Aerosol–cloud interactions (ACIs) are uncertain and the estimates
of the ACI effective radiative forcing (ERF
The aerosol–liquid water path relationship in ECHAM6-HAM2 is systematically
stronger than in AATSR–CAPA data and cannot be explained by an
overestimation of autoconversion when using diagnostic precipitation but
rather by aerosol swelling in regions where humidity is high and clouds are
present. When aerosol water is removed from the analysis in ECHAM6-HAM2 the
strength of the susceptibilities of liquid water path, cloud droplet number
concentration and cloud albedo as well as ERF
We further find that the statistical relationships inferred from different satellite sensors (AATSR–CAPA vs. MODIS–CERES) as well as from ECHAM6-HAM2 are not always of the same sign for the tested environmental conditions. In particular the susceptibility of the liquid water path is negative in non-raining scenes for MODIS–CERES but positive for AATSR–CAPA and ECHAM6-HAM2. Feedback processes like cloud-top entrainment that are missing or not well represented in the model are therefore not well constrained by satellite observations.
In addition to aerosol swelling, wet scavenging and aerosol processing have an impact on liquid water path, cloud albedo and cloud droplet number susceptibilities. Aerosol processing leads to negative liquid water path susceptibilities to changes in aerosol index (AI) in ECHAM6-HAM2, likely due to aerosol-size changes by aerosol processing.
Our results indicate that for statistical analysis of aerosol–cloud interactions the unwanted effects of aerosol swelling, wet scavenging and aerosol processing need to be minimised when computing susceptibilities of cloud variables to changes in aerosol.
Aerosol particles emitted from natural and anthropogenic sources are
important for Earth's climate because of their interactions with radiation
and clouds. In particular, the uncertainty of aerosol–cloud interactions is
large (Boucher et al., 2013) and impairs the investigation of historical
climate records and the prediction of future changes in climate. Several
studies revealed differences in the response of cloud properties to changes
in aerosol optical depth (AOD) in model simulations and satellite
observations (e. g. Lohmann and Lesins, 2003; Quaas et al., 2009; McComiskey
and Feingold, 2012; Boucher et al., 2013; Schmidt et al., 2015). These
differences can be explained by the growth of aerosol particles in the humid
environment surrounding clouds (Twohy et al., 2009; Boucher and Quaas, 2012),
misclassification of partly cloudy satellite pixels as cloud-free (cloud
contamination), brightening of aerosol particles by sunlight reflected at the
edge of clouds (3-D-effects; Varnái and Marshak, 2009), processing of
aerosol particles in clouds by nucleation or impact scavenging, subsequent
growth by heterogeneous chemistry and re-evaporation, wet scavenging of
aerosol particles in particular in areas of strong precipitation (Grandey et
al., 2014; Gryspeerdt et al., 2015), by stability/humidity changes due to
absorbing aerosol above or near clouds, structural uncertainties due to
differences in the analysis/observational scale and the process
scale (McComiskey and Feingold, 2012), or covariation of aerosol and cloud
properties with meteorology (Chen et al., 2014; Andersen et al., 2017).
Andersen et al. (2016) showed that cloud droplet size sensitivity to aerosol
loading depends on the magnitude of the aerosol loading and that the
magnitude of greatest sensitivity is larger for larger total columnar water
vapour (with a possible explanation being aerosol swelling). Quaas et
al. (2010) identified the swelling of aerosols (Zhao et al., 2017) as the
most likely explanation for the larger cloud cover susceptibility (to AOD) in
observations than in models. Gryspeerdt et al. (2014) showed that the
cloud-top height susceptibility is not a direct response to aerosol changes
but mediated by changes in cloud cover (which as the study by Quaas et
al. (2010) showed is likely due to covariation of relative humidity). To
circumvent the covariation of relative humidity in the cloud cover
susceptibility, Gryspeerdt et al. (2016) use the cloud droplet number
susceptibility to mediate the cloud cover susceptibility. Thus, cloud cover
can only change through a change in cloud droplet number concentration. The
mediated cloud cover susceptibilities are much smaller than the “direct”
cloud cover susceptibility, hinting at the large influence of other factors
like humidity. Bender et al. (2016) used a different approach for analysing
albedo-cloud cover histograms. Because of the correlation of cloud cover and
AOD they subtract for each cloud cover bin the mean AOD to obtain the
correlation of AOD anomalies to the albedo-cloud cover histograms. After the
subtraction they find indications that absorbing aerosol influences the cloud
albedo in Namibian and Canarian stratocumulus regions. Boucher and
Quaas (2012) and Grandey et al. (2014) used dry AOD to remove the effect of
humidity on the susceptibility of the precipitation rate to changes in AOD.
However, Koren et al. (2013) showed, with basic hygroscopic growth and
radiative transfer calculations, that aerosol swelling alone cannot explain
the large difference in AOD in polluted and clean conditions. The algorithm
applied to the MODIS (Moderate Resolution Imaging Spectroradiometer) AOD
product that they used filters pixels within 1 km of detectable clouds, and
25 % of the brightest pixels are rejected within each
10
The liquid water path (LWP) response to AOD changes also shows a difference
between model simulations and satellite observations, such that it is in
general larger in model simulations than in satellite observations (Quaas et
al., 2009). Although this difference can be explained by similar influences
to those in the cloud cover susceptibility, it also depends on the ratio (autoconversion
rate/autoconversion rate
To study aerosol–cloud interactions in observational data a proxy for cloud condensation nuclei (CCN) is necessary. Liu and Li (2014) show based on surface measurements that aerosol index (AI) is a better proxy for CCN than AOD and that in situ scattering AI at the surface (i.e. not vertically integrated) has the highest correlation to CCN at the surface. Stier (2016) has shown using model simulations that vertically resolved measurements of aerosol radiative properties (i.e. as a function of altitude) would be necessary to obtain a good CCN proxy for most of the globe. In the absence of vertical information AI is considered better as a CCN proxy than AOD due to the higher weight of smaller aerosols at larger optical depths (Nakajima et al., 2001). Gryspeerdt et al. (2017) showed that including vertical information is beneficial for several global aerosol–climate models, but these benefits are smaller than when using AI instead of AOD as a CCN proxy for most analysed models. The simulations by Stier (2016), Gryspeerdt et al. (2017) and surface measurements do not account for aerosol processing in clouds, which could affect the suitability of these aerosol quantities as a CCN proxy. Shinozuka et al. (2015) propose using the in situ dry extinction coefficient and Ångström exponent to parameterise CCN, which accounts for ambient relative humidity, vertical information and aerosol size. Interestingly, in the parameterisation of Shinozuka et al. (2015) the CCNs do not increase linearly with the dry extinction coefficient, which is an indication of growth processes like condensation, coagulation or in-cloud aerosol processing. Aerosol particles can activate as CCN, collide and coalesce with cloud droplets and atmospheric gases can be taken up by cloud droplets and undergo chemical reactions in the aqueous phase. Aerosol particles release by evaporation of cloud droplets or raindrops are larger than before the processing in the clouds. We compare simulations with and without aerosol processing in clouds to obtain an indication of how aerosol processing affects the suitability of different aerosol properties as proxies for CCN.
In Sect. 2 the methodology is outlined and satellite products and model experiments are described in Sect. 3. The results are presented in Sect. 4 and summarised in Sect. 5, where conclusions also are drawn.
For a statistical analysis of aerosol–cloud interactions from satellite
data, the data from aerosol and cloud retrievals need to be paired. The
Cloud–Aerosol Pairing Algorithm (CAPA), used here for the satellite data, is
described in Sect. 2.1. In a model, however, the model
parameterisations use the aerosol in a grid box to compute cloud
microphysical processes, so the aerosol and cloud data in a grid box match
each other all the time due to the model parameterisations, and no further
association is necessary. The computation of susceptibilities for the paired
aerosol and cloud data from satellite products and the model data is
described in Sect. 2.2. As a proxy for CCN, the AI is used. AI is
computed by multiplying AOD by the Ångström exponent (AE). For
ECHAM6-HAM2 and the Aerosol_cci products we compute the
Ångström exponent from AOD at 550 and 865 nm (see Sect. 2.3). For the Cloud_cci AATSR products the effective cloud
droplet number concentration (CDNC) is derived. By combining Eqs. (6) and (9) from
Bennartz (2007) and assuming a cloud fraction
The cloud albedo (
CAPA applied to paired aerosol and cloud pixels is described in detail in the companion paper, Christensen et al. (2017). By pairing high-resolution retrievals of aerosol and cloud properties CAPA aims to minimise data aggregation effects at coarser resolution (McComiskey and Feingold, 2012) and provides sufficient data pairs for significant susceptibilities. To reduce cloud contamination, 3-D radiative effects and aerosol swelling, a minimum distance of 15 km is required between the aerosol and cloud pixels.
Average frequency of the occurrence of low liquid clouds (cloud-top
pressure > 500 hPa, cloud-top temperature > 273.15 K) in
E6_Ref between 1995 and 2012 in
Susceptibilities (ACI
Our analysis uses the pixel-scale (1 km spatial resolution) level 2 Aerosol
and Cloud_cci AATSR products. Only data points are analysed
where (fully overcast) cloud and aerosol pixels can be paired using CAPA.
The AATSR cloud properties therefore represent in-cloud properties. The
ECHAM6-HAM2 cloud properties are divided by the low liquid cloud cover (cloud-top pressures > 500 hPa and cloud-top temperatures
> 273.15 K) to obtain in-cloud values also for the global model
data. The computation of mean susceptibilities in Eq. (6) uses the number of
aerosol–cloud data pairs
Susceptibilities are computed for each grid area for each season using all available years (e.g. all summer seasons during 1995–2012 for the model data, 2002–2012 for AATSR data and 2006–2010 for MODIS data). Annual mean susceptibilities are computed as a weighted mean from the seasonal susceptibilities.
Multiple linear regression could be used in principle to assess the importance of relative humidity on aerosol–cloud susceptibilities. Due to the non-linear dependence of AOD and cloud properties on relative humidity, the ambient relative humidity would need to be observed with high precision at high resolution (horizontal and vertical). As such high-resolution satellite observations of humidity are not available, we therefore use CAPA for AATSR products and remove aerosol water from AOD and AI in ECHAM6-HAM2 data.
The AI is computed as the product of AOD and the Ångström
exponent (ANG; Ångström, 1964):
The effective radiative forcing due to aerosol–cloud
interactions (ERF
As a reference forcing for ECHAM6-HAM2, ERF
Data for the environmental conditions are taken for both satellite data sets (AATSR and MODIS) from the European Center for Medium-Range Weather Forecast-AUXiliary analysis (ECMWF-AUX) product.
The susceptibilities for the Advanced Along-Track Scanning Radiometer (AATSR) data
have been computed with CAPA, described in Christensen et al. (2017), from the
ESA Aerosol_cci L2 aerosol products, ORAC
V4.01, which are available at 10
The A-train satellite products are the same as described in Christensen et al. (2016). The data include CloudSat radar data, CERES (Clouds and the Earth's Radiant Energy System) radiative fluxes and Moderate Resolution Imaging Spectroradiometer (MODIS) level 2 (MYD06) cloud and MODIS (MYD08) aerosol products. The methodology follows Chen et al. (2014). All sensors were matched to the nearest CloudSat footprint. The CloudSat precipitation flag is used to identify raining scenes.
Aerosol data are taken from the gridded MODIS (MYD08) atmospheric
product (1
ECHAM-HAMMOZ is a global aerosol-chemistry climate model of which in this
study only the global aerosol–climate model part is used. Two versions of
ECHAM-HAM are used because they have different options to treat
aerosol–cloud interactions. ECHAM6.1-HAM2.2 (Neubauer et al., 2014), for the
sake of brevity referred to as ECHAM6-HAM2, consists of the general
circulation model ECHAM6 (Stevens et al., 2013) coupled to the aerosol
module HAM2 (Zhang et al., 2012), which includes a size-dependent in-cloud
scavenging parameterisation (Croft et al., 2010). ECHAM5.5-HAM, for the sake
of brevity referred to as ECHAM5-HAM, consists of the general circulation
model ECHAM5 (Roeckner et al., 2003) coupled to the aerosol module HAM (Stier
et al., 2005). Some of the model components of ECHAM6-HAM2 and
ECHAM5-HAM are similar, although in ECHAM6-HAM2 several software errors have
been fixed. Both model versions use a two-moment cloud microphysics scheme
which solves prognostic equations for both mass mixing ratios and number
concentrations of cloud liquid water and cloud ice (Lohmann et al., 2007;
Lohmann and Hoose, 2009). The Lin and Leaitch (1997) aerosol activation
scheme and the Khairoutdinov and Kogan (2000) autoconversion scheme are used
in both model versions as well. A minimum cloud droplet number concentration
of 40 cm
ECHAM6-HAM2 and ECHAM5-HAM use a 1.5-order turbulence closure scheme with a simplified prognostic equation for turbulent kinetic energy (TKE) (Brinkop and Roeckner, 1995) to compute vertical diffusion (mixing) in the boundary layer.
In the ECHAM6-HAM2 simulation with aerosol processing in stratiform clouds, the scheme from Hoose et al. (2008a, b) is applied in order to extend the seven aerosol modes of HAM2 through an explicit representation of aerosol particles in cloud droplets and ice crystals in stratiform clouds. The in-cloud aerosol is represented by five tracers for sulfate, black carbon, organic carbon, sea salt and mineral dust for cloud droplets and ice crystals (see details in Neubauer et al., 2014). ECHAM-HAM in its standard configuration does not track aerosol particles in hydrometeors. In the standard configuration scavenged aerosol particles (by nucleation and/or impaction scavenging) are removed from the interstitial aerosol (evaporation of rain or sublimation of snow below cloud base release part of the scavenged aerosol particles back to the atmosphere though), and sulfate produced by heterogeneous chemistry is added to the interstitial aerosol. With the aerosol processing scheme, however, aerosol mass transfers to and from in-cloud aerosol tracers by nucleation and impact scavenging, freezing and evaporation of cloud droplets, and melting and sublimation of ice crystals are tracked. These processes are computed explicitly. Sulfate produced by heterogeneous chemistry is added to the in-cloud sulfate aerosol tracer. Aerosol particles from evaporating/sublimating clouds and precipitation are released to the modes that correspond to their size with the aerosol processing scheme.
In the ECHAM5-HAM simulation with prognostic precipitation, the prognostic precipitation scheme by Sant et al. (2015), which builds on work by Posselt and Lohmann (2008) and Sant et al. (2013), is applied, and in addition to the standard cloud liquid water and cloud ice classes it uses rain, drizzle and snow. For all five water classes (three liquid, two solid) prognostic equations for both mass mixing ratios and number concentrations are solved.
The experiment set-up follows the guidelines of the AeroCom aerosol–climate
model intercomparison initiative (
To focus only on warm, liquid clouds in the analysis, model cloud-top pressure and temperature (from the 3 h instantaneous output) are used to identify low liquid clouds as those with cloud-top pressures greater than 500 hPa and cloud-top temperatures exceeding 273.15 K. The model variables are used for the sampling and environmental regime discrimination for the model data. Minimum and maximum values for aerosol and cloud properties are applied to mimic the sensitivity of the satellite retrievals and remove unrealistically large values that could influence the linear regression (Table 1). The same conditions (cloud type and environmental conditions) on the selection criteria are used for the satellite analysis (environmental data were taken from the ECMWF-AUX product).
Minimum and maximum values for aerosol and cloud properties used in
this study. AOD is aerosol optical depth, AI is aerosol index, CDNC is cloud
droplet number concentration, LWP is liquid water path, COD is cloud optical
depth and
Four experiments were conducted: a reference simulation with ECHAM5-HAM (E5_Ref), a reference simulation with ECHAM6-HAM2 (E6_Ref), a simulation with ECHAM5-HAM and the prognostic precipitation scheme (E5_Prog) and a simulation with ECHAM6-HAM2 and the aerosol processing scheme (E6_AProc). The E5_Ref and E5_Prog simulations were run for 12 years (2000–2011) as some input files for this older ECHAM-HAM version were not available for the years 1995–1999 and 2012.
Susceptibility of LWP to changes in AI or AOD for ECHAM6-HAM2 (E6_Ref and E6_AProc) when low liquid clouds
and aerosol are present during the simulation period 1995–2012 between
60
In Fig. 2a the annual mean susceptibility of the LWP to changes in AI during
1995–2012 between 60
To further remove the effects of covarying variables, in Fig. 2c the LWP
susceptibility to changes in AIdry is shown only for non-raining scenes.
This minimises the effect of wet scavenging of aerosol particles by
precipitation but cannot fully remove it (Gryspeerdt et al., 2015). Clouds
with higher LWP are more likely to remove aerosol particles by wet
scavenging, leading to a negative LWP susceptibility in particular in regions
where heavy precipitation occurs frequently. In Fig. 2c the LWP
susceptibility is positive everywhere except in regions where deep
convection and moderate and heavy precipitation are frequent, so the
negative LWP susceptibilities in Fig. 2b seem to be due to wet scavenging.
Moderate and heavy precipitation originates predominantly from convective
clouds in ECHAM6-HAM2, whereas light precipitation comes mainly from
stratiform clouds. In Fig. 2c the LWP susceptibility of precipitating
convective clouds is therefore still largely masked by wet scavenging. In
Fig. 2a the effect of wet scavenging is not as easily identifiable as in
Fig. 2b as the effect of aerosol swelling is overshadowing other factors
that influence the statistical relationship of LWP and aerosol such as wet
scavenging. In Fig. 2d the same is shown as in Fig. 2c but using a
Figure 2e shows the same as Fig. 2c but for the simulation with processing of aerosol in stratiform clouds. The LWP susceptibility is negative almost everywhere in Fig. 2e, although only non-raining scenes are shown; i.e. the effect of wet scavenging should be minimal. A possible mechanism that explains the negative LWP susceptibilities is the growth of aerosol particles in cloud droplets (by collisions of the cloud droplets with interstitial aerosol particles and heterogeneous chemistry; Hoose et al., 2008a) and release of the larger aerosol particles when the cloud droplets evaporate (as AIdry decreases for larger particles). The larger the LWP (or cloud lifetime), the more the aerosol may be processed and grow in size in the cloud, therefore leading to negative LWP susceptibilities and to changes in AIdry. A further indication that the negative LWP susceptibility in Fig. 2e is due to the growth of aerosol particles by aerosol processing is that the LWP susceptibility to changes in AODdry is positive in most regions (see Fig. 2f) even with aerosol processing. AODdry is less sensitive to aerosol size than AIdry, so the negative LWP susceptibility shown in Fig. 2e should rather be due to changes in aerosol size than in aerosol number or mass (for comparison the LWP susceptibility to changes in AODdry of E6_Ref ,i.e. without aerosol processing, is shown in Fig. 2g). It should be noted here that ECHAM6-HAM2 overestimates the lifetime of sea salt particles when aerosol processing is used (Hoose et al., 2008a) and it uses a modal approach to simulate aerosol size and this may be too coarse to well capture the size changes by aerosol processing. Because of these limitations of ECHAM6-HAM2 we use both AI and AIdry as proxies for CCN in this study. Further research, for example using a bin representation of aerosol size, could give further insight into the effect of aerosol processing on aerosol–cloud interactions.
In Fig. 2a–e the regions over the oceans, where typically shallow convective clouds are present, show a particularly strong LWP susceptibility (positive or negative). Clouds in these regions are not frequent (see Fig. 1a), so these regions do not contribute much to global or regional mean susceptibilities.
Note that the wave structures visible in Fig. 2 and some other figures are due to spurious numerical oscillations (SNOs), which commonly appear in spectral but also in non-spectral models (Geil and Zeng, 2015). The SNOs in Fig. 2 are weaker than in most of the cloud and aerosol input fields (only AIdry and AODdry fields show no SNOs; not shown) and the impact of humidity, wet scavenging and aerosol processing also occurs in regions where there are weak or no SNOs (see Fig. 2), so the results do not seem to be affected by these SNOs.
To assess the impact of environmental regimes, susceptibilities averaged over all grid boxes of each environmental regime (see Fig. 1b, c) are examined in this section. In the weighted averaging only grid boxes over the global oceans are taken into account.
Susceptibility of CDNC to changes in AI for ECHAM6-HAM2 (E6_Ref), E6_Ref without aerosol water uptake (dry)
during 1995–2012, for AATSR–CAPA using the full satellite record span
2002–2012 and for MODIS–CERES during 2006–2010. The definitions of the
different environmental regimes are given in the text.
The response of CDNC to changes in AI (dlnCDNC/dlnAI) averaged over the global oceans is shown in Fig. 3. For ECHAM6-HAM2, AATSR–CAPA and MODIS–CERES, the CDNC susceptibility to AI varies only a little between moist or dry free-tropospheric conditions and a stable or unstable lower troposphere. The CDNC susceptibility of ECHAM6-HAM2 to AIdry is generally smaller, up to 50 % less depending on the regime. The CDNC susceptibility of AATSR–CAPA is smaller than for MODIS–CERES or ECHAM6-HAM2 (AI or AIdry). The minimum distance of the CAPA algorithm should reduce the effects of aerosol swelling, cloud contamination and 3-D radiative effects by selecting aerosols farther away from clouds where these satellite artefacts should be minimal. For AATSR–CAPA this seems to lead to a small CDNC susceptibility. For ECHAM6-HAM2 and MODIS–CERES the differences between non-raining and raining scenes are small and in general the CDNC susceptibility is smaller in the raining scenes than in the non-raining scenes, which is an indication of wet scavenging affecting aerosol concentrations in the raining scenes. For AATSR–CAPA the CDNC susceptibility to AI is smaller in the moist stable regime in the raining than in the non-raining and even negative in the other regimes in the raining scenes, which is also indicative of wet scavenging in the raining scenes. Part of the differences between raining and non-raining scenes may be due to different updraught velocities, which may be higher in the raining than in the non-raining scenes.
Same as Fig. 3 but for the LWP susceptibility to changes in AI for ECHAM6-HAM2 (E6_Ref), E6_Ref without aerosol water uptake (dry), AATSR–CAPA and MODIS–CERES. The MODIS–CERES data are from Christensen et al. (2016).
The response of LWP to changes in AI (dlnLWP/dlnAI), averaged over the
global oceans, shown in Fig. 4, reveals larger susceptibilities and lower
variability in susceptibilities between environmental regimes in ECHAM6-HAM2
than in satellite observations. When AIdry is used instead, the magnitude of
the LWP susceptibility is close to that of AATSR–CAPA and MODIS–CERES and
the variability between environmental regimes in ECHAM6-HAM2 is similar to
AATSR–CAPA. In most regimes, the LWP susceptibility to changes in AI or
AIdry is larger in the non-raining than in the raining scenes and even
negative in some regimes in the raining scenes for AATSR–CAPA, similarly to
the CDNC susceptibility. In the non-raining scenes of the MODIS–CERES data
the LWP susceptibility to changes in AI is negative which could be an
indication of cloud-top entrainment. Chen et al. (2014) found negative LWP
susceptibilities to changes in AI in all environmental regimes for
non-raining scenes from MODIS–CERES as shown in Fig. 4. They attribute this
to entrainment of dry and warm air from the free troposphere into the
boundary layer due to decreased cloud droplet sedimentation of smaller cloud
droplets at higher AI. The entrainment is stronger if the free troposphere
is drier and/or the lower troposphere is more unstable. Although AATSR–CAPA
and MODIS–CERES observed similar scenes, this effect of entrainment seems
not to appear in the non-raining scenes in the AATSR–CAPA data. A reason
could be the different sampling between AATSR–CAPA and MODIS–CERES, where
AATSR has a longer time series and wider swath. The MODIS–CERES data are
along the CloudSat nadir-view track. Other differences could be in the
retrieval scheme used to obtain cloud and the aerosol properties – ORAC,
which uses an optimal estimation method to acquire radiative consistency in
the retrieval using all of the channels simultaneously, is compared to MODIS,
which uses discrete channel selection to retrieve aerosol and cloud
properties (King et al., 1998) separately. The aerosol retrieval has been
validated and evaluated within ESA's Aerosol_cci project and
a comparable quality of the AATSR and MODIS aerosol retrievals over ocean
has been found (Popp et al., 2016). Another reason could be that a
Same as Fig. 3 but for the shortwave cloud albedo susceptibility to changes in AI for ECHAM6-HAM2 (E6_Ref), E6_Ref without aerosol water uptake (dry), AATSR–CAPA and MODIS–CERES. The MODIS–CERES data are from Christensen et al. (2016).
In addition to changes in cloud microphysical parameters (CDNC, LWP) it is
interesting to investigate the impact of changes in aerosol on a cloud
macrophysical parameter like
Same as Fig. 3 but for the LWP susceptibility to changes in AI for E5_Prog, E5_Ref and E6_Ref.
For the evaluation of the impact of a prognostic precipitation scheme on
aerosol susceptibilities we use the prognostic precipitation scheme
developed by Sant et al. (2013), which has recently been implemented in
ECHAM5-HAM (Sant et al., 2015) and solves prognostic equations for rain,
drizzle and snow. Compared to conventional prognostic precipitation schemes,
the additional drizzle class allows a better representation of the drop size
distribution and the drizzling conditions that often occur in marine
stratocumulus clouds. Previous studies found a shift of precipitation
formation from autoconversion to accretion when using a prognostic instead
of a diagnostic precipitation scheme, in better agreement with observations (Posselt
and Lohmann, 2008; Gettelman and Morrison, 2015). The change to a
prognostic precipitation scheme or an autoconversion scheme that depends
less on the CDNC results in a smaller effective radiative forcing due to
aerosol–radiation and aerosol–cloud interactions (ERF
A similar increase occurs for other susceptibilities (not shown). There are two reasons for this. First the LWP in stratocumulus regions is higher in E5_Prog than in E5_Ref (Fig. 7b) because of the change in rain (E5_Ref) to drizzle (E5_Prog) in these regions. The increased LWP in E5_Prog (and the increased variability in LWP) seems to increase the (present day) LWP susceptibility in these regions. This is in contrast to the smaller increase in LWP due to anthropogenic aerosol reported in Sant et al. (2015), who computed this increase from simulations with present-day versus pre-industrial aerosol. Carslaw et al. (2013) and Ghan et al. (2016) found that present day variability is a poor proxy for the change due to anthropogenic aerosol for several susceptibilities such as the LWP susceptibility. Our results are similar to their findings as the difference between the prognostic and the diagnostic precipitation scheme leads to a weaker LWP response to anthropogenic aerosols (Sant et al., 2015) but a stronger LWP response determined by present day variability (Fig. 6). Note that covarying variables might affect the LWP susceptibility as well. The other reason for the stronger response of LWP to AI is that AI is larger in E5_Prog than in E5_Ref over the oceans. This leads to a general increase of the susceptibilities. Because AOD is more closely related to the aerosol mass, whereas AI also takes into account the aerosol size, it is instructive to compare AOD and AI in E5_Prog and E5_Ref as it gives an indication whether smaller or larger particles are removed more efficiently by the different precipitation schemes. The AOD is smaller in E5_Prog than in E5_Ref, whereas AI is larger over the oceans in E5_Prog than in E5_Ref (in the global mean AI is similar in E5_Prog and E5_Ref). The prognostic precipitation scheme therefore seems to remove more efficiently larger aerosol particles than the diagnostic precipitation scheme.
These differences between LWP and AI in the simulations have a strong impact on the computed susceptibilities. Global observations with low uncertainty would be necessary to constrain the simulated LWP and AI. Current satellite observations of LWP and AI (e.g. MODIS, AATSR) show considerable differences. Without more observations to better constrain LWP (or other cloud properties) and AI it is not clear which present day simulation (E5_Prog, E5_Ref, E6_Ref) is most realistic and which susceptibilities computed from these simulations (E5_Prog, E5_Ref, E6_Ref) are most realistic.
Same as Fig. 5 but for the shortwave cloud albedo susceptibility
to changes in AI for ECHAM6-HAM2 (E6_Ref), E6_Ref without aerosol water uptake (dry), AATSR–CAPA and MODIS–CERES in the
Because buffering effects of aerosol–cloud interactions can depend on cloud
type (Stevens and Feingold, 2009; Christensen et al., 2016) and some areas
are also affected by wet scavenging in the non-raining scenes (see Fig. 2c),
we compute, in addition to global mean values (between 60
From the susceptibility of
Estimates of
Not including aerosol water leads to a better agreement of intrinsic
ERF
The estimates for extrinsic ERF
The considerably larger estimates of intrinsic and extrinsic ERF
It has been recognised in the scientific community that the statistical
analysis of aerosol–cloud interactions can be affected by artefacts like
cloud contamination or 3-D-effects, by covariations with relative humidity,
by effects of clouds on aerosols like wet scavenging or aerosol processing,
by absorbing aerosols or by differences in the analysis/observational scale
and the process scale. Aerosol swelling has further been identified as the
most likely reason for the large cloud cover susceptibility to changes in
aerosol in satellite observations. Whereas the effect of aerosol swelling on
the cloud cover and precipitation rate susceptibilities and how to minimise
it has received attention in the literature, the effect on susceptibilities
of other cloud variables is less explored. Our results with the global
aerosol–climate model ECHAM6-HAM2 show that the LWP and
Our results show further that, in addition to aerosol swelling, wet scavenging
and aerosol processing have an impact on LWP,
A simulation with prognostic precipitation (rain, drizzle and snow) scheme
in ECHAM5-HAM showed that the large LWP susceptibility cannot be explained
by overemphasising autoconversion instead of accretion (Sant et al., 2015).
While using a prognostic precipitation scheme considerably reduces the ratio
of autoconversion to autoconversion
A differentiation of susceptibilities by different environmental regimes (precipitation, stability in the lower troposphere, RH in the lower free troposphere) revealed that AATSR–CAPA, MODIS–CERES and ECHAM6-HAM2 do not always agree in their dependence on environmental regimes. The susceptibility of liquid water path is negative in non-raining scenes for MODIS–CERES but positive for AATSR–CAPA (and ECHAM6-HAM2). A negative LWP susceptibility in non-raining scenes has been interpreted as cloud-top entrainment (Chen et al., 2014). Feedback processes such as cloud-top entrainment that are missing or not well represented in ECHAM6-HAM2 are therefore not well constrained by the satellite observations. Further research with multiple satellite aerosol and cloud products could help to better understand such feedback processes and provide better constraints for climate models.
The Centre for Environmental Data Analysis (CEDA;
LWP response to changes in AI for ECHAM6-HAM2 (1995–2011).
AATSR observations are done at a mean local solar time of 10:30, while for
ECHAM6-HAM2 3 h instantaneous data are used. For ECHAM6-HAM2 data,
therefore, the diurnal cycle of clouds and aerosol are resolved, while AATSR
data are always available at the same time. Resolving the diurnal cycle or
not can potentially lead to a difference in the computed susceptibilities. To
estimate the effect of the different sampling frequencies and lack of
temporal collocation (Schutgens et al., 2016), we compute the LWP
susceptibility to changes in AI of a 17-year ECHAM6-HAM2 simulation once from
3 h output and once from data at 10:30, temporally collocated with AATSR.
The results are shown in Fig. A1. The maxima and minima of the LWP
susceptibility are more pronounced with the 10:30 local time sampling than
with the 3 h sampling. The general geographical pattern and magnitude of the
LWP susceptibility are quite similar for the two sampling methods. As the
global ECHAM6-HAM2 simulations have to use a relatively coarse
resolution (T63, 1.9
DN designed the analysis, conducted the simulations and computed susceptibilities for ECHAM-HAM and computed the effective radiative forcing estimates. MC computed susceptibilities for MODIS–CERES and MODIS–CAPA. CP provided support needed to run ORAC. UL contributed to the analysis and interpretation of findings. DN prepared the manuscript with contributions from co-authors.
The authors declare that they have no conflict of interest.
This work was supported by the European Space Agency as part of the
Aerosol_cci project (ESA Contract No. 4000109874/14/I-NB). We thank the ESA
Cloud_cci project for providing the cloud satellite data.
The MODIS satellite data used in this study were acquired from
NASA Goddard (