Abstract.
The
effective
radiative
forcing
due
to
aerosol-cloud
interactions
(ERFaci)
is
difficult
quantify,
leading
large
uncertainties
in
model
projections
of
historical
and
climate
sensitivity.
In
this
study,
satellite
observations
reanalysis
data
are
used
examine
the
low-level
cloud
responses
aerosols.
While
some
studies
it
assumed
that
activation
rate
droplet
number
concentration
(Nd)
response
variations
sulfate
aerosols
(SO4)
or
aerosol
index
(AI)
has
a
one-to-one
relationship
estimation
ERFaci,
we
find
assumption
be
incorrect,
demonstrate
explicitly
accounting
for
crucial
accurate
ERFaci
estimation.
This
corroborated
through
“perfect-model”
cross
validation
using
state-of-the-art
models,
which
compares
our
estimates
with
“true”
ERFaci.
Our
results
suggest
smaller
less
uncertain
value
global
than
previous
(-0.39
±
0.29
W
m-2
SO4
-0.24
0.18
AI,
90
%
confidence),
indicating
may
impactful
previously
thought.
also
consistent
observationally
constrained
total
feedback
“top-down”
models
weaker
better
match
observed
hemispheric
warming
asymmetry
over
period.
Geophysical Research Letters,
Journal Year:
2024,
Volume and Issue:
51(7)
Published: April 9, 2024
Abstract
This
study
addresses
a
critical
gap
in
understanding
anthropogenic
influences
on
tropical
climate
dynamics
by
investigating
the
impact
of
aerosol‐cloud
interactions
large‐scale
circulation.
Despite
extensive
research
greenhouse
gas‐induced
warming
and
its
effects
circulation,
aerosols,
particularly
their
with
clouds,
circulation
remains
understudied.
Utilizing
large‐domain
radiative
convective
equilibrium
cloud‐resolving
simulations,
this
demonstrates
that
increasing
aerosol
concentration
intensifies
overturning
evaluated
at
mid‐troposphere
,
strongly
correlating
domain
mean
cloud
properties.
Employing
weak
temperature
gradient
approximation,
I
attribute
variations
to
changes
clear‐sky
cooling
rather
than
stability.
These
are
linked
humidity
driven
warm
rain
suppression
aerosols.
study's
findings
underscore
need
take
into
account
microphysical
changes,
concentrations,
when
studying
Authorea (Authorea),
Journal Year:
2024,
Volume and Issue:
unknown
Published: July 17, 2024
Marine
low
clouds
significantly
cool
the
climate,
but
predicting
these
remains
challenging:
response
of
to
various
factors
is
highly
non-linear.
Previous
studies
usually
overlook
effects
cloud
droplet
number
concentration
(Nd)
and
non-local
information
target
grids.
To
address
challenges,
we
introduce
a
convolutional
neural
network
model
(CNNMet-Nd)
that
uses
both
local
includes
Nd
as
cloud-controlling
factor
enhance
predictive
ability
cover,
albedo,
radiative
(CRE)
for
global
marine
clouds.
CNNMet-Nd
demonstrates
superior
performance,
explaining
over
70%
variance
in
three
variables
instantaneous
scenes
1°×1°,
notable
improvement
past
efforts.
also
accurately
replicates
geographical
patterns
trends
from
2003
2022.
In
contrast,
similar
without
input
(CNNMet)
fails
predict
mean
properties
effectively,
underscoring
critical
role
Nd.
Further
comparisons
with
an
artificial
(ANNMet-Nd)
model,
which
same
inputs
considering
spatial
dependence,
show
CNNMet-Nd's
performance
R2
values
CRE
being
0.16,
0.11,
0.18
higher,
respectively.
This
highlights
importance
incorporating
into
predictions
climate
parameterizations.
Atmospheric chemistry and physics,
Journal Year:
2023,
Volume and Issue:
23(15), P. 8515 - 8530
Published: Aug. 1, 2023
Abstract.
Reflection
of
solar
radiation
by
tropical
low-level
clouds
has
an
important
cooling
effect
on
climate
and
leads
to
decreases
in
surface
temperatures.
Still,
the
pollution
ubiquitous
continental
investigation
related
impact
atmospheric
rates
are
poorly
constrained
situ
observations
modeling,
particular
during
West
African
summer
monsoon
season.
Here,
we
present
comprehensive
measurements
microphysical
properties
over
Africa,
measured
with
Deutsches
Zentrum
für
Luft-
und
Raumfahrt
(DLR)
aircraft
Falcon
20
DACCIWA
(Dynamics–Aerosol–Chemistry–Cloud
Interactions
Africa)
campaign
June
July
2016.
Clouds
below
1800
m
altitude,
identified
as
boundary
layer
clouds,
were
classified
according
their
carbon
monoxide
(CO)
level
into
pristine
less
polluted
(CO
<
135
ppbv)
>
155
confirmed
linear
CO
accumulation
aerosol
number
concentration
correlation.
Whereas
slightly
enhanced
background
levels
from
biomass
burning
across
entire
area,
substantially
outflow
major
coastal
cities,
well
rural
conurbations
hinterlands.
Here
investigate
cloud
droplet
size
Our
results
show
that
(CDNC)
range
3
50
µm
around
noon
increases
26
%
elevated
cities
loadings
median
CDNC
240
cm−3
(52
501
interquartile
range)
324
(60
740
range).
Higher
resulted
a
17
decrease
effective
diameter
deff
14.8
12.4
clouds.
Radiative
transfer
simulations
non-negligible
influence
higher
concentrations
smaller
particle
sizes
diurnally
averaged
(noon)
net
radiative
forcing
at
top
atmosphere
−3.9
W
m−2
(−16.3
m−2)
respect
lead
change
instantaneous
heating
−22.8
K
d−1
(−17.7
d−1)
Thus,
only
case
due
already
concentrations.
Additionally,
occurrence
mid-
high-level
layers
atop
buffer
this
further,
so
rate
turn
out
be
sensitive
towards
projected
future
anthropogenic
Africa.
Atmospheric chemistry and physics,
Journal Year:
2024,
Volume and Issue:
24(15), P. 8653 - 8675
Published: Aug. 6, 2024
Abstract.
Aerosols
can
cause
brightening
of
stratocumulus
clouds,
thereby
cooling
the
climate.
Observations
and
models
disagree
on
magnitude
this
cooling,
partly
because
aerosol-induced
liquid
water
path
(LWP)
adjustment,
with
climate
predicting
an
increase
in
LWP
satellites
observing
a
weak
decrease
response
to
increasing
aerosols.
With
higher-resolution
global
models,
which
allow
simulation
mesoscale
circulations
clouds
are
embedded,
there
is
hope
start
bridging
gap.
In
study,
we
present
boreal
summertime
simulations
conducted
ICOsahedral
Non-hydrostatic
(ICON)
storm-resolving
model
(GSRM).
Compared
geostationary
satellite
data,
ICON
produces
realistic
cloud
coverage
regions;
however,
these
look
cumuliform,
sign
adjustments
disagrees
observations.
We
investigate
disagreement
causal
approach,
combines
time
series
knowledge
processes,
allowing
us
diagnose
sources
observation–model
discrepancies.
The
positive
adjustment
results
from
superposition
overestimated
due
(1)
precipitation
suppression,
(2)
lack
wet
scavenging,
(3)
deepening
under
inversion,
despite
(4)
small
negative
influences
cloud-top
entrainment
enhancement.
also
find
that
suppression
enhancement
occur
at
different
intensities
during
day
night,
implying
daytime
studies
suffer
selection
bias.
This
methodology
guide
modelers
how
modify
parameterizations
setups
reconcile
conflicting
concerning
across
data
sources.
Abstract.
The
effective
radiative
forcing
due
to
aerosol-cloud
interactions
(ERFaci)
is
difficult
quantify,
leading
large
uncertainties
in
model
projections
of
historical
and
climate
sensitivity.
In
this
study,
satellite
observations
reanalysis
data
are
used
examine
the
low-level
cloud
responses
aerosols.
While
some
studies
it
assumed
that
activation
rate
droplet
number
concentration
(Nd)
response
variations
sulfate
aerosols
(SO4)
or
aerosol
index
(AI)
has
a
one-to-one
relationship
estimation
ERFaci,
we
find
assumption
be
incorrect,
demonstrate
explicitly
accounting
for
crucial
accurate
ERFaci
estimation.
This
corroborated
through
“perfect-model”
cross
validation
using
state-of-the-art
models,
which
compares
our
estimates
with
“true”
ERFaci.
Our
results
suggest
smaller
less
uncertain
value
global
than
previous
(-0.39
±
0.29
W
m-2
SO4
-0.24
0.18
AI,
90
%
confidence),
indicating
may
impactful
previously
thought.
also
consistent
observationally
constrained
total
feedback
“top-down”
models
weaker
better
match
observed
hemispheric
warming
asymmetry
over
period.
