Investigation of the summer Kara Sea circulation employing a variational data assimilation technique

Transcription

1 Investigation of the summer Kara Sea circulation employing a variational data assimilation technique G. Panteleev International Arctic Research Center-Frontier Research System for Global Change,University of Alaska Fairbanks, Alaska, Fairbanks A. Proshutinsky Woods Hole Institute of Oceanography, MA, USA M. Kulakov Arctic Antarctic Research Institute, St.Petersburg, Russia D. A. Nechaev University of Southern Mississippi, Stennis Space Center, MS, 959, USA W. Maslowski Naval Postgraduate School,Monterey, CA, USA

2 Short title: KARA SEA CIRCULATION

3 Abstract. An estimate of the summer Kara Sea circulation is constructed as a variational inverse of the summer hydrographic and atmospheric climatologies. The reconstructed evolution of temperature, salinity, and velocity fields provides the best fit to climatological data and satisfies dynamical and kinematic constraints of a quasi-stationary primitive equation ocean circulation model. The variational approach allowed us to obtain the circulation which agrees with the available velocity measurements in the region but differs from the traditional scheme of the Kara Sea circulation. The circulation is characterized by inflow of.6 Sv through Kara Gate and.8 Sv between Novaya Zemlya and Franz Josef Land, respectively. The water entering the Kara Sea between Novaya Zemlya and the Franz Josef Land outflows through the St. Anna trough, while 75% of Kara Gate inflow forms the outflow through the Vilkitsky/Shokalsky straits. The reconstruction of the Kara Sea circulation during the periods of positive and negative phases of the Arctic Oscillation allowed us to identify the qualitative difference between the two states. Comparison of the obtained optimized climatological circulation with the results of two numerical models reveals significant differences. Several numerical experiments were performed in order to understand the reasons that could be responsible for these discrepancies.

4 4. Introduction The general surface circulation pattern of the Kara Sea established from historical ship and ice drift (Soviet Arctic, 97) is shown in Figure. This classic circulation scheme has been reproduced in numerous publications (e.g. Pavlov and Pfirman, 995; Volkov et al., and others) but was published first by Berezkin and Ratmanov [94]. The circulation of the Kara Sea below the surface is known from moorings and from ADCP (Acoustic Doppler Current Profiler) measurements. Approximately 5 moorings were deployed during along the southern coast of the Kara Sea to collect information about currents along the Northern Sea Route, primarily for navigation needs. During the last two decades, 5 moorings were deployed in the central and northwestern parts of the Kara Sea. These observations were analyzed by King et al. [996], Loeng et al. [997] and Scherbinin, []. For this paper, we have compiled data from a number of different sources (published figures and tables, and information from several declassified Arctic and Antarctic Research Institute data sets) which are shown in Figure. Unfortunately, because of the relatively short mooring records, most of the data were averaged weekly and can not be treated as robust characteristics of the summer circulation regime but some useful information about the prevailing circulation could still be obtained. The ADCP measurements were summarized for by King et al. [996], and McClimans et al., [], (hereafter McC) and are shown in Figure. Interestingly, that the major features of the measured circulation from Figures and are not really

5 5 in agreement with currents presented in Figure. There are several reasons for this. First, the circulation pattern of Figure is known since the 97s, but it is based on observations before the 94s. Circulation patterns derived relatively recently can be different from the classic circulation because of climate change (e.g. changes in wind forcing, ice coverage, river runoff). Second, the original pattern (Berezkin and Ratmanov, 94) reflected not only summer data, but also information from other seasons so must be taken with caution. Third, there is much more data in our data archives now than was available for Berezkin and Ratmanov [94]. The sparseness of velocity measurements and strong variability of the currents make quantitative estimates of the general circulation of the Kara Sea based on observations a difficult task. In this situation, numerical modeling is an appropriate tool to compensate for observational gaps. The first studies of the Kara Sea employing different models were conducted by Doronin [98, 987] and Doronin et al. [99]. They simulated circulations for different seasons and years based on atmospheric forcing and observed temperature (T) and salinity (S) fields employing relatively simple barotropic and baroclinic diagnostic numerical models. These studies showed that the circulation of the Kara Sea is influenced by different factors and that the circulation changes significantly from season to season and from year to year depending on the prevailing forcing. Among forcing parameters, wind, water exchange with the Barents Sea and the Arctic Ocean, and river runoff were identified as governing. Volkov et al., [] provided another diagnostic study of the Kara Sea circulation based on hydrographic surveys in Augusts of 977, 9 and 98. These results allowed them to reproduce some known features

6 6 of the Kara Sea circulation from Figure, but the interannual variability of simulated patterns for these years was significant, and circulation patterns for different years differed from the pattern in Figure. McC studied the circulation of the Kara Sea by employing a rotating laboratory model. The model was forced by climatological transports at the open model boundaries and river discharge. Despite many model simplifications, the McC results agree very well with observations. Trajectories of water particles reproduced by this model (Plate from McC) clearly indicate both an anticyclonic circulation in the central Kara Sea and a flow along the eastern flank of the Novaya Zemlya Trough (NZT). Unfortunately, laboratory modeling provides only qualitative patterns of circulation and is not able to resolve vertical circulation features and a more or less real range of transports. Recently, a more detailed numerical study of the Kara Sea circulation was carried out by Harms and Karcher, [999, 5], (hereafter HK99 and HK5). The circulation was simulated with a high resolution (9.4 km) prognostic Hamburg Shelf Ocean Model (HAMSOM) [Backhaus, 985]. The model was run under forcing of seasonal atmospheric climatology, water, salt, and heat fluxes and tides. The summer water circulation in the upper 5-m layer is shown in Figure 4. Comparing Figure 4 with Figures - reveals several differences. Among them is absence of a pronounced anticyclonic circulation in the central part of the Kara Sea. Instead, the fresh water from the Ob and Yenisey rivers flows northward and eastward to form three distinct branches. Among AOMIP (Arctic Ocean Model Intercomparison Project) models, only two models with horizontal resolutions less than km could be used for circulation

7 7 studies in the Kara Sea. These are the models of the Naval Postgraduate School (NPS, Maslowski et al., [4]) with a horizontal resolution of 8.5 km, and the Los Alamos Global Ocean model (Hunke and Holland, [6]) with a resolution of approximately 9km. Other AOMIP models do not really resolve the complex bathymetry of the Kara Sea and are not discussed here. In addition, model output from the Los Alamos model was not available for this study so only the NPS model results are validated here. One of the important problems of regional numerical modeling is that the regional model solutions significantly depend on the open boundary conditions which are usually highly uncertain. The accumulation of boundary condition errors may gradually deteriorate the solution of any numerical or laboratory model and cause disagreements with the observations. Volkov et al., [], demonstrated that a simple adjustment of boundary conditions can easily improve model results. But the question is how to adjust these boundary conditions correctly to satisfy observational data and to improve the model solution inside the model domain. The variational data assimilation approach [Le Dimet and Talagrand, 987] provides an efficient way to achieve a better model-data agreement through the optimization of the model initial and boundary conditions. During the two last decades, this method was proved to be an extremely useful and efficient tool for the study of the ocean circulation [Wunsch, 994, Stammer et al.,, Awaji et al., ]. This approach closes the gap between circulation studies relying heavily on observations (such as the dynamical method, water mass analysis and diagnostic modeling) and methods based upon dynamical constraints alone (such as model simulations).

8 8 The major goal of this paper is to reconstruct a quasi-stationary summer Kara Sea circulation that agrees with the observed hydrographic fields, and, at the same time, satisfies the dynamical constraints of a primitive equation model. To solve this problem we have utilized a variational data assimilation approach proposed by of Tziperman and Thacker, [989]. Previously, the variational algorithm for reconstruction of a quasi-stationary circulation was successfully implemented in a number of studies (e.g. Grotov et al., 998; Yaremchuk et al., 998; Panteleev et al., 6b). Another goal of this publication is to show that the AOMIP (Arctic Ocean Model Intercomparison Project) models can be validated based on the results of modeling with data assimilation. There are several measurable oceanic parameters which could be used for model validation, namely: T, S, ocean currents, and sea surface heights (SSH) or sea level. Among these components, information about ocean currents is less available than for other parameters and the variational data assimilation approach provides the opportunity to derive the most probable velocity field for the region under investigation. Sea level data are available for approximately 7 tide gauge stations along Siberian coastline. Model validation based on these data is discussed in Proshutinsky et al. [6] (this volume). This paper is organized as follows. Section begins with a description of available data and their preliminary analysis. The numerical model, statistical hypotheses and specific details of the variational data assimilation procedure are outlined in Section. In Section,4 the major results of the reconstruction of the summer Kara Sea circulation are presented and are compared with observations and other models. Discussion and

9 9 conclusions are provided in Section 5.. Data and data sources The procedure of the variational data assimilation approach depends heavily on the data available for the region under investigation, so it is logical to analyze the existing data first and then describe the data assimilation technology. The reconstruction of the summer circulation in the Kara Sea is based upon the following data sets: i) the summer climatological fields of T and S; ii) the mean summer wind stress and sea surface heat and salt fluxes; and iii) the estimates of total transports through the segments of the western open boundary. The data assimilation procedure prescribes relative weights to different parameters according to the reliability of observations. The observational data is accompanied by standard deviations (STD) for each parameter and is treated as characteristic of the data error. Variance takes into account both instrumental and interpolation errors. Signals of the processes not described by the model (e.g. interannual variability of observations and data variability at weekly time scales) are also treated as data errors... Hydrography Approximately, T and S summer profiles from 9- were extracted from the NOAA Atlas NESDIS 58, [4], and the AARI database. Only data from August and September were used (Figure 5b) to avoid significant noise in water T and S fields associated with sea ice melt in June and July. The assimilation of the

10 raw T and S profiles into the model is not computationally efficient since, in many cases, the individual profiles do not represent statistically independent climatological observations under the constraints of the model dynamics. In order to avoid this problem, the existing data were averaged for the period of the data reconstruction in the vicinity of each grid point. The number of T-S profiles in some model grid boxes was not sufficient to derive reliable estimates of data variance and for these cases, horizontal interpolation of the statistics characterizing data variability was carried out. The obtained climatological data and the estimates of the corresponding data STDs were used in the data assimilation procedure. For example, Figures 5a,b and 5c,d show surface T and S fields and their STDs... Meteorological data The.5.5 o gridded wind stresses, heat and salt fluxes (evaporation minus precipitation, E P) were extracted from the DaSilva et al., [995], climatology. Figure 6 shows the mean summer wind stresses from this climatology. We have compared these data with wind stresses calculated based on NCAR/NCEP sea level pressure reanalysis data and found very close agreement between these two data sets. The August-September mean E P vary from. 7 to kg/m /s and spatially do not change significantly from region to region in the Kara Sea... Water transports Kara Gate: Historical assessments [Uralov, 96; Turanov, 96; Potanin and

11 Korotkov, 988; Loeng et al., 99,995] show that the net inflow of the Barents Sea water into the Kara Sea via Kara Gate is approximately.5.6 Sv. This water transport was used by McClimans et al. [] to simulate the Kara Sea circulation in his laboratory model. Recent direct measurements of the water transport in this region were conducted by King et al. [996] using several day-long ADCP surveys in September, 995. Results of their measurements coincide with earlier studies and also show that the maximum current speed is approximately cm/s. Mooring observation obtained by Scherbinin, [], show a persistent water flow from the Barents Sea to the Kara Sea with mean velocities approximately 5 cm/s in the vicinity of the Novaya Zemlya (NZ) and 6 cm/s near Vaigach Island. Unfortunately Scherbinin [] does not provide the total water transport in the Kara Gate, but because of high velocities one can assume that the eastward inflow was higher than.6 Sv observed by King et al., [996]. Panteleev et al., [4] analyzed volume, heat and salt fluxes through the open boundaries of the Barents Sea with the application of simplified variational data assimilation section model. They found out that the mean water transport via the Kara Gate has to be approximately.7 Sv in order to correctly explain and satisfy the structures of the velocity, T and S fields along the open boundaries. Taking into account these estimates, for this study it is assumed that the summer climatologic water transport via the Kara Gate is.65±. Sv. Franz Josef Land (FZL) Novaya Zemlya (NZ) passage: Information about water transport between the FZL and Cape Zhelania of NZ are rather controversial. The

12 geostrophic calculations of Uralov [96] and Timofeev [96] indicated that there is an inflow of the Barents Sea water to the Kara Sea of approximately..5 Sv, while Loeng et al. [997] estimated this inflow as.6 Sv. This was based on the analysis of salt, heat and water volume balance in the Barents Sea. The only direct water transport measurements in this passage were reported by Loeng et al. [99] from four moorings deployed for one year (99 99). The fifth mooring in the vicinity of FJL was lost and the Loeng et al. [99] results did not take into account transport in the northern part of the strait. The rest of the mooring records revealed a well pronounced seasonal cycle in the water transport toward the Kara Sea, with net fluxes varying from.5 to. Sv in winter, and with approximately Sv flow in summer. Taking into account some indirect estimates discussed above, water transport of.5±. Sv was prescribed along this section for our study. Water transports via northern and eastern segments of the Kara Sea model domain (Figure ) were not constraint to the observed fluxes because these data do not exist and the model has derived optimal water transports at these boundaries in order to satisfy model constraints for the other assimilated forcing and water parameters. The Ob and Yenisey river runoffs were adopted from Pavlov and Pfirman [995] and prescribed as.sv±.5 for Ob river and as.5sv±.5 for Yenisey. The water transports via different segments of the Kara Sea open boundary are summarized in Table.

13 . Data assimilation technique.. Forward and adjoint models To derive correctly the Kara Sea circulation from the data, the observational information has been combined with information imposed by the dynamical constraints. For this paper, the dynamical constraints are formulated as a numerical model based on a set of conventional primitive equations under Boussinesq and hydrostatic approximations. This model is a modification of the C-grid, z-coordinate OGCM designed in the Laboratoire d Oceanographie Dynamique et de Climatologie [Madec et al., 999]. The model is implicit both for barotropic and baroclinic modes permitting model runs with relatively large time steps. The model is used in climatological non-eddy-resolving mode on a relatively coarse grid with a -hour time step. Detailed description of the numerical scheme can be found in Nechaev et al. [5], and Panteleev et al. [6a]. The model grid resolution is. o along meridians and.5 o along latitudes (approximately km). The model bathymetry is from the ETOPO data set ( There are 8 unequally spaced levels in the vertical direction with a minimum resolution of m near the surface and 5 m near the bottom. The adjoint code of the model was constructed analytically by transposition of the operator of the tangent linear model, linearized in the vicinity of the given solution of the forward model [Penenko, 98, Wunsch, 996]. Application of the implicit scheme

14 4 with large time steps results in a considerable reduction of storage requirements for variational data assimilation, since for nonlinear problems running the adjoint model requires storing the solution of the forward model every time step. The tangent linear model was obtained by direct differentiation of the forward model code. Therefore, the tangent linear and adjoint model are the exact analytical consequences of the forward model. In the course of data assimilation, the model solution is optimized by tuning free parameters (the so-called control vector) of the model. The control vector of the model is composed of values of the functions specifying initial conditions, open boundary conditions and surface fluxes. The set of initial conditions of the model includes the fields of sea surface height (SSH), T, S and horizontal components of velocity. Boundary components of the control vector comprise the distributions of T, S, and normal components of velocity specified on the open boundaries. The free-slip boundary condition for the tangent velocity component is set at the open boundary, therefore the values of the tangent velocity are not included into the control vector... Cost function The variational data assimilation can be formulated as a traditional least square problem [Marchuk, 9; Penenko, 98; Le Dimet and Talagrand, 986; Thacker and Long, 988]. The optimal solution of the model is found through constrained minimization of a quadratic cost function on the space of the model control vectors, where the cost function measures squared weighted distances between the model solution

15 5 and data. Statistical interpretation of the least square method [Thacker, 989; Wunsch, 996] considers the cost function as an argument of the Gaussian probability distribution with the cost function weights being the inverse covariances of the corresponding data errors. Under the statistical interpretation the optimal solution is the most probable model state for the given data realization and prior error statistics. Because of the sparseness of the oceanographic data and limited duration of the observational time series, practical data assimilation methods commonly rely on the assumption that the errors of different observations are δ-correlated [Thacker, 989]. Under this assumption, the cost function weights are represented by the diagonal matrices, with diagonal elements being equal to the reciprocals of the corresponding data error variances. In the present study, we use the cost function J, which, in addition to the real climatological data described in Section, contains the so-called bogus data [Thacker, 989] in the form of the smoothness and stationarity terms: J = J C + Ju + J stat J C = Ω,t Ju = J stat = n=,n z= Ω,t [WC (C C ) + WC s ( C) ]dωdt + WV,n( [W s ζ [W st C t Γ n H udzdγ Vn ) + Ω z= [W B W s u ( u) dωdt + ( ζ) + W (τ τ ) + W s ( τ ) ]dsdt τ Ct + W st Ctt + W st u t + W st u tt + W st C tt u t τ u tt (B B ) + W s ( B) ]dsdt ζ t B ζt + Wζ st tt ζtt]dωdt

16 6 () Here u denotes the horizontal velocity; ζ is sea surface height ; C = (T, S) is T and S; τ is wind stress; B represents surface heat and salt fluxes; Γ n denote the segments of the open boundary; V n is the estimate of transport through the n-th segment of the open boundary; and W are the variances of corresponding data. Asterisks denote the observed fields. The smoothness or bogus data terms in J are proportional to the squared Laplacians of the model fields. These terms were introduced into the cost function to regularize the data assimilation problem. Two groups of terms J C and Ju constrain baroclinic (T, S and heat/salt fluxes at the surface) and barotropic (SSH, velocity and wind stress) variables of the model respectively. The physical meaning of the different terms in J C and Ju is straightforward: minimization of these terms enforces smoothness of the model solution and attracts it to the observed data. According to Tziperman and Thacker [989], the third group J stat ought to force the model solution to be quasistationary with a degree defined by weights (W st... ). The spatial and temporal distributions of real data variances W... have been discussed in section. The variances of the bogus data are determined through the scale analysis: W s C = C s /L 4 C, W s B = B s/l 4 C, W s u = V s /L 4 u, W s ζ = ζ s /L 4 u, W s τ = τ s /L 4 τ, WC st t = Cs /t s, WC st tt = Cs /ts, 4 Wu st t = Vs /t st, Wu st tt = Vs /t 4 st, Wζ st t = ζs /t st, Wζ st tt = ζs /t 4 st, t st =6 months. The scales V s =5 cm/s and ζ s = cm are defined as typical variations of these variables in the first guess solution, while the parameters C s, B s, τ s are derived

17 7 from characteristic spatial variability of the corresponding data. We utilized a uniform spatial scale for the wind stress fields L τ =5 km, while spatial scales L C and Lu are the functions of the local depth gradient and vary within the ranges km and 5 5 km respectively. This setting of the characteristic scales allows us to avoid oversmoothing of the model solution in regions with strong topography steering. The procedure of the first guess initialization and the technique of sequential minimization of the cost function J are outlined in Panteleev et al. [6a]. Starting with some prior estimate of the model control vector the model run is performed to obtain the first guess solution. Given the solution of the forward model the value of the cost function J is computed and the adjoint model backward is simulated in time to estimate the gradient of the cost function with respect to the control vector. The gradient of the cost function is then used in the quasi-newtonian optimization algorithm [Gilbert and Lemarechal, 989] to find a better estimate of the control vector of the model. The procedure is repeated until the norm of the cost function gradient is sufficiently small. Because of the model s non-linearity, the cost function J may have multiple local minima. The quasi-newtonian optimization algorithm finds only one local minimum, which is the closest to the first guess solution. The procedure of the first guess initialization and gradual minimization of cost function J is outlined in Panteleev et al. [6a].

18 8 4. Reconstruction results Theoretically, the reconstructed summer circulation and hydrography fields obtained by employing the variational data assimilation technique represent the most probable state of the Kara Sea conditions derived from the existing data. In this section, the reconstructed fields are described and the circulation parameters are compared with direct measurements, classic circulation schemes and with results from the models. This comparison between observations and results and results of different studies benefits all approaches. 4.. Comparison with observations The optimized velocity and SSH fields are shown in Figure 7. Thick arrows depict the available mooring velocities from Figure. These data have not been assimilated into the model and were used to validate reconstructed results. Sections - are shown by thick black lines. Water transports through these sections calculated for different experiments are shown in Table. The water dynamics of the Kara Sea are analyzed here for five distinctive regions (see Figure 7c for region locations): (i) the western region with Novaya Zemlya Trough (NZT) (55 o 67 o E and 68 o 77 o N); (ii) the central region or Region Of Fresh water Inflow (ROFI) (67 o 85 o E and 7 o 77 o N); (iii) the eastern region (85 o -95 o E and o - o N); (iv) the northwest region, or St. Anna Trough (55 o 77 o E and 77 o 8 o N); and (v) the northeast region, or Voronin Trough (77 o 95 o E and o 8 o N). Region : The currents of the upper m layer in the southeast of this region (Figure 7a and 7b) resemble a cyclonic circulation pattern with a week ( cm/s)

19 9 south westward current along the NZ coast and a stronger ( 4 cm/s) north eastward branch along the eastern flank of the NZT. Velocities in deeper levels have a persistent northeast direction ranging from to cm/s (Figure 7c). Vertically integrated transport has a northeast direction and gradually increases from.7 Sv at section to. Sv at section (see section locations in Figure 7a) such that, as the south westward current along the eastern NZ coast exists only in the upper layer and its influence on the total transport in the NZT is not significant. Our results do not show Litke Current in the Kara Gate. The ADCP survey during September,996 (Figure ) and mooring observations in September 997 [Scherbinin, ] also did not record the Litke Current in the Kara Gate. This current is a relatively narrow flow and likely has a baroclinic origin associated with a frontal zone separating fresh waters running from NZ to the sea and saline waters flowing to the Kara Sea via the Kara Gate. It is possible that the model and data horizontal resolution do not allow us to resolve this current. Another interesting feature of the circulation in the NZT region is that the fresh water from the Ob and Yenisey rivers penetrates into the region and flows westward along Yamal Peninsula. Figure 7a (see also Figures 8a and 9a) shows that this flow then turns southward, reaches 7 o N and deflects offshore as a north eastward current along the eastern flank of the NZT and joins the major branch of the Barents Sea water inflowing via Kara Gate. A similar flow pattern was observed in the McC laboratory model and in the numerical modeling results of the freshwater plume in the Kara Sea by Kulakov and Stanovoi []. Figure 7 also shows that this current follows the steep

20 slope of the NZT and gradually accelerates, reaching approximately cm/s in the vicinity of section with a total transport of.75 Sv (section is shown in Figure 7a). The structure of this NZT through-flow is in a good agreement with ADCP velocities (Figure ) and with trajectories of ice beacons reported by McC. The optimized water T and S fields (Figure 8 and Figure 9) agree with the reconstructed dynamics. The relatively warm and saline Barents Sea water enters the Kara Sea via Kara Gate causing a T and S increase in the surface and deeper layers. Further downstream, the surface T gradually decreases from 5 o C near Kara Gate to o near the northern tip of the NZ. According to Figure 8, the T distribution in the layer -45 m reveals a thin strip of warm water along the NZ coastline. This is a results of downwelling (vertical velocities along NZ in Figure indicate downwelling) but the origin (wind forcing, for example) of this downwelling is not clear and additional experiments are needed for robust conclusions. Region : The S distribution in Figure 9a and velocity field at Figure 7a clearly indicate that the river discharge in this region forms an area of very fresh and relatively warm waters with clockwise/anticyclonic rotation (Figure 7a). An anticyclonic circulation in the central part of the Kara Sea (ROFI) is a prominent feature of the reconstructed circulation, but the classic scheme in Figure does not show this anticyclonic circulation cell. At the same time, the reconstructed velocity field agrees well with the mooring data and with the ADCP data in Figure. McC speculated that this remarkable circulation cell is generated by freshwater

21 accumulation in the shallow ROFI zone during intensive river discharge in June and July. In August and September, the river discharge reduces and the ROFI freshwater pool acts as a zonal barrier deflecting river runoff to the left. It is interesting that water transport through section is.9sv and is times larger than the total river runoff. This means that the anticyclonic circulation driven by baroclinic effects in this region returns waters into the area. This is confirmed by estimates of water transport through section (eastern branch of ROFI circulation) which is.8 Sv and is rather significant. The optimized S fields shown in Figure 9 also agree with the water circulation discussed above. S gradients and frontal zones observed in the upper layers of the Kara Sea indicate that the summer circulation of the Kara Sea (when strong winds are absent) is density driven and is regulated by river discharge and water inflow from the Barents Sea. Region : The circulation pattern in region is characterized by an eastward through flow toward Vilkitsky and Shokalski straits and agrees with Figure. Another feature of this region is a week anticyclonic circulation around several islands. This disagrees with Figure which shows more cyclonic type water rotation, but Figure a with observations from moorings clearly shows anticyclonic circulation cell and agrees well with reconstructed velocity field in Figure 7a. The reconstructed water transports through Shokalsky and Vilkitsky Straits are shown in Table and are indicated by sections 9 and, respectively. The total water

22 transport to the Laptev Sea is estimated as.5 Sv. The Vilkitsky and Shokalsky straits play a very important role in the Siberian coastal water dynamics by allowing transportation of the Kara Sea waters eastward to the Laptev Sea and further east toward Bering Strait with the East-Siberian Coastal Current that originates in the Laptev Sea (Sverdrup, 99). Moreover, we speculate that there is a coastal fresh water transport along the arctic coastline starting with a freshwater outflow from the Baltic Sea, via the North Sea, then along Scandinavia (with a significant input of fresh water from Scandinavia) to the Barents Sea, then to the Kara Sea via Kara Gate and along the coastline toward the Chukchi Sea and Bering Strait. This idea was introduced recently by Eddy Carmack (personal communication) and also is supported by numerical studies of Maslowski [4] and Kulakov [6]. Region 4: The reconstructed circulation in region 4 generally agrees with Figure, revealing a strong water inflow via the NZ FJL strait along the northern coast of NZ, and an outflow along the southern coast of FJL (which could be identified as the beginning of Persey current, known more as a current of the northern Barents Sea) and an inflow/outflow along the western and eastern sides of the St.Anna Trough, respectively. The reconstructed optimized transports through sections and 4 showing water dynamics associated with the St. Anna Trough, are presented in Table. Balancing water inflow via section and 4 (.8 Sv) with transports through sections 5 and 6 (.7 Sv) allows us to conclude that practically all water inflowing to the Kara Sea between NZ and FJL outflows through the St.Anna Trough.

23 Several velocity measurements observed at 5 m during and 997 (Figure 7c) confirm the reconstructed circulation fields at this level, but the reconstructed circulation in the upper layers disagrees significantly with the observations of McC. McC analyzed this problem and concluded that the wind regime during their observations was not typical for summer conditions, resulting in water outflow from the Kara Sea instead of inflow. In the deep layers, at m depth and below, the inflow pattern agrees with our reconstructed circulation scheme. The water from the Barents Sea inflows between the NZ and FJL as an intense current with velocities of -5 cm/s near the NZ coast (Figure 7). The current follows the m isobath and deflects northward near the NZ tip, presumably due to the sharp topography break. Thus, the currents along the western and eastern NZ coasts diverge in the vicinity of the northern NZ tip. The outlined behavior of these currents allows warm water to be accumulated in the vicinity of the NZ tip (Figure 8b). Recently, the presence of a relatively warm water (.5 o C up to.5 o C) in this area was observed by Pivovarov et al. []. Region 5: The water circulation in this region is significantly influenced by the bathymetry of the Voronin Trough and reveals a weak inflow of the Arctic Ocean waters via section 7 (.7 Sv) and a major outflow of the Kara Sea waters through section 8 (. Sv). It is important to note that the variational data assimilation approach allowed us to reconstruct the circulation in the the St. Anna and Voronin Troughs without prescribing water transports through the northern and eastern boundaries, and only

24 4 observed T and S fields assimilated into the model were used under the constraints of the model dynamics. The S distribution (Figure 9) reveals a local S minimum in the north-east corner of region 5. Probably, this feature is related to the intensive summer melting of sea ice that accumulates in the region due to prevailing currents and wind regime. This S minimum is responsible for an anticyclonic type of water motion in addition to the bathymetric dominating factor which governs the major circulation features in the Kara Sea. The classic circulation scheme in Figure partially disagrees with the circulation discussed above. Reasons for this will be analyzed later considering different model experiments. 4.. Comparison with model results 4... Laboratory model, McC. Reconstructed circulation results agree well with the circulation patterns of the McC laboratory model. Both studies cover common model domains and utilize more or less similar water transports through the Kara Gate, NZ FJL passage and river discharge (see Table ). As a result, the optimized Kara Sea circulation in the NZT and ROFI zone resembles trajectories of particles in the McC model (see Plate from McC). At the same time, both laboratory model and variational data assimilation results agree well with the ADCP-based circulation scheme in Figure. On the other hand, optimized circulation pattern requires water inflow through sections 5 and 7 of.5 Sv that is two times smaller than the prescribed boundary condition in the McC laboratory model. One can expect significant differences

25 5 between MC model results and the reconstructed circulation in regions 4 and 5 but unfortunately, detail patterns of water velocities from McC model are not available for analysis Regional high resolution numerical HK99 model. The mean summer Kara Sea surface circulation pattern of HK99 (Figure 4) differs significantly from the reconstructed velocity fields. In region, the HK99 circulation shows an intensification of the currents along the east NZ coast in the southern part of the NZT (7 o o N). As a result, the SSH derived by HK99 has a small maximum in the center of the NZT, while our results indicate a small minimum in the SSH distribution (Figure 7d) resulting in a weak south eastward current along the east NZ coast (Figure 7a). Despite the simple preprocessing method for the raw ADCP data and strong smoothing with a typical scale of 6 km, the composite ADCP velocities (Figure ) show very weak currents along the east NZ coast near 7 o N. Several other models (e.g. Karcher et al.,) also do not reveal the intense northeast current along the east NZ coast. In region, at the location of section, the HK99 model shows a strong inflow into the model domain (Figure 4). This agrees with the classic circulation scheme of the Kara Sea (Figure ), but contradicts the available current meter data (Figure ), ADCP survey results (Figure ), and the local S distribution (Figure 9). The summer S distribution indicates a zonal front in this area (Figure 9a) but HK99 S fields do not have this feature because of several reasons associated with a weak imbalance between fresh water river runoff, salt fluxes due to ice melt and growth and due to the transport

26 6 of sea ice from the Kara Sea to the Arctic Ocean. The underestimate of freshening of the Kara Sea by this model was discussed by HK99 and is a known problem of this simulation. Third, the HK99 surface circulation in the ROFI zone is not so pronounced as in the optimized reconstructed fields and in the McC results. There are two relatively broad outflow areas formed by the Ob and Yenisey discharges shown in Figure 4 with zero currents in the vicinity of the ROFI zone. This is mostly because of the underestimation of water freshening in the Kara Sea by this model as discussed above. For the HK99 model results, our speculations are based on a simple visual analysis of the circulation patterns. Below, a more detailed examination provides some quantitative estimates of the differences between the reconstructed fields and fields extracted from the results of the Pan-Arctic regional ice-ocean coupled model of the Naval Postgraduate School Regional high resolution model of the Arctic Ocean. Results of the Naval Postgraduate School regional coupled Arctic Ocean and sea ice general circulation model are available for the AOMIP investigators at ftp://ftp.arsc.edu/private/maslowsk/aomip. The ocean model is based on the GFDL formulation adapted for parallel computers [Smith et al., 99]. The rotated numerical grid covers the Arctic Ocean, the sub-polar seas and the North Atlantic to approximately 5 o N latitude. The Bering Strait is closed. No mass flux is allowed through the closed lateral boundaries. The grid is relatively fine (8 km and levels). The non-linear Navier-Stokes equations are solved in spherical form. Hydrostatic balance is assumed.

27 7 Laplacian diffusivity and viscosity in the vertical direction and biharmonic in the horizontal are used. More details about the ocean model can be found in Smith et al. [99]. The ice model is a version of the Hibler [979] viscous-plastic, dynamicthermodynamic ice model, adapted for parallel computers. At the ice surface, heat flux is calculated according to the energy budget, as in Parkinson and Washington [979]. The surface energy budget is calculated using the ECMWF data, including the short wave radiation, cloud cover and -m air temperature and dew point. Downward long wave radiation was calculated from -m air temperature and cloud cover. Horizontal stress at the surface was calculated from the ECMWF -m wind fields. Using this forcing, the model has been shown to give realistic distributions of sea ice properties [Zhang et al., 999]. The -year model output consists of monthly parameters of three dimensional T, S and water velocity fields at depth layers between limits:,, 45, 7,, 4, 8, 4,, 8, 46, 58, 7, 9,,, 5, 7, 9,,, 5, 7, 9,,, 5, 7, 9, 4, and 4 meters, such that the first layer is between and meters, the eighth layer is between 4 and meters, etc. In order to compare the results of this model with the reconstructed fields, we averaged the reconstructed data for the layers of the NPS model. Summer climatological currents, T and S fields in the original model grid are shown in Figures -. Figures 4-6 show the differences between reconstructed fields and NPS model results for currents, T and S in different layers, respectively.

28 8 Both reconstructed and NPS model solutions (Figure 7 and Figure ) reveal the southward current along the Yamal Peninsula, strong northeast flow along the eastern flank of the NZT, the eastward inflow in the southern part of the NZ FJL passage, and outflow in its northern part. At the same time, there are significant differences in the magnitude of the velocities. The reconstructed velocities are stronger and are well documented in Figure 4. The optimized velocities (Figure 7) in Kara Gate and NZT are nearly twice higher than velocities of NPS model. On the other hand, the Persey Current is better represented by the NPS model than in the reconstructed results. The NPS currents in this region agree better with available observations (compare Figure with Figure 7 and Figure ). There are significant discrepancies between the NPS and reconstructed velocity fields in the other regions of the Kara Sea and these differences can be summarized as: (a) The NPS circulation pattern does not show the anticyclonic circulation in the central Kara Sea (ROFI zone). Instead, the NPS solution indicates the broad westward flow along the Siberian coast which extends westward from the Vilkitsky Strait. The available mooring observation and ADCP measurements (Figure and ) do not support these results. Obviously, the variationally optimized vectors in Figure 7 agree better with the observations. (b) The NPS model inflow via the Vilkitsky Strait contradicts the T and S fields and the well known phenomenon of the westward freshwater transport along the Siberian coast. (c) There are also notable differences in the circulation in region 4. According to

29 9 the NPS circulation pattern (Figure ), the major outflow of all waters coming to the Kara Sea occurs via St. Anna Trough. This disagrees with the observed zonal S and T fronts near the northern NZ tip (Figure 8 and Figure 9). Comparing T and S reconstructed fields with NPS model results (see Figures 5 and 6) shows that the NPS model water T is o C to 4 o C lower than the reconstructed T in the surface layers of the river deltas and in the center of region, o C to o C lower than reconstructed along the eastern coast of NZ, and approximately o C lower in the other regions of the Kara Sea. In the deeper layers (-45m, 45-7m) except for the region in the vicinity of Mys Zhelania, this difference changes sign and NPS water T are higher than in the reconstructed T and S fields. Below 7m, both models show good agreement except in the region of the NZ tip, where the reconstructed water T is approximately degree higher than the NPS model results. These T and S distributions show that vertical water stratification is not well reproduced by the NPS model. Figure 7, where vertical water T and S profiles are presented for different regions, confirms this. Differences in the water salinities are even larger than in the water T between the modeling with data assimilation and the NPS model. Figure 6 reveals that the S of NPS model is much higher in the upper -m layer than the reconstructed S. This is especially pronounced in the river deltas where the difference reaches 5 S units. In the deeper layers, the NPS salinities are less than the reconstructed salinities, indicating that the vertical water stratification in the NPS model does not reproduce the thermocline and halocline in summer.

30 5. Discussion and conclusions In this section we discuss some possible reasons for the disagreements between the reconstructed and simulated summer currents, and T and S fields in the Kara Sea. 5.. Possible causes of model differences In order to clarify reasons that cause the differences between the HK99 and the reconstructed circulation results, two numerical experiments were carried out. In the first experiment, the assimilation procedures described above ( control model run) were repeated but water transport in Kara Gate was reduced to. Sv. This mean summer transport via Kara Gate was utilized by HK99. The major goal of this experiment was to investigate how changes in Kara Gate inflow influence the circulation structure of the Kara Sea. In the second experiment, Kara Gate transport was defined as.65 Sv as in the control model run, but the T and S fields were prescribed uniform without gradients in the vertical and horizontal directions (T=5 o C and S= ppt in all grid points of model domain). In this experiment we investigated how the absence of baroclinic effects influenced circulation in the region. The surface circulation patterns for these experiments are shown in Figure 8. Comparing Figure 8a with Figure 7a shows an intensification of cyclonic circulation in the southern part of region. There are no more visible differences between these two figures and therefore, the significant disagreement between the optimized solution shown in Figure 7 and the HK99 results presented in Figure 4 are not due to reduced water inflow via Kara Gate.

31 The second experiment shows that there are striking differences in circulation patterns of Figure 8b and Figure 7a. Without baroclinic effects, the circulation is driven by water fluxes via open boundaries because wind stresses are very small in summer. In this case, the major circulation flows are regulated by bathymetry and the Coriolis effect. The major flow from Kara Gate follows the bathymetry and runs through the central Kara Sea toward Vilkitsky and Shokalsky straits. River runoffs from the Ob and Yenisey rivers join the major flow at o N and do not show any signs of the ROFI zone configuration. This circulation agrees better with the HK99 circulation pattern than with the control model run results. This confirms our conclusion that the HK99 underestimated horizontal gradients in the Kara Sea S fields. These conclusions explain the absence of the ROFI zone in the NPS model results. The major problems of the NPS model, at least for the Kara Sea, is that all boundaries of this regional Arctic model are closed and therefore there are no direct water volume fluxes to the sea from river deltas. This situation leads to the underestimation of the role of river runoff in the marginal seas, smoothing of water mass fronts, and reduction of vertical water stratification. These also result in the decrease of upper layer T and increase of upper layer salinities. The vertical model resolution is too coarse and does not resolve well the bathymetry, halocline and thermocline structures in the marginal seas. The specific problem of the Kara Sea circulation reproduced by the NPS model is a flow from the Laptev Sea to the Kara Sea via Vilkitski Strait. Numerous previous studies and observational data show that there is a current from the Kara Sea to the

32 Laptev Sea, and biological studies also confirm this indicating high abundance of Kara Sea foraminifera in the western part of the Laptev Sea [Lukina, ]. Recently, the new NPS Pan-Arctic coupled ice-ocean model has been significantly improved and now has higher horizontal (approximately 9 km) and vertical (45 levels) resolutions. In addition, Bering Strait water inflow from the Pacific Ocean has been introduced and the water circulation in the Chukchi Sea has been significantly improved. Figure 9 shows new model results for the Kara Sea and this pattern agrees very well with the classic circulation pattern and with the reconstructed summer circulation of the Kara Sea. The only problem of this new model results is that the ROFI zone is still under-represented in this simulation. It is assumed that this problem partially could be solved by allowing water transports via the model open boundaries including the river deltas. Second, the model restores winter climatological S. It seems that this is not appropriate for summer conditions and summer climatology has to be used for restoring at least for summer months. In the future, it will be important to validate the new NPS model results with the reconstructed fields and to study the degree that our conclusions about needed model improvements are correct. 5.. Circulation change under Arctic Oscillation The employed quasi-stationary approach can be easily modified and applied for the reconstruction of monthly evolution of the Kara Sea circulation (e.g. Nechaev et al., 5; Panteleev et al., 6a). This would allow investigation not only of the circulation patterns and their variability in the region, but also validation of the different

33 numerical models against more accurate data fields obtained by the data assimilation techniques. Unfortunately, the sparseness of the monthly T and S data does not allow us to reconstruct the monthly evolution of the Kara Sea circulation. But in these final remarks, changes of the Kara Sea circulation associated with different phases of the Arctic Oscillation (AO)index and show water circulation patterns for the years with positive (AO+) and negative phases (AO ) of the AO are discussed. The 6-hourly wind stresses for August and September were calculated from NCAR/NCEP reanalysis sea level pressure fields and averaged to force our model for the years with the highest (967, 975, 989, 99, and 99) and lowest (958, 96, 966, 969, 98, 985) AO index years. The obtained wind stress fields are shown in Figures 6 b,c. Note that the wind stress distribution during the AO phase is rather similar to the climatological August-September wind stress pattern, while AO+ wind stresses differ significantly from the typical climatological distribution. Similarly, the summer observations of T and S during the outlined above years were extracted from the AARI hydrographic database and NOAA Atlas NESDIS 58, [4]. The Ob/Yenisey discharges were defined as.5/.5 Sv and./.5 Sv during the period of AO+ and AO, respectively ( ). Due to the lack of other measurements, the climatological transports through the western boundaries discussed earlier were assimilated. Because of the sparseness of T and S data and uncertain transports along the western and other boundaries, we the northern regions were excluded from consideration and only qualitative comparison between the near-surface Kara Sea circulation during the periods of the AO+ and AO are provided.

34 4 The optimized AO+ and AO T, S and velocity fields at.5 m are shown in Figure o and reveal at least three distinct features: (i) during the AO+ phase, the T in Kara Gate and in the southern part of the NZT is approximately.5 o o C higher than during the AO phase; (ii) during the AO phase the warm water is accumulated near the northern tip pf NZ. This process leads to the formation of a bulge of warm water that is absent during the AO+ phase; (iii) during the AO+ phase the ROFI zone is shifted approximately km eastward. These features of the AO+ state agree with the domination of the southwest AO+ wind stresses (Figure 6b). We speculate that the AO+ southwest and western winds over the Barents and Kara seas push the surface waters on-shore and increase the eastward transport of the warm water through the Kara Gate. Our results indicate a small. Sv increase of the eastward Kara Gate transport during the AO+ phase. The real difference between Kara Gate transport for AO and AO+ years can be even larger, because the mean climatological value of Kara Gate transport was assimilated in both cases due to the lack of the other data. Similar influence of the wind stresses on the inflow of the Atlantic water into the Barents Sea was observed by Ingvaldsen et al. [4]. Because of southwest winds, the whole system of the NZT circulation is shifted eastward during the AO+ years. As a result, the south westward current along the east NZ coast is stronger, and the north eastward flow along the NZT is localized along the m isobath of the eastern flank of the NZT. Similarly, the current along the western NZ coast does not deflect to the north near the northern NZ tip but continues to flow

35 5 westward. During the AO+ phase these two currents join near the northern NZ tip and flow eastward as a broad current, while in the mean summer climatological state (Figure 7) and in the AO state (Figure f), these currents diverge and there is a distinct spot with week currents and warm water between them. Comparing Figures e and f, the western AO+ winds (Figure 6b) cause the increase of the onshore transport in the northern part of the KS. That weakens the westward Persey Current along the FJL and intensifies the eastward current along the Severnaya Zemlya. Another effect of the AO+ winds is a change of the circulation in the ROFI zone. Figures e and f indicate significant weakening of the the westward current along the Siberian coast during the AO+ phase resulting in a moderate (.8 Sv) increase of the total eastward transport through the eastern boundary. The weakening of the coastal current also results in the decrease of the intrusion of the salt water into the Yenisey Bay during the AO+ phase. This phenomenon was recently modeled by Harms et al., []. The water transports through sections for the AO+ and AO phases are shown in Table. The AO+ and AO solutions were obtained by assimilation of the relatively sparse T and S data. These results could be inaccurate and should be used only for the qualitative comparison between the two states. Finally, it maybe concluded that the data assimilation approach has some advantages compared with the traditional estimates of the ocean state obtained through the geostrophic calculations and numerical modeling. The major advantage of the obtained circulation is that it is reasonably close to the existing data, and at the same

36 6 time all reconstructed fields (T, S, velocity, surface heat/salt and momentum fluxes) are dynamically balanced. Acknowledgments. This work was supported by the International Arctic Research Center, University of Alaska Fairbanks, and the U.S. National Science Foundation The development of the data assimilation system, utilized in this study, was also supported by the ONR Grant N4--- and NSF Grant OCE--8.

37 7 References Awaji, T., S. Masuda, Y. Ishikawa, N. Sugiura, T. Toyoda, and T. Nakamura (), State estimation of the North pacific Ocean by a four-dimensional variational data assimilation experiment. J. Oceanography, 59, Backhaus J.O. (985), A three-dimension model for the simulation of shelf sea dynamics, Dsch. Hydrogr. Z., 8(H4), Berezkin Vs. A., G. Ye. Ratmanov (94), A General scheme of currents of the Arctic Ocean and adjoint seas, Hydrograpic Administration of the Navy, pp., Leningrad (in Russian). DaSilva, A., Young, C.C., Levitus, S., 995. Atlas of Surface Marine Data, NOAA Atlas NESDIS 6, Natl. Oceanogr. Data Cent., Silver Spring, MD. Doronin N.Y., V.L.Kuznetcov, A.Y.Proshutinsky (99), Circulation of the water masses in the Kara Sea. Transactions of the Arctic and Antarctic Research Institute, 44, 4-4. Doronin N. Yu. (98), Simulation of the barotropic circulation in the Kara Sea, Transactions of the Arctic and Antarctic Research Institute, 8, 54-6 (in Russian). Doronin N.Y. (987), Diagnostic calculation of the three-dimensional circulation in the Kara Sea. Problems of Arctic and Antarctic, 6, 47-5 (in Russian). Gilbert, J.C., C. Lemarechal (989), Some numerical experiments with variable storage quasi-newton algorithms. Math.Prog. 45, Grotov, A.S., D.A. Nechaev, G.G. Panteleev, M.I. Yaremchuk (998), Circulation In Bellingshausen and Amundsen Seas, J. Geoph. Res.,, -. Harms I.H., M.J.Karcher. Modelling the seasonal variability of hydrography and circulation in the Kara Sea. J.Geophys.Res., 4,(C6), 54,,4-,448, 999.

38 8 Harms I.H., U.Huebner, J.Backhaus, M.Kulakov, V.Stanovoy, O.Stepanets, L.Kodina, R.Schlitzer. Salt intrusions in Siberian River Estuaries: Observations and model experiments for Ob and Yenisei. Proceedings in Marine Science, 6, 47-7,. Harms I.H., M.J.Karcher. Kara Sea freshwater dispersion and export in the late 99s. J.Geophys.Res.,, C87,doi:.9/4JC4, 5. Hibler W. D.,III (979), A dynamic thermodynamic sea ice model, J. Phys. Oceanogr., 9, Hunke E. C. and M. Holland (6). Global atmospheric forcing data for Arctic ice-ocean modeling (this volume). Ingvaldsen, R., Asplin, L. and H. Loeng (4), Velocity field of the western entrance to the Barents Sea. Geophys. Res., 9, c, doi:.9/jc8. Karcher M.J, M.Kulakov, S.Pivovarov, U.Schauer, F.Kauker, R.Schlitzer, Atlantic Water flow to the Kara Sea: comparing model results with observations. Siberian river run-off in the Kara Sea, R.Stein, K.Fahl, E.M.Galimov and O.V. Stepanets (Editors), Elseviar Science B.V.,. King S.E., D.R.Johnson, J.Carroll, EPOCA-95 cruise report, Rep.NRL/MR/ , 5pp., Nav.Res.Lab., Washington, D.C., 996. Kulakov M., V. Stanovoi, Frontal zones in the Kara Sea: observation and modeling. Extended Abstracts of the -th International Niennial Conference on Physics of Estuaries and Coastal zones, Hamburg, Germany, September 7-, 9-97,. Kulakov, M.Yu., V.B. Pogrebov, S. F. Timofeyev, N.V. Chernova and O.A. Kiyko (6), Ecosystem of the Barents and Kara Seas, Coastal Segment. In: The Sea, Vol. 4B, The Global Coastal Ocean, Interdisciplinary Regional Studies and Syntheses Edited by Allan

39 9 R. Robinson, Kenneth Brink, Harvard University Press, Cambridge, Massachusetts, USA, 7pp. Le Dimet, F.X., O. Talagrand (986), Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus, 8A, 97. Loeng, H., V. Ozhigin, R. Adlansvik, H. Sagen (99), Current measurements in the northeastern Barents Sea. ICES C.M, C:p. Loeng H., Ozhigin, V., Adlansvik, B., Water fluxes through the Barents Sea, ICES Journal of Marine Science, 54, -7, 997. Lukina, T.G. (), Foraminifera of the Laptev Sea, Protistology, (), 5-. Madec, G., P. Delecluse, M. Imbard, C. Levy, (999), OPA8. Ocean General Circulation Model. Reference Manual, Note du Pole modelisation, Institute Pierre-Simon Laplace (IPSL), France, 9pp. Marchuk, G.I. (9), Basic and adjoint equation of the ocean and atmosphere dynamic. Meteorologija and gidrologija, 7-4. Maslowski, W., D. Marble, W. Walczowski, U. Schauer, J. Clement, A. Semtner (4), On climatological mass, heat, and salt transports through the Barents Sea and Fram Strait from a pan-arctic coupled ice-ocean model simulation J. Geophys. Res., 9, C, C,.9/JC9. McClimans, T.A., D.R. Jonson, M. Krosshavn, E.E. King, J. Carrol, and O. Grennes (), Transport processes in the Kara Sea, J.Geoph.Res., 5,(C6), 4,-4,9. Nechaev, D.A., G.G. Panteleev, M.I. Yaremchuk (5), Reconstruction of the circulation in the limited region with open boundaries: circulation in the Tsushima Strait. Okeanologiya, 45(6),

40 4 NOAA Atlas NESDIS, 58, 4. Panteleev, G., Ikeda, M., Grotov, A., Nechaev, D., Yaremchuk, M., Mass, Heat and Salt Balances in the Eastern Barents Sea Obtained by Inversion of a Hydrographic Section Data. Journal of Oceanography, 6, 6-6, 4. Panteleev, G.G., D.A. Nechaev, M.Ikeda, Reconstruction of summer Barents Sea circulation from climatological data, Atmosphere-Ocean, in press, 6a. Panteleev, G. G., P. Stabeno, V. A. Luchin, D. A. Nechaev, and M. Ikeda (6b), Summer transport estimates of the Kamchatka Current derived as a variational inverse of hydrophysical and surface drifter data, Geophys. Res. Lett.,, L969, doi:.9/5gl49. Parkinson, C.L. and W.M. Washington (979), A large scale numerical model of sea ice. J. Geophys. Res., 84, -7. Pavlov V.K., S.I.Pfirman. (995), Hydrographic structure and variability of the Kara Sea: Implication for pollutant distribution. Depp Sea Res., Part II, 4, Penenko, V.V. (98), Methods of numerical simulation of atmospheric processes. Hydrometeoizdat, 47 pp. Pivovarov S., R. Schlitzer, A.Novikhin (), River run-off influence on the water mass formation in the Kara Sea. Proceedings in Marine Science, 6, 9-6. Potanin, V.A., S.V. Korotkov, (988), Seasonal variability of the main currents in the southern Barents Sea and water exchange with the adjacent areas. Geological and Geographical Problems of natural resources exploitation in the Northern Seas, Murmansk, 8-9, (in Russian). Proshutinsky, A., I. Ashik, S. Hakkinen, E. Hunke, R. Krishfield, W. Maslowski (6), Sea

41 4 level variability in the Arctic Ocean from AOMIP models, (this volume). Scherbinin, A. (), Measurements in the Kara strait. Experience of system oceanologic studies in the Arctic, Moscow, Scientific World, 644p., 8-. Smith, R.D., J.K. Dukowicz, and R.C. Malone (99), Parallel ocean general circulation modeling, Physica D, 6, 8-6. Soviet Arctic, (97), Seas and Islands of th Arctic Ocean, Moscow, Nauka, 56 pp., (In Russian). Stammer, D., C. Wunsch, R. Giering, C. Eckert, P. Heimbach, J. Marotzke, A. Adcroft, C.N. Hill, J. Marshal (), Global circulation during , estimated from ocean observations and a general circulation model. J. Geoph. Res., 7(C9), 8, doi:.9/jc888. Sverdrup, H. U., (99) The waters on the North-Siberian Shelf, The Norwegian North Polar Expedition with the Maud 98-95, Scientific Results, vol. 4(), pp., Geofys. Inst., Bergen, Norway. Timofeyev V.T. (96), Interaction of Arctic Ocean water with the Atlantic and Pacific waters. Oceanography,, 569-5,(in Russian). Thacker, W.C., R. Long (988), Fitting dynamics to data. J.Geophys. Res., 9, Thacker, W.C. (989), The role of the Hessian matrix in fitting models to measurements. J. Geophys. Res., 94, (C5), Turanov I.(96), Water exhange through the Arctic Straits (An overview of the present studies, Scientific Research Project OK-9/, pp., Leningrd: The Arctic and Antarctic Research Institute Arhives (in Russian). Tziperman, E., W.C.Thacker (989), An optimal-control/ adjoint equation approach to

42 4 studying the oceanic general circulation. J. Phys. Oceanogr., 9, Volkov et al., (), Polar Seas Oceanography: An integrated case study of the Kara Sea, Springer. Uralov, N.S. (96), On the advective component of the heat balance in the southern Barents Sea. Trudy Gosudarstvennogo Oceanograficheskogo Instituta, 55, - (in Russian). Wunsch, C., (994), Dynamically consistent hydrography and absolute velocity in the eastern North Atlantic Ocean, J. Geophys. Res., 99, (C7), Wunsch, C.,(996), The Ocean Circulation Inverse Problem. Cambridge University Press, Cambridge, 44 p.. Yaremchuk, M.I., D.A. Nechaev, J. Schroeter, and E. Fahrbach, (998), A dynamically consistent analysis of circulation and transports in the southwestern Weddell Sea. Annales Geophysicae, 6, 4-8. Zhang Y., W. Maslowski, A.J. Semtner, Impact of mesoscale ocean currents on sea ice in high-resolution Arctic ice and ocean simulations, J. Geophys. Res., 4, 8,49-8,49, 999. Received ; revised ; accepted. 6. Figure captions Figure. (a) The Kara Sea model domain and bathymetry (m); (b) The classic Kara Sea surface summer circulation pattern (adapted from Soviet Arctic, 97). Figure. Vectors of summer observed currents for 956-present for different layers.

43 4 Figure. Late summer circulation vectors, composited from ADCP and moored current meters using data from : (a) 5, (b) 5, (c) (), and (d) 5m depth (adopted from McClimans et al. []. Figure 4. Simulated climatological summer -5 m layer circulation in the Kara Sea adopted for this publication from HK99. Figure 5. Some characteristics of the observational data: (a, c) the summer climatological surface water T and S derived from observations with a km correlation radius; and (b,d) water T and S standard deviations for these fields, respectively. Distribution of sites with T-S observations in summer for the 9- period is shown by dots at (b). Figure 6. Summer wind stresses: (a) climatologic, (b) for AO positive and (c) for AO negative years. Figure 7. Reconstruction results. Optimized currents at (a) 4.5m, (b) 7.5m, (c) 5 m depth. Thick lines with numbers denote sections where water transports were calculated (see also Table ). Sea surface heights (cm) are shown in (d). Dashed lines in (d) depict boundaries between five regions of the Kara Sea shown by numbers,,, 4 and 5 in bold font. Figure 8. The optimized climatological T ( o C) averaged for layers (a) -m,(b) -45 m, (c), 45-7 m, and (d) 7- m. Figure 9. The optimized climatological S ( o C) averaged for layers of (a) -m,(b) -45 m, (c), 45-7 m, and (d) 7- m. Figure. The optimized climatological vertical velocity ( cm/s) at 7.5 m. Figure. August-September circulation patterns from NPS model results for layers of (a) - m,(b) -45 m, (c), 45-7 m, and (d) 7- m. Figure. August-September water T fields from NPS model results for layers of (a) -m,(b) -45 m, (c), 45-7 m, and (d) 7- m.

44 44 Figure. August-September water S from NPS model results for layers of (a) - m,(b) -45 m, (c), 45-7 m, and (d) 7- m. Figure 4. Differences between vectors of reconstructed currents and NPS model results for layers of (a) - m,(b) -45 m, (c), 45-7 m, and (d) 7- m. Figure 5. Temperature differences between the reconstructed Kara Sea fields and NPS model results for the layers m (a), 45 m (b), 45 7 m (c), and 7 m (d). T opt and T nps are the mean values of the reconstructed and NPS fields. The correlation coefficients (ρ) and relative errors (ξ) between the mean reconstructed fields and NPS fields are calculated for each layer. Figure 6. Salinity differences between the reconstructed Kara Sea fields and NPS model results for the layers m (a), 45 m (b), 45 7 m (c), and 7 m (d). S opt and S nps are the mean values of the reconstructed and NPS fields. The correlation coefficients (ρ) and relative errors (ξ) between the mean reconstructed fields and NPS fields are calculated for each layer. Figure 7. Typical T and S vertical profiles for 5 regions of the Kara Sea. Solid lines represent reconstructed data and dashed lines depict parameters from the NPS model results. Figure 8. Circulation patterns at 4.5 m level for two numerical experiments. Upper panel shows vectors representing results of experiment when water transport via the Kara Gate was reduced to. SV and corresponded to water transport used in HK99 model. Lower panel shows results of experiment when water transport via all boundaries corresponded to the control model run but water T and S were uniform in vertical and horizontal directions reproducing a pure barotropic circulation forcing regime.

45 45 Figure 9. Climatological circulation of the Kara Sea in the upper m layer from the NPS 9 km horizontal resolution model results. Figure. Figure. Hydrography and circulation of the Kara Sea reconstructed for periods of AO+ and AO. (a,c,d): Water T, S and circulation patterns for AO+; (b,d,f): Water T, S and circulation patterns for AO.

46

47 8 N 7 a) m m 7 8 N 4 cm/s E 5 7 b) m 5 m 7 4 cm/s 8 N E 5 7 c) 5 m 4 m 7 4 cm/s E

48

49