Introduction
Modeling climate on a regional scale is essential for assessing the
impact of climate change on society, the economy and natural
resources. Regional climate models are limited-area models that
simulate climate processes being often used to downscale dynamically
global model simulations or global reanalysis data for specific
regions in order to provide more detailed results (Laprise, 2008;
Rummukainen, 2010). Several studies suggest that we can benefit from
the use of regional climate models, especially due to the higher
resolution of stationary features like topography and coastlines and from
the improved representation of small-scale processes such as
convective precipitation (see Flato et al., 2013, and references
therein). Usually, regional climate models are evaluated and “tuned”
according to their ability to simulate temperature and precipitation
(e.g., Giorgi et al., 2012; Vautard et al., 2013; Kotlarski et al.,
2014). However, as discussed in Katragkou et al. (2015), the role of
other climatological parameters should be included in the evaluation
procedure of regional climate models (e.g., radiative fluxes, sensible
and latent heat fluxes and cloud properties).
The ability of regional climate models to assess surface
solar radiation (SSR) patterns has not received much attention
despite the fact that SSR plays a core role in various climatic
processes and parameters such as (1) evapotranspiration (e.g., Teuling
et al., 2009), (2) hydrological cycle (e.g., Allen and Ingram, 2002;
Ramanathan et al., 2001; Wang et al., 2010; Wild and Liepert, 2010),
(3) photosynthesis (e.g., Gu et al., 2002; Mercado et al., 2009),
(4) oceanic heat budget (e.g., Lewis et al., 1990; Webster et al., 1996;
Bodas-Salcedo et al., 2014), and (5) global energy balance (e.g., Kim and
Ramanathan, 2008; Stephens et al., 2012; Trenberth et al., 2009; Wild
et al., 2013) and solar energy production (Hammer et al., 2003) and
largely affects temperature and precipitation. The same holds for the
parameters that drive SSR levels, such as cloud macrophysical and
microphysical properties (cloud fractional cover, CFC; cloud optical
thickness, COT; and cloud effective radius, Re), aerosol optical
properties (aerosol optical depth, AOD; asymmetry factor, ASY; and single-scattering albedo, SSA), surface broadband albedo (ALB) and atmospheric
water vapor amount (WV). However, in the last few years, there have been
a few regional climate model studies focusing on the SSR levels or the
net surface shortwave radiation, either to examine the
dimming/brightening effect (e.g., Zubler et al., 2011; Chiacchio
et al., 2015) or to evaluate the models (e.g., Jaeger et al., 2008;
Markovic et al., 2008; Kothe and Ahrens, 2010; Kothe et al., 2011,
2014; Güttler et al., 2014). These studies highlight the
dominating effect of cloud cover and surface albedo.
List of the parameters being analyzed in this work, their sources,
the original resolution at which the data were acquired and the corresponding time periods.
Parameter
Source
Resolution
Period
SSR
CM SAF MFG
0.03∘ × 0.03∘
2000–2005
SSR
CM SAF MSG
0.05∘ × 0.05∘
2006–2009
CFC
CM SAF MSG
0.05∘ × 0.05∘
2004–2009
COT
CM SAF MSG
0.05∘ × 0.05∘
2004–2009
Re
CM SAF MSG
0.05∘ × 0.05∘
2004–2009
AOD
MACv1
1∘ × 1∘
Climatology
ASY
MACv1
1∘ × 1∘
Climatology
SSA
MACv1
1∘ × 1∘
Climatology
ALB
CERES
1∘ × 1∘
Climatology
WV
ERA-Interim
1∘ × 1∘
2006–2009
All above
RegCM4
50 km × 50 km
2000–2009
In this work, we go a step further, proceeding to a detailed
evaluation of the ability of RegCM4 regional climate model to simulate
SSR patterns over Europe, taking into account not only CFC and ALB but
also COT, Re, AOD, ASY, SSA and WV. For the scope of this study, the same
parameters are extracted from satellite-based observational data (Satellite Application Facility on Climate Monitoring (CM SAF), CERES),
data from an aerosol climatology (MACv1) and data from the ERA-Interim reanalysis
(see Table 1). First a decadal simulation (2000–2009) is implemented with the model
and the output is evaluated against observations from the EUMETSAT
geostationary satellites of CM SAF. SSR data from the Meteosat First Generation (MFG)
satellites (Tessier, 1989) are available for the period 2000–2005, while data from the Meteosat
Second Generation (MSG) satellites (Schmetz et al., 2002) are available for the period 2006–2009.
These data are characterized by a high spatial (∼3–5 km) and temporal resolution (15–30 min) and
have been validated in the past, constituting a well-established
product (e.g., Sanchez-Lorenzo et al., 2013; Posselt et al., 2014).
In Sect. 2.1, the basic features of the model are described
along with the simulation setup and the way various parameters are
calculated by the model. In Sects. 2.2 and 2.3, a description of the
satellite data from CM SAF and the other data which are used for the
evaluation of RegCM4 is given, while in Sect. 2.4 we discuss the
methodology followed in this paper. Section 3.1 includes the
evaluation of RegCM4 SSR against data from MFG and MSG; Sects. 3.2 and 3.3
the evaluation of CFC, COT and Re against data from MSG; Sect. 3.4 the comparison of
RegCM4 AOD, ASY and SSA with data from MACv1 aerosol climatology; and Sect. 3.5 the comparison of
RegCM4 WV and ALB with data from ERA-Interim reanalysis and CERES
satellite sensors, respectively. The CFC, COT, Re, AOD, ASY, SSA, ALB and WV data sets
where chosen so as to be consistent with the CM SAF SSR data set. The potential contribution of various
parameters to the RegCM4–CM SAF SSR differences is estimated with the
combined use of the data mentioned above and a radiative transfer
model for the MSG SSR period (2006–2009). The results are presented in Sect. 3.6, while the main
findings of this paper are summarized in Sect. 4.
Model description, data and methods
RegCM4 description and simulation setup
In this work, a decadal (2000–2009) simulation was implemented with
RegCM4.4 (hereafter denoted RegCM4 or RegCM) for the greater
European region with a horizontal resolution of 50 km. The
model's domain extends from 65∘ W to 65∘ E and 15 to
75∘ N including the largest part of the Sahara and
part of the Middle East (see Fig. S1 in the Supplement of this
paper). RegCM is a hydrostatic, σ-p regional climate model
with a dynamical core based on the hydrostatic version of the PSU/NCAR
Mesoscale Model version 5 (MM5) (Grell et al., 1994). Specifically,
RegCM4 is a substantially improved version of the model compared to
its predecessor RegCM3 (Pal et al., 2007) with regard to software code
and physics (e.g., radiative transfer, planetary boundary layer,
convection schemes over land and ocean, land types and surface
processes, ocean–air exchanges). Details on the historical evolution
of RegCM from the late 1980s until today and a full description of
RegCM4's basic features are given in Giorgi et al. (2012).
Data from ECMWF's ERA-Interim reanalysis were used as lateral boundary
conditions. RegCM4 through a simplified aerosol scheme accounts for
anthropogenic SO2, sulfates, and organic and black carbon (Solmon
et al., 2006). The emissions of these anthropogenic aerosols are based
on monthly, time-dependent, historical emissions from the Coupled Model Intercomparison
Project Phase 5 (CMIP5) (Lamarque et al., 2010) with a 1-year spin-up
time (1999). This inventory is used by a number of climate models in
support of the most recent report of the Intergovernmental Panel on
Climate Change (IPCC, 2013). The model also accounts for maritime
particles through a two-bin sea salt scheme (Zakey et al., 2008) and for
dust through a four-bin approach (Zakey et al., 2006).
For each model layer a concentration of anthropogenic SO2,
sulfates, black carbon, organic carbon, sea salt particles and dust is calculated,
from which, according to a look-up table with associated optical properties, the
model accounts for the aerosol extinction profiles (see Solmon et al., 2006;
Zakey et al., 2006, 2008, for more details). For our simulation, the
MIT–Emanuel convection scheme (Emanuel, 1991; Emanuel and
Zivkovic-Rothman, 1999) was used. Convection is triggered when the buoyancy level
is higher than the cloud base level. The cloud mixing is considered to be episodic and
inhomogeneous, while the convective fluxes are based on a model of sub-cloud-scale updrafts
and downdrafts (see Giorgi et al., 2012). Zanis et al. (2009) reported for
RegCM3 that the low stratiform clouds are systematically denser and
more persistent with the use of the Grell (1993) convective
scheme than with the Emanuel scheme, a result with major importance
for the cloud–radiation feedback. The boundary layer scheme of
Holtslag et al. (1990) was utilized, while the Subgrid Explicit
Moisture Scheme (SUBEX) handles large-scale cloud and precipitation
computations. The ocean flux scheme was taken from Zeng et al. (1998)
with the Biosphere–Atmosphere Transfer Scheme (BATS) (Dickinson
et al., 1993) accounting for land surface processes.
The Community Climate Model version 3 (CCM3) (Kiehl et al., 1996)
radiative package handles radiative transfer within RegCM4. The CCM3
scheme employs the δ-Eddington approximation following its
predecessor (CCM2) (Briegleb, 1992). Especially for the shortwave
radiation, the radiative transfer model takes into account the effect
of atmospheric water vapor and greenhouse gasses, aerosol amount and
optical properties per layer (e.g., aerosol optical thickness, asymmetry factor,
single-scattering albedo), and cloud macrophysical (e.g., cloud
fractional cover) and microphysical properties (e.g., effective droplet
radius, liquid water path, cloud optical thickness) and land surface
properties (surface albedo). The radiative transfer equation is solved
for 18 discrete spectral intervals from 0.2 to 5 µm for
the 18 RegCM vertical sigma layers from 50 hPa to the surface.
The effect of clouds on shortwave radiation is manifested by CFC,
cloud droplet size and cloud water path (CWP), which is based on the
prognostically calculated parameter of cloud water amount (Giorgi
et al., 2012). Within the model, the effective droplet radius for
liquid clouds (Rel) is considered constant (10 µm) over
the ocean, while over land it is given as a function of temperature
(Kiehl et al., 1998; Collins et al., 2006). On the other hand, the ice
particle effective radius (Rei) is given as a function of normalized
pressure, starting from 10 µm. The equations used for the
calculation of Rel and Rei are given below.
Rel=5µmT>-10∘C5-5T+1020µm-30∘C≤T≤-10∘CReiT<-30∘C,Rei=Reiminp/ps>pIhighReimin-(Reimax-Reimin)(p/ps)-pIhighpIhigh-pIlowµmp/ps≤pIhigh,
where T is the atmospheric temperature, p is the atmospheric pressure,
ps is the surface pressure, Reimax=30 µm,
Reimin=10 µm, pIhigh=0.4
and pIlow=0.0. The fraction (fice) of cloud water that
consists of ice particles is given as a function of T, the fraction (fliq)
of the liquid water droplets being calculated as fliq=1-fice.
fice=0T>-10∘C-0.05T+10-30∘C≤T≤-10∘C1T<-30∘C
Then, the radiative properties of liquid and ice clouds in the
shortwave spectral region are given by the following
parameterizations, originally found in Slingo (1989) and revisited by
Briegleb (1992).
COTphλ=CWPaphλ+bphλRephfph,SSAphλ=1-cphλ-dphλReph,ASYphλ=ephλ+fphλReph,ϕphλ=ASYphλ2,
where superscript λ denotes the spectral interval and
subscript ph denotes the phase (liquid/ice), while ϕ is
the phase function of clouds. It needs to be mentioned here that all
the equations presented above are given in Kiehl et al. (1998) and
Collins et al. (2004) with slightly different annotation. The
coefficients a–f for liquid clouds are given in Slingo (1989), while
those for ice clouds are given in Ebert and Curry (1992) for the four pseudo-spectral
intervals (0.25–0.69, 0.69–1.19, 1.19–2.38 and
2.38–4.00 µm) employed in the radiative scheme of
RegCM. Especially for COT, in this paper we calculated this parameter for the
spectral interval 0.25–0.69 µm for both liquid and ice
clouds so that it is comparable to the CM SAF satellite-retrieved COT at
0.6 µm (see Sect. 2.2). Following the approach of Cess
(1985), to derive the bulk COT for the whole atmospheric column, the
COTs calculated for each layer are simply added. The total COT for
each layer is calculated by merging the COT values for liquid and ice
clouds.
Within RegCM, CFC at each layer is calculated from relative humidity
and cloud droplet radius. The surface radiation flux in RegCM4 is
calculated separately for the clear and cloud-covered part of the
sky. The total CFC for each model grid cell is an intermediate value
between the one calculated using the random overlap approach, which
leads to a maximum cloud cover, and the one found by assuming a full
overlap of the clouds appearing in different layers, which minimizes
cloud cover. As discussed in Giorgi et al. (2012), this approach
allows for a more realistic representation of surface radiative
fluxes.
CM SAF satellite data
To evaluate the RegCM4 SSR simulations described previously, we use
high-resolution satellite data from the SIS (surface incoming
shortwave radiation) product of CM SAF. The data sets were obtained
from EUMETSAT's MFG (10.5676/EUM_SAF_CM/RAD_MVIRI/V001)
and MSG (10.5676/EUM_SAF_CM/CLAAS/V001) geostationary satellites. SSR data are available from
1983 to 2005 from six MFG satellites (Meteosat
2–7) and from 2005 onwards from MSG satellites
(Meteosat 8–10). These satellites fly at an altitude of ∼36 000 km, being located at longitudes around
0∘ above the Equator and covering an area extending from
80∘ W to 80∘ E and from 80∘ S to
80∘ N. In the case of MFG satellites, the SSR data are
retrieved from measurements with the Meteosat Visible and Infrared
Instrument (MVIRI) sensor. MVIRI is a radiometer that takes
measurements at three spectral bands (visible, water vapor, infrared)
every 30 min. SSR is retrieved using MVIRI's broadband visible
channel (0.45–1 µm) only, at a spatial resolution of
∼2.5 km (at the sub-satellite point). The data are
afterwards re-gridded on a 0.03∘×0.03∘ regular
grid.
The MagicSol–Heliosat algorithm, used for the derivation of the SSR
data analyzed in this work, has been extensively described in several
papers (see Posselt et al., 2011a, b, 2012, 2014; Mueller et al., 2011; Sanchez-Lorenzo et al., 2013). The
algorithm includes a modified version of the original Heliosat method
(Beyer et al., 1996; Cano et al., 1986). Heliosat utilizes the digital
counts obtained from the visible channel to calculate the so-called
effective cloud albedo. The modified version incorporates the
determination of the monthly maximum normalized digital count (for
each MVIRI sensor) that serves as a self-calibration parameter. To
derive the clear-sky background reflection, a 7-day running average
of the minimum normalized digital counts is used instead of fixed
monthly mean values. This method minimizes changes appearing in the
radiance data recorded by different MVIRI sensors due to the
transition from one Meteosat satellite to another, ensuring a data set that
is as homogeneous as possible. Then, the clear-sky
irradiances are derived using the look-up-table-based clear-sky model
MAGIC (Mueller et al., 2009) and finally SSR is retrieved by combining
them with the effective cloud albedo.
On the other hand, MSG satellites carry the Spinning Enhanced Visible
and Infrared Imager (SEVIRI), a radiometer taking measurements at 12
spectral bands (from visible to infrared) every 15 min with a spatial
resolution of ∼3 km (at the sub-satellite point). The
data used here are available in a 0.05∘×0.05∘
regular grid. The SEVIRI broadband high-resolution visible channel
(HRV), which is very close to MVIRI's broadband visible channel, cannot
be used for the continuation of the SSR data set, since, unlike MVIRI,
it does not cover the full Earth disk. Furthermore, the use of
one of SEVIRI's narrow-band visible channels directly in the same
algorithm as MVIRI (MagicSol) is not feasible – firstly because
of the spectral differences with MVIRI's broadband visible channel,
and secondly because of the sensitivity of cloud albedo to spectral
differences of the land surfaces below the clouds (especially for
vegetated areas) (see Posselt et al., 2011a, 2014). In this case, an
artificial SEVIRI broadband visible channel that corresponds to
MVIRI's broadband visible channel is simulated following the approach
of Cros et al. (2006). SEVIRI's two narrow-band visible channels (0.6
and 0.8 µm) and MVIRI's broadband channel spectral
characteristics are used to establish a simple linear model. This
model is afterwards applied to SEVIRI's 0.6 and 0.8 µm
radiance measurements to calculate the broadband visible channel
radiance (see Posselt et al., 2014, for more details).
The CM SAF SSR satellite-based product is characterized by a threshold
accuracy of 15 Wm-2 for monthly mean data and
25 Wm-2 for daily data (Mueller et al., 2011; Posselt
et al., 2012, 2014; Sanchez-Lorenzo et al., 2013). Posselt et al. (2012) evaluated CM SAF SSR data on a daily and
monthly basis against ground-based observations from 12 BSRN (Baseline
Surface Radiation Network) stations around the world, showing that
both daily and monthly CM SAF data are below the target accuracy for
∼90 % of the stations. Specifically for Europe,
Sanchez-Lorenzo et al. (2013), using monthly SSR data from 47 GEBA
(Global Energy Balance Archive) ground stations, proceeded to
a detailed validation of the CM SAF SSR data set for the period
1983–2005. They found that CM SAF slightly overestimates SSR by
5.2 Wm-2 (4.4 % in relative values). Also, the mean
absolute bias was found to be 8.2 Wm-2, which is below the
accuracy threshold of 15 Wm-2 (10 Wm-2 for
the CM SAF retrieval accuracy and 5 Wm-2 for the surface
measurements uncertainties). Applying the standard normal homogeneity
test (SNHT), Sanchez-Lorenzo et al. (2013) revealed that the MFG SSR
data over Europe can be considered homogeneous for the period
1994–2005. Recently, Posselt et al. (2014) verified the results of
the previous two studies by using a combined MFG-MSG SSR data set
spanning from 1983 to 2010. They found that the monthly mean data set
exhibits a mean bias of +3.16 Wm-2 and
a mean absolute bias of 8.15 Wm-2 compared to
BSRN, which is again below the accuracy threshold of CM SAF. Also, the
data set was found to be homogeneous for the period 1994–2010 in most
of the investigated regions except for Africa.
To investigate the differences appearing between the RegCM4 and CM SAF
SSR fields we also use CFC, COT and Re CM SAF observations from MSG
satellites for the period 2004–2009. A description of this cloud
optical properties product, also known as CLAAS (CLoud property
dAtAset using SEVIRI), can be found in Stengel et al. (2014). The MSG
NWC software package v2010 is used for the detection of cloudy pixels,
the determination of their type (liquid/ice) and their vertical
placement (Derrien and Le Gléau, 2005; NWCSAF, 2010). The
detection of cloudy pixels is based on a multispectral threshold
method incorporating parameters such us illumination (e.g., daytime,
twilight, nighttime, sunglint) and type of surface. According to
Kniffka et al. (2014), the CM SAF cloud mask accuracy is ∼90 % (successful detection of cloudy pixels for ∼90 %
of the cases) when evaluated against satellite data from
CALIOP/CALIPSO and CPR/CloudSat. The bias of the CFC product was found
to be +2 and +3 % for SEVIRI's disk when compared to ground-based data from SYNOP
(lidar-radar measurements) and satellite-based data from MODIS,
respectively (Stengel et al., 2014). The cloud physical properties
(CPP) algorithm (Roebeling et al., 2006; Meirink et al., 2013) is used
to retrieve COT at 0.6 µm, Re and CWP. The algorithm is
based on the use of SEVIRI's spectral measurements at the visible
(0.64 µm) and near infrared (1.63 µm)
(Nakajima and King, 1990). First, COT and Re are retrieved for the
cloudy pixels and then CWP is given by the following equation:
CWPph=2/3ρphRephCOTph,
where ph stands for the clouds' phase (liquid/ice) and ρ is the
density of water. According to Stengel et al. (2014), the CM SAF COT
bias was estimated at -9.9 % compared to MODIS observations. The
corresponding bias for CWP is -0.3 % for liquid-phase clouds and
-6.2 % for ice-phase clouds. COT and CWP data are available from
CM SAF at a spatial resolution of 0.05∘×0.05∘
on a daily basis. In this work, Re values were calculated from the COT
and CWP CM SAF available data using Eq. (8).
Seasonal NMB patterns of RegCM4–CM SAF SSR over Europe for
(a) winter (DJF), (b) spring (MAM), (c) summer
(JJA) and (d) autumn (SON) from MSG SEVIRI
observations. The seven sub-regions used for the generalization of the
results are marked in (a): northern Europe (NE), central Europe
(CE), eastern Europe (EE), Iberian Peninsula (IP), central
Mediterranean (CM), eastern Mediterranean (EM) and northern Africa
(NA).
Other data
In addition to the CM SAF SSR and cloud optical properties data used
for the evaluation of RegCM4, we also use ancillary data from other
sources, namely AOD, ASY and SSA at 550 nm monthly
climatological values from the MACv1 climatology (Kinne et al., 2013);
monthly climatological broadband surface shortwave fluxes retrieved
from CERES sensors aboard EOS TERRA and AQUA satellites for a 14-year
period starting from March 2000 (Kato et al., 2013); and finally monthly
mean total column WV data from ECMWF's ERA-Interim reanalysis (Dee
et al., 2011) for the period 2006–2009. All the data were obtained at
a spatial resolution of 1∘×1∘. It should be mentioned that these data are similar to the ones used as input
within the MAGIC clear-sky radiative transfer code (Mueller et al.,
2009), which is used for the calculation of CM SAF SSR. Therefore, they
can be used in order to examine the reasons for possible deviations
appearing between RegCM4 and CM SAF SSR (see Sect. 2.4).
To our knowledge, the uncertainty in the MACv1 aerosol parameters
used here has not been reported anywhere in detail. The CERES broadband
surface albedo over land exhibits a relative bias of -2.4 % compared to MODIS.
Specifically, over deserts, the relative bias drops to -2.1 % (Rutan et al., 2009). A
detailed evaluation of the ERA-Interim WV total column product does not exist.
Only recently, the upper troposphere–lower stratosphere WV data were evaluated
against airborne campaign measurements, showing good agreement (30 % of the observations
were almost perfectly represented by the model) (Kunz et al., 2014).
Methodology
In this study, first of all, the RegCM4 SSR fields are evaluated against SSR
fields from CM SAF (MFG for 2000–2005 and MSG for 2006–2009) for the European region (box region
in Fig. S1). Prior to the evaluation, the
model and satellite data are averaged on a monthly basis and brought
to a common 0.5∘×0.5∘ spatial resolution. It
should be mentioned that the same temporal and spatial resolution was
used for all the data utilized in this study. Maps with the normalized
mean bias (NMB) (hereafter denoted as bias) are produced on an annual
and seasonal basis. NMB is given by the following equation:
NMB=∑i=1N(RegCMi-CMSAFi)∑i=1NCMSAFi100%=RegCMCMSAF‾‾-1100%,
where RegCMi and CMSAFi represent the RegCM4 and CM SAF mean
values for each month i, N is the number of months, and
RegCM‾ and CMSAF‾ are the
RegCM4 and CM SAF mean values. The statistical significance of the
results at the 95 % confidence level is checked by means of a two-independent-sample t test:
t=(RegCM‾-CMSAF‾)/σRegCM2+σCMSAF2/N,
where σRegCM and σCMSAF are the standard deviations of
RegCM4 and CM SAF total means. When |t| is greater than a critical
value that depends on the degrees of freedom (here 2n-1), the bias is
considered statistically significant. In addition to the whole
European region (EU) and the land-covered (LA) and ocean-covered (OC)
part of Europe, seven other sub-regions are defined for the
generalization of our results: northern Europe (NE), central Europe
(CE), eastern Europe (EE), Iberian Peninsula (IP), central
Mediterranean (CM), eastern Mediterranean (EM) and northern Africa
(NA) (see Figs. 1a and S1). The bias on an annual and seasonal basis
is calculated per region. Apart from bias, other statistical metrics
(correlation coefficient, R; normalized standard deviation, NSD;
modified normalized mean bias, MNMB; root mean square error, RMSE) are
also defined, calculated and presented in the Supplement of this
paper. Specifically for the SSR results presented in the paper
the normalized mean error (NME) is calculated along with the bias in order
to get an insight into the absolute bias between the model simulations
and the satellite observations.
NME=∑i=1NRegCMi-CMSAFi∑i=1NCMSAFi100%
The latitudinal variability in model and satellite-based SSR and their difference
is examined by means of seasonal plots. Finally, the seasonal
variability in SSR from RegCM4 and CM SAF and their differences is
investigated for each of the 10 regions mentioned above.
While NMB is primarily used in this work for the investigation of
the spatiotemporal variability in RegCM4–CM SAF deviations, the real
difference is given in the plots with the latitudinal and seasonal variability
for each region in order to get an insight into the performance of the model,
regardless of the SSR levels. The same procedure is done separately
for MFG data (2000–2005) and MSG data (2006–2009) to see whether
the two data sets lead to similar results. Our results are mostly
focused on MSG satellite-based observations, since CFC and
cloud optical properties data are only available from MSG SEVIRI.
In order to interpret the observed differences between RegCM4 and CM
SAF SSR, the same detailed procedure is repeated for CFC and COT for
the period 2004–2009. CFC and COT are the two major determinants of
the transmission of shortwave radiation through clouds (Gupta et al.,
1993) and along with AOD constitute the major controllers of SSR
(Kawamoto and Hayasaka, 2008). Therefore, we also proceed to
a detailed comparison of RegCM4 AOD at 550 nm (AOD550)
against MACv1 climatological data. However, other cloud- (Re) and
aerosol- (ASY, SSA) related parameters also play a significant
role. Here, RegCM4 Re is evaluated against observational data from CM
SAF, while RegCM4 ASY and SSA are compared against climatological data
from MACv1 (see Supplement). Specifically, the comparison of RegCM4
data with MACv1 does not constitute an evaluation of the RegCM4
aerosol-related parameters, like in the case of the cloud-related
parameters above, since MACv1 data (Kinne et al., 2013) are
climatological (based on a combination of models and observations) and
not pure observational data. However, a similar climatology (Kinne
et al., 2006) is used for the production of CM SAF SSR (Trentmann
et al., 2013). In addition, Mueller et al. (2014) showed that the use
of MACv1 aerosol climatology instead of the Kinne et al. (2006)
climatology does not significantly affect the CM SAF SSR
product. Hence, this comparison allows us to reach useful conclusions
about the effect of aerosol representation within RegCM4 on the
simulated SSR fields by the model. The same holds for the comparison
of RegCM4 ALB data with climatological data from CERES satellite
sensors and RegCM4 WV data with WV data from ERA-Interim reanalysis
(see Supplement). The CERES ALB 14-year climatology is temporally
constant, similar to the CERES climatology used for the production of
CM SAF SSR (Trentmann et al., 2013). Finally, the ERA-Interim WV data
used here are the same as the WV data incorporated by the radiative
scheme of CM SAF. Unlike the RegCM4 evaluation results, the comparison
results discussed in this paragraph are presented in the
Supplement.
Average RegCM4 SSR and CM SAF SSR (MSG SEVIRI) with their standard deviations
(±1σ) and the corresponding normalized mean bias (NMB) and normalized mean
error (NME) per season and region. When the difference between RegCM4 and CM SAF SSR
is statistically significant at the 95 % confidence level due to a two-independent-sample
t test, the NMB values are marked with bold letters, while in the opposite case they are
marked with an asterisk. Positive NMBs are italic, while negative
NMBs are underlined. ANN denotes annual results and DJF, MAM, JJA and SON the winter, spring, summer and autumn results, respectively.
ANN
DJF
MAM
MOD
SAT
bias (NME)
MOD
SAT
bias (NME)
MOD
SAT
bias (NME)
EU
175.0±106.5
169.3±96.7
3.3 (11.7)
77.1±57.1
74.2±57.2
3.9 (13.3)
206.8±83.0
206.7±67.0
0.0∗ (12.2)
LA
173.1±106.9
171.9±97.2
0.7 (11.7)
78.1±61.0
78.0±60.8
0.1∗(12.7)
202.7±85.7
208.7±68.6
–2.9 (13.0)
OC
178.2±105.6
164.9±95.7
8.1 (11.8)
75.3±49.7
67.7±49.8
11.3 (14.5)
213.8±77.8
203.2±64.2
5.2 (10.9)
NE
104.0±81.2
113.7±93.4
–8.5 (17.4)
19.3±12.0
12.7±16.8
52.4 (71.0)
137.6±53.4
160.4±60.8
–14.2 (17.0)
CE
134.5±89.2
136.1±83.1
–1.2 (14.3)
42.3±20.8
42.8±24.4
-1.1∗ (22.9)
158.1±55.6
174.0±51.3
–9.1 (15.6)
EE
132.3±92.0
139.5±89.8
–5.2 (12.9)
37.5±17.5
38.8±22.1
–3.4 (20.1)
155.2±61.2
179.4±57.7
–13.5 (16.4)
IP
197.9±95.1
194.7±84.4
1.7 (9.7)
91.7±26.9
98.6±27.5
–7.0 (12.6)
224.8±56.5
224.0±46.3
0.4∗ (9.6)
CM
209.8±98.6
195.1±85.1
7.5 (10.6)
97.3±29.1
96.7±27.1
0.6∗(9.1)
243.7±59.2
225.9±46.2
7.9 (11.1)
EM
219.3±101.6
205.6±90.3
6.7 (9.9)
105.1±36.8
101.8±33.7
3.3 (11.4)
251.4±68.8
235.6±54.4
6.7 (10.7)
NA
261.8±82.3
243.8±69.5
7.4 (8.9)
164.7±35.2
161.8±31.9
1.8 (6.3)
303.8±41.3
280.2±33.7
8.4 (9.3)
JJA
SON
MOD
SAT
bias (NME)
MOD
SAT
bias (NME)
EU
281.6±70.6
265.2±55.2
6.2 (11.1)
126.3±77.4
123.3±71.3
2.4 (11.3)
LA
278.6±71.7
267.0±55.0
4.4 (10.7)
124.9±79.0
126.1±72.8
–0.9 (11.0)
OC
286.7±68.2
262.1±55.3
9.4 (11.9)
128.7±74.5
118.6±68.4
8.4 (11.8)
NE
198.7±45.5
219.4±43.3
–9.4 (13.8)
52.9±38.2
53.4±44.3
-1.0∗ (22.1)
CE
245.6±47.9
228.9±38.2
7.3 (12.0)
84.4±46.8
90.9±48.2
–7.2 (13.9)
EE
248.4±44.9
242.8±36.5
2.3 (9.0)
80.1±46.0
88.8±48.8
–9.8 (13.6)
IP
317.5±29.1
296.3±32.3
7.2 (8.9)
148.6±53.9
151.8±50.4
–2.1 (9.6)
CM
331.3±27.3
299.9±25.1
10.4 (10.8)
157.7±53.5
149.8±45.4
5.3 (10.4)
EM
339.3±29.1
312.8±28.1
8.5 (8.9)
171.8±63.0
163.7±55.9
5.0 (9.5)
NA
353.5±20.5
320.5±21.7
10.3 (10.3)
217.2±49.5
205.8±39.7
5.5 (8.1)
Apart from a qualitative approach, we also proceed to a quantitative
study of the reasons that could potentially lead to deviations between the RegCM4 and CM
SAF SSR. Using data from RegCM4 and CM SAF and the Santa Barbara
DISORT Atmospheric Radiative Transfer (SBDART) model (Ricchiazzi
et al., 1998), we estimate the potential relative contribution of the
parameters CFC, COT, Re, AOD, ASY, SSA, ALB and WV to the percent
RegCM4–CM SAF SSR difference (ΔSSR) over the seven
sub-regions mentioned above. ΔSSR is given by
Eq. (12), expressing the percentage of SSR deviation caused by the
observed difference between RegCM4 and CM SAF for each parameter
(p). First, a SBDART simulation is implemented with a 3 h time step
for the 15th day of each month (Ming et al., 2005) using monthly mean
RegCM4 data as input (control run) for each region. The average of all
the time steps per month expresses the monthly SSR flux
(SSRcontrol). The SSR fields simulated with
SBDART are almost identical to the RegCM4 SSR fields. This indicates
that SBDART indeed can be used to study the sensitivity of RegCM4's
radiative scheme to various parameters. Then, several SBDART
simulations are implemented in the same way, replacing each time only
one of the aforementioned input parameters with corresponding values
from CM SAF, MACv1 or ERA-Interim
(SSR(p)). SSRcontrol and
SSR(p) are then used in Eq. (12) to calculate
ΔSSR for each month (i) and parameter (p).
ΔSSRi(p)=100SSRcontroli-SSRi(p)/SSRcontroli
The results of this analysis are presented by means of bar plots for
each sub-region. The procedure described above was repeated assuming the simulated
SSR fields with all the CM SAF, MACv1 and ERA-Interim input data as
the control run and replacing each time the corresponding parameter
with data from RegCM4. This was done in order to make sure that the
interdependence (the effect of changing a parameter is different
under different conditions) of the examined parameters does not
impact the validity of our results. In addition, a method like the one introduced by
Kawamoto and Hayasaka (2008, 2010, 2011), which is based on the
calculation of the sensitivities of SSR on CFC, COT, AOD and WV, was
also implemented with similar results (not shown here).
Results and discussion
Surface solar radiation
As discussed above, we first examine the CM SAF and RegCM4 bias
patterns for the MFG (2000–2005) and MSG (2006–2009) periods
separately. This work focuses on the MSG data set, since cloud
property data, which are used in order to investigate the reasons for
the observed bias between CM SAF and RegCM4 at a later stage, are only
available from MSG. However, we investigate both periods to examine
whether the observed biases are valid for the whole simulation period and
ensure that there are no differences when using one or the other
data set. As shown in Fig. S2a and b, the annual bias patterns are
similar for both MFG-RegCM4 and MSG-RegCM4. The main feature is a low
negative bias over land and a low positive bias over ocean. Overall,
the RegCM4 simulations slightly overestimate SSR compared to CM SAF
over Europe with a bias of +1.5 % in the case of MFG and
+3.3 % in the case of MSG, while SSR from RegCM4 is much closer
to SSR from CM SAF over land (bias of -1.6 % for MFG and
+0.7 % for MSG) than over ocean (bias of +7.2 % for MFG
and +8.1 % for MSG). These values can be found in Table 2 for
the RegCM4-MSG period along with the corresponding values for the seven
sub-regions of interest appearing in Fig. 1a, while the same values for
the RegCM4-MFG period can be found in Table S1 of the Supplement. It should be mentioned that, hereafter, only results for the MSG CM SAF
SSR data set are presented within this paper, while the results for the
MFG data set are included in the Supplement (Figs. S3 to S5).
As presented in Fig. 1, some differences appear in the seasonal bias
patterns. A strong positive bias is observed during winter over
northern Europe. For the rest of the regions the winter patterns are
very close to the spring and the annual patterns. In contrast to the
annual patterns, in summer, the positive bias extends over Europe
until the latitudinal zone of 50∘ N, while in autumn the bias
patterns are quite similar to the annual ones. In winter, the
RegCM4 simulations overestimate SSR compared to CM SAF for the whole
European domain, the bias being +3.9 %. Over land the bias is
nearly zero (+0.1 %), while over ocean there is a significant
bias of +11.3 %. As shown in Fig. 1a, NE is the
sub-region with by far the strongest bias (+52.4 %). Also, NME is
13.3 % for the whole European domain (12.7 % over land and 14.5 %
over ocean), NE and NA being the regions with the highest (71.0 %)
and lowest (6.3 %) value, respectively (Table 2). The seasonal and
annual model- and satellite-derived values with the corresponding
biases and NMEs and their statistical significance at the 95 % confidence
level according to a two-independent-sample t test appear in
Table 2. The latitudinal variability in RegCM4 SSR, CM SAF SSR and
their difference is presented in Fig. 2a. As mentioned in Sect. 2.4,
the differences given in the figures with the latitudinal and the
seasonal variability are not normalized by the average SSR levels of each
region and hence should not be confused with the bias values appearing in the text.
For example, while the RegCM4–CM SAF difference is ∼7 Wm-2 over NE in
winter (comparable to other regions), a strong bias of ∼52 % characterizes
this region due to the low insolation levels at these latitudes. Overall, RegCM4 slightly
overestimates SSR at latitudes lower than ∼40∘ N;
a negligible difference between RegCM4 and CM SAF is observed until
the latitudinal zone of ∼52∘ N, while a significant
difference is observed for higher latitudes. In spring, a zero bias is
observed between the model and CM SAF for Europe. When discriminating
between land- and ocean-covered regions, a negative bias is observed
over land (-2.9 %) and a positive over ocean (+5.2 %).
The regions with the highest negative bias are NE (-14.2 %), EE
(-13.5 %) and CE (-9.1 %), while the regions with the
highest positive bias are NA (+8.4 %), CM (+7.9 %) and
EM (+6.7 %) (see Table 1). This is also reflected in Fig. 2b,
where RegCM4 clearly overestimates SSR for latitudes less than ∼44∘ N, and significantly underestimating SSR thereafter.
NME is 12.2 % for the whole European domain, with 13.0 %
over land and 10.9 % over ocean. NME ranges from 9.3 % (NA)
to 17.0 % (NE) (Table 2). In summer, a positive bias of +6.2 % is
calculated for the whole European domain, with the bias being +4.4 % over
land and +9.4 % over ocean. As seen in Table 2, the bias is positive for
all the sub-regions, ranging from +2.3 % (EE) to +10.4 %
(CM), except for NE (-9.4 %). RegCM4 clearly overestimates SSR
for latitudes less than ∼55∘ N and underestimates SSR
for higher latitudes (Fig. 2c). For the whole European domain, NME is 11.1 %
(10.7 % over land and 11.9 % over ocean), ranging from 8.9 %
(EM, IP) to 13.8 % (NE) (Table 2). A positive bias of +2.4 % is
found for Europe in autumn, with the corresponding values being
-0.9 % over land-covered and +8.4 % over ocean-covered
regions. EE (-9.8 %) and CE (-7.2 %) are the regions
with the strongest negative bias, while the regions with the strongest
positive bias are the ones to the south, namely NA (+5.5 %),
CM (+5.3 %) and EM (+5.0) (see also Table 2). This is also
seen in Fig. 2d, where RegCM4 overestimates SSR for latitudes less than
∼42∘ N. NME is 11.3 % for the whole European domain,
being 11.0 % over land and 11.8 % over ocean. NME ranges from
8.1 % (NA) to 22.1 % (NE) (Table 2).
Latitudinal variability in RegCM4 SSR (red), CM SAF SSR
(blue) and their difference (orange) over Europe for (a) winter
(DJF), (b) spring (MAM), (c) summer (JJA)
and (d) autumn (SON) from MSG SEVIRI observations.
Seasonal variability in RegCM4 SSR (red), CM SAF SSR (blue)
and their difference (orange) over (a) the whole of Europe,
(b) land, (c) ocean, (d) NE, (e) CE,
(f) EE, (g) IP, (h) CM, (i) EM, and
(j) NA from MSG SEVIRI observations.
The seasonal variability in RegCM4 SSR, CM SAF SSR and their
difference, for the whole European domain, for the land- and ocean-covered part of Europe, and for the seven sub-regions of
interest, is presented in Fig. 3a–j. For Europe as a whole, the largest
difference between RegCM4 and CM SAF SSR is observed in summer, with July
being the month with the highest RegCM4–CM SAF difference
(20.3 Wm-2). Over land, the difference between RegCM4
and CM SAF SSR is nearly zero for winter and autumn months. During
spring, in March and April, RegCM4 underestimates SSR, while in summer
SSR is overestimated, especially in July. However, over ocean,
SSR is overestimated by RegCM4 in all months. The
highest RegCM4–CM SAF differences are observed during the warm period
(May–September). Over NE, RegCM4 underestimates SSR for the months
from March to September and overestimates SSR during the winter
months. The seasonal variability in the difference between RegCM4 and
CM SAF is pretty similar over CE and EE. The simulations underestimate
SSR in spring (especially during April) and autumn and overestimate
SSR in summer. Over IP, SSR is overestimated again in May and during
the summer and underestimated in February, March, November and
December. For CM and EM, the seasonal variability in the difference
between RegCM4 and CM SAF is almost identical. RegCM4 significantly
overestimates SSR from April to October, while for the rest of the
months the difference is nearly zero. Finally, over NA, the seasonal
variability in the difference is close to the one appearing over CM
and EM, but here SSR is also overestimated by RegCM4 in March.
Annual normalized mean bias (NMB) of RegCM4–CM SAF CFC, COT, Rel and Rei; RegCM4–MACv1
ASY and SSA; RegCM4–CERES ALB; and RegCM4–ERA-Interim WV. When the difference between
RegCM4 and CM SAF or CERES or ERA-Interim is statistically significant at the 95 %
confidence level due to a two-independent-sample t test, the NMB values are marked
with bold letters, while in the opposite case they are marked with an asterisk.
Positive NMBs are italic, while negative NMBs are underlined.
CFC
COT
Rel
Rei
AOD
ASY
SSA
ALB
WV
EU
–24.3
4.3
–36.1
–28.3
–35.3
–1.1
–4.2
1.6
12.0
LA
–13.7
7.3
–47.7
–26.4
–32.1
–1.8
–4.3
–28.3
11.4
OC
–38.4
–2.5
–18.3
–31.1
–42.0
0.1
–4.1
131.1
12.8
NE
–20.3
54.3
–32.8
–31.3
–75.9
1.0
–5.6
5.2
13.1
CE
–19.7
24.1
–45.1
–24.0
–63.6
0.0∗
–5.9
–22.7
14.0
EE
–16.0
30.9
–44.6
–24.2
–64.6
2.1
–3.5
–40.7
10.8
IP
–13.7
–13.9
–46.1
–27.3
–7.4
–1.5
–4.8
–3.8
14.4
CM
–31.2
–30.7
–26.7
–27.6
–19.3
–0.7
–3.5
85.9
10.4
EM
–28.8
–22.0
–29.3
–28.4
–34.2
–0.0
–2.3
35.4
10.9
NA
0.4∗
–39.8
–47.3
–30.0
25.0
–7.9
–3.5
–26.4
8.7
Cloud fractional cover
CFC plays a determinant role for the SSR levels. Therefore, we compare
the CFC patterns simulated with RegCM4 against CFC patterns from MSG
CM SAF for the common period 2004–2009. Overall, CFC is
underestimated by RegCM4 over Europe by 24.3 % on an annual basis
(13.7 % over land and 38.4 % over ocean) despite the fact
that, over specific regions (e.g., within IP and NA), CFC is
overestimated (see Table 3). Underestimation is observed for all seasons, NA being the only region with a bias of
+8.1 % in winter and a bias of +13.1 % in autumn (see
Table S3). As shown in Fig. 4a–d, the underestimation of CFC from
RegCM4 is stronger over ocean, especially in summer, while strong
overestimation is observed over regions in western NA in winter and
spring, eastern NA in summer, and the whole of NA during autumn. The
latitudinal variability in RegCM4 CFC, CM SAF CFC and their difference
is presented in Fig. 5. A clear, strong underestimation of CFC from
RegCM4 is observed for all the latitudinal bands and seasons apart
from latitudes around 30∘ N, where CFC is slightly
overestimated in autumn. The seasonal variability in RegCM4 CFC, CM
SAF CFC and their difference, for the whole European domain, for the
land- and ocean-covered part of Europe, and for the seven sub-regions of
interest, is presented in Fig. 6a–j. CFC is underestimated
steadily by RegCM4 throughout a year, with the underestimation being much stronger
over the ocean than over land (see Fig. 6b and c). This
underestimation is observed for all the sub-regions except for NA,
where CFC is underestimated from April to September and overestimated
for the rest of the months.
Generally, lower CFCs would lead to higher SSR levels. However,
a comparison of the SSR bias patterns appearing in Fig. 1a–d with the
CFC bias patterns appearing in Fig. 4a–d, as well as the biases
appearing in Table 1 and Table S3 and the differences and other
metrics appearing in Table S2 and Table S4, reveals that, for some areas and
seasons, the RegCM4–CM SAF SSR deviations cannot be explained through
the corresponding CFC deviations (e.g., land-covered regions during
spring and autumn). This is in line with the findings of Katragkou
et al. (2015), where the WRF–ISCCP SSR deviations could not always be
attributed to CFC deviations. As discussed there, the role of
microphysical cloud properties should also be taken into
account. Following this, in the next paragraph we go a step further,
taking into account the effect of COT.
The same as Fig. 1 but for RegCM4 and CM SAF CFC.
The same as Fig. 2 but for RegCM4 and CM SAF CFC.
The same as Fig. 3 but for RegCM4 and CM SAF CFC.
Cloud microphysical properties
Cloud optical thickness
COT is a measure of the transparency of clouds and, along with CFC,
determines the transmission of shortwave radiation through clouds
(Gupta et al., 1993). In this paragraph, the RegCM4 COT patterns are
compared against COT patterns from MSG CM SAF for the common period
2004–2009. Overall, COT is overestimated by RegCM4 over Europe by
4.3 % on an annual basis, the bias being positive over land
(+7.3 %) but negative over ocean (-2.5 %) (see
Table 3). In addition, COT bias varies with seasons, being positive in
spring and autumn and negative in winter and summer (see Tables S5 and S6). As
shown in Fig. 7a–d, positive biases are mostly observed over land-covered regions of CE, EE and NE and negative biases over NA and the
regions around the Mediterranean Sea. In fact, there is a strong
latitudinal variability in the RegCM4–CM SAF COT difference for all
the seasons as presented in Fig. 8a–d. RegCM4 underestimates COT for
latitudes below ∼45∘ N in winter, spring and autumn and
for latitudes below ∼50∘ N in summer. The seasonal
variability in RegCM4 COT, CM SAF COT and their difference for the
whole European domain, for the land- and ocean-covered part of Europe,
and for the seven sub-regions of interest, is presented in Fig. 9a–j. In
general, the RegCM4–CM SAF COT difference is not steadily positive or
negative but varies from month to month over both land and
ocean. RegCM4 steadily overestimates COT throughout a year only over
NE and underestimates COT over CM and NA. It needs to be mentioned
that there are no COT retrievals over NE for December and January due
to limited illumination at those latitudes during this period of the
year. This is also the reason for there being missing grid cells in
the top-right corner of Fig. 7a–d.
A comparison of the SSR bias patterns appearing in Fig. 1a–d with the
CFC (Fig. 4a–d) and the COT (Fig. 7a–d) bias patterns reveals that
COT could explain part of the RegCM4–CM SAF SSR deviations that could
not be explained through CFC (e.g., NE, CE, EE). The same conclusions
can be reached by comparing the seasonal variability in SSR, CFC and
COT over the region of interest (see Figs. 3, 6 and 9). However, other
parameters are expected to be responsible for the remaining
unexplained RegCM4–CM SAF SSR deviation.
The same as Fig. 1 but for RegCM4 and CM SAF COT.
The same as Fig. 2 but for RegCM4 and CM SAF COT.
The same as Fig. 3 but for RegCM4 and CM SAF COT.
Cloud effective radius
Re is a microphysical optical property expressing the size of cloud
droplets in the case of liquid clouds and the size of ice crystals in
the case of ice clouds. Re of liquid (Rel) and ice (Rei) clouds plays
a critical role in the calculation of the optical thickness of clouds
as well as their albedo (see Eqs. 4–7 in Sect. 2.1). The evaluation
of RegCM4 Rel and Rei against observational data from CM SAF reveals
a significant underestimation over the whole European domain (bias of
-36.1 % for Rel and -28.3 % for Rei) (see Tables 3, S7 and S8).
This is also apparent in the maps appearing in Figs. S6 and S8. In the case of ice
clouds, the biases over land and ocean do not differ significantly. However, for liquid clouds, the bias over land is more than
double the bias over ocean (see Tables 3, S7 and S8). This is due to the very low
RegCM4 Rel values appearing over land, while the CM SAF data set does
not exhibit such a land–ocean difference. A possible explanation for
this could be the fact that a different approach is
used over land (constant Rel of 10 µm) and ocean (Eq. 1) for liquid clouds,
while for ice clouds the parameterization is the same for land and
ocean (Eq. 2). The fact that the average Rel value over land
(5.65±1.06 µm) is very close to the lowest Rel
boundary (5 µm) according to Eq. (1) possibly points
towards an underestimation of the liquid cloud height and vertical
development. Also, this Rel land–ocean difference is responsible for the
COT land–ocean difference (see Table 3) according to Eq. (4). In
general, the underestimation of Re would result in more reflective
clouds and hence in underestimated SSR levels. It should be
mentioned here that the latitudinal and monthly variability in RegCM4 Rel and Rei as well as CM
SAF Rel and Rei and their difference, for the whole European domain,
for the land- and ocean-covered part of Europe, and for the seven
sub-regions, is presented in the Supplement of this
paper (Figs. S6 to S9). A constant underestimation of Rel and Rei is
observed for the whole of Europe.
Aerosol optical properties
As discussed in Sect. 2.4, AOD along with CFC and COT constitute the
major controllers of SSR. A comparison of the RegCM4
AOD550 seasonal patterns with climatological
AOD550 values from MACv1 is presented in Fig. S10a–d. On
an annual basis, RegCM4 overestimates AOD over the region of NA (bias
of +25.0 %) (see Table 3). The overestimation is very strong
during winter and much weaker in spring and autumn (see Tables S9 and S10).
This overestimation over regions affected by dust emission
has been discussed comprehensively in Nabat et al. (2012) and has to
do with the dust particle size distribution schemes utilized by RegCM4
(Alfaro and Gomes, 2001; Kok, 2011). Nabat et al. (2012) showed that
the implementation of the Kok (2011) scheme generally reduces the dust AOD
overestimation in RegCM4 over the Mediterranean Basin. However,
a first climatological comparison of RegCM4 dust AODs with data from
CALIOP/CALIPSO (A. Tsikerdekis, personal communication, 2015) has
shown that both schemes overestimate dust AOD over Europe, and
therefore the selection of a specific dust scheme is not expected to drastically
change our results. However, AOD is significantly
underestimated over the rest of the domain. This should be expected as
RegCM does not account for several types of aerosols, both anthropogenic
(e.g., nitrates, ammonium and secondary organic aerosols, industrial
dust) and natural (e.g., biogenic aerosols), which potentially play an
important role (Kanakidou et al., 2005; Zanis et al., 2012). This
overestimation/underestimation dipole in winter, spring and autumn is
also reflected in Fig. S11. RegCM4 overestimates AOD for latitudes
below ∼40∘ N in winter, for latitudes below ∼35∘ N in spring, and for a narrow latitudinal band (∼30–33∘ N) in autumn. In summer, RegCM4 steadily
underestimates AOD compared to MACv1. The seasonal variability in
RegCM4 AOD550, MACv1 AOD550 and their
difference, for the whole European domain, for the land- and ocean-covered part of Europe, and for the seven sub-regions of interest,
is presented in Fig. S12a–j. In general, RegCM4 clearly underestimates
AOD throughout a year over regions that are not affected heavily by
Sahara dust transport. This underestimation would cause an
overestimation of SSR if all the other parameters were kept
constant. The opposite is true for the region of NA, where AOD, except
for summer, is significantly overestimated.
As in the case of COT and Re, in order to fully assess the contribution of aerosols
to the observed RegCM4–CM SAF SSR deviations, one has to take into account ASY
and SSA besides AOD. A comparison of RegCM4 ASY with climatological values
from MACv1 reveals a small underestimation from RegCM4 over Europe (bias of -1.1 %)
(Tables 3 and S11). As shown in Fig. S13, RegCM4 underestimates ASY for latitudes below
∼40∘ N and slightly overestimates ASY for the rest of the region. Except for NA, where
RegCM4 underestimates ASY throughout the year, RegCM4 slightly overestimates ASY
for the warm period over NE, CE and EE, while for the rest of the sub-regions the
RegCM4–MACv1 difference is close to zero (see Fig. S14). In contrast to the case of
ASY, RegCM4 steadily underestimates SSA compared to MACv1 over Europe by
4.2 % (see Tables 3 and S12 and Fig. S15). Moreover, as shown in Fig. S16, SSA
is underestimated on an annual basis for the total of the sub-regions.
ΔSSR (%) caused by CFC, COT, Re, AOD, ASY, SSA, WV and ALB for
(a) CE, (b) EE, (c) IP, (d) CM and (e) EM.
Other parameters
Apart from the major (CFC, COT, AOD) and minor (Re, ASY, SSA) SSR
determinants, which are discussed above in detail, there are also
a number of other parameters that could impact the simulation skills
of RegCM4 compared to CM SAF, since these parameters are used as input
within the radiative scheme of the model.
As previously discussed, WV is another parameter that affects the
transmission of solar radiation within the atmosphere. RegCM4 is found
here to overestimate WV compared to ERA-Interim reanalysis all over
Europe with a bias of ∼12 % (see Tables 3 and S13). This becomes more
than obvious when looking at the bias map and the seasonal and latitudinal
variability in the two data sets (see Figs. S17 and S18).
In line with the study of Güttler et al. (2014), RegCM4 exhibits
a significant underestimation of ALB over CE, EE and NA (see Table 3)
compared to climatological data from CERES (see Sect. 2.3). In
general, there is a striking difference between land- and ocean-covered
regions (Figs. S19 and S20). Over land RegCM4 underestimates ALB by
28.3 %, while over ocean ALB is strongly overestimated by
131 %. As previously mentioned, the comparisons of RegCM4
with non-observational data presented in this paragraph do not
constitute an evaluation of RegCM4. However, these comparisons give us
an insight into how several parameters affect the ability of RegCM4 to
simulate SSR.
Assessing the effect of various parameters on RegCM's SSR
As discussed in detail in Sect. 2.4, the potential contribution of each one of
the aforementioned parameters in the deviation between RegCM4 and CM
SAF SSR is assessed quantitatively with the use of the SBDART radiative
transfer model. The results of this analysis are presented in
Fig. 10. The percent contribution of each parameter to the RegCM4–CM
SAF SSR difference is calculated on a monthly basis. Results for NE
are not included in this paper, since COT and Re are not
available from CM SAF during winter (December, January) and also due
to the low insolation levels for several months at high latitudes. Results
for NA are also not presented. This region is characterized by a significant
day-to-day variability in cloudiness and aerosols and therefore the statistical
significance of a monthly analysis like the one presented here would be limited.
Another source of uncertainty would be the use of spatial averages within the
radiative transfer simulations, since the western and eastern part of the region
differ significantly by means of aerosol load and cloud coverage and hence the
region cannot be considered homogeneous.
It should be mentioned that the potential percent contributions to the RegCM4–CM SAF
SSR difference presented in Fig. 10 do not include the relative contribution due to
algorithmic issues of the CM SAF product used here and also uncertainties
introduced by the method itself (e.g., SBDART simulation accuracy, use of monthly data, spatial
averaging). Therefore the contributions appearing in Fig. 10 are not directly connected
to the RegCM4–CM SAF differences presented in Fig. 3. In fact, part of these differences is
due to the overestimation of SSR by CM SAF due to the method used for the production of the
data set. Hence, the ΔSSR values presented below do not include the bias introduced by the
CM SAF algorithm. As mentioned in Sect. 2.2, CM SAF was found to overestimate SSR
compared to ground observations over Europe by 5.2 Wm-2 for the 1983–2005 MFG
period (Sanchez-Lorenzo et al., 2013) and by 3.16 Wm-2 for the 1983–2010 MFG–MSG
period (Posselt et al., 2014). Following these studies, the CM SAF MSG data (2006–2009)
used in this work are validated using ground-based observations from 26 stations (23
stations from the World Radiation Data Center (WRDC) and 3 independent stations)
evenly distributed around Europe (see Fig. S21). Overall, it is found that CM SAF
overestimates SSR on an annual basis by 4.5 Wm-2 over CE, 8.8 Wm-2
over EE, 2.4 Wm-2 over IP, 7.8 Wm-2 over CM and 4.5 Wm-2
over EM, the overestimation being much higher during the warm period (Fig. S22).
As seen in Fig. 10a, apart from the bias introduced by the CM SAF retrieval
methodology, the percent RegCM4–CM SAF SSR difference
(ΔSSR) over CE is mostly determined by CFC, COT and
AOD. However, for specific months, Re and the other parameters also
play an important role, leading to an underestimation of SSR. CFC leads
to a significant overestimation of SSR on an annual basis ranging from
3.7 % (April) to 18.6 % (January). Apart from in July, COT
leads to an underestimation of SSR, April being the month with
the highest underestimation (ΔSSR of
-13.3 %). AOD, on the other hand, leads to an overestimation
of SSR over CE ranging from +4.6 % (June) to +9.5 %
(January). As mentioned in Sect. 2.4, the procedure was repeated assuming the
simulated SSR fields with all the CM SAF, MACv1 and ERA-Interim input
data as the control run and replacing each time the corresponding parameter
with data from RegCM4. The results from this repetition were similar to the
results presented above, showing that the effect of the interdependence of
the parameters investigated here is low and does not affect the validity of
our results. The same holds for all the sub-regions. The results from the
inverse procedure and the differences to the results presented here are
given in Figs. S23 and S24, respectively.
In line with CE, ΔSSR over EE is mostly determined by
CFC, COT and AOD (Fig. 10b). Apart from in April, CFC leads to an
overestimation of SSR, December being the month with the highest
overestimation (+22.9 %). Apart from in June and July, COT causes
an underestimation of SSR, March/August being the month with the
highest/lowest underestimation (-15.8/-0.2 %). On the other
hand, AOD leads to an overestimation of SSR the whole year,
December/May being the month with the highest/lowest overestimation
(+12.3/+4.2 %). Re also plays a role, leading to an
underestimation of SSR, which ranges from -1.06 % (July) to
-2.5 % (February). All the other parameters play a minor role,
generally leading to an underestimation of SSR.
Over IP, despite the fact that the dominant parameters are CFC and
COT, for some months AOD, SSA and Re contribute substantially to
ΔSSR (Fig. 10c). CFC leads to an overestimation of
SSR, January/September being the month with the highest/lowest
overestimation of SSR (+9.1/+1.1 %). COT causes an important
overestimation of SSR from April to October (e.g., +3.7 % in
June) and a significant underestimation during March
(-2.8 %). On the other hand, Re leads to an underestimation of
SSR that ranges from -1.3 % in April to -0.3 % in
August. The same holds for SSA, with an average annual SSR
underestimation of -1.2 %, while AOD exhibits a mixed behavior
leading to either underestimation (a maximum of -6.1 % in
December) or overestimation (a maximum of +4.9 % in March).
As seen in Fig. 10d, ΔSSR over CM is mostly determined
by CFC, COT, AOD and SSA. CFC causes a significant overestimation of
SSR ranging from +3.2 % (July) to +11.9 %
(December). COT leads to an overestimation of SSR on an annual basis,
October being the month with the highest overestimation
(+4.6 %). AOD causes an overestimation of SSR over CM for the
period from March to October (average ΔSSR of
+2.2 %) and an underestimation during winter (average
ΔSSR of -2.3 %). SSA, on the other hand, causes
an underestimation of SSR on an annual basis ranging from
-0.5 % (July) to -1.9 % (December).
ΔSSR over EM is dominated by the relative contribution
of CFC, AOD and COT (see Fig. 10e). CFC causes an overestimation of
SSR on an annual basis ranging from +1.7 % (August) to
+12.2 % (December). Apart from in February, AOD causes
a significant overestimation ranging from +0.5 % (March) to
+6.0 % (September). Apart from in March, COT leads to an
overestimation of SSR, February being the month with the highest
overestimation (+4.3 %). SSA also plays a role, in some cases
comparable in magnitude to that of COT or AOD (e.g., January, March).
In summary, for the total of the five sub-regions, CFC, COT and AOD
are the most important factors that determine the SSR deviations between
RegCM4 and CM SAF on an annual basis. The underestimations/overestimations
of CFC, COT and AOD by the model cause an annual absolute deviation of the
SSR compared to CM SAF of 8.4, 3.8 and 4.5 %, respectively.
Conclusions
In the present study, a decadal simulation (2000–2009) with the regional
climate model RegCM4 is implemented in order to assess the model's ability
to represent the SSR patterns over Europe. The RegCM4 SSR fields are
evaluated against satellite-based observations from CM SAF. The annual
bias patterns of RegCM4–CM SAF are similar for both MFG (2000–2005) and
MSG (2006–2009) observations. The model slightly overestimates SSR compared
to CM SAF over Europe, the bias being +1.5 % for MFG and +3.3 % for MSG
observations. Moreover, the bias is much lower over land than over ocean, while
some differences appear locally between the seasonal and annual bias patterns.
In order to understand the RegCM4–CM SAF SSR deviations, CFC, COT and Re
data from RegCM4 are compared against observations from CM SAF (MSG period).
For the same reason, AOD, ASY, SSA, WV and ALB from RegCM4 are compared
against data from MACv1, ERA-Interim reanalysis and CERES since these data
are similar to the ones used as input in the retrieval of CM SAF SSR.
CFC is significantly underestimated by RegCM4 compared to CM SAF over
Europe by 24.3 % on an annual basis. Part of the bias between RegCM4 and
CM SAF SSR can be explained through CFC, with the underestimation of CFC
leading to a clear overestimation of SSR. It was also found that RegCM4
overestimates COT compared to CM SAF on an annual basis, suggesting that COT
may explain part of the RegCM4–CM SAF SSR deviations that could not be explained
through CFC over specific regions. In addition, RegCM4 significantly underestimates
Rel and Rei compared to CM SAF over the whole European domain on an annual basis.
A comparison of the RegCM4 AOD seasonal patterns with AOD values from the MACv1
aerosol climatology reveals that RegCM4 overestimates AOD over the region of NA and
underestimates it for the rest of the European domain. ASY and SSA are slightly
underestimated by the model. The comparison of RegCM4 WV against data from
ERA-Interim reanalysis reveals a clear overestimation over Europe. In line with
previous studies, RegCM4 significantly underestimates ALB over CE, EE and NA
compared to climatological data from CERES, with a striking difference between land and ocean.
The combined use of SBDART radiative transfer model with RegCM4, CM SAF, MACv1,
CERES and ERA-Interim data for the common period 2006–2009 shows that the difference
between RegCM4 and CM SAF SSR, apart from the bias introduced by the CM SAF algorithm,
is mostly explained through CFC, COT and AOD deviations. In the majority of the regions,
CFC leads to an overestimation of SSR by RegCM4. In some cases, COT leads to a significant
underestimation of SSR by RegCM4, while for the majority of the regions it leads to an overestimation.
AOD is generally responsible for the overestimation of SSR. The other parameters (Re, ASY, SSA,
WV and ALB) play a less significant role in the RegCM4–CM SAF SSR deviations. Overall, CFC, COT
and AOD are the major determinants of the SSR differences between RegCM4 and CM SAF,
causing an absolute deviation on an annual basis of 8.4, 3.8 and 4.5 %, respectively.
These results highlight the importance of other parameters apart from CFC, which has been examined in
previous model evaluation studies (e.g., Jaeger et al., 2008; Markovic et al., 2008; Kothe and Ahrens,
2010; Kothe et al., 2011, 2014; Güttler et al., 2014).
Overall, it is shown in this study that RegCM4 adequately simulates the SSR patterns over Europe.
However, it is also shown that the model significantly over- or underestimates several
parameters that determine the transmission of solar radiation in the atmosphere.
The good agreement between RegCM4 and satellite-based SSR observations from
CM SAF is to a great extent a result of the conflicting effect of these parameters. Our
results suggest that there should be a reassessment of the way these parameters
are represented within the model so that SSR is well simulated, but also for
the right reasons. This would also allow for a safer investigation of the dimming/brightening
effect since the SSR deviations would be safely dedicated to one or the other parameter.
It is suggested here that a similar approach should be implemented in the future in the same
or other regional climate models, with various setups also utilizing new satellite
products (e.g., CM SAF SARAH).