Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate <i>δ</i><sup>13</sup>C(CH<sub>4</sub>) and CH<sub>4</sub>: a case study with model LMDz-SACS
<p>Atmospheric <span class="inline-formula">CH<sub>4</sub></span> mole fractions resumed their increase in 2007 after a plateau during the 1999–2006 period, indicating relative changes in the sources and sinks. Estimating sources by exploiting observations wit...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-06-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/15/4831/2022/gmd-15-4831-2022.pdf |
| _version_ | 1828773757697654784 |
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| author | J. Thanwerdas M. Saunois A. Berchet I. Pison B. H. Vaughn S. E. Michel P. Bousquet |
| author_facet | J. Thanwerdas M. Saunois A. Berchet I. Pison B. H. Vaughn S. E. Michel P. Bousquet |
| author_sort | J. Thanwerdas |
| collection | DOAJ |
| description | <p>Atmospheric <span class="inline-formula">CH<sub>4</sub></span> mole fractions resumed their increase in 2007 after a plateau during the 1999–2006 period, indicating relative changes in the sources and sinks. Estimating sources by exploiting observations within an inverse modeling framework (top-down approaches) is a powerful approach. It is, nevertheless, challenging to efficiently differentiate co-located emission categories and sinks by using <span class="inline-formula">CH<sub>4</sub></span> observations alone. As a result, top-down approaches are limited when it comes to fully understanding <span class="inline-formula">CH<sub>4</sub></span> burden changes and attributing these changes to specific source variations. <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)<sub>source</sub></span> isotopic signatures of <span class="inline-formula">CH<sub>4</sub></span> sources differ between emission categories (biogenic, thermogenic, and pyrogenic) and can therefore be used to address this limitation. Here, a new 3-D variational inverse modeling framework designed to assimilate <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> observations together with <span class="inline-formula">CH<sub>4</sub></span> observations is presented. This system is capable of optimizing both the emissions and the associated source signatures of multiple emission categories at the pixel scale. To our knowledge, this represents the first attempt to carry out variational inversion assimilating <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> with a 3-D chemistry transport model (CTM) and to independently optimize isotopic source signatures of multiple emission categories. We present the technical implementation of joint <span class="inline-formula">CH<sub>4</sub></span> and <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> constraints in a variational system and analyze how sensitive the system is to the setup controlling the optimization using the LMDz-SACS 3-D CTM. We find that assimilating <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> observations and allowing the system to adjust isotopic source signatures provide relatively large differences in global flux estimates for wetlands (<span class="inline-formula">−</span>5.7 <span class="inline-formula">Tg CH<sub>4</sub> yr<sup>−1</sup></span>), agriculture and waste (<span class="inline-formula">−</span>6.4 <span class="inline-formula">Tg CH<sub>4</sub> yr<sup>−1</sup></span>), fossil fuels (<span class="inline-formula">+</span>8.6 <span class="inline-formula">Tg CH<sub>4</sub> yr<sup>−1</sup></span>) and biofuels–biomass burning (<span class="inline-formula">+</span>3.2 <span class="inline-formula">Tg CH<sub>4</sub> yr<sup>−1</sup></span>) categories compared to the results inferred without assimilating <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> observations. More importantly, when assimilating both <span class="inline-formula">CH<sub>4</sub></span> and <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> observations, but assuming that the source signatures are perfectly known, these differences increase by a factor of 3–4, strengthening the importance of having as accurate signature estimates as possible. Initial conditions, uncertainties in <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> observations, or the number of optimized categories have a much smaller impact (less than 2 <span class="inline-formula">Tg CH<sub>4</sub> yr<sup>−1</sup></span>).</p> |
| first_indexed | 2024-12-11T15:10:23Z |
| format | Article |
| id | doaj.art-90e842cee0064cb3b49fa62b204903d6 |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-12-11T15:10:23Z |
| publishDate | 2022-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-90e842cee0064cb3b49fa62b204903d62022-12-22T01:00:47ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032022-06-01154831485110.5194/gmd-15-4831-2022Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate <i>δ</i><sup>13</sup>C(CH<sub>4</sub>) and CH<sub>4</sub>: a case study with model LMDz-SACSJ. Thanwerdas0M. Saunois1A. Berchet2I. Pison3B. H. Vaughn4S. E. Michel5P. Bousquet6Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, IPSL, Gif-sur-Yvette, FranceLaboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, IPSL, Gif-sur-Yvette, FranceLaboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, IPSL, Gif-sur-Yvette, FranceLaboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, IPSL, Gif-sur-Yvette, FranceINSTAAR, University of Colorado, Boulder, Boulder, CO, USAINSTAAR, University of Colorado, Boulder, Boulder, CO, USALaboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, IPSL, Gif-sur-Yvette, France<p>Atmospheric <span class="inline-formula">CH<sub>4</sub></span> mole fractions resumed their increase in 2007 after a plateau during the 1999–2006 period, indicating relative changes in the sources and sinks. Estimating sources by exploiting observations within an inverse modeling framework (top-down approaches) is a powerful approach. It is, nevertheless, challenging to efficiently differentiate co-located emission categories and sinks by using <span class="inline-formula">CH<sub>4</sub></span> observations alone. As a result, top-down approaches are limited when it comes to fully understanding <span class="inline-formula">CH<sub>4</sub></span> burden changes and attributing these changes to specific source variations. <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)<sub>source</sub></span> isotopic signatures of <span class="inline-formula">CH<sub>4</sub></span> sources differ between emission categories (biogenic, thermogenic, and pyrogenic) and can therefore be used to address this limitation. Here, a new 3-D variational inverse modeling framework designed to assimilate <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> observations together with <span class="inline-formula">CH<sub>4</sub></span> observations is presented. This system is capable of optimizing both the emissions and the associated source signatures of multiple emission categories at the pixel scale. To our knowledge, this represents the first attempt to carry out variational inversion assimilating <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> with a 3-D chemistry transport model (CTM) and to independently optimize isotopic source signatures of multiple emission categories. We present the technical implementation of joint <span class="inline-formula">CH<sub>4</sub></span> and <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> constraints in a variational system and analyze how sensitive the system is to the setup controlling the optimization using the LMDz-SACS 3-D CTM. We find that assimilating <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> observations and allowing the system to adjust isotopic source signatures provide relatively large differences in global flux estimates for wetlands (<span class="inline-formula">−</span>5.7 <span class="inline-formula">Tg CH<sub>4</sub> yr<sup>−1</sup></span>), agriculture and waste (<span class="inline-formula">−</span>6.4 <span class="inline-formula">Tg CH<sub>4</sub> yr<sup>−1</sup></span>), fossil fuels (<span class="inline-formula">+</span>8.6 <span class="inline-formula">Tg CH<sub>4</sub> yr<sup>−1</sup></span>) and biofuels–biomass burning (<span class="inline-formula">+</span>3.2 <span class="inline-formula">Tg CH<sub>4</sub> yr<sup>−1</sup></span>) categories compared to the results inferred without assimilating <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> observations. More importantly, when assimilating both <span class="inline-formula">CH<sub>4</sub></span> and <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> observations, but assuming that the source signatures are perfectly known, these differences increase by a factor of 3–4, strengthening the importance of having as accurate signature estimates as possible. Initial conditions, uncertainties in <span class="inline-formula"><i>δ</i><sup>13</sup>C(CH<sub>4</sub>)</span> observations, or the number of optimized categories have a much smaller impact (less than 2 <span class="inline-formula">Tg CH<sub>4</sub> yr<sup>−1</sup></span>).</p>https://gmd.copernicus.org/articles/15/4831/2022/gmd-15-4831-2022.pdf |
| spellingShingle | J. Thanwerdas M. Saunois A. Berchet I. Pison B. H. Vaughn S. E. Michel P. Bousquet Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate <i>δ</i><sup>13</sup>C(CH<sub>4</sub>) and CH<sub>4</sub>: a case study with model LMDz-SACS Geoscientific Model Development |
| title | Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate <i>δ</i><sup>13</sup>C(CH<sub>4</sub>) and CH<sub>4</sub>: a case study with model LMDz-SACS |
| title_full | Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate <i>δ</i><sup>13</sup>C(CH<sub>4</sub>) and CH<sub>4</sub>: a case study with model LMDz-SACS |
| title_fullStr | Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate <i>δ</i><sup>13</sup>C(CH<sub>4</sub>) and CH<sub>4</sub>: a case study with model LMDz-SACS |
| title_full_unstemmed | Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate <i>δ</i><sup>13</sup>C(CH<sub>4</sub>) and CH<sub>4</sub>: a case study with model LMDz-SACS |
| title_short | Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate <i>δ</i><sup>13</sup>C(CH<sub>4</sub>) and CH<sub>4</sub>: a case study with model LMDz-SACS |
| title_sort | variational inverse modeling within the community inversion framework v1 1 to assimilate i δ i sup 13 sup c ch sub 4 sub and ch sub 4 sub a case study with model lmdz sacs |
| url | https://gmd.copernicus.org/articles/15/4831/2022/gmd-15-4831-2022.pdf |
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