EARTH, ATMOSPHERIC,
AND PLANETARY SCIENCES
Societal shifts due to COVID-19 reveal large-scale
complexities and feedbacks between atmospheric
chemistry and climate change
Joshua L. Laughner
a,1,2
, Jessica L. Neu
b,1
, David Schimel
b,1
, Paul O. Wennberg
a,c,1
,KelleyBarsanti
d,e
,
Kevin W. Bowman
b
, Abhishek Chatterjee
f,g,2
,BartE.Croes
h,i
, Helen L. Fitzmaurice
j
, Daven K. Henze
k
,JinsolKim
j
,
Eric A. Kort
l
,ZhuLiu
m
, Kazuyuki Miyazaki
b
, Alexander J. Turner
b,j,n
, Susan Anenberg
o
, Jeremy Avise
p
,
Hansen Cao
k
, David Crisp
b
, Joost de Gouw
i,q
, Annmarie Eldering
b
, John C. Fyfe
r
,DanielL.Goldberg
o
,
Kevin R. Gurney
s
, Sina Hasheminassab
t
, Francesca Hopkins
u
, Cesunica E. Ivey
d,e,3
, Dylan B. A. Jones
v
, Junjie Liu
b
,
Nicole S. Lovenduski
w,x
, Randall V. Martin
y
, Galen A. McKinley
z
,LesleyOtt
g
, Benjamin Poulter
aa
,MuyeRu
bb,cc
,
Stanley P. Sander
b
,NeilSwart
r
, Yuk L. Yung
a,b
, Zhao-Cheng Zeng
dd
, and the rest of the Keck Institute for Space
Studies “COVID-19: Identifying Unique Opportunities for Earth System Science” study team
4
a
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125;
b
Jet Propulsion Laboratory, California Institute of
Technology, Pasadena, CA 91109;
c
Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125;
d
Department of
Chemical and Environmental Engineering, University of California, Riverside, CA 92521;
e
Center for Environmental Research and Technology, Riverside, CA
92507;
f
Goddard Earth Sciences Technology and Research, Universities Space Research Association, Columbia, MD 21046;
g
Global Modeling and
Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771;
h
Energy Research and Development Division, California Energy Commission,
Sacramento, CA 95814;
i
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO 80309;
j
Department of Earth and
Planetary Science, University of California, Berkeley, CA 94720;
k
Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309;
l
Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI 48109;
m
Department of Earth System Science, Tsinghua
University, Beijing 100084, China;
n
Department of Atmospheric Sciences, University of Washington, Seattle, WA 98195;
o
Milken Institute School of Public
Health, George Washington University, Washington, DC 20052;
p
Modeling and Meteorology Branch, California Air Resources Board, Sacramento, CA
95814;
q
Department of Chemistry, University of Colorado, Boulder, CO 80309;
r
Canadian Centre for Climate Modelling and Analysis, Environment and
Climate Change Canada, Victoria, BC, V8W 2Y2 Canada;
s
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ
86011;
t
Science and Technology Advancement Division, South Coast Air Quality Management District, Diamond Bar, CA, 91765;
u
Department of
Environmental Sciences, University of California, Riverside, CA 92521;
v
Department of Physics, University of Toronto, Toronto, ON, M5S 1A1 Canada;
w
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO 80309;
x
Institute of Arctic and Alpine Research, University of
Colorado, Boulder, CO 80309;
y
McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO 63130;
z
Department of Earth and
Environmental Sciences, Lamont Doherty Earth Observatory, Columbia University, Palisades, NY 10964;
aa
Biospheric Sciences Laboratory, NASA Goddard
Space Flight Center, Greenbelt, MD 20771;
bb
The Earth Institute, Columbia University, New York, NY 10025;
cc
Nicholas School of the Environment, Duke
University, Durham, NC 27707; and
dd
Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA 90095
Edited by Akkihebbal R. Ravishankara, Colorado State University, Fort Collins, CO, and approved September 29, 2021 (received for review June 10, 202
1)
The COVID-19 global pandemic and associated government lock-
downs dramatically altered human activity, providing a window
into how changes in individual behavior, enacted en masse, im-
pact atmospheric composition. The resulting reductions in anthro-
pogenic 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 atmo-
spheric composition, which provide direct insight into feedbacks
between the Earth system and human activity. While air pollu-
tants and greenhouse gases share many common anthropogenic
sources, there is a sharp difference in the response of their at-
mospheric 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 CO
2
and a likely
increase in CH
4
lifetime from reduced NO
x
emissions. Second, the
response of O
3
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 CH
4
and CO
2
, background
pollutant levels, the timing and location of emissions changes, and
climate feedbacks on air quality, such as wildfires and the ozone
climate penalty.
COVID-19 | air quality | greenhouse gases | earth system | mitigation
T
he effects of the COVID-19 pandemic and associated lock-
down measures have provided a way to observationally test
predictions of future atmospheric composition. This is illustrated
conceptually in Fig. 1. With many people working from home
and limiting travel, the pandemic caused a significant decrease
in anthropogenic emissions. These emissions reductions can be
thought of as a jump forward in time to a future where additional
systemic emissions controls have been adopted. However, be-
cause these changes occurred in a matter of months, the changes
to the concentrations of key air quality (AQ) and climate-relevant
gases in the atmosphere were readily observable. Combining
these observations with current state-of-science models allows
us an important window into the underlying processes governing
Author contributions: J.L.L., K.B., K.W.B., A.C., B.E.C., H.L.F., D.K.H., J.K., E.A.K., Z.L., K.M.,
A.J.T., S.A., J.A., H.C., D.C., J.d.G., A.E., J.C.F., D.L.G., K.R.G., S.H., F.H., C.E.I., D.B.A.J., J.L.,
N.S.L., R.V.M., G.A.M., L.O., B.P., M.R., S.P.S., N.S., Y.L.Y., and Z.-C.Z. performed research;
J.L.N., D.S., and P.O.W. designed research; J.L.L., J.L.N., D.S., P.O.W., K.B., K.W.B., A.C.,
B.E.C., H.L.F., D.K.H., J.K., E.A.K., Z.L., K.M., A.J.T., S.A., J.A., H.C., D.C., J.d.G., A.E., J.C.F.,
D.L.G., K.R.G., S.H., F.H., C.E.I., D.B.A.J., J.L., N.S.L., R.V.M., G.A.M., L.O., B.P., M.R., S.P.S.,
N.S., Y.L.Y., and Z.-C.Z. analyzed data; J.L.L., J.L.N., D.S., and P.O.W. wrote the paper; and
K.B., K.W.B., A.C., B.E.C., H.L.F., D.K.H., J.K., E.A.K., Z.L., K.M., A.J.T., S.A., J.A., H.C., D.C.,
J.d.G., A.E., J.C.F., D.L.G., K.R.G., S.H., F.H., C.E.I., D.B.A.J., J.L., N.S.L., R.V.M., G.A.M., L.O.,
B.P., M.R., S.P.S., N.S., Y.L.Y., and Z.-C.Z. edited and approved the manuscript draft.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
This open access article is distributed under
Creative Commons Attribution-
NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND)
.
1
To whom correspondence may be addressed. Email: jlaugh@caltech.edu,
jessica.l.neu@jpl.nasa.gov, david.schimel@jpl.nasa.gov, or wennberg@gps.caltech.edu.
2
Present address: Jet Propulsion Laboratory, California Institute of Technology, Pasadena,
CA 91109.
3
Present address: Department of Civil and Environmental Engineering, University of
California, Berkeley, CA 94720.
4
A complete list of the Keck Institute for Space Studies “COVID-19: Identifying Unique
Opportunities for Earth System Science” study team can be found in the
SI Appendix
.
This article contains supporting information online at
https://www.pnas.org/lookup/
suppl/doi:10.1073/pnas.2109481118/-/DCSupplemental
.
Published November 9, 2021.
PNAS
2021 Vol. 118 No. 46 e2109481118
https://doi.org/10.1073/pnas.2109481118
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Significance
The COVID-19 pandemic and associated lockdowns caused
significant changes to human activity that temporarily altered
our imprint on the atmosphere, providing a brief glimpse
of potential future changes in atmospheric composition. This
event demonstrated key feedbacks within and between air
quality and the carbon cycle: Improvements in air quality
increased the lifetime of methane (an important greenhouse
gas), while unusually hot weather and intense wildfires in
Los Angeles drove poor air quality. This shows that efforts
to reduce greenhouse gas emissions and improve air quality
cannot be considered separately.
the response of the Earth system to reductions in anthropogenic
emissions and thus a preview of the relative effectiveness of
different emissions-control strategies.
Our goal is to synthesize some of the key results from the
past year into a coherent understanding of what we have
learned about the effectiveness of different strategies to reduce
greenhouse gas (GHG) emissions and improve AQ. We briefly
highlight individual components of the changes in composition
(which are well-described in the literature) but focus on the
interactions and feedbacks between different parts of the Earth
system. We will do so in four parts. First, we summarize the
observed changes in anthropogenic emissions during 2020.
Second, we examine how the reduction in CO
2
emissions
impacted the atmospheric CO
2
growth rate. Third, we show
that the response of AQ to NO
x
emissions reductions differs for
cities around the world and depends strongly on the interaction
with meteorology. We focus on ozone and nitrate particulate
matter (PM) as key AQ metrics that are strongly driven by NO
x
emissions. Fourth, we discuss the implications of these results
for future AQ improvement strategies; our understanding of
processes controlling GHG concentrations in the atmosphere;
feedbacks between AQ, GHGs, and climate; and, finally, close by
identifying strengths and gaps in our current observing networks.
We draw three primary conclusions from this synthesis:
1. Despite drastic reductions in mobility and resulting vehicular
emissions during 2020, the growth rates of GHGs in the
atmosphere were not slowed.
2. The lack of clear declines in the atmospheric growth rates
of CO
2
and CH
4
, despite large reductions in human activity,
reflect carbon-cycle feedbacks in air–sea carbon exchange,
large interannual variability in the land carbon sink, and the
chemical lifetime of CH
4
. These feedbacks foreshadow similar
challenges to intentional mitigation.
3. The response of AQ to emissions changes is heavily modulated
by factors including background pollutant levels, the timing
and location of emissions changes, and climate-related factors
like heat waves and wildfires. Achieving robust improvements
to AQ thus requires sustained reductions of both air pollutant
(AP) and GHG emissions.
Summary of Emissions in 2020
As AQ-relevant gases and CO
2
are coemitted by combustion
processes, decreases in human activity are expected to drive
Fig. 1.
Illustration of the conceptual foundation for this study. The COVID-19–induced reductions in human activity led to reduced anthropogenic emission
s.
The fact that these reductions occurred over months rather than decades allows us to observe how the atmosphere, land, and ocean are likely to respond
in a future scenario with stricter emissions controls. This analysis helps to identify effective pathways to mitigate air pollution and climate-relevant GHG
emissions. Image credit: Chuck Carter (Keck Institute for Space Studies, Pasadena, CA).
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Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks
between atmospheric chemistry and climate change
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EARTH, ATMOSPHERIC,
AND PLANETARY SCIENCES
0
20
40
60
80
100
Stringency Index
2020-01
2020-02
2020-03
2020-04
2020-05
2020-06
2020-07
2020-08
2020-09
2020-10
2020-11
2020-12
2021-01
Germany
China
United States
US (state mean)
California
A
2020-01
2020-02
2020-03
2020-04
2020-05
2020-06
2020-07
2020-08
2020-09
2020-10
2020-11
2020-
1
2
2021-01
40
60
80
100
120
Percent of baseline (15 Jan 2020)
C
Bay Area (CalTrans)
Bay Area (Apple)
LA (CalTrans)
LA (Apple)
Global (Apple)
2020-01
2020-02
2020-03
2020-04
2020-05
2020-06
2020-07
2020-08
2020-09
2020-10
2020-11
2020-12
2021-01
0
20
40
60
80
100
120
Percent of baseline (15 Jan 2020)
B
SFO
LAX
United States
China
Germany
Port of Oakland
Ports of LA & Long Beach
2020-
01
2020-02
2020-03
2020-
04
2020-05
2020-0
6
2020-
07
2020-08
2020-09
2020-10
2020-11
2020-12
2021-01
85
90
95
100
105
110
Percent of 2019 use
D
Residential
Commercial
Industrial
Total
Fig. 2.
Metrics for change in human activity at different scales show that the strongest impact of COVID-19 lockdowns was in the transportation sectors
and that these impacts varied substantially from country to country.
A
shows the Oxford Stringency Index (1) for the regions used in this figure. “US (state
mean)” is the average of individual states’ indices, and “United States” is the index attributed to the United States as a whole (not individual states
;see
SI Appendix
for discussion).
B
shows the percent change in flights (2–4) for two California airports (San Francisco International Airport [SFO] and Los Angeles
International Airport [LAX]) and three countries (lines) and container moves for three California ports (bars).
C
shows traffic metrics for two California urban
areas and 26 countries (“global”). CalTrans indicates California Department of Transportation Performance Measurement System data; Apple indica
tes Apple
driving mobility data.
D
shows electricity consumption in the United States by sector, relative to the same month in 2019. The three sectors shown constitute
>
96
%
of US power consumption. In
B
and
C
, daily metrics are relative to 15 January 2020 and presented as 7-d rolling averages, and monthly metrics are
relative to January 2020. Electricity consumption was not available after November 2020 at the time of writing.
decreases in both of these species. Fig. 2 summarizes changes
to key sectors of human activity during the COVID-19 pan-
demic. Fig. 2
A
shows the Oxford Stringency Index (1), which
quantifies the severity of government-imposed restrictions on
travel, businesses, schools, and other aspects of society. Fig. 2
B
–
D
show changes in air travel and maritime shipping; traffic;
and US electricity use, respectively. There was a clear decrease
in air travel and traffic for most of the world in March 2020,
when the first major wave of COVID-19 led governments to
institute quarantine measures (see also high values of the Strin-
gency Index). Maritime shipping (to West Coast US ports) and
power generation (in the United States) were less affected. Power
generation, in particular, remained within
∼
5% of 2019 levels.
Reductions in NO
x
emissions were apparent in both in situ (5)
and satellite (6) observations of NO
2
concentrations due to the
short atmospheric lifetime of NO
x
(
<
1 d). Estimates of NO
x
emissions reductions from assimilating satellite data in global
models (7), combining global chemical models with machine
learning trained on surface measurements (8), or activity data
(including electricity use, traffic/mobility data, flight data, etc.)
(9–11) find regional reductions of 10 to 40% during the strictest
lockdown periods. Generally, methods assimilating satellite data
report smaller reductions (10 to 20%) than studies based on
activity data (25 to 40%). Estimates of the reduction in global
NO
x
emissions in the first half of 2020 range from 5% (8)
to 13% (7).
The change in global CO
2
emissions was comparable to that
of NO
x
emissions, as seen in Fig. 3. Liu et al. (13) report a peak
global reduction of
∼
15% (4 Tg C or 15 Mt CO
2
) in April and
an annual total of 5.4%. In March 2020, Le Quéré et al. (14) pro-
jected a slightly larger 7% decrease in CO
2
over the remainder of
2020. The largest decreases occurred in the first half of 2020, as
showninFig.4
A
, and were primarily associated with reductions
in ground transportation (15). The response of atmospheric CO
2
mixing ratios can be observed near the emissions sources; during
the strictest lockdowns, Turner et al. (16) were able to use CO
2
observations from a local ground-based network to estimate a
48% reduction in traffic CO
2
emissions in the San Francisco Bay
Area. Liu et al. (17) found a 63% (41 parts per million [ppm])
decrease of the typical on-road CO
2
enhancement in Beijing,
2005
2014
2013
2012
2011
2010
2009
2008
2007
2006
2020
2019
2018
2017
2016
2015
300
350
400
375
325
8
11
9
10
Global Anthropogenic Emissions of NO
X
, CH
4
, CO
2
CH
4
(Tg CH
4
/yr),
X
*10 (Tg N/ yr)
CO
2
(Gt C/yr)
NO
CH
CO
NO
x
4
2
?
Fig. 3.
The year 2020 saw reductions in CO
2
,CH
4
, and NO
x
emissions. CH
4
and NO
x
are plotted along the left axis and CO
2
on the right. The dashed
line for CH
4
after 2017 indicates that it is estimated from the average rate of
increase. The 2020 emissions are represented as a range: The IEA estimated
a 10% decrease in CH
4
emissions in 2020 (12), but this is uncertain, as the
CH
4
growth rate increased in 2020. Full details are in
SI Appendix
.
Laughner et al.
Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks
between atmospheric chemistry and climate change
PNAS
3of12
https://doi.org/10.1073/pnas.2109481118
Downloaded at California Institute of Technology on November 11, 2021
A
B
Fig. 4.
Despite substantial reductions in anthropogenic CO
2
emissions in
early 2020, the annual atmospheric CO
2
growth rate did not decline.
A
shows daily global CO
2
emissions for 2019 and 2020, calculated following
Liu et al. (13).
B
shows trends in atmospheric column average CO
2
from the
OCO-2. The small blue and red symbols indicate daily, deseasonalized values
as percent anomalies relative to the global 2018 mean. The solid cyan and
orange lines are linear fits to 2016–2019 data. In
B
, the vertical gray dashed
line marks 1 March 2020 as the approximate beginning of lockdowns in
response to COVID-19. A version of
B
showing the absolute trends and the
data including the seasonal cycle is available in
SI Appendix
, Fig. S8.
China. Distinguishing these signals in CO
2
at regional scales is
more challenging. Buchwitz et al. (18) infer peak decreases in
anthropogenic CO
2
emissions from China of 10% from space-
based total column CO
2
measurements. However, they note that
the uncertainty is
∼
100% and that the expected CO
2
concentra-
tion signal is 0.1 to 0.2 ppm, out of a background of over 400 ppm.
Anthropogenic CH
4
emissions are dominated by sources such
as landfills, oil and gas production, and agricultural activities. The
International Energy Agency (IEA) estimates that CH
4
emis-
sions dropped by 10% in 2020 (Fig. 3), largely due to the decrease
in demand for oil and gas. However, it is unclear whether reduced
demand during 2020 was the primary driver of emissions. It is
likely that decreased maintenance of landfills and oil and gas
infrastructure during the COVID-19 pandemic led to new leaks
in some areas, which can result in those locations becoming CH
4
“superemitters” (19). In general, the type, maintenance level, and
throughput of CH
4
infrastructure can have a large impact on the
amount of fugitive emissions (20, 21). Further, the downturn in
oil and gas prices in 2020 may have resulted in wells being left
uncapped when the owning company went bankrupt, increasing
fugitive CH
4
emissions (22). On a positive note, some of the
decrease in emissions estimated by the IEA was associated with
the installation of new oil and gas infrastructure and the adoption
of new CH
4
regulations in a number of countries (12). Such
decreases would likely be sustained beyond the pandemic period.
CO
2
and CH
4
Atmospheric Growth Rates
The effect of CO
2
emissions reductions, especially from ground
transport, was clearly apparent in urban-scale observations of
atmospheric CO
2
mixing ratios (16, 17). This does not, however,
transfer to global-scale observations. Fig. 4
B
shows deseasonal-
ized trends in column-average CO
2
mixing ratios (referred to as
XCO
2
) observed by the Orbiting Carbon Observatory 2 (OCO-
2) instrument. Despite the reduction in CO
2
emissions in 2020
(Fig. 4
A
), there is no clear deflection of the observed XCO
2
below what would be projected based on previous years’ growth
rates. We compared the variability in actual atmospheric CO
2
growth rates derived from the OCO-2 data with that computed
from fossil fuel emissions (
SI Appendix
, Fig. S8
B
) and found that
the change in atmospheric CO
2
growth caused by the COVID-
19 pandemic is smaller than the natural year-to-year variability.
This is expected, because the percent change in the CO
2
growth
rate, in the absence of feedbacks, will match the percent change
in emissions. For a typical growth rate of 2.45 ppm/y since 2016
(
SI Appendix
, Fig. S8
B
and ref. 23), the 5.4% total reduction in
CO
2
emissions calculated by Liu et al. (13) equals a 0.13 ppm/y
decrease in the CO
2
growth rate for 2020–well within the natural
variability observed by OCO-2 (
SI Appendix
, Fig. S8)andsurface
networks (23).
Wildfires are one element of the variability in CO
2
growth
rate. The 2019/2020 Australian wildfires emitted 173 Tg of C (634
Mt of CO
2
) between November 2019 and January 2020, over
six times more than Australia’s average November to January
CO
2
emissions for 2001 through 2018 (24). This drove an early
increase in CO
2
in 2020, evident in the deseasonalized Southern
Hemisphere OCO-2 XCO
2
(Fig. 4
B
, red series) and growth
rate derived from the OCO-2 data (
SI Appendix
, Fig. S8
B
). This
wildfire anomaly offset a third of the 518 Tg of C (1,901 Mt of
CO
2
) reduction in anthropogenic CO
2
(13) and so does not fully
explain the offset between emissions and atmospheric mixing
ratios for CO
2
.
The atmospheric CO
2
growth rate led to a reduction in the
rate of oceanic CO
2
uptake. Fig. 5 shows the magnitude of ocean
carbon fluxes over 8 y, as computed from a model ensemble under
normal and COVID-like emissions. The COVID-like emissions
scenario was chosen near the beginning of the pandemic and
so had to assume how CO
2
emissions would recover. However,
it does match the bottom-up emissions shown in Fig. 4
A
(13)
reasonably well through November 2020 (26).
Fig. 5 shows that a reduction from normal to COVID-like
emissions results in a decrease in ocean carbon uptake. There is
significant variation in the sea–air and CO
2
flux among the model
ensemble members. This spread represents the potential interan-
nual variability in CO
2
flux; given that variability, the true change
in CO
2
flux in 2020 is uncertain, in part due to corresponding
variability in the land carbon sink (
SI Appendix
, Fig. S9). How-
ever, the ensemble mean indicates that while on short time scales,
the land carbon flux is insensitive to the change in emissions
(
SI Appendix
, Fig. S9), the ensemble mean ocean uptake was
reduced by 70 Tg of C/y in 2020. This would offset 14% of the
∼
520 Tg of C/y (1,901 Mt of CO
2
/y) reduction in anthropogenic
CO
2
emissions in 2020 (13), further dampening the signal from
2019
2020
2021
2022
2023
2024
2025
2026
-3.1
-3
-2.9
-2.8
-2.7
-2.6
-2.5
-2.4
-2.3
globally integrated sea-air
CO
2
flux (Pg C yr
-1
)
Fig. 5.
Sea–air carbon exchange responded quickly to the reduction in
anthropogenic CO
2
emissions during 2020. Shown here are annual mean,
globally integrated sea-to-air carbon dioxide fluxes predicted from the
Canadian Earth System Model Version 5–COVID ensemble (25, 26). Black/gray
lines derive from simulations forced with Shared Socioeconomic Pathways
2–RCP4.5 CO
2
emissions, while red/pink lines derive from simulations forced
with a 25% peak CO
2
emissions reduction in 2020. See refs. 25 and 26 for
more details. Thick lines are ensemble averages, and thin lines are individual
ensemble members, each with different phasing of internal variability.
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PNAS
https://doi.org/10.1073/pnas.2109481118
Laughner et al.
Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks
between atmospheric chemistry and climate change
Downloaded at California Institute of Technology on November 11, 2021