Southern California Megacity CO2, CH4, and CO Flux Estimates Using Ground- and Space-Based Remote Sensing and a Lagrangian Model

Hedelius, Jacob K ; Liu, Junjie ; Oda, Tomohiro ; Maksyutov, Shamil ; Roehl, Coleen M ; Iraci, Laura T ; Podolske, James R ; Hillyard, Patrick W ; Liang, Jianming ; Gurney, Kevin R(Arizona State Univ (ASU), School of Life Science, Tempe, AZ, United States)

关键词: CARBON DIOXIDE;    CARBON MONOXIDE;    DYNAMIC MODELS;    ERRORS;    FLUX QUANTIZATION;    LAGRANGIAN FUNCTION;    LESSONS LEARNED;    METHANE;    ORBITING CARBON OBSERVATORY (OCO);    REMOTE SENSING;    SENSITIVITY;    SOUTHERN CALIFORNIA;   

RP-ID  :  GSFC-E-DAA-TN73293

学科分类：地球科学(综合)

美国|英语

来源: NASA Technical Reports Server

【 摘 要 】

We estimate the overall CO2, CH4, and CO flux from the South Coast Air Basin using an inversion that couples Total Carbon Column Observing Network (TCCON) and Orbiting Carbon Observatory-2 (OCO-2) observations, with the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the Open-source Data Inventory for Anthropogenic CO2 (ODIAC). Using TCCON data we estimate the direct net CO2 flux from the So-CAB to be 104±26 Tg CO2 yr(exp -1) for the study period of July 2013–August 2016. We obtain a slightly higher estimate of 120±30 Tg CO2 yr(exp -1) using OCO-2 data. These CO2 emission estimates are on the low end of previous work. Our net CH4 (360±90 Gg CH4 y(exp -1)) flux estimate is in agreement with central values from previous top-down studies going back to 2010 (342–440 Gg CH4 yr(exp -1)). CO emissions are estimated at 487±122 Gg CO yr(exp -1), much lower than previous top-down estimates (1440 Gg CO yr(exp -1)). Given the decreasing emissions of CO, this finding is not unexpected. We perform sensitivity tests to estimate how much errors in the prior, errors in the covariance, different inversion schemes, or a coarser dynamical model influence the emission estimates. Overall, the uncertainty is estimated to be 25%, with the largest contribution from the dynamical model. Lessons learned here may help in future inversions of satellite data over urban areas.