OSOcean ScienceOSOcean Sci.1812-0792Copernicus PublicationsGöttingen, Germany10.5194/os-12-233-2016Long-term variability of the southern Adriatic circulation in relation to North
Atlantic OscillationShabrangL.lshabrang@ogs.trieste.itMennaM.PizziC.LavigneH.CivitareseG.GačićM.OGS – Istituto Nazionale di Oceanografia e di Geofisica
Sperimentale, Trieste, ItalyL. Shabrang (lshabrang@ogs.trieste.it)12February201612123324115January201510February201511January201615January2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://os.copernicus.org/articles/12/233/2016/os-12-233-2016.htmlThe full text article is available as a PDF file from https://os.copernicus.org/articles/12/233/2016/os-12-233-2016.pdf
The interannual variability of the South Adriatic Gyre and its relation to
the wind vorticity and the large-scale climatic pattern (North Atlantic
Oscillation – NAO) was studied using the time series of satellite
altimetric data and ocean surface wind products. The cyclonic circulation
observed in the southern Adriatic area was partly sustained by the local wind
forcing, as suggested by the positive correlation between the rate of change
of the current vorticity and the wind-stress vorticity. Nevertheless, the
influence of vorticity advection from the adjacent area (northern Ionian Sea)
cannot be ignored and it is more significant during the anticyclonic phase
of Adriatic–Ionian Bimodal Oscillation System. The geostrophic current
vorticities of the southern Adriatic and northern Ionian seas are correlated with
a time lag of 14 months, which approximately corresponds to an advection
speed of ∼ 1 cm s-1. The different wind patterns observed during
two NAO phases in the winter revealed a stronger positive vorticity during
the negative NAO phase. Conversely, during the wintertime positive NAO phase
the wind vorticity is characterized by lower positive or slightly negative
values. Despite a statistically significant negative correlation between the
NAO index and the wind vorticity, no unequivocal relationship between large
climatic system and the interannual variability of the South Adriatic Gyre
intensity was found due to additional effects of the vorticity advection
from the Ionian. This can be explained by the fact that the Ionian
circulation mode does not depend on the NAO variations. Therefore, the main
result of this study is that the interannual variability of the southern
Adriatic cyclonic circulation is a result of the combined influence of the
vorticity advection from the Ionian and the local wind-curl effect.
Introduction
The Adriatic Sea is a source of the Adriatic Dense Water (AdDW), the main
component of the Eastern Mediterranean Dense Water (EMDW). The dense water
formation in the Adriatic Sea takes place both in the northern Adriatic shelf
area (Hendershott and Rizzoli, 1976) and in the South Adriatic Pit (SAP), in
the centre of the permanent topographically trapped South Adriatic Gyre
(SAG), through two different processes. In the northern Adriatic, the dense
water is formed over the large northern shelf area through winter cooling
and mixing, while in the southern Adriatic the dense water is formed via
open-ocean convection mechanism (Gačić et al., 2002; Manca et al.,
2002). These processes occur under the action of cold and severe
northerly winds, more specifically the ENE or NE bora wind associated with
the persistent synoptic condition and orographic configuration (Grisogono
and Belušić, 2009). Major contribution to the outflowing AdDW comes
from the water formed in the SAP (∼ 90 %; Vilibić and
Orlić, 2001), and it presumably varies on interannual scale
(Mihanović et al., 2013). The estimated total average rate of the dense
water formation/outflow from the Adriatic is 0.3 Sv (1 sverdrup (Sv) = 1 000 000 m3 s-1; Lascaratos, 1993). Obviously, this estimate is an
average value and the formation rate is subject to pronounced interannual
and decadal variability. Decadal variability is presumably linked to the
buoyancy variations related to the import of intermediate and surface waters
of varying salinity from the Ionian as associated with Adriatic–Ionian Bimodal
Oscillating System (BiOS; Gačić et al., 2011). On the other hand,
interannual variability of the dense water formation rate is due to a variety
of factors such as surface buoyancy losses, wind forced preconditioning of
surface layer density through doming of isopycnals and advective changes in
density via variations in the near-surface temperature and salinity (Josey
et al., 2011). Preconditioning depends also as we will show here on the
intensity of the SAG due to the local wind vorticity input and the vorticity
advection from the Ionian. Variations in the strength of the SAG result in
changes of the vertical distribution of isopycnals and in general in changes
of the doming shape of the physical and biogeochemical interfaces.
The aim of this paper is to study the interannual variability of the SAG
intensity, i.e. the vorticity of the flow field in the southern Adriatic, to
relate it to the vorticity inputs (from wind and advection), and then
possibly to large-scale climatic regimes (North Atlantic
Oscillation (NAO) index will be considered).
Data and methods
The wind products used in this study were the Cross-Calibrated
Multi-Platform (CCMP) ocean surface wind velocity, downloaded from the NASA
Physical Oceanography DAAC (https://podaac.jpl.nasa.gov/) for the period
July 1987–December 2011 (Atlas et al., 2009). These products were created
using a variational analysis method to combine wind measurements derived
from several satellite scatterometers and microwave radiometers. The CCMP
six-hourly gridded analyses (level 3.0, first-look version 1.1, resolution
of 25 km) were used to quantify the vertical component of the wind-stress
curl [curlτ]z over the Mediterranean Sea:
curlτz=∂τy∂x-∂τx∂y;τx,τy=ρCDuw,vwU10,
where (τx,τy) are the wind-stress components, ρ(1.22 kg m-3) is the density of air, (uw,vw) and U10 are the
components and the magnitude of the wind speed at 10 m, respectively,
and CD is the drag coefficient which has been obtained according to
Yelland and Taylor (1996).
CD=10-3U10≤3msCD=(0.29+3.1U10+7.7U102)×10-33ms≤U10≤6msCD=(0.6+0.07U10)×10-36ms≤U10≤26ms
The six-hourly wind-stress curl estimated from Eq. (1) was firstly
time-averaged over monthly periods and finally spatially averaged in the SAG (the upper black box in Fig. 1).
The vorticity associated with the surface geostrophic circulation in the SAG
and in the northern Ionian was estimated using the gridded
(1/8∘ Mercator projection grid) Ssalto/Duacs weekly
multi-mission delayed time (quality controlled) products from AVISO
(SSALTO/DUACS users handbook 2014). Absolute geostrophic velocity (AGV)
data, derived from the satellite absolute dynamic topography (ADT) through
geostrophic balance equations, were downloaded for the 1992–2014 period. The
ADT is the sum of sea level anomaly and synthetic mean dynamic topography,
estimated by Rio et al. (2014), over the 1993–2012 period. The delayed time
product used in this work was based on pairs of satellites (Jason-2–Altika
or Jason-2–CryoSat or Jason-2–Envisat or Jason-1–Envisat or
TOPEX-Poseidon–ERS) with the same ground track. This data series was
homogeneous all along the available time period, thanks to a stable
sampling. The relative vorticity (ζ) of the AGV data was evaluated
as the vertical component of the velocity field curl (Pedlosky, 1987):
ζ=∂vg∂x-∂ug∂y,
where ug and vg are the components of the AGV.
Geography of the southern Adriatic and northern Ionian seas. The black
squares show the areas used to estimate the time series in Fig. 2. The
grey contours indicate the isobaths between 200 and 1200 m with the 200 m
line space. Colours show the mean altimetry pattern in the period October
1992–December 2013; altimetry grid points located within 50 km from the
coast have been deleted. The Adriatic square includes 81 altimetry
measurement points; the Ionian square includes 232 altimetry measurement
points.
Monthly means of the geostrophic current vorticity fields were spatially
averaged in the region of the SAG and in the northern Ionian (areas of
averaging are shown in Fig. 1). Time series of these spatially averaged
parameters were filtered using a 13-month moving average, in order to
remove the seasonal and intra-annual variations and focus on the interannual
fluctuations. The low-pass procedure consists of a zero-phase forward and
backward digital infinite impulse response filtering, with a symmetric
Hanning window (Yan et al., 2004) of 13 points (months).
The vorticity equation was analysed in order to evaluate the importance of
various sources of current vorticity. Following Ezer and Mellor (1994) and
Schwab and Beletsky (2003), current vorticity equation can be written as
∂ζ∂t=-curl(AD)-div(fv)-1ρ0curl(1D∇Φ)+curl(τsρ0D)-curl(τbρ0D),
where ζ is current vorticity, A is advection and diffusion, D is total
water depth, ρ0 is the reference density, Φ is the potential
energy, f is Coriolis parameter, v is current velocity, and τs and
τb are wind stress and bottom stress, respectively. Since we assume
the predominance of the barotropic flow, the internal pressure gradient (the
third term on the right) can be ignored. We also neglect the bottom stress.
If we separate the current velocity into geostrophic (Vg) and
ageostrophic (Va) parts and consider the non-divergence of the
geostrophic current, we will have
V=Vg+Va;ζ=ζg+ζadiv(fV)=f∂ua∂x+∂va∂y=fDdhdt,
Replacing Eqs. (5) and (6) in Eq. (4) and neglecting the diffusion
A as well as bottom stress and divergence (fD(dhdt), which
is 2 orders of magnitude smaller than rate of change of the vorticity)
implies
∂(ζg+ζa)∂t=-(Vg+Va)⋅∇(ζg+ζa)+1ρDcurl(τs).
Since VaVg=ζaζg∼O(Ro)=O(UfL)=10-2 (U∼10-1ms,L∼105m,f∼10-4s-1), the ageostrophic parts vanish and finally we
obtain the current vorticity equation:
∂ζg∂t=-Vg⋅∇(ζg)+1ρDcurl(τs),
which shows that the variation of the geostrophic current vorticity can be
explained in terms of the wind-stress vorticity as well as vorticity
advection from the neighbouring areas.
The monthly NAO index used in this work was obtained from the National
Weather Service, Climate Prediction Center of NOAA (National Oceanic and
Atmospheric Administration). The procedures used to identify the NAO index
was the rotated principal component analysis (RPCA, Barnston and Livezey,
1987). The RPCA procedure is superior to grid-point-based analyses,
typically determined from one-point correlation maps, in that the
teleconnection patterns in the RPCA approach are identified based on the
entire flow field, and not just from height anomalies at selected locations
(http://www.cpc.ncep.noaa.gov/data/teledoc/nao.shtml).
Time series of the spatially averaged, low-pass filtered (13
months) wind-stress vorticity (a) and current vorticity (b) in the Adriatic
Sea, computed over the domain denoted in Fig. 1. Time series of the
low-pass filtered current vorticity in the Ionian Sea (c) spatially averaged
over the domain presented in Fig. 1.
Results and discussion
Calculations of the spatially averaged current vorticity (Fig. 2b) show that
the southern Adriatic was characterized, as expected, by a permanent positive
vorticity since the SAG is a cyclonic circulation feature. Nevertheless,
prominent interannual or decadal variability was present in the time series
(Fig. 2). The interannual variability prevailed also in the wind-stress curl
(Fig. 2a), while decadal variability was prevalent in the vorticity field of
the northern Ionian (Fig. 2c). In fact, the vorticity field in the northern
Ionian is mainly subject to decadal variability due to BiOS (Gačić
et al., 2010) as opposed to the Adriatic current vorticity and the
wind-stress curl. The vorticity of the wind field was positive for the major
part of the record with only short periods of negative values (Fig. 2a).
Considering the flow vorticity Eq. (8), interannual variability of the
intensity of the geostrophic cyclonic circulation in the southern Adriatic can
be only partly explained in terms of the local wind vorticity input, this
last one being prevalently positive. Thus in addition to the local wind-curl
effect, the vorticity advection from adjacent area should be taken into
consideration.
Spatial distribution of the correlation coefficient between the
JFM time derivative of the vorticity and the wind-stress curl for the period
1993–2011 (colours). Black bold contours outline the 20-year average of the
JFM sea level height (cm), and the white dotted lines indicate the level of
the 95 % significance (a); spatial distribution of the average of the JFM
geostrophic current vorticity (colours and the black lines). The grey
contours indicate the isobaths between 200and 1200 m with the 200 m line
space (b).
The grey squares show the areas used to estimate the time series in Fig. 2;
altimetry grid points located within 50 km from the coast have been
deleted.
First to estimate the importance of the local input in the vorticity
equation, we compared the time series of the current vorticity tendency with
the curl of the wind stress over the winter months, from January to March
(hereafter we refer to this time period as JFM), calculating the linear
correlation coefficient in each data point of the study domain. Wintertime
values were chosen because strong air–sea interaction (wind forcing and
possible relationship to NAO) occurs during the winter months when deep
convection takes place. As it follows from the vorticity equation (Eq. 8)
the vorticity tendency and the wind-stress curl should be positively
correlated.
Correlation between the time series of the spatially averaged
low-pass current vorticity in the Adriatic and 0.25∘×0.25∘ domains in the Ionian for the 14-month time lag (a); lagged
correlation between Adriatic and Ionian spatially averaged vorticities. For
the Adriatic the averaging domain is the upper polygon while for the northern
Ionian the averaging domain corresponds to the area (0.25∘×0.25∘) with the maximum correlation (b); the black polygons show the
areas used to estimate the time series in Fig. 2; the black dotted lines
indicate the level of confidence of 95 %.
The spatial distribution of the correlation coefficients over the study area
shows a rather patchy pattern. The area of the significant positive
correlation (r≥0.6;s≥0.95) northeast of the gyre (see Fig. 3a)
coincides rather well with the maximum of the current vorticity average
(Fig. 3b), and there probably the main wind vorticity input takes place. This
suggests that in a limited area the Ekman suction controls the strength of
the SAG determining the strength of the gyre. In the centre of the gyre, the
correlation diminishes probably due to the generally small values of the
current vorticity. In addition, some small-scale features characterized by
the negative correlation are present west and south of the gyre, which can
be explained in terms of the vortex stretching due to strong bathymetric
features. The significant negative correlation (r≤-0.5;s≥0.95) west
of the gyre (around 17∘15′ E and 41∘40′ N) is probably
due to the topographic anomaly near the Bari Canyon (Cushman-Roisin et al.,
2001), which may generate strong ageostrophic divergence. Therefore, in
accordance with the quasi-geostrophic equation of the vorticity
conservation, the mechanism partially responsible for the variations of the
current vorticity is the wind-stress curl acting in a limited area of the
SAG. The fact that direct forcing from the wind-stress curl could be an
important mechanism determining the vorticity of the mean circulation
pattern was also evidenced in some large lakes (Schwab and Beletsky, 2003).
The second term that may contribute to the vorticity tendency in the SAP is
the advection term. In order to study to what extent the vorticity advection
from the Ionian plays a role in controlling the curl of the flow in the
southern Adriatic, we first calculated the lagged correlation between the
spatially averaged vorticity in the northern Ionian and southern Adriatic
(figure not shown). The correlation between the low-pass Adriatic and Ionian
flow vorticities reached maximum (r∼ 0.4) for the Adriatic
vorticity lagging the Ionian one by about 14 months. It should be mentioned
that by decreasing the degrees of freedom of the time series from
∼ 240 to ∼ 20 due to the filtering procedure,
the level of confidence of the correlation decreases. In other words,
according to the standard t table (e.g. Snedecor and Cochran, 1980) the
correlation coefficients must exceed 0.423 to be significant at 95 %
confidence level. Furthermore, as far as the estimates of the time lag are
concerned the same value (14 months) was obtained using either unfiltered
data or data filtered with different window lengths (figure not shown). This
time lag corresponds approximately to the advection speed of 1 cm s-1, a rather
reasonable value. Then, in order to determine with more precision the
vorticity source area in the northern Ionian Sea, the 14-month lag correlations
between the spatial average of the low-passed current vorticity in the SAG
(the upper domain in the Fig. 4a) and average vorticity in smaller domains
(0.25∘×0.25∘) in the northern Ionian were calculated
(see Fig. 4a). All over the area of the northern Ionian the correlation
coefficients are positive with a maximum located in the northern part of North Ionian Gyre (NIG)
(around 18∘30′ E, 39∘30′ N) where the horizontal shear is
the strongest during the anticyclonic mode of BiOS (Gačić et al.,
2011). Afterwards, the lag correlation between the filtered time series of
the mean vorticities in the SAG and the area where maximum correlation
between SAG and Ionian was evidenced (the small polygon located in
18∘30′ E, 39∘30′ N in the Fig. 4a) was calculated. The
maximum correlation coefficient (r∼ 0.56) with the higher
level of confidence (99 %) is evidenced again for the time lag = 14
months (see Fig. 4b), which confirms the impact of the vorticity in this area
on the SAG circulation. Although the estimated correlation coefficient is
relatively large, the relation between vorticities in these two areas is not
visible inspecting the time series (see Fig. 2b and c). This can be
explained by the fact that, according to Eq. (8), vorticity advection only
partly determines the variation of the circulation in the SAP while the
additional contribution comes from the wind input. Therefore, the influence
of Ionian circulation to the current vorticity of the SAP cannot be clear in
the visual examination of the vorticity time series. More specifically, the
advection term is not equally important in all situations; in 1997, the
reversal of the northern Ionian circulation took place from anticyclonic to
cyclonic mode (Larnicol et al., 2002; Pujol and Larnicol, 2005). The
continuous reduction of the current vorticity term between 1995 and 1999
(see Fig. 5a) is due to this circulation transition. The subsequent
passage from cyclonic to anticyclonic circulation in the northern Ionian Sea
occurred in 2006 (Gačić et al., 2010), which has as a consequence an
increase of the relative importance of the advection term. Therefore, when
the Ionian circulation is in the anticyclonic phase the advection term is
more important than in the cyclonic phase. In the former case, the advection
term is proportional to the sum of the Ionian and Adriatic vorticities while
in the latter case the advection term is proportional to the difference
between the two vorticities (see Eq. 8). In order to examine the relative
importance of the advection term in each mode of BiOS, we compare the
vorticity tendency with the advection term in Eq. (8):
∂ξg∂t=-Vg⋅∇(ξg)+A,
where A is the wind-stress vorticity. Then, Eq. (9) expressed in terms of
the finite differences becomes
ΔξgΔt=-V×ΔξgΔx+A.
Then considering only the advection term, we obtain
ξg-SAG(t+1)-ξg-SAG(t-1)∝C×ξg-SAG(t)-ξg-NIG(t),
in which ξg-SAG and ξg-NIG are the spatial average of the
curl of geostrophic current in SAG and in the small polygon in the NIG
located around 18∘30′ E and 39∘30′ N, respectively.
Furthermore, we assume that C=-VΔtΔx
is a constant obtained from the time step, the distance between the Ionian
vorticity source area and the SAP, and considering the constant advection
speed.
Time series of the low-pass (13 months) current vorticity
advection from the northern Ionian Sea to the southern Adriatic Sea. Areas shaded
in red correspond to the time periods characterized by cyclonic circulation
mode, and the dashed lines show the average values of the advection over each
cyclonic/anticyclonic periods (a); scatter plot of the vorticity
tendency (in finite differences) in the SAP (Fig. 2b) and the difference
of vorticities in the Adriatic and the northern area of NIG during the
cyclonic (red circles) and anticyclonic (blue circles) modes of BiOS (b).
Using Eq. (11), vorticity tendency in the SAP was compared to the difference
of the vorticities between the SAP and the northern portion of NIG plotting
the scatter diagram for the periods associated with the cyclonic (red) and
anticyclonic (blue) modes of BiOS (Fig. 5b). The figure reveals rather
satisfactory linear relation between two terms during the anticyclonic
phase, when the vorticity advection becomes more important. Conversely, in
the cyclonic mode of the Ionian circulation, the difference of the
vorticities in the SAP and NIG is smaller and the advection does not have
the significant influence on the vorticity variations in the SAG. In
addition, from the calculated linear regression between two terms of Eq. (11),
we obtained the advection speed of about 0.8 cm s-1, which is rather consistent
with the estimate obtained from the lagged correlation between the NIG and
SAP vorticities.
Therefore, the local current vorticity input prevailed in the period
1997–2006 when the Ionian was in the cyclonic phase and the advection term
was less important. Before 1997 and after 2006 the Ionian was characterized
by the anticyclonic circulation mode, and the vorticity advection term was
more important.
In order to relate the interannual variability of the wind-stress curl (one
of the important factors affecting the variation of the strength of the gyre
according to Eq. 8) to NAO as a large climatic system, we compared
time series of the wintertime NAO index with the wind-stress curl. More
specifically, we calculated the correlation coefficient between the two
time series in each point of the study area for the period 1988–2011 (Fig. 6). Previous research showed that the correlation between the wind speed and
NAO in the Adriatic was statistically insignificant (Pirazzoli and Tomasin,
2003). Considering however the wind-stress curl, the results revealed the
significant (95 %) negative correlation between the wind-stress curl and
NAO for the major portion of the open Adriatic Sea: NAO index negative
values were concomitant with maximum wind-stress curl, and conversely minima
of the wind-stress curl were associated with NAO maximum values.
Spatial distribution of the correlation coefficient of the JFM NAO
index and wind-stress vorticity, 1988–2011 (coloured area and dotted
lines). The bold solid and dashed lines indicate the 0 correlation and the
95 % significance isoline, respectively. The square shows the area used to
estimate the time series in Fig. 2.
Spatial distribution of the mean JFM wind-stress vorticity
(colours; Nm-3) and wind-stress vectors (arrows; Nm-2) in the positive NAO
phase (a) and negative NAO phase (b), 1988–2011. The squares show the areas
used to estimate the time series in Fig. 2.
During the positive NAO phase, northwesterlies are dominant in southern
Europe and Mediterranean Sea as the result of the enhancement of the
Icelandic Low as well as of the Azores High. Conversely, in the negative
phase, the intensification of the westerlies is observed in these regions
(Jerez et al., 2013). More specifically, depending on the phase of NAO, the
pressure gradient over the North Atlantic changes in the magnitude and
orientation, which causes the differences in the speed and direction of winds
in mid-latitudes (Lamb and Peppler, 1987). In agreement with Trigo et al. (2002), the local maxima of the wind vorticity were present in the southern
Adriatic Sea during both positive and negative NAO phases. The positive
winter NAO indices were followed by strong northwesterly winds over the
Mediterranean, which is the consequence of the intensification of the high
pressure over the Mediterranean region (Fig. 7a). This configuration
resulted in a rather weak low-pressure centre over the southern Adriatic and
a weakening of the cyclonic vorticity. On the contrary, during the negative
NAO periods rather strong northward atmospheric flow along the eastern coast
of the southern Adriatic was observed, reinforcing the wind-stress curl
(Fig. 7b).
Therefore, we can say that the large-scale climatic conditions associated
with a positive NAO phase weaken the positive wind-stress curl, while the
stronger positive wind-stress curl is related to the negative NAO index. The
wind-stress curl, on its turn, affects the current vorticity tendency in the
central part of the southern Adriatic however depending on the circulation
mode of BiOS. In the cyclonic phase, the wind-stress curl is presumably
prevailing in determining the vorticity tendency, while in the anticyclonic
phase the vorticity advection term becomes important. In conclusion, due to
the varying importance of the vorticity advection term, which depends on the
Ionian circulation mode, it is not possible to establish an unequivocal
relationship between NAO and the strength of the SAG.
Conclusions
Intensity of the SAG shows prominent intra-annual and interannual
variability. In this paper its interannual variability was analysed using
the surface geostrophic current vorticity. Local forcing is analysed
considering the wind-stress curl in the area of the southern Adriatic, while
advective contributions were examined to analyse the vorticity in the
adjacent area, i.e. the northern Ionian. Correlation between the wintertime
wind-stress curl and the geostrophic vorticity tendency reaches local
maximum on the northeast of the SAG (Fig. 3a) coinciding with the
maximum of the current vorticity average (Fig. 3b). We thus conclude that
the current vorticity tendency can partially be explained in terms of the
local wind vorticity input.
Subsequently, the moving correlation between the current vorticity in the
northern Ionian, possible source area, and in the southern Adriatic shows that
the vorticity variations in the Adriatic lag those in the Ionian by about 14
months (Fig. 4b). This suggests that the advection speed is about 1 cm s-1.
Calculating the correlation between the average current vorticity at the SAG
and each small polygon of the NIG, the strongest advection signal from
northern Ionian to the South Adriatic Gyre is recognized to be from the
northern area of the NIG (Fig. 4a). This location coincides with the
strongest horizontal shear during anticyclonic BiOS. The scatter diagram
between the SAG vorticity tendency and vorticity differences between SAG and
northern part of NIG reveals the stronger impact of the advection term with
the speed ∼ 0.8 cm s-1 (close to the previously obtained
advection speed) during the anti-cyclonic mode of BiOS. It implies that the
importance of the advective term in the vorticity equation depends on the
BiOS circulation mode. It was revealed that in the BiOS cyclonic phase the
main vorticity input into the SAG comes from the wind-stress curl although
we cannot exclude completely the advection term. In the anticyclonic phase
the advective vorticity input from the Ionian becomes larger and presumably
more important for the overall SAG vorticity tendency than during the
cyclonic phase (Fig. 5a and b). The large-scale climatic conditions were
presented by the NAO index, and the wind-stress curl variations were related
to them. Comparison between the NAO and the wind-stress curl shows that in
both positive and negative NAO phases cyclonic atmospheric circulation is
dominant, but higher vorticity in the wind field coincides with negative
NAO, and conversely smaller values of the wind-stress curl are concomitant
with positive NAO values (Fig. 7). This was explained in terms of the
prevailing atmospheric flows over the larger Mediterranean area.
This analysis therefore suggests that, to a certain extent, the interannual
variations of the strength of the SAG are associated with the large-scale
climatic variations via the wind-stress curl forcing. However, due to the
rather important contribution of the vorticity advection from the Ionian,
characterized by the prevalent decadal variability, there is no clear
evidence of a direct effect of large-scale atmospheric circulation over the
NAO on the interannual variability of the intensity of the
SAG.
Acknowledgements
The altimeter data were produced by SSALTO/DUACS and distributed by AVISO,
with support from CNES (http://www.aviso.oceanobs.com/duacs/). We thank S. Neske for the contribution to the work during her internship stay at OGS. We
express our thanks to A. Mellit for help with the statistical analysis. We
acknowledge the support to H. Lavigne of the European Commission “Cofunded
by the European Union under FP7-People – Co-funding of Regional, National
and International Programmes, GA no. 600407” and of the RITMARE Flagship
Project. The research was partially financed by the national project MedGES
of the Italian Ministry of Education, University and Research.
Edited by: M. Hoppema
References
Atlas, R., Ardizzone, J. V., Hoffman, R., Jusem, J. C., and Leidner, S. M.:
Cross-calibrated, multi-platform ocean surface wind velocity product
(MEaSUREs Project), Guide Document, Physical Oceanography Distributed Active
Archive Center (PO.DAAC), JPL, Pasadena, California, 18 May 2009, Version
1.0., 26 pp., 2009.
Barnston, A. G. and Livezey, R. E.: Classification, seasonality and
persistence of low frequency atmospheric circulation patterns, Mon. Weather
Rev., 115, 1083–1126, 1987.
Cushman-Roisin, B., Gačić, M., Poulain, P. M., and Artegiani, A.:
physical oceanography of the Adriatic Sea; Past, present and future, 126 pp.,
Kluwer Academic Publishers, Dordrecht, 2001.
Ezer, T. and Mellor G. L.: Diagnostic and prognostic calculations of the
North Atlantic circulation and sea level using a sigma coordinate ocean
model, J. Geophys. Res., 99, 14159–14171, 1994.
Gačić, M., Civitarese, G., Miserocchi, S., Cardin, V., Crise A., and
Mauri, E.: The open-ocean convention in the Southern Adriatic: a controlling
mechanism of the spring phytoplankton bloom, Cont. Shelf Res., 22,
1897–1908, 2002.Gačić, M., Eusebi Borzelli G. L., Civitarese G., Cardin V., and Yari
S.: Can internal processes sustain reversals of the ocean upper circulation?
The Ionian Sea example, Geophys. Res. Lett., 37, L09608, 10.1029/2010GL043216,
2010.Gačić, M., Civitarese, G., Eusebi Borzelli, G. L.,
Kovačević, V., Poulain, P-M., Theocharis, A., Menna, M., Catucci A.,
and Zarokanellos, N.: On the relationship between the decadal oscillations
of the Northern Ionian Sea and the salinity distributions in the Eastern
Mediterranean, J. Geophys. Res., 116, C12002, 10.1029/2011JC007280,
2011.
Grisogono, B. and Belušić, D.: A review of recent advances in
understanding the meso- and microscale properties of the severe Bora wind,
Tellus A, 61, 1–16, 2009.
Hendershott, M. C. and Malanotte-Rizzoli, P.: The winter circulation of the
Adriatic Sea, Deep-Sea Res., 23, 353–370, 1976.Jerez, S., Jimenez-Guerrero, P., Montávez, J. P., and Trigo, R. M.: Impact of the North Atlantic Oscillation
on European aerosol ground levels through local processes: a seasonal model-based assessment using fixed
anthropogenic emissions, Atmos. Chem. Phys., 13, 11195–11207, 10.5194/acp-13-11195-2013, 2013.Josey, S. A, Somot, S., and Tsimplis, M.: Impacts of atmospheric modes of
variability on Mediterranean Sea surface heat exchange, J. Geophys. Res.,
116, C02032, 10.1029/2010JC006685, 2011.
Lamb P. J. and Peppler R. A: North Atlantic Oscillation: concept and an
application, B. Am. Meteorol. Soc. 68, 1218–1225, 1987.Larnicol, G., Ayoub, N., and Le Traon, P. Y.: Major changes in Mediterranean
Sea level variability from 7 years of TOPEX/Poseidon and ERS-1/2 data, J.
Mar. Syst., 33–34, 63–89, 10.1016/S0924-7963(02)00053-2, 2002.
Lascaratos, A.: Estimation of deep and intermediate water formation rates in
the Mediterranean Sea, Deep-Sea Res. PT. II, 40, 1327–1332, 1993.
Manca, B. B., Kovačević, V., Gačić, M., and Viezzoli, D.:
Dense water formation in the Southern Adriatic Sea and spreading into the
Ionian Sea in the period 1997–1999, J. Marine Syst., 33–34, 133–154, 2002.Mihanović, H., Vilibić, I., Carniel, S., Tudor, M., Russo, A.,
Bergamasco, A., Bubić, N., Ljubešić, Z., Viličić, D.,
Boldrin, A., Malačič, V., Celio, M., Comici, C., and Raicich, F:
Exceptional dense water formation on the Adriatic shelf in the winter of
2012, Ocean Sci., 9, 561–572, 10.5194/os-9-561-2013, 2013.
Pedlosky, J.: Geophysical Fluid Dynamics, 2nd ed., 710 pp., Springer,
New York, 1987.
Pirazzoli, P. A. and Tomasin, A.: Recent near-surface wind changes in the
Central Mediterranean and Adriatic areas, Int. J. Climatol., 23, 963–973,
2003.Pujol, M. I. and Larnicol G.: Mediterranean Sea eddy kinetic energy
variability from 11 years of altimetric data, J. Mar. Syst., 58, 121–142,
10.1016/j.jmarsys.2005.07.005, 2005.Rio, M.-H., Pascual, A., Poulain, P.-M., Menna, M., Barceló, B., and Tintoré, J.: Computation of a new mean dynamic topography
for the Mediterranean Sea from model outputs, altimeter measurements and oceanographic in situ data, Ocean Sci., 10, 731–744, 10.5194/os-10-731-2014, 2014.Schwab, D. J. and Beletsky D.: Relative effects of wind-stress curl,
topography, and stratification on large-scale circulation in Lake Michigan,
J. Geophys. Res., 108, 3044, 10.1029/2001JC001066, 2003.
Snedecor, G. W. and Cochran, W. G.: Statistical Methods, 7th Edn. Ames:
Iowa State University Press, 1980.
Trigo, R. M., Osborn, T. J., and Corte-Real, J. M.: The North Atlantic
Oscillation influence on Europe: climate impacts and associated physical
mechanisms, Clim. Res., 20, 9–17, 2002.Vilibić, I. and Orlić, M.: Least-squares tracer analysis of water
masses in the South Adriatic (1967–1990), Deep-Sea Res. Pt. I, 48, 2297–2330,
2001.
Yan, H., Zhong, M., and Zhu, Y.: Determination of the Degree of Freedom of
Digital Filtered Time Series With an Application to the Correlation Analysis
Between the Length of Day and the Southern Oscillation Index, Chin. Astron.
Astrophy., 28, 120–126, 2004.
Yelland, M. and Taylor P. K.: Wind-stress measurements from the open ocean,
J. Phys. Oceanogr., 26, 541–558, 1996.