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Technical note: A high-resolution inverse modelling technique for estimating surface CO2 fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjointстатья
Статья опубликована в высокорейтинговом журнале
Информация о цитировании статьи получена из
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Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 3 ноября 2021 г.
- Авторы: Maksyutov Shamil, Oda Tomohiro, Saito Makoto, Janardanan Rajesh, Belikov Dmitry, Kaiser Johannes W., Zhuravlev Ruslan, Ganshin Alexander, Valsala Vinu K., Andrews Arlyn, Chmura Lukasz, Dlugokencky Edward, Haszpra László, Langenfelds Ray L., Machida Toshinobu, Nakazawa Takakiyo, Ramonet Michel, Sweeney Colm, Worthy Douglas
- Журнал: Atmospheric Chemistry and Physics
- Том: 21
- Номер: 2
- Год издания: 2021
- Издательство: European Geophysical Society
- Местоположение издательства: Germany
- Первая страница: 1245
- Последняя страница: 1266
- DOI: 10.5194/acp-21-1245-2021
- Аннотация: We developed a high-resolution surface flux inversion system based on the global Eulerian–Lagrangian coupled tracer transport model composed of the National Institute for Environmental Studies (NIES) transport model (TM; collectively NIES-TM) and the FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM–FLEXPART-variational) as it applies a variational optimization to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve the best fit to atmospheric CO2 data collected by the global in situ network as a necessary step towards the capability of estimating anthropogenic CO2 emissions. We employed the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate surface flux footprints of CO2 observations at a spatial resolution of 0.1∘×0.1∘. The LPDM is coupled with a global atmospheric tracer transport model (NIES-TM). Our inversion technique uses an adjoint of the coupled transport model in an iterative optimization procedure. The flux error covariance operator was implemented via implicit diffusion. Biweekly flux corrections to prior flux fields were estimated for the years 2010–2012 from in situ CO2 data included in the Observation Package (ObsPack) data set. High-resolution prior flux fields were prepared using the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, the Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange, and the Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and a separate simulation of CO2 fluxes at a daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for the terrestrial biosphere was scaled proportionally to the monthly mean gross primary production (GPP) by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17 product. The inverse system calculates flux corrections to the prior fluxes in the form of a relatively smooth field multiplied by high-resolution patterns of the prior flux uncertainties for land and ocean, following the coastlines and fine-scale vegetation productivity gradients. The resulting flux estimates improved the fit to the observations taken at continuous observation sites, reproducing both the seasonal and short-term concentration variabilities including high CO2 concentration events associated with anthropogenic emissions. The use of a high-resolution atmospheric transport in global CO2 flux inversions has the advantage of better resolving the transported mixed signals from the anthropogenic and biospheric sources in densely populated continental regions. Thus, it has the potential to achieve better separation between fluxes from terrestrial ecosystems and strong localized sources, such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as our posterior fit was better than that of the National Oceanic and Atmospheric Administration (NOAA)'s CarbonTracker for only a fraction of the monitoring sites, i.e. mostly at coastal and island locations where background and local flux signals are mixed.
- Добавил в систему: Mаксютов Шамиль Шавратович