The integrity of forest-based climate solutions and carbon credits requires persistent carbon storage, but climate change is increasing the risk of natural disturbances that release carbon back into the atmosphere. Using global satellite data, disturbance modeling, and machine learning, we provide the first spatially explicit and scenario-based maps of long-term probability of carbon loss in global forests under different disturbance severities and climate scenarios. We find that North American conifer forests, tropical rainforests, and Asian (sub)tropical dry forests face the greatest risks, and that Eurasian temperate forests, African (sub)tropical dry forests face the lowest. Globally, the likelihood of reversals over 100 years is 31%-42% across all scenarios. Our work helps to maximize the benefits of forest-based climate solutions by informing more strategic project placement and more robust reversal-risk compensation mechanisms, such as buffer pools, and highlights critical additional science to better understand and manage risks of these essential climate solutions.
Wu, C., Goulden, M. L., Randerson, J. T., Trugman, A. T., Wang, J. A., Yang, L., Acil, N., Cook-Patton, S. C., Cullenward, D., Davis, S. J., Williams, C. A., Anderegg, W. R. L.
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