Global high-resolution simulations of CO2 and CH4 using a NIES transport model to produce a priori concentrations for use in satellite data retrievals
1Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, 305-8506, Japan
2Research Institute for Global Change, JAMSTEC, Yokohama, 236-0001, Japan
3Division for Polar Research, National Institute of Polar Research, Tachikawa, Tokyo 190-8518, Japan
Abstract. The Greenhouse gases Observing SATellite (GOSAT) measures column-averaged dry air mole fractions of carbon dioxide and methane (XCO2 and XCH4, respectively). Since the launch of GOSAT, model-simulated three-dimensional concentrations from a National Institute for Environmental Studies offline tracer Transport Model (NIES TM) have been used as a priori concentration data for operational near real-time retrievals of XCO2 and XCH4 from GOSAT short-wavelength infrared spectra at NIES. Although the choice of a priori profile has only a minor effect on retrieved XCO2 or XCH4, a realistic simulation with minimal deviation from observed data is desirable. In this paper, we describe the newly developed version of NIES TM that has been adapted to provide global and near real-time concentrations of CO2 and CH4 using a high-resolution meteorological dataset, the Grid Point Value (GPV) prepared by the Japan Meteorological Agency. The spatial resolution of the NIES TM is set to 0.5° × 0.5° in the horizontal in order to utilise GPV data, which have a resolution of 0.5° × 0.5°, 21 pressure levels and a time interval of 3 h. GPV data are provided to the GOSAT processing system with a delay of several hours, and the near real-time model simulation produces a priori concentrations driven by diurnally varying meteorology. A priori variance–covariance matrices of CO2 and CH4 are also derived from the simulation outputs and observation-based reference data for each month of the year at a resolution of 0.5° × 0.5° and 21 pressure levels. Model performance is assessed by comparing simulation results with the GLOBALVIEW dataset and other observational data. The overall root-mean-square differences between model predictions and GLOBALVIEW analysis are estimated to be 1.45 ppm and 12.52 ppb for CO2 and CH4, respectively, and the seasonal correlation coefficients are 0.87 for CO2 and 0.53 for CH4. The model showed good performance particularly at oceanic and free tropospheric sites. The high-resolution model also performs well in reproducing both the observed synoptic variations at some sites and stratospheric profiles over Japan. These results give us confidence that the performance of our GPV-forced high-resolution NIES TM is adequate for use in satellite retrievals.