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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 Mar 2020

27 Mar 2020

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This preprint is currently under review for the journal ACP.

Technical note: A high-resolution inverse modelling technique for estimating surface CO2 fluxes based on the NIES-TM – FLEXPART coupled transport model and its adjoint

Shamil Maksyutov1, Tomohiro Oda2,3, Makoto Saito1, Rajesh Janardanan1, Dmitry Belikov1,a, Johannes W. Kaiser4, Ruslan Zhuravlev5, Alexander Ganshin5, Vinu K. Valsala6, Arlyn Andrews7, Lukasz Chmura8, Edward Dlugokencky7, László Haszpra9, Ray L. Langenfelds10, Toshinobu Machida1, Takakiyo Nakazawa11, Michel Ramonet12, Colm Sweeney7, and Douglas Worthy13 Shamil Maksyutov et al.
  • 1National Institute for Environmental Studies, Tsukuba, Japan
  • 2NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 3Universities Space Research Association, Columbia, MD, USA
  • 4Deutscher Wetterdienst, Offenbach, Germany
  • 5Central Aerological Observatory, Dolgoprudny, Russia
  • 6Indian Institute for Tropical Meteorology, Pune, India
  • 7Earth System Research Laboratory, NOAA, Boulder, CO, USA
  • 8AGH University of Science and Technology, Krakow, Poland
  • 9Research Centre for Astronomy and Earth Sciences, Sopron, Hungary
  • 10Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale, VIC, Australia
  • 11Tohoku University, Sendai, Japan
  • 12Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL, Gif-sur-Yvette, France
  • 13Environment and Climate Change Canada, Toronto, Canada
  • anow at: ChibaUniversity, Chiba, Japan

Abstract. We developed a high-resolution surface flux inversion system based on the global Lagrangian–Eulerian coupled tracer transport model composed of National Institute for Environmental Studies Transport Model (NIES-TM) and FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM-FLEXPART-variational) as it applies variational optimisation to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve best fit to atmospheric CO2 data collected by the global in-situ network, as a necessary step towards capability of estimating anthropogenic CO2 emissions. We employ the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate the surface flux footprints of CO2 observations at a 0.1° × 0.1° spatial resolution. The LPDM is coupled to 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 is being 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) dataset. High-resolution prior flux fields were prepared using Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange and Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and separate simulation of CO2 fluxes at daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for 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 vegetation productivity gradients. The resulting flux estimates improve fit to the observations at continuous observations sites, reproducing both the seasonal variation and short-term concentration variability, including high CO2 concentration events associated with anthropogenic emissions. The use of high-resolution atmospheric transport in global CO2 flux inversion has the advantage of better resolving the transport from the mix of the anthropogenic and biospheric sources in densely populated continental regions and shows potential for better separation between fluxes from terrestrial ecosystems and strong localised sources such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as the posterior fit is better than that by the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker only for a fraction of the monitoring sites, mostly at coastal and island locations experiencing mix of background and local flux signals.

Shamil Maksyutov et al.

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Shamil Maksyutov et al.

Shamil Maksyutov et al.


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Publications Copernicus
Short summary
In order to improve the top-down estimation of the anthropogenic greenhouse gas emissions, a high-resolution inverse modeling technique was developed for applications to global transport modeling of carbon dioxide and other greenhouse gases. A coupled Eulerian-Lagrangian transport model and its adjoint are combined with surface fluxes at 0.1-degree resolution to provide high-resolution forward simulation and inverse modeling of surface fluxes accounting for signals from emission hotspots.
In order to improve the top-down estimation of the anthropogenic greenhouse gas emissions, a...