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| Titel |
Estimation of regional CO2 fluxes in 2009-2010 with GOSAT observations using two inverse modeling approaches. |
| VerfasserIn |
S. Maksyutov, H. Takagi, R. Zhuravlev, A. Ganshin, M. Saito, T. Oda, V. Valsala, D. Belikov, T. Saeki, R. Saito, S. Oshchepkov, A. Bril, A. Lukyanov, R. J. Andres, B. Khattatov, T. Yokota |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250062622
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| Zusammenfassung |
| We provide inverse estimation of surface CO2 fluxes using atmospheric transport model and
GOSAT observations. The NIES-retrieved CO2 column mixing ratio is used together with
ground-based observations. The column averaged CO2 mixing ratio (XCO2) and column
averaging kernel are provided by GOSAT Level 2 product and PPDF-DOAS method.
Monthly mean CO2 fluxes for 64 regions are estimated together with a global mean offset
between GOSAT data and Globalview. We used the fixed-lag Kalman smoother
to infer monthly fluxes for 42 sub-continental terrestrial regions and 22 oceanic
basins. We estimate fluxes and compare results obtained by two approaches. In basic
approach adopted in GOSAT Level 4 product we use aggregation of the GOSAT
observations into monthly mean over 5x5 degree grids and fluxes are estimated
independently for each region, and NIES atmospheric transport model is used for forward
simulation. In the alternative method the model-observation misfit is estimated for each
observation separately and fluxes are spatially correlated using EOF analysis of
the simulated flux variability. Transport simulation is enhanced by coupling with
Lagrangian transport model Flexpart. Both methods use using same set of prior fluxes and
region maps. Daily net ecosystem exchange (NEE) is predicted by the Vegetation
Integrative SImulator for Trace gases (VISIT) optimized to match seasonal cycle of the
atmospheric CO2. Monthly ocean-atmosphere CO2 fluxes are produced with an
ocean pCO2 data assimilation system. Biomass burning fluxes were provided by the
Global Fire Emissions Database (GFED); and monthly fossil fuel CO2 emissions are
estimated with ODIAC inventory. The results of the analyzing one year of the GOSAT
data suggest that when both GOSAT and ground-based data are used together the
fluxes change compared to using only ground-based data in the tropical and other
remote regions, for those regions flux uncertainties are reduced when compared to
ground-based data only case. Although the fluxes appear reasonable for many regions and
seasons, there is still a need for improving the retrieval, data filtering and the inverse
modeling method to reduce apparent estimated flux anomalies visible in some areas.
We observe that application of spatial flux correlations reduces flux anomalies. |
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