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On the detection of COVID-driven changes in atmospheric carbon dioxide
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  • Nicole Suzanne Lovenduski,
  • Abhishek Chatterjee,
  • Neil C. Swart,
  • John Fyfe,
  • Ralph F. F. Keeling,
  • David Schimel
Nicole Suzanne Lovenduski
University of Colorado Boulder

Corresponding Author:nicole.lovenduski@colorado.edu

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Abhishek Chatterjee
NASA Goddard Space Flight Center
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Neil C. Swart
Environment Canada
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John Fyfe
Canadian Centre for Climate Modelling and Analysis
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Ralph F. F. Keeling
University of California, San Diego
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David Schimel
Jet Propulsion Laboratory
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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. Internal variability and carbon-concentration feedbacks obscure the detectability of short-term emission reductions in atmospheric CO2. COVID-driven changes in the simulated interhemispheric CO2 gradient and column-averaged dry air mole fractions of CO2 (total column or XCO2) 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.
28 Nov 2021Published in Geophysical Research Letters volume 48 issue 22. 10.1029/2021GL095396