Speaker: Archana Dayalu
Archana DAYALU, Ph.D. candidate, Department of Earth and Planetary Sciences, Harvard University
Sponsored by the China Project, Harvard Paulson School of Engineering and Applied Sciences
China’s reported carbon dioxide emissions inventories have large uncertainty due to significant differences in data reported at the national and provincial levels. Accurate emissions estimates are essential for China to successfully achieve its carbon emission reduction targets. This study uses in-situ hourly CO2 measurements (2005-2009) from a site in Northern China to conduct top-down optimization of annual bottom-up CO2 emissions inventories focusing on the heavily populated and industrialized regions of northern and eastern China. Surface influences (“footprints”, in CO2 ppm per unit flux) attributing upwind surface fluxes to downwind concentration measurements are estimated from a Lagrangian Particle Dispersion Model (LPDM) run in backward time mode driven with high resolution meteorological fields from the Weather Research and Forecasting model (WRF). NOAA CarbonTracker provides boundary conditions, validated with CO2 observations from the NOAA/WMO flask network. A biogenic CO2 surface flux inventory is constructed to model the biological contributions to observed CO2, validated with ChinaFlux and FluxNet eddy flux data and scaled up regionally using WRF meteorology and satellite-derived surface reflectances. The footprints are ultimately multiplied with the anthropogenic and biogenic flux estimates in an inverse model framework to optimize the surface flux inventories by minimizing the mismatch between modeled and measured CO2 concentrations at the measurement site.