DOI: 10.5061/dryad.qrfj6q5hz
title: Supporting data for: Emissions background, climate, and season determine the impacts of past and future pandemic lockdowns on atmospheric composition and climate

description: 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.
description: 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. <em>2.1 Emissions  </em>
 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.  <em>2.2 Greenhouse gas
 concentrations</em> 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<sub>y</sub>  =  GHG_cov<sub>y-1</sub> * AGR Where AGR is the annual growth rate in greenhouse gas concentrations under SSP2-4.5 between year<sub>y-1</sub>and year<sub>y</sub> and GHG_cov<sub>y-1</sub> is the estimate of greenhouse concentrations under the seasonally-shifted lockdown scenario in year<sub>y-1</sub>.