Tracer-based Rapid Anthropogenic Carbon Estimation (TRACE)
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| Опубликовано в:: | Earth System Science Data vol. 17, no. 6 (2025), p. 3073 |
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| Главный автор: | |
| Другие авторы: | , , , , , |
| Опубликовано: |
Copernicus GmbH
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| Предметы: | |
| Online-ссылка: | Citation/Abstract Full Text Full Text - PDF |
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| 100 | 1 | |a Carter, Brendan R |u Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, Seattle, WA 98115, USA; NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, WA 98115, USA | |
| 245 | 1 | |a Tracer-based Rapid Anthropogenic Carbon Estimation (TRACE) | |
| 260 | |b Copernicus GmbH |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The ocean is one of the largest sinks for anthropogenic carbon dioxide (Canth) and its removal of carbon dioxide (CO<inline-formula>2)</inline-formula> from the atmosphere has been valued at hundreds of billions to trillions of US dollars in climate mitigation annually. The ecosystem impacts caused by planet-wide shifts in ocean chemistry resulting from marine Canth accumulation are an active area of research. For these reasons, we need accessible tools to quantify ocean Canth inventories and distributions and to predict how they might evolve in response to future emissions and mitigation activities. Unfortunately, Canth estimation methods are typically only accessible to trained scientists and modelers with access to significant computational resources. Here, we make modifications to the transit time distribution approach for Canth estimation that render the method more accessible. We also release software (BRCScienceProducts, 2025) called “Tracer-based Rapid Anthropogenic Carbon Estimation version 1” (TRACEv1) that allows users – with one line of code – to obtain Canth and water mass age estimates throughout the global open ocean from user-supplied values of geographic location, pressure, salinity, temperature, and the estimate year. We use this code to generate a data product of global gridded open-ocean Canth distributions (TRACEv1_GGCanth; Carter, 2025) that ranges from the preindustrial era through 2500 under a range of Shared Socioeconomic Pathways (SSPs, or atmospheric CO2 concentration pathways). We estimated the skill of these estimates by reconstructing Canth in models with known distributions of Canth and transient tracers and by conducting perturbation tests. In the model-based reconstruction test, TRACEv1 reproduces the global ocean Canth inventory to within <inline-formula>±10</inline-formula> % in 1980 and 2014. We discuss implications and limitations of the projected Canth distributions and highlight ways that the estimation strategy might be improved. One finding is that the ocean will continue to increase its net Canth inventory at least through 2500 due to deep-ocean ventilation, even with the SSP in which intense mitigation successfully decreases atmospheric Canth by <inline-formula>∼60</inline-formula> % in 2500 relative to the 2024 concentration. A notable limitation of this and similar projections made with TRACEv1 is that ongoing and potential future warming and changing oceanic circulation patterns with climate change are not captured by the method. The data products generated by this research are available as MATLAB code (10.5281/zenodo.15692788, BRCScienceProducts, 2025) and a spatially and temporally gridded data product (10.5281/zenodo.15692788, BRCScienceProducts, 2025). | |
| 653 | |a Climate change | ||
| 653 | |a Carbon dioxide removal | ||
| 653 | |a Accessibility | ||
| 653 | |a Carbon dioxide | ||
| 653 | |a Regression analysis | ||
| 653 | |a Biogeochemistry | ||
| 653 | |a Ventilation | ||
| 653 | |a Anthropogenic factors | ||
| 653 | |a Estimates | ||
| 653 | |a Carbon dioxide concentration | ||
| 653 | |a Ocean circulation patterns | ||
| 653 | |a Human influences | ||
| 653 | |a Oceans | ||
| 653 | |a Climate change mitigation | ||
| 653 | |a Marine chemistry | ||
| 653 | |a Salinity | ||
| 653 | |a Circulation patterns | ||
| 653 | |a Tracers | ||
| 653 | |a Water masses | ||
| 653 | |a Marine ecosystems | ||
| 653 | |a Environmental impact | ||
| 653 | |a Neural networks | ||
| 653 | |a Seawater | ||
| 653 | |a Ocean circulation | ||
| 653 | |a Transit time | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Schwinger, Jörg |u Norwegian Research Center, University of Bergen, Nygårdsgaten 112, 5008 Bergen, Norway | |
| 700 | 1 | |a Sonnerup, Rolf |u Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, Seattle, WA 98115, USA | |
| 700 | 1 | |a Fassbender, Andrea J |u NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, WA 98115, USA | |
| 700 | 1 | |a Sharp, Jonathan D |u Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, Seattle, WA 98115, USA; NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, WA 98115, USA | |
| 700 | 1 | |a Dias, Larissa M |u Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, Seattle, WA 98115, USA; NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, WA 98115, USA | |
| 700 | 1 | |a Sandborn, Daniel E |u School of Oceanography, University of Washington, Seattle, WA 98195, USA | |
| 773 | 0 | |t Earth System Science Data |g vol. 17, no. 6 (2025), p. 3073 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3225641902/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
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| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3225641902/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |