1. Introduction
Observations and modeling studies seem to support the idea that Mediterranean climate is changing. In particular, the possibility that the climate in this region could become more variable and extreme is currently under investigation. An increased frequency of extreme events has been registered during the most recent years (e.g., the European summer heat wave in 2003, the 2004 winter cold wave in Turkey, and heavy snow in the Balkans in 2005 and in Italy in 2012). Both observational (Alexander et al. 2006; Klein Tank and Konnen 2003) and modeling studies (Meehl et al. 2004; Kharin et al. 2007; Kodra et al. 2011) suggest that while global-mean temperature will significantly increase by the end of the twenty-first century, extreme cold events will not disappear.
During the last decades, pronounced warming was observed in the Arctic during winter. This warming was accompanied by a rapid decrease in sea ice cover that is particularly pronounced in the Barents Sea (Stroeve et al. 2007). The Arctic winter sea ice retreat has been related to an increase of the Atlantic inflow to the western Barents Sea and the increased delivery of oceanic heat to the ice sheet margin (Stroeve et al. 2012; Joughin et al. 2012; Arthun et al. 2012). Future scenarios also indicate an abrupt reduction of Arctic summer sea ice related to increasing ocean heat transport to the Arctic (Holland et al. 2006).
Some dynamical mechanisms have been suggested to explain the influence of sea ice variability on the atmospheric circulation. These include the formation of stationary Rossby wave trains and the forcing of the North Atlantic Oscillation toward a negative phase (see, e.g., Yamamoto et al. 2006; Honda et al. 2009). An involvement of change in cyclone paths, also explaining the persistence during winter of the response to anomalous ice cover during autumn, has been recently suggested by Inoue et al. (2012). The authors, by using National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis, showed that during light ice years the lower baroclinicity over the Barents Sea prevents cyclones from traveling eastward. They also used a composite analysis of heavy and light ice years of all cyclone events to show that during light ice years an anticyclonic anomaly prevailed along the Siberian coast of the Barents Sea, leading to anomalous warm advection over the Barents Sea and cold advection over eastern Siberia.
Honda et al. (2009) and Petoukhov and Semenov (2010) showed that the anomalous decrease of wintertime Arctic sea ice concentration could increase the probability of cold winter extremes over large areas, including continental Europe. Also, Liu et al. (2012) found that the recent decline of Arctic sea ice has played a critical role in recent cold winters over a large part of the northern continents.
In light of the above mentioned studies, in the present work we focus our interest on the possibility that dynamical perturbations driven by Arctic sea ice decrease could also impact the Mediterranean area, in particular changing the intensity and the probability of extreme climate events.
An increasing trend in the occurrence of extreme winter precipitation events (from heavy to torrential) in Spain and Italy has been identified by Toreti et al. (2010). Between January 1950 and October 2009, 395 severe flood and storm events were also reported by EM-DAT (2009) for 19 Mediterranean countries. During autumn 2011, severe flood events hit the regions of Liguria, Tuscany, and Sicily in Italy.
General circulation models (GCMs) are useful tools to study future climate change, but their application to regional climatic process studies is limited due to their coarse spatial resolution (approximately 2.5° in latitude/longitude). A common problem with global climate models is the fact that their grids do not always resolve important topographic features that determine the spatial variability of rainfall at regional scales (Smith et al. 2013). Regional modeling studies have shown that an increase in resolution generally leads to a better simulation of the precipitation statistics, including extremes (e.g., Huntingford et al. 2003). Finescale processes have been identified to play a critical role in the response of extreme precipitation events (Diffenbaugh et al. 2005). For these reasons, global GCMs, while representing the best tool to make future climate scenario projections, usually require a downscaling procedure for regional impact-oriented studies because of their coarse spatial resolution. Brankovic et al. (2012) assessed a good ability of a regional climate model (RegCM) to reproduce the spatial distribution of extreme temperatures and precipitations over Croatia by comparing results from a present-day simulation driven by the ECHAM5/Max Planck Institute Ocean Model (MPI-OM) global climate model simulation for 30 yr during the twentieth century (1961–90) with Croatian station data. They also highlighted the need for using a high-resolution regional model to accurately reproduce climate extremes, which are often related to sharp orographic gradients or to complex small-scale orography.
Both Gao et al. (2006), by using a high-resolution regional climate model, and Goubanova and Li (2007), by using a variable-grid general circulation model, evaluated changes in precipitation around the Mediterranean Basin, under the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 emission scenario. In particular, during winter, they suggested that this region will experience a decrease of total precipitation but more intense precipitation events.
In this work, we estimate the potential effects of Arctic sea ice reduction on the Mediterranean Basin during the winter period. To this end, global dynamical fields from an atmospheric GCM are used to initialize and force a regional climate model at higher horizontal resolution.
Section 2 describes the models used and the simulations performed. Also, a description of the indices used to identify extreme events is provided. Results are examined in section 3 and conclusions are summarized in section 4.
2. Models and simulations
Two idealized simulation cases have been performed with the NCAR Community Atmosphere Model, version 3 (CAM3), GCM (Collins et al. 2006). The model has been used at a T42 resolution (about 2.8° × 2.8°) with 26 vertical layers in a hybrid-sigma coordinate system, ranging from the surface to 2.917%hPa. Ozone, sea surface temperature (SST), and sea ice climatological boundary conditions have been taken from the standard datasets provided with the model. At sea level, CAM3 at a T42 resolution reproduces the basic observed patterns of the pressure field. However, the model is known to have a bias at high latitudes characterized by simulated pressures that are too low poleward of 50° latitude in both the Northern and Southern Hemispheres. In particular, during winter, the Iceland low is too deep and it extends too far over Eurasia and the Arctic Basin (Hurrell et al. 2006).
Following the approach used in previous studies (Grassi et al. 2008, 2012), namely, considering a repeating annual cycle of the boundary conditions, we run the model for 12 consecutive years, producing 12 yearly realizations that have been used to generate a statistical base for analysis. More specifically, CAM3 simulations have been forced by prescribing analyzed climatological monthly-mean values of SST, provided with the model and obtained by merging the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST; Rayner et al. 2003) dataset with the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation Sea Surface Temperature, V2 (OISST2; Reynolds et al. 2002), dataset. The climatological value is obtained by averaging data for 1950–2001. Sea ice concentration has also been globally set to the climatological value, with the only exception of the Barents–Kara region where, from November to April, sea ice concentration has been set to 50% and 20% to produce the two simulation cases hereafter referred as 50%ICE and 20%ICE, respectively. Based on Kern et al. (2010), these two concentration values have been chosen as representative of present (50%ICE) and future (20%ICE) winter conditions during the next decades. Sea ice concentration fields for the two simulation cases are shown in Fig. 1. The prescribed idealized sea ice concentrations are consistent with present-day (1980–2000 time period) and projected (2080–2100 time period) ice cover for the SRES A1B scenario, during winter (Solomon et al. 2007; http://www.ipcc.ch/publications_and_data/ar4/wg1/en/figure-10-14.html).
Mean sea ice concentration (%) boundary conditions in November–April used in the (left) 50%ICE and (right) 20%ICE simulation cases.
Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00697.1
Results from CAM3 simulations have then been used to force a regional climate model during the winter period (i.e., from January to March). The three-dimensional mesoscale model used in this study is the Abdus Salam International Centre for Theoretical Physics (ICTP) RegCM, version 4 (RegCM4; Giorgi et al. 2012). The chosen model domain covers the Mediterranean region and surrounding areas, from Spain to Turkey and from northern Africa to the Baltic Sea. The model is run with a horizontal grid spacing of about 60 km and the standard vertical configuration with 18 sigma layers. To obtain a larger ensemble of initial/boundary conditions for RegCM4, each set of u, υ, and T fields simulated by CAM3 has been perturbed by adding to/subtracting from each variable a random quantity equal to 0–0.5 times the standard deviation of its weekly distribution. This process has been repeated three times, obtaining a total ensemble of 48 (3 times 12 perturbed plus 12 unperturbed fields) sets of initial/boundary conditions for RegCM4 for each simulated case (50%ICE and 20%ICE). The lateral boundary conditions are provided from CAM3 to RegCM4 through the selection of a lateral buffer zone and the use of a nudging procedure that interpolates the driving large-scale fields onto the model grid and then applying a relaxation/diffusion term (Giorgi et al. 1993). Topography of RegCM4 is provided at a resolution of approximately 1 km.
Cold temperature and heavy precipitation events have been analyzed on a gridpoint basis (see, e.g., Bell et al. 2004). From the distributions of daily minimum Tmin and maximum Tmax near-surface temperature and total daily precipitation Ptot values, the 5th 
3. Results
The response to the prescribed idealized sea ice reduction is investigated in terms of difference in means between 20%ICE- and 50%ICE-simulated cases. The means are, respectively, 12 or 48 winter averages for CAM3 or RegCM4. The significance of the difference in means is assessed using a Student's t test.
The dynamical response to sea ice reduction has been preliminarily studied on the large scale by examining CAM3 simulation results. The analysis of mean changes of sea level pressure (SLP) shows the development during January of a statistically significant signal (Fig. 2, left). SLP shows an anticyclonic anomaly near the Taymyr Peninsula that strongly resembles the SLP anomaly pattern found by Inoue et al. (2012) in their composite analysis, performed on NCEP–NCAR reanalysis data, between light and heavy ice years. In the study by Inoue et al., the SLP signal was attributed to a change of the cyclone tracks due to lower baroclinicity over the Barents Sea during the light ice years that prevented cyclones from traveling eastward. To this end, an algorithm for cyclone identification and tracking has been used. Even if a complete study of the cyclone tracks is beyond the scope of the present paper, the analysis of the change in the baroclinicity between the 20%ICE and 50%ICE simulations seems to support the hypothesis that a mechanism similar to that discussed by Inoue et al. could also be active in our model. Figure 2 also shows the difference in the baroclinicity, which we calculate as the vertical zonal wind shear between 500 and 925 hPa caused by the introduction in November of the prescribed sea ice perturbation. The statistically significant negative signal over the Scandinavian Peninsula indicates a reduction of baroclinicity simulated in the 20%ICE compared to the 50%ICE background case. Even if a different origin of the anticyclonic pattern cannot be definitively ruled out (i.e., it could follow from a difference in the model bias in the two simulated cases), the characteristics of the baroclinicity reduction in November are, however, consistent with a decrease in the number of cyclones reaching the Siberian coast during the following months. The signal on baroclinicity disappears during the following months. These results are also consistent with previous studies (Deser et al. 2007, 2010; Jaiser et al. 2012) showing an initial early winter baroclinic response, followed then by a midwinter barotropic response that reaches equilibrium from two to two and a half months after the initial sea ice cover change and that resembles the North Atlantic Oscillation (NAO)–Arctic Oscillation (AO) pattern. The 500-hPa geopotential height response (Fig. 3, left) shows, during January, statistically significant positive anomalies over the Arctic region extending toward the North Atlantic sector and negative anomalies over the Balkan region with a minimum lower than −40 m. The pattern of 500-hPa geopotential height simulated for the 50%ICE background case is also shown for comparison purposes (Fig. 3, right). Results suggest that the response, which shows some resemblance to the pattern of the AO in its negative phase, leads to a weakening of the positive AO–like background structure. The 500-hPa geopotential height response seems to be related to the anticyclonic anomaly near the Taymyr Peninsula that leads to a clockwise circulation, then bringing anomalous cold air from northeastern Siberia toward central Europe and warm air toward the North Atlantic. The persistence of this warming can activate the wave flux toward the Mediterranean region, which in turn creates the lower pressure anomaly. Figure 4 shows the response to the prescribed ice perturbation in terms of 250-hPa geopotential height, surface sensible and latent heat fluxes, and 250-hPa wave activity flux (WAF), averaged during winter. Geopotential height wavelike anomalies suggest the occurrence of stationary Rossby waves excited by anomalous heat flux and associated with the propagation of WAF. The horizontal WAF (Takaya and Nakamura 2001) shows strong wave propagation from the North Atlantic region southeastward into the lower latitudes reaching the Mediterranean Basin. Honda et al. (2009) also found an eastward propagation of the horizontal WAF, impacting the Far East. The Tmin anomalies, calculated at the bottom level of CAM3 for January, February, and March (Fig. 5), show positive values over the Arctic region and negative values over Eurasia, with a strong minimum over Asia, that progressively decrease from January to March, and a secondary minimum over continental Europe that is located over the Balkan Peninsula during January and shifts toward the central and western Europe during February and March, with values up to −2°C. While the minimum over Eurasia is probably driven by the anticyclonic circulation over the eastern Arctic region, the minimum over Europe seems related to the intrusion of cold air from the North Atlantic into the Mediterranean Basin due to WAF. This intrusion, which is a large-scale dynamical effect and is prescribed in RegCM4 through the boundary conditions provided by CAM3, leads, when reaching the Mediterranean Basin, to a heat flux from the sea. The higher grid resolution of RegCM4 produces differences between the global and regional model in the simulation of the processes that involve orographic characteristics of the domain. The heat fluxes simulated by RegCM4 and CAM3 in the 50%ICE background case and in the response to the prescribed sea ice reduction are shown in Fig. 6. The patterns of the heat fluxes are similar for the background case (Figs. 6c,d), with maximum values in the eastern Mediterranean, and for the response to sea ice reduction (Figs. 6a,b), with maximum values over the western Mediterranean region. However, CAM3 simulates a heat flux perturbation that is generally 50% higher than that found with RegCM4. This is probably related to the coarse resolution of CAM3, which seems unable to resolve the topography of the Italian Peninsula.
CAM3-simulated change (20%ICE − 50%ICE) of (left) SLP (hPa) in January and (right) baroclinicity (m s−1 km−1) in November. Green contour lines encompass anomalies that are statistically significant at 95% confidence level.
Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00697.1
CAM3-simulated (left) change (20%ICE − 50%ICE) of 500-hPa geopotential height (m) and (right) 500-hPa geopotential height (km) in the background 50%ICE case. Both plots are for January. Green contour lines encompass anomalies that are statistically significant at 95% confidence level.
Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00697.1
CAM3-simulated difference map (20%ICE − 50%ICE) for winter surface sensible and latent heat fluxes (W m−2; positive: upward; color shaded), 250-hPa horizontal wave activity flux (m s−2; arrows), and 250-hPa geopotential height (m; isolines).
Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00697.1
CAM3-simulated change (20%ICE − 50%ICE) of monthly-mean Tmin for (top) January, (middle) February, and (bottom) March. Green contour lines encompass anomalies that are statistically significant at 95% confidence level.
Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00697.1
(a) RegCM4- and (b) CAM3-simulated change (20%ICE − 50%ICE) of sensible and latent heat flux. (c) RegCM4- and (d) CAM3-simulated sensible and latent heat flux for the background 50%ICE case. Only statistically significant anomalies at the 95% confidence level are shown.
Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00697.1
The response to sea ice reduction in terms of temperature extreme events appears to be correlated with the characteristics of the simulated heat fluxes. Figure 7a shows statistically significant negative anomalies of 
Difference maps (20%ICE − 50%ICE) of (a),(d) 
Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00697.1
Changes of 
Difference maps (20%ICE − 50%ICE) of (a),(d) 
Citation: Journal of Climate 26, 24; 10.1175/JCLI-D-12-00697.1
Analyses performed on 
4. Conclusions
In this paper, we estimated the potential climate response over the Mediterranean region to sea ice concentration reduction (from 50% to 20%) on the Barents–Kara Seas, by using the RegCM4 regional climate model driven by large-scale fields from the CAM3 GCM. This response has been investigated in terms of changes in extremes of winter minimum temperature and total precipitation and in the number of extreme cold and rainy events, identified by examining high and low quantiles of the seasonal distributions.
Based on Arctic sea ice trends (e.g., Kern et al. 2010) and IPCC projections (Solomon et al. 2007), the prescribed idealized sea ice concentrations are reasonably representative of present and future (i.e., within the next decades) Barents–Kara sea ice conditions, and so we can look at the results as a possible future climatic scenario for the Mediterranean area.
Our main conclusions can be summarized as follows:
Results suggest a shift toward an overall more cold/rainy winter condition in the Mediterranean Basin and, in particular, a tendency toward an increased risk of flooding in southern Italy, Greece, and the Iberian Peninsula. Also, temperature extreme events show an increase in intensity (over the Balkan Peninsula) and in number (over continental Europe).
The investigation of the dynamical perturbation leading to the remote response on the Mediterranean Basin suggests the activation of a large-scale mechanism consistent with those discussed in previous works. The response, following an initial baroclinic stage of adjustment, becomes progressively more barotropic and is characterized by a wave activity flux from the North Atlantic to the Mediterranean Basin and by a negative AO-like pattern.
When comparing global to regional model results, we find that RegCM4 shows, with respect to CAM3, an intensification of extreme precipitation events that we relate to the higher resolution in RegCM4 of the orography of the Mediterranean Basin. Only minor differences can be found between RegCM4 and CAM3 simulations of temperature extreme events, which are probably related to the differences in the simulated heat fluxes. These results highlight the importance of regional downscaling when local climate/impact studies are performed.
Further studies, performed on simulation ensembles produced with a variety of GCMs and regional circulation models, could enable us to better distinguish the response to sea ice reduction from internal variability, enhancing the significance of the results.
Acknowledgments
We thank Filippo Giorgi and ICTP for making available the RegCM4 model. Our thanks also go to Graziano Giuliani for assistance with model implementation and guidance with model pre- and post-processing. We thank the anonymous reviewers for their valuable comments and suggestions.
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