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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #384549

Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

Title: Global and northern-high-latitude terrestrial carbon sinks in the 21st century from CMIP6 experiments

item QUI, H. - University Of Wisconsin
item HAO, D. - Pacific Northwest National Laboratory
item ZENG, Y. - University Of Wisconsin
item Zhang, Xuesong
item CHEN, M. - University Of Wisconsin

Submitted to: Science of the Total Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/15/2022
Publication Date: 1/9/2023
Citation: Qui, H., Hao, D., Zeng, Y., Zhang, X., Chen, M. 2023. Global and northern-high-latitude terrestrial carbon sinks in the 21st century from CMIP6 experiments. Science of the Total Environment. 14(1):1-16.

Interpretive Summary: Understanding the global carbon cycle under global change is critical for projecting future temperature and designing carbon management practices to reduce greenhouse gas emissions. Previous studies have shown large uncertainties associated with the climate models used to project future carbon fluxes for global terrestrial ecosystems. We analyzed the most up-to-date modeling results from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to quantify the uncertainties associated with state-of-the-science climate models for the period 2015-2100. Our findings show that the uncertainties of global and northern high latitudes terrestrial carbon fluxes from CMIP6 are similar in magnitude to those reported in previous studies, and the uncertainties grow larger over time. The results highlight the need to constrain the large uncertainties associated with CMIP6 model projections for supporting effective climate mitigation strategies.

Technical Abstract: Climate warming is accelerating the ongoing changes in the global terrestrial ecosystems and particularly those in the northern high latitudes (NHL), and rendering the balances of the land atmosphere carbon exchange highly uncertain in the future. The Coupled Model Intercomparison Project Phase 6 (CMIP6) employs the most updated climate models to estimate terrestrial ecosystem carbon dynamics driven by a new set of socioeconomic and climate change pathways generated by the most updated Integrated Assessment Models. By analyzing the future (2015-2100) carbon fluxes estimated by ten CMIP6 models, we quantitatively evaluated the projected magnitudes, trends, spatial patterns and uncertainties of global and NHL carbon fluxes under four global change scenarios (i.e., SSP126, SSP245, SSP370 and SSP585) as well as the role of NHL in the global terrestrial ecosystem carbon dynamics. Overall, the ten CMIP6 models consistently suggest that the global and NHL terrestrial ecosystems will be consistent carbon sinks in the future, and the magnitude of the carbon sinks is projected to be larger under scenarios with higher radiation forcing. By the end of this century, the models by average estimate the NHL net ecosystem productivity (NEP) as 0.54 ±0.77, 1.01 ±0 .98, 0.97 ±1.62, and 1.05 ±1.83 PgC/yr under SSP126, SSP245, SSP370 and SSP585, respectively. The model uncertainties are not substantially reduced compared with those from earlier intercomparison projects, e.g., the Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) and the inter-Sectoral Impact Model Intercomparison Project (ISIMIP) and will grow under future scenarios. Although NHL contributes a small fraction of the global carbon sink (~13%) with a small uncertainty (i.e. the spread of the projections), the relative uncertainties (i.e. standard deviation divided by ensemble mean) of NHL NEP are much larger than the global level. The results presented in this study provide insights into future carbon flux evolutions under various future global change scenarios and highlight the urgent need to constrain the large uncertainties associated with CMIP6 model projections for making better climate mitigation strategies.