Joint Estimation of Fossil Fuel and Biogenic CO2 Fluxes in an Urban Environment

Open Access
- Author:
- Wu, Kai
- Graduate Program:
- Meteorology and Atmospheric Science
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- January 06, 2020
- Committee Members:
- Kenneth James Davis, Dissertation Advisor/Co-Advisor
Kenneth James Davis, Committee Chair/Co-Chair
Thomas Lauvaux, Committee Chair/Co-Chair
William Henry Brune, Committee Member
Klaus Keller, Outside Member
David Jonathan Stensrud, Program Head/Chair
Thomas Lauvaux, Dissertation Advisor/Co-Advisor - Keywords:
- urban CO2 emissions
atmospheric inversion
eddy covariance measurements
flux partitioning
fossil fuel CO2 emissions
biogenic CO2 fluxes - Abstract:
- Evaluation of the effectiveness of emission reduction policies requires accurate assessments of anthropogenic and biogenic carbon dioxide (CO2) fluxes in urban areas. Atmospheric observations provide a valuable independent assessment to test emissions inventories and biogenic flux models. The Indianapolis Flux Experiment (INFLUX) aims to use multiple integrated atmospheric measurements and a high-resolution inversion system to estimate the temporal and spatial variation of anthropogenic and biogenic fluxes from an urban environment. This study presents a Bayesian inversion system solving for fossil fuel and biogenic CO2 fluxes over the city of Indianapolis, IN. Both components are described at one km resolution to represent point sources and fine-scale structures such as highways in the a priori fluxes. With a series of Observing System Simulation Experiments (OSSEs), we evaluate the sensitivity of inverse flux estimates to various measurement deployment strategies. We also test the impacts of flux-error structures, biogenic CO2 fluxes and atmospheric transport errors on estimating fossil fuel CO2 emissions and their uncertainties. The results indicate that high-accuracy and high-precision measurements are necessary to quantify urban CO2 emissions. Systematic measurement errors of one ppm produce biased inverse solutions, degrading the accuracy of retrieved emissions by about one μmol m−2 s−1 compared to the spatially averaged anthropogenic CO2 emission of five μmol m−2 s−1. The presence of uncertain biogenic CO2 fluxes (similar error magnitude to anthropogenic emissions) limits our ability to correct for random and systematic emission errors. However, assimilating continuous fossil fuel CO2 measurements with one ppm random error in addition to total CO2 measurements can partially compensate for the interference from biogenic CO2 fluxes. Moreover, systematic and random flux errors can be further reduced by reducing model-data mismatch errors caused by atmospheric transport uncertainty. The precision of the inverse flux estimates is highly sensitive to the correlation length scale in the prior emission errors. This work suggests that improved fossil fuel CO2 measurement technology, and better understanding of both prior flux and atmospheric transport errors are essential to improve the accuracy and precision of high-resolution urban CO2 flux estimates. For conducting real data inversion experiments, we need to quantify prior flux errors in anthropogenic CO2 emissions. We use eddy covariance flux measurements to evaluate a high-resolution emissions inventory (Hestia). We develop a method to partition net flux measurements into anthropogenic and biogenic components using tower-based measurements of CO2 and CO mole fractions. We use 14C measurements to estimate emission ratios between CO and fossil fuel CO2. Moreover, we use a flux footprint model to match with the Hestia inventory, and estimate the emissions predicted by the inventory at a tower location. The Hestia emissions inventory is biased by 14% compared to the mean observed emissions. There are some differences in the off-season diurnal cycle between the Hestia inventory and flux data, which may be related to errors in residential emissions. However, their growing-season diurnal cycles are very similar. The Hestia inventory exceeds flux measurements in most wind directions, except when the wind comes from the west. To reduce the impact of uncertain biogenic CO2 fluxes on anthropogenic flux estimates, we measure biogenic CO2 fluxes from turf grass, corn, and soybean fields in and around the city of Indianapolis, IN. We use these data to optimize model parameters, and estimate errors in a simple biogenic flux model. Flux measurements indicate that turf grass has non-negligible photosynthesis in winter, and the threshold of air temperature for turf grass photosynthesis is –5°C. Moreover, corn shows remarkable photosynthesis and respiration compared to soybean and turf grass during the growing season. The Vegetation Photosynthesis and Respiration Model (VPRM) with optimized parameters can simulate these characteristics. High correlations between observed and modeled fluxes demonstrate the effectiveness of the VPRM in simulating temporal variation of biogenic CO2 fluxes for different ecosystems, although the modeled fluxes are slightly underestimated compared to flux data. Future work is needed to estimate random measurement errors, and quantify errors in emissions inventories and biogenic flux models for atmospheric inversions to improve the estimation of high-resolution urban CO2 emissions.