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Estimating regional fossil fuel CO(2) concentrations from (14)CO(2) observations: challenges and uncertainties
The direct way to estimate the regional fossil fuel CO(2) surplus (ΔffCO(2)) at a station is by measuring the Δ(14)CO(2) depletion compared with a respective background. However, this approach has several challenges, which are (i) the choice of an appropriate Δ(14)CO(2) background, (ii) potential co...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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The Royal Society
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10642792/ https://www.ncbi.nlm.nih.gov/pubmed/37807691 http://dx.doi.org/10.1098/rsta.2022.0203 |
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author | Maier, Fabian Levin, Ingeborg Gachkivskyi, Maksym Rödenbeck, Christian Hammer, Samuel |
author_facet | Maier, Fabian Levin, Ingeborg Gachkivskyi, Maksym Rödenbeck, Christian Hammer, Samuel |
author_sort | Maier, Fabian |
collection | PubMed |
description | The direct way to estimate the regional fossil fuel CO(2) surplus (ΔffCO(2)) at a station is by measuring the Δ(14)CO(2) depletion compared with a respective background. However, this approach has several challenges, which are (i) the choice of an appropriate Δ(14)CO(2) background, (ii) potential contaminations through nuclear (14)CO(2) emissions and (iii) masking of ΔffCO(2) by (14)C-enriched biosphere respiration. Here we evaluate these challenges and estimate potential biases and typical uncertainties of (14)C-based ΔffCO(2) estimates in Europe. We show that Mace Head (MHD), Ireland, is a representative background station for the Integrated Carbon Observation System (ICOS) atmosphere station network. The mean ΔffCO(2) representativeness bias when using the MHD Δ(14)CO(2) background for the whole observation network is of order 0.1 ± 0.3 ppm. At ICOS sites, the median nuclear contamination leads to 25% low-biased ΔffCO(2) estimates if not corrected for. The ΔffCO(2) masking due to (14)C-enriched heterotrophic CO(2) respiration can lead to similar ΔffCO(2) biases as the nuclear contaminations, especially in summer. Our evaluation of all components contributing to the uncertainty of ΔffCO(2) estimates reveals that, due to the small ffCO(2) signals at ICOS stations, almost half of the (14)C-based ΔffCO(2) estimates from integrated samples have an uncertainty that is larger than 50%. This article is part of the Theo Murphy meeting issue 'Radiocarbon in the Anthropocene'. |
format | Online Article Text |
id | pubmed-10642792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-106427922023-11-14 Estimating regional fossil fuel CO(2) concentrations from (14)CO(2) observations: challenges and uncertainties Maier, Fabian Levin, Ingeborg Gachkivskyi, Maksym Rödenbeck, Christian Hammer, Samuel Philos Trans A Math Phys Eng Sci Articles The direct way to estimate the regional fossil fuel CO(2) surplus (ΔffCO(2)) at a station is by measuring the Δ(14)CO(2) depletion compared with a respective background. However, this approach has several challenges, which are (i) the choice of an appropriate Δ(14)CO(2) background, (ii) potential contaminations through nuclear (14)CO(2) emissions and (iii) masking of ΔffCO(2) by (14)C-enriched biosphere respiration. Here we evaluate these challenges and estimate potential biases and typical uncertainties of (14)C-based ΔffCO(2) estimates in Europe. We show that Mace Head (MHD), Ireland, is a representative background station for the Integrated Carbon Observation System (ICOS) atmosphere station network. The mean ΔffCO(2) representativeness bias when using the MHD Δ(14)CO(2) background for the whole observation network is of order 0.1 ± 0.3 ppm. At ICOS sites, the median nuclear contamination leads to 25% low-biased ΔffCO(2) estimates if not corrected for. The ΔffCO(2) masking due to (14)C-enriched heterotrophic CO(2) respiration can lead to similar ΔffCO(2) biases as the nuclear contaminations, especially in summer. Our evaluation of all components contributing to the uncertainty of ΔffCO(2) estimates reveals that, due to the small ffCO(2) signals at ICOS stations, almost half of the (14)C-based ΔffCO(2) estimates from integrated samples have an uncertainty that is larger than 50%. This article is part of the Theo Murphy meeting issue 'Radiocarbon in the Anthropocene'. The Royal Society 2023-11-27 2023-10-09 /pmc/articles/PMC10642792/ /pubmed/37807691 http://dx.doi.org/10.1098/rsta.2022.0203 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Articles Maier, Fabian Levin, Ingeborg Gachkivskyi, Maksym Rödenbeck, Christian Hammer, Samuel Estimating regional fossil fuel CO(2) concentrations from (14)CO(2) observations: challenges and uncertainties |
title | Estimating regional fossil fuel CO(2) concentrations from (14)CO(2) observations: challenges and uncertainties |
title_full | Estimating regional fossil fuel CO(2) concentrations from (14)CO(2) observations: challenges and uncertainties |
title_fullStr | Estimating regional fossil fuel CO(2) concentrations from (14)CO(2) observations: challenges and uncertainties |
title_full_unstemmed | Estimating regional fossil fuel CO(2) concentrations from (14)CO(2) observations: challenges and uncertainties |
title_short | Estimating regional fossil fuel CO(2) concentrations from (14)CO(2) observations: challenges and uncertainties |
title_sort | estimating regional fossil fuel co(2) concentrations from (14)co(2) observations: challenges and uncertainties |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10642792/ https://www.ncbi.nlm.nih.gov/pubmed/37807691 http://dx.doi.org/10.1098/rsta.2022.0203 |
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