–mortality model and empirical niche models are developed in Python programming language. The growth–mortality model is adapted from a previous study (https://doi.org/10.5281/zenodo.4741329). The climate niche model is available at https://doi.org/10.6084/m9.figshare.20069408.
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We thank O. S. Chegwidden and J. Freeman from CarbonPlan who gave helpful comments about this work. C.W. and W.R.L.A. acknowledge support from the David and Lucille Packard Foundation. W.R.L.A. acknowledges support from the US National Science Foundation (NSF) grants 1714972, 1802880, 2003017 and 2044937, and the USDA National Institute of Food and Agriculture, Agricultural and Food Research Initiative Competitive Programme, Ecosystem Services and Agro-Ecosystem Management, grant no. 2018-67019-2. S.R.C. acknowledges support from the NSF Graduate Research Fellowship Program, grant no. DGE-1839285. S.R.C., J.T.R. and M.L.G. acknowledge support from the UCOP National Laboratory Fees Research Program (grant no. LFR-18-542511) and from the California Strategic Growth Council’s Climate Change Research Program with funds from California Climate Investments as part of the Center for Ecosystem Climate Solutions. J.T.R. also acknowledges funding from the Department of Energy Office of Science’s Reducing Uncertainty in Biogeochemical Interactions through Synthesis and Computation (RUBISCO) Science Focus Area and NASA’s Modeling Analysis and Prediction programme. A.T.T. acknowledges funding from the NSF grants 2003205, 2017949 and 2216855, the USDA National Institute of Food and Agriculture, Agricultural and Food Research Initiative Competitive Programme grant no. 2018-67012-31496, and the University of California Laboratory Fees Research Program award no. LFR-20-652467.
The authors declare no competing interests.
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Extended Data Fig. 1 Projections for end-of-century C storage potential in CONUS forests using the growth-mortality model in the scenario that includes climate stress and fire mortality (50% tree mortality) and the role of inclusion of harvest in reducing this potential.
a,b, Simulated changes in the average two-decadal (the 2080 s and 2090 s) AGL C relative to 1995–2014 from 100 Monte Carlo runs using the growth-mortality model in the scenario of climate stress and fire mortality (50% tree mortality) in (a) SSP2-4.5 and (b) SSP5-8.5. c,d, Simulated difference in the projections for end-of-century C storage potential in CONUS forests using the growth-mortality model in the scenario of climate stress, fire mortality (50% tree mortality) and harvest and those in the scenario of climate stress and fire mortality (50% tree mortality) (that is, no harvest treatment included; a,b) in (c) SSP2-4.5 and (d) SSP5-8.5. The mean total AGL C changes in CONUS forests (percentage, weighted by grid area) between the future and historical period are shown in a,b. Total difference (c,d) is calculated as the difference in the total AGL C change between the scenarios with harvest treatment included and those without. Shown is the multiple-model mean across a second six ESMs ensemble from CMIP6.
Extended Data Fig. 2 Projections for end-of-century C storage potential in CONUS forests diverge depending on scientific approach (SSP5-8.5).
a, Simulated multiple-model mean changes in 20-year average annual AGL C between 2081–2100 and 1995–2014 across 22 ESMs from CMIP6. b, Simulated changes in the average two-decadal (the 2080 s and 2090 s) AGL C relative to 1995–2014 from 100 Monte Carlo runs using the growth-mortality model in the scenario that includes climate stress, fire mortality (50% tree mortality) and harvest. Shown is the multiple-model mean across a second six ESMs ensemble from CMIP6 (see Methods). c, Simulated changes in 20-year average annual AGL C between 2081–2100 and 1995–2014 by the climate niche model. d, a synthesis map of forest C storage potential, showing averaged changes in AGL C between the future and historical period across the three approaches. Hatched areas indicate model consensus in the sign of C change across the three methods. The mean total AGL C changes in CONUS forests (percentage, weighted by grid area) between the future and historical period are shown in a–c. All results are provided for the SSP5-8.5 scenario.
Extended Data Fig. 3 Escalating climate stress and fire disturbance in the western CONUS with climate change are major permanence risks to C storage potential (SSP5-8.5).
a–e, Simulated changes in average two-decadal (the 2080 s and 2090 s) AGL C relative to 1995–2014 by the growth-mortality model in the scenarios without any climate-sensitive disturbances (a), considering only climate-stress-related tree mortality (b) and considering climate-stress mortality plus three fire mortality scenarios of 25% (c), 75% (d) and 100% (e) tree mortality. Panels c–e show the mean of 100 Monte Carlo runs using the growth-mortality model. Panels a–e show the multiple-model mean across a second six ESMs ensemble from CMIP6. f, Simulated multiple-model mean changes in 20-year average annual fire CO2 emissions between 2081–2100 and 1995–2014 across 11 ESMs from CMIP6. The mean total AGL C (a–e) and fire CO2 emissions (f) changes in CONUS forests (percentage, weighted by grid area) between the future and historical period are shown. g–l, The effects of statistically significant (p < 0.05; two-sided F-test) climate-related predictors identified on the forest C storage potential (log-transformed) in the growth–mortality model (g–i) and ESMs (j–l). We used precipitation change (Δpr, mm per month), temperature change (Δtas, °C), and future burn fraction (log-transformed) as the climate-related drivers. The grey vertical line is the zero-line. The error bars indicate standard deviation of the error. The numbers in the corner of the panels indicate the linear generalized least-squares regression R2 and P values that indicate the statistical significance of that regression after accounting for spatial autocorrelation. N represents a total number of grid cells from the CONUS forest domain. Each point indicates an individual grid cell and redder colours indicate a higher density of points. Red solid and dashed lines show ordinary least-squares regression lines of best fit and their 95% confidence interval, respectively. All results are provided for the SSP5-8.5 scenario.
Extended Data Fig. 4 Large uncertainties in the long-term C storage of current compliance forest C offset projects with climate change (SSP5-8.5).
a,d,g, Spatial distributions of the 139 C offset projects in CONUS forests (green points show C gains and dark grey points represent C losses; the size of the points scales with the log-transformed project area). Some projects include multiple sub-projects, which are shown as ‘transparent’ colour points. The background maps are the same as those in Extended Data Fig. 2a–c and show the future change in AGL C expected by the end of the century. b,c,e,f,h,i, Number (b,e,h) and summed total area (×105 ha; c,f,i) of forest C offset projects in different intervals of projected changes in 20-year average annual AGL C (kg C m−2) between 2081–2100 and 1995–2014 by the ESMs (b,c), growth-mortality model (e,f) and climate niche model (h,i) are shown. The blue dashed line is the zero-line used to identify C losses. The red solid and dashed lines indicate median and mean AGL C change across projects, respectively. The number (x/139), the total areas of the projects (x/2 million ha), and the percentage of the number and total area of C offset projects (%) showed C gain or loss are shown. All results are provided for the SSP5-8.5 scenario.
Raw data to reproduce Fig. 1 and a README file.
Raw data to reproduce Fig. 2 and a README file.
Raw data to reproduce Fig. 3 and a README file.
Raw data to reproduce Fig. 4 and a README file.
Raw data to reproduce Extended Data Fig. 1 and a README file.
Raw data to reproduce Extended Data Fig. 2 and a README file.
Raw data to reproduce Extended Data Fig. 3 and a README file.
Raw data to reproduce Extended Data Fig. 4 and a README file.
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Wu, C., Coffield, S.R., Goulden, M.L. et al. Uncertainty in US forest carbon storage potential due to climate risks. Nat. Geosci. 16, 422–429 (2023). https://doi.org/10.1038/s41561-023-01166-7