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

We assess the detectability of COVID-like emissions reductions in global atmospheric CO2 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 CO2 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 CO2. COVID-driven changes in the simulated, column-averaged dry air mole fractions of CO2 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.

Automated summary

This study assesses the detectability of COVID-like emissions reductions in global atmospheric CO2 concentrations using a simulation model. The unique fingerprint of COVID in the simulated CO2 growth rate is obscured by internal variability and carbon-concentration feedbacks, making it formally detectable only with unrealistically large emissions reductions. COVID-driven changes in CO2 concentrations are overshadowed by internal variability, while carbon-concentration feedbacks further complicate signal detection in the atmosphere.