An Atmosphere-Ocean Regional Climate Model for the
Mediterranean area: Assessment of a Present Climate
Simulation
ARTALE V., CALMANTI S., CARILLO A., DELL’AQUILA A., HERRMANN1 M., PISACANE G., RUTI PM.,
SANNINO G., STRUGLIA MV
ACS/CLIM-MOD, ENEA, Roma, Italy ,
GIORGI F., BI X., PAL2 J.S., RAUSCHER S.,
ABDUS SALAM ICTP,TRIESTE, ITALY
THE PROTHEUS GROUP
Corresponding authors:
Alessandro Dell’Aquila, Sandro Calmanti
ACS-CLIM MOD, ENEA, Italian National Agency for New Technologies, Energy and the
Environment , Bldg F19, Room 112 Sp. 91 CR Casaccia, Via Anguillarese, 301
00060 Santa Maria di Galeria - Rome- ITALY tel: +39-06-3048 6870 (fax 4264)
alessandro.dellaquila@enea.it ; sandro.calmanti@enea.it
1
CNRM, Météo-France, Toulouse, 2Loyola Marymount University, CA
1
Abstract
We present an atmosphere-ocean regional climate model (AORCM) for the Mediterranean basin,
called the PROTHEUS system, composed by the regional climate model RegCM3 as the
atmospheric component and by a regional configuration of the MITgcm model as the oceanic
component. The model is applied to an area encompassing the Mediterranean Sea and compared to a
stand-alone version of its atmospheric component. An assessment of the model performances is
done by using available observational datasets.
Despite a persistent bias, the PROTHEUS system is able to capture the inter-annual variability of
seasonal SST and also the fine scale spatio-temporal evolution of observed SST anomalies, with
spatial correlation as high as 0.7 during summer. The close inspection of a 10-day strong wind event
during the summer of 2000 proves the capability of the PROTHEUS system to correctly describe
the daily evolution of SST under strong air-sea interaction conditions. As a consequence of the
model’s skill in reproducing observed SST and wind fields, we expect a reliable estimation of airsea fluxes. The model skill in reproducing climatological land surface fields is in line with that of
state of the art regional climate models.
2
1. Introduction
A number of regional climate model (RCM) systems have been developed during the last
two decades in order to downscale the output from large scale global climate model simulations and
produce fine scale regional climate change information useful for impact assessment and adaptation
studies (e.g. Giorgi 2006a). To date most RCMs have been essentially composed by an atmospheric
component coupled to a land surface scheme and driven over ocean areas by prescribed sea surface
temperature (SST). Although such a RCM can be sufficient for many applications, there are cases in
which the fine scale feedbacks associated with air-sea interactions can substantially influence the
spatial and temporal structure of regional climates. A typical example is the Indian Ocean and its
effects on the South Asia monsoon, for which it has been clearly shown that air-sea feedbacks are
essential in regulating the development of the South Asia monsoon (e.g. Meehl, 1994). Ratnam et al.
(2008) coupled the regional atmospheric model RegCM3 (Pal et al. 2007) with the regional ocean
model POM (Mellor,
2004) over the Indian ocean and found that the coupling considerably
improved the simulation of the Indian monsoon rain band both over the ocean and land areas.
Moreover, the Max-Planck AORCM has recently been employed over the Indonesian region
(Aldrian et al 2005), with a remarkable improvement in the simulation of rainfall.
Different AORCMs have also been developed for the Baltic Sea region (Döscher et al 2002,
Lehmann et al 2004) and for the Arctic (Rinke et al 2003).
Strong air-sea interactions take place also in the Mediterranean basin. This region is characterized
by extremely complex coastlines and topographical features, such as the Alpine, Apennine,
Pyrenees and Balkan mountain chains, the Italian and Hellenic peninsulas and large islands
(Balearic, Sicily, Sardinia, Corsica, Crete and Cyprus). From the atmospheric point of view this
morphological complexity leads to the formation of intense weather phenomena. A typical example
of such phenomena is the Mistral wind, which blows through the Garonne and Rhone valleys into
the Gulf of Lions and across to Corsica and Sardinia through the Strait of Boniface. Another
example is the Bora wind, which blows in a north-easterly direction across a series of topographical
channels into the North Adriatic Sea. Several coastal locations of the Central (e.g. the Gulf of
Genoa) and Eastern (e.g. Cyprus island) Mediterranean are also centers of topographically-induced
intense cyclogenesis (e.g. Buzzi and Tibaldi 1978; Alpert et al., 1995).
Such events dramatically influence the Mediterranean ocean circulation. The Mediterranean Sea is a
semi-enclosed and evaporative ocean basin in which a wide range of oceanic processes and
interactions of regional and global interest occur. It is connected to the Atlantic Ocean by the
shallow Strait of Gibraltar and is composed of two basins of similar size, i.e. the Western and the
3
Eastern Mediterranean Seas, separated by the shallow and narrow Strait of Sicily (Fig. 1). To the
northeast it is also connected to the Black Sea through the Bosporus channel. In the Strait of
Gibraltar, at the surface, the comparatively fresher Atlantic water flows into the Mediterranean Sea
to replace both the evaporated water and the denser, saltier Mediterranean water flowing out at
depth into the Atlantic. Deep Mediterranean water is produced at different locations by intense airsea interactions: in the Gulf of Lions (western Mediterranean), the Southern Adriatic, the northeast
Levantine basin and the Aegean Sea in the eastern Mediterranean (see Golnaraghi and Robinson,
1994; Roether et al., 1996). The salty and dense outflow from the Mediterranean maintains a higher
salinity in the North Atlantic than in the Pacific Ocean and may trigger the formation of North
Atlantic deep water (Reid, 1979). Progress in the knowledge of the circulation of the Mediterranean
Sea comes both from recent observational programs and modelling efforts (Millot 1999, Marullo et
al 2007, Sannino et al 2009). The basin’s circulation is characterized by the presence of sub-basin
gyres, intense mesoscale variability and a strong seasonal signal. Interannual variability is also
observed and most of it is directly related to interannual variability of atmospheric forcings ( Josey
2003; Mertens and Schott 1998 ). Such physical processes have two critical characteristics: first,
they derive from strong air-sea coupling and, second, they occur at fine spatial scales. In order to
explicitly resolve the two-way interactions at the atmosphere-ocean interface in the Mediterranean
region, Somot et al. (2008) coupled the global atmospheric model ARPEGE (Déqué and Piedelievre
1995) with the regional ocean model OPAMED (Somot et al 2006). As the ARPEGE spatial
resolution was locally increased over the region of interest, results are effectively comparable to
those of a regional model simulation. Comparison of coupled and uncoupled experiments showed
that in the coupled simulations the climate change signal was generally more intense over large
areas, with wetter winters over northern Europe and drier summers over southern and eastern
Europe. The better simulated Mediterranean SST appeared to be one of the factors responsible for
such differences, which were found to be highly significant.
A high resolution coupled Atmosphere-Ocean Regional Climate Model (AORCM) might therefore
prove to be an optimal, if not necessary, tool for accurate simulation of past, present and future
climate in the Mediterranean region, a task that is urged by the recent recognition of the
Mediterranean region as one of the most vulnerable to global warming (Giorgi 2006b).
In this work we present the development and first testing phase of a coupled AORCM for the
Mediterranean basin, which we refer to as the PROTHEUS system. We use the RegCM3 and the
MITgcm (Marshall et al., 1997a, b) models as the atmospheric and oceanic component, respectively.
Both models are well tested and have been extensively used over different regions of the world for a
variety of applications (Giorgi and Mearns 1999; Giorgi et al. 2006a; Marshall et al., 1997a, b).
They
are
coupled
through
the
OASIS
software
4
(http://www.cerfacs.fr/globc/software/oasis/oasis.html), which allows flexible coupling for different
model configurations (Valcke and Redler, 2006). In this work, we mainly focus on the performance
of the coupled system in reproducing the atmospheric land surface conditions, the ocean surface
temperatures and the air-sea fluxes. The main goal of the paper is to assess the ability of the
PROTHEUS system to simulate present-climate in a hindcast experiment using the ERA40
reanalyses as boundary and initial conditions. Numerical results are compared to available
observational datasets, with the corresponding atmospheric stand-alone RegCM simulations and
with the ERA40 fields.
The paper is organized as follows. In section 2 we describe the model components, the coupling
procedure and the experiment design; the overall model climatology is evaluated in section 3, while
section 4 presents the analysis of a case study of particular interest. Section 5 presents our final
considerations and discussion.
2. Model description and experiments
The PROTHEUS system is composed of the RegCM3 atmospheric regional model and the MITgcm
ocean model, coupled through the OASIS3 coupler.
2.1 REGCM3
RegCM3 is a 3-dimensional, sigma-coordinate, primitive equation, hydrostatic RCM. It was
originally developed by Giorgi et al. (1993a,b) and then successively upgraded as described by
Giorgi and Mearns (1999) and Pal et al. (2007). RegCM3 includes different physics options (Pal et
al. 2007). In this study we employ the CCM3 radiative transfer scheme (Kiehl et al. 1996) with
specified GHG concentrations, the planetary boundary layer scheme of Holtslag et al. (1990), the
Biosphere-Atmosphere Transfer Scheme (BATS1E) of Dickinson et al. (1993), the resolvable
precipitation scheme of Pal et al. (2000) and the cumulus convection scheme of Grell (1993) with
the Fritsch and Chappell (1980) closure assumption. Air-sea exchanges are treated using the
parameterization of Zeng et al. (1998). This scheme was implemented to improve some of the
problems found in the original BATS package, most noticeably the excessive evaporation from
warm ocean surfaces (Pal et al. 2007). In experiments over the continental U.S. and adjacent ocean
water the Zeng scheme lead to a considerable improvement of evaporation and precipitation over the
Gulf of Mexico and the tropical western Atlantic (Pal et al. 2000).
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The configuration employed in this study has a uniform horizontal grid spacing of 30 km on a
Lambert conformal projection. The model domain is centered at 41N and 15E with 160 grid points
in the meridional direction, 150 grid points in the zonal direction and 18 σ-levels. This model
domain covers the entire Mediterranean (Figure 1).
Lateral boundary conditions for the RegCM3 simulations are supplied by interpolating 6-hourly
large scale horizontal wind component, temperature, specific humidity and surface pressure onto the
model grid and by relaxing the prognostic model variables towards these conditions over a 12 grid
point lateral buffer zone with an exponentially decreasing relaxation coefficient (Giorgi et al.
1993b). In its standard configuration RegCM3 reads SST from an input file. This procedure is
maintained in the coupled configuration. However, after the observed SST (or Tf ) are read, they are
merged with the temperatures To received from the ocean model according to the formula:
SST = α  To + 1  α   T f
We started with a sharp transition between externally supplied temperatures (α=0) outside the strait
of Gibraltar and temperatures received from the partner model (α=1) inside the Mediterranean Sea.
The transition can be easily turned into a smoother one by introducing suitable values of α in the
coupling mask.
2.2 MITgcm
The stand-alone oceanic component has been recently implemented and validated by Sannino et al.
(2009). It is based on the MITgcm developed by Marshall et al. (1997a, b) which is used in its
hydrostatic, implicit free-surface, partial step topography formulation (Adcroft et al. 1997). In the
following we describe only the main features of the model, referring the reader to Sannino et al.
(2009) for a complete description and validation analysis. The ocean model has a spatial resolution
of 1/8°x1/8°, which corresponds to a non-uniform resolution of 14 km x (9÷12) km, the finer
spacing being achieved in the northern part of the domain. As in the Mediterranean Sea the first
internal Rossby radius of deformation is approximately 10-15 Km (with the exception of the
Adriatic Sea, where it is  5Km, Bergamasco and Gacic, 1996), the model can be considered eddypermitting. Horizontal viscous and diffusive terms are modelled with a bi-harmonic formulation
10
with diffusivity and viscosity coefficients equal to 1.5×10
4
−1
m s . Vertical eddy-diffusivity is
−5
modelled via a Laplacian formulation with the diffusivity coefficient ranging from 3.0× 10 m s at
−7
the surface to 1.0× 10 m s at the ocean bottom. The viscous coefficient is kept constant at 1.5 ×
6
−4
2 −1
10 m s over the whole water column. In order to ensure the occurrence of deep convection, the
2
vertical diffusivity is locally enhanced to 1 m s
−1
in regions where the stratification becomes
unstable. The bottom topography is interpolated from the 1/12° ETOPO5 database (US National
Geophysical Data Center), with some ad hoc corrections for isolated grid points located in
correspondence of islands and straits. In order to adequately resolve the dynamics of the
Mediterranean Atlantic Water (MAW) and the Levantine Intermediate Water (LIW), 42 unevenly
spaced vertical Z-levels are used, with a resolution varying from 10 m at the surface to 300 m in the
deeper part of the basin, with an intermediate resolution of about 40-50 m in the layer between 200
m and 700 m. We use the equation of state introduced by Jackett and Mcdougall (1995) for the
computation of density. A third order direct space-time flux limiter advection scheme is used for
tracers.
The simulation is initialized with the MEDATLAS-II (MEDAR Group (2002)) climatological data
for January. The two-way water exchange with the Atlantic Ocean was achieved by relaxing the 3D
salinity and temperature fields to climatology (Levitus, 1982). In order to reduce spurious
recirculations, relaxation was applied to all grid points within a “box” that extends as far as
13°41.35’W and spans 30 longitude grid points, with a relaxation time linearly varying with
longitude from 5 days to 100 days. The bathymetry in the box was held constant with longitude,
while latitudinal variations were allowed.
Except for the Atlantic box, where no surface forcing is applied, the model is forced through the
specification of wind stress and heat fluxes computed by the RegCM3. The freshwater forcing
(evaporation, precipitation, and river runoff) is applied as a virtual salt flux consistent with the
numerically predicted evaporation and precipitation fields and with monthly-mean climatological
runoff data. Runoff has been computed by analyzing historical monthly discharge time series for the
main 80 rivers discharging in the Mediterranean Sea (Struglia et al. 2004), its basin integrated
annual mean value being 10721 m3 s-1. Each single river mouth is located at a single point of the
Mediterranean ocean grid. The net flow from the Black Sea is simulated as an extra river
characterized by a constant discharge value of 8036 m3 s-1. Note that ,since no real surface fresh
water flux is applied in the model and since the volume conservation constraint is imposed, the net
volume transport through the Strait of Gibraltar is zero.
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2.3 OASIS3
The coupling of the RegCM and MITgcm models is accomplished using the OASIS3 coupler.
Besides the synchronization of the two models, the main task of OASIS3 is the interpolation of the
coupling fields from a source to a target grid, which in our experiment have different resolutions, the
ocean model being run at a higher resolution than that of the atmospheric model. In order to reduce
discrepancies between the two models in the definition of coastlines, the RegCM3 land-sea mask
was accordingly modified.
Coupling fields are exchanged every 6 hours, that is with the same frequency at which lateral
boundary conditions are provided to the atmospheric model. In this configuration, MITgcm supplies
the SST field to RegCM3, while RegCM3 computes the surface forcing fields for the ocean, i.e.
wind stress, sensible heat flux, latent heat flux, long and short wave incident radiation.
2.4 Present climate simulations
In all experiments the ERA40 reanalyses (Simmons and Gibson, 2000) have been used as lateral
boundary conditions for RegCM3, over the 43-year period 1958-2000. In the following the
simulations performed with the stand-alone version of RegCM3 will be indicated as RCM
simulations, whereas the abbreviation CPL (CouPLed) will refer to simulations performed with the
PROTHEUS system. The monthly SST fields at 1°x1° resolution which force the RCM simulations
were obtained from the Global Ice and Sea Surface Temperature Dataset (GISST 2.3) released by
the UKMO (Rayner et al., 2006). The same dataset was used to provide surface boundary conditions
for the atmospheric component of the coupled system over the Atlantic.
The land-use module of any atmospheric model usually requires a spin-up time to reach equilibrium.
We therefore performed a set of preliminary experiments with RegCM3, starting from extremely
wet and extremely dry soil conditions, and found that
6-8 months were sufficient for the
simulations to converge towards a similar state. However, in order to also allow the initialization of
the oceanic mixed layer, we skip the first 7 simulation years in the analysis of results.
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3. Model Climatology
3.1 Mediterranean Sea Surface Temperature
In this section we compare the sea surface temperature fields simulated by the coupled
system with two observational datasets, the GISST2.3 dataset used as boundary forcing for the
stand-alone RegCM3 and the daily OISST high resolution (1/16°x1/16°) dataset provided by
Marullo et al. (2007). The former dataset has also used for the production of the ERA40 Reanalysis
until to 1981, and, over the Mediterranean region, it is fully consistent with the ERA40 after 1981 as
well (not shown here). The latter is only available for the period 1985-2000. Figure 2 shows time
series of observed and simulated seasonal SST averaged over the whole Mediterranean basin. In
CPL, all simulated seasonal SSTs closely reproduce the inter-annual variability of the observations,
especially in summer. For the overlapping period 1985-2000, we computed both the SST mean and
the corresponding standard deviation, which are also reported in Figure 2. Excellent agreement with
observations is found for JJA and MAM, whereas a systematic bias of about 1°C characterizes
winter and fall simulated SSTs. In addition, decadal variability appears to be well captured by the
coupled model. Results are all the more encouraging if we consider that no relaxation towards
observations is performed.
In Figure 3 we show the spatial patterns of the mean summer and winter SST fields obtained in CPL
(Fig. 3 a-b), together with the biases with respect to GISST2.3 (Fig. 3c-d) and OISST (Fig. 3e-f).
We restrict our analysis to the period 1985-2000, and report only differences that are statistically
significant at 90% confidence level according to a two-sided t-test. During the winter season, CPL
shows a cold bias with respect to both observational datasets over the whole basin (especially over
the eastern sub-basin).
Relevant exceptions are the Adriatic and the Aegean sea, where a
statistically significant positive anomaly occurs.
During summer there is a prevailing warm bias in the south-eastern Mediterranean (such as in the
Mersa Matrouh gyre close to the Nile mouth), and a cold bias in the Adriatic and in the northeastern Levantine basin. This pattern roughly corresponds to the prevailing anti-cyclonic oceanic
structures along the south-eastern coasts and to the cyclonic structures along the northern
Mediterranean coasts, the two
being separated by the Mid-Mediterranean Jet (Robinson and
Golnaraghi 1993; Robinson et al, 1991).
Notice that the CPL-OISST bias is stronger than the corresponding CPL-GISST2.3 bias. This might
be explained if we consider that GISST2.3 is consistent with the ERA40 reanalysis which force CPL
as to both resolution and spatial coherence, whereas OISST is a high resolution independent dataset
9
derived from optimal interpolation of satellite data and validated against in situ measurements. A
larger CPL-OISST then CPL-GISST2.3 bias is an indication that lateral boundary conditions are a
powerful constraint to the bulk of the sea surface characteristics over this domain. As a
consequence, a smooth SST dataset consistent with the lateral boundary conditions is expected to
be in better agreement with the simulated SST field than an independent direct observation,
especially when looking at long term averages.
To further investigate the relation between the computed SST and the satellite based OISST, we
analyze correlations between the computed SST anomalies and those derived from the observational
dataset. Anomalies are calculated by subtracting the 30-day running mean at each grid point, so that
any correlation only accounts for variability at time scales shorter than 30 days. The map of SST
correlation anomalies (Fig. 4) shows highest correlation in the western basin, in particular over the
Gulf of Lions (about 0.65). As a matter of fact, time variability is best reproduced in the northern
part of the basin and close to Cyprus, where additional relative maxima are found (about 0.55). In
both regions the Mediterranean circulation is mainly driven by the prevailing surface winds, and
therefore the model takes advantage of the simulated high resolution atmospheric fields. Lower
correlations occur in the southern basin, especially along the African coasts, where the surface
structure is more affected by the fresher Atlantic water inflow from the Strait of Gibraltar. Note that
these regions are also influenced by the summer warm bias shown in Fig. 3d-f, which may
contribute to impair the simulation of surface patterns. In this view, a realistic representation of the
Atlantic water inflow appears as a further critical issue for the improvement of model performances.
We then analyze the spatial coherence of the simulated SST fields produced by PROTHEUS
and the OISST high resolution observations. In Fig. 5 we show the time series of spatial correlation
coefficients between PROTHEUS SST and OISST for the whole Mediterranean basin for the period
1985-2000. Also in this case, we consider SST anomalies. A strong seasonality can be detected in
the spatial correlation at the basin scale. In particular, low values are found in winter, when the SST
anomaly patterns are characterized by small scale turbulence whose spatial coherence is not
captured by PROTHEUS system. On the other hand, in the remaining seasons disturbances are well
organized in more coherent spatial structures at larger scales. These type of patterns are well
reproduced by the coupled model, with spatial correlation coefficients of up to 0.7. Despite the
significant large scale warm bias, these results highlight the capability of the PROTHEUS system to
reliably reproduce high resolution SST fields at least in the summer season.
We further highlight the different performance of the PROTHEUS system in simulating the
summer and winter SST variability in the PROTHEUS system by showing 2D plots of the ratio
between the simulated and observed intraseasonal variance of SST anomaly patterns (Fig. 6). Winter
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SST variance is strongly underestimated in CPL over the whole basin, with a spatially uniform ratio
of about 0.28. This confirms that PROTHEUS experiences difficulties in reproducing the winter
small scale disturbances, when the wind induced mixing is quite strong. As a matter of fact, our
oceanic resolution is too close to the typical deformation Rossby radius (5-15 Km), i.e. not fully
resolving the Mediterranean eddies and their horizontal variability. During summer, we obtain a
closer agreement with observations, with an average ratio of about 0.8. A finer ocean model
resolution – from eddy permitting to eddy resolving – is likely to address this critical issue
3.2 Validation of surface climatology
In this section, we analyze the PROTHEUS ability to reproduce mean climatological surface fields.
Evaporation
We compare CPL simulated evaporation at the air-sea interface with the evaporation derived
from the driving ERA40 dataset, with available observational datasets and with the corresponding
RCM field. The evaporation pattern produced in the CPL simulation shows a strong east-west
gradient over the sea, with large positive values over the eastern basin (Fig. 7a -b) for DJF as well as
for JJA. Comparison with ERA40 highlights small differences during winter, except along the
coasts, where discrepancies are probably related to the relatively coarse resolution of the ERA40
dataset (Fig. 7c). During summer the CPL simulation features higher evaporation with respect to
ERA40, especially over the sea and in the northern land areas of the domain. Conversely, along the
Mediterranean coasts we find negative anomalies (Fig. 7d), again probably due to the different
resolution of coastlines. We also compare the model evaporation against HOAPS-3 1°x1° monthly
data (Andersson et al 2007) available for the period 1988-2000 over most of the Mediterranean sea
(Fig. 7e-f). The HOAPS data do not cover the Adriatic region, the Aegean Sea and coastal regions.
In winter we do not find statistically significant discrepancies between the CPL evaporation and
HOAPS data, except over small regions of the Tyrrhenian and the Ionian basins. During summer
CPL overestimates evaporation over the western basin and underestimates it in the eastern basin,
south of Cyprus. The difference in evaporation between the CPL and RCM simulations (Fig. 7g-h)
closely follows the SST bias discussed in section 3.1 (Fig. 3 c-d). The positive correlation between
SST and evaporation implies that SST changes drive evaporation changes. As a matter of fact, SST
affects both the vertical stability of the atmospheric boundary layer and the saturation vapour
pressure at the sea surface.
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The winter pattern shows small negative differences over all basins, except for the Aegean
and the Adriatic where the coupled model exhibits a slightly larger positive flux. During summer,
the pattern highlights the strong signature of the coastal upwelling anomalies as observed for the
SST bias. Similar considerations are also valid for the patterns of sensible heat flux differences (not
shown).
Temperature
For 2-meter temperature and precipitation, we compare model results to the ERA40 reanalysis and
monthly land observations provided by the Climatic Research Unit (CRU) of the University of East
Anglia (New et al., 2002). We show bias maps as well as seasonal values averaged over the subdomains, reported in Table 1, following the diagnostic methods developed in the PRUDENCE
project (Christensen and Christensen, 2007). Seasonal averages and standard deviations are
computed over the period 1965-2000. In order to allow inter-comparison of the temperature fields,
these were extrapolated onto the RegCM3 topography via a dry adiabatic lapse rate. It is worth
recalling that an inter-comparison study carried out in the framework of the PRUDENCE project
indicated that the biases exhibited by a coarser resolution implementation of RegCM3 were
comparable in magnitude to those of other regional models (Jacob et al. (2007).
We find a prevailing cold bias in the CPL simulation with respect to ERA40 (Fig. 8 c-d), especially
in the summer season, when the negative anomaly extends from the Mediterranean region to central
Europe and reaches magnitudes of up to 1.5-2 degrees. Such a dominant cold bias was also detected
by Jacob et al. (2007), and it persists when the CPL simulation is compared to the CRU
observations. In the latter case, however, it is of smaller magnitude and some areas of warm bias
appear, especially in south-eastern Europe. Temperatures over land are similar in the CPL and RCM
simulations, while the discrepancies between CPL and RCM over the sea (Fig.8 g-h) follow the bias
already shown in Fig. 3c-d, and extend up to the neighbouring coastal regions, especially in the
southeastern part of the domain.
Our results are summarized in Table 2, which reports the average seasonal 2-meter temperature over
land and its associated interannual standard deviation for each examined dataset and for each
PRUDENCE sub-region. There is an overall good agreement between CPL and the other datasets,
except for a few cases during winter and summer. During winter, the land temperature fields over
most sub-regions in both RCM and CPL achieve a closer agreement with the CRU observations than
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the ERA40 reanalyses, probably as a consequence of the dynamical downscaling. During JJA the
regional simulations exhibit a marked cold bias with respect to CRU, especially over France and
Mid-Europe. CPL and RCM have similar average temperatures during all seasons. Finally, in all
seasons the interannual variability characterizing the two regional simulations is comparable with
that of both ERA40 and CRU. In particular, largest interannual variabilities are detected for MAM
and SON in both simulations and observations.
Precipitation
If compared to ERA40, the CPL simulation produces more precipitation, especially over
mountainous regions and during summer (Fig.9 c-d), whereas over the sea the CPL produces less
precipitation than ERA40, particularly during winter. Compared to CRU, the CPL experiment
produces too much orographic precipitation (Fig. 9e). For example, in the northern flank of Alps a
systematic overestimation occurs during winter, associated with passages of cyclones in Central
Europe. Instead, precipitations are generally underestimated along the southern flank of the Alps.
This dipole in the precipitation bias underlines the dominant role of orography in shaping the model
rainfall patterns. Finally, in most areas of the southern Mediterranean PROTHEUS seemingly
underestimates precipitation.
In assessing the model precipitation biases it should be noted that the CRU data do not include a rain
gauge undercatch correction, which can be up to 10-30% in the cold seasons (Adam and
Lettenmaier 2003), and therefore the RCM and CPL overestimate is likely artificially enhanced. In
summer we find small biases (Fig.9f) especially over Italy and Greece, whereas in central Europe an
overestimation of observed rainfall is still present. The precipitation in the CPL and RCM
simulations do not present substantial differences, suggesting a weak effect of air-sea feedbacks on
seasonal mean of land precipitation in our modeling system. This result is somewhat expected from
the well established notion that the Mediterranean area is mainly subject to large scale orographic
precipitation associated to synoptic systems traveling eastward from the North Atlantic. However, in
the CPL simulation, mostly in the cold seasons, we find less rainfall over the sea than in the
corresponding RCM configuration. This is attributed to the SST cold bias discussed above (see
Section 3.1), though a closer inspection of the atmospheric water budget is necessary to confirm this
hypothesis. Note that the only region where a significant CPL-RCM difference in precipitation
occurs in the warm season is the Po valley, probably due to the strong cold bias highlighted in Fig.
3d over the North Adriatic that could provide a weaker moisture source over this region. Table 3
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shows the seasonal precipitation and associated inter-annual standard deviation spatially averaged
over the PRUDENCE sub-regions. ERA40 tends to substantially underestimate precipitation over
land, whereas RegCM3 (in the stand-alone and in the coupled configuration) tend to overestimate
precipitation. This occurs especially during DJF and MAM, when we observe a persistent
overestimation of precipitation compared to the CRU data. Only in SON we do not find a systematic
overestimation. Similarly to the 2 meter temperature, also for precipitation we point out close values
for the interannual variability in all the datasets considered. Finally, Table 3 shows that both the
RCM and CPL runs improve the rainfall field representation with respect to ERA40 over the
Mediterranean area (MD) and the Alpine region (AL).
Fluxes
To conclude our analysis of surface fields, we consider the integrated values of heat and fresh water
fluxes over the Mediterranean basin. We obtain a time-mean Mediterranean surface heat loss of 5.88
W m-2 with an inter-annual standard deviation of 6.1 W m−2. This value is in good agreement with
previous measurements. For example, Bethoux (1979) estimated an annual mean heat loss of 7±3 W
m−2, successively confirmed by Bunker et al. (1982). We note that the mean heat flux trough the
Strait of Gibraltar has a value of 5.87 W m−2 , which balances the Mediterranean surface heat loss.
The time-mean volume transport simulated by the model through the Strait of Gibraltar is 1.06 Sv
with an inter-annual standard deviation of 0.03 Sv. This value is consistent with the corresponding
observational estimates ranging from 0.72 Sv in Bryden et al. (1994) to 1.68 Sv in Bethoux (1979).
For the time-mean fresh water flux (evaporation minus precipitation minus river runoff) we obtain a
value of 0.83 m yr−1 with an inter-annual standard deviation of 0.06 m yr−1 for the entire
Mediterranean basin (with a fresh water loss of 1.04 m yr−1 and 0.64 m yr−1 in the Eastern and
Western Mediterranean respectively, and a gain of fresh water in the Adriatic Sea of −0.42 m yr −1).
The value obtained over the entire Mediterranean Sea is in good agreement with previous estimates,
which range from 0.37 to 0.95 m yr−1 (Mariotti et al. 2002; and references therein).
4. A case study for 2000
In this section, we address the capability of PROTHEUS to describe the high spatio-temporal
variability of the Mediterranean system. We compare simulated and observed daily data for the year
2000, when a large amount of observational data is available. We use daily QuikSCAT LEVEL3
wind data (Physical Oceanography DAAC, GuideDocument, 2001) validated over the
14
Mediterranean region by Ruti et al. (2008), the daily OISST data computed by Marullo et al. (2007),
and the daily precipitation from the GPCP dataset (Huffman et al., 2001) .
The time correlation between observed and modelled daily wind data averaged over the whole basin
is 0.92 in the case ERA40, 0.84 for RCM and 0.83 for CPL. As for the case of sea surface
temperature (see section 3.1), correlations are computed between the anomalies with respect to a 30day running mean. The high correlation with ERA40 is a direct consequence of the assimilation of
wind data in the reanalysis. The good correlation between QuikSCAT and RCM or CPL indicates
that the dynamical downscaling of ERA40 preserves the high-frequency chronology of wind data. A
map of the temporal correlation between QuikSCAT and CPL daily wind speed illustrates the
model’s good performances in the representation of wind speed (Figure 10), especially over the
western part of the basin and the Aegean sea - where the structure of the Etesian winds is well
reproduced (correlation around 0.65).
Despite the high correlation of anomalies, ERA40 strongly underestimates the wind speed, with a
yearly average of 4.5 m.s-1 vs. 6.0 m.s-1 observed by QuikSCAT. Figure 11a shows that ERA40
underestimates all sectors of the wind distribution. Conversely, the RCM and CPL wind speeds are
much closer to the QuikSCAT observations and yearly averages are arounf 6.3 m.s-1 for both
simulations.
Regarding precipitation, the temporal correlation with GPCP data over the whole basin is 0.71 for
ERA40, 0.67 for CPL and 0.68 for RCM.
A comparison of ERA40, CPL, and RCM precipitation with the GPCP daily data over the
Mediterranean sea shows that CPL and RCM tend to correct the underestimation occurring in
ERA40 (Figure 11b). However GPCP intense winter precipitation events (greater than 4 mm/day,
the last 10 percentiles) are still underestimated.
Rainfall differences over the sea are not as large as for precipitations over land (see Sec. 3.2),
suggesting that the large scale precipitation schemes adopted in the reanalysis and in RegCM have
similar behavior over the sea. A greater amount of precipitation over sea in RCM with respect to
CPL is due to the winter cold bias present in CPL which reduces the evaporation source, especially
in the eastern basin (see also Fig. 9).
In terms of the representation of surface ocean temperature, Figure 12a shows the evolution of the
daily SST anomalies computed by removing a 30-day running mean. Even if the SST in CPL shows
a detectable bias compared to observations (Fig. 2), the daily SST anomalies are well reproduced,
15
both in terms of amplitude and timing (temporal correlation of 0.83 obtained after filtering). This is
also confirmed by looking at the time series of the spatial correlation PROTHEUS –OISST during
the year 2000 (Figure 12, see also Figure 5). During January and February, the spatial correlation is
very low and has a small variability: during this period, SST patterns are characterized by small
scale features that are not well captured by an eddy permitting ocean model (see also section 3.1).
After the end of March, spatial correlation increases, due to the formation of larger scale patterns of
SST anomalies. Figure 13 shows the seasonal maps of temporal correlations PROTHEUS-OISST
for 2000 and confirms the low correlation values in winter and spring. During fall we find high
values (up to 0.75) over the Gulf of Lions and generally in the western basin and in the Adriatic sea.
During fall high correlations (around 0.8) are obtained over the Aegean sea.
A closer inspection of the cold windy and rainy event of July 9th -17th illustrates the capability of
PROTHEUS to describe SST dynamics. Observations show a 0.7°C decrease of the average SST
during this time interval (Fig. 12a). CPL represents correctly an SST decrease, actually stronger than
observed (-1.2°C in CPL). The maps of SST anomaly during this episode (Fig.14) show that CPL
correctly reproduces the daily evolution of the spatial pattern of this event associated to a strong
cooling in the northwestern area. In particular, CPL captures the increasing negative anomaly over
the Gulf of Lions that in some days extends up to the Tyrrhenian sea and most of the western basin,
eventually affecting also the Adriatic region. Other small scale features in the eastern basin (over
Aegean sea and around Cyprus) are also well reproduced. During the considered time frame the
spatial correlation CPL-OISST over the whole basin reaches a relative maximum of 0.57 (Fig. 12b)
A comparison of the large scale structure of the total heat flux during the event for ERA40, RCM
and CPL shows that the large scale spatial patterns of the total heat flux is generally related to that
of wind speed (not shown). In particular, during the time frame considered, mid July, the sensible
heat flux is negligible and the longwave and shortwave fluxes are relatively constant in time.
Changes in the total heat flux are thus driven by changes in the latent heat flux. The good
performance of CPL in reproducing the spatial patterns of daily SST suggests that the small scale
patterns of the simulated SST play a key role in shaping air-sea interactions. For example, the
average heat flux difference between RCM and CPL is about 40 W/m^2 but it can be as high as 150
W/m^2 in the north-western Mediterranean Sea during the most intense wind event. By affecting
air-sea interactions, the spatial and temporal high resolution SSTs have a strong impact on the heat
content of the atmospheric boundary layer. For example, the basin averaged 2m Temperature
decreases by 3.4 °C in CPL vs. 2.4 °C in RCM during the considered event. At the end of the event,
16
the temperature difference between the two simulations reaches 2°C in the north-western
Mediterranean.
We conclude that the ERA40 fields can be effectively downscaled by RegCM3, both in the stand
alone and in the coupled configuration. Such a dynamical downscaling corrects the underestimation
of near-surface wind speed and precipitation which is present in the ERA40 dataset over the
Mediterranean region, thereby producing more realistic heat and water fluxes. Furthermore, we have
demonstrated that the coupled PROTHEUS model is able to closely follow the high-frequency
spatio-temporal variability of SST fields described in the state-of-the-art observational datasets. This
capability is important for the description of air-sea interactions especially in the case of intense
events.
5. Conclusions
We present the results of an hindcast experiment using a new AORCM for the Mediterranean
region, the PROTHEUS system, composed of the RegCM3 as the atmospheric component, the
MITgcm as the ocean component and OASIS3 as the model coupler. We performed a 43-year
control simulation using ERA40 reanalysis fields as lateral boundary conditions for the atmospheric
model. Simulated surface fields were compared to the ERA40 reanalysis, to different observational
datasets and to the corresponding simulation performed with a stand-alone configuration of the
atmospheric component. We validated the ability of the coupled model to simulate present climate
and examined the advantages of running an AORCM for climate studies.
The climatology of the coupled model over land is in good agreement with the results of the
stand-alone configuration of the atmospheric model, whose performance is in line with that of other
state-of-the-art regional climate models. This is not surprising in consideration of the fact that in the
Euro-Mediterranean area atmospheric processes are basically driven by large scale processes (i.e.
the advection of moist air over steep topography), which mainly benefit from the high resolution
description of topography allowed by regional models. The two-way interaction with the ocean does
not impair the quality of the predicted atmospheric fields, despite the presence of a temperature bias
over the sea that induces a corresponding anomaly in rainfall and evaporation. Our results confirm
the findings of Somot et al. (2008), who show that the atmosphere-ocean coupling over this region
does not significantly change the simulation of present climate. Regional coupled models are
therefore to be considered a robust instrument for future climate studies, which in the Mediterranean
area definitely require good quality prediction of high resolution SST and air-sea fluxes, as also
stated in Somot et al. (2008). In this respect, we proved that the PROTHEUS system predicts SST
17
temporal and spatial patterns which closely follow high resolution observations, and might be
further improved by an increased resolution oceanic component.
The coupled system has also proved skilful in the simulation of a very local and intense event. We
studied a cold windy and rainy 10-day event during the summer of year 2000 showing that the high
resolution SST predicted fields are in accordance with the most recent fine scale datasets, as a
consequence of the good quality surface wind, sensible heat flux and latent heat flux fields.
Besides climatological studies, we trust that the PROTHEUS system could be useful for a variety of
applications, from offering a support for observations in the construction of high quality, fine scale
SST datasets, to providing an estimate of air-sea fluxes over large areas where high-resolution
observations are hardly available (e.g. past decades, paleoclimate). The model can be also used in
case studies aiming to investigate the role of local air-sea feedbacks during extreme events.
In the future, we plan to use the PROTHEUS system in climate change scenario experiments under
increased greenhouse gas forcing and for seasonal forecast applications over the Mediterranean
region.
Acknowledgements
The authors wish to thank the anonymous reviewers for very useful suggestions and comments. We
thank the JPL PO.DAAC that makes the QuikSCAT data freely available The ECMWF ERA-40
data have been obtained from the ECMWF data server at http://data.ecmwf.int/data/ . The HOAPS
latent heat fluxes were obtained from the CERA database (http://cera-www.dkrz.de/CERA/) . The
GPCP-1DD data were provided by the NASA/Goddard Space Flight Center's Laboratory for
Atmospheres, which develops and computes the 1DD as a contribution to the GEWEX Global
Precipitation Climatology Project. Reformatted CRU TS 2.1 data downloaded from CGIAR-CSI at:
http://cru.csi.cgiar.org. This work is partially funded by CIRCE-EU project. (EU Project No.
036961)
18
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Figures
Figure 1: Domain for the PROTHEUS simulation with corresponding topography and bathymetry.
Units are m.
24
DJF
MAM
JJA
SON
Figure 2: Mediterranean average seasonal SST (°C) in CPL (PROTHEUS) and in the observational
datasets GISST2.3 and OISST. GISST2.3 data are also employed as a surface boundary condition
for RCM. The figure legend reports mean value and the corresponding standard deviation for the
overlapping period 1985-2000. The legend also shows the correlation coefficient r between the
observations and the PROTHEUS simulation.
25
a) CPL
DJF
b) CPL
c) CPL-GISST2.3
d) CPL-GISST2.3
DJF
JJA
e) CPL-OISST
DJF
f) CPL-OISST
JJA
JJA
Figure 3: Spatial patterns of winter and summer mean SST (°C) in CPL(a, b) and mean seasonal
anomalies CPL-GISST2.3 (c, d) and CPL-OISST (e, f). Seasonal averages are computed for the
period 1985-2000. White areas over the ocean correspond to differences that are not statistically
significant at the 90% confidence according to a two-sided t-test.
26
Figure 4: Spatial map of temporal correlation between the CPL SST and OISST daily anomalies for
the period 1985-2000. Anomalies are computed by subtracting the 30-day running mean from the
daily fields.
Figure 5: Time series of the basin-wide spatial correlation between the CPL SST and OISST daily
anomalies for the period 1985-2000. Anomalies are computed by subtracting the 30-day running
mean from the daily fields.
27
a) Variance ratio PROTHEUS/OISST DJF
b) Variance ratio PROTHEUS/OISST JJA
Figure 6: Maps of the ratio of intraseasonal variances of PROTHEUS CPL SST and OISST daily
anomalies for the period 1985-2000 computed for DJF (a) and JJA (b). Anomalies are computed by
subtracting the 30-day running mean from the daily fields.
28
a) CPL
DJF
b) CPL
JJA
c) CPL-ERA40
DJF
d) CPL-ERA40
JJA
e) CPL-HOAPS
DJF
f) CPL-HOAPS
JJA
g) CPL-RCM
DJF
h) CPL-RCM
JJA
Figure 7: Spatial patterns of winter and summer mean evaporation (mm/day) in the PROTHEUS
simulation and mean seasonal anomalies CPL-ERA40, CPL-HOAPS data and CPL-RCM ,
respectively. Seasonal averages are computed for the period 1988-2000. We report only the
differences statistically significant at the 90% confidence level ( t-test)
29
a) CPL
DJF
b) CPL
JJA
c) CPL-ERA40
DJF
d) CPL-ERA40
JJA
e) CPL-CRU
DJF
f) CPL-CRU
JJA
g) CPL-RCM
DJF
h) CPL-RCM
JJA
Figure 8: Spatial patterns of winter and summer mean 2m-Temperature (°C) in the PROTHEUS
CPL simulation and mean seasonal anomalies CPL-ERA40, CPL-CRU data and CPL-RCM ,
respectively. Seasonal averages are computed for the period 1965-2000. We report only the
differences statistically significant at the 90% confidence level ( t-test)
30
a) CPL
c) CPL-ERA40
DJF
DJF
b) CPL
JJA
d) CPL-ERA40
JJA
e) CPL-CRU
DJF
f) CPL-CRU
JJA
g) CPL-RCM
DJF
h) CPL-RCM
JJA
Figure 9: Spatial patterns of winter and summer mean precipitation (mm/day) in the PROTHEUS
CPL simulation and mean seasonal anomalies CPL-ERA40, CPL-CRU data and CPL-RCM ,
respectively. Seasonal averages are computed for the period 1965-2000. We report only the
differences statistically significant at the 90% confidence level ( t-test)
31
Figure 10: Maps of temporal correlation between PROTHEUS CPL data and QuikSCAT wind
speed anomalies for the year 2000. Anomalies are computed by subtracting the 30-day running
mean from the daily fields.
32
Figure 11: QQ plot of daily wind speed and precipitation distribution for the year 2000 over sea
points in ERA40 (black crosses), RCM (Blue crosses) and CPL (red crosses) compared against
QuikSCAT wind data (left panel) and GPCP precipitation data (right panel).
Figure 12a). Time series of SST anomalies (see text for details) for the PROTHEUS CPL simulation
(red line) and OISST (black line). Values are averaged over the whole Mediterranean basin. b) Time
series of spatial correlation between the daily CPL SST and OISST for the year 2000 computed over
the whole Mediterranean basin (see Fig. 5)
33
a) CPL-OISST
DJF
b) CPL-OISST
MAM
c) CPL-OISST
JJA
d) CPL-OISST
SON
Figure 13: Maps of temporal correlation between CPL SST and OISST daily anomalies for the year
2000 for the four seasons. Anomalies are computed by subtracting the 30-day running mean from
the daily fields.
34
CPL
9-7-2000
CPL 11-7-2000
OISST 9-7-2000
OISST 11-7-2000
CPL 13-7-2000
OISST 13-7-2000
CPL
OISST 15-7-2000
CPL
15-7-2000
17-7-2000
OISST 17-7-2000
Figure 14. SST anomalies (degrees C) between 09/07/2000 and 17/07/2000 in CPL and in fine scale
observations (see text).
35
AREA
WEST
IP
Iberian Peninsula
FR
France
ME
EAST
SOUTH
NORTH
-10
3
36
44
-5
5
44
50
Mid-Europe
2
16
48
55
AL
Alps
5
15
44
48
MD
Mediterranean
3
25
36
44
EA
Eastern Europe
16
30
44
55
Table1: Definition of PRUDENCE sub-regions.
T2m
DJF
CPL
4.69 0.48
6.91 0.42
6.09 0.46
FR
4.69 0.54
3.95 0.62
4.57 0.62
4.69 0.54
ME
1.52 0.70
1.07 0.82
1.12 0.84
1.51 0.70
-0.54 0.60 -0.43 0.64 -0.03 0.60 -0.49 0.64
4.37 0.48
3.54 0.48
5.68 0.44
4.49 0.48
EA
-1.37 0.74 -2.02 0.92 -1.85 0.94 -1.39 0.79
IP
10.81 0.92 10.01 0.90 11.90 0.88 10.83 0.90
FR
9.36 0.94
9.69
ME
7.84 1.20
7.90 1.22
8.43 1.20
7.86 1.20
AL
5.78 1.26
7.50 1.28
6.89 1.20
5.87 1.23
MD
EA
SON
RCM
6.02 0.48
MD
JJA
CRU
IP
AL
MAM
ERA40
1.0 10.00 0.96
9.37 0.94
10.72 1.24 10.61 1.28 11.91 1.20 10.82 1.22
7.94 1.62
8.37 1.74
8.47
1.6
7.96 1.62
IP
20.04 0.64 19.21 0.62 21.24 0.68 20.03 0.64
FR
16.47 0.48 17.90 0.60 18.00 0.62 16.44 0.46
ME
15.50 0.40 16.37 0.52 16.98 0.54 15.49 0.40
AL
15.01 0.54 16.67 0.52 15.85 0.56 14.97 0.52
MD
21.07 0.70 20.65 0.48 21.79 0.54 21.07 0.68
EA
17.74 0.44 18.24 0.42 18.08 0.44 17.75 0.46
IP
12.40 1.34 12.21
1.4 14.48 1.40 12.47 1.32
FR
10.38 1.12 11.10
1.3 11.71
1.3 10.42 1.12
ME
8.39 1.22
8.91 1.34
9.37 1.42
8.38 1.22
AL
6.87
1.4
8.38 1.52
8.50 1.44
6.92 1.36
MD
EA
12.11 1.42 12.67 1.44 14.55 1.40 12.32
7.66 1.52
8.40 1.62
8.71 1.64
1.4
7.69 1.52
Table 2. Seasonal mean values (1965-2000) for T2m (degrees C) in CPL, CRU, ERA40, and RCM
over the European PRUDENCE sub-regions with the associated standard error. The shaded cells are
for significant discrepancies (90% confidence level) with respect to the CPL data.
36
PRECIP
DJF
MAM
JJA
SON
CPL
ERA40
CRU
RCM
IP
2.64 0.40
1.50
0.24
2.36
0.42
2.65
0.40
FR
3.13 0.41
2.40
0.32
2.39
0.30
3.13
0.42
ME
2.46 0.30
1.79
0.24
1.74
0.26
2.46
0.30
AL
3.87 0.42
1.97
0.26
2.93
0.36
3.90
0.42
MD
3.05 0.38
1.69
0.22
2.45
0.32
3.12
0.38
EA
2.01 0.20
1.44
0.14
1.27
0.16
2.03
0.20
IP
2.60 0.34
1.44
0.18
1.90
0.28
2.62
0.32
FR
3.14 0.36
2.11
0.24
2.15
0.32
3.16
0.36
ME
2.79 0.28
1.44
0.18
1.78
0.22
2.78
0.26
AL
4.31 0.44
2.23
0.22
3.39
0.38
4.36
0.44
MD
3.08 0.30
1.50
0.18
1.90
0.20
3.16
0.30
EA
2.82 0.28
1.42
0.12
1.61
0.20
2.85
0.28
IP
1.61 0.28
0.98
0.14
0.95
0.18
1.63
0.28
FR
2.89 0.30
1.78
0.18
1.89
0.22
2.93
0.32
ME
3.30 0.24
1.71
0.14
2.35
0.22
3.33
0.24
AL
3.84 0.40
2.61
0.18
4.01
0.34
3.96
0.38
MD
1.46 0.30
1.06
0.10
1.22
0.16
1.53
0.32
EA
3.07 0.36
2.16
0.14
2.54
0.22
3.09
0.36
IP
2.13 0.36
1.40
0.22
2.11
0.38
2.17
0.34
FR
2.73 0.40
2.0
0.30
2.51
0.38
2.73
0.40
ME
2.32 0.28
1.54
0.22
1.92
0.24
2.35
0.30
AL
3.65 0.54
2.11
0.28
3.67
0.50
3.66
0.34
MD
2.35 0.36
1.44
0.26
2.40
0.34
2.53
0.38
EA
1.96 0.26
1.33
0.18
1.63
0.22
2.00
0.26
Table 3 Seasonal mean values (1965-2000) for precipitation (mm/day) in CPL, CRU, ERA40, and
RCM over the European PRUDENCE sub-regions with the associated standard error. The shaded
cells are for significant discrepancies (90% confidence level) with respect to the PROTHEUS CPL
data
37