COVID-19 pandemic responses affected atmospheric composition and climate. These effects are historically contingent, depending on the background emissions, climate, and season in which they occur. We used the GISS ModelE Earth System Model to evaluate how atmospheric and climate impacts depend on the decade and season in which lockdowns occurred. Data underlying the figures and analysis are provided as Python numpy arrays as a courtesy for peer reviewers. These data are annual means of diagnostic variables from ModelE.

We used the GISS ModelE2.1, an Earth system model with a resolution of 2° x 2.5° and 40 vertical layers. The model includes the ocean component GISS Ocean version 1, which has a horizontal resolution of 1◦ latitude by 1.25◦ longitude, and 40 vertical layers.

Briefly, ModelE2.1 has fully coupled chemistry and aerosols. ModelE2.1 only includes the first indirect effect, which is the effect of aerosols on cloud optical depth (the Twomey effect). The version used in this study, ModelE2.1, includes atmospheric chemistry and is coupled to an aerosol microphysical scheme, MATRIX (Multiconfiguration Aerosol TRacker of mIXing state.  

In addition to COVID-19 lockdown effects for 2020–2024, we evaluate the effect of lockdowns at two points in history when emissions were near their peak in North America and Europe (1980–1984) and in East Asia (2008–2012) and under a future scenario when global emissions are projected to be lower (2051–2055 under SSP2-4.5).  To evaluate the influence of seasonality, we also assess how the effects of COVID-19 lockdowns would have changed if they had been delayed by 3, 6, or 9 months. We conducted an ensemble of 10 runs for each treatment in which only initial conditions differ to facilitate the detection of small changes in climate.  Regional analyses use definitions derived from the Global Fire Emissions Database’s basis regions.

2.1 Emissions  

ModelE2.1 uses prescribed anthropogenic emissions from the Community Emissions Data System (CEDS).  We use a COVID-19 emissions scenario (“Two-year blip”) that uses changes in activity data to estimate monthly 2020 emissions of greenhouse gases and other species relative to the SSP2-4.5 scenario  (Forster et al., 2021; Lamboll et al., 2020), and used for the COVID-19 Model Intercomparison Project (CovidMIP; Jones et al., 2021).  Emission reductions for December 2020 are projected forward at 2/3 of their value during 2021, with linear recovery to the SSP2-4.5 baseline in 2022. 

For the seasonal shifts, we first calculated the relative emission decreases for each month under the Two-year blip scenario relative to the baseline SSP2-4.5 scenario, where month 1 is January 2020.  We then applied these relative decreases to the appropriate months for the seasonally-shifted scenarios. 

For the runs starting in 1980 and 2008, we applied the relative decreases in the Two-year blip scenario for 2020–2022 to the historical CEDS emissions for 1980–1982 and 2008–2010, respectively.  For the runs starting in 2051, we applied those relative decreases to SSP2-4.5 scenario emissions for 2051-2053. 

2.2 Greenhouse gas concentrations

For the simulations starting in 1980, 2008, and 2051, we multiply the annual greenhouse concentrations for each year in the relevant 5-year period from CEDS or SSP2-4.5 by the proportion difference for the SSP2-4.5 and Two-year blip scenarios for the corresponding year in 2020–2024.  For the seasonal shift, we calculate the concentrations of each greenhouse gas for 2020 by modifying the baseline SSP2-4.5 to reflect the fact that greenhouse gas emissions were affected by COVID-19 lockdowns for either ¼, ½, or ¾ of the year.  For 2021 and following years, greenhouse gas concentrations under the COVID-19 scenarios are calculated using a baseline concentration estimate GHG_base_esty that accounts for the effects of lockdowns in the previous year:

GHG_base_est_y  =  GHG_cov_y-1 * AGR

Where AGR is the annual growth rate in greenhouse gas concentrations under SSP2-4.5 between year_y-1and year_y and GHG_cov_y-1 is the estimate of greenhouse concentrations under the seasonally-shifted lockdown scenario in year_y-1.