Effect of a positive Sea Surface Temperature anomaly on a Mediterranean tornadic supercell

Extreme events represent a topic of paramount importance and a challenge for modelling investigations. Due to the need of high-resolution models, the study of severe localized convective phenomena is even more critical, especially in relation to changes in forcing factors, such as sea surface temperatures (SSTs), in future climate scenarios. Here, we analyze the effect of changes in SSTs on the intensity of a tornadic supercell in the Mediterranean through modelling investigations. We show dramatic (nonlinear) changes for updraft helicity and vertical velocity, which measure the intensity of the supercell, even for variations of SST only of + /−1 K.

deficiency of tornado datasets in most of the world. Thus, at present, the best one can do is to analyze the change in the parameters favorable to severe convection, obtained from downscaled high-resolution simulations nested into global circulation model projections. This category of studies has generally predicted an increase in the frequency of these events in future scenarios, associated with greater potential instability that offsets the predicted reduction in deep-layer shear, resulting in environments more favorable for severe thunderstorms [20][21][22][23] .
However, the latter approach provides only a rough indication of the expected changes of severe convection in regions with complex morphology, like the Mediterranean. In these areas, the location and intensity of severe convective weather depend decisively on meso-γ features, which can be properly simulated only using grid spacing of 1-2 km 24 . For example, high-resolution numerical simulations were necessary to identify the crucial role of the circulations induced by small-scale terrain features for the development of a tornadic event in Spain 25 , to show that mesoscale patterns controlled the evolution of a supercell in northeastern Italy 26 , and that the presence of steep mountains may represent an important factor for tornadogenesis in Greece 27 . Thus, the conditions of development in the Mediterranean are different from the more homogeneous, synoptic-scale setting typical of the US Great Plains 26 .
Considering also that the most comprehensive database available in Europe 28 suffers from relevant gaps in the southern regions, only partially mitigated in the last few years 29,30 , one can understand that a classical "climatological" approach would not work properly to identify climatic changes in severe convection over the Mediterranean. Thus, for the points discussed above, the sensitivity analysis to SST even in a single case study can be of some interest for both its meteorological and climatic implications.
In the present study, we started from the simulation of a supercell spawning a tornado in the Mediterranean 31 . Since the simulation was able to properly reproduce the tracking and timing of the cell, one can better understand the mechanisms responsible for its triggering and development by undertaking some sensitivity tests with modified control parameters. In particular, additional simulations are performed with modified SST.

Results
Case study. The tornado originated as a waterspout over the Ionian Sea, made landfall in the port of Taranto (Apulia region, southeastern Italy) after about 30 minutes 31 (0950 UTC, 1050 Local Time, 28 November 2012), and was responsible for one fatality and estimated damages for 60 M€ 32 .
The exact track of the tornado over land 33 is shown here in Fig. 1 (red line). The "proximity" sounding in Brindisi (70 km far from Taranto) at 12:00 UTC, 2 hours after the landfall, documented the extraordinary The radar reflectivity images 32 suggest that the orography south-southwest of Taranto played a key role in the development of the supercell: a line of convective cells was triggered by Sila mountains (top height of about 2000 m), then moved downstream and approached the coast near Taranto 31,32 . Motivated by the need for a better understanding of the dynamics of the event, numerical simulations were performed using the Weather Research and Forecasting (WRF) model 34 , implemented with 3 one-way nested grids, with horizontal spacing of 9, 3, and 1 km respectively 31 . This configuration is too coarse to simulate the tornado but can reproduce the supercell spawning it. A "control" simulation, using the ECMWF analysis/forecasts initialized at 00:00 UTC, 27 November 2012 as initial/boundary conditions (horizontal resolution of about 16 km) and lasting 36 hours, was able to simulate properly the evolution of the supercell, both in timing and track. Numerical simulations confirm the central role of Sila mountains in triggering convection and that both the increasing instability, due to the advection of high-θ e (equivalent potential temperature) low-level air and cold mid-tropospheric air, and the intensification of deep-and low-level wind shear provided an environment favorable to supercell convection 31 .
Sensitivity simulations. We hypothesize that modifications in SST may affect the supercell intensity. SST values in the simulation (extracted from the ECMWF analysis at the initial time) are increased/decreased uniformly all over the domain by 0.5 K and 1 K with respect to the control run, while the atmospheric fields in the initial and boundary conditions are kept fixed. Figure   of the landfall (simulated in the control run at around 10:20 UTC, 28 November), thus during the lifetime of the supercell the PBL has already relaxed to the modified lower boundary condition. As a consequence, in the morning of 28 November, the low-level temperature in each sensitivity experiment is different from that in the control run, being the difference maximum near the surface (by approximately the difference in SST between the runs) and progressively reduced moving to higher altitude.
The impact of SST on the supercell development is analyzed by comparing the sensitivity simulations with the control run. An indication of the intensity of the supercell is provided by the 2-5 km updraft helicity UH, a diagnostic parameter designed for identifying rotation in simulated storms. UH is computed by taking the integral of the vertical vorticity ζ times the updraft-vertical velocity w between 2 and 5 km: A typical threshold used to predict mesocyclones 37 is UH = 50 m 2 s −2 , while UH = 100 m 2 s −2 was found to most reliably predict tornadoes 38 .
In the control run, UH reaches peak values of 250 m 2 s −2 just before landfall (Fig. 3). Within small variations of SST (+/− 0.5 K), the supercell still forms and the evolution appears similar to that in the control run (see Table 1), although convective activity appears more spread in the warmer simulation.
However, when SST is modified by 1 K, the changes are dramatic and highly nonlinear. Near the time of the observed landfall, the simulated UH span two orders of magnitude for a SST variation of just 2 K. In the coldest run, only a limited peak of UH ≈ 20 m 2 s −2 is simulated (some cells are triggered by the Sila mountains, but no supercell forms); in contrast, the case with the warmest SST produces a strong intensification of the updraft rotation in the supercell, since a peak of UH higher than 800 m 2 s −2 is reached when the cell gets close to the coastline near Taranto (Fig. 3). In the latter case, the track is slightly shifted to the east compared to the control run ( Fig. 1, purple line), thus the longer persistence of the cell over the sea may have also cooperated to its stronger intensification.
The difference in UH follows from changes in sea surface fluxes, in particular latent heat fluxes. Indeed, the lower troposphere is moistened and warmed with greater intensity for higher SST, thus the low-level profiles of temperature and humidity differ significantly among the experiments, affecting potential instability. As a consequence, the values of MUCAPE (Convective Available Potential Energy of the Most Unstable parcel 39 ) around the time when the supercell developed, averaged in the area where it originated, range from 1180 J kg −1 for the coolest case to 1940 J kg −1 for the warmest case (Table 1): greater CAPE means stronger updrafts, hence more intense stretching of low-level environmental vorticity. Figure 3a show that the maximum vertical velocity w at 600 hPa increases with SST. Table 1 shows that the range of variation within 0.5 °C around the SST of the control run is quite limited, while w is much greater in the warmest case and smaller in the coldest case. Similar results come out for other levels (e.g., w at 450 hPa, about the level where the uplift was found to be maximum, is shown in Table 1). Comparing w at different levels, one can note that w is lower at 450 hPa than at 600 hPa only in the coldest case, thus suggesting the presence of less vigorous and shallower convection in that run.
Finally, several instability indices used to diagnose severe convection were analyzed. Although most parameters show conditions slightly more favorable to severe convection for higher SST (not shown), only CAPE identifies a significant change of the environmental characteristics (Table 1). While an increase in CAPE by around 200 J kg −1 was recently simulated for a 2 K increase in the Mediterranean SST 40 , in the present study Table 1 shows that for the analyzed supercell the same change in SST would produce greater modifications in CAPE and, consequently, in the updraft velocity.
In contrast, the 0-3 km storm relative helicity (where: v is the horizontal wind vector, c is the storm motion vector, ω is the horizontal vorticity vector associated with the vertical wind shear), which is a measure of the potential for cyclonic updraft rotation, slightly decreases for increasing SST. This change may contribute to explain the non-monotonic variation of UH max between the control run and the simulation with SST increased by 0.5 K (Table 1).

Discussion
In the last years, due to the critical impacts of extreme events on territories, ecosystems and humans, a strong interest for their attribution has raised in order to understand how much the changes in their features can be due to the anthropogenic factors manifested in the climate change. Modelling studies are usually oriented to understand if the number of extreme events increases with global warming, but the topic of their intensity is only partially addressed 41,42 . Of course, attention is paid mainly to large-scale processes and phenomena, due to difficulties in modelling severe localized weather. In this framework, the work presented here represents a first attempt at investigating how an increase in SST, predicted for the next decades, may affect the thermodynamics of a tornadic supercell and lead to stronger effects/impacts. The analysis of this specific event suggests that, in similar environmental conditions, a warm SST anomaly, due to a temporary and local fluctuation in the field or induced by a general SST warming associated with climate change, may favor supercell formation and intensification over the Mediterranean. Considering that the supercell developed over the Ionian Sea, which was warmer than average by about 1 K, one can speculate that no supercell development would have occurred over a normal SST, while an even warmer anomaly would have drastically increased the intensity of the supercell (and, possibly, of the associated tornado) in a nonlinear manner. However, this does not necessarily imply that the frequency of these events should increase in future climate scenarios, considering that the present study only deals with changes in thermodynamics; dynamical modifications in weather circulation patterns, important as well to explain climatological changes in extreme weather events 43 , are not analyzed here. (Incidentally, some studies agree that the effect of climate change on Mediterranean tropical-like cyclones is to decrease their frequency, although they would develop over a warmer SST 44 ). Nonetheless, our analysis is consistent with climate change studies, which predict an increase of tornado activity in global warming scenarios due to the increasing CAPE (which more than compensates for the predicted decrease in SREH). Idealized simulations are actually in progress to better understand the role of the forcing mechanisms in the development of the analyzed supercell.

Methods
Numerical setup. The Weather Research and Forecasting (WRF) model, version ARW-3.5.1 (ref. 33 ) is used for the numerical simulations of the case study. WRF is a limited area model, which solves the fully compressible, nonhydrostatic primitive equations. Forty terrain-following vertical levels are used, more closely spaced near the ground to better represent the boundary layer (their vertical distance ranges from 58 m in the boundary layer to 600 m).
Three one-way-nested domains are implemented. The outer grid covers the central part of the southern Mediterranean (210 × 150 grid points, dx = 9 km), the intermediate grid represents southern Italy, part of Greece and Albania (271 × 193 points, dx = 3 km); the inner domain is centered over the Ionian regions of southern Italy (211 × 271 points, dx = 1 km).
The following parameterization schemes are used: the Thompson microphysics 45 ; the longwave radiation Rapid Radiative Transfer Model (RRTM) 46 ; the Dudhia shortwave radiation 47 ; the land-surface unified Noah model 48 ; the Mellor-Yamada-Janjic planetary boundary layer 49 . Cumulus convection is switched off in all domains.
The "control" simulation uses the ECMWF analysis (forecasts) initialized at 00:00 UTC, 27 November 2012 as initial (boundary) conditions and lasts 36 hours. The sensitivity runs use the same setup, apart from the modified sea surface temperature. Data availability. Simulation outputs and data to reproduce the numerical experiments are available on request.