Towards monitoring the CO<sub>2</sub> source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO<sub>2</sub> mole fraction
<p>Improving the estimates of CO<span class="inline-formula"><sub>2</sub></span> sources and sinks over India through inverse methods calls for a comprehensive atmospheric monitoring system involving atmospheric transport models that make a realistic accountin...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-12-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/22/15287/2022/acp-22-15287-2022.pdf |
| _version_ | 1811210742840426496 |
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| author | V. Thilakan V. Thilakan D. Pillai D. Pillai C. Gerbig M. Galkowski M. Galkowski A. Ravi A. Ravi T. Anna Mathew |
| author_facet | V. Thilakan V. Thilakan D. Pillai D. Pillai C. Gerbig M. Galkowski M. Galkowski A. Ravi A. Ravi T. Anna Mathew |
| author_sort | V. Thilakan |
| collection | DOAJ |
| description | <p>Improving the estimates of CO<span class="inline-formula"><sub>2</sub></span> sources and sinks over India through inverse methods calls for a comprehensive atmospheric monitoring system involving atmospheric transport models that make a realistic accounting of atmospheric CO<span class="inline-formula"><sub>2</sub></span> variability along with a good coverage of ground-based monitoring stations. This study investigates the importance of representing fine-scale variability in atmospheric CO<span class="inline-formula"><sub>2</sub></span> in models for the optimal use of observations through inverse modelling. The unresolved variability in atmospheric CO<span class="inline-formula"><sub>2</sub></span> in coarse models is quantified by using WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) simulations at a spatial resolution of 10 km <span class="inline-formula">×</span> 10 km. We show that the representation errors due to unresolved variability in the coarse model with a horizontal resolution of 1<span class="inline-formula"><sup>∘</sup></span> (<span class="inline-formula">∼</span> 100 km) are considerable (median values of 1.5 and 0.4 ppm, parts per million, for the surface and column CO<span class="inline-formula"><sub>2</sub></span>, respectively) compared to the measurement errors. The monthly averaged surface representation error reaches up to
<span class="inline-formula">∼</span> 5 ppm, which is even comparable to half of the magnitude of the
seasonal variability or concentration enhancement due to hotspot emissions.
Representation error shows a strong dependence on multiple factors such as
time of the day, season, terrain heterogeneity, and changes in meteorology
and surface fluxes. By employing a first-order inverse modelling scheme
using pseudo-observations from nine tall-tower sites over India, we show
that the net ecosystem exchange (NEE) flux uncertainty solely due to
unresolved variability is in the range of 3.1 % to 10.3 % of the total NEE of the region. By estimating the representation error and its impact on flux estimations during different seasons, we emphasize the need to take account of fine-scale CO<span class="inline-formula"><sub>2</sub></span> variability in models over the Indian subcontinent to better understand processes regulating CO<span class="inline-formula"><sub>2</sub></span> sources and sinks. The efficacy of a simple parameterization scheme is further demonstrated to capture these unresolved variations in coarse models.</p> |
| first_indexed | 2024-04-12T05:00:49Z |
| format | Article |
| id | doaj.art-e7d63b90c1444cf0b402d5366f77649e |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-04-12T05:00:49Z |
| publishDate | 2022-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-e7d63b90c1444cf0b402d5366f77649e2022-12-22T03:47:01ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242022-12-0122152871531210.5194/acp-22-15287-2022Towards monitoring the CO<sub>2</sub> source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO<sub>2</sub> mole fractionV. Thilakan0V. Thilakan1D. Pillai2D. Pillai3C. Gerbig4M. Galkowski5M. Galkowski6A. Ravi7A. Ravi8T. Anna Mathew9Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research Bhopal (IISERB), Bhopal, IndiaMax Planck Partner Group (IISERB), Max Planck Society, Munich, GermanyDepartment of Earth and Environmental Sciences, Indian Institute of Science Education and Research Bhopal (IISERB), Bhopal, IndiaMax Planck Partner Group (IISERB), Max Planck Society, Munich, GermanyDepartment of Biogeochemical Signals, Max Planck Institute for Biogeochemistry, Jena, GermanyDepartment of Biogeochemical Signals, Max Planck Institute for Biogeochemistry, Jena, GermanyFaculty of Physics and Applied Computer Science, AGH University of Science and Technology, Kraków, PolandDepartment of Earth and Environmental Sciences, Indian Institute of Science Education and Research Bhopal (IISERB), Bhopal, IndiaMax Planck Partner Group (IISERB), Max Planck Society, Munich, GermanyDepartment of Earth and Environmental Sciences, Indian Institute of Science Education and Research Bhopal (IISERB), Bhopal, India<p>Improving the estimates of CO<span class="inline-formula"><sub>2</sub></span> sources and sinks over India through inverse methods calls for a comprehensive atmospheric monitoring system involving atmospheric transport models that make a realistic accounting of atmospheric CO<span class="inline-formula"><sub>2</sub></span> variability along with a good coverage of ground-based monitoring stations. This study investigates the importance of representing fine-scale variability in atmospheric CO<span class="inline-formula"><sub>2</sub></span> in models for the optimal use of observations through inverse modelling. The unresolved variability in atmospheric CO<span class="inline-formula"><sub>2</sub></span> in coarse models is quantified by using WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) simulations at a spatial resolution of 10 km <span class="inline-formula">×</span> 10 km. We show that the representation errors due to unresolved variability in the coarse model with a horizontal resolution of 1<span class="inline-formula"><sup>∘</sup></span> (<span class="inline-formula">∼</span> 100 km) are considerable (median values of 1.5 and 0.4 ppm, parts per million, for the surface and column CO<span class="inline-formula"><sub>2</sub></span>, respectively) compared to the measurement errors. The monthly averaged surface representation error reaches up to <span class="inline-formula">∼</span> 5 ppm, which is even comparable to half of the magnitude of the seasonal variability or concentration enhancement due to hotspot emissions. Representation error shows a strong dependence on multiple factors such as time of the day, season, terrain heterogeneity, and changes in meteorology and surface fluxes. By employing a first-order inverse modelling scheme using pseudo-observations from nine tall-tower sites over India, we show that the net ecosystem exchange (NEE) flux uncertainty solely due to unresolved variability is in the range of 3.1 % to 10.3 % of the total NEE of the region. By estimating the representation error and its impact on flux estimations during different seasons, we emphasize the need to take account of fine-scale CO<span class="inline-formula"><sub>2</sub></span> variability in models over the Indian subcontinent to better understand processes regulating CO<span class="inline-formula"><sub>2</sub></span> sources and sinks. The efficacy of a simple parameterization scheme is further demonstrated to capture these unresolved variations in coarse models.</p>https://acp.copernicus.org/articles/22/15287/2022/acp-22-15287-2022.pdf |
| spellingShingle | V. Thilakan V. Thilakan D. Pillai D. Pillai C. Gerbig M. Galkowski M. Galkowski A. Ravi A. Ravi T. Anna Mathew Towards monitoring the CO<sub>2</sub> source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO<sub>2</sub> mole fraction Atmospheric Chemistry and Physics |
| title | Towards monitoring the CO<sub>2</sub> source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO<sub>2</sub> mole fraction |
| title_full | Towards monitoring the CO<sub>2</sub> source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO<sub>2</sub> mole fraction |
| title_fullStr | Towards monitoring the CO<sub>2</sub> source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO<sub>2</sub> mole fraction |
| title_full_unstemmed | Towards monitoring the CO<sub>2</sub> source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO<sub>2</sub> mole fraction |
| title_short | Towards monitoring the CO<sub>2</sub> source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO<sub>2</sub> mole fraction |
| title_sort | towards monitoring the co sub 2 sub source sink distribution over india via inverse modelling quantifying the fine scale spatiotemporal variability in the atmospheric co sub 2 sub mole fraction |
| url | https://acp.copernicus.org/articles/22/15287/2022/acp-22-15287-2022.pdf |
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