[M-IS01] Environmental, socio-economic, and climatic changes in Northern Eurasia

[MIS01-01] Evaluation of a revised global high-resolution inverse model with CO₂ observations over Siberia.

*Shamil Maksyutov¹、Motoki Sasakawa¹、Toshinobu Machida¹、Tomohiro Oda²、Jiye Zeng¹、Tsuneo Matsunaga¹、Johannes W Kaiser³、Mikhail Arshinov⁴、Boris Belan⁴、Ed Dlugokencky⁵ (1.National Institute for Environmental Studies、2.Universities Space Research Association、3.Deutscher Wetterdienst、4.Inst. Atmospheric Optics、5.Global Monitoring Laboratory, NOAA)

キーワード：carbon dioxide, inverse modeling, Siberia

We apply a revised version of the global inverse model NTFVAR (NIES-TM-FLEXPART-variational) to make top-down estimates of regional carbon dioxide fluxes based on data from the Japan-Russia Siberian tall tower inland observation network (JR-STATION) in Siberia and NOAA's global monitoring network for 2008-2017. In the revised coupled modeling system, the global tracer transport model NIES-TM uses ERA-5 reanalysis winds interpolated to 42 hybrid-pressure levels with a horizontal resolution of 2.5 and 3.75 degrees, while the FLEXPART model uses 0.1-degree resolution and includes a diurnal cycle. Simulation of advection in the transport model and its adjoint was revised to a 3rd order upwind scheme. The land biosphere fluxes are provided by the global upscaling product based on tower flux data at a resolution of 0.1 degrees (Zeng et al. 2020). Fossil emissions are provided by ODIAC, fire emissions by GFAS, and oceanic fluxes by the upscaling product based on surface ocean pCO₂ observations (Landschützer et al. 2015). The diurnal cycle of biospheric CO₂ exchange is simulated by combining data for gross ecosystem production and ecosystem respiration. The respiration and oceanic fluxes are scaled to match the global CO₂ growth rate. Corrections to the prior fluxes are estimated on a bi-weekly time step for two sets of observational data, one includes only background observation sites of the global network and another adds JR-STATION data to it by taking afternoon averaged data once a day for each site. Flux optimization with the global background network results in a good match and strong correlation with JR-STATION data over West Siberia, demonstrating that the combination of accurate transport and prior fluxes make background observations useful for constraining large scale fluxes. Addition of the Siberian observations further reduces the bias of the CO₂ simulations at the sites and improves the match in the cold season. The comparison shows the importance of adding regional observations for improving regional carbon dioxide flux estimates.

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