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Abstract We assess the detectability of COVID‐like emissions reductions in global atmospheric CO2concentrations 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 CO2sampled 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 CO2are 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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The Ocean Carbon Response to COVID‐Related Emissions Reductions
Abstract The decline in global emissions of carbon dioxide due to the COVID‐19 pandemic provides a unique opportunity to investigate the sensitivity of the global carbon cycle and climate system to emissions reductions. Recent efforts to study the response to these emissions declines has not addressed their impact on the ocean, yet ocean carbon absorption is particularly susceptible to changing atmospheric carbon concentrations. Here, we use ensembles of simulations conducted with an Earth system model to explore the potential detection of COVID‐related emissions reductions in the partial pressure difference in carbon dioxide between the surface ocean and overlying atmosphere (ΔpCO2), a quantity that is regularly measured. We find a unique fingerprint in global‐scale ΔpCO2that is attributable to COVID, though the fingerprint is difficult to detect in individual model realizations unless we force the model with a scenario that has four times the observed emissions reduction.
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- PAR ID:
- 10402859
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 48
- Issue:
- 6
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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