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Effect of assimilating CO(2) observations in the Korean Peninsula on the inverse modeling to estimate surface CO(2) flux over Asia

To investigate the impact of two CO(2) observation datasets obtained from the Korean Peninsula on the surface CO(2) flux estimation over Asia, the two datasets are assimilated into the CarbonTracker (CT) inverse modeling system and the estimated surface CO(2) fluxes are analyzed. Anmyeon-do (AMY) an...

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Autores principales: Cho, Minkwang, Kim, Hyun Mee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8856549/
https://www.ncbi.nlm.nih.gov/pubmed/35180259
http://dx.doi.org/10.1371/journal.pone.0263925
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author Cho, Minkwang
Kim, Hyun Mee
author_facet Cho, Minkwang
Kim, Hyun Mee
author_sort Cho, Minkwang
collection PubMed
description To investigate the impact of two CO(2) observation datasets obtained from the Korean Peninsula on the surface CO(2) flux estimation over Asia, the two datasets are assimilated into the CarbonTracker (CT) inverse modeling system and the estimated surface CO(2) fluxes are analyzed. Anmyeon-do (AMY) and Gosan (GSN) sites in the Korean Peninsula have observed surface CO(2) mole fraction since the late 1990s. To investigate the effect of assimilating the additional Korean observations on the surface CO(2) flux estimation over Asia, two experiments are conducted. The reference experiment (CNTL) only assimilates observations provided by National Oceanic and Atmospheric Administration (NOAA), while the other experiment (EXP1) assimilates both NOAA observations and two Korean observation datasets. The results are analyzed for 9 years from 2003 to 2011 in Asia region because both AMY and GSN datasets exist almost completely for this period. The annual average of estimated biosphere CO(2) flux of EXP1 shows more flux absorption in summer and less flux emission from fall to spring compared to CNTL, mainly on Eurasia Temperate and Eurasia Boreal regions. When comparing model results to independent CO(2) concentration data from surface stations and aircraft, the root mean square error is smaller for EXP1 than CNTL. The EXP1 yields more reduction on uncertainty of estimated biosphere CO(2) flux over Asia, and the observation impact of AMY, GSN sites on flux estimation is approximately 11%, which is greater than other observation sites around the world. Therefore, the two CO(2) observation sets in the Korean Peninsula are useful in reducing uncertainties for regional as well as global scale CO(2) flux estimation.
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spelling pubmed-88565492022-02-19 Effect of assimilating CO(2) observations in the Korean Peninsula on the inverse modeling to estimate surface CO(2) flux over Asia Cho, Minkwang Kim, Hyun Mee PLoS One Research Article To investigate the impact of two CO(2) observation datasets obtained from the Korean Peninsula on the surface CO(2) flux estimation over Asia, the two datasets are assimilated into the CarbonTracker (CT) inverse modeling system and the estimated surface CO(2) fluxes are analyzed. Anmyeon-do (AMY) and Gosan (GSN) sites in the Korean Peninsula have observed surface CO(2) mole fraction since the late 1990s. To investigate the effect of assimilating the additional Korean observations on the surface CO(2) flux estimation over Asia, two experiments are conducted. The reference experiment (CNTL) only assimilates observations provided by National Oceanic and Atmospheric Administration (NOAA), while the other experiment (EXP1) assimilates both NOAA observations and two Korean observation datasets. The results are analyzed for 9 years from 2003 to 2011 in Asia region because both AMY and GSN datasets exist almost completely for this period. The annual average of estimated biosphere CO(2) flux of EXP1 shows more flux absorption in summer and less flux emission from fall to spring compared to CNTL, mainly on Eurasia Temperate and Eurasia Boreal regions. When comparing model results to independent CO(2) concentration data from surface stations and aircraft, the root mean square error is smaller for EXP1 than CNTL. The EXP1 yields more reduction on uncertainty of estimated biosphere CO(2) flux over Asia, and the observation impact of AMY, GSN sites on flux estimation is approximately 11%, which is greater than other observation sites around the world. Therefore, the two CO(2) observation sets in the Korean Peninsula are useful in reducing uncertainties for regional as well as global scale CO(2) flux estimation. Public Library of Science 2022-02-18 /pmc/articles/PMC8856549/ /pubmed/35180259 http://dx.doi.org/10.1371/journal.pone.0263925 Text en © 2022 Cho, Kim https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Cho, Minkwang
Kim, Hyun Mee
Effect of assimilating CO(2) observations in the Korean Peninsula on the inverse modeling to estimate surface CO(2) flux over Asia
title Effect of assimilating CO(2) observations in the Korean Peninsula on the inverse modeling to estimate surface CO(2) flux over Asia
title_full Effect of assimilating CO(2) observations in the Korean Peninsula on the inverse modeling to estimate surface CO(2) flux over Asia
title_fullStr Effect of assimilating CO(2) observations in the Korean Peninsula on the inverse modeling to estimate surface CO(2) flux over Asia
title_full_unstemmed Effect of assimilating CO(2) observations in the Korean Peninsula on the inverse modeling to estimate surface CO(2) flux over Asia
title_short Effect of assimilating CO(2) observations in the Korean Peninsula on the inverse modeling to estimate surface CO(2) flux over Asia
title_sort effect of assimilating co(2) observations in the korean peninsula on the inverse modeling to estimate surface co(2) flux over asia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8856549/
https://www.ncbi.nlm.nih.gov/pubmed/35180259
http://dx.doi.org/10.1371/journal.pone.0263925
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