Anthropogenic CO2 emission estimates in the Tokyo metropolitan area from ground-based CO2 column observations

. Urban areas are responsible for more than 40 % of global energy-related carbon dioxide (CO 2 ) emissions. The Tokyo Metropolitan Area (TMA), Japan, one of the most populated regions in the world, includes various emission sources, such as thermal power plants, automobile traffic, and residential facilities. We conducted an intensive field campaign in the 15 TMA from February to April 2016 to measure column-averaged dry-air mole fractions of CO 2 (XCO 2 ) with three ground-based Fourier transform spectrometers (one IFS 125HR and two EM27/SUN spectrometers). At two urban sites (Saitama and Sodegaura), measured XCO 2 values were generally larger than those at a rural site (Tsukuba) by up to 9.5 ppm, and average diurnal variations increased toward evening. To simulate the XCO 2 enhancement ( D XCO 2 ) resulting from emissions at each observation site, we used the Stochastic Time‐Inverted Lagrangian Transport (STILT) model driven by meteorological fields 20 at a horizontal resolution of ~1 km from the Weather Research Forecast (WRF) model, which was coupled with anthropogenic (large point source and nonpoint source) CO 2 emissions and biogenic fluxes. Although some of the diurnal variation of D XCO 2 was not reproduced and plumes from nearby large point sources were not captured, primarily because of a transport modeling error, the WRF–STILT simulations using prior fluxes were generally in good agreement with the observations (mean bias, 0.30 ppm; standard deviation, 1.31 ppm). By combining observations with high-resolution modeling, we developed an urban-25 scale inversion system in which spatially resolved CO 2 emission fluxes at >3 km resolution and a scaling factor of large point source emissions were estimated on a monthly basis by using Bayesian inference. The D XCO 2 simulation results from the posterior CO 2 fluxes were improved (mean bias, –0.03 ppm; standard deviation, 1.21 ppm). In addition, the inverse analysis reduced the uncertainty in total CO 2 emissions in the TMA by a factor of ∼ 2. The posterior total CO 2 emissions agreed with emission inventories within the posterior uncertainty at the 95 % confidence level, demonstrating that the EM27/SUN 30 spectrometer data can constrain urban-scale monthly CO 2 emissions.