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The Effectiveness of Agricultural Carbon Dioxide Removal using the University of Victoria Earth System Climate Model
| Description: | This dataset comprises output from simulations used in "The Effectiveness of Agricultural Carbon Dioxide Removal using the University of Victoria Earth System Climate Model" by Rebecca C Evans and H Damon Matthews, published in Biogeosciences (2025). The data was collected to improve our understanding of how the climate responds to carbon removal when the sequestered carbon is stored temporarily in pools that can continue to interact with the atmosphere (nature-based removal). Included are time series from 12 future climate simulations: - 3 simulations that represent the climate's response to future emissions under the SSP 1-1.9, 2-4.5, and 5-8.5 emissions scenarios. - 3 simulations that have the same SSP scenarios (1-1.9, 2-4.5, 5-8.5) with the addition of a small amount of prescribed carbon dioxide removal in agricultural areas - 3 simulations that have the same SSP scenarios (1-1.9, 2-4.5, 5-8.5) but with moderate rates of agricultural CDR - 3 simulations that have the same SSP scenarios (1-1.9, 2-4.5, 5-8.5) but with high rates of agricultural CDR. The data provided here is the output of these simulations, and can be found in a netcdf format. The data is annual globally-averaged output of temperature, atmospheric CO2, carbon pools, etc. for the 100 years from the year 2000 to 2100. Brief description of the methodology: (1) Take projections for future CO2 under specific SSP scenarios from https://tntcat.iiasa.ac.at/SspDb (2) Modify the code of the UVic ESCM (https://terra.seos.uvic.ca/model/2.10/) to include a prescribed atmosphere-to-soil CO2 flux in agricultural areas (3) Prescribe this CO2 flux to linearly ramp from 0 in the present day, to a maximum value in 2050 based literature estimates of CDR that will be possible by 2050 for low, moderate, and high costs (4) Hold the CO2 flux constant after 2050 (5) Compare the results to the simulations with no prescribed CDR, to elucidate the impact of CDR on the climate A more detailed description of the methodology can be found in the published paper. Model output data to create the figures in the published manuscript Manuscript: Evans, R. C., & Matthews, H. D. (2024). The effectiveness of agricultural carbon dioxide removal using the University of Victoria Earth System Climate Model. Biogeosciences. https://doi.org/10.5194/egusphere-2024-1810 |
| Notes: | Direct link to the model code: https://terra.seos.uvic.ca/model/2.10/ is not functional. Item exited embargo and became publicly available on 2025-04-16 |
| Authors: | Rebecca, Evans; Concordia University;  0000-0001-7699-5217Matthews, Damon; Concordia University; 0000-0003-3625-390X |
| Keywords: | University of Victoria Earth System Climate Model UVic ESCM Nature-based Climate Solutions Carbon Dioxide Removal Agriculture Soil Carbon Sequestration Numerical Modeling |
| Field of Research: | Earth and related environmental sciences > Soil sciences > Carbon sequestration science
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| Publication Date: | 2025-04-16 |
| Publisher: | Federated Research Data Repository / dépôt fédéré de données de recherche |
| Funder: | Mitacs Elevate Postdoctoral Fellowship; Simulating the Use of Agriculture as a Nature-Based Carbon Capture Solution for Mitigating Climate Change; IT34880 Microsoft (Canada); Simulating the Use of Agriculture as a Nature-Based Carbon Capture Solution for Mitigating Climate Change Environment and Climate Change Canada; Simulating the Use of Agriculture as a Nature-Based Carbon Capture Solution for Mitigating Climate Change |
| URI: | https://doi.org/10.20383/103.0978 |
| Related Identifiers: |
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This dataset is derived from
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| Geographic Coverage: | Place Name Global |
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Access to this dataset is subject to the following terms:
Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
Citation
Rebecca, E., Matthews, D. (2025). The Effectiveness of Agricultural Carbon Dioxide Removal using the University of Victoria Earth System Climate Model. Federated Research Data Repository. https://doi.org/10.20383/103.0978
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