The observation of seiches in the Baltic Sea using a multi data set of water levels

2000 ◽  
Vol 24 (1-2) ◽  
pp. 67-84 ◽  
Author(s):  
Margitta Metzner ◽  
Martin Gade ◽  
Ingo Hennings ◽  
Alexander B Rabinovich
Keyword(s):  
Baltic Sea ◽  
Water Levels ◽  
The Baltic Sea ◽  
Data Set ◽  
The Baltic

Simulating extreme sea levels at the Baltic Sea coast from synthetic cyclones

2020 ◽  
Author(s):  
Jani Särkkä ◽  
Jani Räihä ◽  
Matti Kämäräinen ◽  
Kirsti Jylhä
Keyword(s):  
Baltic Sea ◽  
Sea Level ◽  
Water Volume ◽  
The Baltic Sea ◽  
Data Set ◽  
Sea Levels ◽  
Extreme Sea Levels ◽  
Level Model ◽  
The Baltic ◽  
Sea Coast

<p>Coastal areas are under rapid changes. Management to face flooding hazards in changing climate is of great significance due to the major impact of flooding events in densely populated coastal regions, where also important and vulnerable infrastructure is located. The sea level of the Baltic Sea is affected by internal fluctuations caused by wind, air pressure and seiche oscillations, and by variations of the water volume due to the water exchange between the Baltic Sea and the North Sea through the Danish Straits. The highest sea level extremes are caused by cyclones moving over the region. The most vulnerable locations are at the ends of the bays. St. Petersburg, located at the eastern end of the Gulf of Finland, has experienced major sea floods in 1777, 1824 and 1924.</p><p>In order to study the effects of the depths and tracks of cyclones on the extreme sea levels, we have developed a method to generate cyclones for numerical sea level studies. A cyclone is modelled as a two-dimensional Gaussian function with adjustable horizontal size and depth. The cyclone moves through the Baltic Sea region with given direction and velocity. The output of this method is the gridded data set of mean sea level pressure and wind components which are used as an input for the sea level model. The internal variations of the Baltic Sea are calculated with a numerical barotropic sea level model, and the water volume variations are evaluated using a statistical sea level model based on wind speeds near the Danish Straits. The sea level model simulations allow us to study extremely rare but physically plausible sea level events that have not occurred during the observation period at the Baltic Sea coast. The simulation results are used to investigate extreme sea levels that could occur at selected sites at the Finnish coastline.</p>

scholarly journals The relative importance of selected factors controlling the oxygen dynamics in the water column of the Baltic Sea

2009 ◽  
Vol 6 (3) ◽  
pp. 2115-2156
Author(s):  
S. Miladinova ◽  
A. Stips
Keyword(s):  
Baltic Sea ◽  
Water Levels ◽  
Biogeochemical Model ◽  
Physical Factors ◽  
Intermediate Water ◽  
Relative Importance ◽  
The Baltic Sea ◽  
Salinity Field ◽  
The Baltic ◽  
Oxygen Dynamics

Abstract. A 1-D biogeochemical/physical model of marine systems has been applied to study the oxygen cycle in four stations of the different sub-basins of the Baltic Sea, namely, in Gotland Deep, Bornholm, Arkona and Fladen. The model consists of biogeochemical model of Neumann et al. (2002) coupled with the 1-D General Ocean Turbulence Model (GOTM). The model has been forced with meteorological data from the ECMWF reanalysis project for the period 1998–2003, producing a 6-year hindcast validated with datasets from the Baltic Environmental Database (BED) for the same period. The vertical profiles of temperature and salinity are relaxed towards both profiles provided by 3-D simulations of General Estuarine Turbulent Model (GETM) and observed profiles from BED. Modifications in the parameterisation of the air/sea oxygen fluxes have led to significant improvement of the model results in the surface and intermediate water levels. The largest mismatch with observation is found in simulating the oxygen dynamics in the Baltic Sea bottom waters. The model results demonstrate the good capability of the model to predict the time-evolution of the physical and biogeochemical variables at all different stations. Comparative analysis of the modelled oxygen concentrations with respect to the observation data is performed to distinguish the relative importance of several factors on the seasonal, interannual and long-term variations of oxygen. It is found that the natural physical factors, like the magnitude of the vertical turbulent mixing, wind speed, the variation in temperature and salinity field are the major factors controlling the oxygen dynamics in the Baltic Sea. The influence of limiting nutrients is less pronounced, at least under the nutrient flux parameterisation assumed in the model.

Towards operational NEMO model for the Baltic Sea

2020 ◽  
Author(s):  
Tuomas Kärnä ◽  
Jonni Lehtiranta ◽  
Laura Tuomi
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Model Development ◽  
Circulation Model ◽  
Water Levels ◽  
Sea Surface Salinity ◽  
The Baltic Sea ◽  
Surface Salinity ◽  
Ice Coverage ◽  
The Baltic

<p>We are developing a new operational circulation model for the Baltic Sea using NEMO v4.0. The model configuration is derived from the NEMO v3.6 1 nmi NemoNordic setup (Hordoir et al., Geoscientific Model Development, 2019). A pre-operational version of the model has been implemented to produce daily forecasts of water level, temperature, salinity, and currents, as well as sea ice coverage. In this poster we present model validation for a two-year hindcast simulation. The results indicate that daily and seasonal variability of water levels and sea surface salinity are well captured. Sea ice coverage is well represented, although slightly over-estimated. Comparisons at several mooring locations show realistic vertical salinity structure, and verify that the model can simulate Baltic inflow events. Overall, the model skill has significantly improved compared to previous operational models.</p>

Wind conditions over the Baltic Sea – comparing reanalysis data sets with observations

2020 ◽  
Author(s):  
Christoffer Hallgren ◽  
Erik Sahlée ◽  
Stefan Ivanell ◽  
Heiner Körnich ◽  
Ville Vakkari
Keyword(s):  
Baltic Sea ◽  
Wind Power ◽  
Reanalysis Data ◽  
Data Sets ◽  
The Baltic Sea ◽  
Data Set ◽  
Low Level ◽  
Seasonal And Diurnal Variations ◽  
Low Level Jets ◽  
The Baltic

<p>The potential of increasing the amount of offshore wind energy production in the Baltic Sea has been of great interest for many countries and wind power companies for a long time. From a meteorological point of view, there are several special wind characteristics that are observed in this area that needs to be taken into consideration when planning for a wind farm. For example, as the Baltic Sea is a semi-enclosed basin surrounded by coastlines in all directions, phenomenon such as low-level jets occur frequently.</p><p>In order to create a climatology of the wind conditions over the Baltic Sea, with wind power applications in mind, four different state-of-the-art reanalysis data sets (MERRA2, ERA5, UERRA and NEWA) have been compared with measurements from LIDAR systems and high meteorological towers (Anholt, Finnish Utö, FINO2 and Östergarnsholm). The performance of the data sets has been analyzed in terms of stability and governing synoptic weather conditions as well as seasonal and diurnal variations. By selecting the most suitable reanalysis data set and using the observations to make corrections, a climatology for wind conditions over the Baltic Sea, focusing on the low-level jets, has then been constructed.</p>

DETERMINATION OF COMBINED PROBABILITIES OF OCCURRENCE OF WATER LEVELS AND WAVE HEIGHTS FOR THE SAFETY OF DIKES AT THE BALTIC SEA

2017 ◽  
pp. 6
Author(s):  
Christian Kaehler ◽  
Christian Schlamkow ◽  
Fokke Saathoff
Keyword(s):  
Baltic Sea ◽  
Storm Surges ◽  
Water Levels ◽  
The Baltic Sea ◽  
Copula Models ◽  
Protective Function ◽  
Safety Standards ◽  
Wave Heights ◽  
The Baltic

Large parts of the Baltic Sea coast in Germany are protected by dikes against storm surges and floods. The dikes are designed to resist storm surges and floods, also taking into consideration of climate changes and sea level rise. To ensure the protective function the safety standards of the dikes are validated in regular intervals. This paper presents an approach to determine combined probabilities of occurrence of water level and wave heights for three selected sections. The probabilities of occurrence for defined return periods have been calculated by comparing several Copula models from the Archimedean Copula family.

scholarly journals Comparisons of Satellite and Modeled Surface Temperature and Chlorophyll Concentrations in the Baltic Sea with In Situ Data

2021 ◽  
Vol 13 (15) ◽  
pp. 3049
Author(s):  
Malgorzata Stramska ◽  
Marta Konik ◽  
Paulina Aniskiewicz ◽  
Jaromir Jakacki ◽  
Miroslaw Darecki
Keyword(s):  
Baltic Sea ◽  
Satellite Data ◽  
In Situ Measurements ◽  
Data Sets ◽  
The Baltic Sea ◽  
Data Set ◽  
In Situ Data ◽  
The Baltic ◽  
Good Agreement

Among the most frequently used satellite data are surface chlorophyll concentration (Chl) and temperature (SST). These data can be degraded in some coastal areas, for example, in the Baltic Sea. Other popular sources of data are reanalysis models. Before satellite or model data can be used effectively, they should be extensively compared with in situ measurements. Herein, we present results of such comparisons. We used SST and Chl from model reanalysis and satellites, and in situ data measured at eight open Baltic Sea stations. The data cover time interval from 1 January 1998 to 31 December 2019, but some satellite data were not always available. Both the model and the satellite SST data had good agreement with in situ measurements. In contrast, satellite and model estimates of Chl concentrations presented large errors. Modeled Chl presented the lowest bias and the best correlation with in situ data from all Chl data sets evaluated. Chl estimates from a regionally tuned algorithm (SatBaltic) had smaller errors in comparison with other satellite data sets and good agreement with in situ data in summer. Statistics were not as good for the full data set. High uncertainties found in chlorophyll satellite algorithms for the Baltic Sea highlight the importance of continuous regional validation of such algorithms with in situ data.

scholarly journals Submarine Groundwater Discharge From Non-Tidal Coastal Peatlands Along the Baltic Sea

2021 ◽  
Vol 9 ◽  
Author(s):  
Erwin Don Racasa ◽  
Bernd Lennartz ◽  
Miriam Toro ◽  
Manon Janssen
Keyword(s):  
Hydraulic Conductivity ◽  
Baltic Sea ◽  
Submarine Groundwater Discharge ◽  
Groundwater Discharge ◽  
Water Levels ◽  
Peat Layer ◽  
Discharge Region ◽  
The Baltic Sea ◽  
Submarine Groundwater ◽  
The Baltic

Submarine groundwater discharge (SGD) is an important pathway for water and materials within the land-ocean transition zone that can impact coastal environments and marine life. Although research from sandy shorelines has rapidly advanced in recent years, there is very little understanding of coastal areas characterized by a low hydraulic conductivity, such as carbon-rich coastal peatlands. The objective of this study was to determine the magnitude and location of terrestrial SGD to be expected from a non-tidal low-lying coastal peatland located along the Baltic Sea and to understand the controlling factors using numerical modeling. We employed the HYDRUS-2D modeling package to simulate water movement under steady-state conditions in a transect that extends from the dune dike-separated rewetted fen to the shallow sea. Soil physical properties, hydraulic gradients, geological stratifications, and topography were varied to depict the range of properties encountered in coastal peatlands. Our results show that terrestrial SGD occurs at the study site at a flux of 0.080 m2 d−1, with seepage rates of 1.05 cm d−1 (upper discharge region) and 0.16 cm d−1 (lower discharge region above submerged peat layer). These calculated seepage rates compare to observations from other wetland environments and SGD sites in the Baltic Sea. The groundwater originates mainly from the dune dike—recharged by precipitation and infiltration from ponded peatland surface water—and to a lesser extent from the sand aquifer. The scenario simulations yielded a range of potential SGD fluxes of 0.008–0.293 m2 d−1. They revealed that the location of terrestrial SGD is determined by the barrier function of the peat layer extending under the sea. However, it has little impact on volume flux as most SGD occurs near the shoreline. Magnitude of SGD is mainly driven by hydraulic gradient and the hydraulic conductivity of peat and beach/dune sands. Anisotropy in the horizontal direction, aquifer and peat thickness, and peatland elevation have little impacts on SGD. We conclude that SGD is most probable from coastal peatlands with high water levels, large Ks and/or a dune dike or belt, which could be an essential source for carbon and other materials via the SGD pathway.

scholarly journals Spatial sea-level reconstruction in the Baltic Sea and in the Pacific Ocean from tide gauges observations

2016 ◽  
Vol 59 (3) ◽  
Author(s):  
Marco Olivieri ◽  
Giorgio Spada
Keyword(s):  
Pacific Ocean ◽  
Baltic Sea ◽  
Sea Level ◽  
Tide Gauge ◽  
Tide Gauges ◽  
The Baltic Sea ◽  
Data Set ◽  
The Pacific ◽  
The Pacific Ocean ◽  
The Baltic

<p>Exploiting the Delaunay interpolation, we present a newly implemented 2-D sea-level reconstruction from coastal sea-level observations to open seas, with the aim of characterizing the spatial variability of the rate of sea-level change. To test the strengths and weaknesses of this method and to determine its usefulness in sea-level interpolation, we consider the case studies of the Baltic Sea and of the Pacific Ocean. In the Baltic Sea, a small basin well sampled by tide gauges, our reconstructions are successfully compared with absolute sea-level observations from altimetry during 1993-2011. The regional variability of absolute sea level observed across the Pacific Ocean, however, cannot be reproduced. We interpret this result as the effect of the uneven and sparse tide gauge data set and of the composite vertical land movements in and around the region. Useful considerations arise that can serve as a basis for developing sophisticated approaches.</p>

scholarly journals Diurnal cycle of the CO<sub>2</sub> system in the coastal region of the Baltic Sea

2020 ◽  
Author(s):  
Martti Honkanen ◽  
Jens Daniel Müller ◽  
Jukka Seppälä ◽  
Gregor Rehder ◽  
Sami Kielosto ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Baltic Sea ◽  
Diurnal Cycle ◽  
Coastal Region ◽  
Sampling Period ◽  
Effect Of Temperature ◽  
Carbon Dioxide Exchange ◽  
The Baltic Sea ◽  
Data Set ◽  
The Baltic

Abstract. The direction and magnitude of carbon dioxide exchange between the atmosphere and the sea is regulated by their difference in partial pressure of carbon dioxide (pCO2). Typically, observations of pCO2 are carried out by using research vessels and voluntary observing ships which cannot easily detect the diurnal cycle of pCO2 at a given location. This study evaluates the magnitude and driving processes of the diurnal cycle of pCO2 in a coastal region of the Baltic Sea during the different seasons.We present pCO2 data from July 2018–June 2019 carried out in the vicinity of the island of Utö in the Archipelago Sea and quantify the relevant physical, biological and chemical processes affecting pCO2. The highest monthly median diurnal pCO2 peak-to-peak amplitude (31 μatm) was observed in August. This high diurnal variation was found to be related predominantly to biological processes. The biological transformations of carbon generated a sinusoidal diurnal pCO2 variation, with a maximum in the morning and a minimum in the afternoon. Compared to the biological carbon transformations, the effect of air sea exchange of carbon dioxide and the effect of temperature changes on pCO2 are smaller, with their monthly median peak-to-peak amplitudes were up to 12 and 5 μatm, respectively. Single diurnal peak-to-peak amplitudes can be significantly larger (up to 500 μatm), during upwelling. If the net exchange of carbon dioxide between the sea and atmosphere on our study site and sampling period is calculated based on a data set that consists of only one measurement per day, the error in the budget depends on the sampling time and can be up to &amp;pm;12 %.

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