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:

Uncertainty in US forest carbon storage potential due to climate risks

Nature Geoscience volume 16, pages 422–429 (2023)Cite this article

Subjects

Abstract

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.

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: Projections for end-of-century C storage potential in CONUS forests diverge depending on scientific approach.
Fig. 2: Escalating climate stress and fire disturbance in the western CONUS with climate change are major permanence risks to C storage potential.
Fig. 3: Tree species niche models suggest large C losses for the majority of US forest groups across the CONUS with climate change.
Fig. 4: Large uncertainties in the long-term C storage of current compliance forest C offset projects with climate change.

Similar content being viewed by others

Data availability

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.

Code availability

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.

References

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

    Article  Google Scholar 

  2. Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).

    Article  Google Scholar 

  3. Rockström, J. et al. We need biosphere stewardship that protects carbon sinks and builds resilience. Proc. Natl Acad. Sci. USA 118, e2115218118 (2021).

    Article  Google Scholar 

  4. Williams, C. A., Collatz, G. J., Masek, J. & Goward, S. N. Carbon consequences of forest disturbance and recovery across the conterminous United States. Glob. Biogeochem. Cycles 26, GB1005 (2012).

    Article  Google Scholar 

  5. Xu, L. et al. Changes in global terrestrial live biomass over the 21st century. Sci. Adv. 7, eabe9829 (2021).

    Article  Google Scholar 

  6. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2019 (US Environmental Protection Agency, 2021).

  7. Fargione, J. E. et al. Natural climate solutions for the United States. Sci. Adv. 4, eaat1869 (2018).

    Article  Google Scholar 

  8. Hurtt, G. C. et al. Projecting the future of the U.S. carbon sink. Proc. Natl Acad. Sci. USA 99, 1389–1394 (2002).

    Article  Google Scholar 

  9. Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).

    Article  Google Scholar 

  10. Iglesias, V., Balch, J. K. & Travis, W. R. U.S. fires became larger, more frequent, and more widespread in the 2000s. Sci. Adv. 8, eabc0020 (2022).

    Article  Google Scholar 

  11. Juang, C. S. et al. Rapid growth of large forest fires drives the exponential response of annual forest‐fire area to aridity in the western United States. Geophys. Res. Lett. 49, e2021GL097131 (2022).

    Article  Google Scholar 

  12. Williams, A. P. et al. Observed impacts of anthropogenic climate change on wildfire in California. Earth’s Future 7, 892–910 (2019).

    Article  Google Scholar 

  13. Wu, C. et al. Historical and future global burned area with changing climate and human demography. One Earth 4, 517–530 (2021).

    Article  Google Scholar 

  14. McDowell, N. G. et al. Mechanisms of woody-plant mortality under rising drought, CO2 and vapour pressure deficit. Nat. Rev. Earth Environ. 3, 294–308 (2022).

    Article  Google Scholar 

  15. Kannenberg, S. A., Driscoll, A. W., Malesky, D. & Anderegg, W. R. L. Rapid and surprising dieback of Utah juniper in the southwestern USA due to acute drought stress. For. Ecol. Manag. 480, 118639 (2021).

    Article  Google Scholar 

  16. Schwalm, C. R. et al. Reduction in carbon uptake during turn of the century drought in western North America. Nat. Geosci. 5, 551–556 (2012).

    Article  Google Scholar 

  17. Anderegg, W. R. L., Trugman, A. T., Badgley, G., Konings, A. G. & Shaw, J. Divergent forest sensitivity to repeated extreme droughts. Nat. Clim. Change 10, 1091–1095 (2020).

    Article  Google Scholar 

  18. Coffield, S. R., Hemes, K. S., Koven, C. D., Goulden, M. L. & Randerson, J. T. Climate-driven limits to future carbon storage in California’s wildland ecosystems. AGU Adv. 2, e2021AV000384 (2021).

    Article  Google Scholar 

  19. Meddens, A. J. H. et al. Patterns and causes of observed piñon pine mortality in the southwestern United States. New Phytol. 206, 91–97 (2015).

    Article  Google Scholar 

  20. 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  Google Scholar 

  21. van Wees, D. et al. The role of fire in global forest loss dynamics. Glob. Change Biol. 27, 2377–2391 (2021).

    Article  Google Scholar 

  22. Rogers, B. M., Soja, A. J., Goulden, M. L. & Randerson, J. T. Influence of tree species on continental differences in boreal fires and climate feedbacks. Nat. Geosci. 8, 228–234 (2015).

    Article  Google Scholar 

  23. Wang, J. A., Randerson, J. T., Goulden, M. L., Knight, C. A. & Battles, J. J. Losses of tree cover in California driven by increasing fire disturbance and climate stress. AGU Adv. 3, e2021AV000654 (2022).

    Article  Google Scholar 

  24. Wang, J. A., Baccini, A., Farina, M., Randerson, J. T. & Friedl, M. A. Disturbance suppresses the aboveground carbon sink in North American boreal forests. Nat. Clim. Change 11, 435–441 (2021).

    Article  Google Scholar 

  25. McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).

    Article  Google Scholar 

  26. Parmesan, C. et al. In Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Pörtner, H.-O. et al.) Chapter 2, 197–377 (Cambridge Univ. Press, 2022).

  27. Fisher, R. A. et al. Vegetation demographics in Earth system models: a review of progress and priorities. Glob. Change Biol. 24, 35–54 (2018).

    Article  Google Scholar 

  28. 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 

  29. Wu, C. et al. Reduced global fire activity due to human demography slows global warming by enhanced land carbon uptake. Proc. Natl Acad. Sci. USA 119, e2101186119 (2022).

    Article  Google Scholar 

  30. Xie, Y. et al. Tripling of western US particulate pollution from wildfires in a warming climate. Proc. Natl Acad. Sci. USA 119, e2111372119 (2022).

    Article  Google Scholar 

  31. Hurtt, G. C. et al. Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geosci. Model Dev. 13, 5425–5464 (2020).

    Article  Google Scholar 

  32. Zhu, K., Zhang, J., Niu, S., Chu, C. & Luo, Y. Limits to growth of forest biomass carbon sink under climate change. Nat. Commun. 9, 2709 (2018).

    Article  Google Scholar 

  33. Pacala, S. W. et al. Forest models defined by field measurements: estimation, error analysis and dynamics. Ecol. Monogr. 66, 1–43 (1996).

    Article  Google Scholar 

  34. Bugmann, H. A review of forest gap models. Clim. Change 51, 259–305 (2001).

    Article  Google Scholar 

  35. Loarie, S. R. et al. Climate change and the future of California’s endemic flora. PLoS ONE 3, e2502 (2008).

    Article  Google Scholar 

  36. Rehfeldt, G. E., Crookston, N. L., Warwell, M. V. & Evans, J. S. Empirical analyses of plant–climate relationships for the western United States. Int. J. Plant Sci. 167, 1123–1150 (2006).

    Article  Google Scholar 

  37. Jackson, S. T., Betancourt, J. L., Booth, R. K. & Gray, S. T. Ecology and the ratchet of events: climate variability, niche dimensions, and species distributions. Proc. Natl Acad. Sci. USA 106, 19685–19692 (2009).

    Article  Google Scholar 

  38. Keenan, T., Maria Serra, J., Lloret, F., Ninyerola, M. & Sabate, S. Predicting the future of forests in the Mediterranean under climate change, with niche- and process-based models: CO2 matters! Glob. Change Biol. 17, 565–579 (2011).

    Article  Google Scholar 

  39. Bossio, D. A. et al. The role of soil carbon in natural climate solutions. Nat. Sustain. 3, 391–398 (2020).

    Article  Google Scholar 

  40. Anderegg, W. R. L. et al. Future climate risks from stress, insects and fire across US forests. Ecol. Lett. 25, 1510–1520 (2022).

    Article  Google Scholar 

  41. Yu, K. et al. Pervasive decreases in living vegetation carbon turnover time across forest climate zones. Proc. Natl Acad. Sci. USA 116, 24662–24667 (2019).

    Article  Google Scholar 

  42. Morin, X. & Thuiller, W. Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change. Ecology 90, 1301–1313 (2009).

    Article  Google Scholar 

  43. Compliance Offset Protocol U.S. Forest Projects (California Air Resources Board, 2015).

  44. Gea-Izquierdo, G. & Sanchez-Gonzalez, M. Forest disturbances and climate constrain carbon allocation dynamics in trees. Glob. Change Biol. 28, 4342–4358 (2022).

    Article  Google Scholar 

  45. Sanderson, B. M. & Fisher, R. A. A fiery wake-up call for climate science. Nat. Clim. Change 10, 175–177 (2020).

    Article  Google Scholar 

  46. Bugmann, H. & Seidl, R. The evolution, complexity and diversity of models of long-term forest dynamics. J. Ecol. 110, 2288–2307 (2022).

    Article  Google Scholar 

  47. Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytol. 229, 2413–2445 (2021).

    Article  Google Scholar 

  48. Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Change 9, 684–689 (2019).

    Article  Google Scholar 

  49. Abatzoglou, J. T. et al. Projected increases in western US forest fire despite growing fuel constraints. Commun. Earth Environ. 2, 227 (2021).

    Article  Google Scholar 

  50. Cabon, A. et al. Cross-biome synthesis of source versus sink limits to tree growth. Science 376, 758–761 (2022).

    Article  Google Scholar 

  51. Anderson-Teixeira, K. J. & Kannenberg, S. A. What drives forest carbon storage? The ramifications of source–sink decoupling. New Phytol. 236, 5–8 (2022).

    Article  Google Scholar 

  52. Gómez-Pineda, E. et al. Suitable climatic habitat changes for Mexican conifers along altitudinal gradients under climatic change scenarios. Ecol. Appl. 30, e02041 (2020).

    Article  Google Scholar 

  53. Rogers, B. M., Jantz, P. & Goetz, S. J. Vulnerability of eastern US tree species to climate change. Glob. Change Biol. 23, 3302–3320 (2017).

    Article  Google Scholar 

  54. Fisher, R. A. et al. Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED). Geosci. Model Dev. 8, 3593–3619 (2015).

    Article  Google Scholar 

  55. Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).

    Article  Google Scholar 

  56. He, B. et al. Worldwide impacts of atmospheric vapor pressure deficit on the interannual variability of terrestrial carbon sinks. Natl Sci. Rev. 9, nwab150 (2022).

    Article  Google Scholar 

  57. Wickham, J., Stehman, S. V., Sorenson, D. G., Gass, L. & Dewitz, J. A. Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United States. Remote Sens. Environ. 257, 112357 (2021).

    Article  Google Scholar 

  58. Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).

    Article  Google Scholar 

  59. Chegwidden, O. S. et al. Risks to Forest Carbon in a Changing Climate (CarbonPlan, 2021). https://carbonplan.org/research/forest-risks-explainer

  60. Gillespie, A. J. R. Rationale for a national annual forest inventory program. J. For. 97, 16–20 (1999).

    Google Scholar 

  61. Whittier, T. R. & Gray, A. N. Tree mortality based fire severity classification for forest inventories: a Pacific Northwest national forests example. For. Ecol. Manag. 359, 199–209 (2016).

    Article  Google Scholar 

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

    Article  Google Scholar 

  63. Archibald, S., Roy, D. P., van Wilgen, B. W. & Scholes, R. J. What limits fire? An examination of drivers of burnt area in southern Africa. Glob. Change Biol. 15, 613–630 (2009).

    Article  Google Scholar 

  64. Faivre, N. R., Jin, Y., Goulden, M. L. & Randerson, J. T. Spatial patterns and controls on burned area for two contrasting fire regimes in southern California. Ecosphere 7, e01210 (2016).

    Article  Google Scholar 

  65. Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).

    Article  Google Scholar 

  66. Ahlström, A., Schurgers, G., Arneth, A. & Smith, B. Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections. Environ. Res. Lett. 7, 044008 (2012).

    Article  Google Scholar 

  67. Pedregosa, F. et al. scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  68. Badgley, G. et al. Systematic over-crediting in California’s forest carbon offsets program. Glob. Change Biol. 28, 1433–1445 (2022).

    Article  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. Wilkes Center for Climate Science and Policy, University of Utah, Salt Lake City, UT, USA

    Chao Wu & William R. L. Anderegg

  2. School of Biological Sciences, University of Utah, Salt Lake City, UT, USA

    Chao Wu & William R. L. Anderegg

  3. Department of Earth System Science, University of California Irvine, Irvine, CA, USA

    Shane R. Coffield, Michael L. Goulden & James T. Randerson

  4. Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA, USA

    Michael L. Goulden

  5. Department of Geography, University of California Santa Barbara, Santa Barbara, CA, USA

    Anna T. Trugman

Authors
  1. Chao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shane R. Coffield
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael L. Goulden
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. James T. Randerson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anna T. Trugman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. William R. L. Anderegg
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.W., S.R.C. and W.R.L.A. designed the study with input from all co-authors. C.W., S.R.C. and W.R.L.A. performed the analyses. C.W. wrote a first draft and S.R.C, M.L.G., J.T.R., A.T.T. and W.R.L.A provided extensive comments and revisions.

Corresponding author

Correspondence to Chao Wu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks Harald Bugmann, Christopher Woodall and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

Additional information

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

Extended data

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.

Source data

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.

Source data

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.

Source data

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.

Source data

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Results, Supplementary Discussion, Supplementary Figs. 1–25, Supplementary Tables 1–5 and Supplementary References.

Source data

Source Data Fig. 1

Raw data to reproduce Fig. 1 and a README file.

Source Data Fig. 2

Raw data to reproduce Fig. 2 and a README file.

Source Data Fig. 3

Raw data to reproduce Fig. 3 and a README file.

Source Data Fig. 4

Raw data to reproduce Fig. 4 and a README file.

Source Data Extended Data Fig. 1

Raw data to reproduce Extended Data Fig. 1 and a README file.

Source Data Extended Data Fig. 2

Raw data to reproduce Extended Data Fig. 2 and a README file.

Source Data Extended Data Fig. 3

Raw data to reproduce Extended Data Fig. 3 and a README file.

Source Data Extended Data Fig. 4

Raw data to reproduce Extended Data Fig. 4 and a README file.

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

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41561-023-01166-7

This article is cited by

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
pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy