On the modelling of ice-thickness redistribution

AbstractAn ice-thickness distribution model based on physical ice classes is formulated. Pack ice is subdivided into open water, two different types of undeformed ice, and rafted, rubble and ridged ice. Evolution equations for each ice class are formulated and a redistribution between the ice classes is calculated according to a functional form depending on the ice compactness, thickness and velocity divergence. The ice-thickness distribution model has been included in a coupled ice–ocean model, and numerical experiments have been carried out for a simulation of the Baltic Sea ice season. The extended ice classification allows separation of thermally and mechanically produced ice. Inherent thermodynamic growth/melting rates of the ice classes can be introduced into the model, giving a more detailed seasonal evolution of the pack ice. In addition, the model provides more information about the surface properties of pack ice.Numerical experiments for the Baltic Sea show that both the sub-basin and inter-basin ice characteristics were realistically simulated by the model. Deformed-ice production was related to storm activity. Most of the deformation was produced in the coastal zone, which is also an important region for thermodynamically produced ice because of the ice growth in leads. The modelled mechanical growth rates of ice were 0.5–3 cm d−1 on a basin scale, close to the thermodynamic ice-production rates. The deformed-ice fraction was 0.2 in mid-winter and increased to 0.5–1.0 during spring.

Download Full-text

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

Geoscientific Model Development ◽

10.5194/gmd-10-3105-2017 ◽

2017 ◽

Vol 10 (8) ◽

pp. 3105-3123 ◽

Cited By ~ 13

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.

Download Full-text

Predicting ice-induced load amplitudes on ship bow conditional on ice thickness and ship speed in the Baltic Sea

Cold Regions Science and Technology ◽

10.1016/j.coldregions.2016.12.006 ◽

2017 ◽

Vol 135 ◽

pp. 116-126 ◽

Cited By ~ 18

Author(s):

Mikko Kotilainen ◽

Jarno Vanhatalo ◽

Mikko Suominen ◽

Pentti Kujala

Keyword(s):

Baltic Sea ◽

Ice Thickness ◽

The Baltic Sea ◽

The Baltic ◽

Ship Speed

Download Full-text

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

10.5194/egusphere-egu2020-19573 ◽

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

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.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. &#160;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.In this work sea ice parameters such as sea ice concentration, thickness and volume obtained from both &#8211; 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.The ice extent, thickness and volume were compared in several regions within the Baltic Sea.&#160; 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.&#160;This work was partly funded by Polish National Science Centre, project number 2017/25/B/ST10/00159

Download Full-text

Relationships between the Baltic Sea ice extent and ice parameters in the sheltered basins of the southern Baltic coast

Oceanological and Hydrobiological Studies ◽

10.1515/ohs-2020-0026 ◽

2020 ◽

Vol 49 (3) ◽

pp. 291-303

Author(s):

Józef P. Girjatowicz ◽

Małgorzata Świątek

Keyword(s):

Baltic Sea ◽

Coastal Lagoons ◽

Correlation Coefficients ◽

Ice Thickness ◽

The Baltic Sea ◽

Sea Ice Extent ◽

Regression Methods ◽

Baltic Coast ◽

Southern Baltic ◽

The Baltic

AbstractIn the study, archive data on the maximum annual ice extent in the Baltic Sea (MIB) for the period 1961–2018 were used. They were obtained from the FIMR database. Data on ice parameters for the four largest southern Baltic coastal lakes: Jamno, Bukowo, Gardno and Łebsko, and for Szczecin, Puck, and Vistula Lagoons, come from the Maritime Branch of Institute of Meteorology and Water Management – National Research Institute (in Polish: Instytut Meteorologii i Gospodarki Wodnej – Panstwowy Instytut Badawczy, IMGW-PIB) in Gdynia. The time series for the lakes cover the years from 1960 to 2018, and for the lagoons – from 1946 to 2018. Three ice parameters were selected for this study: the number of days with ice, the duration of the ice season and the maximum ice thickness for a given winter. Relationships between the selected ice parameters for the studied basins and the MIB were examined using correlation and regression methods.Correlations between the MIB and values of the ice parameters for the lakes and the southern Baltic coastal lagoons do not differ significantly. Considerable differences are observed amongst the correlation coefficients for individual ice parameters and the MIB.Larger differences are found in relationships between the values of individual ice parameters in the sheltered basins and the MIB. The strongest correlation with the MIB is observed for the maximum ice thickness and the number of days with ice.

Download Full-text

Direct estimates of the lateral eddy diffusivity in the gulf of finland of the Baltic Sea (based on the results of numerical experiments with an eddy resolving model)

Oceanology ◽

10.1134/s0001437008020033 ◽

2008 ◽

Vol 48 (2) ◽

pp. 175-181 ◽

Cited By ~ 3

Author(s):

V. M. Zhurbas ◽

J. Laanemets ◽

N. P. Kuzmina ◽

S. S. Muraviev ◽

J. Elken

Keyword(s):

Baltic Sea ◽

Numerical Experiments ◽

Eddy Diffusivity ◽

Gulf Of Finland ◽

The Baltic Sea ◽

The Baltic ◽

The Gulf Of Finland

Download Full-text

Understanding past and future sea surface temperature trends in the Baltic Sea

Climate Dynamics ◽

10.1007/s00382-021-06084-1 ◽

2021 ◽

Author(s):

C. Dutheil ◽

H. E. M. Meier ◽

M. Gröger ◽

F. Börgel

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Baltic Sea ◽

Heat Fluxes ◽

Sea Surface ◽

Atmospheric Forcing ◽

The Baltic Sea ◽

Climate Simulations ◽

Basin Scale ◽

The Baltic

AbstractThe Baltic Sea is one of the fastest-warming semi-enclosed seas in the world over the last decades, yielding critical consequences on physical and biogeochemical conditions and on marine ecosystems. Although long-term trends in sea surface temperature (SST) have long been attributed to trends in air temperature, there are however, strong seasonal and sub-basin scale heterogeneities of similar magnitude than the average trend which are not fully explained. Here, using reconstructed atmospheric forcing fields for the period 1850–2008, oceanic climate simulations were performed and analyzed to identify areas of homogenous SST trends using spatial clustering. Our results show that the Baltic Sea can be divided into five different areas of homogeneous SST trends: the Bothnian Bay, the Bothnian Sea, the eastern and western Baltic proper, and the southwestern Baltic Sea. A classification tree and sensitivity experiments were carried out to analyze the main drivers behind the trends. While ice cover explains the seasonal north/south warming contrast, the changes in surface winds and air-sea temperature anomalies (along with changes in upwelling frequencies and heat fluxes) explain the SST trends differences between the sub-basins of the southern part of the Baltic Sea. To investigate future warming trends climate simulations were performed for the period 1976–2099 using two RCP scenarios. It was found that the seasonal north/south gradient of SST trends should be reduced in the future due to the vanishing of sea ice, while changes in the frequency of upwelling and heat fluxes explained the lower future east/west gradient of SST trend in fall. Finally, an ensemble of 48 climate change simulations has revealed that for a given RCP scenario the atmospheric forcing is the main source of uncertainty. Our results are useful to better understand the historical and future changes of SST in the Baltic Sea, but also in terms of marine ecosystem and public management, and could thus be used for planning sustainable coastal development.

Download Full-text

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

Polish Maritime Research ◽

10.2478/pomr-2018-0094 ◽

2018 ◽

Vol 25 (3) ◽

pp. 35-43 ◽

Cited By ~ 1

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.

Download Full-text

Developing benthic monitoring programmes to support precise and representative status assessments: a case study from the Baltic Sea

Environmental Monitoring and Assessment ◽

10.1007/s10661-020-08764-7 ◽

2020 ◽

Vol 192 (12) ◽

Author(s):

Henrik Nygård ◽

Mats Lindegarth ◽

Alexander Darr ◽

Grete E. Dinesen ◽

Ole R. Eigaard ◽

...

Keyword(s):

Baltic Sea ◽

Marine Ecosystem ◽

Monitoring Data ◽

Data Series ◽

Sampling Effort ◽

The Baltic Sea ◽

Basin Scale ◽

The Status ◽

The Baltic

AbstractBenthic habitats and communities are key components of the marine ecosystem. Securing their functioning is a central aim in marine environmental management, where monitoring data provide the base for assessing the state of marine ecosystems. In the Baltic Sea, a > 50-year-long tradition of zoobenthic monitoring exists. However, the monitoring programmes were designed prior to the current policies, primarily to detect long-term trends at basin-scale and are thus not optimal to fulfil recent requirements such as area-based periodic status assessments. Here, we review the current monitoring programmes and assess the precision and representativity of the monitoring data in status assessments to identify routes for improvement. At present, the monitoring is focused on soft-bottoms, not accounting for all habitat types occurring in the Baltic Sea. Evaluating the sources of variance in the assessment data revealed that the component accounting for variability among stations forms the largest proportion of the uncertainty. Furthermore, it is shown that the precision of the status estimates can be improved, with the current number of samples. Reducing sampling effort per station, but sampling more stations, is the best option to improve precision in status assessments. Furthermore, by allocating the sampling stations more evenly in the sub-basins, a better representativity of the area can be achieved. However, emphasis on securing the long-term data series is needed if changes to the monitoring programmes are planned.

Download Full-text