Why do inverse models disagree? A case study with two European CO<sub>2</sub> inversions
<p>We present an analysis of atmospheric transport impact on estimating CO<span class="inline-formula"><sub>2</sub></span> fluxes using two atmospheric inversion systems (CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm (LUMIA)) over E...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-03-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/23/2813/2023/acp-23-2813-2023.pdf |
| _version_ | 1811161545204301824 |
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| author | S. Munassar S. Munassar G. Monteil M. Scholze U. Karstens C. Rödenbeck F.-T. Koch F.-T. Koch K. U. Totsche C. Gerbig |
| author_facet | S. Munassar S. Munassar G. Monteil M. Scholze U. Karstens C. Rödenbeck F.-T. Koch F.-T. Koch K. U. Totsche C. Gerbig |
| author_sort | S. Munassar |
| collection | DOAJ |
| description | <p>We present an analysis of atmospheric transport impact on
estimating CO<span class="inline-formula"><sub>2</sub></span> fluxes using two atmospheric inversion systems
(CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm (LUMIA)) over Europe in 2018. The main focus of
this study is to quantify the dominant drivers of spread amid CO<span class="inline-formula"><sub>2</sub></span>
estimates derived from atmospheric tracer inversions. The Lagrangian
transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of
mesoscale transport. The impact of lateral boundary conditions for CO<span class="inline-formula"><sub>2</sub></span>
was assessed by using two different estimates from the global inversion
systems CarboScope (TM3) and TM5-4DVAR. CO<span class="inline-formula"><sub>2</sub></span> estimates calculated with
an ensemble of eight inversions differing in the regional and global
transport models, as well as the inversion systems, show a relatively large
spread for the annual fluxes, ranging between <span class="inline-formula">−</span>0.72 and 0.20 PgC yr<span class="inline-formula"><sup>−1</sup></span>, which is
larger than the a priori uncertainty of 0.47 PgC yr<span class="inline-formula"><sup>−1</sup></span>. The discrepancies
in annual budget are primarily caused by differences in the mesoscale
transport model (0.51 PgC yr<span class="inline-formula"><sup>−1</sup></span>), in comparison with 0.23 and 0.10 PgC yr<span class="inline-formula"><sup>−1</sup></span> that resulted from the far-field contributions and the inversion
systems, respectively. Additionally, varying the mesoscale transport caused
large discrepancies in spatial and temporal patterns, while changing the
lateral boundary conditions led to more homogeneous spatial and temporal
impact. We further investigated the origin of the discrepancies between
transport models. The meteorological forcing parameters (forecasts versus
reanalysis obtained from ECMWF data products) used to drive the transport
models are responsible for a small part of the differences in CO<span class="inline-formula"><sub>2</sub></span>
estimates, but the largest impact seems to come from the transport model
schemes. Although a good convergence in the differences between the
inversion systems was achieved by applying a strict protocol of using
identical prior fluxes and atmospheric datasets, there was a non-negligible
impact arising from applying a different inversion system. Specifically, the
choice of prior error structure accounted for a large part of
system-to-system differences.</p> |
| first_indexed | 2024-04-10T06:17:15Z |
| format | Article |
| id | doaj.art-595fe329c8f8406f912ff6f4638e1b53 |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-04-10T06:17:15Z |
| publishDate | 2023-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-595fe329c8f8406f912ff6f4638e1b532023-03-02T06:41:23ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242023-03-01232813282810.5194/acp-23-2813-2023Why do inverse models disagree? A case study with two European CO<sub>2</sub> inversionsS. Munassar0S. Munassar1G. Monteil2M. Scholze3U. Karstens4C. Rödenbeck5F.-T. Koch6F.-T. Koch7K. U. Totsche8C. Gerbig9Department of Biogeochemical Signals, Max Planck Institute for Biogeochemistry, Jena, GermanyDepartment of Physics, Faculty of Sciences, Ibb University, Ibb, YemenDepartment of Physical Geography and Ecosystem Science, Lund University, Lund, SwedenDepartment of Physical Geography and Ecosystem Science, Lund University, Lund, SwedenICOS Carbon Portal at Lund University, department of Physical Geography and Ecosystem Science, Lund University, Lund, SwedenDepartment of Biogeochemical Signals, Max Planck Institute for Biogeochemistry, Jena, GermanyDepartment of Biogeochemical Signals, Max Planck Institute for Biogeochemistry, Jena, GermanyMeteorological Observatory Hohenpeissenberg, Deutscher Wetterdienst, Hohenpeißenberg, GermanyInstitute of Geoscience, Friedrich Schiller University, Jena, GermanyDepartment of Biogeochemical Signals, Max Planck Institute for Biogeochemistry, Jena, Germany<p>We present an analysis of atmospheric transport impact on estimating CO<span class="inline-formula"><sub>2</sub></span> fluxes using two atmospheric inversion systems (CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm (LUMIA)) over Europe in 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO<span class="inline-formula"><sub>2</sub></span> estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO<span class="inline-formula"><sub>2</sub></span> was assessed by using two different estimates from the global inversion systems CarboScope (TM3) and TM5-4DVAR. CO<span class="inline-formula"><sub>2</sub></span> estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems, show a relatively large spread for the annual fluxes, ranging between <span class="inline-formula">−</span>0.72 and 0.20 PgC yr<span class="inline-formula"><sup>−1</sup></span>, which is larger than the a priori uncertainty of 0.47 PgC yr<span class="inline-formula"><sup>−1</sup></span>. The discrepancies in annual budget are primarily caused by differences in the mesoscale transport model (0.51 PgC yr<span class="inline-formula"><sup>−1</sup></span>), in comparison with 0.23 and 0.10 PgC yr<span class="inline-formula"><sup>−1</sup></span> that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions led to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO<span class="inline-formula"><sub>2</sub></span> estimates, but the largest impact seems to come from the transport model schemes. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical prior fluxes and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences.</p>https://acp.copernicus.org/articles/23/2813/2023/acp-23-2813-2023.pdf |
| spellingShingle | S. Munassar S. Munassar G. Monteil M. Scholze U. Karstens C. Rödenbeck F.-T. Koch F.-T. Koch K. U. Totsche C. Gerbig Why do inverse models disagree? A case study with two European CO<sub>2</sub> inversions Atmospheric Chemistry and Physics |
| title | Why do inverse models disagree? A case study with two European CO<sub>2</sub> inversions |
| title_full | Why do inverse models disagree? A case study with two European CO<sub>2</sub> inversions |
| title_fullStr | Why do inverse models disagree? A case study with two European CO<sub>2</sub> inversions |
| title_full_unstemmed | Why do inverse models disagree? A case study with two European CO<sub>2</sub> inversions |
| title_short | Why do inverse models disagree? A case study with two European CO<sub>2</sub> inversions |
| title_sort | why do inverse models disagree a case study with two european co sub 2 sub inversions |
| url | https://acp.copernicus.org/articles/23/2813/2023/acp-23-2813-2023.pdf |
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