scholarly journals Harmonizing large data sets reveals novel patterns in the Baltic Sea phytoplankton community structure

2013 ◽  
Vol 473 ◽  
pp. 53-66 ◽  
Author(s):  
K Olli ◽  
O Trikk ◽  
R Klais ◽  
R Ptacnik ◽  
T Andersen ◽  
...  
Keyword(s):  
Community Structure ◽  
Baltic Sea ◽  
Phytoplankton Community ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Phytoplankton Community Structure ◽  
The Baltic Sea ◽  
The Baltic

scholarly journals Mechanisms of variability in decadal sea-level trends in the Baltic Sea over the 20th century

2017 ◽  
Vol 8 (4) ◽  
pp. 1031-1046 ◽  
Author(s):  
Sitar Karabil ◽  
Eduardo Zorita ◽  
Birgit Hünicke
Keyword(s):  
Regression Model ◽  
Baltic Sea ◽  
Sea Level ◽  
Atmospheric Circulation ◽  
Data Sets ◽  
Climatic Data ◽  
The Baltic Sea ◽  
Underlying Factor ◽  
Coastal Sea ◽  
The Baltic

Abstract. Coastal sea-level trends in the Baltic Sea display decadal-scale variations around a long-term centennial trend. In this study, we analyse the spatial and temporal characteristics of the decadal trend variations and investigate the links between coastal sea-level trends and atmospheric forcing on a decadal timescale. For this analysis, we use monthly means of sea-level and climatic data sets. The sea-level data set is composed of long tide gauge records and gridded sea surface height (SSH) reconstructions. Climatic data sets are composed of sea-level pressure, air temperature, precipitation, evaporation, and climatic variability indices. The analysis indicates that atmospheric forcing is a driving factor of decadal sea-level trends. However, its effect is geographically heterogeneous. This impact is large in the northern and eastern regions of the Baltic Sea. In the southern Baltic Sea area, the impacts of atmospheric circulation on decadal sea-level trends are smaller. To identify the influence of the large-scale factors other than the effect of atmospheric circulation in the same season on Baltic Sea sea-level trends, we filter out the direct signature of atmospheric circulation for each season separately on the Baltic Sea level through a multivariate linear regression model and analyse the residuals of this regression model. These residuals hint at a common underlying factor that coherently drives the decadal sea-level trends in the whole Baltic Sea. We found that this underlying effect is partly a consequence of decadal precipitation trends in the Baltic Sea basin in the previous season. The investigation of the relation between the AMO index and sea-level trends implies that this detected underlying factor is not connected to oceanic forcing driven from the North Atlantic region.

scholarly journals Upwelling-induced trace gas dynamics in the Baltic Sea inferred from 8 years of autonomous measurements on a ship of opportunity

2021 ◽  
Vol 18 (8) ◽  
pp. 2679-2709
Author(s):  
Erik Jacobs ◽  
Henry C. Bittig ◽  
Ulf Gräwe ◽  
Carolyn A. Graves ◽  
Michael Glockzin ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Gas Dynamics ◽  
Coastal Upwelling ◽  
Regional Distribution ◽  
Trace Gas ◽  
Data Sets ◽  
The Baltic Sea ◽  
Spatial And Temporal Scales ◽  
In Situ Data ◽  
The Baltic

Abstract. Autonomous measurements aboard ships of opportunity (SOOP) provide in situ data sets with high spatial and temporal coverage. In this study, we use 8 years of carbon dioxide (CO2) and methane (CH4) observations from SOOP Finnmaid to study the influence of upwelling on trace gas dynamics in the Baltic Sea. Between spring and autumn, coastal upwelling transports water masses enriched with CO2 and CH4 to the surface of the Baltic Sea. We study the seasonality, regional distribution, relaxation, and interannual variability in this process. We use reanalysed wind and modelled sea surface temperature (SST) data in a newly established statistical upwelling detection method to identify major upwelling areas and time periods. Large upwelling-induced SST decrease and trace gas concentration increase are most frequently detected around August after a long period of thermal stratification, i.e. limited exchange between surface and underlying waters. We found that these upwelling events with large SST excursions shape local trace gas dynamics and often lead to near-linear relationships between increasing trace gas levels and decreasing temperature. Upwelling relaxation is mainly driven by mixing, modulated by air–sea gas exchange, and possibly primary production. Subsequent warming through air–sea heat exchange has the potential to enhance trace gas saturation. In 2015, quasi-continuous upwelling over several months led to weak summer stratification, which directly impacted the observed trace gas and SST dynamics in several upwelling-prone areas. Trend analysis is still prevented by the observed high variability, uncertainties from data coverage, and long water residence times of 10–30 years. We introduce an extrapolation method based on trace gas–SST relationships that allows us to estimate upwelling-induced trace gas fluxes in upwelling-affected regions. In general, the surface water reverses from CO2 sink to source, and CH4 outgassing is intensified as a consequence of upwelling. We conclude that SOOP data, especially when combined with other data sets, enable flux quantification and process studies addressing the process of upwelling on large spatial and temporal scales.

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>

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 Seasonality of Coastal Picophytoplankton Growth, Nutrient Limitation, and Biomass Contribution

2021 ◽  
Vol 12 ◽  
Author(s):  
Javier Alegria Zufia ◽  
Hanna Farnelid ◽  
Catherine Legrand
Keyword(s):  
Baltic Sea ◽  
Nutrient Limitation ◽  
Seasonal Dynamics ◽  
Phytoplankton Biomass ◽  
Phytoplankton Community ◽  
The Baltic Sea ◽  
Coastal Site ◽  
Total Phytoplankton ◽  
Temperature Ranges ◽  
The Baltic

Picophytoplankton in the Baltic Sea includes the simplest unicellular cyanoprokaryotes (Synechococcus/Cyanobium) and photosynthetic picoeukaryotes (PPE). Picophytoplankton are thought to be a key component of the phytoplankton community, but their seasonal dynamics and relationships with nutrients and temperature are largely unknown. We monitored pico- and larger phytoplankton at a coastal site in Kalmar Sound (K-Station) weekly during 2018. Among the cyanoprokaryotes, phycoerythrin-rich picocyanobacteria (PE-rich) dominated in spring and summer while phycocyanin-rich picocyanobacteria (PC-rich) dominated during autumn. PE-rich and PC-rich abundances peaked during summer (1.1 × 105 and 2.0 × 105 cells mL–1) while PPE reached highest abundances in spring (1.1 × 105 cells mL–1). PPE was the main contributor to the total phytoplankton biomass (up to 73%). To assess nutrient limitation, bioassays with combinations of nitrogen (NO3 or NH4) and phosphorus additions were performed. PE-rich and PC-rich growth was mainly limited by nitrogen, with a preference for NH4 at >15°C. The three groups had distinct seasonal dynamics and different temperature ranges: 10°C and 17–19°C for PE-rich, 13–16°C for PC-rich and 11–15°C for PPE. We conclude that picophytoplankton contribute significantly to the carbon cycle in the coastal Baltic Sea and underscore the importance of investigating populations to assess the consequences of the combination of high temperature and NH4 in a future climate.

scholarly journals Seasonality of Coastal Picophytoplankton Growth, Nutrient Limitation and Biomass Contribution

2021 ◽  
Author(s):  
Javier Alegria Zufia ◽  
Hanna Farnelid ◽  
Catherine Legrand
Keyword(s):  
High Temperature ◽  
Baltic Sea ◽  
Nutrient Limitation ◽  
Seasonal Dynamics ◽  
Phytoplankton Biomass ◽  
Phytoplankton Community ◽  
The Baltic Sea ◽  
Coastal Site ◽  
Total Phytoplankton ◽  
The Baltic

Abstract Picophytoplankton in the Baltic Sea includes picocyanobacteria (Synechococcus/Cyanobium) and photosynthetic picoeukaryotes (PPE). Picophytoplankton are thought to be a key component of the phytoplankton community but their seasonal dynamics and relationships with nutrients and temperature are largely unknown. We monitored pico- and larger phytoplankton at a coastal site in Kalmar Sound (K-Station) weekly during 2018. Among the picocyanobacteria, phycoerythrin-rich Synechococcus (PE-rich) dominated in spring and summer while phycocyanin-rich Synechococcus (PC-rich) dominated during autumn. PE-rich and PC-rich abundances peaked during summer (1.1x105 and 2.0x105 cells mL− 1) while PPE reached highest abundances in spring (1.1x105 cells mL− 1). PPE was the main contributor to the total phytoplankton biomass (3–73%). To assess nutrient limitation, bioassays with combinations of nitrogen (NO3 or NH4) and phosphorus additions were performed. PE-rich and PC-rich growth was mainly limited by nitrogen, with a preference for NH4 at 15–19°C. The three groups had distinct seasonal dynamics and optimal temperatures for growth were 10°C and 17–19°C for PE-rich, 13–16°C for PC-rich and 11–15°C for PPE. We conclude that picophytoplankton contribute significantly to the carbon cycle in the coastal Baltic Sea and underscore the importance of investigating functional groups to assess the consequences of the combination of high temperature and NH4 in a future climate.

scholarly journals SPATIAL-TEMPORAL DETECTION OF CHANGES ON THE SOUTHERN COAST OF THE BALTIC SEA BASED ON MULTITEMPORAL AERIAL PHOTOGRAPHS

2016 ◽  
Vol XLI-B2 ◽  
pp. 49-53 ◽  
Author(s):  
K. Michalowska ◽  
E. Glowienka ◽  
A. Pekala
Keyword(s):  
Baltic Sea ◽  
Aerial Images ◽  
Geographical Information ◽  
Aerial Photographs ◽  
Annual Data ◽  
Data Sets ◽  
Base Line ◽  
The Baltic Sea ◽  
Multitemporal Data ◽  
The Baltic

Digital photogrammetry and remote sensing solutions applied under the project and combined with the geographical information system made it possible to utilize data originating from various sources and dating back to different periods. Research works made use of archival and up-to-date aerial images, satellite images, orthophotomaps. Multitemporal data served for mapping and monitoring intermediate conditions of the Baltic Sea shore zone without a need for a direct interference in the environment. The main objective of research was to determine the dynamics and volume of sea shore changes along the selected part of coast in the period of 1951-2004, and to assess the tendencies of shore development in that area. For each of the six annual data sets, the following were determined: front dune base line, water line and the beach width. The location of the dune base line, which reflects the course of the shoreline in a given year was reconstructed based on stereoscopic study of images from each annual set. Unidirectional changes in the period of 1951-2004 occurred only within 10% of the examined shore section length. The examined shore is marked by a high and considerable dynamics of changes. Almost half of the shore, in particular the middle coast shows big changes, in excess of 2 m/year. The limits of shoreline changes ranged from 120 to -90 m, and their velocity from 0 to 11 m/year, save that the middle and west parts of the examined coast section were subjected to definitely more intense shore transformations. Research based on the analysis of multitemporal aerial images made it possible to reconstruct the intermediate conditions of the Baltic Sea shoreline and determine the volume and rate of changes in the location of dune base line in the examined period of 53 years, and to find out tendencies of shore development and dynamics.

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