PUBLICATIONS. Journal of Geophysical Research: Oceans

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1 PUBLICATIONS Journal of Geophysical Research: Oceans RESEARCH ARTICLE Key Points: Ocean model derived field was combined with the satellite-derived observations The surface circulation characteristics of the Caspian Sea is investigated Interannual variability of the surface circulation of the Caspian is examined Correspondence to: M. Gunduz, Citation: Gunduz, M. (2014), Caspian Sea surface circulation variability inferred from satellite altimeter and sea surface temperature, J. Geophys. Res. Oceans, 119, , doi:1002/ 2013JC Received 30 OCT 2013 Accepted 30 JAN 2014 Accepted article online 5 FEB 2014 Published online 25 FEB 2014 Caspian Sea surface circulation variability inferred from satellite altimeter and sea surface temperature Murat Gunduz 1 1 Institute of Marine Sciences and Technology, Dokuz Eylul University, Izmir, Turkey Abstract Multiyear ( ) satellite-derived Sea Level Anomaly (SLA), Sea Surface Temperature (SST), and model-derived mean dynamic topography were used together to analyze climatological and interannual variations of the Caspian Sea surface circulation. Constructed geostrophic currents are in good agreement with the known circulation features of the Caspian Sea, obtained from models and verified by some drifter observations. It is shown that the climatological surface circulation of the Middle Caspian Sea (MCS) is dominated by a basin-wide cyclonic circulation in winter, switching to an anticyclonic circulation in summer. A dipole pattern (an anticyclonic eddy near 39.5 N and a cyclonic one near 38 N) exist in the Southern Caspian Sea (SCS) (stronger from September to January). Evaluation of the multiyear geostrophic velocities shows that the Caspian Sea surface circulation exhibits strong interannual variations, with the location and intensity of the circulation patterns changing from one year to another. 1. Introduction Knowledge on the surface circulation of the Caspian Sea and its variability on interannual time scales are limited, despite the efforts involving in situ observations and modeling. The available hydrographic data are mostly localized on the continental shelf regions and only of sufficient detail allowing to characterize the synoptic variability of the currents [Tsytsarev, 1967; Klevtsova, 1967; Bondarenko, 1993; Ghaffari and Chegini, 2010; Jamshidi and Abu Bakar, 2012]. On the other hand, modeling studies made until now only provide information on the climatological surface circulation, without revealing details of its interannual variability [Knysh et al., 2008; Ibrayev et al., 2010; Tuzhilkin and Kosarev, 2005]. The general surface circulation characteristics deduced from the earlier model results are as follows: by assimilating climatological temperature and salinity into a primitive equation circulation model, Knysh et al. [2008] have shown the existence of a basin-wide cyclonic circulation in the Middle Caspian Sea (MCS) in winter. Kara et al. [2010] found a similar pattern, using a climatologically forced model (HYbrid Coordinate Ocean Model (HYCOM)) [Bleck and Boudra, 1981] of the Caspian Sea. The northward surface coastal current and the southward subsurface current along the eastern coast of the MCS were demonstrated in both of the above studies. In the South Caspian Sea (SCS), a pair of anticyclonic and cyclonic eddies at the western part of the basin was found by these authors, together with a strong coastal current along the southern coast. In the eastern part of the SCS basin, northerly currents were found to follow the shelf topography. Tuzhilkin and Kosarev [2005] have generated annual mean surface circulation by averaging the seasonal fields of a numerical ocean model results by Tuzhilkin et al. [1997]. They found similar dipole pattern in the SCS with a rather wide cyclonic gyre located at the south eastern part of the Sea. A schematic description of the seasonal circulation of the Caspian Sea has been articulated by Gunduz [2008]. In addition to the above modeling efforts, the surface circulation of the Caspian Sea was also investigated by using observational data on satellite-derived chlorophyll and Sea Surface Temperature (SST). The results of Sur et al. [2000] were consistent with the above mentioned circulation features. A southward current was shown to transport cold surface water from the north to the south along the western coast, in addition to a cyclonic gyre in the MCS. A vein of warm water originating from the SCS moves northward along the eastern coast of the Sea. None of the above mentioned modeling studies have provided a detailed description of the interannual variations of the surface circulation of the Caspian Sea. Satellite altimetry provides continuous measurement of the Sea Level Anomaly (SLA), suitable for the analysis of interannual variations of the Caspian Sea circulation. The analyses based on combination of satellite data and model predicted fields have been successfully GUNDUZ VC American Geophysical Union. All Rights Reserved. 1420

2 Table 1. Time Coverage of the Satellites Time Coverage Repeat Cycle Jason 1 Aug 2002 to Dec days T/P 1 Sep 1992 to Aug days T/P 2 Sep 2002 to Oct days GFO Jan 2000 to Dec days Envisat Mono. Oct 2002 to Dec days applied to the global ocean by Rio and Hernandez [2004]. On a regional scale, Rio et al. [2007] have used this approach to investigate the current patterns and structures of the Mediterranean Sea. In this study, the SLA obtained from the satellite altimeter and mean dynamic topography from an ocean model are used together to investigate the seasonal and interannual variability of the Caspian Sea surface circulation. 2. Data and Methodology 2.1. SLA and SST Data The along-track Sea Level Anomaly (SLA) data were obtained from the AVISO web page ( oceanobs.com/duacs). Ssalto/Duacs multimission altimeter product is used in this study. This product is available for three satellites (Jason1, TOPEX/Poseidon (T/P), and GFO). The Monomission Envisat product was obtained from the same web page. The time periods and repeat cycle of each satellite mission are summarized in Table 1. The tracks of each satellite are given in Figure 1. The details of the process to generate SLA product can be found in Ssalto/Duacs User Handbook (Ssalto/Duacs User Handbook, 2013). Here it will be given the basic steps performed by AVISO to obtain SLA. First, the Mean Profile (MP) should TP/J1 GFO E2/ENVI TP2 Figure 1. Tracks of the four satellite altimeters. Topex Poseidon and Jason1 (red), GFO (blue), Envisat and ERS2 (magenta), and TP2 (orange). In general, the tracks of TP/J1 and TP2 are the same except for a couple of points. be calculated. When the satellites flies over a repetitive orbit, measurements are resampled along a theoretical ground track associated with each mission. The MP is a time average of similarly resampled data over a long period. The SLA is calculated by subtracting this MP from the Sea Surface Height (SSH). It is known that combining of data from different measurements improves the estimation of mesoscale signals [Le Traon et al., 2001, 2003]. The merging of the different satellite data are given in detail by Ducet et al. [2000]. In this study, we used AVHRR Pathfinder Version 5.2 (PFV5.2) data, obtained from the US National Oceanographic Data Center and GHRSST ( The PFV5.2 data are an updated version of the Pathfinder Version 5.0 and 5.1 collection described in Casey et al. [2010]. The SST data was interpolated to the analysis grid before being used for any comparison. The wind data used in this study to generate the climatological wind speed and direction is GUNDUZ VC American Geophysical Union. All Rights Reserved. 1421

3 obtained from National Climatic Data Center (NCDC). It is on horizontal grid. The monthly mean data were obtained and climatological fields were generated from these. The product details can be found in Zhang et al. [2006] Methodology The along track data were interpolated by optimal interpolation analysis on a regular grid of 5 3 horizontal resolution with zonal and meridional grid size of , respectively. Fifteen years of integration was made covering the period from 1 January 1993 to 31 December The Navy Coupled Ocean Data Assimilation (NCODA) system was used for this purpose. The NCODA system uses the multivariate optimum interpolation (MVOI) technique. The detailed documentation of the NCODA system and the interpolation technique can be found in Cummings [2005]. The horizontal correlation length-scale used in the analysis is 50 km. The monthly mean results shown here are the daily averaged analyses of the interpolated dynamic topography. Absolute dynamic topography (h) can be considered to consist of two parts, the mean <h> and the anomaly h 0 ; h5 < h > 1h 0 Satellite altimeter provides the anomaly h 0, and an ocean model may provide the mean dynamic topography <h>. In this study, the annual mean dynamic topography was obtained from the climatologically forced Caspian Sea model of Gunduz [2008], based on the HYCOM. The nominal resolution of the model is 3.2 km in the horizontal, with 25 sigma-z levels in the vertical. The model has been forced with the climatological ERA40 atmospheric reanalysis data set obtained from European Centre for Medium-Range Weather Forecasts (ECMWF), Uppala et al. [2005]. The model has been run for 12 years and the last 4 years of the integration was averaged to obtain the mean dynamic topography. This model-derived mean dynamic topography was then added to the satellite-derived SLA fields to construct the dynamic topography. The zonal and meridional components of geostrophic current (u g, v g ), respectively, on the tangent plane are calculated as follows: u g 52ðg=fÞðdh=dyÞ v g 5ðg=fÞðdh=dxÞ where g m/s 2 is the gravity acceleration, f 5 2*X*sin(U) is the Coriolis parameter, X rad/s is the rotation rate of earth, U is the latitude (rad), and h is the absolute dynamic topography (m). In the following section, this derived product will be investigated in detail. 3. Results and Discussion 3.1. Comparison With Drifters The analyzed geostrophic currents were compared with the drifter observations for a minimal evaluation of the calculated monthly mean geostrophic currents with respect to available observations. In the framework of the NATO SfP Project Multidisciplinary Analysis of the Caspian Sea Ecosystem (MACE) ( three drifters have been released in October 2006 and operated for 3 months (until December 2006). There were two drifters released in the MCS, and one drifter in the SCS. Figure 2 shows the comparison of drifter trajectories with calculated geostrophic currents (vectors) and superposed on the SST fields (color). In October 2006, the two drifters in the MCS (#1: magenta and #2: red) move northward, in the same direction as the calculated currents. In the SCS, the drifter (#3: cyan) first moves westward and then turns to the south, showing similar behavior with the geostrophic currents. In November 2006, one of the drifters in MCS continues to move northward (#2) and the other (#1) turns west (following the cyclonic circulation in these months) and then moves southward along the western coast of the MCS. In the SCS, the drifter (#3) turns to south after moving eastward, along with the southward general direction of the calculated climatological currents. In December 2006, the drifter at western part of the MCS GUNDUZ VC American Geophysical Union. All Rights Reserved. 1422

4 (a) October (b) November (c) December Figure 2. Monthly mean dynamic topography in (a) October, (b) November, and (c) December The starting positions of the drifters are shown by white dots. Note that the color scales are different for each plot. (#1, close to Apsheron peninsula) changes direction and moves once again to the north, as indicated by the geostrophic currents. The (#3) drifter in the SCS follows the strong coastal current indicated by the geostrophic estimate along the southern coast of the Sea. In general, the calculated currents are in good agreement with the observed drifter trajectories. The daily averages of the zonal and meridional velocities for the three drifters were calculated, and the constructed geostrophic velocities were sampled for each day at the drifter points. Figure 3 shows the comparison of the daily averaged drifter velocities with the constructed geostrophic velocities. The drifter and constructed velocities have similar ranges. The constructed geostrophic velocities are most correlated with the drifter #1 among the three drifters. The correlation increases in November and December in drifter #1. For example, the v velocities are southward (negative) in November, and they turn synchronously to northward (positive) in October. V velocities for the drifter #2 are northward from October to mid November and they turns synchronously to southward in December in both time series. Similar synchronization for v velocity is also observed for the drifter #3. It should be noted that the drifters are influenced by Ekman drift, and the comparison is given for the sake of reliability of the generated data Climatological Variability Samples of climatological monthly mean geostrophic currents overlaid on absolute dynamic topography are shown in Figure 4. In January (Figure 4a), the most striking feature of the climatological circulation of the MCS is the cyclonic gyre covering the whole basin. This cyclone weakens in March and May (Figures 4b and 4c) and disappears completely in the summer months, finally turning into an anticyclonic circulation in July as shown in Figure 4d. This anticyclone begins to decay in the autumn (Figure 4e), and becomes cyclonic once again in November (Figure 4f). During the summer upwelling period of Figure 4e, Ekman transport along eastern coast of the MCS causes the surface water to move toward the west. The mechanism of a complete reversal of the currents from a cyclonic to an anticyclonic circulation in July is not clear, although it appears possible that the fresh and cold water flowing north along the western coast of the basin may generate this anticyclone [Gunduz, 2008]. It is also possible that change in wind direction may be responsible for the reversal. Figures 5a 5c show the climatological wind speed at 10 m over the Caspian Sea, and Figure 5d shows the area averaged climatological wind direction. It is clear that there are two dominant wind regimes in the Caspian Sea. While in winter, the easterly winds are stronger, in summer the northerly winds become dominant. The dramatic change in wind regime is very clear especially in the MCS. GUNDUZ VC American Geophysical Union. All Rights Reserved. 1423

5 #1 u #1 v #2 u #2 v #3 u #3 v October November December Figure 3. Daily averaged zonal (u) and meridional (v) velocities (m/s) for each drifter. Drifter #1 is magenta, drifter #2 is red, and drifter #3 is cyan; black line is constructed geostropic velocities sampled at each drifter points. The time series were filtered with a moving average filter with 3 days length. In the SCS, there is a dipole pattern structure. An anticyclonic gyre is located at the northeastern part of the basin near 39.5 N, and a cyclonic gyre is located at the south-eastern part of the basin near 38 N. These results are in agreement with Tuzhilkin and Kosarev [2005]; similar dipole pattern is also evident in their results. This dipole pattern preserves its structure during the whole year. However, it is more intense in summer, and the cyclonic pattern weakens in winter. The jet-like coastal current flowing toward north along the eastern coast of the sea is seen during the winter months. The easterly coastal current along the southern coast can be seen during the whole year. The above mentioned features are consistent with the results of Sur et al. [2000], Knysh et al. [2008], and Gunduz [2008] Interannual Variability Compared to other enclosed water bodies (e.g., the Black Sea), the Caspian Sea surface circulation shows strong interannual variability. Figure 6 shows the SST overlaid with the calculated currents in the month of January for the specific years 1993, 1994, 1997, 1999, 2000, and While a basin wide cyclonic circulation exists in the January of every year in the MCS, the strength of this gyre changes from year to year. For example, it appears stronger in the years 1993, 1994, and 2004 (Figures 6a, 6b, and 6f), but in other years (Figures 6c, 6d, and 6e) the cyclone is not so well organized in the MCS. A strong northward current along the eastern coast of the MCS exists during winters of the whole investigated period. The northward current, after following the eastern coast at a distance of 50 km from the coast, turns to west at the northern edge of the sea and later flows south along the western coast of the basin, closing the basin-wide cyclonic circulation. Relatively colder water is detected in the satellite-derived SST inside this cyclonic circulation, also observed throughout the investigated period, and consistent with the cyclonic circulation. In the SCS, the dipole pattern at the western part of the basin shows interannual variability. In 1993 and 1994 (Figures 6a and 6b), the cyclone can be observed at the south-western part of the sea, while it is not so well organized in other years. The anticyclonic member of the pair appears stronger in 1993, 1994, and 2004, and not so much in other years. The interannual variation of the surface circulation and SST in April is shown in Figure 7. The cyclonic winter circulation in the MCS starts to diminish in this month. The most obvious feature of the circulation in April is the northward warm water intrusion along the eastern coast, carried by the current originating from the SCS and moving to the interior of the MCS. Similar behavior of the current has also been detected by Suretal. GUNDUZ VC American Geophysical Union. All Rights Reserved. 1424

6 (a) January (b) March (c) May (d) July (e) September (f) November Figure 4. Climatological monthly mean dynamic topography (m) and calculated geostrophic currents (m/s) in (a) January, (b) March, (c) April, (d) July, (e) September, and (f) November. GUNDUZ VC American Geophysical Union. All Rights Reserved. 1425

7 (a) January (b) July (c) December (d) Wind Stick Plot Figure 5. Climatological monthly mean wind velocity (m/s) over the Caspian Sea in (a) January, (b) July, (c) December, and (d) wind stick plot showing the area averaged climatological wind direction. [2000], based on satellite-derived SST patterns. This current seems to occur during all investigated years, but with varying intensity from one year to another. The dipole pattern observed earlier in the SCS is not evident in April. However, the coastal jet along the southern coast exists during the whole investigated period. Figure 8 shows SST overlaid with geostrophic currents for the month of August in the years 1993, 1997, 1998, 1999, 2000, and It is well known from earlier studies [Sur et al., 2000; Ibrayev et al., 2010; Gunduz, 2008] that upwelling occurs in August along the eastern coast of the MCS, forced by the northerly winds in summer resulting in the particular circulation. It is observed in Figure 8 that the intensity and location of the upwelling are subject to interannual variations. For example, the upwelling is very intense and the cold water covers the whole eastern coast of the MCS basin in 1993 and 1994, while it is much more localized in 2000, and there is no evidence of upwelling detected by SST in 1999 and The westward flowing Ekman drift current at the surface, responsible for creating the coastal upwelling is evident in all of the years. When there is strong upwelling, the current along the eastern coast appears to slow down relative to the prevailing situation before upwelling. The location of the cyclone in the SCS also changes from year to year, and in some years it is not formed at all. GUNDUZ VC American Geophysical Union. All Rights Reserved. 1426

8 (a) 1993 (b) 1994 (c) 1997 (d) 1999 (e) 2000 (f) Figure 6. Monthly mean January SST ( C, color) overlaid with currents (m/s) in (a) 1993, (b) 1994, (c) 1997, (d) 1999, (e) 2000, and (f) GUNDUZ VC American Geophysical Union. All Rights Reserved. 1427

9 (a) 1993 (b) 1997 (c) 1998 (d) 1999 (e) 2000 (f) Figure 7. Monthly mean April SST ( C, color) overlaid with currents (m/s) in (a) 1993, (b) 1997, (c) 1998, (d) 1999, (e) 2000, and (f) GUNDUZ VC American Geophysical Union. All Rights Reserved. 1428

10 (a) 1993 (b) 1997 (c) 1998 (d) 1999 (e) 2000 (f) Figure 8. Monthly mean August SST ( C, color) overlaid with currents (m/s) in (a) 1993, (b) 1997, (c) 1998, (d) 1999, (e) 2000, and (f) GUNDUZ VC American Geophysical Union. All Rights Reserved. 1429

11 4. Conclusions Geostrophic velocities calculated from satellite altimetry and SST data were used together with modelderived mean dynamic topography to document and try to better understand the seasonal and interannual variations of the Caspian Sea surface circulation. It was shown that the generated geostrophic currents are in good agreement with the randomly released drifter trajectories. The monthly variations of the generated geostrophic velocity were compared with the satellite SST data, confirming qualitative agreement between the two fields. A basin-wide cyclonic circulation was found in the MCS, which however briefly turned into an anticyclonic gyre in summer. In the SCS, there is an anticyclonic-cyclonic dipole pattern preserving its structure during whole year. The results of this analysis have shown that the Caspian Sea surface circulation exhibits strong interannual variability comparable to other closed basins (like the semiclosed Black Sea). Mean SSH from the numerical ocean model used in this study to construct dynamic height could possibly influence the quality of the generated geostrophic currents. The analysis should be performed by using the means obtained from different numerical model outputs as a further analysis. A major weakness of the interpolation methodology we used was in the treatment of the coastal data, possibly arising from the technical problems of considering nonisotropic statistics near the coast as well as the quality of the satellite data near the coast. We hope to improve the method and overcome these limitations in the future. Acknowledgments AVHRR Pathfinder data were provided by GHRSST and the US National Oceanographic Data Center. This project was supported in part by a grant from the NOAA Climate Data Record (CDR) Program for satellites. References Bleck, R., and D. B. Boudra (1981), Initial testing of a numerical ocean circulation model using a hybrid (quasi-isopycnic) vertical coordinate, J. 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