On the Detection of COVID-Driven Changes in Atmospheric Carbon Dioxide.

We assess the detectability of COVID-like emissions reductions in global atmospheric CO 2 concentrations using a suite of large ensembles conducted with an Earth system model. We find a unique fingerprint of COVID in the simulated growth rate of CO 2 sampled at the locations of surface measurement sites. Negative anomalies in growth rates persist from January 2020 through December 2021, reaching a maximum in February 2021. However, this fingerprint is not formally detectable unless we force the model with unrealistically large emissions reductions (2 or 4 times the observed reductions). Internal variability and carbon-concentration feedbacks obscure the detectability of short-term emission reductions in atmospheric CO 2 . COVID-driven changes in the simulated, column-averaged dry air mole fractions of CO 2 are eclipsed by large internal variability. Carbon-concentration feedbacks begin to operate almost immediately after the emissions reduction; these feedbacks reduce the emissions-driven signal in the atmosphere carbon reservoir and further confound signal detection.