Artificial Intelligence Reveals a Weaker CMIP6 Terrestrial Carbon Sink with Reduced Uncertainty

Terrestrial ecosystems have cumulatively sequestered 24% of anthropogenic carbon dioxide (CO₂) emissions since 1850 and are critical for mitigating future climate change. However, current Earth System Models (ESMs) remain highly uncertain in projecting future trajectories of this carbon sink capacity, hampering our predictive understanding of climate mitigation potential and impeding effective climate and carbon management policies. This study develops a novel framework that harnesses deep-learning (DL) to constrain uncertainties of ESM-projected Gross Primary Production (GPP) and Net Ecosystem Production (NEP) through 2100. Specifically, we apply DL to characterize the “offset” between ESM-simulated output (using CMIP6 models) and best-available observational products (top-down, bottom-up). This offset is treated as unresolved processes by current ESMs that could be effectively resolved by DL, which, once trained during historical periods, can be applied to adjust CMIP6 projections of the future. We find that DL significantly reduces the inter-model spread of GPP by ~56% and NEP by ~66% across the CMIP6 ESM ensemble . Under the medium emission scenario (SSP 245), the ensemble mean for NEP in 2100 is much weaker, 2.42 ± 1.16 PgC yr⁻¹ compared to 5.52 ± 3.45 PgC yr⁻¹ in the raw CMIP6 projections, suggesting a current overestimation of future carbon sequestration capability. Interestingly, DL revealed a slower trajectory of NEP growth compared to the raw CMIP6 projection. Beyond curbing the uncertainties of CMIP6 projections, DL also captures key environmental sensitivities of carbon cycle processes such as CO₂ fertilization and sensitivity to warming. These findings demonstrate the power of DL in effectively curbing ESMs projection uncertainties and suggest that relying solely on natural terrestrial carbon sinks for climate mitigation is unlikely to slow down climate warming.