Editing IPCC:AR6/WGI/Chapter-6 (section)

== Cross-Chapter Box 6.1 | Implications of COVID-19 Restrictions for Emissions, Air Quality and Climate ==

<div id="h2-32-siblings" class="h2-siblings"></div>

'''Coordinators:''' Astrid Kiendler-Scharr (Germany/Austria), John C. Fyfe (Canada)

'''Contributors:''' Josep G. Canadell (Australia), Sergio Henrique Faria (Spain/Brazil), Piers Forster (UK), Sandro Fuzzi (Italy), Nathan P. Gillett (Canada), Christopher Jones (UK), Zbigniew Klimont (Austria/Poland), Svitlana Krakovska (Ukraine), Prabir Patra (Japan/India), Joeri Rogelj (Austria/Belgium), Bjørn Samset (Norway), Sophie Szopa (France), Izuru Takayabu (Japan), Hua Zhang (China)

In response to the outbreak of COVID-19 (officially the severe acute respiratory syndrome–coronavirus 2 or SARS-CoV-2), which was declared a pandemic on March 11 2020 by the World Health Organization (WHO), regulations were imposed by many countries to contain the spread of COVID-19. Restrictions were implemented on the movement of people, such as closing borders or requiring the majority of population to stay at home, for periods of several months. This Cross-Chapter Box assesses the influence of the COVID-19 containment on short-lived climate forcers (SLCFs) and long-lived greenhouse gases (LLGHGs), and related implications for the climate. Note that this assessment was developed late in the AR6 WGI process and is based on the available emerging literature.

'''Emissions'''

Global fossil CO <sub>2</sub> emissions are estimated to have declined by 7% (''medium confidence'') in 2020 compared to 2019 emissions, with estimates ranging from 5.8% to 13.0% based on various combinations of data on energy production and consumption, economic activity and proxy activity data for emissions and their drivers (Forster et al. , 2020; Friedlingstein et al. , 2020; Le Quéré et al. , 2020; Liu et al. , 2020) . However, the concentration of atmospheric CO <sub>2</sub> continued to grow in 2020 compared to previous years ([[#Dlugokencky--2021|Dlugokencky and Tans, 2021]]). Given the large natural interannual variability of CO <sub>2</sub> ([[IPCC:Wg1:Chapter:Chapter-5#5.2.1|Section 5.2.1]]), and the small expected impact of emissions in the CO <sub>2</sub> growth rate, there were no observed changes in CO <sub>2</sub> concentration that could be attributed to COVID-19 containment (''medium confidence'') ([[#Chevallier--2020|Chevallier et al., 2020]] ; [[#Tohjima--2020|Tohjima et al., 2020]]).

Global daily CO <sub>2</sub> emissions from fossil fuel sources had a maximum decline of 17% in early April, compared with the mean 2019 levels, and coinciding with the global peak pandemic lockdown ([[#Le%20Quéré--2020|Le Quéré et al., 2020]]). The reductions in CO <sub>2</sub> emissions in 2020 were dominated by the drop in emissions from surface transport followed, in order of absolute emissions reductions, by industry, power and aviation ([[#Le%20Quéré--2020|Le Quéré et al., 2020]] ; [[#Liu--2020|Liu et al., 2020]]). Residential emissions showed little change ([[#Liu--2020|Liu et al., 2020]]) or rose slightly ([[#Forster--2020|Forster et al., 2020]] ; [[#Le%20Quéré--2020|Le Quéré et al., 2020]]). Aviation had the biggest relative drop in activity. CO <sub>2</sub> emissions due to land use (based on early and uncertain evidence on deforestation and forest fires) were higher than average in 2020 ([[#Amador-Jiménez--2020|Amador-Jiménez et al., 2020]]).

Using similar methodologies, [[#Forster--2020|Forster et al. (2020)]] assembled activity data and emissions estimates for other greenhouse gases and aerosols and their precursors. Anthropogenic NO <sub>x</sub> emissions, which are largely from the transport sector, are estimated to have decreased by a maximum of 35% in April (''medium confidence''). Species whose emissions are dominated by other sectors, such as methane and NH <sub>3</sub> from agriculture, saw smaller reductions.

'''Abundances and air quality'''

Owing to the short atmospheric lifetimes of SLCFs relevant to air quality, changes in their concentrations were detected within a few days after lockdowns had been implemented (e.g., Bauwens et al. , 2020; Venter et al. , 2020; Gkatzelis et al. , 2021; Shi et al. , 2021) . The COVID-19-driven economic slowdown has illustrated how complex the relationship is between emissions and air pollutant concentrations due to non-linearity in the atmospheric chemistry leading to secondary compound formation (Section 6.1, Box 6.1; [[#Kroll--2020|Kroll et al., 2020]]).

Several studies have examined the effect of COVID-19 containment on air quality, showing that multi-year datasets with proper statistical/modelling analysis are required to discriminate the effects of meteorology from that of emissions reduction (Dhaka et al. , 2020; L. Li et al. , 2020; Wang et al. , 2020; Zhao et al. , 2020b) . Accounting for meteorological influences and with an increasing stringency index, the median observed change in NO <sub>2</sub> decreased from –13% to –48%, and in PM <sub>2.5</sub> decreased from –10% to –33%, whereas the median change in ozone increased from 0% to 4% ([[#Gkatzelis--2021|Gkatzelis et al., 2021]]). The latter can be explained by the decrease of NO emissions that titrate ozone in specific highly polluted areas, leading to the observed increase in surface ozone concentration in cities (Le et al. , 2020; Sicard et al. , 2020; Huang et al. , 2021).

The temporary decrease of PM <sub>2.5</sub> concentrations should be put in perspective of the sustained reduction (estimated at 30–70%), which could be achieved by implementing policies addressing air quality and climate change (Section 6.6.3). Such sustained reductions can lead to multiple benefits and simultaneously achieve several SDGs (Section 6.6.3). These policies would also result in reduction of ground-level ozone by up to 20% (Section 6.7.1.3).

Except for ozone, temporary improvement of air quality during lockdown periods was observed in most regions of the world (''high confidence''), resulting from a combination of interannual meteorological variability and the impact of COVID-19 containment measures (''high confidence''). Estimated air pollution reductions associated with lockdown periods are lower than what can be expected from integrated mitigation policy leading to lasting reductions (''medium confidence'').

'''Radiative forcings'''

COVID-19-related emissions changes primarily exerted effective radiative forcing (ERF) through reduced emissions rates of CO <sub>2</sub> and methane, altered abundance of SLCFs, notably ozone, NO <sub>2</sub> and aerosols, and through other changes in anthropogenic activities, notably a reduction in the formation of aviation-induced cirrus clouds.

[[#Forster--2020|Forster et al. (2020)]] combined the FaIR emulator (Cross-Chapter Box 7.1) with emissions changes for a range of species, relative to a continuation of Nationally Determined Contributions ([[#Rogelj--2017|Rogelj et al., 2017]]). They found a negative ERF from avoided CO <sub>2</sub> emissions that strengthens through 2020 to –0.01 W m <sup>–2</sup> . During the spring lockdown, they found a peak positive ERF of 0.1 W m <sup>–2</sup> from loss of aerosol-induced cooling, and a peak negative ERF of –0.04 W m <sup>–2</sup> from reductions in tropospheric ozone (from reduced photochemical production via NO <sub>x</sub>). Overall, they estimated a net ERF of +0.05 W m <sup>–2</sup> for spring 2020, declining to +0.025 W m <sup>–2</sup> by the end of the year.


[[File:e5663caf3f762ec596b8100c7e22007c IPCC_AR6_WGI_CCBox_6_1_Figure_1.png]]

'''Cross-Chapter Box 6.1, Figure 1''' '''|''' '''Emissions reductions and their effect on aerosols and climate in response to COVID-19.''' Estimated reductions in emissions of CO <sub>2</sub> , SO <sub>2</sub> and NO <sub>x</sub> are shown in panel '''(a)''' based on reconstructions using activity data (updated from [[#Forster--2020|Forster et al., 2020]]). Eight Earth system models (ESMs) performed multiple ensemble simulations of the response to COVID-19 emissions reductions forced with these assumed emissions reductions up until August 2020 followed by a constant continuation near the August value to the end of 2020. Emissions reductions were applied relative to the SSP2-4.5 scenario. Panel '''(b)''' shows ESM-simulated AOD at 550nm (only seven models reported this variable). Panel '''(c)''' shows ESM-simulated GSAT anomalies during 2020; curves denote the ensemble mean result for each model with shading used for ±1 standard deviation for each model. ESM data from these simulations (‘ssp245-covid’) is archived on the Earth System Grid CMIP6 database. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where &lt;80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1.
[[#Gettelman--2021|Gettelman et al. (2021)]] extended Forster et al.’s (2020) results using two ESMs, and found a spring peak aerosol-induced ERF ranging from 0.12 to 0.3 W m <sup>–2</sup> , depending on the aerosol parametrization. They also estimated an ERF of –0.04 W m <sup>–2</sup> from loss of contrail warming. Overall, they report a peak ERF of 0.04 to 0.2 W m <sup>–2</sup> , and a subsequent decline to around half the peak value. Two independent ESM studies [[#Weber--2020|Weber et al. (2020)]] and [[#Yang--2020|Yang et al. (2020)]] found consistent results in time evolution and component contributions but included fewer forcing components.

The available studies are in broad agreement on the sign and magnitude of contributions to ERF from COVID-19-related emissions changes during 2020. The range in peak global mean ERF in spring 2020 was [0.025 to 0.2] W m <sup>–2</sup> (''medium confidence''), composed of a positive forcing from aerosol–climate interactions of [0.1 to 0.3] W m <sup>–2</sup> , and negative forcings from CO <sub>2</sub> (–0.01 W m <sup>–2</sup>), NO <sub>x</sub> (–0.04 W m <sup>–2</sup>) and contrail cirrus (–0.04 W m <sup>–2</sup>) (''limited evidence'' , ''medium agreement''). By the end of 2020, the ERF was at half the peak value (''medium confidence'').

'''Climate responses'''

Changes in atmospheric composition due to COVID-19 emissions reductions are not thought to have caused a detectable change in global temperature or rainfall in 2020 (''high confidence''). A large ensemble of Earth system model (ESM) simulations show an ensemble average reduction in Aerosol Optical Depth (AOD) in some regions, notably Eastern and Southern Asia ([[#Fyfe--2021|Fyfe et al., 2021]]). This result is supported by observational studies finding decreases in optical depth in 2020 ([[#Gkatzelis--2021|Gkatzelis et al., 2021]] ; [[#Ming--2021|Ming et al., 2021]] ; [[#van%20Heerwaarden--2021|van Heerwaarden et al., 2021]]), which may have contributed to observed increases in solar irradiance ([[#van%20Heerwaarden--2021|van Heerwaarden et al., 2021]]) or solar clear-sky reflection ([[#Ming--2021|Ming et al., 2021]]).

Model simulations of the response to COVID-19 emissions reductions indicate a small warming of global surface air temperature (GSAT) due to a decrease in sulphate aerosols ([[#Forster--2020|Forster et al., 2020]] ; [[#Fyfe--2021|Fyfe et al., 2021]]), balanced by cooling due to an ozone decrease ([[#Forster--2020|Forster et al., 2020]] ; [[#Weber--2020|Weber et al., 2020]]), black carbon decrease ([[#Weber--2020|Weber et al., 2020]]) and CO <sub>2</sub> decrease. It is noted that observational studies report little SO <sub>2</sub> change, at least locally near the surface ([[#Shi--2021|Shi et al., 2021]]), and do not correlate with emissions inventory-based changes ([[#Gkatzelis--2021|Gkatzelis et al., 2021]]). One study suggests a small net warming while another using idealized simulations suggests a small cooling ([[#Weber--2020|Weber et al., 2020]]). Simulated GSAT and rainfall changes are unlikely to be detectable in observations (''high confidence'') ([[#Samset--2020|Samset et al., 2020]] ; [[#Fyfe--2021|Fyfe et al., 2021]]). Multi-model ESM simulations based on a realistic COVID-19 containment forcing scenario ([[#Forster--2020|Forster et al., 2020]]) indicate a model mean reduction in regional AOD but no discernible response in GSAT (Figure 1, Cross-Chapter Box 6.1).
</div>
<div id="6.7" class="h1-container"></div>

<span id="future-projections-of-atmospheric-composition-and-climate-response-in-ssp-scenarios"></span>