Stratocumulus clouds are important for climate as they reflect large amounts of solar radiation back into space. However they are difficult to simulate in global climate models because they form under a sharp inversion and are thin. A comparison of model simulations with the ECHAM6-HAM2 global aerosol climate model to observations, reanalysis and literature data revealed too strong turbulent mixing at the top of stratocumulus clouds and a lack of vertical resolution. Further reasons for cloud biases in stratocumulus regions are the too “active” shallow convection scheme, the cloud cover scheme and possibly too low subsidence rates.
To address some of these issues and improve the representation of stratocumulus clouds, we made three distinct changes to ECHAM6-HAM2. With a “sharp” stability function in the turbulent mixing scheme we have observed, similar to previous studies, increases in stratocumulus cloud cover and liquid water path. With an increased vertical resolution in the lower troposphere in ECHAM6-HAM2 the stratocumulus clouds form higher up in the atmosphere and their vertical extent agrees better with reanalysis data. The recently implemented in-cloud aerosol processing in stratiform clouds is used to improve the aerosol representation in the model.
Including the improvements also affects the anthropogenic aerosol effect.
In-cloud aerosol processing in ECHAM6-HAM2 leads to a decrease in the anthropogenic aerosol effect in the global annual mean from
Stratocumulus clouds are important for future climate predictions as they
have a strong cooling effect (Bretherthon et al., 2004; Williams and Webb,
2009). In a global climate model it is challenging to model stratocumulus
clouds because of their small vertical extent. The feedback of low clouds is
believed to be a major cause for the model discrepancy in the
It is also challenging to represent the complex interaction between aerosol
and clouds in a global climate model. Recent high-resolution large eddy
simulation studies showed that the liquid water path may either
increase or decrease with increased cloud droplet number concentrations
(
Typical biases of global climate models and numerical weather prediction models when simulating stratocumulus clouds are a too low cloud amount, a too shallow planetary boundary layer and an underestimation of the liquid water path (Hannay et al., 2009; Medeiros and Stevens, 2011). The diversity that exists among models in simulating stratocumulus clouds increases the uncertainty of the influence of aerosol particles on climate. In an intercomparison study by Stier et al. (2013), the uncertainty in the direct aerosol forcing due to the differences in the cloud albedo simulated and surface albedo used among the participating models was assessed. Stratocumulus cloud regions were identified to be among the regions responsible for the largest host model uncertainty in the direct aerosol effect and can therefore be expected to be important for the total anthropogenic aerosol effect.
For the first indirect aerosol effect (cloud albedo effect), Carslaw et al. (2013) systematically evaluated the sources of uncertainty for the simulation of aerosol. Uncertainties in natural emissions cause most uncertainty in cloud radiative forcing, followed by uncertainties in anthropogenic emissions and aerosol processes. Stratocumulus regions were identified as regions with a strong cloud albedo effect and large model uncertainty. Surface albedo and cloud optical depth fields from International Satellite Cloud Climatology Project (ISCCP; Rossow and Schiffer, 1999) D2 data for low-level stratiform clouds were used in their study. To evaluate the uncertainty stemming from the simulation of clouds Carslaw et al. (2013) performed extra simulations with the 1983–2008 multi-annual ISCCP cloud climatology but found that the sensitivity to the cloud climatology was very small.
As stratocumulus regions are areas of a strong anthropogenic aerosol effect, simulations of the anthropogenic aerosol effect can be expected to depend on the representation of stratocumulus clouds. In our study we investigate the total anthropogenic aerosol effect (also referred to as the effective radiative forcing due to aerosol–cloud and aerosol–radiation interactions; Boucher et al., 2013), including the direct, semi-direct and indirect aerosol effects (cloud albedo, cloud lifetime), as well as effects on mixed-phase and ice clouds, but not convective clouds.
A number of physical processes have to be accounted for when modeling stratocumulus clouds, including cloud top radiative cooling, which drives turbulent fluxes in the planetary boundary layer; absorption of shortwave fluxes in the cloud layer; entrainment of warm, dry air from the free atmosphere; and microphysical processes. The representation of several of these processes is addressed in the general circulation model ECHAM6 (Stevens et al., 2013) coupled to the aerosol module HAM2 (Zhang et al., 2012) and a two-moment cloud microphysics scheme (Lohmann et al., 2007) in this study.
Section 2 summarizes the methodology to evaluate stratocumulus clouds in a global climate model and observational data used. Section 3 gives a description of the model and experiments conducted, the results from which are presented in Sect. 4. The discussion of the results and conclusions follow in Sect. 5.
The focus of this study is on the representation of marine stratocumulus clouds. The analysis of the experiments is therefore confined to stratocumulus regions (and global values where appropriate). Two approaches have been used in recent years for analysis in different cloud regimes. The first one is based on cloud characteristics, where a statistical cluster analysis method is used to identify cloud clusters in joint histograms of cloud optical depth and cloud top pressure (Jakob and Tselioudis, 2003; Gordon et al., 2005; Williams and Tselioudis, 2007; Zhang, 2007; Williams and Webb, 2009; Tsushima et al., 2013). The second approach is based on dynamic and/or thermodynamic regimes (Tselioudis et al., 2000; Norris and Weaver, 2001; Tselioudis and Jakob, 2002; Bony et al., 2004; Williams et al., 2006; Medeiros and Stevens, 2011). We have used the latter approach as it is straightforward to apply to a global climate model and provides information for the frequency of occurrence of environmental conditions favorable for stratocumulus clouds. This definition of the stratocumulus regime allows, to the extent possible in a global climate model simulation, for separation of dynamical (large-scale environment) and other influences on the simulation of stratocumulus clouds.
We define the stratocumulus regime as
For model evaluation we use satellite data and ERA-Interim reanalysis data (Dee et al., 2011). To take into account limitations in satellite observations (e.g., detection thresholds), different definitions of model variables vs. variables in satellite retrievals, and different scales of model grids vs. satellite pixels, we use the Cloud-Aersol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO; Winker et al., 2010) simulator from the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al., 2011). This simulator also separates cloud cover into high-, mid- and low-cloud fractions according to the International Satellite Cloud Climatology Project (ISCCP; Rossow and Schiffer, 1999) definition.
CFMIP also provides satellite data products for the evaluation of climate
and weather prediction models (CFMIP-OBS,
The total anthropogenic aerosol effect (AAE) is calculated using effective
radiative forcing (also called the radiative flux perturbation method) that
takes fast feedbacks and interactions into account (cloud lifetime effect,
semi-direct effect or aerosol interactions with mixed-phase and ice clouds).
Effective radiative forcing is computed as the difference in the top of the
atmosphere radiation budget between simulations with and without
anthropogenic aerosol emissions using the same sea surface temperatures
(Hansen et al., 2005; Haywood et al., 2009; Lohmann et al., 2010; Boucher et
al., 2013):
On the one hand, using only grid boxes in the analysis where the
environmental conditions are suitable for stratocumulus clouds provides
additional information and allows for one cloud regime to be focused on. Where and
when the stratocumulus conditions occur depends on the temporal evolution of
the modeled atmospheric conditions (see Appendix A). On the other hand, such a conditional
sampling is therefore a source of internal variability when comparing different simulations. Global differences by changes in the
model physics or resolution or the global anthropogenic aerosol effect are
typically much larger than internal variability. In the stratocumulus regime,
however, due to the conditional sampling internal variability can become as
large as changes in variables due to model changes or the anthropogenic
aerosol effect. Furthermore, differences in the stratocumulus regime between
simulations cannot be computed as a difference of each grid box at each
month, as is typically done for global differences. Due to the conditional
sampling, an averaging step is necessary before two simulations can be
compared. Therefore the statistical significance of model changes or the
anthropogenic aerosol effect in the stratocumulus regime is highly relevant.
Statistical significance is assessed by applying an unpaired two-tailed
The general circulation model ECHAM6 (Stevens et al., 2013) coupled to the latest version of the aerosol module HAM2 (Zhang et al., 2012) is used in this study. It includes a two-moment cloud microphysics scheme for cloud droplets and ice crystals where prognostic equations are computed for cloud water, cloud ice, cloud droplet number concentrations and ice crystal number concentrations (Lohmann et al., 2007). The latest version, HAM2.2, includes a size-dependent in-cloud scavenging parameterization (Croft et al., 2010) and optionally orographic cirrus clouds (Joos et al., 2010). Hereinafter, for the sake of brevity, we will refer to it as HAM2. Aerosol effects on convective clouds are not included, but there is a dependence of cloud droplets detrained from convective clouds on aerosol. The condensate detrained from convective clouds is added to that of the existing stratiform clouds. For liquid clouds the cloud droplet number added from detrainment depends on the number of aerosol particles that can be activated at the convective cloud base.
The impact of aerosols on warm, mixed-phase and ice clouds can be studied using ECHAM6-HAM2. In all experiments we use a fractional cloud cover scheme that diagnoses fractional cloud cover from relative humidity when a critical relative humidity is reached (Sundqvist et al., 1989).
The vertical turbulent diffusion scheme uses a 1.5-order turbulence closure scheme, which includes a simplified prognostic equation for turbulence kinetic energy (TKE) with moist Richardson number (Brinkop and Roeckner, 1995).
Comparison of “sharp” and ECHAM6 stability function
We made three distinct changes to ECHAM6-HAM2 for this study:
Sharp stability function (STAB):
In the TKE scheme used in ECHAM6, the turbulent diffusivities ( The stability function used in ECHAM6 is a so-called “long-tail” function,
which decays slowly with increasing Richardson number (see Fig. 1). We replaced the long-tailed stability function
with a “sharp” stability function (King et al., 2001; Brown et al., 2008;
see Fig. 1). As the stability functions differ the
most for large Richardson numbers, the largest differences in the simulations
occur at stable atmospheric conditions. Long-tailed functions, also used in
numerical weather prediction models, are known to result in excessive mixing
at high stabilities. This artificially increased mixing was introduced to
offset a cold bias in the near-surface temperature and too active synoptic
cyclones (see Sandu et al., 2013, and references therein). In the European
Centre for Medium-Range Weather Forecasts (ECMWF) numerical weather
prediction model, the mixing at stable conditions was relaxed in 2007 to
avoid the erosion of capping inversions of the planetary boundary layer and
thereby dissipation of stratocumulus clouds (Köhler et al., 2011;
Holtslag et al., 2013; Sandu et al., 2013). Brown et al. (2008) found
improvements in the operational verification scores in a numerical weather
prediction model by changes to the boundary layer scheme that included the
use of a “short-tailed” or sharp stability function over the ocean. They
also noted that in the Met Office Hadley Centre climate model (HadGEM2;
Martin et al., 2011) the sharp stability function cloud be used everywhere
(ocean and land). Pithan and Mauritsen (2012) found an increase in
subtropical stratocumulus cloud cover and a decrease in trade wind cumulus
when using ECHAM6 with a sharp function. No near-surface temperature cold
bias was apparent with the sharp stability function (Pithan, 2013,
personal communication). In a recent study, Possner et al. (2014) showed
that reducing the mixing at high stability (by reducing the limit for the
prescribed minimum eddy diffusivity in their model) improves the simulation
of inversions in the regional climate and weather prediction model COSMO. Increased vertical resolution (VRES):
The low vertical resolution used in global climate models (GCMs) results in
numerical artifacts such as numerical entrainment (Lendering and Holtslag,
2000) and spurious radiative–dynamical interactions at the cloud top
interface of stratocumulus clouds (Stevens et al., 1999). We therefore
increase the vertical resolution in the lower troposphere in ECHAM6-HAM2
(see Fig. 2). Grenier and Bretherton (2001) showed that a 1.5-order turbulence closure model can provide good simulations
of dry convective boundary layers. With 15 hPa vertical resolution also in
stratocumulus-capped boundary layers, mixing was simulated properly. The
performance of the model simulations of Grenier and Bretherton (2001),
especially at coarser resolution, were dependent on further details of the
model like the implementation of the entrainment closure and the vertical
advection scheme. In the current study we use two new vertical grids: L47bl
and L95bl. In both grids the new layers are inserted primarily in the
boundary layer/lower atmosphere. To avoid numerical instabilities, the time step needs to be increased at
higher vertical resolution. From the standard 31 vertical levels (L31) to
L47bl, the vertical resolution is approximately doubled and the time step is
reduced from 720 to 300 s. With L95bl the vertical resolution is
approximately doubled again compared to L47bl or quadruplicated compared to
L31 and the time step is reduced to 180 s. The effect of reducing the time
step alone is presented in Sect. 4.2.2. Aerosol processing (AP):
Aerosol processing in stratiform clouds by uptake into cloud particles,
collision–coalescence, chemical processing inside the cloud particles and
release back into the atmosphere changes the aerosol concentration, size
distribution, chemical composition and mixing state. By modeling aerosol
processing, the representation of the mixing state and the size distribution
of particles released by evaporation of clouds and precipitation is more
realistic. These changes in the aerosol can influence cloud droplet and ice
crystal number concentrations and subsequently cloud liquid and ice water
paths as well as cloud lifetime and cloud radiative forcing. HAM2 uses seven modes to describe the total aerosol. We adapted the scheme
from Hoose et al. (2008a, b) to ECHAM6-HAM2 in order to extend the seven modes through an
explicit representation of aerosol particles in cloud droplets and ice
crystals in stratiform clouds, which are each represented by five tracers for
sulfate (SO
Vertical resolution of the reference L31 vertical grid and new L47bl and L95bl grids as well as the L60 vertical grid used in ERA-Interim. The (pressure) height of the model layers is shown as a function of the height above the surface for a surface pressure of 1000 hPa.
Description of experiments conducted in this study.
The simulations, summarized in Table 1, were
conducted with sea surface temperatures and sea ice cover fixed to observed
values (AMIP simulations) at T63 (1.9
In addition to the standard experiments, a sensitivity simulation with the reference configuration was performed in which the precipitation in stratocumulus regions was turned off, and another simulation in which the parameterization for shallow convective clouds was turned off. Both simulations were run with climatological sea surface temperatures and sea ice cover for 1 year with present-day greenhouse gas and aerosol emissions.
Processes and tracers used in the aerosol processing scheme. New tracers for aerosol particles in cloud droplets (CD) and ice crystals (IC) are added to the tracers for the soluble/mixed modes of HAM2 (nucleation, NS; Aitken, KS; accumulation, AS; coarse, CS) and insoluble modes (Aitken, KI; accumulation, AI; coarse, CI) .
The changes described in Sect. 3.1 lead to an imbalance of the radiative fluxes at the top of the atmosphere. The model was therefore re-tuned for the different experiments. Most parameters are kept to the values of the reference simulation and changes are kept to a minimum. Although this may result in the parameter settings not being the optimal ones to be used, the comparison between the different experiments is facilitated. In most experiments, only the tuning parameter for the autoconversion rate (ccraut) is changed (see Table 1), which by itself has a small effect on AAE (Lohmann and Ferrachat 2010). Lohmann and Ferrachat (2010) varied ccraut values between 1 and 10; in this study, ccraut values between 3.5 and 12 are used (see Table 1). In this study the same autoconversion parameterization (Khairoutdinov and Kogan 2000) as in Lohmann and Ferrachat (2010) is used. The tuning of the experiments with the new vertical grids L47bl and L95bl is described in more detail in Sect. 4.2.2.
Frequency of occurrence of stratocumulus conditions in ERA-Interim and ECHAM6-HAM2 in the REF, STAB, AP, VRES47 and VRES95 experiments. In the panel for the REF experiment, the six stratocumulus regions which are used in assessing the effect of anthropogenic aerosol are also shown.
The stratocumulus conditions (see Sect. 2) are met in ECHAM6-HAM2 in similar
areas to those in ERA-Interim but less frequently (Fig. 4). This is because large values of lower troposhperic stability (LTS) occur 12 % less often in ECHAM6-HAM2 than in the reanalysis data (the same is
true for other GCMs; see Medeiros and Stevens, 2011) in areas where both
stratocumulus conditions are met. Note that, with the frequency of occurrence
of stratocumulus conditions, the simulation of the large-scale environment
can be investigated separately from other factors controlling stratocumulus
cloud formation, which are discussed below. The criterion for subsidence is
met 9 % less often in ECHAM6-HAM2 than in ERA-INTERIM in these areas. As
the conditions of strong LTS and subsidence together are less frequently met
in ECHAM6-HAM2, stratocumulus clouds form less often than in ERA-Interim.
The stratocumulus regime covers 4.8 % of the global area in the reanalysis
data, 4.4 % in REF, 4.2 % in STAB, 3.0 % in VRES47 3.0 % in VRES95
and 4.5 % in AP. Gettelman et al. (2012) altered the stability threshold
to adjust the area covered by the stratocumulus regime in their simulations
to the same area fraction as in the reanalysis data but found that the
results did not change. Due to the smaller area (compared to reanalysis)
covered by the stratocumulus regime in our simulations cloud properties like
cloud cover, liquid water path or cloud radiative effect will therefore be
too low compared to observations. The regime-based analysis allows for investigation of cloud properties only when the environmental conditions for
stratocumulus clouds are met (see Sect. 2. and Appendix A) and therefore for
separation between in-regime uncertainties (all influences on stratocumulus
clouds formation excluding large-scale dynamical factors) and total
uncertainties (in-regime plus frequency of occurrence uncertainty, all
influences on stratocumulus clouds formation including dynamical factors).
We therefore differentiate in the following between cloud properties in
stratocumulus areas (total uncertainty) and stratocumulus regime cloud
properties (in-regime uncertainty). As values in the stratocumulus areas
include the average frequency of occurrence (
Low-level cloud cover in stratocumulus cloud regions in the
reference simulation and the CALIPSO and ISCCP satellite data. Values below
each panel show in-regime values (subscript
In Fig. 5 a clear underestimation of low-level cloud fraction (LCC) is visible in stratocumulus cloud regions in the reference simulation compared to CALIPSO/ISCCP satellite data. When looking only at (stratocumulus) in-regime values, i.e., similar large-scale environmental conditions, the underestimation is less severe: on average 48 % of the stratocumulus regions are cloud-covered in the reference simulation, compared to 65 % in CALIPSO data. The low-cloud cover is significantly lower in ISCCP compared to CALIPSO, whereas it is the opposite for mid-cloud cover, indicating a problem with the cloud top height in stratocumulus regions in the ISCCP data.
Liquid water path in stratocumulus cloud regions in the reference simulation, MODIS, ERA-Interim and a climatology from the University of Wisconsin. Values below the panels are in-regime values.
Similar to the cloud fraction, the liquid water path (LWP) is also too low in the reference simulation as compared to observations in stratocumulus areas (see Fig. 6). ERA-Interim reanalysis data agree fairly well with Moderate Resolution Imaging Spectroradiometer (MODIS; MYD08_D3 daily mean level 3 cloud product; King et al., 2003) data and the LWP climatology of the University of Wisconsin (UWisc; O'Dell et al., 2008) derived from satellite-based passive microwave observations (1988–2005) over oceans. On the other hand, when looking only at the LWP in the stratocumulus regime, the (in-regime) values for LWP are higher in the reference simulation than in ERA-Interim. The apparent underestimation of LWP is therefore due to the less frequent simulation of large LTS and subsidence in ECHAM6-HAM2.
Shortwave and longwave cloud radiative effect in stratocumulus cloud regions in the reference simulation and a 5-year CERES climatology. Values below each panel are in-regime values.
The shortwave and longwave cloud radiative effects (SWCRE/LWCRE) are too low (see Fig. 7) in the ECHAM6-HAM2 reference simulation compared to CERES data (Loeb et al., 2009). The in-regime value for the shortwave cloud radiative effect of the simulation agrees quite well with the observational data. The LWCRE, on the other hand, is also underestimated when only grid points that meet stratocumulus conditions are considered. This is not associated with stratocumulus clouds but is due to a lack of mid-level and high clouds in stratocumulus regions in the reference simulation. The net cloud radiative effect is therefore too negative in stratocumulus regions in ECHAM6-HAM2.
In Fig. 8, vertical profiles of relative humidity, potential temperature, cloud cover and liquid water content in stratocumulus regions for the reference simulation and ERA-Interim are shown. The inversion in temperature and humidity is not represented well in the reference simulation, which is mostly due to the coarse resolution used in the reference simulation.
The cloud cover and liquid water content profiles show that stratocumulus clouds form too low in the atmosphere and are too shallow in ECHAM6-HAM2. The liquid water content is too, high resulting in the observed overestimation of LWP.
Vertical profiles of relative humidity, potential temperature,
cloud cover and liquid water content in the stratocumulus regime. The red
line is for the ECHAM6-HAM2 reference simulation, the green line for the
STAB simulation, the black line for the VRES47
The mean diurnal cycle of liquid water path (LWP) in all stratocumulus regions from 1 month of an ECHAM6-HAM2 simulation is displayed in Fig. 9. Also shown is the diurnal cycle in different regions from Wood et al. (2002), who examined 2 years of TMI (Tropical Rainfall Measuring Mission Microwave Imager) satellite microwave radiometer data. Wood et al. (2002) found that the diurnal cycle was more pronounced in the SE Pacific and in the SE Atlantic. For a comparison, we therefore chose the month of October (2006), when the stratocumulus cloud cover is large in the SE Pacific and SE Atlantic (because of the large amount of data involved we were not able to compute the output for longer time periods). The mean LWP is lower in this particular month than the multiyear average (see Fig. 6). The difference in the morning maximum and the afternoon minimum of LWP, normalized to the mean LWP, in ECHAM6-HAM2 (26 %) agrees quite well with the TMI data (20–28 %, depending on the region).
To summarize, ECHAM6-HAM2 has cloud biases in stratocumulus cloud regions that are typical for GCMs: the clouds form too low and are too shallow, and low-cloud cover, liquid water path and the shortwave cloud radiative effect are underestimated. When looking only at data points where the environmental conditions are favorable for stratocumulus clouds (in-regime values), these biases are reduced. The monthly average diurnal cycle of stratocumulus clouds simulated with ECHAM6-HAM2 agrees well with observations.
In Fig. 10, changes in cloud properties are shown
when the long-tailed stability function of ECHAM6-HAM2 is replaced by a
sharp stability function. Both the cloud cover and the liquid water path
increase in the stratocumulus regime, whereas in other regions the changes
are small. The in-regime low-cloud cover increases by 5.3 % and the LWP
increases by 8.2 g m
The vertical cloud properties shown in Fig. 8 in the stratocumulus regime reveal subtle changes by using a sharp stability function. While stratocumulus clouds still form too low and their vertical extension seems to be limited, cloud cover and liquid water content are reduced above the inversion and reduced below, as would be expected from a reduction of mixing at cloud top.
Two 1-year simulations with climatological sea surface temperatures and
sea ice cover, but otherwise the same setup as REF and STAB, were conducted to
diagnose vertical profiles of the turbulent diffusion coefficients
(
An increase in the vertical resolution leads to a degradation of the
simulations, as parameters used in the parameterization of sub-grid processes
may depend on the resolution. In a sensitivity simulation, an autoconversion
rate parameter (ccraut) of 12 was necessary to achieve a balance of
radiative fluxes at the top of the atmosphere. This large autoconversion
rate leads to more precipitation in the stratocumulus regime as well as
strong reductions in cloud cover and liquid water path. For the experiments
with increased vertical resolution we therefore used tuning parameters when
possible, which showed no strong effect on stratocumulus clouds cloud cover
in sensitivity simulations. For L47bl, ccraut was kept as in the reference
simulation and a parameter for the entrainment rate of deep convection was
adjusted instead (entrpen
To estimate the effect of the reduction of the time step, the present-day
reference simulation (L31) was repeated with reduced time steps of 300 s and
180 s. This leads to significant increases in condensation and deposition
rates at shorter time steps and reduced vertical velocities due to reduced
TKE. This time step dependence will be fixed in
newer versions of the ECHAM6 GCM (ECHAM6.2 onwards; Mauritsen T., personal communication, 2014), but unfortunately they are not yet coupled to the aerosol scheme.
The reduced TKE leads to a reduced vertical velocity, which then favors
depositional growth of ice crystals at the expense of condensational growth
of cloud droplets (Wegener–Bergeron–Findeisen process). In stratocumulus
regions the reduced TKE reduces the cloud cover significantly when the time
step is reduced. The reduction in cloud cover in the stratocumulus regime in
the VRES experiments can therefore be attributed to the reduction of the
time step and the subsequent reduction of TKE. The changes in
condensation/deposition/TKE also lead to changes in convection. Mid-level
convection in the storm tracks is replaced with shallow convection. In the
tropics and subtropics, shallow convection is replaced by deep and mid-level
convection. These changes in convection correlate with changes in AAE. AAE
increases from
The different tuning and the reduced time steps are necessary for increasing the vertical resolution. The effects of changing the vertical resolution described below are not entirely due to the change in the vertical resolution alone but also to these necessary changes in the model setup.
The increase in the vertical resolution has an ambiguous impact on stratocumulus clouds. Figure 12 shows that, with L47bl, the already small low-cloud cover and the LWP in the stratocumulus regime decrease and the net cloud radiative effect is less negative compared to L31 in the reference simulation. The smaller low-cloud cover in the stratocumulus regime can be explained in part by the decreased TKE due to the smaller time step necessary. As a result of the decrease in the time step in the reference simulation, a decrease in 3 % in the low-cloud cover occurred. The decrease in low clouds is partly compensated for by a small increase in mid-level clouds, but the total cloud cover decreases with L47bl in the stratocumulus regime (not shown). The cloud cover in regions of shallow convective clouds increases (not shown) and compensates for the decrease in the stratocumulus regime, whereas other regions show only small changes. The vertical profiles of relative humidity and potential temperature do not change significantly with L47bl in the stratocumulus regime compared to the reference simulation (see Fig. 13). The clouds seem to form higher up in the atmosphere but the cloud cover and the liquid water content are reduced. Around 800 hPa the liquid water content is larger than in the reanalysis data. This is the result of too much vertical transport, as the cloud cover in the simulation with L47bl is not significantly larger around 800 hPa compared to the reanalysis data. Increasing the vertical resolution further has a somewhat different effect. With the highest vertical resolution grid L95bl used in this study, there is an increase in cloud cover and liquid water path in the stratocumulus regime (Fig. 12). The pattern appears like a spatial shift of the clouds, but in actual fact there are two changes partly compensating for each other. The increase in cloud cover and LWP is in areas where shallow cumulus clouds may also appear (the shallow convection frequency is reduced in the VRES95 experiment; see Appendix Fig. C1) and not in the “core” stratocumulus regions, where the same decrease in cloud cover and LWP as in the VRES47 simulation occurs (due in part to reduced turbulent vertical velocity). In VRES95 the vertical cloud properties are improved further, i.e., the clouds form higher up in the atmosphere and their vertical extent agrees better with reanalysis data. That there is no clear improvement in ECHAM6-HAM2 when increasing the vertical resolution is in agreement with other studies. Stevens et al. (2007) showed that LWP and the planetary boundary layer (PBL) depth are underestimated in ERA-40 (Uppala et al. 2005) and ERA-15 (Gibson et al. 1997) although the vertical resolution was increased from ERA-15 to ERA-40. With the Köhler (2005) PBL scheme the representation of stratocumulus clouds was improved in the ECMWF model without increasing the vertical resolution. Although increasing the vertical resolution in single-column models often improves the representation of stable/cloudy boundary layers (Grenier and Bretherton, 2001; Zhu et al., 2005; Wyant et al., 2007; Gettelman and Morrison, 2014), the same need not necessarily be true in a global model. Feedbacks between the dynamics and the physical parameterizations can cause differences in the biases of a parameterization in a global model and a single-column model (Petch et al., 2007; Zhang et al., 2013).
Aerosol, cloud and forcing parameters for present-day CLIM
simulations for all experiments. Global values and values in the
stratocumulus regime are given. Note that the results with L47bl and L95bl
are from 1-year simulations. LWP is liquid water path, IWP is ice water
path,
Diurnal cycle of liquid water path from TMI microwave radiometer data in different regions in 1999–2000 and ECHAM6-HAM2 in the stratocumulus regime in October 2006.
Difference in low-cloud cover, LWP and SWCRE in stratocumulus regions between a simulation with a sharp stability function and the reference simulation. Values below each panel are in-regime values.
Vertical profiles of turbulent kinetic energy (TKE, in
m
Same as in Fig. 10 but for increased vertical resolution (L47bl and L95bl). Values below each panel are in-regime values.
Vertical profiles of relative humidity, potential temperature, cloud cover and liquid water content in stratocumulus regions (in-regime values). The green line is for a simulation with the L47bl vertical grid, the black line for L95bl, the red line for the ECHAM6-HAM2 reference simulation and the blue line for ERA-Interim data.
The change in wet deposition of aerosol mass and the change in
production of SO
Changes in aerosol, cloud and forcing parameters between
simulations with preindustrial and present-day aerosol for all experiments.
Global values and values in the stratocumulus regime are given. Note that
the results with L47bl and L95bl are from 1-year simulations. LWP is
liquid water path, CC is cloud cover, AAE is the anthropogenic aerosol
effect,
The vertical profiles of relative humidity and cloud properties improve with
the L95bl resolution and are quite similar to reanalysis data. The clouds
are forming higher up in the atmosphere and have a larger vertical extent
(see Fig. 13). The higher cloud cover and LWP at
higher altitudes in the VRES experiments compared to ERA-Interim and the
lower cloud cover and LWP at lower altitudes indicate too much turbulent and
convective vertical transport at the cloud top in the VRES experiments.
There are still too few stratocumulus clouds even with L95bl in ECHAM6-HAM2,
as only the cloud cover increases in stratocumulus regions, whereas the
frequency of occurrence of those regions is still too low or even lower in
the VRES experiments compared to reanalysis data (Fig. 4). The aerosol burden decreases for all aerosol
species except sulfate (SO
In the VRES47
The total anthropogenic aerosol effect (AAE) is shown globally. The average value is shown below the panel.
The cloud condensation nuclei concentration at 0.1 % supersaturation
roughly doubles in the AP experiment compared to the reference simulation in
the stratocumulus regime, while the cloud droplet number concentration only
increases by 13 %. Although the aerosol load, aerosol size distribution
and mixing state change when using in-cloud aerosol processing (not shown),
this hardly affects cloud properties in stratocumulus cloud regions. In a
simulation with aerosol processing, the cloud cover is lower by 0.3 %, LWP
increases by 0.4 g m
In the experiment STAB
In Fig. 15 the total anthropogenic aerosol effect
(AAE) is shown globally. Stratocumulus regions are regions of a strong
negative AAE, as are regions close to the industrial centers of the world
and biomass burning regions. Table 2 lists aerosol, cloud and forcing
parameters for present-day CLIM simulations for all experiments. The large
SS burden and AOD in the AP experiment are due to too large sea salt
emissions (see Hoose et al., 2008a). Table 3 lists AAE and other
parameters for all experiments globally and in the stratocumulus regime. The
focus of this study is on the representation of marine stratocumulus
clouds. Therefore AAE is also computed in the stratocumulus regime. For
the computation of the change in the aerosol effect in the stratocumulus
regime (AAE
The change in AAE between the STAB, AP, VRES47 and VRES95 simulation and the reference simulation is shown globally. Values below each panel are average values for the areas above. Stippling marks statistically significant differences at the 90 % significance level.
Figure 16 shows the change in AAE between the
reference simulation and simulations with the sharp stability function
(STAP), aerosol processing (AP) and increased vertical resolution (VRES47,
VRES95), respectively. In the experiment with the sharp stability function
the change in LWP between the simulation with present-day and preindustrial
aerosol and the change in cloud cover are comparable to the reference
experiment (see Table 3, Fig. 16). AAE increases globally
(
There is a reduction in AAE compared to the reference simulation in the
experiment with aerosol processing, i.e., in regions of a negative AAE in
the reference simulation, AAE becomes less negative; in regions of a
positive AAE in the reference simulation, AAE becomes less positive, and
in the global average, AAE is less negative. Note that the impact of
aerosol processing may be different in high-resolution simulations (e.g., large eddy simulations) of stratocumulus clouds, as in our GCM simulation the important
“evaporation–entrainment” feedback (Xue and Feingold, 2006) is not accounted
for explicitly. In the AP experiment the background aerosol is increased.
This leads to a reduced susceptibility of the clouds to anthropogenic
aerosol. The reduction occurs everywhere over the globe in the simulation
with aerosol processing. Both shortwave and longwave forcings are weaker,
but the forcing becomes less negative on average (
Running the model with the sharp stability function and aerosol processing
together (STAB
In the VRES experiments there is a strong increase in AAE. As discussed in
Sect. 4.2.2 there are changes in aerosol emission and removal in the VRES
experiments compared to the reference simulation, leading to smaller aerosol
burdens. These changes do not seem to be the direct result of the changed model
resolution but instead the changes in the clouds. Changes in clouds, as they
occur in the VRES experiments, also change the atmospheric aerosol by
changing wet deposition or production of SO
In the VRES47 experiment, both shortwave and longwave aerosol forcing
increase compared to the REF experiment. The resulting AAE is stronger in
VRES47 than in REF. The change in the shortwave and longwave aerosol forcing probably
comes from changes in cloud regimes due to the increased vertical
resolution and different entrainment rates for deep convection. In the
stratocumulus regimes there is a similarly strong increase in AAE
Combining the increased vertical resolution with the sharp stability
function (VRES47
In the VRES95 experiment, AAE is strongly increased. This is due to the lower
aerosol load described above in the present-day and preindustrial aerosol simulations at
this high vertical resolution and the subsequent increased susceptibility to
anthropogenic aerosol. In the stratocumulus regime a similarly strong increase
compared to REF in AAE
We have performed several simulations to identify cloud biases in the stratocumulus regime and to improve the representation of stratocumulus clouds and the aerosol in the stratocumulus regime. The impact of these changes on the anthropogenic aerosol effect have also been investigated. The biases in ECHAM6-HAM2 are typical for global models: the clouds form too low and are too shallow, and low-cloud cover, liquid water path and the shortwave cloud radiative effect are underestimated. In the stratocumulus regime (diagnosed by environmental conditions) these biases are reduced.
The formation of stratocumulus clouds depends on many factors. Their
representation in large-scale models requires a correct simulation of the
large-scale environment. The main reasons for the cloud biases in regions
with high stratocumulus cloud cover in ECHAM6-HAM2 are as follows:
Too strong turbulent mixing at stable conditions: at high vertical resolution the
vertical cloud properties indicate a too strong mixing at the top of stratocumulus clouds in
ECHAM6-HAM2 and too much convective transport. The turbulent mixing at stable conditions can be
reduced by using a “sharp” stability function in the TKE scheme of ECHAM6. This improves the
stratocumulus cloud cover and liquid water path but changes the vertical cloud properties only
modestly. The stratocumulus clouds in ECHAM6-HAM2 at high vertical resolution have a larger vertical
extent but their coverage is smaller at lower altitudes than in ERA-Interim. This may be explained by
too strong entrainment of warm, dry free-tropospheric air into the PBL, which is reduced with the
sharp stability function, and too much convective transport of moisture to higher levels. The improvement through use of a sharp stability function is not sufficient to reconcile the simulated low-cloud cover with that of
satellite observations. Too “active” shallow convective scheme: another reason for the lack of stratocumulus
clouds appears to be the over-active shallow convection scheme in ECHAM6-HAM2. Isotta et al. (2011)
showed that the Tiedtke shallow-convection scheme (Tiedtke, 1989) used in ECHAM5-HAM (Roeckner et al., 2003;
Stier et al., 2005; also used in ECHAM6-HAM2) activates too frequently compared to large eddy
simulations and observations of the frequency of cumulus clouds. Their transient shallow-convection
scheme decreased the frequency of shallow convection which was compensated for by increased stratus and
stratocumulus (a similar decrease in shallow-convection frequency and increase in LWP in the
stratocumulus regime was observed in the VRES95 experiment; see Fig. C1). In a recent study, Nam
et al. (2014) compared three boundary layer cloud schemes in ECHAM5 to the standard scheme used
in ECHAM5 and CALIPSO and CloudSat satellite observations. All three schemes improved low-cloud
cover and precipitation in the (sub)tropics compared to the standard scheme (note that their ECHAM5_Trig model is
similar to what is used in ECHAM6). Two of the new schemes reduced the frequency of shallow convection compared to
standard ECHAM5. The third new scheme does not compute shallow convection separately.
By turning off shallow convection completely in a sensitivity study we found
that stratocumulus clouds were forming higher up and were thicker. The
improvement is almost as large as that from increasing the vertical resolution.
Turning off shallow convection also increased the low-cloud cover in the
stratocumulus regime. Changing the shallow convection scheme in ECHAM6 would
probably be beneficial for representing stratocumulus clouds. The relative-humidity-based cloud cover scheme: a sensitivity study where precipitation
in the stratocumulus regime was turned off showed an impact mainly on liquid water path, cloud
optical properties and cloud radiative effects. LWP and cloud optical depth (COD) approximately
double in the stratocumulus regime without precipitation compared to the reference simulation, and
SWCRE is increased by 21 %, resulting in a more negative net cloud radiative effect (NETCRE in
worse agreement with observations). The low-cloud cover increases only by 3 % from 47.7 to 50.7 %.
This strong increase in LWP resulting from turning off precipitation, which hardly affects low-cloud cover, indicates that the
relative-humidity-based cloud cover scheme used for the simulations does not produce enough cloud cover in the
stratocumulus regime (see also Fig. 5). Lack of vertical resolution: stratocumulus clouds in ECHAM6-HAM2 form too low and are too
shallow. With an increased vertical resolution, the clouds form higher up and are quite
similar to the clouds in the ERA-Interim stratocumulus regime. A simple increase in the vertical
resolution (at unchanged horizontal resolution) improves the vertical cloud properties in the stratocumulus
regime but affects other parts of the model and leads to a degradation of the simulation. Diagnosing the
actual inversion height (cloud top) in stratocumulus regions as in the schemes of Grenier and Bretherton (2001;
applied to ECHAM5-HAM in Siegenthaler-Le Drian, 2010) could improve stratocumulus clouds while keeping the
interaction with other parts of the model at a minimum. Possibly too low subsidence rates: environmental conditions suitable for stratocumulus clouds appear
8 % less frequently in ECHAM6-HAM2 (4.4 % of the global area in the REF experiment) than in reanalysis
data (4.8 %) due to a too low LTS and too low subsidence rates. The underestimation of the frequency of
stratocumulus conditions appears in all simulations conducted in this study, in particular also in the simulations
with reduced turbulent mixing at the top of the stratocumulus clouds and increased vertical resolution. Subsidence
rates are lower in ECHAM6-HAM2 than in ERA-Interim, which might explain the lack of inversions. The monthly average diurnal cycle of liquid water path of stratocumulus clouds modeled in
ECHAM6-HAM2, on the other hand, agrees well with observations.
Our simulations indicate that no single measure brings the simulated
stratocumulus clouds in ECHAM6-HAM2 into agreement with observations. Changes
to three parts of the model will be necessary to further improve the
simulation of stratocumulus clouds in ECHAM6-HAM2:
changes in the cloud cover scheme, changes in the shallow convection scheme, changes in the boundary layer scheme.
From our simulations with changes in model resolution and physics to better
represent clouds and aerosol in the stratocumulus regime, we conclude that
the anthropogenic aerosol effect (AAE) is sensitive to changes in
(stratocumulus) clouds.
Aerosol processing in stratiform clouds has only a small impact on cloud
properties in ECHAM6-HAM2 but it reduces the anthropogenic aerosol effect
globally from
The stratocumulus regime is defined by environmental conditions (Eqs. 1,
2). At T63 (1.9
As environmental conditions change over time, the such defined areas also change over time. Thus, at each point in time, the stratocumulus regime may consist of different geographical areas. Appendix Fig. A1 shows the stratocumulus regime in January and July 2006. The variation that occurs between different months makes it difficult to compare values from a specific month between two simulations. However, the annual average where the environmental conditions favorable for stratocumulus clouds are met is quite constant. Furthermore, the conditions are often met in specific geographical areas. Monthly mean values of LTS and vertical velocity were used to compute the stratocumulus regime.
Note that the term stratocumulus regime used in this study refers only to the presence of specific environmental conditions and not necessarily to the presence of clouds. The conditions were chosen to be favorable for stratocumulus clouds, but that does not mean that a cloud must be present in every area within the stratocumulus regime.
This definition of the stratocumulus regime allows, to the extent possible in a GCM simulation, for separation of dynamical and other influences on the simulation of stratocumulus clouds. Dynamics alter when and where stratocumulus conditions are present, but once they are met the properties of stratocumulus clouds in the stratocumulus regime (in-regime values) can be considered to mainly depend on the parameterizations used in the model and not on the (resolved) large-scale dynamics.
The stratocumulus regime in January and July 2006.
Figure 4 shows a 5-year average of the occurrence of the environmental conditions favorable for stratocumulus clouds. In some geographical areas it is apparent that the environmental conditions favorable for stratocumulus clouds are met more than 25 % of the time; in some areas they are met even more than 50 % of the time, or even more frequently. We use this to define six geographically distinct stratocumulus regions by hand (also shown in Fig. 4).
These are average values of a certain quantity over all areas where the
environmental conditions favorable for stratocumulus clouds are met, i.e.,
average values for the stratocumulus regime. Note that a cloud need not be present in every area
within the stratocumulus regime. For example, average
values of low-cloud cover are shown in Fig. 5.
In-regime values are shown in many figures below the panels in this study
(marked by the subscript
The in-regime values can be multiplied by the frequency of occurrence of stratocumulus conditions. These values then give the total uncertainty due to the dynamics of the models and other model parts compared to reanalysis data and observations. In-regime values multiplied by the frequency of occurrence of stratocumulus conditions are displayed in many figures of the present study to facilitate the assessment of the total model uncertainty.
Results of the
Probability computed with an unpaired two-tailed
Same as Table B1 but the
The frequency of the activation of the shallow-convection scheme in the REF, STAB, VRES47 and VRES95 experiments is shown in Fig. C1.
Frequency of the activation of the shallow-convection scheme in the REF, STAB, VRES47 and VRES95 experiments.
D. Neubauer gratefully acknowledges the support by the Austrian Science Fund (FWF): J 3402-N29 (Erwin Schrödinger Fellowship Abroad) and ETH Zurich. The EU FP7 projects EUCLIPSE (244067) and COMBINE (226520) are acknowledged for financial support. This work was supported by a grant from the Swiss National Supercomputing Centre (CSCS) under project ID s431. We would like to thank Bjorn Stevens, Thorsten Mauritsen, Felix Pithan, Sebastian Rast, Andreas Chlond, Erich Roeckner, Andrew Gettelman, Graham Feingold, Colombe Siegenthaler-Le Drian, Anna Possner, Sylvaine Ferrachat, Angela Meyer and Franziska Glaßmeier for discussions and technical help. Edited by: M. C. Facchini