A decadal inversion of CO2 using the Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA): sensitivity to the ground-based observation network
We present an assimilation system for atmospheric carbon dioxide (CO2) using a Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA), and demonstrate its capability to capture the observed atmospheric CO2 mixing ratios and to estimate CO2 fluxes. With the efficient data handling scheme in GEL...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Stockholm University Press
2017-01-01
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| Series: | Tellus: Series B, Chemical and Physical Meteorology |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/16000889.2017.1291158 |
| _version_ | 1818539917771276288 |
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| author | T. Shirai M. Ishizawa R. Zhuravlev A. Ganshin D. Belikov M. Saito T. Oda V. Valsala A.J. Gomez-Pelaez R. Langenfelds S. Maksyutov |
| author_facet | T. Shirai M. Ishizawa R. Zhuravlev A. Ganshin D. Belikov M. Saito T. Oda V. Valsala A.J. Gomez-Pelaez R. Langenfelds S. Maksyutov |
| author_sort | T. Shirai |
| collection | DOAJ |
| description | We present an assimilation system for atmospheric carbon dioxide (CO2) using a Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA), and demonstrate its capability to capture the observed atmospheric CO2 mixing ratios and to estimate CO2 fluxes. With the efficient data handling scheme in GELCA, our system assimilates non-smoothed CO2 data from observational data products such as the Observation Package (ObsPack) data products as constraints on surface fluxes. We conducted sensitivity tests to examine the impact of the site selections and the prior uncertainty settings of observation on the inversion results. For these sensitivity tests, we made five different site/data selections from the ObsPack product. In all cases, the time series of the global net CO2 flux to the atmosphere stayed close to values calculated from the growth rate of the observed global mean atmospheric CO2 mixing ratio. At regional scales, estimated seasonal CO2 fluxes were altered, depending on the CO2 data selected for assimilation. Uncertainty reductions were determined at the regional scale and compared among cases. As measures of the model–data mismatch, we used the model–data bias, root-mean-square error, and the linear correlation. For most observation sites, the model–data mismatch was reasonably small. Regarding regional flux estimates, tropical Asia was one of the regions that showed a significant impact from the observation network settings. We found that the surface fluxes in tropical Asia were the most sensitive to the use of aircraft measurements over the Pacific, and the seasonal cycle agreed better with the results of bottom-up studies when the aircraft measurements were assimilated. These results confirm the importance of these aircraft observations, especially for constraining surface fluxes in the tropics. |
| first_indexed | 2024-12-11T21:48:28Z |
| format | Article |
| id | doaj.art-02970e456c3d44fc845f0ef7707448a7 |
| institution | Directory Open Access Journal |
| issn | 1600-0889 |
| language | English |
| last_indexed | 2024-12-11T21:48:28Z |
| publishDate | 2017-01-01 |
| publisher | Stockholm University Press |
| record_format | Article |
| series | Tellus: Series B, Chemical and Physical Meteorology |
| spelling | doaj.art-02970e456c3d44fc845f0ef7707448a72022-12-22T00:49:32ZengStockholm University PressTellus: Series B, Chemical and Physical Meteorology1600-08892017-01-0169110.1080/16000889.2017.12911581291158A decadal inversion of CO2 using the Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA): sensitivity to the ground-based observation networkT. Shirai0M. Ishizawa1R. Zhuravlev2A. Ganshin3D. Belikov4M. Saito5T. Oda6V. Valsala7A.J. Gomez-Pelaez8R. Langenfelds9S. Maksyutov10Center for Global Environmental Research, National Institute for Environmental StudiesCenter for Global Environmental Research, National Institute for Environmental StudiesInstitute of Applied Physics of the Russian Academy of ScienceCentral Aerological ObservatoryCenter for Global Environmental Research, National Institute for Environmental StudiesCenter for Global Environmental Research, National Institute for Environmental StudiesUniversities Space Research AssociationIndian Institute for Tropical MeteorologyIzaña Atmospheric Research Center, Meteorological State Agency of SpainCommonwealth Scientific and Industrial Research OrganizationCenter for Global Environmental Research, National Institute for Environmental StudiesWe present an assimilation system for atmospheric carbon dioxide (CO2) using a Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA), and demonstrate its capability to capture the observed atmospheric CO2 mixing ratios and to estimate CO2 fluxes. With the efficient data handling scheme in GELCA, our system assimilates non-smoothed CO2 data from observational data products such as the Observation Package (ObsPack) data products as constraints on surface fluxes. We conducted sensitivity tests to examine the impact of the site selections and the prior uncertainty settings of observation on the inversion results. For these sensitivity tests, we made five different site/data selections from the ObsPack product. In all cases, the time series of the global net CO2 flux to the atmosphere stayed close to values calculated from the growth rate of the observed global mean atmospheric CO2 mixing ratio. At regional scales, estimated seasonal CO2 fluxes were altered, depending on the CO2 data selected for assimilation. Uncertainty reductions were determined at the regional scale and compared among cases. As measures of the model–data mismatch, we used the model–data bias, root-mean-square error, and the linear correlation. For most observation sites, the model–data mismatch was reasonably small. Regarding regional flux estimates, tropical Asia was one of the regions that showed a significant impact from the observation network settings. We found that the surface fluxes in tropical Asia were the most sensitive to the use of aircraft measurements over the Pacific, and the seasonal cycle agreed better with the results of bottom-up studies when the aircraft measurements were assimilated. These results confirm the importance of these aircraft observations, especially for constraining surface fluxes in the tropics.http://dx.doi.org/10.1080/16000889.2017.1291158carbon cycletop-down approachflux estimationcarbon dioxideinversioncoupled modeltropical Asia |
| spellingShingle | T. Shirai M. Ishizawa R. Zhuravlev A. Ganshin D. Belikov M. Saito T. Oda V. Valsala A.J. Gomez-Pelaez R. Langenfelds S. Maksyutov A decadal inversion of CO2 using the Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA): sensitivity to the ground-based observation network Tellus: Series B, Chemical and Physical Meteorology carbon cycle top-down approach flux estimation carbon dioxide inversion coupled model tropical Asia |
| title | A decadal inversion of CO2 using the Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA): sensitivity to the ground-based observation network |
| title_full | A decadal inversion of CO2 using the Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA): sensitivity to the ground-based observation network |
| title_fullStr | A decadal inversion of CO2 using the Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA): sensitivity to the ground-based observation network |
| title_full_unstemmed | A decadal inversion of CO2 using the Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA): sensitivity to the ground-based observation network |
| title_short | A decadal inversion of CO2 using the Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA): sensitivity to the ground-based observation network |
| title_sort | decadal inversion of co2 using the global eulerian lagrangian coupled atmospheric model gelca sensitivity to the ground based observation network |
| topic | carbon cycle top-down approach flux estimation carbon dioxide inversion coupled model tropical Asia |
| url | http://dx.doi.org/10.1080/16000889.2017.1291158 |
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