CC BY
93
МОДЕЛИРОВАНИЕ ГИДРОДИНАМИКИ ШЕЛЬФОВОЙ ЗОНЫ МОРЯ ЛАПТЕВЫХ
Вера Фофонова
Институт Полярных и Морских Исследований Альфреда Вегенера, отдел “Динамика климата” и “Экология шельфовых зон”; D-27570, Бремерхафен, Германия, аспирант, тел. +49(471)4831-1703, e-mail: [email protected]
Сергей Данилов
Институт Полярных и Морских Исследований Альфреда Вегенера, отдел “Динамика климата”, D-27570, Бремерхафен, Германия; к.ф.-м.н., зам. начальника отдела “Динамика климата”, тел. +49(471)4831-1703, e-mail: [email protected]
Алексей Андросов
Институт Полярных и Морских Исследований Альфреда Вегенера, отдел “Динамика климата”, D-27570, Бремерхафен, Германия, к.ф.-м.н., научный сотрудник, тел. +49(471)4831-2106, e-mail: Alexey. Andro sov@awi. de
Михаил Жуков
Южный Федеральный Университет, факультет Математики, Механики и Компьютерных наук, кафедра Вычислительной Математики и Математической Физики, 344090, Россия, Ростов-на-Дону, ул. Мильчакова 8а, профессор, заведующий кафедрой Вычислительной Математики и Математической Физики, тел. +7(863)2-975-114, e-mail: zhuk@math. sfedu.ru
Ольга Семёнова
Российский Г осударственный Гидрометеорологический Институт, Гидрологический факультет, 195196, Россия, Санкт-Петербург, Малоохтинский проспект, дом 98, к.ф.-м.н., научный сотрудник, тел. +7 (911) 213-26-57, e-mail: [email protected]
Пол Овердуин
Институт Полярных и Морских Исследований Альфреда Вегенера, отдел “Исследование пе-ригляциальных областей”, Германия, Потсдам, к.ф.-м.н., научный сотрудник, тел. +49(331)288-2113, e-mail: [email protected]
Карен Вилтшаир
Институт Полярных и Морских Исследований Альфреда Вегенера, отдел “Экология шельфовых зон”, D-27498, Германия, Хельголанд, профессор, начальник отдела “Экология шельфовых зон”, тел. +49(4725)819-3238, e-mail: [email protected]
Основная цель работы заключается в подготовке физической трехмерной модели шельфовой зоны моря Лаптевых для анализа климатических и биологических изменений, происходящих в регионе за последние 50 лет, а также для создания рабочей базы для моделирования экосистемы шельфовой зоны моря Лаптевых. Рассматривается динамика региона под влиянием атмосферной циркуляции, приливной динамики, температуры и объема стока реки Лена. Моделирование осуществляется на базе современного конечно-объемного пакета FVCOM c использованием неструктурированной сетки.
Ключевые слова: море Лаптевых, дельта реки Лена, FVCOM, шельфовая зона.
SIMULATIONS OF SHELF CIRCULATION DYNAMICS IN THE LAPTEV SEA
Vera Fofonova
Alfred Wegener Institute for Polar and Marine Research, Climate Dynamics and Coastal Ecology, Bussestrasse 24, D-27570 Bremerhaven (Building F-408), Germany, PhD student,
tel. +49(471)4831-1703, e-mail: [email protected]
Sergey Danilov
Alfred Wegener Institute for Polar and Marine Research, Climate Dynamics, Bussestrasse 24, D-27570 Bremerhaven (Building F-305), Germany, tel. +49(471)4831-1764, e-mail: [email protected],
Alexey Androsov
Alfred Wegener Institute for Polar and Marine Research, Climate Dynamics, Bussestrasse 24, D-27570 Bremerhaven (Building F-302), Germany, Dr., tel. +49(471)4831-2106, e-mail: Alexey. Andro sov@awi .de,
Michael Zhukov
Southern Federal University, Faculty Mathematics, Mechanics, and Computer Science, Department of Computational Mathematics and mathematical Physics, 344090, Russia, Rostov-on-Don, Milchakova 8a, head of the department, Prof., tel. +7(863)2-975-114, e-mail: zhuk@math. sfedu.ru
Olga Semenova
Russian State Hydrometeorological University, Department of Hydrology, 195196, Russia, Saint-Petersburg, Malookhtinsky prospect 98, Dr., tel. +7 (911) 213-26-57, e-mail: omakarieva@gmail. com
Paul Overduin
Alfred Wegener Institute for Polar and Marine Research, Periglacial Research, Telegrafenberg A43, D-14473 Potsdam (Building A43-116), Dr., tel. +49(331)288-2113, e-mail: [email protected]
Karen Wiltshire
Alfred Wegener Institute for Polar and Marine Research. Shelf Sea System Ecology | Coastal Ecology, Head of the Department, Kurpromenade, D-27498 Helgoland (Building C-51), Germany, head of the department, Prof., tel. +49(4725)819-3238, e-mail: [email protected]
The article describes the modeling processes of the shelf circulation dynamics in the Laptev Sea with focus on the Lena Delta region. We try to estimate the role of different factors such as atmospheric forcing, Lena runoff and tidal forcing on the dynamics of the region. An unstructured-grid Finite Volume Coastal Ocean Model (FVCOM) is used as a modeling tool.
Key words: Laptev Sea, Lena Delta, shelf circulation dynamics, FVCOM.
Introduction
The polar shelf zones are highly dynamic and diverse systems. They form a border between warm and fresh water continental drain and the cold currents of the northern seas. In the Arctic shelf region, multiple river deltas accumulate organic carbon. They host a unique and very diverse northern fauna and flora.
The Lena delta region of Laptev Sea acquires a special focus in this context as it can serve as an indicator of climate change. A large number of observations in this region suggest a strong climate and biological changes for the last fifty years (AARI, web source; Bauch et al., 2009; Holemann et al., 2011). Organized as a part of the In-
ternational Polar Year (2007 - 2008), joint study by the National Research Center of France, University of Alaska (USA) and Melnikov Permafrost Institute (Siberian Branch of Russian Academy of Sciences) has found that the Lena water temperature in the flood period had increased by 2 ° C compared to the values of 1950 (Costard et al., 2007).
Based on the results of observations in the Lena Delta region (Russian-German expeditions «Lena-2007», «Lena-2008») and Laptev Sea (Russian-German expedition «BARKALAV-2007/TRANSDRIFT-XII», «P0LYNIA-2008/TRANSDRIFT-XIII», «BARKALAV-2008/TRANSDRIFT-XIV») it was established, that in summer 2007 a positive anomaly of temperature and negative of salinity existed in the central and eastern part of the Laptev Sea in the mixed layer. The same structure of temperature and salinity field was observed in summer 2008, but the magnitudes of anomalies were smaller. A continuant temperature increase was also found for Atlantic water. Such a powerful invasion of warm Atlantic waters into the Arctic Basin was not previously observed for the entire period of instrumental observations since 1897 (AARI, web source).
The long-term analysis by (Polyakov et al., 2008) of the surface salinity change in the Arctic Basin and Arctic Seas, including the Laptev Sea, showed, that ice-related processes, freshwater runoff and its spreading under the influence of atmospheric processes play a key role in desalination and salinity changes of the upper layer over the past decades.
Johnson (2001) modeling studies showed that atmospheric forcing greatly determines the direction of freshwater transport in the Laptev Sea. The observations have confirmed that the variability of summer surface salinity in the Laptev Sea is mainly governed by local wind patterns associated with positive and negative phases of atmospheric vorticity over the adjacent Arctic Ocean (Dmitrenko et al., 2005). It should be emphasized that the winter water dynamics has very small impact to riverine water pathways in the summer (Dmitrenko et al., 2010). In the end of the winter season (March-April) the surface hydrography pattern is nearly the same as in September modified by thermodynamic ice formation.
The prevailing cyclonic regime (positive vorticity) in the summer leads to spread of the Laptev Sea riverine water to the east and hence a negative salinity anomaly east of the Lena Delta and farther to the East Siberian Sea, and a positive anomaly to the north of the Lena Delta. The prevailing of anticyclonic regime (negative vorticity) in the summer leads negative salinity anomalies northward from the Lena Delta due to freshwater advection toward the north, and a corresponding salinity increase eastward (Dmitrenko et al., 2005).
Despite the fact that the anomalies of atmospheric circulation have a significant impact on local salinity and temperature patterns, the surface salinity field over the shelf area east of the Lena Delta is less sensitive to the atmospheric circulation, with the standard deviation varying between 2 and 4 psu (Dmitrenko et al., 2010). Apparently, an as of yet undetermined interplay between the atmospheric circulation, river runoff, topography and ice related processes may explain those components of the salinity variance that are not well described by local wind patterns (Dmitrenko et al., 2005).
Driven by the need to explain and understand the processes in the Lena Delta, the main goal of our work is the modeling the shelf circulation dynamics in the Laptev Sea with focus on the Lena Delta region. Our more distant goal is the ecosystem modeling in the region, for which a model with consistent dynamics is a necessary step.
This note describes our results obtained while tuning the model so that it is able to simulate the climatic changes in the region, and studying with its help the variability of circulation under the action of atmospheric, tidal and run-off forcing. We examine the role of local wind pattern, tidal dynamics, structure and temperature of freshwater runoff, characteristics of heat fluxes in determining the features of the temperature and salinity distributions in the region. Additionally, we estimate the impact of improved bathymetry representation on the shelf in the vicinity of Lena Delta on tidal dynamics and temperature and salinity local patterns. The numerical simulations were based on Finite Volume Coastal Ocean Model (FVCOM) (Chen et al., 2006).
Model description
We use the Finite Volume Coastal Ocean Model (FVCOM) to carry out our simulations. It is developed for simulations of flooding/drying processes in estuaries and tidal-, buoyancy- and wind-driven circulation in the coastal region featuring with complex irregular geometry and steep bottom topography. FVCOM is a prognostic, unstructured-grid, finite-volume, free-surface, 3-D primitive equation coastal ocean circulation model (Chen et al., 2003; Chen et al., 2006).
Our model domain covers water depths up to 65 m (Fig. 1). The minimum water depth in the model is 0.5 m. We use high quality unstructured grid, which allows to take into account complexity of coastline, characteristics of the bathymetry and peculiarities of the problem. The grid was constructed using Persson’s algorithm (Persson, Strang, web source). Elements sizes are vary from 400m near the cost to 5 km on the open boundary. The model contains 6 vertical sigma-layers with 250000 nodes on each of them. FVCOM was run using spherical coordinates, including wet/dry treatment of domain, open boundary Temperature/Salinity time series nudging. For vertical and horizontal mixing simulation we use modified Mellor and Yamada level 2.5 and Smagorinsky turbulent closure schemes respectively. As advection scheme we use second order upwind scheme. The FVCOM version used in this study is numerically solved by a mode splitting method (Chen et al., 2009). The time step for external mode is 4.6 sec for barotropic case and 2.5 sec for baroclinic case, the ratio of internal mode time step to external mode time step is 10.
Input data
The bathymetry data were taken from GEBCO (The General Bathymetric Chart of the Oceans, (GEBCO, web source)). For coastline construction we compared GEBCO bathymetry data and NOAA (The National Oceanic and Atmospheric Administration) coastline data (NOAA, web source). To smooth the coastline we used cubic b-splines technique (Fig. 1).
7fis 120 125 130 135 140 145 °
Ion
Fig. 1. The selected domain, bathymetry data from GEBCO (resolution of GEBCO grid is 30 arc-second), m. In red is shown coastline based on NOAA data, in green - coastline, which was obtained from GEBCO bathymetry data. On the right picture in blue is shown constructed coastline (smoothed using cubic b-splines technique).
The wind magnitudes and direction, radiation fluxes were taken from the regional, non-hydrostatic model provided by the consortium for Small-scale Modeling (COSMO). The time resolution of COSMO forcing is 1 hour. The COSMO model with included thermodynamic sea-ice module provides a high quality atmospheric forcing allowing to take into account the presence of a thin layer of ice and can be applied for short-range simulations (Steppeler et al., 2003), (Schattler et al., 2008), (Schroder et al., 2011). We used results from COSMO simulations with 5 km resolution performed for the Laptev Sea area with and without assumption that the Laptev Sea polynyas are ice-free. Also for long simulations (more then 1 month) we were using NOAA and ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric forcing.
The temperature and salinity fields for initializing the model (Fig.2) and for daily nudging on the open boundary were taken from Arctic simulations by R. Gerdes and P. Rozman with focus on the Laptev Sea region (Rozman et al., 2011). This model provides data, which are in a good agreement with long-term mean (19202008) surface salinity distribution for winter season (February-April) described in (Dmitrenko et al., 2010) and salinity observation data for May 2008 (provided by M. Janout). Also, the provided salinity/temperature patterns are close to the pattern of seasonal cycle from summer 2007 to late winter/spring of 2008 shown in (Holemann et al., 2011). This sea-ice model provides daily data for temperature and salinity field in the region for 6 vertical layers.
a) b)
Fig. 2. Initial of salinity fields (9 of May, 2008 at 00:00:00), PSU, in
The input daily Lena runoff data, derived from observations, were provided by Hydrological Institute, St. Petersburg. The runoff temperature was set to either 0.5°C or 5°C, which present, respectively, the approximately lower and upper bounds for mean temperature in the river mouth during May based on ( Yang et al., 2002), (Yang et al., 2005), (Costard et al., 2007). For assessment of the influence of local bathymetry on temperature and salinity patterns we used additionally bathymetry measurement data in Lena Delta region. The observation bathymetry data at 27686 locations (the average distance between points is about 800m) in close proximity to Lena Delta were provided by Paul Overduin (Alfred Wegener Institute, Potsdam).
The model is forced by tidal elevation prescribed at the open boundary from different models: TPX06.2, TPXO7.1 and AOTIM with Doodson correction. We paid special attention to tuning the conditions at open boundaries so as to obtain best agreement with the observational data. The model simulates the four most energetic tidal constituents: M2,s2,O1 ,K (Sofina, 2008), (Lenn et al., 2011), (Kowalik, 1993; Dmitrenko et al., 2012).
Tidal dynamics analysis
Observations of tidal currents over the Laptev Sea continental are rare and fragmentary. The starting point of the analysis were tide gauges data provided by Kowalik and Proshutinsky (KP) (can be downloaded from http://www.ims.uaf.edu/tide/). Based on observation data near the open boundary and features of different model we designed new open boundary condition. To specify the correct open boundary condition is one of the central problem of our modeling due to small depths in the area under consideration. We should emphasize that for current domain the amplitudes and phases on open boundary taken from any models near the cost (depth<10-15m) should be corrected. The horizontal resolution of TPX06.2 and TPXO7.1 and associated inaccuracies in bathymetry data limits its to resolve the tidal features in the coastal zone. In addition to the 2-D character, the linear assumption used in AOTIM, makes this model incapable of resolving residual currents and tide-induced water transports in coastal regions where the interaction of tidal currents with topography is highly nonl inear (Chen et al., 2009)
The AOTIM (The Arctic Ocean Tidal Inverse Model) was created based on (Egbert et al., 1994) data assimilation scheme by computing the inverse solution with all available tidal gauge data (Padman and Erofeeva, 2004). As a ‘prior’ solution was used the Arctic Ocean Dynamics-based Tide Model(the numerical solution to the shallow water equations). This pan-Arctic 2-D linear model is highly resolved ( 5km regular grid), simulates 4 most energetic tides constituents (M2, S2, O1, and K1). Assimilated data consists not only coastal and benthic tide gauges (between 250 and 168 310 gauges per tidal constituent) but also available satellite altimeters (Padman and Erofeeva, 2004). Model bathymetry is based on the International Bathymetric Chart of the Arctic Ocean (Jakobsson et al., 2008). AOTIM5 does not include the effects of sea ice presence.
The TPXO7.1 and TPXO6.2 is a global inverse tide model developed by Gary Egbert and Lana Erofeeva at Oregon State University. The resolution of this models is 1/4o x 1/4o. TPXO7.1 and TPXO 6.2 assimilates TOPEX/Poseidon (T/P) and TOPEX Tandem satellite radar altimetry (available for the ice-free ocean between +/-66o latitude), and in situ tide gauge data in the Antarctic and the Arctic. TPXO7.1 is one of the most accurate global tidal solutions.
(Chen et al., 2009) presented high resolution unstructed grid finite volume Arctic Ocean model (AO-FVCOM) in application for tidal studies. A spherical coordinate version of the instructed grid 3-D FVCOM was applied to the Arctic Ocean for tides simulation. The size of elements varies from 1 km in the near coastal areas to 15 km in the deep ocean. This model resolves accurately the irregular geometry of bays, inlets and islands in the Arctic coastal zone and produces a detailed description of the topographically trapped diurnal tidal waves. However the largest amplitude and phase differences between modeled and observed diurnal tides (tide gauge of coastal observations) were caused by the model errors along the Russian coast. The designed open boundary condition provides better agreement with observation data compared to the case when the condition directly derived from AOTIM, TPXO6.2 or TPXO7.1 is used. The results from the tidal simulations for East Siberian shelf provided by Sof’ina have been also included in the analysis. The table below shows the results of comparison for M2 constituent.
AO-FVCOM with stations coord. corrections (R<40km) East Siberian shelf model AOTIM5 TPX07.1 TPXO6.2 Simulation based on AOTIM5 Simulation based on TPXO7.1 Simulation based on designed open boundary conditions Simulation based on designed open boundary conditions with stations coord. corrections (R<20km)
Error ■102 30.94 41.07 45.74 36.86 50.78 33.09 19.61 15.24 3.61
Error = (Yi + gssWN _ 2 . cos(Sph(i) - oph (0) ■ I
i=1
Where , - simulated amplitude and phase respectively, - ob-
served amplitude and phase respectively, N = 1 0 - number of stations.
Fig.3. The results of simulation with desined open boundary condition for amplitude and phase for M2 constituent: a) Amplitude, m b) Phase, deg
Temperature and salinity patterns variability
We compare salinity and temperature fields in mixed layer under the ice in simulations with and without atmospheric forcing, tidal dynamics, with different temperatures of freshwater and using different techniques for freshwater input. We present here only a schematic overview of obtained results.
The salinity fields, freshwater runoff input from the boundary, calculation period from 9 of May 2008 to 31 of May 2008, PSU: a) atmospheric forcing from COSMO without open polynya assumption, b) no atmospheric forcing, c) atmospheric forcing is the same as in a), but no tidal dynamics.
The surface salinity to the north and west from Lena Delta is mainly determined by the local wind pattern. Note that the locking westward winds drive the freshwater distribution to the east in the western part and to the north in the northern part in the vicinity of Lena Delta, the locking eastward winds drive flows to mouth due to the Coriolis force.
We should emphasize the importance of the local wind pattern for the explanation of local salinity pattern in the vicinity zone. The NOAA wind with 1 ° resolution cannot provide adequate picture due to unstable and heterogeneous wind pattern in the region of interest with smaller scale features.
Tidal dynamics also have an important influence on the local salinity and temperature distributions. The insensitivity of salinity and temperature in the mixed layer to atmospheric circulation over the shelf east of the Lena Delta is explained, according to the model, by buoyancy forcing and the structure of tidal flows in the region. These factors play a main role in this area due to nearby presence of the large freshwater ducts. Also, tides play a significant role in water mass modification through vertical mixing of seawater properties in the mixed layer.
The dominating of southward and westward winds (cyclonic atmospheric circulation) in late spring/summer season strengthens the freshening effect over the shelf east of the Lena Delta. It leads the appearance of big temperature and salinity anomalies compared to the climatic mean (Fig. 7).
Fig.7. The salinity field in practical scale. Simulations cover summer months with prevailing cyclonic regime
We have found that the variability of runoff temperature influences only little the freshwater plume direction. However, the structure of heat fluxes strongly affects the variability of temperature pattern in the entire mixed layer.
We have also observed that because of weak winds in the region, the detailed representation of freshwater channels and distribution of total runoff volume over these channels becomes particularly important and influences detail of simulated distributions of temperature and salinity.
REFERENCES
1. "Arctic and Antarctic Research Institute" Federal State Budgetary Institution, The current state and variability of the natural environment of the Laptev Sea region as a reflection of global climate processes. http://portalrp.ru/atmosphere-and-hvdrosphere-monitoring-2007-2010-projects/tech-02-515-11-5080 Last accessed: 30.07.2012 (in Russian).
2. Bauch, D., Dmitrenko, I., Kirillov, S., Wegner, C., Holemann, J., Pivovarov, S., Timokhov, L., Kassens, H., 2009. Eurasian Arctic shelf hydrography: Exchange and residence time of southern Laptev Sea waters. Continental Shelf Research 29 (15), 1815-1820, doi:10.1016/j.csr.2009.06.009.
3. Chen, C., Beardsley, R.C., Cowles, G., 2006. An Unstructured Grid, Finite-Volume Coastal Ocean Model. FVCOM User Manual, second ed. SMAST/UMASSD-06-0602.
4. Chen, C., Liu, H., Beardsley, R. C., 2003. An Unstructured Grid, Finite-Volume, Three-
Dimensional, Primitive Equations Ocean Model: Application to Coastal Ocean and Estuaries. Journal of Atmospheric and Oceanic Technology 20(1), 159, doi: 10.1175/1520-
0426(2003)020<0159:AUGFVT>2.0.TO;2
5. Chen C., Gao G.,Qi J., Proshutinsky A., Beardsley R.,Kowalik Z.,Lin H., Cowles G., 2009. A new high-resolution unstructed grid finite volume Arctic Ocean model (AO-FVCOM): An application for tidal studies J. Geophys. Res., Vol. 114, C08017, doi:10.1029/2008JC004941.
6. Costard, F., Gautier, E., Brunstein, D., Hammadi, J., Fedorov, A., Yang, D., Dupeyrat, L., 2007. Impact of the global warming on the fluvial thermal erosion over the Lena River in Central Siberia. Geophys. Res. Lett. 34, L14501, doi:10.1029/2007GL030212.
7. Dmitrenko, I., Kirillov, S., Eicken, H., Markova, N., 2005. Wind-driven summer surface hydrography of the eastern Siberian shelf. Geophysical ReSearch Letters 32, L14613.
8. Dmitrenko, I. A., Kirillov, S. A., Krumpen, T., Makhotin, M., Abrahamsen, E. P., Willmes, S., Bloshkina, E., Holemann, J. A., Kassens, H., Wegner, C., 2010. Wind-driven diversion of summer river runoff preconditions the Laptev Sea coastal polynya hydrography: Evidence from summer-to-winter hydrographic records of 2007-2009. Continental Shelf Research 30(15), 1656-1664.
9. Dmitrenko, I. A., Kirillov, S. A., Bloshkina, E., Lenn, Y.D., 2012. Tide-induced vertical mixed in the Laptev Sea coastal polynya, J. Geophys. Res. 117, doi:10.1029/2011JC006966.
10. Ebner, L., Schroder, D., Heinemann, G., 2011. Impact of Laptev Sea flaw polynyas on the atmospheric boundary layer and ice production using idealized mesoscale simulations. Polar Research 30, 7210, doi:10.3402/polar.v30i0.7210.
11. Ernsdorf, T., Schroder, D., Adams, S., Heinemann, G., Timmermann, R., Danilov, S., 2011. Impact of atmospheric forcing data on simulations of the Laptev Sea polynya dynamics using the Sea-ice ocean model FESOM. J. Geophys. Res., 116, C12038, doi:10.1029/2010JC006725.
12. GEBCO. http://www.gebco.net/data_and_products/gridded_bathymetry_data/ Last accessed: 30.07.2012
13. Holemann, J., Kirillov, S., Klagge, T., Novikhin, A., Kassens, H., Timokhov, L., 2011. Near-bottom water warming in the Laptev Sea in response to atmospheric and sea ice conditions in 2007. Polar Research 30, doi: 10.3402/polar.v30i0.6425
14. Jakobsson , M., Macnab, R., Mayer, L., Anderson, R., Edwards, M.,Hatzky, J., Schenke, H.W., Johnson, P., 2008. An improved bathymetric portrayal of the Arctic Ocean: Implications for ocean modeling and geological, geophysical and oceanographic analyses. Geophys. Res. Lett. 35, DOI: 10.1029/2008GL033520
15. Kowalik, Z., Proshutinsky, A. Y., 1993. Diurnal Tides in the Arctic Ocean, J. Geophys. Res. 98(C9), 16,449-16,468, doi:10.1029/93JC01363.
16. Kowalik, Z., Proshutinsky, A. Y., 1994. The Arctic Ocean tides, in: Johannessen, O. M., Muench, R. D., Overland, J. E., The Polar Oceans and Their Role in Shaping the Global Environment, Geophys. Monogr. Ser. 85, 137-158, AGU, Washington, D. C., doi:10.1029/GM085p0137.
17. Kowalik, Z., and A. Proshutinsky, 1995. Topographic enhancement of tidal motion in the western Barents Sea. J. Geophys. Res., 100(C2), 2613-2637, doi:10.1029/94JC02838.
18. Lenn, Y. D., Rippeth, T. P., Old, C. P., Bacon, S., Polyakov, I., Ivanov, V., Holemann, J., 2011. Intermittent Intense Turbulent Mixed under Ice in the Laptev Sea Continental Shelf. Journal of Physical Oceanography 41 (3), 531-547, doi:10.1175/2010JPO4425.1.
19. NOAA. http://www.ngdc.noaa.gov/mgg/shorelines/shorelines.html Last accessed: 30.07.2012
20. Padman L. and Erofeeva S., 2004. A Barotropic Inverse Tidal Model for the Arctic Ocean. J. Geophys. Res., Vol. 31, DOI: 10.1029/2003GL019003
21. Persson, P.O., Strang, G., A simple mesh generator in MATLAB. http://persson.berkeley.edu/distmesh/persson04mesh.pdf Last accessed: 30.07.2012
22. Proshutinsky, A., Timmermans, M.-L., Ashik, I., Beszczynska-Moeller, A., Carmack, E.,
Eert, J., Frolov, I., Itoh, M., Kikuchi, T., Krishfield, R., McLaughlin, F., Rabe, B., Schauer, U., Shimada, K., Sokolov, V., Steele, M. , Toole, J., Williams, W., Woodgate, R., Zimmermann, S., 2011. Arctic Report Card: update for 2011,
Ocean,http://www.arctic.noaa.gov/reportcard/ocean.html Last accessed: 30.07.2012
23. Rozman, P., Holemann, J. A., Krumpen, T., Gerdes, R., Koberle, C., Lavergne, T., Adams, S., Girard-Ardhuin, F., 2011. Validating satellite derived and modelled Sea-ice drift in the Laptev Sea with in situ measurements from the winter of 2007/08. Polar Research 30, 7218, doi:10.3402/polar.v30i0.7218
24. Schattler, U., Doms, G., Schraff, C., 2012. A Description of the Nonhydrostatic Regional COSMO-Model, Part VII: User’s Guide. Printed at Deutscher Wetterdienst, Germany.
25. Schroder, D., Heinemann, G., Willmes, S., 2011. The impact of a thermodynamic Sea-ice module in the COSMO numerical weather prediction model on simulations for the Laptev Sea, Siberian Arctic. Polar Res. 30, 6334, doi:10.3402/polar.v30i0.6334.
26. Sofina, E.V., 2008. The simulation of tidal ice drift and ice-related changes in tidal dynamics and energy in Siberian continental shelf. Dissertation, Russian State Hydrometeorological University (in Russian).
27. Steppeler, J., Doms, G., Schattler, U., Bitzer, H.W., Cassmann, A., Damrath, U., Gregoric, G., 2003. Meso-gamma scale forecasts using the nonhydrostatic model LM.s.l. Meteorol. Atmos. Phys. 82, 75-96, doi:10.1007/s00703-001-0592-9.
28. Voinov, G., 2002. Tide and tidal streams, in Polar Seas Oceanography: An Integrated
Case Study of the Kara Sea. Springer, 147 - 214, New York.
29. Yang, D., Liu, B., Ye, B., 2005. Stream temperature changes over Lena River Basin in Si-
beria. Geophys. Res. Lett. 32, L05401, doi:10.1029/2004GL021568.
30. Yang, D., Kane, D. L., Hinzman, L., Zhang, X., Zhang, T., Ye, H., 2002. Siberian Lena
River hydrologic regime and recent change. J. Geophys. Res. 107(D23), 4694, doi:10.1029/2002JD002542.
© V. Fofonova, S. Danilov, A. Androsov, M. Zhukov, O. Semenova, P. Overduin, K. Wiltshire, 2013
