scholarly journals Bacteria in sea ice and underlying brackish water at 54°26'5"N (Baltic Sea, Kiel Bight)

1997 ◽  
Vol 158 ◽  
pp. 23-40 ◽  
Author(s):  
T Mock ◽  
KM Meiners ◽  
HC Giesenhagen
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Brackish Water ◽  
Kiel Bight

Discovery of bacterial polyhydroxyalkanoate synthase (PhaC)-encoding genes from seasonal Baltic Sea ice and cold estuarine waters

Extremophiles ◽  
2014 ◽  
Vol 19 (1) ◽  
pp. 197-206 ◽  
Author(s):  
Katariina Pärnänen ◽  
Antti Karkman ◽  
Marko Virta ◽  
Eeva Eronen-Rasimus ◽  
Hermanni Kaartokallio
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Estuarine Waters ◽  
Polyhydroxyalkanoate Synthase ◽  
Encoding Genes

Application of the Regional Ocean Modelling System (ROMS) for Baltic Sea area

2021 ◽  
Author(s):  
Maciej Muzyka ◽  
Jaromir Jakacki ◽  
Anna Przyborska
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Shortwave Radiation ◽  
Ocean Modelling ◽  
Efficient System ◽  
Curvilinear Grid ◽  
The Baltic ◽  
Modelling System

<p>The Regional Ocean Modelling System has been begun to implement for region of Baltic Sea.  A preliminary curvilinear grid with horizontal resolution ca. 2.3 km has been prepared based on the grid, which was used in previous application in our research group (in Parallel Ocean Program and in standalone version of Los Alamos Sea Ice Model - CICE).  Currently the grid has 30 sigma layers, but the final number of levels will be adjusted accordingly.</p><p>So far we’ve successfully compiled the model on our machine, run test cases and created Baltic Sea case, which is working with mentioned Baltic grid. The following parameters: air pressure, humidity, surface temperature, long and shortwave radiation, precipitation and wind components are used as an atmospheric forcing. The data arrive from our operational atmospheric model - Weather Research and Forecasting Model (WRF).</p><p>Our main goal is to create efficient system for hindcast and forecast simulations of Baltic Sea together with sea ice component by coupling ROMS with CICE. The reason for choosing these two models is an active community that takes care about model’s developments and updates. Authors also intend to work more closely with the CICE model to improve its agreement with satellite measurements in the Baltic region.<br><br>Calculations were carried out at the Academic Computer Centre in Gdańsk.</p>

scholarly journals Operational SAR-based sea ice drift monitoring over the Baltic Sea

Ocean Science ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 473-483 ◽  
Author(s):  
J. Karvonen
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Vector Fields ◽  
Phase Correlation ◽  
The Baltic Sea ◽  
Sar Images ◽  
Envisat Asar ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
The Baltic

Abstract. An algorithm for computing ice drift from pairs of synthetic aperture radar (SAR) images covering a common area has been developed at FMI. The algorithm has been developed based on the C-band SAR data over the Baltic Sea. It is based on phase correlation in two scales (coarse and fine) with some additional constraints. The algorithm has been running operationally in the Baltic Sea from the beginning of 2011, using Radarsat-1 ScanSAR wide mode and Envisat ASAR wide swath mode data. The resulting ice drift fields are publicly available as part of the MyOcean EC project. The SAR-based ice drift vectors have been compared to the drift vectors from drifter buoys in the Baltic Sea during the first operational season, and also these validation results are shown in this paper. Also some navigationally useful sea ice quantities, which can be derived from ice drift vector fields, are presented.

scholarly journals Spatial variation of biogeochemical properties of landfast sea ice in the Gulf of Bothnia, Baltic Sea

2006 ◽  
Vol 44 ◽  
pp. 80-87 ◽  
Author(s):  
M. Steffens ◽  
M.A. Granskog ◽  
H. Kaartokallio ◽  
H. Kuosa ◽  
K. Luodekari ◽  
...  
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Snow Depth ◽  
Large Scale ◽  
Spatial Scales ◽  
Thick Layer ◽  
Ice Cores ◽  
Ice Thickness ◽  
Gulf Of Bothnia ◽  
Landfast Sea Ice

AbstractHorizontal variation of landfast sea-ice properties was studied in the Gulf of Bothnia, Baltic Sea, during March 2004. In order to estimate their variability among and within different spatial levels, 72 ice cores were sampled on five spatial scales (with spacings of 10 cm, 2.5 m, 25 m, 250m and 2.5 km) using a hierarchical sampling design. Entire cores were melted, and bulk-ice salinity, concentrations of chlorophylla(Chla), phaeophytin (Phaeo), dissolved nitrate plus nitrite (DIN) as well as dissolved organic carbon (DOC) and nitrogen (DON) were determined. All sampling sites were covered by a 5.5–23 cm thick layer of snow. Ice thicknesses of cores varied from 26 to 58 cm, with bulk-ice salinities ranging between 0.2 and 0.7 as is typical for Baltic Sea ice. Observed values for Chla(range: 0.8–6.0 mg ChlaL–1; median: 2.9 mg ChlaL–1) and DOC (range: 37–397 μM; median: 95 μM) were comparable to values reported by previous sea-ice studies from the Baltic Sea. Analysis of variance among different spatial levels revealed significant differences on the 2.5km scale for ice thickness, DOC and Phaeo (with the latter two being positively correlated with ice thickness). For salinity and Chla, the 250 m scale was found to be the largest scale where significant differences could be detected, while snow depth only varied significantly on the 25 m scale. Variability on the 2.5 m scale contributed significantly to the total variation for ice thickness, salinity, Chlaand DIN. In the case of DON, none of the investigated levels exhibited variation that was significantly different from the considerable amount of variation found between replicate cores. Results from a principal component analysis suggest that ice thickness is one of the main elements structuring the investigated ice habitat on a large scale, while snow depth, nutrients and salinity seem to be of secondary importance.

scholarly journals Sea Ice Concentration Analyses for the Baltic Sea and Their Impact on Numerical Weather Prediction

2006 ◽  
Vol 45 (7) ◽  
pp. 982-994 ◽  
Author(s):  
Matthias Drusch
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Baltic Sea ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
The Baltic Sea ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Concentration ◽  
The Baltic

Abstract Sea ice concentration plays a fundamental role in the exchange of water and energy between the ocean and the atmosphere. Global real-time datasets of sea ice concentration are based on satellite observations, which do not necessarily resolve small-scale patterns or coastal features. In this study, the global National Centers for Environmental Prediction (NCEP) 0.5° sea ice concentration dataset is compared with a regional high-resolution analysis for the Baltic Sea produced 2 times per week by the Swedish Meteorological and Hydrological Institute (SMHI). In general, the NCEP dataset exhibits less spatial and temporal variability during the winter of 2003/04. Because of the coarse resolution of the NCEP dataset, ice extent is generally larger than in the SMHI analysis. Mean sea ice concentrations derived from both datasets are in reasonable agreement during the ice-growing and ice-melting periods in January and April, respectively. For February and March, during which the sea ice extent is largest, mean sea ice concentrations are lower in the NCEP dataset relative to the SMHI product. Ten-day weather forecasts based on the NCEP sea ice concentrations and the SMHI dataset have been performed, and they were compared on the local, regional, and continental scales. Turbulent surface fluxes have been analyzed based on 24-h forecasts. The differences in sea ice extent during the ice-growing period in January cause mean differences of up to 30 W m−2 for sensible heat flux and 20 W m−2 for latent heat flux in parts of the Gulf of Bothnia and the Gulf of Finland. The comparison between spatially aggregated fluxes yields differences of up to 36 and 20 W m−2 for sensible and latent heat flux, respectively. The differences in turbulent fluxes result in different planetary boundary height and structure. Even the forecast cloud cover changes by up to 40% locally.

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