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.

Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks between atmospheric chemistry and climate change.

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. Furthermore, the abrupt reduction in emissions during the lockdown periods led to clearly observable changes in atmospheric composition, which provide direct insight into feedbacks between the Earth system and human activity. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes, due in large part to their different lifetimes. Here, we discuss several key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed, in part due to decreased ocean uptake of CO2 and a likely increase in CH4 lifetime from reduced NO x emissions. Second, the response of O3 to decreased NO x emissions showed significant spatial and temporal variability, due to differing chemical regimes around the world. Finally, the overall response of atmospheric composition to emissions changes is heavily modulated by factors including carbon-cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality, such as wildfires and the ozone climate penalty.

The Ocean Carbon Response to COVID-Related Emissions Reductions.

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 ΔpCO2 that 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.

Large near-term projected snowpack loss over the western United States.

Peak runoff in streams and rivers of the western United States is strongly influenced by melting of accumulated mountain snowpack. A significant decline in this resource has a direct connection to streamflow, with substantial economic and societal impacts. Observations and reanalyses indicate that between the 1980s and 2000s, there was a 10-20% loss in the annual maximum amount of water contained in the region's snowpack. Here we show that this loss is consistent with results from a large ensemble of climate simulations forced with natural and anthropogenic changes, but is inconsistent with simulations forced by natural changes alone. A further loss of up to 60% is projected within the next 30 years. Uncertainties in loss estimates depend on the size and the rate of response to continued anthropogenic forcing and the magnitude and phasing of internal decadal variability. The projected losses have serious implications for the hydropower, municipal and agricultural sectors in the region.