The COVID-19 lockdowns: a window into the Earth System

Abstract

Restrictions to reduce human interaction have helped to avoid greater suffering and death from the COVID-19 pandemic, but have also created socioeconomic hardship. This disruption is unprecedented in the modern era of global observing networks, pervasive sensing and large-scale tracking of human mobility and behaviour, creating a unique test bed for understanding the Earth System. In this Perspective, we hypothesize the immediate and long-term Earth System responses to COVID-19 along two multidisciplinary cascades: energy, emissions, climate and air quality; and poverty, globalization, food and biodiversity. While short-term impacts are dominated by direct effects arising from reduced human activity, longer-lasting impacts are likely to result from cascading effects of the economic recession on global poverty, green investment and human behaviour. These impacts offer the opportunity for novel insight, particularly with the careful deployment of targeted data collection, coordinated model experiments and solution-oriented randomized controlled trials, during and after the pandemic.

Full text

COVID-19 is disrupting lives and
livelihoods around the world. The most
important consequences are the public
health crisis and associated economic and
humanitarian disasters, which are having
historic impacts on human well-being.
Inaddition, after more than four months of
widespread sheltering and other restrictions,
it is clear that the scale and persistence of
socioeconomic disruption represent an
unprecedented modification of human
interactions with the Earth System, the
impacts of which will be long-lasting,
widespread and varying across space and
time (FIG.1).
Some obvious and immediate effects
are reflected in the worldwide reports of
reduced traffic congestion, clearer skies,
cleaner waterways and the emergence
of wildlife into human settlements.
Inaddition to anecdotal reports, effects
questions, such as the processes linking
heterogeneous local pollutant emissions
and regional atmospheric chemistry and air
quality, or the relationship between global
economic integration and poverty-driven
environmental degradation. The uniquely
pervasive disruption also has the potential
to reveal novel questions about the Earth
System that have not previously been
asked, and many diverse efforts are already
underway to learn from this inadvertent
Earth System modulation.
In this Perspective, we examine the
impacts of COVID-19-related social
disruption on two multidisciplinary
pathways: energy, emissions, climate and air
quality; and poverty, globalization, food and
biodiversity. We first consider hypotheses
about how the COVID-19 disruption could
influence the Earth System along these
pathways and then explore the potential
for rapid advances in understanding if
we are able to carefully observe, test and
characterize Earth System processes during
and after the COVID-19 event.
COVID-19 disrupts the Earth System
Under usual daily life, the human footprint
on the Earth System is vast. As a result,
a very large perturbation is required to
cause an observable difference from this
‘business-as-usual’ baseline: COVID-19
is providing that perturbation. As of
July 2020, as much as half the world’s
population has been under some version
of sheltering orders7 (FIG.2a). These orders
have substantially reduced human mobility
and economic activity (FIG.2b), with ~70% of
the global workforce living in countries that
have required closures for all non-essential
workplaces and ~90% living in countries
with at least some required workplace
closures8.
The scale of this socioeconomic
disruption is likely to be detected in the
Earth System at local to global scales (FIG.1).
Some responses are direct, while others will
result from interactions between humans,
ecosystems and climate. The impacts of the
socioeconomic disruption are, thus, also
likely to vary across timescales: although
the direct impacts of the reduction in
human mobility will be strongest during the
sheltering period, many of the most lasting
impacts could result from cascading effects
are being detected in a variety of long-term
physical observations (from improved
air quality to reduced seismic noise) and
socioeconomic indicators (such as reduced
mobility and declining economic growth
and greenhouse-gas emissions). While
some of these impacts might be considered
beneficial to the environment, negative
consequences are also emerging, including
cascading effects for poverty, food security,
mental health, disaster preparedness and
biodiversity.
As with previous calamities, such as
volcanic eruptions1–3, electrical blackouts4
and the short-term reductions in human
mobility following the 11 September attacks5,
the current COVID-19 crisis will inevitably
present a new test bed for understanding
how the Earth System works, including
the critical role of humans6. This test bed
could provide answers to long-standing
The COVID-19 lockdowns: a window
into the Earth System
NoahS.Diffenbaugh , ChristopherB.Field , EricA.Appel , InesL.Azevedo,
DennisD.Baldocchi, MarshallBurke , JenniferA.Burney , PhilippeCiais ,
StevenJ.Davis , ArleneM.Fiore , SarahM.Fletcher, ThomasW.Hertel,
DanielE.Horton, SolomonM.Hsiang , RobertB.Jackson , XiaomengJin ,
MargaretLevi, DavidB.Lobell , GalenA.McKinley , FrancesC.Moore,
AnastasiaMontgomery, KariC.Nadeau , DianeE.Pataki, JamesT.Randerson ,
MarkusReichstein, JordanL.Schnell , SoniaI.Seneviratne , DeeptiSingh,
AllisonL.Steiner and GabrielleWong-Parodi
Abstract | Restrictions to reduce human interaction have helped to avoid greater
suffering and death from the COVID-19 pandemic, but have also created socio-
economic hardship. This disruption is unprecedented in the modern era of global
observing networks, pervasive sensing and large-scale tracking of human mobility
and behaviour, creating a unique test bed for understanding the Earth System. In
this Perspective, we hypothesize the immediate and long-term Earth System
responses to COVID-19 along two multidisciplinary cascades: energy, emissions,
climate and air quality; and poverty, globalization, food and biodiversity. While
short-term impacts are dominated by direct effects arising from reduced human
activity, longer-lasting impacts are likely to result from cascading effects of the
economic recession on global poverty, green investment and human behaviour.
These impacts offer the opportunity for novel insight, particularly with the careful
deployment of targeted data collection, coordinated model experiments and
solution-oriented randomized controlled trials, during and after the pandemic.
PERSPECTIVES
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Earth & EnvironmEnt

initiated by the economic recession, some of
which (such as those induced by changes in
public policy, the structure of the economy
and/or human behaviour) could persist
for decades following the initial economic
recovery.
The reduction of human activities, and
the efforts to manage their revival, have
varied around the world (FIG.2). Given
the variations in the timing, strength and
approach to sheltering7, it may be possible
to track effects through the components
of the Earth System. Likewise, because the
large-scale reduction in human activity will
necessarily be temporary, it will be possible
to observe whether or how Earth System
processes return to their previous states after
activity returns to something approaching
pre-pandemic levels. The event, therefore,
provides a unique test bed for probing
hypotheses about Earth System sensitivities,
feedbacks, boundaries and cascades6,9–11,
presuming that the observing systems are in
place to capture these responses (BOX1).
Path I: Energy, emissions, climate and air
quality. Impacts on energy consumption,
and associated emissions of greenhouse
gases and air pollutants, are likely to cascade
across timescales (FIG.1). In the near-term,
reductions in mobility and economic activity
have reduced energy use in the commercial,
industrial and transportation sectors, and
might have increased energy use in the
residential sector12,13. These direct impacts
will interact with secondary influences
from energy markets, such as the severe
short-term drop in oil prices in March and
April 2020 (REF.14). Further, as with past
economic recessions15,16, energy demands —
and the mix of energy sources — are likely
to evolve over the course of the economic
recovery in response to market forces, public
preferences and policy interventions17,18.
This evolution could have long-term effects
on the trajectory of decarbonization if, for
example, the economic disruption delays
the implementation of ambitious climate
policy or results in decreased investments in
low-carbon energy systems16. Alternatively,
large government stimulus spending could
target green investments that overhaul
outdated infrastructure and accelerate
decarbonization18.
Misunderstandings have arisen with
regards to declines in carbon dioxide
emissions caused by COVID-19-related
disruption, with some interpreting
short-term reductions to suggest that
austerity of energy consumption could
be sufficient to curb the pace of global
warming. A reduction in fossil CO2
emissions proportional to the economic
decline15 would be dramatic relative to
previous declines. For example, the decline
in daily CO2 emissions peaked at >20% in
the largest economies during the period
of sheltering13 (FIG.2c) and the cumulative
reduction in global emissions was ~7% from
January through April 2020 (REF.12) (FIG.2d).
However, these daily-scale declines are
temporary13 and the rebound in emissions
that is already evident13,19 (FIG.2c) supports
the likelihood of a reduction in annual
emissions that is smaller than 7%.
Nevertheless, a 5% drop in annual fossil
CO2 emissions from 37 billion metric tonnes
per year20 would exceed any decline since the
end of World War II (REF.13). There is a strong
basis that such a reduced atmospheric CO2
growth rate would lead to a reduced ocean
carbon sink21 and, thus, also a temporary
reduction in the rate of ocean acidification.
On the other hand, a 5% decrease would still
leave annual 2020 emissions at ~35 billion
metric tonnes, comparable to emissions
in 2013 (REF.20). Such a decline — and
associated changes in the ocean and land
carbon sinks — might not be statistically
detectable above the year-to-year variations
Pollution
GHGs
COVID-19
Human health
Labour
Residential
Energy and industry
Clouds/precipitation
WildlifeEcosystemsWildfires
Agriculture
Restrictions
Economic activity
Transport
Morbidity and mortality
Temperature
Forcing
Forcing
CCN
Climate
dynamics
Income
Jobs
Labour
Land use, poaching
Food supply Mobility
Health care
Demand Demand
Emissions
Emissions
Weather
Fig. 1 | Earth System interactions linked to the COVID-19 socioeconomic disruption. Two pathways highlight the potential for multi-dimensional Earth
System responses: energy, emissions, climate and air quality; and poverty, globalization, food and biodiversity. Interactions will manifest differently in
different regions and on different timescales, with the sign of the interaction potentially changing across different phases of the event. Note that these
interactions are indicative of primary hypotheses, but not all possible interactions are shown. CCN, cloud condensation nuclei; GHGs, greenhouse gases.
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PersPectives

in the natural carbon cycle and, regardless,
global atmospheric CO2 concentrations
will inevitably rise in 2020, continuing a
long-term trend. Progress in understanding
the carbon-cycle responses to COVID-19
will, therefore, be challenging and, at a
minimum, will require new methods for
tracking the unprecedented short-term
perturbation in emissions through the
Earth System.
Based on past events and fundamental
understanding, there are a number of
hypotheses of how sheltering-induced
changes in atmospheric emissions could
influence the climate system more broadly
(FIG.1). On short timescales, reduced
air travel decreases the abundance of
contrails, which can be detected in the
radiation budget (as occurred during the
brief cessation of air travel following
the 11 September attacks5). The response of
atmospheric aerosols to sheltering is likely
to vary regionally, with changes in emissions,
meteorology and atmospheric chemistry
influencing the outcome (BOX2). While
reductions in aerosols have occurred in
many locations (FIG.3), they have also been
observed to increase in others22, highlighting
the important role of secondary chemistry in
these assessments. Changes in atmospheric
aerosols could further influence cloud
and precipitation processes23,24, and might
be detectable in the local surface energy
budget25. A reduction in scattering aerosols
will also cause warmer surface temperatures
over emitting regions26 (FIG.4), potentially
manifesting as more frequent and/or intense
heatwaves27,28. If aerosol reductions persist
across the Northern Hemisphere, this could
have short-term impacts on the onset,
intensity and/or intraseasonal variability
of monsoon rainfall29–31, particularly
given that both local and remote aerosol
emissions can influence variability within
the monsoon season31.
1 February 2020 1 March 2020 15 March 2020
1 April 2020 1 May 2020 1 June 2020
20 30 40 50 60 70 80 90 100
Government response strategy index
2020 day of year
% of people staying at home
0306090 120
20
30
40
50
60
a
Timing of sheltering intensity
b Sheltering intensity c Daily CO2 emissions
∆Daily CO2 emissions (%)
2020 da
y
of
y
ear
−30
−20
−10
0
0306090120 150
–14
–12
–10
–8
–6
–4
–2
0
Global China
USA
EU+UK
Residential
Power
Industry
Ground
Aviation
∆Cumulative CO2 emissions (%)
d Cumulative CO2 emissions
Mississippi
Iowa
Ohio
California
New York
Weakest Strictest
Global
USA
China
EU+UK
Fig. 2 | Sheltering orders and changes in mobility and CO2 emissions.
a | The Oxford Government Response Stringency Index7 on six different
dates between 1 February and 1 June. b | Percentage of people staying at
home, as estimated by mobility data from cell phones91, for five US states.
c | Percentage change in carbon dioxide emissions13,92 for the World, China,
the USA and Europe. Each day’s value is the percentage departure in
2020 from the respective day-of-year emissions in 2019, accounting for
seasonality. d | Percentage change in cumulative carbon dioxide
emissions12,93 for January through April 2020 compared with January
through April 2019 for the World, China, the USA and Europe. The differ-
ences in timing of sheltering and mobility in different areas of the world are
a source of information that can be used in understanding causality in the
Earth System response. In the case of carbon dioxide emissions, the early
onset and subsequent relaxation of sheltering in China is clearly reflected
in the timing of reduction and subsequent recovery of emissions in China
relative to the USA and Europe.
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PersPectives

On longer timescales, changes in
theenergy intensity of the economy, the
carbon intensity of energy or the pace of
deforestation could affect the long-term
trajectory of global climate (through
thetrajectory of greenhouse gas emissions
and associated land and ocean carbon-cycle
feedbacks). These effects could go in either
direction: for example, in the US electricity
sector, coal plants will likely shut down
at an accelerated pace as a result of the
economic slowdown, continuing a long-term
decline32. However, in the transportation
sector, policy intervention to stimulate
the economy might loosen emissions
standards33, increasing emissions relative
to the pre-pandemic trajectory.
The short-term reductions in pollutant
emissions have already resulted in noticeable
changes in air quality in some regions
(BOX2). If sustained, improved air quality
could yield multiple benefits. These include
improved crop health34, as air pollution can
reduce regional harvests by as much as 30%
(REF.35). In addition, ambient air pollution is
a significant cause of premature death and
disease worldwide36, even from short-term
exposure37,38. Several well-documented
historical examples illustrate how decreased
ambient air pollution can improve human
health39. These include effects from
short-term reductions in traffic, travel
and/or industrial activities associated with
events such as the 1996 Atlanta Olympic
Games40 and 2008 Beijing Olympics41–45.
While associations between air quality and
health outcomes are hypothesized in studies
of the current pandemic46,47, understanding
the role of air quality as an indicator
forthe epidemic trajectory is an emerging
challenge. Further, any health improvements
resulting from improved air quality during
the pandemic should not be viewed as a
‘benefit’ of the pandemic but, rather, as an
accidental side effect of the sheltering that
was imposed to protect public health from
the virus.
Some of the most lasting impacts of the
COVID-19 crisis on climate and air quality
could occur via insights into the calculation
of critical policy parameters. Two of the
most important, and controversial, are the
value of mortality risk reduction (sometimes
termed the value of a statistical life, or
VSL) and the pure rate of time preference
(or PRTP), which is one component of
the social discount rate and measures
willingness to trade off well-being over
time. The VSL is important to the analysis
of all environmental regulation in the
United States and can determine whether
environmental regulations as mundane as
a labelling requirement for toxic chemicals
will pass a cost–benefit test. The PRTP is
important in evaluating long-term societal
trade-offs — most notably, climate-change
regulation — and can be important in
calculating an economic value of avoiding
climate damages48,49. With a higher PRTP,
aggressive mitigation of greenhouse gases
becomes less attractive, while a low rate,
which places relatively higher value on the
well-being of future generations, suggests
that far more aggressive regulation of today’s
emissions is warranted.
Both the VSL and the PRTP can
be difficult to quantify. However, the
COVID-19 crisis is making these trade-offs
more explicit, as governments, communities
and individuals make historic decisions that
reflect underlying preferences for current
and future consumption and the trade-off
between different types of economic activity
and individual and collective risk. The
diverse responses to the unusual conditions
during the pandemic could reveal far more
about how different societies manage
these trade-offs than has been revealed in
the last half-century. As those insights are
incorporated into the formal policy-making
apparatus, they will have lasting effects on
the regulations that impact the long-term
trajectory of climate and air quality.
Path II: Poverty, globalization, food and
biodiversity. By amplifying underlying
inequities in the distribution of resources,
the socioeconomic disruption caused by the
response to COVID-19 will almost certainly
have negative long-term impacts on human
health and well-being. In particular,
theeconomic shock is likely to increase the
extent and severity of global poverty50, both
from direct impacts on health, employment
and incomes and through disruptions
of supply chains and global trade51. The
severe impacts on poverty rates and food
security that are already emerging50 are
indicative of these disruptions and are a sign
of how tightly many of the world’s poorest
households are now interwoven into the
global economy. The unwinding of these
relationships in the wake of restrictions on
human mobility and associated economic
shocks will provide insight into the role
of economic integration in supporting
livelihoods around the world. A severe and
prolonged deepening of global poverty is
also likely to reduce available resources
for climate mitigation and adaptation,
Box 1 | Datasets for understanding the Earth System impacts of COVID-19 disruption
A wide range of data could be leveraged to understand Earth System changes during the
COVID-19 pandemic. These include long-term, operationally deployed Earth observations from
satellite remote-sensing platforms and atmospheric, oceanic and surface measurement networks.
Although long-term socioeconomic data are also operationally available, a 1–2-year processing
lag can inhibit real-time analysis. Access to long-term private-sector data could remove some
ofthese barriers. A range of shorter-term and/or intermittent observations are also available.
These include stationary and mobile measurements of the atmosphere, ocean and near-surface
environment, as well as energy, trade, transportation and other socioeconomic data available at
either fine resolution for short periods or coarse resolution for longer periods.
One of the most potent opportunities will be to safely deploy observations in geographic areas
or economic sectors where there is already a rich pre-existing data baseline; where Earth System
models have generated specific, testable hypotheses; or where initial observations suggest that
a strong or unexpected response is already emerging. This strategy could include deployment
of stationary and/or mobile sensors, short-term online or phone surveys, and ‘citizen-science’
opportunities via crowd-sourcing platforms such as the USA National Phenology Network,
iNaturalist, PurpleAir and Smoke Sense. There are also abundant opportunities to leverage newer,
emerging datasets — such as from cell-phone GPS, social media, e-commerce and the private
satellite industry — that, if handled with care to preserve privacy, could help to bridge the gaps
in long-term, operational data.
Despite the prevalence of extensive datasets, the current COVID-19 crisis is revealing limitations
in the ability to measure critical variables in real time. For example, the event has made clear that
the world is ill-equipped to make real-time measurements of economic activity and its immediate
consequences. It is also revealing deficiencies in real-time-measurement capacity for emissions
of some air pollutants and greenhouse gases, as well as highlighting longer-known issues like
a relative inability to assess the vertical structure of pollution in the atmosphere. The crisis is
demonstrating the urgent need for improved data, models and analysis to understand and correct
those deficiencies.
Many sectors would benefit from a public repository containing the heterogeneous data that are
critical to fully understand this unique planetary-scale disruption. Some data sources are public,
some are proprietary and some do not yet exist. As has been proven repeatedly in recent years, an
open, public repository providing all of these heterogeneous data in a uniform, coordinated format
would enable novel, unpredictable insights across multiple research disciplines, long after the
event has passed.
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increasing climate risks and exacerbating
climate-related inequities.
The global agriculture sector is a key
sentinel for the response of poverty to the
pandemic. Primary near-term questions
centre around how food security and
agriculture-dependent incomes might
be affected by unprecedented shocks to
local labour supply and global supply
chains. A first-order impact has been the
income shock associated with widespread
sheltering8. Loss of wages in both
low-income and high-income countries
with limited social safety-nets will drive
food insecurity and poverty50.
It is possible that agricultural production
in rural areas will proceed largely unaffected,
particularly for larger producers of field
crops that tend to be heavily mechanized.
However, in many locations and for many
specialty crops, agriculture still relies
heavily on field labour; sufficient labour
supply during the key planting and harvest
periods is crucial, and there are frequently
labour shortages at these critical times. How
these pre-existing labour-supply challenges
are affected by the scale and scope of
sheltering remains to be seen. In the USA,
meat-packing plants have become hotbeds of
COVID-19, raising the question of whether
excessive concentration of this industry
might have led to a loss of resilience52.
Sheltering-induced return migration from
urban to rural areas, as has been widely
reported in India, could alleviate agricultural
labour shortages in some developing
countries. However, mandated sheltering
could cause reductions in plantings, which,
in combination with the prospect of
sheltering during the harvest season, could
reduce subsequent harvests.
Such supply-side shocks could combine
with general disruption of global trade53
to trigger a cascading series of export bans
like those that occurred in 2007–2008
(REF.54), which caused a spike in grain
prices and contributed to unrest around
the world55. Initial export restrictions are
already emerging56. Given that agriculture
prices are important for both consumers
and producers, such bans tend to hurt rural
producers in favour of protecting urban
consumers in the exporting countries57.
They can also lead to food shortages in
import-dependent countries and rapid
increases in international commodity
prices58, as well as acting to amplify the
impacts of climate variability on poverty59.
However, global grain stocks are much
larger today than they were in 2007, which
should help buffer some sheltering-related
production shortfalls, should they arise.
Deepening of global poverty is likely
to have lasting negative environmental
impacts (including deforestation, land
degradation, poaching, overfishing and
loosening of existing environmental
policies), as a larger share of the global
population is pushed towards subsistence.
For example, after decades of efforts to
replace environmental degradation with
earnings from ecotourism, the collapse
of tourism in the wake of COVID-19 is
coinciding with a rapid increase in illegal
poaching in southern African parks60. The
rapid response is a potential indicator of
the importance of the large African tourism
industry for the preservation of endangered
species. However, further analysis is
needed to distinguish the contributions
of income and governance/enforcement.
Likewise, deforestation in the Brazilian
Amazon surged to >2,000 km2 in the first
five months of 2020, an increase of ~35%
compared to the same period in 2019 (REF.61).
Governance appears to be playing a key
role in this initial short-term resurgence
during the COVID-19 sheltering. Over the
longer term, historical drivers62,63 suggest
that a prolonged poverty shock is likely to
increase deforestation and biodiversity loss.
These cascading impacts on ecosystems and
biodiversity offer a sobering contrast to the
reports of wildlife ‘rebounds’ occurring in
response to local sheltering64.
Changes in human behaviour and
decision-making induced by the pandemic
are also likely to cascade through the
globalized Earth System over the long
term. For example, although sheltering
orders are reducing personal vehicle use,
the long-term impacts are less clear and
will be determined, in part, by how human
behaviours respond to the pandemic. If, for
instance, the pandemic causes people to feel
more dependent on cars as ‘safe places’, that
dependence could act to further reinforce
the prominence of the automobile at the
Box 2 | Interpreting energy, emissions, climate and air quality responses
Changes in atmospheric pollutants have co-occurred with COVID-19 sheltering restrictions22,78,79,
including broadly publicized reductions in satellite-derived tropospheric NO2 columns95 (FIG.3a).
The sheltering period can shed light on processes controlling atmospheric constituents on local
to global scales. However, accurate attribution requires careful consideration of emissions,
meteorology and atmospheric chemistry.
Anthropogenic forcing
The large regional variations in pollutant emissions will create spatial heterogeneity in the
response of air quality to sheltering. While some regions show decreases in aerosols (FIG.3b),
post-shutdown increases have been observed in urban regions in China due to secondary
chemistry22. Sheltering measures were implemented during spring/autumn transitions (FIG.2),
when energy demand, usage and fuel mix fluctuate sharply. Further, observed changes in
atmospheric constituents might also be influenced by longer-term emission reductions.
These factors must be carefully considered when attributing changes to COVID-19 restrictions.
The COVID-19 disruption provides impetus to combine existing energy-consumption data with
robust ground-based and space-based atmospheric-chemical measurements to characterize
local pollutant emissions and the resulting atmospheric chemistry that drives air quality.
Distinguishing signal from noise
Natural climate variability must be accounted for to quantify the human influence on short-term
Earth System changes96–98. In the case of quantifying the response of regional air pollution to
sheltering, several limitations must be overcome. Irregular sampling frequencies over limited
observing periods are a primary barrier. For example, space-based retrievals of air pollutants such as
NO2 are sensitive to physical (such as daily boundary-layer variations) and chemical (such as seasonal
lifetime variability) processes. In the Northern Hemisphere, peak sheltering has coincided with the
period when NO2 lifetimes are transitioning from winter maximum to summer minimum, affecting
estimation of emissions differences from satellite column density retrievals (FIG.3a). Further, as NO2
columns cannot be retrieved under clouds, concentration differences calculated within the period
of sheltering, or between 2020 and previous years, could arise due to variable meteorology.
Opportunities for the future
COVID-19 sheltering could help elucidate Earth System processes along the energy–emissions–
climate–air quality pathway. For example, observations during this period could yield insights
into road-traffic contributions to local air quality, as passenger-car emissions decline but trucking
emissions persist. Connections between emissions and climate may be revealed from observations
in regions with large aerosol forcing signals, offering much-needed tests for local-to-global
responses simulated by Earth System models (FIG.4). For example, asymmetric hemispheric
warming is a robust model response to regional reductions in aerosol emissions26; can this signal
be distinguished from long-term aerosol trends when accounting for internal variability? These
queries sample the rich opportunities to advance understanding of processes governing linkages
between energy use, emissions, climate and air quality.
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a Tropospheric NO2 column densities
b Aerosol optical depth
Jan–FebMar–Apr
Jan–FebMar–Apr
0.50.30.1–0.1–0.3–0.5
∆AOD
∆NO2 (1015 molecules per cm2)
0246810–2–4–6–8–10
Fig. 3 | Variability in air-quality indicators during the 2020 winter–
spring transition. Difference in tropospheric NO2 column density (panel a)
and aerosol optical depth (panel b) for select months between 2020 and
2019. Aerosol optical depth (AOD) data are from the NASA Visible Infrared
Imaging Radiometer Suite; NO2 data are from the NASA Ozone Monitoring
Instrument, processed as in REF.94. Year-to-year changes in air quality
reflect a complex array of processes in addition to COVID-19 restrictions.
For example, strong NO2 decreases over Northeast China coincide with the
Wuhan lockdown95, while those over the UK in January–Febuary predate
COVID-19 restrictions. Relative to NO2, AOD data show less regional coher-
ency. Confident attribution to COVID-19 restrictions highlights a new
challenge to explain these observed spatio-temporal differences and to
place them in the context of the longer-term satellite and ground-based
observations (BOX2).
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expense of public transit. On the other
hand, some cities might seek to maintain
reductions in traffic by permanently closing
some streets and encouraging residents to
rely more on walking and bicycles. Another
potentially consequential outcome could
be a change in the kind of housing and
work environments people will prefer in
the future. The pandemic favours access
to outdoor space and disfavours use of tall
buildings with elevators. If these human
preferences are sustained for years after
thepandemic passes, over the long term, the
combination could lead to more sprawling
suburbs and fewer residential and office
towers, with corresponding consequences
for the Earth System.
More broadly, priorities and incentives
embedded in government aid and economic
stimulus will influence financial investment.
For example, rollbacks of environmental
restrictions by governments seeking to
accelerate economic recovery33 (including
fuel standards, mercury, clean water, and
oil and gas production on federal lands)
could have consequences that outlast the
pandemic. Alternatively, efforts to support
economic recovery could be directed
towards electrification of transportation,
along with green jobs that rebuild public
transit, housing and critical infrastructure
in an environmentally sensitive way18. In the
private sector, pandemic-induced changes
in perceptions of economic security and
human needs could increase investment
in technologies or platforms that lower the
risk of future pandemics, such as reducing
human interactions by introducing more
robotics into workplaces. Although the
precise trajectory is unknown, the long-term
impacts of the pandemic on resource
demand and efficiency will be heavily
influenced by the response of human
behaviour and decision-making, which is
likely to vary among and within countries,
as has occurred with health practices and
policies during the pandemic.
Investigative frameworks
The COVID-19 sheltering has, thus far, been
relatively brief, but its impacts are already
emerging in the Earth System. Some of these
responses, such as those directly connected
to mobility and emissions of atmospheric
pollutants, might pass when the sheltering
passes (FIG.2c, BOX2), while others will
persist long past the economic recovery
(FIG.1). Given the complexity of Earth System
interactions, understanding these short-term,
medium-term and long-term responses
will require careful deployment of a diverse
portfolio of investigative frameworks.
A major challenge will be to test causality
when so many important, interacting
influences are changing simultaneously.
These include potentially confounding
effects from large reductions in human
activity, government interventions to
stem the economic collapse, simultaneous
market responses to both the economic
All power-plant emissions removed
∆NO2∆SO2
∆PM2.5∆PM2.5
All traffic emissions removed
ab
cd
ef
∆Surface temperature∆Surface temperature
0
3
6
–3
–6
0
6
12
–6
–12
0
6
12
–6
–12
0
42
84
–42
–84
0.1
0
0.2
0.3
–0.2
–0.1
–0.3
0.1
0
0.2
0.3
–0.
2
–0.
1
–0.3
(ppbV) (ppbV)
(µg m–3
)(
µg m–3)
(˚C) (˚C)
Fig. 4 | Idealized sensitivity to removal of emissions from traffic and power generation. NO2
(panel a), SO2 (panel b), PM2.5 (panels c and d) and surface-temperature (panels e and f) changes for
the month of January simulated by the Community Multiscale Air Quality/Weather Research and
Forecasting (CMAQ-WRF) model in response to domain-wide removal of traffic (left panels) or
power-plant (right panels) emissions. Experiments simulate one month using January 2010 emission
factors and January 2013 meteorological fields. They are, thus, idealized illustrations of the potential
for Earth System models to pose hypotheses, illuminate and constrain key processes, and identify
data-gathering priorities; as these simulations predate the COVID-19 pandemic, they should not be
considered an attempt to recreate COVID-19 conditions.
Nature reviews
|
Earth & EnvironmEnt
PersPectives

shock and government stimulus, and
underlying variations such as climate
variability and pre-COVID-19 economic
conditions. In addition, observational
continuity is being affected by sheltering,
including atmospheric, oceanic and land
surface observations that contribute to
the global observing system65. Given these
challenges, insight must be generated
from a combination of ongoing and newly
deployed observations, dedicated modelling
experiments, solutions-oriented randomized
controlled trials (RCTs) and sophisticated
quantitative analysis. To maximize
effectiveness, these approaches will need
to place as much focus on Path II (poverty,
globalization, food and biodiversity) as on
Path I (energy, emissions, climate and air
quality). A key imperative will be to quickly
develop and deploy techniques that can
bring multiple lines of evidence together
todistinguish causality.
A new view to spatial and temporal
dynamics of Earth System processes. Because
the timing of different government actions
is known7, the spatio-temporal phasing
of the socioeconomic disruption can be
used to understand regional variations
in the Earth System response. In essence,
although interventions are occurring around
the globe, we are not really experiencing
a global shutdown but, rather, a complex
patchwork of slowdowns in activity that
vary widely in timing, duration, magnitude
and baseline starting conditions (FIG.2a).
This variation is increasing as the event
moves from the initial global disruption to
heterogeneous resumption of activity (FIG.2a)
and extends across the seasonal transition
from Northern Hemisphere winter to
summer (and potentially beyond). Further,
the scale of economic impacts suggest the
possibility of sustained recession — or
even depression — following the cessation
of large-scale sheltering51,66. An extended
period of substantially reduced economic
activity would produce a trajectory of Earth
System forcing that remains different from
the pre-COVID-19 forcing, well after the
COVID-19 restrictions are removed.
These spatial and temporal gradients in
human activity are a source of information
that becomes even more valuable in the
context of observations that are repeated
through time67 or that take advantage of the
fact that variations in human interventions
are at least partly independent of other
co-varying, confounding factors68. The
magnitude of the socioeconomic disruption
is also large enough that it presents the
opportunity to design data-gathering
campaigns to systematically test hypotheses
about both Path I and Path II that would not
be observable without the disruption.
For example, the unprecedented
reduction in daily fossil CO2 emissions
(FIG.2c) could lend insight into the processes
governing land and ocean carbon sinks,
provided that careful testing demonstrates
that a signal can be detected amid the noise
of natural variability, and that observations
can be safely maintained during the event.
Rapid declines in emissions can also help
to narrow existing uncertainties around
anthropogenic sources and their imprint
on atmospheric trace gas and aerosol
concentrations (BOX2). Methane emissions
from oil and gas fields offer one immediate
example: so far during the event, oil and
gas companies in the USA still maintained
~11 million barrels of daily crude oil
production throughout the spring of 2020,
despite a 44% reduction in gasoline sales
for the USA in April14. Not surprisingly, US
inventories continue to climb, reaching their
highest levels of the past four decades in
June. If oil production slumps this summer,
monitoring from satellites, aircraft, towers
and on-the-ground sensors will provide an
unprecedented opportunity to quantify any
change in methane and ethane emissions,
including decreases caused by lower
production or increases caused by reduced
oversight from workers or inspectors. But
that will only be possible if the scientific
community organizes and there is sufficient
operational flexibility to allow for the
collection of critical data.
A similar opportunity exists to study
the effectiveness of wildfire suppression on
air quality. In the USA, federal, state and
local fire agencies are adjusting strategies
in order to limit use of ground crews and
their exposure to COVID-19 (REF.69). These
strategies could influence aerosol loads from
wildfires (which would have potential health
consequences70). It will, thus, be possible to
systematically evaluate the effectiveness of
this aggressive fire-suppression approach
using existing satellite and ground-based
observations.
Earth System models that predict responses
and guide observations. Computational
models are frequently used to test the
response of the Earth System to changes in
external forcing, including for quantifying
a counterfactual history without human
emissions and for generating climate
scenarios under future forcing from
greenhouse gases or solar geoengineering.
In recent decades, Earth System models
have become increasingly sophisticated and
complex, and have been shown to accurately
reproduce71, and predict72,73, many aspects
of the Earth System6. However, limitations
to validating the response to large changes
in forcing have remained a persistent source
of uncertainty, and the models still contain
only rudimentary representations of the
Path II impacts. The magnitude of the current
socioeconomic disruption thus presents a
unique setting for systematic Earth System
model evaluation and development.
Earth System models could be deployed
for a number of benefits. Because the
magnitude of COVID-19 socioeconomic
disruption is historically unprecedented, it
will not be possible to identify all possible
Earth System responses based on theory
or historical experience alone. Earth
System models could be used to create
hypotheses that cannot be otherwise
foreseen. Generating simulations early in
the event — and leveraging pre-existing
idealized experiments (FIG.4) — could
inform data collection and preservation,
including any new observations that might
be needed in order to validate unexpected
modelling results (such as predictions
of Path I and Path II impacts generated
using existing empirical relationships74,75).
After the event, when the temporal
and spatial evolution of specific Earth
System forcings is known, coordinated
experiments76 would allow multiple Earth
System models to be compared in a unified
framework. The fact that the socioeconomic
disruption is deliberately temporary will
increase the ability to use data collected
during and after the event to verify
modelling results.
The event could also be used to evaluate
the potential efficacy of specific policy
interventions for both Path I and Path II
impacts. For example, because atmospheric
chemistry and pollutant accumulation in
the near-surface environment are subject
to variable meteorological conditions and
highly nonlinear chemical interactions,
consideration of policy interventions to
improve air quality (such as incentives
for electric-vehicle adoption) have relied
heavily on theoretical arguments and
model simulations. The scale of emissions
reductions induced by the socioeconomic
disruption opens an opportunity to use
observations of primary and secondary
pollutants to evaluate the performance of
chemical-transport models in simulating
a number of complex features of the
event (FIG.4).
For example, comparison of observations
over northern China during the 2020
winter lockdown versus the same calendar
www.nature.com/natrevearthenviron
PersPectives

period in 2019 shows higher ground-level
ozone (as expected from theory and
modelling, as NOx emissions decline in
a high-NOx emission region77), which
enhances atmospheric oxidizing capacity
and subsequent formation of secondary
aerosols, such as occurs in extreme-haze
events22,78,79. In addition, sheltering policies
have affected the emission-producing
transportation, manufacturing and
power-generation sectors12, though the
degree and scope of shutdown in these
individual sectors vary considerably13.
Further, much of this change occurred
against the backdrop of the transition from
winter to spring, a period when insolation,
water vapour and meteorology are changing
rapidly. This transition was made even more
complex this year by a large-scale dynamical
pattern that resulted in a relatively cold
spring over much of the central and eastern
USA. Together, these challenges present
a unique opportunity to evaluate Earth
System model simulations of the air-quality
response to emissions reductions in specific
sectors (BOX2).
In addition to implications for air quality,
the representation of aerosol effects has been
one of the key sources of uncertainties in
Earth System models71,80,81. Should changes
in regional aerosol concentrations occur
as a result of the COVID-19 sheltering, the
event could be used to verify simulated
climatic consequences of policies to improve
air quality, such as meteorological impacts
like short-term increases in heat and
precipitation extremes due to ‘unmasking’
of the effect of greenhouse gases82. A key
concern is that these short-term, local
signals (FIG.4) need to be evaluated in the
longer-term context of both internal climate
variability and regulation-induced trends
in aerosol emissions (BOX2). However,
the pervasiveness and persistence of the
socioeconomic disruption may provide
sufficient statistical power to test predictions
generated by Earth System models.
Solution-oriented interventions that
create randomized research trials. Many
of the long-term impacts hypothesized
in this Perspective will be determined by
the response of human behaviour and
decision-making. Systematically testing
these human responses can be challenging.
However, the scale of government response
to the COVID-19 pandemic creates the
opportunity to leverage solution-oriented
interventions to create randomized research
trials that can simultaneously provide
assistance and insight about both Path I
and Path II impacts.
Similar to the RCTs that are used to test
the efficacy of vaccines and therapeutics,
RCTs have been deployed to study a varietyof
other human outcomes, the effectiveness
of which was recognized with this year’s
Nobel Prize in Economics. Although
RCTs have been less frequently aimed at
environmental outcomes, RCT feasibility has
been demonstrated in a number of relevant
contexts, including agricultural microcredit83
and payment for ecosystem services84–86.
In addition, basic benchmarking studies
have been conducted in single locations87.
Together, these past studies provide the
foundational research infrastructure that
would be necessary to deploy RCT-based
interventions in the COVID-19 context.
RCTs could be used to study vulnerability,
resilience and disaster response in the
face of extreme events that occur during
sheltering88. Another prime candidate would
be policy interventions designed to prevent
the kind of long-term socio-environmental
damage that becomes increasingly likely as
the disruption becomes more severe and
sustained51. For example, the emerging
poverty shock50 can be expected to lead to
substantial deforestation, land degradation
and nutrient loss, even over the next
few growing seasons, as smallholder
farmers struggle to produce food with
fewer inputs and households revert to
harvested biomass for cooking. Similar
socio-environmental cascades might occur
in marine ecosystems. Solution-oriented
RCTs would use random assignment (when
the trial is of limited scale) or randomized
phasing of participation (for comprehensive
programmes) to test whether direct
payments or other conditional mechanisms,
such as payments for protection of
ecosystem services, are effective in staving
off environmental damages. Studies could
compare the efficacy of a given treatment
across different locations or domains,
and could also benchmark generalized
interventions (such as unconditional
cash transfers) against more targeted
solutions. In addition to helping vulnerable
individuals and communities weather
the COVID-19-driven poverty shock,
such RCTs would provide a much deeper
understanding of how and where poverty
and environmental degradation are
most tightly linked, and what types of
interventions are doubly-protective
of people and the environment.
A similar opportunity could exist
in conjunction with COVID-19 relief
and recovery funding. For example, if
infrastructure spending is specifically
included in recovery measures, that
spending would provide an opportunity
to systematically study the long-term
effectiveness of green investments18
(including infrastructure and government
programmes like jobs and conservation
corps) in achieving Path I outcomes such
as reduced greenhouse gas emissions and
Path II outcomes such as increased resilience
to climate extremes18,89. Even if federal or
state stimulus measures do not explicitly
include funding or requirements for these
investments, the existing efforts of various
states and localities to consider climate
and other environmental outcomes in
infrastructure investments89 would create
an opening for well-designed, opportunistic
research trials built around variations in
how government stimulus funding is applied
in the context of varying state and local
jurisdictional constraints.
Voluntary, solution-oriented actions
could create similar opportunities for both
Path I and Path II impacts. For example,
large fractions of residential developments
in the western USA are at the wildland–
urban interface. The lack of ‘defensible
space’ around homes substantially increases
wildfire risk. It has been proposed that
residents who are able to shelter in place
could allocate more effort to reducing their
fire risk by increasing the defensible space
around their homes90. With some foresight
and investment, this effort could be used
to study the effectiveness of defensible
space. Other solution-oriented efforts that
can be voluntarily undertaken while safely
sheltering, such as local food production
and preparation, could also be leveraged
to study the effectiveness of adaptation
and resilience interventions, as well as the
effects of changes in consumption patterns
on household carbon and environmental
footprints.
Summary and future perspectives
The socioeconomic disruption associated
with COVID-19 represents a highly
unusual alteration of the human interaction
with the Earth System. This alteration is
likely to generate a series of responses,
illuminating the processes connecting
energy, emissions, air quality and climate, as
well as globalization, food security, poverty
and biodiversity (FIG.1). In many cases, these
long-term, indirect Earth System responses
could be larger — and of opposite sign —
than the short-term environmental effects
that have been immediately visible around
the world. The potential for long-term
impacts via Earth System cascades and
feedbacks highlights the opportunity to use
this period as an unintended experiment,
Nature reviews
|
Earth & EnvironmEnt
PersPectives

and to use the knowledge gained to better
predict, model and monitor Earth System
processes during and after the event.
Given the uncertainty about the length
of sheltering orders — and the nature of
any interventions that may follow — it
is impossible to know how long this
inadvertent experiment will last. This
uncertainty provides motivation for
documenting hypotheses during this
initial stage of the global crisis, so that
data can be gathered and evaluated within
the framework of apriori predictions,
rather than post hoc analyses. Some
hypotheses are only testable or conclusively
verifiable by maintaining and/or deploying
data collection during this early stage.
Unless prohibited by safety concerns,
it is important that these data continue
to be collected so that the Earth System
response to COVID-19 can be understood.
By generating specific hypotheses based
on initial observations, existing empirical
relationships and process-based models,
and then testing those hypotheses with
existing and novel data sources, the
COVID-19 socioeconomic disruption
can provide novel insights into the
processes that govern Earth System
function and change.
Our primary motivation is to search
for insight about the basic functioning of
the Earth System that could be helpful in
managing and recovering from the event,
and in avoiding future impacts. Predicting
the impacts of the sheltering on different
components of the Earth System can help
to aid in environment-related disaster
preparedness in different regions. For
example, analysis of the Earth System
response can enable early detection
of hotspots of environmental risk or
degradation emerging during the event.
Similarly, predicting, monitoring and
understanding Earth System processes can
help to support a sustainable economic,
social and environmental recovery from
the event. Although there is uncertainty
about the length of the pandemic, the
economic effects seem very likely to last
for years. The individual, societal and
government responses to these economic
effects will influence the long-term
trajectory of the human footprint on the
Earth System.
The current socioeconomic disruption
is a singular perturbation of that human
footprint. Advancing understanding of
this forcing, and the processes by which
different components of the Earth System
respond, can help to enhance robustness and
resilience now and in the future.
NoahS.Diffenbaugh
1,2 ✉ , ChristopherB.Field
1,2,
EricA.Appel
2,3, InesL.Azevedo2,4,
DennisD.Baldocchi5, MarshallBurke
1,2,6,
JenniferA.Burney
7, PhilippeCiais
8,
StevenJ.Davis
9, ArleneM.Fiore
10,11,
SarahM.Fletcher2,12, ThomasW.Hertel13,
DanielE.Horton14,15, SolomonM.Hsiang
16,
RobertB.Jackson
1,2, XiaomengJin
10,
MargaretLevi17,18, DavidB.Lobell
1,2,6,
GalenA.McKinley
10,11, FrancesC.Moore19,
AnastasiaMontgomery14, KariC.Nadeau
2,20,
DianeE.Pataki21, JamesT.Randerson
9,
MarkusReichstein22, JordanL.Schnell
15,23,
SoniaI.Seneviratne
24, DeeptiSingh25,
AllisonL.Steiner26 and GabrielleWong-Parodi
1,2
1Department of Earth System Science, Stanford
University, Stanford, CA, USA.
2Stanford Woods Institute for the Environment,
Stanford University, Stanford, CA, USA.
3Department of Materials Science and Engineering,
Stanford University, Stanford, CA, USA.
4Department of Energy Resources Engineering,
Stanford University, Stanford, CA, USA.
5Department of Environmental Science, Policy, and
Management, University of California, Berkeley,
Berkeley, CA, USA.
6Center on Food Security and the Environment,
Stanford University, Stanford, CA, USA.
7School of Global Policy & Strategy, University
ofCalifornia, San Diego, La Jolla, CA, USA.
8Laboratoire des Sciences du Climat et de
l’Environnement, Gif sur Yvette, France.
9Department of Earth System Science, University
ofCalifornia, Irvine, Irvine, CA, USA.
10Department of Earth & Environmental Sciences,
Columbia University, Palisades, New York, NY, USA.
11Lamont-Doherty Earth Observatory, Columbia
University, Palisades, New York, NY, USA.
12Department of Civil and Environmental Engineering,
Stanford University, Stanford, CA, USA.
13Department of Agricultural Economics, Purdue
University, West Lafayette, IN, USA.
14Department of Earth and Planetary Sciences,
Northwestern University, Evanston, IL, USA.
15Institute for Sustainability and Energy at
Northwestern, Northwestern University, Evanston,
IL, USA.
16Goldman School of Public Policy, University of
California, Berkeley, Berkeley, CA, USA.
17Department of Political Science, Stanford University,
Stanford, CA, USA.
18Center for Advanced Study in the Behavioral
Sciences, Stanford University, Stanford, CA, USA.
19Department of Environmental Science and Policy,
University of California, Davis, Davis, CA, USA.
20Division of Allergy, Immunology, & Rheumatology,
Stanford University, Stanford, CA, USA.
21School of Biological Sciences, University of Utah,
Salt Lake City, UT, USA.
22Department of Biogeochemical Integration, Max
Planck Institute for Biogeochemistry, Jena, Germany.
23Cooperative Institute for Research in Environmental
Sciences, University of Colorado Boulder, Boulder,
CO, USA.
24Institute for Atmospheric and Climate Science,
ETH Zurich, Zürich, Switzerland.
25School of the Environment, Washington State
University Vancouver, Vancouver, WA, USA.
26Department of Climate and Space Sciences and
Engineering, University of Michigan, Ann Arbor,
MI, USA.
✉e-mail: diffenbaugh@stanford.edu
https://doi.org/10.1038/s43017-020-0079-1
Published online xx xx xxxx
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Acknowledgements
This article grew from discussions initiated in the Uncommon
Dialogue programme of the Stanford Woods Institute for
the Environment. The authors acknowledge support from
Stanford University. K.C.N. acknowledges financial support
from NIEHS R01 and Sean N. Parker Center at Stanford.
G.A.M. acknowledges support from NSF OCE-1948624.
T.W.H. acknowledges support from USDA-NIFA 2019-67023-
29679 and Hatch 1003642. D.E.H., A.M. and J.L.S.
acknowledge support from the Ubben Program for Climate
and Carbon Science at the Institute for Sustainability and
Energy at Northwestern. P.C. acknoweldges support from
the European Research Council Synergy grant SyG-2013-
610028 IMBALANCE-P and the ANR CLAND Convergence
Institute.
Author contributions
All authors made substantial contributions to discussion of
content and review/editing of the manuscript. N.S.D., C.B.F.,
J.A.B., A.M.F., T.W.H., D.E.H., F.C.M., K.C.N., M.R. and A.L.S.
contributed the initial writing. N.S.D., C.B.F., D.D.B., M.B.,
P.C., S.J.D., A.M.F., D.E.H., R.B.J., X.J., A.M. and J.L.S.
researched data for the article. N.S.D. and C.B.F. convened
the group and coordinated the drafting and revisions of the
figures and manuscript. N.S.D. assembled the initial draft.
Competing interests
The authors declare no competing interests.
Peer review information
Nature Reviews Earth & Environment thanks the anonymous
reviewer(s) for their contribution to the peer review of this
work.
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