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1. Introduction
The lockdown measures instituted to control the spread of Coronavirus Disease 2019 (COVID-19) caused
unprecedented disruptions to many economic sectors, among which manufacturing and transportation
were particularly hard hit. The consequent decrease in emissions of anthropogenic aerosols and their pre
-
cursors generally led to improvements in air quality and visibility (Mahato et al.,
2020
; McNeill,
2020
; Shar
-
ma et al.,
2020
), with notable exceptions (Le et al.,
2020
). These emission reductions may have had an
influence on Earth's radiation budget, and by extension weather and climate, as short-lived aerosol particles
have long been postulated to provide a net cooling by scattering/absorbing insolation (direct effects) under
clear-sky conditions (Bellouin et al.,
2003
; Haywood,
1999
; Mitchell,
1971
) and brightening clouds (indirect
effects) under cloudy conditions (Albrecht,
1989
; Twomey,
1974
).
Satellite observations offer some indications. In March 2020, one month after China implemented a strict
lockdown, the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD)
(Remer et al.,
2005
) exhibited large negative anomalies relative to the climatological (2003–2019) mean,
not only over much of East Asia, but also extending downwind over the Pacific (Figure
1a
). The average
decrease over the East Asian Marginal Seas (EAMS) (defined as the oceanic region in 117°–132°E and
26°–41°N) was 0.16 W m
−2
, or 32% of the climatological mean (Figure
1b
). We choose EAMS as the main
analysis region for its proximity to the upwind source regions, more reliable satellite retrievals over ocean
Abstract
The Coronavirus Disease 2019 (COVID-19) pandemic led to a widespread reduction in
aerosol emissions. Using satellite observations and climate model simulations, we study the underlying
mechanisms of the large decreases in solar clear-sky reflection (3.8 W m
−2
or 7%) and aerosol optical
depth (0.16 W m
−2
or 32%) observed over the East Asian Marginal Seas in March 2020. By separating the
impacts from meteorology and emissions in the model simulations, we find that about one-third of the
clear-sky anomalies can be attributed to pandemic-related emission reductions, and the rest to weather
variability and long-term emission trends. The model is skillful at reproducing the observed interannual
variations in solar all-sky reflection, but no COVID-19 signal is discerned. The current observational and
modeling capabilities will be critical for monitoring, understanding, and predicting the radiative forcing
and climate impacts of the ongoing crisis.
Plain Language Summary
Satellite data showed large reductions in reflected sunlight and
aerosol optical depth over clear (cloudless) sky off the East Asian coast in March 2020. Although these
changes are consistent with a sharp cut in aerosol emissions due to the lockdown put in place to curb the
spread of Coronavirus Disease 2019 (COVID-19), one cannot rule out possible roles played by weather
conditions such as winds and humidity. We use a climate model forced with past known weather to isolate
the latter factor, and to describe the difference from the observation to the former. The main finding is that
the pandemic-related emission reductions are responsible for about one-third of the observed signal. The
model can largely reproduce the year-to-year variations in all-sky reflection, but no influence of COVID-19
is detected.
MING ET AL.
© 2020. The Authors. This article has
been contributed to by US Government
employees and their work is in the
public domain in the USA.
This is an open access article under
the terms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.
Assessing the Influence of
COVID-19 on the Shortwave
Radiative Fluxes Over the East Asian Marginal Seas
Yi Ming
1
, Pu Lin
1
,
Vaishali Naik
1
, Fabien Paulot
1
, Larry
W. Horowitz
1
,
Paul A. Ginoux
1
,
V. Ramaswamy
1
, Norman G. Loeb
2
, Zhaoyi Shen
3
,
Clare E. Singer
3
, Ryan
X.
Ward
3
, Zhibo Zhang
4,5
, and Nicolas Bellouin
6
1
NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA,
2
NASA/Langley Research Center, Hampton, VA,
USA,
3
Department of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA, USA,
4
Department of Physics, University of Maryland Baltimore County, Baltimore, MA, USA,
5
Joint Center for Earth System
Technology, University of Maryland Baltimore County, Baltimore, MA, USA,
6
Department of Meteorology, University
of Reading, Reading, Reading, Whitenights, UK
Key Points:
Solar clear-sky reflection was
observed to drop substantially over
the East Asian Marginal Seas in
March 2020
Climate model simulations nudged
with reanalysis data are used to
separate the impacts of meteorology
and emissions
It is found that about one-third
of the clear-sky anomalies can be
attributed to pandemic-related
emission reductions
Supporting Information:
Supporting Information S1
Correspondence to:
Y. Ming,
yi.ming@noaa.gov
Citation:
Ming, Y., Lin, P., Naik, V., Paulot, F.,
Horowitz, L. W., Ginoux, P. A., et al.
(2021). Assessing the influence of
COVID-19 on the shortwave radiative
fluxes over the east asian marginal
seas.
Geophysical Research Letters
,
48
, e2020GL091699.
https://doi.
org/10.1029/2020GL091699
Received 12 NOV 2020
Accepted 23 DEC 2020
10.1029/2020GL091699
RESEARCH LETTER
1 of 8
Geophysical
Research
Letters
than over land (Hsu et al.,
2013
), and absence of surface snow/ice cover. The concurrently measured Clouds
and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) shortwave clear-sky
top-of-atmosphere (TOA) radiative flux (
F
clr
, upward defined as positive; Loeb et al.,
2018
) was also greatly
reduced during March 2020 (Figure
1c
). The average decrease over EAMS was 3.8 W m
−2
, or 7% (Figure
1d
).
Both anomalies exceed their respective 90% confidence intervals (Figures
1b
and
1d
), and the two quanti-
ties are strongly correlated on interannual timescales. This suggests that a substantial emissions reduction,
presumably caused by COVID-19, gave rise to lower aerosol loading, resulting in more solar absorption by
Earth's surface.
There are, however, inherent difficulties in interpreting the observations. Besides emissions, meteorology
plays a prominent role in modulating AOD and
F
clr
, especially outside of source regions, via multiple path-
ways (e.g. long-range transport, hygroscopic growth, and wet removal). For instance, the negative anomalies
over EAMS in March 2005, when there was no anomalous emissions reduction, were comparable to those
in March 2020 (Figures
1b
and
1d
). Therefore, a confident attribution of the observed decreases in AOD
and
F
clr
to the emissions reduction hinges on a reliable approach for isolating the non-COVID-19 factors.
It is even more challenging to discern possible impacts on shortwave all-sky TOA radiative flux
F
all
due to
the complexities involving clouds. This study addresses these issues with a set of climate model simulations
forced with known meteorological conditions.
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Figure 1.
(a) Spatial distribution of the anomaly in MODIS aerosol optical depth (AOD) in March 2020. The oceanic
region enclosed by the green rectangle (117°–132°E and 26°–41°N) is defined as the East Asian Marginal Seas (EAMS).
(b) Time series of the anomaly in MODIS AOD over EAMS in March from 2003 to 2020. The gray area denotes the
90% confidence interval over the climatological period. (c) Same as (a), but for CERES shortwave clear-sky top-of-
atmosphere (TOA) radiative flux (
F
clr
, upward defined as positive). (d) Same as (b), but for CERES
F
clr
. The climatology
is defined as 2003–2019. MODIS, Moderate Resolution Imaging Spectroradiometer; CERES, Clouds and the Earth's
Radiant Energy System.
(a)
(b)
(d)
(c)
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2. Methods
2.1. Satellite Observations
We use the observed shortwave TOA fluxes and cloud fraction from the CERES project. Observational data
for aerosol and cloud properties are retrieved from the MODIS instrument aboard NASA's Aqua satellite.
While similar products are available from NASA's Terra satellite, others have reported the degradation of
the on-board MODIS instrument over time, specifically with respect to the cloud properties of interest in
this work (Malavelle et al.,
2017
; Polashenski et al.,
2015
). All data are Level 3 (L3) monthly products from
MODIS Collection 6.1. The L3 monthly product (MYD08_M3) are gridded to 1° by 1° and derived from the
daily products (MYD08_D3). The AOD, cloud fraction, cloud effective radius (
R
e
), and liquid water path
(LWP) are retrieved from the MYD08_M3 data set. AOD is derived from the combined Dark Target and
Deep Blue AOD at 0.55
μ
m over the land and ocean. LWP is retrieved from the 3.7
μ
m band and represents
in-cloud properties. To compare with model outputs, the in-cloud LWP is converted to a grid-box mean LWP
by multiplying the in-cloud LWP by the liquid cloud fraction (calculated from the mean cloud fraction and
cloud phase properties). The observational data are interpolated to the AM4 grid for analysis.
2.2. Model Simulations
We conduct a suite of nudged simulations from January 2000 to April 2020 with the GFDL AM4 (Zhao
et al.,
2018
), which participated in the World Climate Research Program (WCRP) Coupled Model Intercom-
parison Project Phase 6 (CMIP6) (Eyring et al.,
2015
) and forms the basis of a climate prediction system
(Delworth et al.,
2020
). The model horizontal winds, temperature, and surface pressure are nudged to the
3-hourly averaged products from the MERRA-2 reanalysis (Gelaro et al.,
2017
) with a nudging time scale
of 6 h, as opposed to generating its own meteorology (typical of climate simulations). Still, aerosols, water
vapor, and clouds are computed interactively and subject to the same dynamical and physical processes as
in a free-running simulation, posing a stringent test for model physics. The simulations use the monthly sea
surface temperatures (SST) and sea ice concentrations prepared for the CMIP6 historical AMIP simulations
(Taylor et al.,
2000
), which are extended to 2020 using the NOAA Optimum Interpolation (OI) SST V2 data
(Reynolds et al.,
2002
). Aerosol concentrations are calculated interactively based on their emissions, chem-
istry, advection, and dry and wet deposition.
The SO
2
and black carbon (BC) emissions used in the control simulations are based on the regional Mul-
tiresolution Emission Inventory for China (MEIC) (Zhang et al.,
2009
) in China for 2000–2015 and the
CMIP6 historical emissions (Hoesly et al.,
2018
) in the rest of the world for 2000–2014. The latter is not used
for China, because it severely underestimates the decline of SO
2
after 2007 (Paulot et al.,
2018
). (Note that
MEIC ends in 2015.) The SO
2
and BC emissions for 2019 are derived by linearly interpolating the CMIP6
SSP (Shared Socioeconomic Pathway) 585 emission scenario between 2015 and 2020 (O'Neill et al.,
2016
).
Emissions for 2016–2018 are derived by interpolating between 2015 and 2019, and those for 2020 are kept
as given by SSP585 for the control simulation. Organic matter (OM) emissions (primary OM only) are based
solely on the CMIP6 historical and SSP585 inventories. The time evolution of anthropogenic emissions
over China is depicted in Figure
S1
in the Supporting Information. After peaking in 2007, SO
2
has been
decreasing steadily due to air pollution control measures, while black carbon (BC) and organic matter (OM)
diverged after 2015, compensating each other to some extent. Three perturbation simulations are created by
reducing the anthropogenic SO
2
, BC, and OM emissions over China for February, March, and April 2020 by
20%, 40%, or 60% to mimic the effects of COVID-19 lockdown. Note that this broad-brush sensitivity study
assumes uniform emissions reduction in various emission factors over China and complements more de
-
tailed analyses (Forster et al.,
2020
; Huang et al.,
2020
). All other forcings (such as greenhouse gases, solar
irradiance, and stratospheric ozone) are based on the CMIP6 historical forcings (Eyring et al.,
2015
) for
2000–2014 and the CMIP6 SSP585 forcings (O'Neill et al.,
2016
) for 2015–2020.
3. Results
The nudged control simulation shows considerable skill in reproducing the observed interannual variations
of AOD and
F
clr
for March over EAMS; the correlation coefficients (
r
) between model and observations
are 0.83 and 0.72, respectively (Figure
2
). The model performance is comparable in February and April
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(Figure
S2
). This suggests that the nudged AM4 simulations provide an
effective way to quantify the non-COVID-19 influence. The most notable
deficiency is that the simulation does not capture the full extent of the
negative anomalies in March 2005. However, the model-simulated AOD
and
F
clr
anomalies are strongly correlated (Figure
3b
), with a slope that
is very close to the observationally based counterpart (Figure
3a
). This
supports the fidelity of the model's representation of the aerosol direct ef-
fects. Both observed anomalies emerge from the lower bounds of the de
-
tection limits, meaning that they are likely to contain forced components;
likelihood is 92% for AOD and 80% for
F
clr
. The anomalies are estimated
at −0.06 for AOD (1.9 standard deviations) and −1.3 W m
−2
for
F
clr
(1.3
standard deviations) by subtracting the control values from the respec-
tive observations. When compared with the perturbation simulations,
the observations are consistent with a 40%–60% anthropogenic emissions
reduction over China (relative to 2020), which roughly translates into a
reduction of 31%–47% in SO
2
emissions relative to 2015, as the baseline
SO
2
emissions in 2015 are 28% higher than in 2020 (Figure
S1a
). Further,
it is important to note that this top-down estimate is obtained in a way
that is fundamentally different from, but complementary to, convention-
al bottom-up approaches based on socioeconomic data. One study of the
latter kind (Forster et al.,
2020
) suggests that SO
2
emissions over China
decreased by about 20% in March 2020 (relative to 2015). Given the com-
plicated nature of producing such bottom-up estimates, it is not expected
that they should agree perfectly with our result. It may help reconcile the
difference between the two types of estimates to take into account the
precise spatiotemporal pattern of the emissions reduction, once known.
We choose the 60% perturbation simulation to illustrate the spatial dis
-
tributions of the model-simulated AOD and
F
clr
anomalies in Figure
4
(Figure
S3
is the same plot for the 40% perturbation simulation). The sim-
ulation exhibits a clear land-sea contrast; the large AOD anomaly over
mainland China decreases gradually down the prevailing southwester
lies over the ocean (Figure
4a
). This pattern is in broad agreement with
MODIS (Figure
1a
). The overall anomaly can be decomposed into the part
due to both the meteorology and long-term emission trends (non-COV
-
ID-19) and into the part due to the COVID-19-related emissions reduc-
tion. The former is the anomaly in the control simulation (Figure
4b
),
and the latter is the difference between the 60% perturbation and control simulations (Figure
4c
). The two
contributors to the overall anomaly are of comparable magnitudes, but show different spatial patterns.
For instance, the plume cutting across northern China, the Korean Peninsula, and Northern Japan in the
non-COVID-19 component is not present in the COVID-19 counterpart. The impact of COVID-19 on AOD
is concentrated over Southern China. These features largely carry over to
F
clr
(Figures
4d–4f
). The afore
-
mentioned decomposition yields insights into the physical mechanisms of regional anomalies. An example
is the dipole structure immediately north of the northern boundary of EAMS (41°N), characteristic of the
large positive anomalies over parts of Inner Mongolia and Mongolia and the negative anomalies over North-
east China. It can be attributed to meteorology as it exists only in the non-COVID-19 component, realized
through land surface albedo changes caused by snow melting or accumulation (not shown). A notable
discrepancy is that the model projects a large decrease in
F
clr
over much of China (Figure
4d
), which is not
found in the CERES observations (Figure
1c
). Although the underlying cause is not entirely clear, it is dif-
ficult to reconcile the substantial decrease in MODIS AOD over northern China with the lack of any signif-
icant change in CERES
F
clr
over the same region given the strong correlation between them (Figure
3
). On
the other hand, the model substantially overestimates the decrease in AOD and
F
clr
over Southern China,
but for different reasons. The former is due to the COVID-19-related emissions reduction, while the latter
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Figure 2.
(a) Times series of the anomaly in AOD over EAMS in March
from 2003 to 2020. The black line is from MODIS, and the blue line is
from the control simulation. The vertical bar denotes the detection limit
(one standard deviation of the differences between the observations and
the control simulation from 2003–2019). The green, orange, and red dots
denote the perturbation simulations of 20%, 40%, and 60% emissions
reductions, respectively.
r
is the correlation coefficient. (b) Same as (a), but
for CERES
F
clr
. AOD, aerosol optical depth; MODIS, Moderate Resolution
Imaging Spectroradiometer; CERES, Clouds and the Earth's Radiant
Energy System. EAMS, East Asian Marginal Seas.
(a)
(b)
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belongs to the non-COVID-19 component. This seems to suggest that the emissions reduction over Central
China may have been overestimated (Figures
S9–S11
).
Excellent agreement (
r
= 0.94) is seen between CERES and AM4-simulated shortwave all-sky flux (
F
all
)
(Figure
5a
). This result is somewhat counterintuitive since
F
all
is heavily influenced by clouds, which GCMs
historically have struggled to simulate owing to the intrinsic difficulties in representing the effects of cloud-
scale turbulence in coarse-resolution models. We cross-check this result by comparing the modeled cloud
fraction with CERES observations (Figure
5b
). The equally impressive model skill (
r
= 0.92) affirms the
prominent role of atmospheric motion in dictating cloud fraction and the quality of AM4's cloud scheme.
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Figure 3.
Scatter plots of the anomalies in AOD and
F
clr
in March over EAMS. Open dots represent the climatological
period (2003–2019) and solid dots represent the year 2020. (a) Observations from MODIS and CERES. (b) Blue dots
are from the control model simulation. Green, orange, and red dots correspond to the 20%, 40%, and 60% perturbation
simulations, respectively. The regression line is calculated for the climatological period (2003–2019). AOD, aerosol
optical depth; MODIS, Moderate Resolution Imaging Spectroradiometer; CERES, Clouds and the Earth's Radiant
Energy System.
(a)
(b)
Figure 4.
(a) Spatial distribution of the anomaly in AOD in March 2020 from the 60% perturbation simulation. The green rectangle denotes EAMS. (b) Same as
(a), but for the control simulation. (c) The difference between (a) and (b). (d)–(f) Same as (a)–(c), but for simulated
F
clr
. The climatology is defined as 2003–2019
in the control simulation. AOD, aerosol optical depth; EAMS, East Asian Marginal Seas.
(a)
(d)
(e)
(f)
(b)
(c)
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(More work is needed to better understand the contributions from different cloud types.) The negative
anomaly in
F
all
(−2.1 W m
−2
) for March 2020 is just within the detection limit, while the negative anomaly
in cloud fraction is barely outside. Interestingly, MODIS cloud fraction shows a much larger negative anom-
aly than its CERES counterpart (Figure
5b
). Although the sign of the model-simulated forced signal in
F
all
(less reflection) in the 60% perturbation simulation is consistent with our expectation for a COVID-19-relat
-
ed emissions reduction, the magnitude (−0.5 W m
−2
) is well within the detection limit. One may interpret
this discrepancy as an indication that the model underestimates the strength of the aerosol indirect effects
since the clear-sky results substantiate the scale of the emissions reduction and the realism of the direct
effects. There is, however, no clear signal in MODIS-retrieved cloud effective radius (
R
e
) (Figure
5c
) or LWP
(Figure
5d
). In summary, our all-sky analyses indicate that the observed negative anomaly in
F
all
for March
2020 was likely caused by weather variability. While nominally consistent with the all-sky radiative impacts
of an emissions reduction, it was realized through lower cloud fraction, instead of higher
R
e
or lower LWP,
the two main pathways through which the aerosol indirect effects manifest in GCMs (including AM4).
To test the sensitivity to the definition of the analysis region, we more than double the original domain
by expanding beyond EAMS further to the open ocean (the oceanic region in 117°–150°E and 26°–41°N).
The results are qualitatively the same (Figures
S4
and
S5
). In fact, the model performs better in terms of
the interannual variations of AOD and
F
clr
, reflecting the good agreement between the observed and sim-
ulated spatial structures (Figures
1
and
4
). A series of additional simulations are conducted to assess the
robustness of the key findings. They cover the long-term emission trends and locations and speciation of
the emissions reduction. Although quantitative differences exist, the main conclusions remain valid (see
Figures
S6–S14
and Text
S1
).
The above analyses are also performed for February and April (Figures
S15–S18
). The MODIS AOD in
February 2020 is the lowest since 2005 (Figure
S15
). The control simulation projects a negative anomaly
in 2020, but of only half of the observed magnitude. The discrepancy can be accounted for by a 20%–40%
emissions reduction. In terms of
F
clr
, the model is less skillful for February than for March, resulting in a
larger detection limit. Unlike AOD, the observed
F
clr
falls within the limit. Note that the observed
F
clr
is not
nearly as variable as the observed AOD in the few years before 2020, breaking the tight linkage between
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Figure 5.
(a) Time series of the anomaly in shortwave all-sky TOA radiative flux (
F
all
) over EAMS in March from 2003
to 2020. The black line is from CERES, and the blue line is from the AM4 control simulation. The vertical bar denotes
the detection limit. The green, orange, and red dots denote the perturbation simulations of 20%, 40% and 60% emissions
reductions, respectively.
r
is the correlation coefficient. (b) Same as (a), but for cloud fraction. The black line is from
CERES, and the gray line is from MODIS. The detection limit is based on CERES. (c) Same as (a), but for cloud effective
radius (
R
e
). (d) Same as (a), but for liquid water path (LWP). In (c) and (d) the observation is from MODIS. MODIS,
Moderate Resolution Imaging Spectroradiometer; CERES, Clouds and the Earth's Radiant Energy System; TOA, top-of-
the-atmosphere; EAMS, East Asian Marginal Seas.
(a)
(c)
(b)
(d)
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the two quantities for March (Figure
3a
). Since the physics governing the AOD-
F
clr
relationship is simple
and robust, more needs to be done to reconcile the two retrievals. One possibility is compensation between
scattering and absorbing aerosols. Both the observed
F
all
and cloud fraction anomalies are smaller than
those in the control simulation (qualitatively similar to March), but within their respective detection limits
(Figure
S16
).
Any sign of AOD decrease is gone by April. Although the MODIS AOD is anomalously low in April 2020,
the fact that it is very close to the control suggests no significant COVID-19-related emissions reduction
(Figure
S17
). This inference is supported by the observed
F
clr
, which is slightly above the upper bound of
the detection limit, opposite to the perturbation simulations. In stark contrast, the observed
F
all
shows an
outsized negative anomaly of −18.1 W m
−2
, the largest in the entire CERES data record (Figure
S18
). This
coincides with the largest decrease in CERES cloud fraction. The control simulation captures the timing
and magnitude of both anomalies, allowing us to attribute them to the specific meteorological conditions
in April 2020, as opposed to the anthropogenic aerosol effects. The above findings are consistent with a
recent study of CO
2
emissions during COVID-19 (Le Quéré et al.,
2020
), which suggests that the emissions
over China decreased substantially in February and March 2020, but almost fully recovered by April. If
one assumes that there is no emissions reduction after April, the annual mean change in
F
clr
over EAMS
in the 40% emissions reduction simulation (−0.19 W m
−2
) is similar to that in the FAST simulation in Yang
et al. (
2020
), where SO
2
and BC emissions over China are reduced by about 20%–30%. Note that in Yang
et al. (
2020
), aerosol emissions in other parts of the world are reduced after March, a factor that is not con-
sidered in this study.
4. Discussion and Conclusions
The COVID-19 pandemic provides an opportunity to evaluate the model representation of the aero
-
sol-cloud-radiation interactions, a major source of uncertainty in global weather and climate modeling.
The observational evidence for aerosol direct effects is unequivocal, and their model representation is satis
-
factory. In contrast, it is more difficult to draw definitive conclusions about aerosol-cloud interactions and
indirect effects from the observed shortwave all-sky flux. This is fundamentally due to the highly variable,
fine-scale nature of clouds, the challenges in retrieving cloud properties on the observational side, and in
parameterizing subgrid cloud processes on the modeling side. Nonetheless, the fact that both the mod-
el-simulated perturbations and the observations stay within the detection limits leads us to conclude that
there is no evidence suggesting that the model-simulated aerosol indirect effects are too strong. The obser
-
vations underline the dominant role of cloud fraction in determining the all-sky flux. Any attempt at dis
-
cerning the manifestation of the aerosol indirect effects through cloud microphysical properties (such as
R
e
and LWP) is contingent on separating out interference from the synoptic-scale variations in cloud fraction.
Running in the nudged mode to separate the effects of meteorology from emissions, AM4 is skillful at
reproducing the observed interannual variations in shortwave TOA radiative fluxes, clear- and cloudy-sky
alike. This allows us to distinguish forced signal from weather variability, a prerequisite for interpreting ob
-
servations. We find that about one-third of the observed decrease in shortwave clear-sky reflection over East
Asian Marginal Seas (1.3 out of 3.8 W m
−2
locally) in March 2020 was likely caused by COVID-19-related
emissions reduction. On the other hand, the concurrent decrease in shortwave all-sky reflection (2.1 W m
−2
)
is within the detection limit, and thus is thought to be caused mainly by weather variability. By leveraging
the latest observational and modeling capabilities, the framework described here is ideal for studying the ra-
diative impacts of the ongoing COVID-19 pandemic, and the resulting perturbations to the energy balance,
in other parts of the world (such as Europe and North America).
Data Availability Statement
The AQUA/MODIS MYD08 L3 Global 1 Deg. data set was acquired from the Level-1 and Atmosphere Ar
-
chive and Distribution System (LAADS) Distributed Active Archive Center (DAAC), located in the Goddard
Space Flight Center in Greenbelt, Maryland (
https://ladsweb.nascom.nasa.gov/
). The CERES data were ob
-
tained from the NASA Langley Research Center Atmospheric Science Data Center (
https://doi.org/10.5067/
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TERRA-AQUA/CERES/EBAF-TOA_L3B004.1
). Primary AM4 simulation results that may be used to pro
-
duce the plots are available are available online (
https://data.caltech.edu/records/1666
).
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Acknowledgments
The authors thank L. Donner and D.
Paynter for their helpful comments
on an early draft. Z. Shen acknowl-
edges support from the Ronald and
Maxine Linde Challenge for Climate
Science Fund. C. E. Singer acknowl-
edges support from the NSF Graduate
Research Fellowship under Grant No.
DGE-1745301. R. X. Ward. acknowledg
-
es fellowship support from the Resnick
Sustainability Institute at Caltech.