Developing methods for estimating the anthropogenic carbon dioxide and methane emissions based on GOSAT and OCO-2 satellite observations
Abstract
We apply a statistical method of comparing anomalies in global atmospheric CO _{2} and CH _{4} (2009-2014) fields due to anthropogenic activities, using GOSAT observations of column-averaged dry air mole fractions (X _{CO2} and X _{CH4}) with simulations by Lagrangian transport model FLEXPART. The CO _{2} and CH _{4} concentration enhancements due to anthropogenic activities, are estimated with the transport model for GOSAT observations using high-resolution emission inventories (ODIAC and EDGAR respectively). To account for strong vegetation signal we add biospheric CO _{2} fluxes simulated with VISIT model at 0.1° resolution. Based on these simulated values, anthropogenic greenhouse gas abundance is calculated using GOSAT observations as anomalies from clean background observations. These anomalies are binned and analysed for continental scale regions and countries. For CO _{2} , we have found linear relationships between model and observed anomalies especially for the globe, Eurasia and North America. The analysis for East Asian region showed systematically higher observed enhancements (around 15%) that is comparable in magnitude to the reported uncertainties in emission inventories in that region. In the case of CH _{4} , we found a good match between inventory-based estimates and GOSAT observations for continental regions. The inventory-based estimate over North American region is biased upward (around 35%) which is in qualitative agreement with recent reports. The results indicate the potential utility of GOSAT observations in monitoring reported anthropogenic emissions over different regions of varying spatial scales.We use the same kind of high resolution transport modeling for the analysis of the CO _{2} emission signatures in the total column X _{CO2} data observed by OCO-2 satellite in 2014-2016. To reduce computational load, the OCO-2 observations are aggregated into 1 second averages prepared separately for two groups (left and right) made of simultaneously measured eight OCO-2 observations (footprints). Each group has surface footprint size of approximately 0.1°x0.1°. Same as for GOSAT, with OCO-2 data we have found linear relations between model and observed anomalies. Enhancements observed by OCO-2 match the simulated ones with a regression slope close to unity for global domain. Even after aggregation of OCO-2 data the number of enhanced X _{CO2} observations by OCO-2 is larger than that of GOSAT more than 15 times for same time period. The result confirms high potential of using OCO-2 observations for analyzing anthropogenic emission signatures. To fully exploit OCO-2 capability of observing narrow CO _{2} plumes emitted by individual power plants we prepare high resolution (1 arc min) global simulation of the CO _{2} transport with emissions based on ODIAC gridded emission inventory (30" resolution) by applying a forward Lagrangian modeling approach. Preliminary results for 2015 demonstrate good correlation of the forward simulations with X _{CO2} enhancements observed by OCO-2 in dormant season, when correction for vegetation fluxes is not critical.To extend the methane emission analysis made with GOSAT data we apply global high-resolution methane flux inversion based on the Lagrangian-Eulerian coupled tracer transport model, aiming at estimating global methane emissions using atmospheric methane data collected at global in-situ network, which is archived at WDCGG, and GOSAT satellite observation. For better accounting for anthropogenic emissions, localized around in large cities, we use the Lagrangian particle dispersion model FLEXPART to model local tracer transport at 0.1° spatial resolution. FLEXPART is coupled to a global atmospheric tracer transport model (NIES-TM). The adjoint of the coupled transport model is used in an iterative optimization procedure. High-resolution prior fluxes were prepared for anthropogenic emissions (EDGAR), biomass burning (GFAS), and wetlands (VISIT). High resolution wetland emission dataset was constructed using a 0.5° monthly emission data simulated by VISIT model and wetland area fraction map by Global Lake and Wetlands Database (GLWD). Inverse model optimizes corrections to two categories of fluxes: anthropogenic and natural (wetlands). Biweekly methane emissions at high spatial resolution are estimated for the period of 2009 to 2012. The inverse model provides optimized fit to the ground-based observations around the globe. Notably, the coupled transport model manages to better reproduce the ground based continuous observations in mid- and high latitudes in winter, due to resolving both anthropogenic emission plumes and near-surface transport in the shallow boundary layer. Forward simulation with surface fluxes optimized by assimilating ground-based observations is used for reducing mismatch with GOSAT Level 2 X _{CH4} data. The monthly mean difference between GOSAT data and optimized forward simulation estimated for each 10x10° latitude-longitude box is subtracted from GOSAT data before including the GOSAT data in inversion. The inverse modeling using combined ground-based and GOSAT data show the bias correction scheme is successful in retaining good fit to the ground-based observations. The suggested correction removes large scale biases in GOSAT data with respect to model simulation, while retaining local scale variability that contains most information on anthropogenic emissions, so it favors information on localized high emissions of anthropogenic origin over large scale atmospheric signals from natural fluxes.
- Publication:
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42nd COSPAR Scientific Assembly
- Pub Date:
- July 2018
- Bibcode:
- 2018cosp...42E2136M