On the detection of COVID-driven changes in atmospheric carbon dioxide | CU Experts

; 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. Internal variability and; carbon-concentration feedbacks obscure the detectability of short-term; emission reductions in atmospheric CO; 2; . COVID-driven; changes in the simulated interhemispheric CO; 2; gradient; and column-averaged dry air mole fractions of CO; 2; (total; column or XCO; 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.;

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