Global carbon dioxide (CO2) and methane (CH4) flux distributions were derived by the inverse model analyses of CO2 and CH4 column-average concentration data from Greenhouse gases Observing SATellite (GOSAT) launched in 2009 and its successor (GOSAT-2) launched in 2018. GOSAT Level 4A CO2 and CH4 flux products are freely available from GOSAT Data Archive Service. Monthly net CO2 and CH4 flux values are calculated for 42 land regions and 22 ocean regions for CO2 and one ocean region for CH4 using the inversion system with NIES TM atmospheric transport model. The preliminary version of GOSAT-2 Level 4A CO2 product is being evaluated and will be released to the public in FY2023. GOSAT-2 Level 4A CH4 product will be generated after the release of CO2 product. GOSAT-2 Level 4A CO2 product is generated using the inversion system named NICAM-based Inverse Simulation for Monitoring CO2 (NISMON- CO2) with about 2.5-degree grids and monthly time interval. The spatial distributions of small CO2 sources and sinks will be shown by this product. GOSAT-GW, the third satellite in GOSAT Series to be launched in FY2024, will map CO2 and CH4 column concentrations with two spatial observation modes, Wide mode with 910 km swath and 10 km resolution, and Focus Mode with 90 km swath and 3 km or better resolution. Such image data will be used in the inversion analysis to obtain global and regional CO2 and CH4 net flux with higher spatial and temporal resolution than GOSAT and GOSAT-2.
Carbon dioxide and methane flows are studied from measurements at the Fonovaya Observatory and two measurement sites in the Iksinsk swamp (Tomsk Region, Russia) in August and September 2019. The measurements were carried out with automatic static chambers. It is shown that in August all the three measurements sites are sinks of carbon dioxide, while in September the flow character becomes intermittent. The both sites in the Iksinsk swamp are sources of methane for the entire period of measurements.
We present an algorithm for the rapid retrieval of the carbon dioxide total column amounts (XCO2) using short wave infrared (SWIR) spectra of the reflected sunlight measured from space. The algorithm takes advantage of the combined processing of observational data from two different satellite missions. For the algorithm implementation we adopted the previously developed EOF (Empirical Orthogonal Functions)-based approach that exploits regression relations of the principal components of the measured spectra with target XCO2 values. In the original algorithm version the regression coefficients were derived by using training sets of collocated satellite and ground-based observations (ground-based observations were treated as “true values”). In this paper we implemented similar approach in which training set for one satellite mission is created using collocated observations of the another “reference” space mission simultaneously on-orbit (in this case XCO2 retrievals of the “reference” mission were treated as “true values”). This approach enables rapid data processing of the new satellite missions omitting expensive and time consuming stage of retrieval algorithm development. The feasibility of the approach was tested by joint processing of GOSAT and OCO-2 observation data. For the analysis of the algorithm precision/accuracy characteristics we used the collocated observations from the Total Carbon Column Observing Network (TCCON).
Wetland CO2 and CH4 fluxes were observed at the Bakchar bog (N56°51’, E82°51’), West Siberia. Measurements were performed by two solar powered automated systems (Flux-NIES), each consisting of NDIR CO2 analyzer, an SnO2-based methane sensor, six static chambers installed along transects, the air drying and distribution units, and a data-logger. Observations were made during the May to October period in 2014 to 2018 at two types of open wetlands: mesotrophic open bog (E-site) and patterned wetland with forested ridges, flat hollows and water pool (O-site). Each chamber is automatically opened and closed with pneumatic actuator. Water level is measured in the wetland and surrounding forest locations. On the basis of the conducted research, the daily dynamics for CO2 and CH4 fluxes was revealed. Correlation analysis made it possible to describe the dependences for CO2 and CH4 fluxes on the local hydrometeorological conditions of the surface. For example, the high flood of bog’s waters had a decreasing effect on the methane genesis during the period of observation and seasonal variation of the CH4 emissions correlates well with the soil temperature at peat bed depth. Wetland CO2 and CH4 fluxes correlate spatially: higher net uptake CO2 and CH4 emissions are observed at wet mesotrophic locations with higher photosynthesis/respiration rates; lower net uptake CO2 and CH4 emissions were observed on oligotrophic patterned wetland. Another objective for investigation was the integration of carbon fluxes from aquatic systems into terrestrial ones, in order to quantify and better understand the catchment scale carbon budget.
The ecosystem-atmosphere exchange of methane and carbon dioxide were measured during the summer campaigns 2014 – 2017 in Plotnikovo (N56°51’, E82°51’) on the wetland Flux-NIES automatic chamber complexes. Eleven vegetative groups on the bog’s surface and one lake site were taken in comparison. The objective of these particular measurements was to estimate the growing season carbon fluxes at sedge fen and on hollow-ridges nearby lake at Siberia ecosystems. The carbon balance of these ecosystems deeply relies on the peat wetness. Another objective was the integration of carbon fluxes from aquatic systems into terrestrial ones, in order to quantify and better understand the catchment scale carbon budget.
We present further development of the empirical orthogonal functions (EOF)-based retrieval algorithm. The algorithm output is a regression formula that relates principal components of the reflected sunlight spectra with CO2 total column amount. The algorithm was implemented and tested for the observations from the Japanese satellite Greenhouse gases Observing Satellite (GOSAT). Training of the EOF-based algorithm with the collocated ground-based and space-borne data (e.g., Total Carbon Column Observing Network and GOSAT observations, respectively) was shown to impose some errors that were interpreted as a result of implicit averaging over the collocation area. Alternative training with the small subset (∼5 % to 10%) of the full-physics algorithm is free of such errors; however, this option requires additional filtering of the space-borne observations that are strongly affected by atmospheric light scattering. This filtering was implemented by the comparison of the EOF-regression estimates of surface pressure with corresponding meteorological data.
Data terrain-atmosphere fluxes of methane and carbon dioxide overseen for measurement campaign Plotnikovo-2014 on the bog’s Flux-NIES automatic complex (N56°51.29’ E82° 50.91’) in the warn season. Six vegetative groups on the bog’s surface were taken in comparison. Improvement precise method used to determinate the sensitivity for the gases analyzers and calculating of the CO2 and CH4 fluxes measured by automated chamber-based technique.
We present satellite-based data of the column-averaged dry air mole fraction of atmospheric carbon dioxide (XCO2) and methane (XCH4), which were derived from the radiance spectra measured by Greenhouse gases Observing SATellite (GOSAT). We have applied new version of the Photon path-length Probability Density Function (PPDF)-based
algorithm to estimate XCO2 and PPDF parameters. These parameters serve to allow for optical path modification due to
atmospheric light scattering and they are retrieved simultaneously with CO2 concentration using radiance spectra from
all available GOSAT short wave infrared (SWIR) bands (oxygen A-band, 1.6-μm, and 2.0-μm CO2 absorption bands). For the methane abundance, retrieved from 1.67-μm absorption band, we applied optical path correction based on PPDF parameters from 1.6-μm CO2 absorption band. Similarly to widely used CO2-proxy technique, this correction assumes identical light path modifications in 1.67-μm and 1.6-μm bands. This approach is believed to offer some advantages over the proxy technique since it does not use any prior assumptions on carbon dioxide concentrations. Both carbon dioxide and methane GOSAT retrievals were validated using ground-based Fourier Transform Spectrometer (FTS)
measurements provided by the Total Carbon Column Observing Network (TCCON). For XCO2 retrievals we found subppm station-to-station bias (GOSAT versus TCCON); single-scan precision of mostly below 2 ppm (0.5%); and
correlation coefficient for the Northern Hemisphere TCCON stations above 0.8. For XCH4 retrievals over TCCON sites
we found single-scan precision below 1 % and correlation coefficient above 0.8.
Inverse estimation of surface C02 fluxes is performed with atmospheric transport model using ground-based and GOSAT observations. The NIES-retrieved C02 column mixing (Xc02) and column averaging kernel are provided by GOSAT Level 2 product v. 2.0 and PPDF-DOAS method. Monthly mean C02 fluxes for 64 regions are estimated together with a global mean offset between GOSAT data and ground-based data. We used the fixed-lag Kalman filter to infer monthly fluxes for 42 sub-continental terrestrial regions and 22 oceanic basins. We estimate fluxes and compare results obtained by two inverse modeling approaches. In basic approach adopted in GOSAT Level4 product v. 2.01, we use aggregation of the GOSAT observations into monthly mean over 5x5 degree grids, 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 similar to geostatistical approach, while transport simulation is enhanced by coupling with a Lagrangian transport model Flexpart. Both methods use using the 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 C02 . Monthly ocean-atmosphere C02 fluxes are produced with an ocean pC02 data assimilation system. Biomass burning fluxes were provided by the Global Fire Emissions Database (GFED); and monthly fossil fuel C02 emissions are estimated with ODIAC inventory. The results of analyzing one year of the GOSAT data suggest that when both GOSAT and ground-based data are used together, fluxes in tropical and other remote regions with lower associated uncertainties are obtained than in the analysis using only ground-based data. With version 2.0 of L2 Xc02 the fluxes appear reasonable for many regions and seasons, however there is a need for improving the L2 bias correction, data filtering and the inverse modeling method to reduce estimated flux anomalies visible in some areas. We also observe that application of spatial flux correlations with EOF based approach reduces flux anomalies.
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