Spatial and temporal variability of the sea surface temperature in the Gulf of Trieste between January 2000 and December 2006

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113,, doi: /2007jc004537, 2008 Spatial and temporal variability of the sea surface temperature in the Gulf of Trieste between January 2000 and December 2006 Elena Mauri, 1 Pierre-Marie Poulain, 1 and Giulio Notarstefano 1 Received 4 September 2007; revised 22 May 2008; accepted 18 June 2008; published 16 October [1] The variability of the sea surface temperature (SST) of the Gulf of Trieste (northeastern Adriatic Sea) is studied using satellite observations spanning After interpolation and validation of the SST data with in situ observations near Trieste, the satellite data are used to compute various statistical estimates (7-year, yearly and monthly means and empirical orthogonal functions modes). The main results obtained are: 1. The SST in the Gulf of Trieste is mostly variable at the seasonal scale, with temperature differences between summer and winter of 14 and 16 C in the northern and southern areas, respectively. The larger seasonal variations in the northern section are related to the Isonzo and Tagliamento river runoffs and to its shallower bathymetry. In the mean, the meridional SST difference between north and south is only 2 C. 2. Significant interannual variations are observed, with winter 2001 and summer 2003 showing the largest monthly anomalies (2 3 C). Most of the seasonal and interannual variations are related to variability in the local heat flux with a time lag of about 2 months. No robust long-term cooling/warming trend is apparent in the observations. 3. The spatial structure of the SST in the Gulf is affected by the Isonzo and Tagliamento rivers (to the north), the warmer Adriatic water (to the south), the nonuniform warming in spring and wind induced mixing and cooling (to the east). The latter effect is mostly in the form of rapid temperature changes triggered by strong Bora events. Citation: Mauri, E., P.-M. Poulain, and G. Notarstefano (2008), Spatial and temporal variability of the sea surface temperature in the Gulf of Trieste between January 2000 and December 2006, J. Geophys. Res., 113,, doi: /2007jc Introduction [2] The Gulf of Trieste (GoT) is located in the northeastern part of the Mediterranean Sea. Its bathymetry is quite shallow with an average of 21 m, with depths increasing gently from north to south (Figure 1). The maximum depth of 25 m is reached in a narrow area in front of the Istrian Peninsula. This midlatitude area is characterized by a well defined seasonal cycle. The water and air temperatures are well correlated [Raicich and Crisciani, 1999], which indicates that the heat content is mainly, but not only, controlled by the local surface heat flux. The fluxes are strongly affected by episodic events due to both strong wind episodes and freshwater buoyancy inputs. The main wind regimes that characterize the area are: the cold dry and gusty Bora, blowing from ENE, and the humid Sirocco coming from SE. The largest fresh water contribution to the Gulf is given by the Isonzo River with an annual average flow rate of 204 m 3 s 1 [Raicich, 1994], while other rivers (Timavo, Ospo) only amount to 10% of the total runoff 1 Istituto Nazionale di Oceanografia e Geofisica Sperimentale, Trieste, Italy. Copyright 2008 by the American Geophysical Union /08/2007JC [Olivotti et al., 1986]. Fresh water input also occurs through groundwater flux or submarine springs near the eastern and southern karstic coasts [Olivotti et al., 1986; Sekulic and Vertacnik, 1996]. To the northwest of the GoT, the Tagliamento River (with an annual average rate of 97 m 3 s 1 according to Raicich [1994]) seems to play a negligible influence on the circulation [Ferraro et al., 1986]. The combination of northeasterly winds and conspicuous Isonzo runoff increase the extension of the Isonzo plume to the southern GoT. The general GoT circulation is mainly wind driven as seen in several in situ measurements [Stravisi, 1987] while the haline stratification is due to the river buoyancy input [Stravisi, 1983; Malačič et al., 1999]. Commonly the Gulf circulation is characterized by a clockwise flow in the surface layer (3 5 m deep) with speeds (5 6 cm s 1 ) depending on the sea/land breeze [Celio et al., 2006]. However, this circulation can be reversed under Bora winds. In summer 2002, a specific event of Bora during stratified conditions was studied numerically by Querin et al. [2006]. In this study, satellite and in situ measurements along with numerical experiments show that strong wind events mix the water and homogenize the entire water column in a few hours. Strong wind-driven currents can flush the whole basin in almost 3 days. In the same paper, the influence of river floods was investigated during 1of15

2 MAURI ET AL.: SST IN THE GULF OF TRIESTE ( ) Figure 1. Geography and bathymetry of the Gulf of Trieste. The OSMER station at the Molo Fratelli Bandiera where in situ data were collected is indicated. spring The fresh buoyant water, which is cooler than the surrounding salty water, increases the stratification and can be tracked in the remotely sensed sea surface temperature (SST) images. From the numerical experiment the fresh water, that affects only the surface layer (down to 5 m), creates strong density gradients and induces a general estuarine circulation. [3] Intermittent temperature and salinity measurements performed between World Wars and more recent studies are summarized by Malačič and Petelin [2001], showing a quasi-permanent riverine layer in front of the northern Italian coast. In situ thermohaline properties from 1991 to 2003 are analyzed by Malačič et al. [2006]. Objective analysis was performed on near-surface (0.3 m) in situ temperature measurements to interpolate the incomplete fields. Summer positive temperature trends of C a 1 with an error of 0.05 C a 1 were found. Since summer 2003 was affected by a heat wave [Grazzini and Viterbo, 2003] the whole year 2003 was excluded from the analysis and, as a result, the summer trend is reduced to Ca 1. In winter, a nonzero trend was only found when 2003 was excluded (0.1 Ca 1 ). The authors conclude that the GoT thermohaline structure is also influenced by interannual variations of the Mediterranean and Adriatic general circulations [Pinardi et al., 2003; Zavattarelli and Pinardi, 2003]. Variations at 3 5-year timescale seem to be important and they are difficult to be filtered out in their decadal data set to properly detect a long-term trend. Barale et al. [2004] used Adriatic SST measured by satellites in the period to find a general positive trend of 2 C over 20 years. This increase seems to be due to the steady rise of summer values. A study of the seasonal and interannual Adriatic SST variability over the period was performed by Gačić et al. [1997] using the empirical orthogonal function (EOF) method. A small constant SST decrease in the northern Adriatic was found for the time period considered. [4] In this paper, we exploit high-resolution satellitederived SST data to describe quantitatively the spatial structure and the temporal evolution of the GoT surface temperature during 7 years ( ), at scales ranging from days to years, and from a few kilometers (the SST image spatial resolution) to the basin scale (20 40 km). Variations of SST in the GoT are also related to the local wind forcing, local heat flux and river runoffs. The satellite data are described in section 2, as well as the ancillary in situ observations of meteo-marime parameters near Trieste (wind speed and direction, seawater temperature, etc. from which heat fluxes are calculated) and the Isonzo River runoff. Details about the statistical methods applied to the SST data, including EOF-based interpolation and EOF analysis, are included in section 3. The satellite processed data are also compared to in situ measurements of surface temperature near Trieste. The results (section 4) are presented in terms of 7-year, yearly, monthly and EOF statistics. Specific events of SST variations corresponding to signal in the third EOF mode are also explored. Section 5 includes discussions and conclusions. 2. Data 2.1. Remotely Sensed Data [5] Advanced very high resolution radiometer (AVHRR) data of the National Oceanic and Atmospheric Administration (NOAA) satellites for the period between January 2000 and December 2006 were acquired and processed at the 2of15

3 MAURI ET AL.: SST IN THE GULF OF TRIESTE ( ) Figure 2. (a) Number of images per month and (b) percentage of cloud coverage in the RDS. (c) Geographical distribution of the percentage of missing data (cloudy pixels) in the RDS. A star identifies the pixel chosen for the validation with the in situ data of the OSMER station in Trieste. Istituto Nazionale di Oceanografia e Geofisica Sperimentale (OGS) in Trieste, Italy using the TeraScan software. Each Local Areal Coverage (LAC) image with nominal pixel size of 1 km was georeferenced, mapped on a 1.2 km grid, and the SST was estimated using the multichannel sea surface temperature (MCSST) algorithm described by McClain et al. [1985]. [6] Since clouds are opaque to infrared radiation, cloudy pixels have to be eliminated using cloud masking tests [Notarstefano et al., 2003]. Ad hoc cloud masking was applied to discriminate between clouds and strong horizontal gradients typical of river plumes. The NOAA-12 nighttime passes considered in this paper, because of the drift of the orbit, cover the area of the GoT between 0340 and 0530 UT. This is a good compromise to avoid the diurnal warming contamination all year-round. We do not use the daytime (early afternoon) images to avoid problems with diurnal warming effects [Notarstefano et al., 2006; Nardelli et al., 2005]. [7] From the initial data set (from now on called IDS) with one image per day, all the images with cloud coverage (on the sea) higher than 70% were excluded. The data set was further reduced to a maximum of 10 images per month (the least cloudy ones were chosen) to uniformize the data distribution throughout the year (winter is usually more cloudy with less images). The temporal distribution of the reduced data set (referred to as RDS) is presented in Figure 2. In Figure 2a, the maximum number of images corresponds to the warmest months whereas the minimum occurs mostly in winter. February, represented twice by only two images, is the cloudiest month of the year during the study period, followed by November. The percentage of cloud coverage corresponding to the number of images selected (Figure 2b) shows that generally the months with more images are also the least cloudy. From the 2392 initial images the data set was reduced to 669 in the RDS. [8] The spatial distribution of the percentage of the missing data over the 7-year period (Figure 2c) shows minimum values in the open sea area while along the coast the data coverage is reduced due mainly to navigation errors. Finally, pixels with more than 65% missing data over the entire period considered were discarded in all the images of the RDS in order to exclude coastal areas where data are scarce, including the Grado Lagoon In Situ Data [9] Meteo-marine in situ data collected by Agenzia Regionale per la Protezione dell Ambiente del Friuli Venezia Giulia Osservatorio Meteorologico Regionale (OSMER) were made available for the 7-year ( ) period considered. The parameters utilized are wind direction and speed measured at a height of 10 m above sea 3of15

4 MAURI ET AL.: SST IN THE GULF OF TRIESTE ( ) Figure 3. Example of (a) noninterpolated and (b) interpolated SST images on 9 January 2002 at 0420 UT. level, incoming irradiance, relative humidity, cloudiness, air temperature and seawater temperature at 0.5 m depth. All parameters were recorded hourly by the meteorological station at the Molo Fratelli Bandiera in Trieste (see Figure 1). The heat fluxes were estimated from the hourly meteomarine data using the formulae of Supić and Orlić [1999] as the sum of the flux due to insolation, longwave radiation, and the latent and sensible heat fluxes. In situ measurements in front of the Isonzo and Tagliamento Rivers are not available to confirm that the fluxes are similar at these locations. [10] The Isonzo daily discharge rates were computed from the hydrometric height at Turriaco (30 km northwest of Trieste) and were provided by the Direzione Centrale Ambiente e Lavori Pubblici Servizio Idraulica Unità Operativa Idrografica. 3. Methods 3.1. EOF Interpolation [11] The data interpolation empirical orthogonal function (DINEOF) described by Beckers and Rixen [2003] was used to reconstruct the missing data due to the clouds, that is, to interpolate spatially the SST images. The Singular Value Decomposition (SVD) technique was applied on the m n initial matrix (obtained from the RDS), where m is the number of pixels in one image (1683) and n is the number of images (669). The initial matrix was normalized by subtracting the temporal mean, then dividing by the standard deviation (s.d.) and setting to zero the missing data. The interpolation method consists in an iterative process that replaces the values with a better guess. The procedure starts by computing the first EOF mode; the first spatial mode is then multiplied by the respective amplitude and used to replace the missing data. The iteration goes on using the newest guess until convergence is reached. The next set of iterations starts using the first and the second modes multiplied by the respective amplitudes to compute another best guess and this is iterated again until convergence is reached. This procedure continues by adding successive EOF modes. The convergence is reached when the absolute value of the difference between the sum of the singular values obtained from the SVD of the current and the previous iterations is below a defined threshold (in our case 10). [12] The optimum number of EOFs to use in the reconstruction is defined by the cross-validation technique [Wilks, 1995]. In this method, 3% of good data are set aside from the RDS to be compared later with the interpolated data. The optimal number (N, in our case 5) of EOFs is determined by minimizing the root mean squared difference and maximizing the correlation between the reconstructed and real data. Once N is known the entire process is restarted reincluding the 3% of data used for the crosscorrelation, and the reconstructed matrix is obtained. Finally the reconstructed matrix is multiplied by the s.d. and the mean is added to obtain the interpolated data set. Only the first five spatial modes and their respective temporal amplitudes where used since they hold most of the variance (93%). It is important to note that the DINEOF method only reconstructs existing gappy images of the reduced data set. No interpolation in time is performed to create missing images. Moreover, the chronological sequence of images is not important in the reconstruction process. Application of the DINEOF method on SST data in the Adriatic can be found in the paper by Alvera-Azcárate et al. [2005], while the same method was used to reconstruct MODIS chlorophyll data in the Northern Adriatic by Mauri et al. [2007]. An example of noninterpolated and interpolated images on 9 January 2002 is illustrated in Figure 3. The cooler waters in the north and east of the study area are well reconstructed by the method Validation With in Situ Data [13] Validation of the satellite-derived SST products was performed using a pixel close to the coast near Trieste (Figure 2c) and the OSMER in situ data collected nearby (at a distance of 2 km). We chose a pixel at some distance from the coast in order to perform the validation with more remotely sensed data (only 30% of missing data). In Figure 4 and Table 1, the relationship between the in situ OSMER temperature and the remotely sensed SST (IDS, RDS, interpolated RDS) is shown. The regression lines between the different data sets have slopes very close to one. The smallest and largest intercepts correspond to the RDS interpolated and the RDS true values, respectively. The correlations are quite good with the coefficients of determination (r 2 ) equal to The number of points (n) ranges between 669 in the interpolated RDS data set (largest number) and the lowest number (302) for the RDS true values. Almost the same number of points (367) were interpolated. If the mean and s.d. of the differences between 4of15

5 MAURI ET AL.: SST IN THE GULF OF TRIESTE ( ) monthly means. Monthly and perpetual monthly means of the total heat fluxes near Trieste were constructed in the same way from the hourly values. Figure 4. Satellite-derived SST at pixel shown in Figure 2c versus in situ temperature data collected at the OSMER station in Trieste. The IDS, true and interpolated RDS values are shown as dots, open circles and triangles, respectively. The 1:1 relationship is depicted by the dashed line. the OSMER in situ temperatures and the satellite SST data sets are computed, the lowest value is found for the interpolated data of the RDS. The interpolated RDS has a mean difference (satellite minus in situ) of about 0.28 C, with a s.d. of less than 1 C. Hence, all the results presented in this paper, based on statistics computed with the interpolated RDS, have a negligible positive offset of about 0.3 C with respect to in situ measurements Basic Statistics [14] Basic statistics were computed with the interpolated RDS, including monthly temporal means and s.d.s. Monthly means were first estimated for each of the 84 months spanning January 2000 and December 2006 (84 images). Then, these mean images were averaged all together to create the temporal 7-year mean (one image), were averaged over the individual 7 years to construct the yearly temporal means (7 images), and were averaged over the 12 months of the year (January to December) to construct the perpetual monthly means (12 images). Thus, the s.d. associated with these statistics corresponds to temporal variability at monthly scales and longer. Given the expected significant seasonal variability in the GoT SST and the nonuniform sampling (with less images in winter because of increased cloud cover), taking the mean of the monthly means results in statistics less biased toward the summer conditions. Spatial averages were also constructed from the monthly and perpetual monthly mean images to study the temporal variability of the GoT as a whole. SST temperature anomalies were defined by subtracting the perpetual monthly means. For instance, SST and in situ temperature anomalies were constructed by removing the respective perpetual 4. Results 4.1. Seven-Year and Perpetual Monthly Mean SST Images [15] The 7-year temporal mean of the GoT SST shows a significant meridional temperature difference (Figure 5a). The northern part has, on average, lower temperatures compared to the southern region. The highest temperatures are recorded off the Istrian Peninsula. In the entire Gulf, the mean temperature ranges between and C. This range is small compared to the difference between the absolute temperature extrema observed in ; the minimum and maximum values are, respectively, 5.0 and 29.2 C (using the IDS) and 5.1 and 28.6 C (using the RDS). The highest variability, with a s.d. close to 7 C, occurs in the colder northern area while the lowest (s.d. <6 C) corresponds to the warmer southern part (Figure 5b). [16] The perpetual monthly means computed with the 7-year SST data set show three different temperature patterns (Figure 6): [17] 1. The fall/winter months from September to March are characterized by a meridional SST gradient with lower temperatures in the northern part of the basin and warmer in the south. [18] 2. In April, the warmest area is located in front of the Grado Lagoon. Such a feature is also evident in the following months until July. The April SST pattern is unique because of the colder temperatures in the southwest and northeast areas. This month can be considered as a transitional period because the meridional temperature gradient becomes zonal in May. The temperature in the inner part of the GoT decreases compared to the rest of the area. The coldest area moves eastward from the shallowest part, affected by the runoff of the Tagliamento and Isonzo rivers, to the area between the Isonzo mouth and the city of Trieste. [19] 3. From May to September, the coastal area north of Trieste is typically 1 C colder compared to the western GoT. The decrease in temperature also involves, but to a lesser extent (a fraction of a degree), the area in front of the Istrian Peninsula. This pattern is mostly recognizable in May and June. In July and August, in the southern part of the inner GoT, the temperature increases while the colder Table 1. Statistical Comparison Between the in Situ SST Observations in Trieste and the Satellite-Derived Products (IDS and RDS) a a b r 2 n Mean Difference s.d. Difference IDS RDS All True Values Interpreted Values a Comparison was evaluated at a pixel about 2 km away from the in situ site. The number of pairs (n), regression intercept and slope (a and b) and the coefficient of determination (r 2 ) are listed. The mean difference (satellite minus in situ) and its s.d. are also included. SST, sea surface temperature; IDS, initial data set; RDS, reduced data set. 5of15

6 MAURI ET AL.: SST IN THE GULF OF TRIESTE ( ) Figure 5. (a) Temporal mean and (b) standard deviation SST images of the GoT. These statistics were estimated by averaging monthly mean SST images over the 7 years considered ( ). strip in the north is still evident. In general, temperature spatial contrasts are larger in the coldest months with differences between the northern and southern areas of 4 5 C. A strong zonal SST front is evident around N. Minimum average temperatures of 6 C in the north and 9 C in the south are reached in February. The spatial temperature differences in summer are reduced to 1 2 C, when the SST can exceed 25 C Seasonal and Interannual Variabilities [20] A clear seasonal cycle is evident in the spatial mean of the monthly temporal means (Figure 7 (top)) with highest temperatures generally recorded in July and August. The mean seasonal temperature range estimated from the perpetual monthly means is C, corresponding to a minimum of 8.43 C in February and a maximum of C in August. In addition, a marked interannual variability is evident. Summer 2003 stands out as the warmest summer with C (in July) and C (in August) followed by August 2004 with C. The coldest month is February 2004 with 7.53 C and the warmest winter month is February 2001 with C. If we define SST anomalies with respect to the perpetual monthly means, it can be seen that in they are mostly positive, especially in the first months of the year: April to June 2000, January to May in 2001, June 2002 and June to August February 2001 is characterized by an anomaly as large as 2.52 C, responsible for the warmer 2001 yearly mean. Summer 2003 is abnormally warmer with anomalies of monthly mean SST reaching 1.96 C (in August) and 1.62 C (in June). The SST anomalies in 2004 and 2006 are mostly negative. August 2006 is 1.84 C below the average as well as June 2004 where the anomaly is 2.23 C. There are some positive anomalies not exceeding 1 C, only in the last period of the 2 years. The SST anomalies in 2005 are mostly negative with exception of January (0.86 C), May (0.23 C) and September (0.44 C), when they are slightly positive. Negative anomalies from February to April and from September to December significantly contribute to the cooler temporal 2005 yearly mean. [21] The spatial averages of the yearly mean SST images (Table 2) indicate that 2005 is the coldest year (16.39 C) and 2001 the warmest (17.77 C). Years 2000 and 2002 are also warm but cooler than 2001, while the other years are slightly warmer than 2005 (with SST in C). [22] The monthly total heat fluxes calculated from the meteo-marine data collected near Trieste are plotted in Figure 7 (bottom). As was done for the satellite-derived SST, perpetual monthly mean fluxes were also calculated. The latter estimates show a seasonal variation between W m 2 in December and W m 2 in May. In contrast, monthly mean fluxes can reach W m 2 (in December 2002, that is, an heat flux anomaly of about 50 W m 2 ) and W m 2 in June 2003 (corresponding to an anomaly of about 30 W m 2 ) EOF [23] The EOF technique was applied to the interpolated RDS data set to investigate the spatiotemporal variability of the surface temperature fields in the GoT. Note that before the EOF decomposition method, the interpolated RDS was normalized by subtracting the temporal mean (similar to the one shown in Figure 5a) and by dividing by the s.d. (similar to the image depicted in Figure 5b). The variance explained in the first three modes represents 99.6% of the total variance. The first mode corresponds to most of the SST variability (99.2%). Its spatial structure has no zero crossing and values are all positive with slightly smaller magnitudes in front of the Grado Lagoon and larger values outside the Gulf and offshore (Figure 8a). Note that to reconstruct SST differences, this signal has to be multiplied by the s.d. (Figure 5b), resulting in temperature excursions 1 2 C larger near Grado (reaching 16 C) with respect to the rest of the GoT (reaching 14 C). The corresponding temporal amplitude exhibits a marked seasonal cycle with interannual variability (Figure 9a). The positive (negative) values in summer (winter) produce higher (lower) temperature throughout the basin. Winter 2001 shows higher temperatures compared to the other winters and a similar positive anomaly is found in the particularly warm summer of [24] The second mode corresponds to a considerably lower percentage of the total variance (0.3% or 40% of the total variance excluding the first mode). This mode splits the area in two parts: one to the north, shallower and influenced by the rivers and the Grado Lagoon, and the second to the south and deepest part of the area (Figure 8b). These two areas vary in phase opposition and are modulated by the second temporal amplitude (Figure 9b). In the 6of15

7 MAURI ET AL.: SST IN THE GULF OF TRIESTE ( ) Figure 6. Perpetual monthly mean SST images in the GoT for the 12 months of the year. 7of15

8 MAURI ET AL.: SST IN THE GULF OF TRIESTE ( ) Figure 6. (continued) 8of15

9 MAURI ET AL.: SST IN THE GULF OF TRIESTE ( ) Figure 7. (top) Spatial averages of the SST monthly mean images (black curve and dots) and the perpetual monthly mean images (gray curve and dots) over the 7 years studied ( ). (bottom) Monthly means (black) and perpetual means (gray) of the total heat fluxes computed from the in situ observations of the OSMER station in Trieste. Positive values correspond to flux into the sea. northern area, lower negative amplitude values corresponding to higher temperature occur from March to June, whereas maximal colder temperatures occur in December and January. Hence, in addition to the seasonal variation in the northern GoT (mostly represented by the first EOF mode) there is, in the second mode, an additional, or anticipated, warming (cooling) in spring (late fall and early winter). The southern part of the GoT reacts in the opposite way, that is, the warming (cooling) is slightly postponed to fall (spring). Note that the temporal amplitude of the second mode is characterized by a sharp decrease in winter (intermittent fast cooling due to mixing) and a slow increase (gradual warming) during the other seasons. [25] The third mode (Figure 8c) divides the inner part of the GoT from the rest of the study area and it represents only 0.1% of the total variance (or 13% of the total variance excluding mode 1). The temporal amplitude is extremely variable (see Figure 9c). Selected major peaks (positive and negative) in the third mode amplitude are analyzed below. The spatial structure and temporal evolution of the SST in the GoT are illustrated and the influence of the wind forcing and the Isonzo River runoff is investigated. Note that the third EOF amplitude was compared statistically (through correlations) to the time series of Isonzo discharge rates and Bora wind speeds. The results, however, are not statistically significant. For instance the largest squared correlation coefficient (r 2 ) was obtained between the third mode amplitude and the Bora wind speed in summer (2.2%). Just as a comparison, the same calculation with the OSMER in situ temperature yields 7.2%. The lack of robust overall correlation between the EOF amplitudes and the time series of Isonzo discharge and Bora wind motivated us to describe qualitatively some events in which the forcing affects significantly the SST in the GoT Specific SST Events [26] Selected events of SST variations corresponding to peaks in the amplitude of the third EOF mode are now discussed. The first event, on 26 June 2002, corresponds to a major positive peak in the third EOF mode amplitude (about 8 units, see Figure 9c). After scaling with the matching spatial mode value (Figure 8c) and the s.d. (Figure 5b), this peak represents a rapid decrease of SST of more than 2.5 C inside the GoT and a rapid increase of about 1.5 C near the western open sea. If we focus on the temporal variations in the pixel close to the OSMER station in Trieste (see location in Figure 2c), we can see that this peak corresponds to a sudden decrease of temperature of 8 C in both the in situ measurements and the satellite-derived SST (Figure 10b) starting on 25 June. The drop actually occurred in less than half a day. A strong Bora event prevailed with wind speeds almost reaching 20 m s 1 a few hours before the sharp temperature variation (Figures 10c and 10d). After the event, temperatures slowly started rising to reach, only after 5 days, the climatological values of the perpetual monthly means. Subsequent weaker Bora events on June did not produce any significant SST change because the water column was already homogenized to the bottom. More details on this major cooling event in summer 2002 can be found in the paper by Querin et al. [2006]. Figure 10a also shows the spatial extent of the Bora-induced cooling and mixing in the inner GoT. The cooled SST spreads from Table 2. Annual Means of the SST in the Gulf of Trieste a Year SST ( C) a Values were obtained by spatial averaging of the yearly SST images, constructed from the monthly images. 9of15

10 MAURI ET AL.: SST IN THE GULF OF TRIESTE ( ) of 1 2 C, compared to the actual differences of 3 4 C seen in the images. [28] The peak on 3 March 2000 followed a major flooding of the local rivers (the Isonzo discharge rate reached 600 m 3 s 1 on 2 March (Figure 11e)). As a result, the areas in front of the Isonzo and Tagliamento were characterized by a cold water plume (Figure 11a). Apparently, the temperature effect of the Tagliamento was more pronounced (as low as 6 C) than the thermal impact of the Isonzo (barely reaching 8 C). Bora winds up to about 10 m s 1 occurred in early March (Figures 11c and 11d), but did not significantly influence the surface temperature of the GoT because the water mass properties were still homogenized in the vertical. [29] The second example took place on 7 June 2001 (Figure 12). In this case, the enhanced zonal SST gradient was not triggered by the local forcing (there are no particular wind or Isonzo events) but was created by the gradual late spring nonuniform warming which was more significant in the inner GoT than in the open sea area (Figure 12a). Figure 8. Spatial distribution corresponding to the three first EOF modes. The zero crossing is depicted by a black curve. the eastern coast (where it reaches 19 C) to the west and southwest, including a zonal intrusion south of the Grado Lagoon. In the rest of the studied area, the SST remained near 25 C. [27] Two negative peaks in the third EOF amplitude (reaching 4 units) were selected and analyzed together with the forcing factors. These peaks correspond to SST images on 3 March 2000 and 7 June 2001 that show, respectively, cooling areas in the northwest area (in front of the Tagliamento mouth (Figure 11a)) and near the southwestern opening of the GoT (Figure 12a). Reduced positive temperature anomalies are only evident in the satellite SST data and the third EOF mode. These features are not apparent in the in situ observations because the region off Trieste is out of the cooling area (Figures 11b and 12b). If the third EOF amplitude is multiplied by the spatial mode (Figure 8c) and by the s.d. (Figure 5b), cooling (warming) to the west (east) amounts to about C (0.5 1 C). Thus, the EOF mode represents a zonal difference 5. Discussion and Conclusions [30] Seven years of satellite AVHRR data were utilized to study the spatial structure and the temporal variability of the SST in the GoT between 2000 and After processing, in particular to interpolate the SST in cloudy pixels, the satellite images were used to compute statistics such as means and s.d.s (covering periods spanning from a month to 7 years), and EOF modes. Ancillary in situ meteo-marine data near the city of Trieste were used to validate the satellite products and to compute local heat fluxes. They were also used to describe specific events of major SST variations, in concert with wind and Isonzo River runoff observations. [31] The SST in the GoT varies more temporarily than spatially. Indeed, the 7-year mean temperature difference across the basin is less than 2 C (with colder waters to the north (see Figure 5a)), while seasonal temporal excursions can be 1 order of magnitude large (15 20 C (see Figure 7)). This temporal variability is slightly larger in the cooler northern area (see the temporal s.d. reaching 7 C in Figure 5b). As shown in Figures 7 and 9a, the GoT SST variations are mostly seasonal, with maxima in July and August and minima in February. This seasonal signal is highly correlated with the local air temperature variations typically spanning 0 30 C (as seen in the OSMER data, not shown). Our results confirm those of Malačič etal.[2006] on the basis of in situ data. Indeed, cross-basin differences of about 2 C are also seen in their seasonal temperature maps at 0.3 m depth. Their fit of a mean plus an annual oscillation over the time period yields a mean SST of about 16.5 C and a peak-to-peak seasonal amplitude of about 16 C, with maximum values occurring in August. Our values are similar (see annual means in Table 2 and seasonal variations in Figure 7). Spatial variations and temporal evolutions, however, are better represented in our study because of the higher spatiotemporal resolution (1 km, 1 month) of the SST statistics. [32] There are important interannual variations in addition to the seasonal cycle. For instance, in summer 2003 the spatial average of the SST reached 27 C following the heat 10 of 15

11 MAURI ET AL.: SST IN THE GULF OF TRIESTE ( ) Figure 9. Temporal amplitudes of the three first EOF modes. The events discussed in section 4.4 are identified by star symbols in the third EOF amplitude. wave that affected Europe in late spring and summer [Grazzini and Viterbo, 2003]. The coldest months were February 2004 and 2006 (8 C) whereas in 2001 the winter was rather mild (11 C). When compared to the perpetual seasonal cycle calculated from the 7 years of data, these extremes correspond to anomalies of +3 C in August 2003, about 1 C in February 2004 and 2006, and more than +3 C in January and March 2001 (see Figure 7). The coolest summer month was August 2006 with less than 25 C (anomaly of about 2 C). All these SST anomalies vary in concert with the variations in the local meteorological conditions, in particular with the air temperature measured in Trieste. To be more specific, Figure 7 shows that the monthly SST means lag by about 2 months the monthly heat fluxes. For the anomalous winter 2001 and summer 2003, the warmer SST is mainly due to the warmer atmospheric conditions prevailing during these periods. Hence, we can conclude that seasonal and interannual SST variations in the GoT are mostly driven by the local heat fluxes, in agreement with the results of Raicich and Crisciani [1999]. [33] The meridional SST gradient in the GoT, with lower values to the north near the river mouths and the Grado Lagoon, and higher temperatures to the south and near the southwestern opening, prevails mostly during the cooling period from September to March (Figure 6). The coastal waters northwest of Trieste are characterized by cooler temperatures year-round but they are mostly pronounced from March through September. A cooling of about 1 Cina 5 10 km wide coastal strip prevails mostly in July and August. Some cooling is also evident in spring and summer along the southern coast of the GoT and off the Istrian Peninsula. We speculate that this coastal cooling is related to the groundwater flux and submarine springs of cold water in the vicinity of the Karstic land [Olivotti et al., 1986; Sekulic and Vertacnik, 1996]. This feature is not well documented in the literature because of the poor spatial resolution of the data [Gačić etal., 1997; Malačič etal., 2006]. [34] The EOF analysis of the SST images (interpolated RDS) in the GoT confirms the strong seasonality, mostly represented by the first EOF mode characterized by annual excursions of about 15 C, similar to the monthly statistics illustrated in Figure 7, with a weak meridional gradient (16 C to the north and 14 C to the south). Annual excursions of 4 C to the north near the Grado Lagoon and of 1 C to the south also emerge in the second EOF mode. Since they are not in phase with those of the first mode, they tend to anticipate the warming (cooling) in the spring (fall) in the northern area, and vice versa to the south. We speculate that the larger seasonal range to the north and the phase difference between modes 1 and 2 are related to the runoff of the Isonzo and Tagliamento rivers and to the fact that bathymetry is shallower there. In contrast, the third EOF mode represents a zonal gradient that is rather intermittent and whose peaks can sometimes be related to forcing events. [35] The influence of the local forcing on the GoT SST could not be studied statistically with the satellite data available (the interpolated RDS). For instance, all the correlations between the Bora wind or the Isonzo River discharge and the SST at selected locations or the EOF temporal amplitudes turned out to be very low and not significant. We believe that the main cause is the fact that many SST images are missing because of cloud coverage (especially in the RDS in which a maximum of 10 best images per month were considered). Bora wind and Isonzo flooding events are mostly intermittent strong phenomena of short duration during which the number of clear satellite images can be reduced. [36] As a result, we decided to study qualitatively a few cases in which the forcing is evident and the SST images capture some related signal (see Figures 10, 11, and 12). In summer, the outbreak of Bora winds can dramatically decrease the air and water temperatures in the GoT area. The SST decreases because of the surface cooling, the upwelling of cooler waters and related mixing processes. For example, on June 2002 Bora winds reaching 11 of 15

12 MAURI ET AL.: SST IN THE GULF OF TRIESTE ( ) Figure 10. (a) SST image on 26 June 2002 at 0405 UT and (b) temporal evolution of the temperature anomalies near the city of Trieste from in situ OSMER (thin curve) and satellite (IDS; thick curve) data around 26 June Anomalies were created by removing the perpetual seasonal cycle from each time series individually. The temporal evolution of the SST signal represented by the third EOF mode is also shown (dashed curve). It was obtained by multiplying the temporal amplitude by the spatial mode and the s.d. relative to the pixel near Trieste. Times series of (d) wind speed and (c) direction, depicting several Bora events, are also displayed. 20 m s 1 triggered a sudden decrease of the SST in the inner GoT of 8 C in less than half a day (Figure 10b). This event is represented by a significant peak in the third EOF mode amplitude (Figure 9c). In situ observations in the water column [Querin et al., 2006] revealed that the strong winds cooled the GoT and mixed the water all the way down to the bottom, breaking temporarily the stratification. The latter was not reestablished before 1 July, that is, 5 days later. A similar case was described for the period around 19 June [37] The influence of the Isonzo and Tagliamento flooding was illustrated for a case in late winter (around 3 March 2000). The flooding (with the Isonzo discharge reaching 600 m 3 s 1 ) occurred on 2 March and the SST image of next day shows clearly the area of lower temperatures extending in the northwest sector of the study area. On 5 March, the next satellite image confirms that this cold signature has disappeared. [38] In contrast to the work of Malačič etal.[2006] we did not try to fit linear trends to the GoT SST. Excluding the exceptionally warm winter 2001 and summer 2003, a linear decrease of the GoT SST extremes can eventually be seen in the spatial average of the monthly SST (Figure 7 and Table 2). This subtle trend is mainly due to the slight 12 of 15

13 MAURI ET AL.: SST IN THE GULF OF TRIESTE ( ) Figure 11. Same as Figure 10 but for period around 3 March (SST image on 3 March 2000 at 0551 UT). (e) The Isonzo River discharge rates are also shown. 13 of 15

14 MAURI ET AL.: SST IN THE GULF OF TRIESTE ( ) Figure 12. Same as Figure 10 but for period around 7 June 2001 (SST image on 7 June 2001 at 0509 UT). (e) The Isonzo River discharge rates are also shown. cooling of the winters. This result, however, differs from the findings of Barale et al. [2004] and Malačič et al.[2006] in which positive trends support the idea of global warming. Positive and negative interannual variations of SST in the GoT are by far more important than any long-term trend, especially during the relatively short period considered ( ). [39] Acknowledgments. We thank the Agenzia Regionale per la Protezione dell Ambiente del Friuli Venezia Giulia Osservatorio Meteorologico Regionale for making available the in situ data. The Direzione Centrale Ambiente e Lavori Pubblici Servizio Idraulica Unità Operativa Idrografica provided the Isonzo River discharge rates. This research is part of the scientific program ADRIANE funded by the Fondo Trieste. The comments of the anonymous reviewers significantly contributed to improve this paper. References Alvera-Azcárate, A., A. Barth, M. Rixen, and J. M. Beckers (2005), Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: Application to the Adriatic Sea surface temperature, Ocean Modell., 9, , doi: /j.ocemod Barale, V., C. Schiller, C. Villacastin, and R. Tacchi (2004), The Adriatic Sea surface temperature historical record from advanced very high resolution radiometer data ( ), Int. J. Remote Sens., 25, , doi: / of 15

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