scholarly journals Numerical Simulations of Sea Ice Conditions in the Baltic Sea for 2010–2016 Winters Using the 3D CEMBS Model

2018 ◽  
Vol 25 (3) ◽  
pp. 35-43 ◽  
Author(s):  
Maciej Janecki ◽  
Artur Nowicki ◽  
Alicja Kańska ◽  
Maria Golenko ◽  
Lidia Dzierzbicka-Głowacka
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Ecosystem Model ◽  
Ice Thickness ◽  
The Baltic Sea ◽  
Current State ◽  
Ice Concentration ◽  
Ice Conditions ◽  
The Baltic ◽  
Modelling Approach

Abstract Sea ice conditions in the Baltic Sea during six latest winters – 2010/2011 to 2015/2016 are analysed using coupled ice–ocean numerical model 3D CEMBS (3D Coupled Ecosystem Model of the Baltic Sea). Simulation results are compared with observations from monitoring stations, ice charts and satellite data. High correlation between model results and observations has been confirmed both in terms of spatial and temporal approach. The analysed period has a high interannual variability of ice extent, the number of ice days and ice thickness. Increasing number of relatively mild winters in the Northern Europe directly associated with climate change results in reduced ice concentration in the Baltic Sea. In this perspective, the implementation and development of the sea ice modelling approach (in addition to standard monitoring techniques) is critical to assess current state of the Baltic Sea environment and predict possible climate related changes in the ecosystem and their influence for human marine–related activities, such as fishery or transportation.

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>

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 Ice and AIS: ship speed data and sea ice forecasts in the Baltic Sea

2014 ◽  
Vol 8 (6) ◽  
pp. 2409-2418 ◽  
Author(s):  
U. Löptien ◽  
L. Axell
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Automatic Identification ◽  
Identification System ◽  
Random Component ◽  
The Baltic Sea ◽  
Mixed Effect ◽  
Ice Conditions ◽  
The Baltic ◽  
Ship Speed

Abstract. The Baltic Sea is a seasonally ice-covered marginal sea located in a densely populated area in northern Europe. Severe sea ice conditions have the potential to hinder the intense ship traffic considerably. Thus, sea ice fore- and nowcasts are regularly provided by the national weather services. Typically, the forecast comprises several ice properties that are distributed as prognostic variables, but their actual usefulness is difficult to measure, and the ship captains must determine their relative importance and relevance for optimal ship speed and safety ad hoc. The present study provides a more objective approach by comparing the ship speeds, obtained by the automatic identification system (AIS), with the respective forecasted ice conditions. We find that, despite an unavoidable random component, this information is useful to constrain and rate fore- and nowcasts. More precisely, 62–67% of ship speed variations can be explained by the forecasted ice properties when fitting a mixed-effect model. This statistical fit is based on a test region in the Bothnian Sea during the severe winter 2011 and employs 15 to 25 min averages of ship speed.

scholarly journals Ice and AIS: ship speed data and sea ice forecasts in the Baltic Sea

2014 ◽  
Vol 8 (4) ◽  
pp. 3811-3828
Author(s):  
U. Löptien ◽  
L. Axell
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Automatic Identification ◽  
Identification System ◽  
Random Component ◽  
The Baltic Sea ◽  
Mixed Effect ◽  
Ice Conditions ◽  
The Baltic ◽  
Ship Speed

Abstract. The Baltic Sea is a seasonally ice covered marginal sea located in a densely populated area in northern Europe. Severe sea ice conditions have the potential to hinder the intense ship traffic considerably. Thus, sea ice fore- and nowcasts are regularly provided by the national weather services. Typically, several ice properties are allocated, but their actual usefulness is difficult to measure and the ship captains must determine their relative importance and relevance for optimal ship speed and safety ad hoc. The present study provides a more objective approach by comparing the ship speeds, obtained by the Automatic Identification System (AIS), with the respective forecasted ice conditions. We find that, despite an unavoidable random component, this information is useful to constrain and rate fore- and nowcasts. More precisely, 62–67% of ship speed variations can be explained by the forecasted ice properties when fitting a mixed effect model. This statistical fit is based on a test region in the Bothnian Bay during the severe winter 2011 and employes 15 to 25 min averages of ship speed.

scholarly journals Sea-ice evaluation of NEMO-Nordic 1.0: a NEMO–LIM3.6-based ocean–sea-ice model setup for the North Sea and Baltic Sea

2017 ◽  
Vol 10 (8) ◽  
pp. 3105-3123 ◽  
Author(s):  
Per Pemberton ◽  
Ulrike Löptien ◽  
Robinson Hordoir ◽  
Anders Höglund ◽  
Semjon Schimanke ◽  
...  
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
North Sea ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
The Baltic Sea ◽  
Sea Ice Extent ◽  
The North ◽  
The North Sea ◽  
The Baltic

Abstract. The Baltic Sea is a seasonally ice-covered marginal sea in northern Europe with intense wintertime ship traffic and a sensitive ecosystem. Understanding and modeling the evolution of the sea-ice pack is important for climate effect studies and forecasting purposes. Here we present and evaluate the sea-ice component of a new NEMO–LIM3.6-based ocean–sea-ice setup for the North Sea and Baltic Sea region (NEMO-Nordic). The setup includes a new depth-based fast-ice parametrization for the Baltic Sea. The evaluation focuses on long-term statistics, from a 45-year long hindcast, although short-term daily performance is also briefly evaluated. We show that NEMO-Nordic is well suited for simulating the mean sea-ice extent, concentration, and thickness as compared to the best available observational data set. The variability of the annual maximum Baltic Sea ice extent is well in line with the observations, but the 1961–2006 trend is underestimated. Capturing the correct ice thickness distribution is more challenging. Based on the simulated ice thickness distribution we estimate the undeformed and deformed ice thickness and concentration in the Baltic Sea, which compares reasonably well with observations.

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.

scholarly journals CLASSIFYING THE ICE SEASONS 1982-2016 USING THE WEIGHTED ICE DAYS NUMBER AS A NEW WINTER SEVERITY CHARACTERISTIC

2017 ◽  
Vol 5 ◽  
pp. 49-56 ◽  
Author(s):  
Jevgeni Rjazin ◽  
Victor Alari ◽  
Ove Pärn
Keyword(s):  
Baltic Sea ◽  
Ice Cover ◽  
The Baltic Sea ◽  
Concentration Data ◽  
Temporal Dimension ◽  
Ice Concentration ◽  
Long Time ◽  
Ice Conditions ◽  
The Baltic ◽  
Severity Degree

Sea ice is a key climate factor and it restricts considerably the winter navigation in severe seasons on the Baltic Sea. So determining ice conditions severity and describing ice cover behavior at severe seasons are necessary. The ice seasons severity degree is studied at the years 1982 to 2016. A new integrative characteristic named the weighted ice days number of the season is introduced to determine the ice season severity. The ice concentration data on the Baltic Sea published in the European Copernicus Programme are used to calculate the maximal ice extent and the weighted ice days number of the seasons. Both the ice season severity characteristics are used to classify the winters with respect of severity. The ice seasons 1981/82, 1984/85, 1985/86, 1986/87, 1995/96 and 2002/03 are classified as severe by the weighted ice days number. Only three seasons of this list are severe by both the criteria. We interpret this coincidence as the evidence of enough-during extensive ice cover in these three seasons. In the winter 2010/11 ice cover extended widely for some time, but did not last longer. At 2002/03 and a few other ice seasons the Baltic Sea was ice-covered in moderate extent, but the ice cover stayed long time. For 11 winters (32 % of the period) the relational weighted ice days number differs considerably (> 10 %) from the relational maximal ice extent. These winters yield one third of the studied ice seasons. Statistically every 6th winter is severe by the weighted ice days number whereas only statistically every 8th winter is severe by the maximal ice extent on the Baltic. Hence there are more intrinsically severe seasons than the maximal ice extent gives. The maximal ice extent fails to account with the ice cover durability. The weighted ice days number enables to describe the ice cover behavior more representatively. Using the weighted ice days number adds the temporal dimension to the ice season severity study.

scholarly journals Sea-ice evaluation of NEMO-Nordic 1.0: a NEMO–LIM3.6 based ocean–sea ice model setup for the North Sea and Baltic Sea

2017 ◽  
Author(s):  
Per Pemberton ◽  
Ulrike Löptien ◽  
Robinson Hordoir ◽  
Anders Höglund ◽  
Semjon Schimanke ◽  
...  
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
North Sea ◽  
Ice Thickness ◽  
The Baltic Sea ◽  
Sea Ice Extent ◽  
The North ◽  
The North Sea ◽  
The Baltic

Abstract. The Baltic Sea is a seasonally ice covered marginal sea in northern Europe with intense wintertime ship traffic and a sensitive ecosystem. Understanding and modeling the evolution of the sea-ice pack is important for climate effect studies and forecasting purposes. Here we present and evaluate the sea-ice component of a new NEMO–LIM3.6 based ocean–sea ice setup for the North Sea and Baltic Sea region. The setup includes a new depth-based fast ice parametrization for the Baltic Sea. The evaluation focuses on long-term statistics, from a 45-year long hindcast, although short-term daily performance is also briefly evaluated. Different sea-ice metrics such as sea-ice extent, concentration and thickness are compared to the best available observational dataset to identify model biases. Overall the model agrees well with the observations in terms of the long-term mean sea-ice extent and thickness. The variability of the annual maximum Baltic Sea ice extent is well in line with the observations but the 1961–2006 trend is underestimated. Based on the simulated ice thickness distribution we estimate the undeformed and deformed ice thickness and concentration in the Baltic Sea, which compares reasonably well with observations. We conclude that the new North Sea/Baltic Sea ocean–sea ice setup is well suited for further climate studies and sea ice forecasts.

Comparison sea ice data for Baltic Sea region based on modelling simulations and remote sensing measurements. 

2021 ◽  
Author(s):  
Jaromir Jakacki ◽  
Maciej Muzyka ◽  
Marta Konik ◽  
Anna Przyborska ◽  
Małgorzata Stramska
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Satellite Data ◽  
Ice Thickness ◽  
Data Sets ◽  
Operational Mode ◽  
The Baltic Sea ◽  
Baltic Sea Region ◽  
Atmospheric Data ◽  
The Baltic

<p>A comprehensive analysis of the results of remote measurements of the Baltic Sea ice cover has been performed. For this purpose, two modelling integrations were made. Two modelling simulations have been compared with two satellite data sets. As a modelling tool Community Ice Code (CICE) was implemented for Baltic Sea region. It was forced by two independent atmospheric data sets.  In the first simulation, the eBalticGrid system was the source of the atmospheric data, which has been operating in operational mode for almost five years. The second simulation used data from the SatBałtyk system. The satellite data differed in the method of evaluating the quality of the results - in some cases, the result was supervised by ice experts, and in the other, the quality was assessed automatically.  Comparisons with model we have performed using the daily ice concentration and ice thickness maps over the Baltic Sea. Datasets are produced by the Finnish Meteorological Institute (FMI) and disseminated through the central dissemination unit: Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/services-portfolio/ access-to-products/). The analysis showed an unnatural increase in the average ice thickness obtained from satellite data at the end of the ice season, for selected regions. The possibility of water appearance on the surface of the analyzed cells was assumed as the source of the potential error, which has a significant impact on the optical properties of the surface. It was proposed to eliminate cells containing a specific surface wetting fraction. However, the results do not allow this approach to be considered correct and therefore the work needs to be continued.</p><p><br><br></p>

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