Investigations of Sea Ice Dynamics in the Baltic Sea

2001 ◽  
pp. 387-394 ◽  
Author(s):  
Matti Leppäranta
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
The Baltic Sea ◽  
Ice Dynamics ◽  
Sea Ice Dynamics ◽  
The Baltic

scholarly journals Numerical modelling of thermodynamics and dynamics of sea ice in the Baltic Sea

Ocean Science ◽  
2011 ◽  
Vol 7 (2) ◽  
pp. 257-276 ◽  
Author(s):  
A. Herman ◽  
J. Jedrasik ◽  
M. Kowalewski
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Air Temperature ◽  
Ice Cover ◽  
Surface Fluxes ◽  
Rate Of Change ◽  
The Baltic Sea ◽  
Ice Dynamics ◽  
Ice Concentration ◽  
The Baltic

Abstract. In this paper, a numerical dynamic-thermo-dynamic sea-ice model for the Baltic Sea is used to analyze the variability of ice conditions in three winter seasons. The modelling results are validated with station (water temperature) and satellite data (ice concentration) as well as by qualitative comparisons with the Swedish Meteorological and Hydrological Institute ice charts. Analysis of the results addresses two major questions. One concerns effects of meteorological forcing on the spatio-temporal distribution of ice concentration in the Baltic. Patterns of correlations between air temperature, wind speed, and ice-covered area are demonstrated to be different in larger, more open sub-basins (e.g., the Bothnian Sea) than in the smaller ones (e.g., the Bothnian Bay). Whereas the correlations with the air temperature are positive in both cases, the influence of wind is pronounced only in large basins, leading to increase/decrease of areas with small/large ice concentrations, respectively. The other question concerns the role of ice dynamics in the evolution of the ice cover. By means of simulations with the dynamic model turned on and off, the ice dynamics is shown to play a crucial role in interactions between the ice and the upper layers of the water column, especially during periods with highly varying wind speeds and directions. In particular, due to the fragmentation of the ice cover and the modified surface fluxes, the ice dynamics influences the rate of change of the total ice volume, in some cases by as much as 1 km3 per day. As opposed to most other numerical studies on the sea-ice in the Baltic Sea, this work concentrates on the short-term variability of the ice cover and its response to the synoptic-scale forcing.

Basin-scale granular ice dynamics modelling

2020 ◽  
Author(s):  
Jonni Lehtiranta
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
The Baltic Sea ◽  
Ice Dynamics ◽  
Length Scales ◽  
The North ◽  
Acceleration Techniques ◽  
Basin Scale ◽  
Ice Floes ◽  
The Baltic

<p>Current operational sea ice models solve primitive equations on a grid and treat sea ice as a continuum with smoothly varying properties. This is the same method that is used in ocean models. The continuum assumption is unrealistic for sea ice which consists of separate rigid ice floes. The assumption works best for length scales much larger than typical floe size, and worst for very small length scales.</p><p>Winter shipping in finnish ports depends on timely sea ice information on the Baltic Sea. Due to climate change, the yearly ice covered area and thermodynamic ice growth are decreasing. However, sea ice is also becoming more mobile and dynamic, especially in the Bay of Bothnia which lies in the north end of the Baltic Sea.</p><p>A particle-based granular approach is more realistic in the length scales of individual ice floes. Such models have been developed (eg. by Mark Hopkins and Agnieszka Herman) and used successfully in limited scales, such as fjords. For larger horizontal scales, they have been computationally too expensive. Using modern GPU acceleration techniques, discrete element simulation of sea ice is becoming possible in the scale required for Baltic sea basins.</p><p>This work presents an ongoing project for building a granular sea ice model for forecasting ice dynamics. This includes ice movement and deformation and describes ridge and lead formation and similar phenomena. Existing accelerated solvers are examined, and the most suitable is adapted for Baltic sea ice and applied for the Bay of Bothnia.</p>

scholarly journals Numerical modelling of thermodynamics and dynamics of sea ice in the Baltic Sea

2011 ◽  
Vol 8 (1) ◽  
pp. 113-157
Author(s):  
A. Herman ◽  
J. Jedrasik ◽  
M. Kowalewski
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Air Temperature ◽  
Ice Cover ◽  
Surface Fluxes ◽  
Rate Of Change ◽  
The Baltic Sea ◽  
Ice Dynamics ◽  
Ice Concentration ◽  
The Baltic

Abstract. In this paper, a numerical dynamic-thermodynamic sea-ice model for the Baltic Sea is used to analyze the variability of ice conditions in three winter seasons. The modelling results are validated with station (water temperature) and satellite data (ice concentration) as well as by qualitative comparisons with the Swedish Meteorological and Hydrological Institute ice charts. Analysis of the results addresses two major questions. One concerns effects of meteorological forcing on the spatio-temporal distribution of ice concentration in the Baltic. Patterns of correlations between air temperature, wind speed, and ice-covered area are demonstrated to be different in larger, more open sub-basins (e.g., the Bothnian Sea) than in the smaller ones (e.g., the Bothnian Bay). Whereas the correlations with the air temperature are positive in both cases, the influence of wind is pronounced only in large basins, leading to increase/decrease of areas with small/large ice concentrations, respectively. The other question concerns the role of ice dynamics in the evolution of the ice cover. By means of simulations with the dynamic model turned on and off, the ice dynamics is shown to play a crucial role in interactions between the ice and the upper layers of the water column, especially during periods with highly varying wind speeds and directions. In particular, due to the fragmentation of the ice cover and the modified surface fluxes, the ice dynamics influences the rate of change of the total ice volume, in some cases by as much as 1 km3 per day. As opposed to most other numerical studies on the sea-ice in the Baltic Sea, this work concentrates on the short-term variability of the ice cover and its response to the synoptic-scale forcing.

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 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.

scholarly journals TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

2020 ◽  
pp. 1-20
Author(s):  
Marjan Marbouti ◽  
Oleg Antropov ◽  
Jaan Praks ◽  
Patrick B. Eriksson ◽  
Vahid Arabzadeh ◽  
...  
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
The Baltic Sea ◽  
The Baltic

Comparison of the main sea ice parameters received from satellite measurements and based on numerical simulation for the Baltic Sea region

2020 ◽  
Author(s):  
Jaromir Jakacki ◽  
Maciej Muzyka ◽  
Marta Konik ◽  
Anna Przyborska ◽  
Jan Andrzejewski
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Baltic Sea ◽  
Melting Time ◽  
Ice Thickness ◽  
Satellite Measurements ◽  
The Baltic Sea ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Baltic

<p>During the last decades remote sensing observations as well as modelling tools has been developed and become key elements of oceanographic research. One of the main advantages of both tools is a possibility of measuring large-scale areas.</p><p>The remote sensing measurements deliver only snapshots of the ice situation with no information about backgroundconditions. Moreover, providing picture of the whole area requires sometimes combining various datasets that increases uncertainties.  Modelling simulations provide full history of external conditions, but they also introduce errors that are the result of parameterizations. Also, an inaccuracy provided by forcing fields at the top and bottom boundaries are accumulated in the model.</p><p>In this work sea ice parameters such as sea ice concentration, thickness and volume obtained from both – satellite measurements and modelling has been compared. Numerical simulations were performed using standalone Community Ice Code (CICE) model (v. 6.0). It is a descendant of the basin scale dynamic-thermodynamic and thickness distribution sea ice model. The model is well known by scientific community and was widely used in a global as well as regional research, even operationally. The satellite derived ice thickness products were based on the C band HH-polarized SAR measurements originating from the satellites Sentinel-1 and RADARSAT-2. The sea ice concentration maps contain also visual and infrared information from MODIS and NOAA.</p><p>The ice extent, thickness and volume were compared in several regions within the Baltic Sea.  Seasonal changes were analyzed with a particular attention to ice formation and melting time. The sea ice extent datasets were compatible. Inconsistencies were observed for the sea ice thickness delivered by satellite measurements, especially during the ice melt. The work presents direction for ignoring satellite data with an error related to ice melting that allows for excluding erroneous satellite maps and obtain reliable intercalibration.</p><p> </p><p>This work was partly funded by Polish National Science Centre, project number 2017/25/B/ST10/00159</p>

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