A Comparison of Annual CO2 Emissions from Inverse Modeling and the National Greenhouse Gases Inventory Report

Abstract:

Each country under the Paris Agreement is obliged to set the nationally determined contribution (NDC) for the greenhouse gases emission reduction and report its annual greenhouse gases mitigation status to the United Nations Framework Convention on Climate Change (UNFCCC) with the form of the national greenhouse gases emission inventory report. Since the annual greenhouse gases emission on the inventory report contains uncertainties, the inverse modeling using observations is required to supplement the report. In this study, CarbonTracker developed by the National Oceanic and Atmospheric Administration (NOAA) is utilized for estimating the annual CO₂ emission of selected countries, and the results are compared with the CO₂ emission data of the national inventory reports.

To see the impact of Asian observations on the CO₂ emission estimates of selected nations, two inverse modeling experiments are conducted: CNTL experiment that uses the basic setting of CarbonTracker and EXP1 experiment that assimilates two Korean observation datasets (Anmyeon-do and Gosan) into CarbonTracker. The resulting surface CO₂ fluxes from two experiments are converted to annual CO₂ emission values and compared with those of the national inventory report.

The CO₂ emissions derived from the biosphere CO₂ fluxes in CarbonTracker are compared with those in the inventory. The annual CO₂ emissions from the inventory report show low yearly variations, whereas the CO₂ emissions estimated from CarbonTracker show relatively high yearly variations. Compared to CNTL, EXP1 shows increasing biosphere CO₂ flux absorption in Korea and Japan, which results in the enhanced CO₂ uptake by the land biosphere and less spread of estimated CO₂ emission ensembles. Further analysis on the relation between the annual CO₂ emissions from the inverse model and the inventory report will be presented at the meeting.

Acknowledgements

This study was supported by the Korea Meteorological Administration Research and Development Program under grant KMI2018-03712 and a National Research Foundation of Korea (NRF) grant funded by the South Korean government (Ministry of Science and ICT) (Grant 2017R1E1A1A03070968).