Tomohiro Oda

TL;DR: In this paper, the authors quantify uncertainties in simulations of North American atmospheric CO2 concentrations using a probabilistic approach and demonstrate that uncertainty in fossil fuel emissions is a key factor in the uncertainty surrounding biospheric flux estimates.

TL;DR: This study analyzed vertical atmospheric CO2 mole fraction data obtained onboard commercial aircraft in proximity to 36 airports worldwide, as part of the Comprehensive Observation Network for Trace gases by Airliners (CONTRAIL) program, and found that the magnitude ofCO2 variability near the ground at an airport was correlated with the intensity of CO2 emissions from a nearby city.

TL;DR: Li et al. as mentioned in this paper collected and analyzed seven published emission datasets to comprehensively evaluate the spatiotemporal distribution of provincial CO2 emissions and found that the provincial emissions ranged from 20 to 649 mt CO2 and that the standard deviations (SDs) ranged from 8 to 159 mt.

TL;DR: In this paper, a machine learning algorithm driven by information from the NASA GEOS-CF model was used to assess changes in nitrogen dioxide (NO$2) and ozone (O$3}$) at 5,756 observation sites in 46 countries.

TL;DR: In this article, a Bayesian inverse modeling framework over Salt Lake City, Utah, which utilizes available CO2 emission inventories to establish a synthetic data simulation aimed at exploring model uncertainties is presented.