Introduction
Aerosols represent the largest uncertainty in our estimates of current
anthropogenic forcing of climate , limiting our ability to
constrain the sensitivity of the current climate to radiative forcing.
Aerosols affect climate through direct effects of absorption or scattering,
and indirect effects by changing the number of cloud drops
and resulting complex microphysical interactions. Increased aerosol number
concentrations are associated with more cloud condensation nuclei (CCN)
, leading to higher cloud drop number
concentrations (Nc). The relationship between aerosols and CCN is
affected by a number of factors , including the aerosol
type and meteorological conditions. The result is a different population of
cloud droplets, depending on aerosol distribution and meteorology.
But that is only the beginning of aerosol effects on clouds. Cloud
microphysics (the interactions of a distribution of cloud drops at the
micro-meter scale) determines how much water precipitates, the amount of
water remaining in the cloud, and the resulting population of cloud drops. In
global modeling experiments, aerosol–cloud interactions (ACI) can be altered by the representation of
cloud microphysical processes (the “C” in ACI) while the aerosol processes
(“A”) remain largely unchanged. , ,
, and all looked at
changes to autoconversion, while looked at changes to
precipitation.
ACI are typically quantified by the change in cloud radiative effect
. ACI occur most readily with liquid sulfate aerosol
(H2SO4) derived from sulfur dioxide (SO2) assisting the formation of
cloud droplets, thus increasing cloud drop numbers. Higher drop numbers
affect cloud albedo , and potentially also affect cloud
lifetime and dynamics . Cloud lifetime and
dynamics effects are highly uncertain .
Recent work found large sensitivities
of ACI to uncertainty in natural emissions and thus pre-industrial aerosols:
the “A” in ACI. But these studies used fixed assumptions about how clouds
interact with aerosols, assuming aerosols translated into cloud drop numbers
based on fixed cloud dynamics and water content , largely
ignoring the “C” in ACI. The cloud microphysical state, defined as the
combination of cloud liquid water path and drop number, determines cloud
microphysical (precipitation rates) and radiative properties. As a result,
perturbations to this state from aerosols (ACI) may depend on the base
state;
i.e., the response of a cloud to a change in CCN may depend on the unperturbed
CCN and resulting drop number.
In this work we quantify the sensitivity of ACI to cloud microphysics with
detailed off-line tests and global sensitivity tests of ACI with a cloud
microphysics scheme. First, detailed off-line tests will isolate the
different components of ACI in a cloud microphysics scheme; off-line tests
will include exploration of lifetime effects and microphysical process rates.
Then global simulations will analyze the sensitivity of ACI to many different
aspects of cloud microphysics, including sensitivity to (1) activation,
(2) precipitation, (3) mixed phase processes, (4) autoconversion treatment,
(5) coupling to other parameterizations and (6) background aerosol emissions.
These processes have been highlighted in previous studies.
The methodology is described in Sect. . Detailed off-line
tests are in Sect. . Global results and sensitivity tests are in
Sect. , and conclusions are in Sect. .
Methods
The double-moment (mass and number predicting), bulk cloud microphysics
scheme described by (hereafter MG1) and
(hereafter MG2) is used for this study. The scheme
handles a variable number of droplets specified from an external activation
scheme . It can also run with a fixed droplet and
crystal number. The scheme is implemented both in an off-line idealized
kinematic driver (KiD) , as well as in a general circulation model (GCM) – the Community Earth System Model (CESM)
. The susceptibility of an earlier version of the
scheme to aerosols has been shown by to be similar to
detailed models with explicit bin microphysics that represent more accurately
the precipitation process .
Off-line tests
To isolate and test the microphysics we use a simple one-dimensional off-line
driver, the KiD with the same
microphysical parameterization as used in the global model. We use a 1 s
time step, 25 m vertical resolution and a 3 km vertical domain in KiD. In
the off-line implementation, specified drop numbers are assumed. Here we
focus only on warm rain cases. We use several different cases for analysis.
The basic case (warm 2 or W2) features multiple 2 ms-1 updrafts over
2 h . We have examined three other cases as
well, with notation following . These cases represent
some basic idealized clouds commonly used to evaluate cloud microphysical
processes such as condensation, precipitation and evaporation. Case 1 (W1) is
a single 2 ms-1 updraft that decays in time (1 h). Case 3 (W3)
features multiple updrafts that weaken over time. Case 7 (W7) has shallower
updrafts of maximum 0.5 ms-1 over 8 h. To assess the impact of
aerosols, experiments are conducted with variable drop number from
10 to 2000 cm-3. This spans the range from pristine to very polluted
conditions.
In off-line tests, we estimate first the cloud albedo, and then divide albedo
(A) changes into contributions from (1) liquid water path (LWP), (2) cloud
drop number concentration (Nc) and (3) cloud coverage (C). To
estimate albedo (A) we make the assumption that
A=C⋅τ/(β+τ),
where β=6.8, τ=α⋅LWP5/6⋅Nc1/3 and α=0.19 Eqs. 19–20.
Strictly speaking the albedo should include a surface reflectance term, which
over ocean would be (1-C)⋅Asfc, where for ocean
Asfc=0.05. For these idealized cases we assume
Asfc=0. The change in albedo (dA) can then be represented as
ΔA=dAdNcΔNc+dAdLWPΔLWP+dAdCΔC+r.
C (cloud cover or cloud fraction) has one value for each simulation.
Nc has one specified value for each simulation and LWP is an
average over the simulation period. r is a residual. The changes are
discrete differences between simulations with different specified
Nc for each case.
Description of sensitivity tests used in the text, including the
case short name (including the microphysics scheme used), a brief
description, and the type of experiment. All tests are pairs of simulations
as described in the text.
Case
Description
Type
MG2
Base case
MG2-2000-1750
ACI with no human emissions
Emissions
MG2-1850-1750
Pre-industrial vs. no human emissions
Emissions
MG2-Nat0.5
MG2 with natural aerosol emissions × 0.5
Emissions
MG2-Nat2
MG2 with natural aerosol emissions × 2
Emissions
MG1
Base case cloud microphysics
Activation
MG1-Hoose
New mixed phase ice nucleation
Mixed phase
MG2-Berg0.1
MG2 with vapor deposition rate × 0.1
Mixed phase
MG1.5
MG1 + different activation, MG2 tuning
Prog precipitation
MG2-NoER
MG2 without evaporation of rain number
Prog precipitation
MG2-CLUBB
New moist turbulence scheme
Coupling
MG2-NoLif
MG2 with lifetime effects removed
Lifetime
MG2-K2013
MG2 with K2013 autoconversion
Autoconversion
MG2-SB2001
MG2 with SB2001 autoconversion
Autoconversion
The idealized one-dimensional kinematic driver is designed to test different
microphysical schemes in the same framework. Results of such idealized
off-line tests are qualitatively useful for examining the relative importance
of individual processes for ACI. We use them for illustration, and will use
global sensitivity tests of the full GCM for quantification.
Global sensitivity tests
The MG2 scheme is implemented in version 5 of the Community Atmosphere Model
(CAM5.3; ) as described by . The MG2
scheme in CAM is coupled to aerosol activation on liquid
and ice hydrometeors, and can also
take specified number concentrations for liquid and ice. CAM5 features a
modal aerosol model . The MG scheme has prognostic drop
number with no minimum drop number.
For the global model, we run simulations with specified climatological sea
surface temperatures (SSTs) and greenhouse gases representing year 2000
conditions. We then vary aerosol emissions in two simulations for the year
2000 and 1850; differences represent only the effects of aerosol emissions.
1850 refers only to the aerosol emissions greenhouse gases and SSTs
remain at year 2000 conditions. Simulations are 1.9∘ latitude by
2.5∘ longitude horizontal resolution, they are 6 years long,
and the last 5 years are analyzed and are similar to previous work
. Sensitivity tests are described below.
To understand the uncertainty in using 5 years of simulation, we performed an
uncertainty analysis. This consisted of running the MG2 experiment out for 20
years (for 2000 and 1850 conditions). Analysis of separate 5-year periods
indicates uncertainty of 0.08 Wm-2 for ACI and long-wave (LW)/shortwave (SW) components (about
10 %) and within 0.04 Wm-2 for direct effects relative to 20-year
means. We also performed nudged experiments where winds or winds and
temperatures were fixed to a previous CAM simulation, but these produced
slightly different cloud radiative effects, and thus slightly different
quantitative values for ACI (different by 20–40 %). Qualitative patterns
and zonal mean structure of ACI are similar to the free-running experiments.
In global simulations, ACI can be defined as the change in cloud radiative
effects (CRE) in the LW and SW, where CRE are equal to
the all sky top-of-atmosphere (TOA) radiative flux minus an estimate of what
the clear-sky flux would be without clouds, but with the same state
(temperature, humidity and surface structure). CRE are adjusted following
to use the “clean-sky” effects based on TOA fluxes
estimated with a diagnostic call to the radiation code without aerosols.
Results are similar, but with a slightly higher magnitude, to a direct
estimate of ACI using CRE. Direct absorption and scattering by aerosols is
also estimated by differencing the TOA radiative fluxes to TOA fluxes
estimated with a diagnostic call to the radiation code without aerosols.
Table describes the different sensitivity tests. As noted
below, tests are motivated by previous studies identifying microphysical
sensitivities. All tests are pairs of simulations with emissions of aerosols
set to 2000 and 1850, except for the MG2-2000-1750 and MG2-1850-1750, which
use different emissions years to explore different magnitudes of emissions
changes. To explore how linear the changes in emissions are, we look at
emissions without any human influence (no biomass burning, domestic or
industrial emissions) and term this 1750. We also explore modifying
background natural emissions in both 1850 and 2000 by a factor of 0.5 or 2.
These experiments test the impact of emissions , not cloud
microphysics.
Tests also track the evolution of the cloud microphysics in CAM from MG1
to MG2 . MG1.5 is an interim
version that has (a) changes to the location where activated numbers are
applied to before estimation of microphysical processes (which thickens the
stratiform clouds) and (b) compensating increases in the threshold relative
humidity for cloud formation to thin clouds back to radiative balance. The
difference between MG1 and MG1.5 tests the changes to the activation scheme.
The impact of prognostic precipitation is tested by the differences between
MG2 and MG1.5.
Warm 2 (W2) off-line tests of (a) time-averaged cloud mass
(g kg-1), (b) time series of surface rain rate (mm h-1),
(c) time-averaged rain
mass (g kg-1), (d) time series of albedo, time-averaged
(e) autoconversion and (f) accretion rates (kg kg-1 s-1).
Different colors correspond to different fixed cloud drop number concentrations.
Two experiments test sensitivity to mixed phase cloud processes. MG1-Hoose
contains a representation of mixed phase ice nucleation that is tied to
aerosols , instead of the temperature-dependent scheme in
MG1 . This change tests the mixed phase ice scheme. The
MG2-Berg0.1 simulations reduce the efficiency of the vapor deposition process
by a factor of 10. This sensitivity test is motivated by the work of
and , who suggested that due to updraft
rates in clouds at least half the time the vapor deposition rate may not
apply. It is also motivated by tests in extending this to a
large-scale model that would also assume inhomogeneity in a grid box, and
found improvements in Antarctic radiative fluxes.
Perturbations to the MG2 microphysics itself are also explored by first,
removing evaporation of rain number (MG2-NoER) present in MG2 but not MG1,
and then removing lifetime effects by fixing cloud drop numbers in
autoconversion, sedimentation and freezing (MG2-NoLif). A fixed number of
100 cm-3 for liquid drops and 0.1 cm-3 for ice crystals is used.
An additional simulation with 300 cm-3 for liquid drops yields
quantitatively and qualitatively similar results. A simulation is performed
changing the moist turbulence scheme and coupling to cloud microphysics using
a higher-order closure scheme called Cloud Layers Unified By
Binormals
(CLUBB; ) in MG2-CLUBB . As
noted in the introduction, several previous studies have focused on
sensitivity of ACI to the autoconversion process. Accordingly, we alter the
autoconversion scheme in the simulations MG2-K2013 and
MG2-SB2001 .
These tests and the parameter values are motivated by previous work.
conducted a perturbed parameter ensembles with a similar
version of CESM and focused on radiative effects. However,
and other perturbed parameter ensembles have not focused on the radiative
perturbations due to aerosols, and here the experiments are all pairs of
simulations with pre-industrial and present-day aerosols.
Results: off-line tests
Figure illustrates basic results from the off-line experiments
with different specified drop numbers. As drop number increases, average
cloud condensate mass increases (Fig. a) and the surface rain
rate (Fig. b) and rain mass (Fig. c) drop rapidly
to zero for Nc>500 cm-3. The cloud albedo (estimated
using Eq. ) increases substantially (Fig. d) for
increasing drop number. The mechanism for the microphysical changes as
described by is the decrease in the autoconversion rate
with increasing drop number (Fig. e), which also causes
decreases in accretion rate as the rain mass decreases (Fig. f).
The W2 case initiates two separate layers of cloud in subsequent updrafts
after the first. There is larger autoconversion and accretion in the lower
layer, creating the peaks in cloud mass (Fig. a), rain mass
(Fig. c), autoconversion (Fig. e) and accretion
(Fig. f). Autoconversion and accretion are not increasing at the
bottom of the cloud. Instead, this is a different layer of cloud not seen as
separate in the time average.
The impact of these changes on albedo is highlighted in Fig. .
The albedo increases with higher drop numbers (Fig. d). This
actually changes the slope of the relationship between albedo and liquid water path (LWP), seen in Fig. a. At low liquid water paths,
the albedo changes are more sensitive to LWP. In Fig. a, the
slope (dA / dLWP) is constant at low LWP, but shifts to reduced
sensitivity at high LWP. Using the decomposition of the albedo change in
Eq. (), we can break down the change between pairs of
simulations (e.g., Nc=20 to Nc=10) by the different
components: the total change in albedo (Tot), the change due to LWP
(dA/dLWP×ΔLWP), the change due to changes in
Nc (dA/dNc×ΔNc) and the change due to cloud cover changes (dA/dC×ΔC). Differences are calculated based on the difference in time-averaged albedo between two simulations. The residual is the difference
between the total and the sum of the three terms, which is small. In the W2 case
with an oscillating updraft, the change in cloud coverage dominates the
albedo change (Fig. b). Note that cloud mass
(Fig. a) is changing along with cloud coverage
(Fig. d). Most of the difference in Fig. a
(cloud mass) is change to the extent of clouds with the same in-cloud water
content; hence for this case, the coverage is identified as being critical.
(a) Liquid water path (LWP) vs. albedo and (b) albedo
change by different sensitivity (dA) terms from the oscillating warm rain
case (W2). Different colors correspond to different fixed cloud drop number
concentrations. The albedo terms in (b) correspond to
the total (Tot) change and the portion due to LWP,
number concentration (Nc), cloud coverage (CC) and a residual (Res).
Figure illustrates the same set of albedo sensitivity terms
for four different cases. The mean and 1 standard deviation of pairs of
adjacent drop numbers (seven pairs from eight values of drop number) is
indicated by the error bar range and midpoint, and the median is shown as a
diamond. The W2 case from Fig. b is illustrated in
Fig. b (black line), where cloud coverage dominates the change
in albedo. Some cases have mostly small differentials for the terms, and only
some values of Nc have large differentials, so the median is
often near zero but the average (dominated by 1–2 cases) is non-zero. The
base case (black) is the basic case using the autoconversion scheme of
, hereafter KK2000. KK2000 represent autoconversion from a fit to
cloud-resolving model experiments as a function of the cloud mass and an
inverse function of drop number; the autoconversion rate (Au) is Au=1350qc2.47Nc-1.79. This is also true for W1
(Fig. a), with lower sensitivity. However, the LWP and
Nc changes are important in the W3 and W7 cases
(Fig. c and d). These are weaker multiple updraft cases.
Albedo change by different sensitivity (dA) terms from
different warm rain cases. (a) Warm 1, (b) warm 2, (c) warm 3 and (d) warm 7.
Albedo terms in each panel correspond to the total (Tot) change and the portion
due to liquid water path (LWP), number concentration (Nc),
cloud coverage (CC) and a residual (Res); the standard case (Base) is in black.
Also shown are the no lifetime effects case (dark blue) and the two
components of the lifetime effect: sedimentation (cyan) and autoconversion (green).
Warm 1 (W1) single updraft case results with cloud drop number concentration of 200 cm-3 for
(a) cloud liquid mass (contour interval 0.2 g kg-1), (b) surface precipitation rate,
(c) warm rain mass
(contour interval 0.05 g kg-1) and (d) rain number
(contour interval 1.5×104 kg-1),
(e) autoconversion rate (contour interval 4×10-9 kg-1) and
(f) accretion rate (contour interval 3×10-9 kg-1) from
MG2 with KK2000 (black) and SB2001 (red).
Also shown in Fig. are three additional sets of experiments
where the microphysics has been modified to limit the lifetime effects. This
has been done by specifying a constant fixed drop number of 100 cm-3 to
(a) the autoconversion scheme (Au), and (b) the sedimentation (Sed) or both
(NoLif). Different drop numbers ranging from 10 to 2000 cm-3 are used
for all other processes in the microphysics. The NoLif cases (dark blue in
Fig. ) are similar to the Au cases (green: autoconversion
effects only) indicating that autoconversion is the dominant process for
lifetime effects. In particular, removing the lifetime effects by specifying
the number concentration going into autoconversion removes the cloud coverage
effects in the W2 case (Fig. b), and perhaps more
significantly removes the LWP effects on albedo in all cases. This leaves
only the drop number effects on albedo. Thus, for some cases with partial
cloud cover (e.g., like the W2 case in Fig. b), lifetime
effects are important for cloud cover changes, but in all cases the effect of
autoconversion in drop number seems to impact LWP.
Recognizing that the representation of autoconversion is important, we
explore two alternatives. , hereafter K2013, use a similar
representation as KK2000 and derive Au=7.90×1010qc4.22Nc-3.01. , hereafter SB2001, derive
expressions for autoconversion and accretion that include the rain water
mixing ratio as a proxy for large cloud droplets to describe the broadening
of the drop size distribution and reduce the efficiency of accretion in the
early stage of the rain formation. We have implemented both of these
parameterizations into the microphysics scheme.
Figure shows the impact of the SB2001 scheme in the single
updraft W1 case with a fixed drop number of 200 cm-3. Relative to KK2000
(black), the use of SB2001 (red) for autoconversion results in higher cloud
mass (Fig. a), significantly less precipitation
(Fig. b) and delayed and smaller rain formation
(Fig. c) and rain number concentration
(Fig. d). Autoconversion (Fig. e) is
delayed, but has a higher magnitude, and accretion is also delayed
(Fig. f), but has a lower magnitude; the changes are
significant. At lower number concentrations the differences are smaller, and
they are larger at higher number concentrations (not shown).
The impact of these changes on the albedo changes in the off-line driver
cases is illustrated in Fig. . KK2000 are the same as the
base case in Fig. . Results are similar with different
autoconversion schemes in case W1 (single updraft: Fig. a) and
case W7 (shallow updraft: Fig. d). In case W2, there is a
significant reduction in the cloud coverage and LWP effects on albedo with
SB2001 (Fig. b); furthermore, there is a significant reduction in the
LWP effect in case W3 for SB2001 and K2013 (Fig. c), which is
compensated for in-cloud cover changes. Autoconversion matters in the cases
with multiple updrafts where cloud coverage is most sensitive (W2 and W3),
and it matters more for the oscillating (W2) than decaying (W3) or weak (W7)
updraft case. This is likely because with a limited temporal updraft the
timing of precipitation matters.
As for Fig. but for standard case (KK2000) in
black and two other autoconversion schemes: SB2001 (blue) and K2013 (red);
see text for details.
Results: global sensitivity tests
Global sensitivity tests with CESM explore how different perturbations to
cloud microphysics impact ACI. All tests are pairs of simulations with
emissions of aerosols set to 2000 and 1850, except for the 2000–1750 and
1850–1750 cases, which use different emissions years to explore different
magnitudes of emissions changes. The experiments described in
Sect. and Table fall into several
categories chosen to span key sensitivities in different microphysical
processes. These are based on a number of previous studies that have
identified these different processes as critical for the interaction of
aerosols with clouds. These studies are highlighted below. The different
processes include (1) aerosol activation (MG1) ,
(2) precipitation (MG1.5, NoER: evaporation of rain)
, (3) mixed phase (Berg0.1: vapor deposition and
Hoose: ice nucleation) , (4) autoconversion
(lifetime effects and two other autoconversion schemes: K2013, SB2001)
, (5) coupling to other schemes (CLUBB)
and (6) natural emissions (Nat 0.5 and Nat2) .
In particular, the range of natural aerosol emissions is identical to the
range in .
The radiative changes between the pairs of simulations in each sensitivity
experiment are indicated in Table . ACI use clean-sky CRE as
discussed by . Differences in microphysical quantities are in
Table . For Table and the figures,
simulated cloud-top liquid microphysical values are estimated by taking the
highest level (first from the top of the model going down) where cloud
condensate is found. This is done at each point in the model and averaged
over those points which are non-zero. The values and figures in the text come
from these simulations. The net CRE for all the simulations
(Table ) is broadly similar, within about ±1 Wm-2,
except for the MG2-CLUBB simulation, which has a different balance of CRE,
drop number and effective radius (Table ).
Radiative impacts of ACI for the different sensitivity tests. Change
in top-of-atmosphere (TOA) flux (ΔR), ACI as change in clean-sky cloud radiative effect (ΔCRE), and its long-wave (LW) and shortwave (SW)
components following . Direct effects (DE) of aerosols as
described in the text. Finally, a residual
(Res = ACI + DE - ΔR).
Case
ΔR
ACI
ΔLW CRE
ΔSW CRE
DE
Res
Wm-2
Wm-2
Wm-2
Wm-2
Wm-2
Wm-2
MG1
-1.59
-1.57
0.44
-2.01
-0.06
0.05
MG1-Hoose
-1.61
-1.51
0.81
-2.32
-0.05
-0.04
MG1.5
-1.22
-1.13
0.23
-1.36
-0.07
-0.02
MG2
-1.08
-0.98
0.15
-1.14
-0.07
-0.03
MG2-2000-1750
-1.29
-1.23
0.21
-1.44
-0.08
0.01
MG2-1850-1750
-0.21
-0.25
0.06
-0.30
-0.01
0.04
MG2-Nat0.5
-1.46
-1.24
0.21
-1.44
-0.11
-0.12
MG2-Nat2
-0.87
-0.68
0.18
-0.86
0.09
-0.28
MG2-CLUBB
-1.43
-1.56
-0.05
-1.50
-0.02
0.14
MG2-NoLif
-0.78
-0.72
0.36
-1.08
-0.08
0.02
MG2-K2013
-1.21
-1.11
0.21
-1.32
-0.08
-0.01
MG2-SB2001
-0.70
-0.77
0.46
-1.23
-0.05
0.12
MG2-NoER
-1.19
-1.11
0.29
-1.39
-0.08
0.00
MG2-Berg0.1
-1.53
-1.41
0.26
-1.67
-0.06
-0.06
The radiative changes between the pairs of simulations in each sensitivity
experiment are indicated in Fig. a. ACI are defined as the change
in clean-sky cloud radiative effect (ΔCRE) between pairs of
simulations with different aerosol emissions . Directly using
CRE yields similar quantitative (%) differences between simulations. ACI for
2000–1850 emissions are -1.57 Wm-2 with MG1, -1.13 Wm-2 with
MG1.5 and -0.98 Wm-2 with MG2 (Table ). Maximum ACI
are
found in Northern Hemisphere midlatitudes (Fig. a), where most
anthropogenic emissions occur. Northern Hemisphere midlatitudes are also where the
largest changes to LWP (Fig. b) and cloud-top drop number
concentration (ΔNc, Fig. c) occur.
Interestingly the changes to cloud-top drop effective radius (ΔRe,
Fig. d) spread farther into high latitudes.
Zonal mean (a) ACI (change in CRE, Wm-2),
(b) percent change in LWP, (c) change in cloud-top drop number concentration
(ΔNc, cm-3) and (d) change in cloud-top effective radius
(ΔRe, m-6) for different sensitivity tests noted with colors and different line styles.
Most of the ACI are due to the shortwave (SW: solar) wavelengths:
brighter clouds (Table ). However, there is a significant
component of positive ACI in the long-wave (LW: terrestrial) wavelengths. This
is a result of two factors. First is the effect of aerosols on cirrus clouds,
where more ice nuclei are formed, and clouds become more opaque in the
long wave than they become brighter in the shortwave .
Second is a compensation effect between LW and SW for cirrus clouds due to
movement of cirrus cloud fraction in the tropics. The second effect accounts
for a good amount of the variance in the magnitude of the LW and SW between
sensitivity tests: increases in LW CRE are compensated for by decreases in
(increased magnitude of negative) SW CRE.
To understand how ACI change with cloud microphysics, we explore how the
radiative effects of ACI are related to microphysical properties strongly
related to radiative effects. Figure illustrates some of the
broad-scale patterns across the simulations, by relating the changes in cloud
radiative effect (ACI = ΔCRE) to other properties of the
simulations, namely, changes to LWP (in percent, Fig. a),
changes to the cloud-top drop number concentration (Fig. b),
changes to cloud-top effective radius (Fig. c) or changes in
total (vertically integrated) cloud coverage or fraction
(Fig. d). There is a strong correlation between ΔLWP and
ACI (Fig. a). The only simulations that differ from the
correlation are those with CLUBB and the simulation without lifetime effects
(NoLif). The CLUBB simulation has a very different coupling of large-scale
condensation and cloud microphysics, as described by ,
where the microphysics is sub-stepped with the CLUBB condensation scheme six
times in each time step. The NoLif simulation has basically no change in LWP,
which is consistent with the off-line KiD tests with a similar formation. The
ACI go from -0.98 (MG2) to -0.72 Wm-2 (NoLif) in
Table . There is no correlation between the change in
cloud-top drop number (Fig. b) or effective radius
(Fig. c) and ACI. Changes in effective radius are negative,
indicating smaller drops in the present with more aerosols than in the past
(pre-industrial). There are small changes in total cloud cover that correlate
slightly with ACI (Fig. d), but mostly because there are large
changes (increases in cloud coverage) in three simulations with large ACI
(CLUBB, MG1, MG1-Hoose).
The simulation without lifetime effects (NoLif) actually has the largest
change (reduction) in averaged drop radius (Fig. c), despite no
change in LWP (Fig. a) and small changes in ACI. Most
simulations have an increase in cloud drop number of ∼ 30 cm-3.
This is an interesting result because many models still prescribe the
radiative effects of aerosols by linking aerosol mass to a change in cloud
drop number or size; on the contrary, in CAM the clearest effects seem to be due to
LWP, though ACI are non-zero even if ΔLWP = 0.
ACI (change in CRE, Wm-2) vs. (a) percent change in LWP, (b) change in cloud-top drop number concentration
(ΔNc, cm-3), (c) change in cloud-top effective radius (ΔREL, m-6 or microns) and
(d) change in total cloud coverage (CTOT, %) for different sensitivity tests.
Red colors are tests of different emissions, blue is the base MG2 case and
black is the other sensitivity tests.
Microphysical impact of different sensitivity tests. Mean CRE,
change in LWP (%), mean LWP, change in cloud-top (CT) effective radius
(REL), mean CT REL, change in CT drop number concentration (Nc) and mean CT
Nc.
Case
CRE
ΔLWP
LWP
ΔREL (CT)
REL (CT)
ΔNc (CT)
Nc (CT)
Wm-2
%
g m-2
m-6
m-6
cm-3
cm-3
MG1
-28.0
7.5
44.6
-0.4
9.5
22.5
89.0
MG1-Hoose
-28.4
8.6
46.2
-0.5
9.5
22.2
88.6
MG1.5
-29.8
7.4
45.0
-0.6
8.8
29.4
110.6
MG2
-27.9
5.5
39.4
-0.6
9.0
28.9
107.2
MG2-2000-1750
-27.9
7.7
39.4
-0.8
9.0
35.0
107.2
MG2-1850-1750
-27.2
2.3
37.2
-0.2
9.6
6.1
78.3
MG2-Nat0.5
-27.6
6.5
38.2
-0.8
9.2
30.9
98.6
MG2-Nat2
-28.4
3.3
40.3
-0.5
8.6
26.0
119.8
MG2-CLUBB
-25.6
4.8
40.1
-0.7
11.3
12.4
59.1
MG2-NoLif
-28.7
-0.6
47.7
-0.9
9.4
27.5
107.9
MG2-K2013
-27.8
7.0
37.6
-0.6
8.9
28.6
107.4
MG2-SB2001
-28.3
3.6
44.6
-0.7
9.2
27.9
109.2
MG2-NoER
-28.2
5.9
39.9
-0.6
9.0
28.6
106.9
MG2-Berg0.1
-28.7
7.2
44.0
-0.6
8.8
27.1
101.7
The following sub-sections detail each of the dimensions of changes to
understand the magnitude of the effects.
Activation
The change to drop activation (moving it before the cloud microphysical
process rates) is seen in the difference between MG1 vs. MG1.5. This is a
substantial reduction in ACI from -1.57 to -1.13 Wm-2 or 28 %
(Table ). The likely cause is that by activating the number first,
other processes in the microphysics act on the revised number, and this
likely buffers the changes in the indirect effects .
Note that there is basically no difference in the LWP change between MG1 and
MG1.5 (Table ). Effects are not simply linear, however, since
MG1.5 with lower ACI has a larger ΔREL and ΔNc.
Prognostic precipitation and rain evaporation
The major scientific changes between MG1.5 (MG1 with the activation change)
and MG2 as described by are the addition of prognostic
precipitation and the addition of evaporation of number when rain
evaporates. The latter change to evaporation of rain number actually does
seem to make a difference: a reduction in ACI (Table ) due
to a reduction in ΔLWP (Table ). The total reduction
between MG1.5 and MG2 is -1.13 to -0.98 Wm-2, or about 14 %. This
occurs through reductions in the ΔLWP (Table ),
especially between 10 and 60∘ N latitude (Fig. b).
ACI as the total change in the top-of-atmosphere clean-sky cloud radiative effect
(CRE) between simulations with 2000 and 1850
aerosol emissions for (a) base (MG2) and (b) no lifetime effect (NoLif) cases.
Mixed phase clouds
Two different sets of experiments were conducted to look at the impact of
altering mixed phase clouds. The changes in MG1-Hoose make the simulations
sensitive to anthropogenic aerosols in the mixed phase regime where they were
not before. This causes increases in the magnitude of the LW and SW
components of ACI (Table ), but a small change in the net
ACI
(-4 %). The sensitivity of LWP goes up (ΔLWP:
Table ). This experiment has the largest LW ACI, which is
expected since it adds ACI in the mixed phase cloud regime between 0 and
-20 ∘C, which will have a significant effect on the LW radiation.
The second experiment used the MG2 configuration to reduce the efficiency of
the vapor deposition onto ice (Bergeron–Findeisen process) by a factor of 10.
This simulates inhomogeneity in cloud liquid and ice (or effectively
inhomogeneity for in-cloud supersaturation or vertical velocity) that does
not effectively mix liquid and ice. noted uncertainties of
at least a factor of 2 in vapor deposition rates based on small-scale cloud
dynamics, and found better agreement with Antarctic mixed
phase clouds when vapor deposition was reduced by a factor of 100. We picked
a value between these limits for a sensitivity test. The reduction of vapor
deposition increases the mean LWP and slightly decreases ΔLWP
(Table ). The stronger long-wave and shortwave components
with more liquid likely lead to an increased magnitude in ACI
(Table ) of +45 %, but the exact mechanism is unclear.
Autoconversion and lifetime effects
As in Sect. , we can also explore the sensitivity of the
microphysics to autoconversion scheme. noted that the
description of autoconversion and accretion matters for ACI, consistent with
a series of previous studies
. One of the reasons for
lower ACI in MG2 is due to the reduction of the ratio of autoconversion to
accretion (more accretion and less autoconversion) with prognostic rain in
MG2 .
Here we explore the impact of different autoconversion schemes on ACI. The
K2013 scheme actually slightly increases the ACI over MG2 with KK2000
(Table ), again consistent with an increase in ΔLWP
(Table ). Conversely, the SB2001 scheme, with a smaller
ΔLWP, reduces ACI from -0.98 to -0.77 Wm-2, or 22 %, and
the NoLif simulation reduces ACI to -0.72 Wm-2 (nearly
-30 %) largely through more compensation between LW and SW effects; i.e.,
larger LW effects, indicating clouds with cold cloud tops may have higher LW
emissivity. This indicates that the lifetime effects themselves may approach
one-third of ACI (the total change in radiative flux changes from -1.04 in MG2
to -0.78 in the NoLif simulation, a reduction of 33 %). The lifetime
effects are not that sensitive to the drop number threshold chosen. Results
of a NoLif simulation with 300 cm-3 rather than 100 cm-3 for
liquid drops yield similar results for ΔR or ACI.
The regional pattern of ACI, based on the total change in top-of-atmosphere
fluxes, is illustrated in Fig. for (a) the base MG2 case and (b)
the NoLif case. The average local standard deviation of annual TOA flux
is about 3 Wm-2, so Fig. shows regions with differences
larger than 1 standard deviation. ACI effects are mostly in the Northern
Hemisphere, and mostly over the oceans. There are some tropical effects in Southeast Asia and off the equatorial eastern Pacific, the latter due to anthropogenic
emissions over the Amazon. The removal of lifetime effects in
Fig. b indicates they are strong over the Northern Hemisphere
midlatitude storm tracks, especially in the North Pacific. Lifetime effects
also are strong in the equatorial eastern Pacific. Lifetime effects do not seem to
impact the stratocumulus region off the coast of California, which has strong
ACI without lifetime effects.
60∘ S to 60∘ N latitude (a) ratio of accretion to autoconversion, (b) autoconversion rate and
(c) accretion rate using KK2000 (red), SB2001 (dark blue), K2013 (cyan) autoconversion and the no lifetime (NoLif)
simulation (green).
Estimates derived from observations from the VOCALS experiment shown as black crosses (see text for details).
The effect of autoconversion and accretion is illustrated in
Fig. . Figure shows autoconversion and accretion
rates and their ratio as a function of LWP. The figure compares results to
estimates based on observations from the VOCALS campaign in the southeastern Pacific
(see the corrigendum to for more details). Note that
the rates are estimated from using observations to approximate the results of
the stochastic collection equation, and may not be exactly comparable to the
model simulations. The slope of the curves with LWP is probably the most
relevant comparison. The figure represents 60∘ S–60∘ N
averages for all liquid clouds treated by the stratiform cloud scheme, so it
does not include convective clouds. A similar figure for just the
southeastern
Pacific region yields similar results, but not as good statistics.
Accretion rates (Ac) are well represented in MG2 with the KK2000
autoconversion (Fig. c), but autoconversion rates (Au) at low
LWP are very large (Fig. b), leading to a low Ac / Au ratio
(Fig. ). With the SB2001 scheme, accretion is high at low LWP,
and autoconversion is 2 orders of magnitude lower. Autoconversion in
particular is much closer to estimates from VOCALS . The
result is a higher Ac / Au ratio, which may be too high at low LWP. The
K2013 scheme (cyan in Fig. ) yields similar results to KK2000:
autoconversion is almost the same, and accretion is a little bit higher. The
no lifetime simulation (green in Fig. ) has accretion rates
similar to KK2000, but lower autoconversion rates due to fixing the drop
number in the autoconversion scheme. The no lifetime simulation has perhaps
the closest representation to the Ac / Au ratio (Fig. a).
Coupling to other schemes
We can also examine the effect of coupling of the microphysics to other cloud
schemes in the model. The CLUBB simulation uses a different unified
higher-order closure scheme to replace the CAM large-scale condensation,
shallow convection and boundary layer scheme, as described by
. It uses MG2 with a different sub-stepping strategy of
5 min time steps, called six times per model time step.
Notably, CLUBB provides a unified condensation scheme for the boundary layer,
stratiform and shallow convection regimes, so that ACI are included in
shallow cumulus regimes in this formulation. This results in a substantial
increase in ACI from -0.98 to -1.56 Wm-2 (just over 50 %). The
change in LWP (ΔLWP) is moderate (Fig. b), and less than
would be expected based on the ACI (Fig. a). CLUBB has a lower
change in cloud-top drop number (Figs. c and b),
but a large increase in cloud coverage (Fig. d), which likely
is contributing to ACI. The increase appears to be occurring in the
sub-tropics of the Northern and Southern hemispheres (Fig. a) mostly from
20 to 40∘ N over the Pacific and Atlantic (not shown). The increase in
ACI over the sub-tropical North Pacific and Atlantic is consistent with ACI
being added in shallow cumulus regimes. Further future exploration of the
impacts of this change is warranted but is beyond the scope of this work.
Also notable is that CLUBB simulations have a decrease in the positive LW
ACI. This occurs in the tropics, and may be related to changes in transport
of water vapor into the upper troposphere, reducing high cloudiness and any
positive ACI associated with high (cirrus) clouds. These changes may also be
due to differences in how CLUBB treats aerosols and aerosol scavenging in
these simulations: it appears that the change in aerosol optical depth (AOD) is larger in CLUBB than
in other simulations, perhaps due to different treatment of aerosol
scavenging and transport in CLUBB. Thus, a very different physical
parameterization suite with the same microphysical process rates can lead to
very different ACI.
Background emissions
Finally, we explore the impact of background emissions on ACI. For these
experiments no changes to the model are made. The experiments here all use
the MG2 code. The only changes are to the emissions files. First, we just
explore what happens with different baselines: a larger period (2000–1750)
or a smaller period (1850–1750) than the basic 2000–1850 (MG2). As noted,
for 1750 emissions, we remove all human sources from the 1850 emissions.
So this is really a no anthropogenic emissions case.
Figure a illustrates that the 2000–1750, MG2 and 1850–1750
changes are fairly linear, with LWP changing about 1 % per
-0.1 Wm-2 change in ACI. The changes are also somewhat linear for
changes to cloud-top drop number (Fig. b) and effective radius
(Fig. c). Larger changes occur with higher emissions
differences. This is not true for cloud coverage changes however
(Fig. d), where MG2 (2000–1850) and 2000–1750 have about the
same decreases in cloud coverage, while there is no change for 1850–1750.
found ±20 % effects on ACI from the assumed level of
background emissions. Similar to we conducted experiments
by halving (Nat0.5) or doubling (Nat2) the natural emissions of aerosols
from dust, volcanoes, ocean dimethylsulfide (DMS) and natural organic carbon
(terpenes and other biological aerosols). This was done for both
pre-industrial and present emissions. Halving natural emissions makes the
model more sensitive to anthropogenic aerosols (-1.13 to -1.24 Wm-2
ACI in Table , a 27 % increase), whereas doubling emissions decreases
the sensitivity significantly (-0.98 to -0.68 Wm-2 ACI in
Table , a 30 % decrease). The total change in TOA flux (dR)
ranges from -1.46 (+34 %) to -0.87 Wm-2 (-17 %) in
Table . There is little change in LW ACI. Thus we can conclude
that the background natural aerosols are important for determining the total
ACI.
The variation in natural emissions alters present-day AOD. Global mean AOD for Nat2, baseline (Nat1) and Nat0.5 is 0.175,
0.137 and 0.117, respectively, with most of the difference caused by the
imposed change to the efficiency of dust production and the dust AOD of
0.042, 0.024 and 0.013, respectively, for natural emissions scaling of 2, 1 and
0.5. This highlights and confirms the need to better constrain background
aerosols identified by .
Summary of sensitivity tests
The sensitivity of ACI in the global model in terms of the percent change in
ACI (ΔCRE) is illustrated in Fig. . Different categories
correspond to groups of sensitivity tests noted above. The autoconversion
scheme is particularly important, also manifested through lifetime
effects (Fig. ) that change the overall mean LWP in
simulations. The SB2001 parameterization that reduces autoconversion at low
LWP reduces ACI, and also reproduces estimates of autoconversion rates better
(Fig. ). Different autoconversion parameterizations can change
ACI by 35 %, and lifetime effects in CESM account for about one-third of total
ACI. The use of prognostic precipitation, and the evaporation of rain also
affect ACI, largely through a similar mechanism of changing the balance
between accretion and autoconversion: with more accretion using prognostic
rain.
Changes to the mixed phase of clouds, in particular a reduction of the rate
of vapor deposition (Berg0.1) to account for sub-grid inhomogeneity, result
in an increase in the sensitivity of ACI to LWP. Reducing vapor deposition in
the mixed phase increases the occurrence of liquid over ice. Liquid has a
longer lifetime (and hence larger average shortwave radiative effect), and
liquid clouds are more readily affected by sulfate aerosols than ice clouds
are (only homogeneous freezing is affected by sulfate). The change to mixed
phase ice nucleation (Hoose) has little impact on the net ACI, but a big
impact on the LW. LW and SW effects for colder clouds tend to nearly cancel,
with a slightly positive residual (similar to the net cloud forcing for cold
clouds), so the LW does not have a strong effect typically on the net ACI in
the sensitivity tests, but it does show that changes to colder clouds that
effect the LW may increase the gross magnitude of ACI.
Coupling of the microphysics to different turbulence closures and adding the
treatment of ACI in shallow convection (CLUBB) alters ACI by over 50 %
(Fig. ). ACI in deep convection are still not treated, and this
may also be important for ACI .
Changing activation to allow all processes to see revised number
concentrations lowers ACI by 25 % (MG1 vs. MG1.5), likely due to buffering
of the change to activation by other processes in the microphysics.
Percent change in ACI for different dimensions of sensitivity tests as described in the text.
These microphysical effects are larger than aerosol processes or emissions
uncertainties (the “A” in ACI). Natural (or background) emissions can alter
the ACI significantly with the same cloud microphysics code, seen in the
emissions bar in Fig. , with variability from -30 to +30 %,
consistent with previous work , indicating ±20 %
sensitivity of ACI to similar perturbations of natural emissions.
also noted ACI sensitivity of ±10 % to aerosol
processes, much smaller than the sensitivity to microphysical processes noted
here.
Discussion/conclusions
Results of idealized and global model tests of a cloud microphysics scheme
indicate strong sensitivity of ACI, the radiative response of clouds to
aerosol perturbations, to cloud microphysics. Idealized experiments
illustrate the different dimensions of aerosol–cloud interactions, and how
different cloud regimes may be affected in different ways by idealized
aerosol perturbations. The idealized tests show that the representation of
the autoconversion process is critical for cloud microphysical response to
different drop numbers. These tests are consistent with and help motivate
global sensitivity tests.
The sensitivity of ACI to the cloud microphysics with MG2 is -30 to
+55 %, larger than the effects of background emissions (-30 to +30 %).
Better representations of cloud microphysical processes (the “C” in ACI)
are critical for representing the total forcing from changes in aerosols.
These effects are stronger than uncertainties in aerosol emissions or
processes. These sensitivity tests are not exhaustive in any statistical
sense but form a baseline based on expert judgement, including processes
identified by previous work that have been found to be important. We also
note that the relative importance between these dimensions of microphysics
and aerosols is important. A more significant perturbed parameter ensemble,
similar in spirit to but including cloud microphysical
uncertainties, is currently being developed.
Uncertain lifetime effects are manifest in CESM through changes to LWP
with changes in aerosols. Lifetime effects in CESM represent about one-third of
the total ACI. The mixed phase and the shallow convective regimes are also
important, indicating that aerosol effects in convective clouds should be
considered. Autoconversion parameterizations in particular seem to specify
lifetime effects that are highly uncertain. Many global models still
prescribe cloud drop number or size based on aerosol mass. This may be
problematic as interactions with different microphysical processes are
important for the magnitude of ACI.
How general are these results across models? The model framework with MG2 is
a typical two-moment bulk microphysics scheme with a framework similar to
other schemes. Many of the process rate formulations for autoconversion
examined here (e.g., KK2000) are used by other schemes as well. The
sensitivity to background aerosol emissions is very similar to that diagnosed
by . In addition, the sensitivity of the microphysical
process rates to autoconversion and accretion that occurs with prognostic
precipitation is qualitatively similar to . However, adding
aerosol effects in all convective clouds (deep and shallow) in a different
GCM reduced the ACI .
Similar tests with different microphysics schemes, and using different GCMs,
would be valuable to confirm the conclusion that ACI sensitivity to cloud
processes is large. We are in the process of developing such a cross-model
comparison. The overall conclusion is that getting better a representation of
ACI is critical for reducing uncertainty in anthropogenic climate forcing:
cloud microphysical development needs to go hand in hand with better
constraints on aerosol emissions to properly constrain ACI and total forcing.