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Southern California megacity CO2, CH4, and CO flux estimates using ground- and space-based remote sensing and a Lagrangian modelстатья
Статья опубликована в высокорейтинговом журнале
Информация о цитировании статьи получена из
Web of Science,
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 6 декабря 2018 г.
- Авторы: Hedelius Jacob K., Liu Junjie, Oda Tomohiro, Maksyutov Shamil, Roehl Coleen M., Iraci Laura T., Podolske James R., Hillyard Patrick W., Liang Jianming, Gurney Kevin R., Wunch Debra, Wennberg Paul O.
- Журнал: Atmospheric Chemistry and Physics
- Том: 18
- Номер: 22
- Год издания: 2018
- Издательство: European Geophysical Society
- Местоположение издательства: Germany
- Первая страница: 16271
- Последняя страница: 16291
- DOI: 10.5194/acp-18-16271-2018
- Аннотация: We estimate the overall CO2, CH4, and CO flux from the South Coast Air Basin using an inversion that couples Total Carbon Column Observing Network (TCCON) and Orbiting Carbon Observatory-2 (OCO-2) observations, with the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the Open-source Data Inventory for Anthropogenic CO2 (ODIAC). Using TCCON data we estimate the direct net CO2 flux from the SoCAB to be 104±26TgCO2yr−1 for the study period of July 2013–August 2016. We obtain a slightly higher estimate of 120±30TgCO2yr−1 using OCO-2 data. These CO2 emission estimates are on the low end of previous work. Our net CH4 (360±90GgCH4yr−1) flux estimate is in agreement with central values from previous top-down studies going back to 2010 (342–440GgCH4yr−1). CO emissions are estimated at 487±122GgCOyr−1, much lower than previous top-down estimates (1440GgCOyr−1). Given the decreasing emissions of CO, this finding is not unexpected. We perform sensitivity tests to estimate how much errors in the prior, errors in the covariance, different inversion schemes, or a coarser dynamical model influence the emission estimates. Overall, the uncertainty is estimated to be 25%, with the largest contribution from the dynamical model. Lessons learned here may help in future inversions of satellite data over urban areas.
- Добавил в систему: Mаксютов Шамиль Шавратович