scholarly journals Using the Baltic Sea to advance algorithms to extract altimetry-derived sea-level data from complex coastal areas, featuring seasonal sea-ice

2020 ◽  
Author(s):  
Marcello Passaro ◽  
Felix L. Müller ◽  
Adili Abulaitijiang ◽  
Ole B. Andersen ◽  
Denise Dettmering ◽  
...  
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Coastal Areas ◽  
Sea Surface ◽  
Coastal Regions ◽  
The Baltic Sea ◽  
Mean Sea Surface ◽  
The Baltic

<p>The use of satellite altimetry at high latitudes and coastal regions is currently limited by the presence of seasonal sea ice coverage, and the proximity to the coast. The semi-enclosed Baltic Sea features seasonal coverage of sea-ice in the northern and coastal regions, and complex jagged coastlines with a huge number of small islands. However, as a semi-enclosed sea with a considerable extent, the Baltic Sea features a much-reduced tidal signal, both open- and coastal- waters, and an extensive multi-national network of tide-gauges. These factors maximise opportunities to drive improvements in sea-level estimations for coastal, and seasonal-ice regions.</p><p>The ESA Baltic SEAL project, launched in April 2019, aims to exploit these opportunities. It is generating and validating a suite of enhanced multi-mission sea level products. Processing is developed specifically for coastal regions, with the objective of achieving a consistent description of the sea-level variability in terms of long-term trends, seasonal variations and a mean sea-surface. These will advance knowledge on adapting processing algorithms, to account for seasonal ice, and complex coastlines. Best practice approaches will be available to update current state-of-the-art datasets.</p><p>In order to fulfill these goals, a novel altimeter re-tracking strategy has been developed. This enables the homogeneous determination of sea-surface heights for open-ocean, coastal and sea-ice conditions (ALES+). An unsupervised classification algorithm based on artificial intelligence routines has been developed and tailored to ingest data from all current and past satellite altimetry missions. This identifies radar echoes, reflected by narrow cracks within the sea-ice domain. Finally, the improved altimetry observations are gridded onto a triangulated surface mesh, featuring a spatial resolution greater than 1/4 degree. This is more suitable for utility for coastal areas, and use by coastal stakeholders.</p><p>In addition to utilizing a wide range of altimetry data (Delay-Doppler and Pulse-Limited systems), the Baltic SEAL initiative harnesses the Baltic Seas unique characteristics to test novel geophysical corrections (e.g. wet troposphere correction), use the latest generation of regional altimetry datasets, and evaluate the benefits of the newest satellite altimetry missions. This presentation outlines the methodology and results achieved to date. These include estimations of a new regional mean sea surface, and insights into the trends of the sea level along the altimetry tracks with the longest records. The transfer of advances to other regions and sea-level initiatives are also highlighted.</p>

scholarly journals Nemo-Nordic 2.0: operational marine forecast model for the Baltic Sea

2021 ◽  
Vol 14 (9) ◽  
pp. 5731-5749
Author(s):  
Tuomas Kärnä ◽  
Patrik Ljungemyr ◽  
Saeed Falahat ◽  
Ida Ringgaard ◽  
Lars Axell ◽  
...  
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Water Temperature ◽  
Sea Level ◽  
Ocean Circulation ◽  
Circulation Model ◽  
Sea Surface ◽  
Model Assessment ◽  
The Baltic Sea ◽  
The Baltic

Abstract. This paper describes Nemo-Nordic 2.0, an operational marine model for the Baltic Sea. The model is used for both near-real-time forecasts and hindcast purposes. It provides estimates of sea surface height, water temperature, salinity, and velocity, as well as sea ice concentration and thickness. The model is based on the NEMO (Nucleus for European Modelling of the Ocean) circulation model and the previous Nemo-Nordic 1.0 configuration by Hordoir et al. (2019). The most notable updates include the switch from NEMO version 3.6 to 4.0, updated model bathymetry, and revised bottom friction formulation. The model domain covers the Baltic Sea and the North Sea with approximately 1 nmi resolution. Vertical grid resolution has been increased from 3 to 1 m in the surface layer. In addition, the numerical solver configuration has been revised to reduce artificial mixing to improve the representation of inflow events. Sea ice is modeled with the SI3 model instead of LIM3. The model is validated against sea level, water temperature, and salinity observations, as well as Baltic Sea ice chart data for a 2-year hindcast simulation (October 2014 to September 2016). Sea level root mean square deviation (RMSD) is typically within 10 cm throughout the Baltic basin. Seasonal sea surface temperature variation is well captured, although the model exhibits a negative bias of approximately −0.5 ∘C. Salinity RMSD is typically below 1.5 g kg−1. The model captures the 2014 major Baltic inflow event and its propagation to the Gotland Deep. The model assessment demonstrates that Nemo-Nordic 2.0 can reproduce the hydrographic features of the Baltic Sea.

Sea level variations in the coastal region from altimetry – Using optimal interpolation in the Baltic Sea for 3-day mean sea level from altimetry, tide gauge and model data

2021 ◽  
Author(s):  
Ida Margrethe Ringgaard ◽  
Jacob L. Høyer ◽  
Kristine S. Madsen ◽  
Adili Abulaitijiang ◽  
Ole B. Andersen
Keyword(s):  
Baltic Sea ◽  
Sea Level ◽  
Temporal Resolution ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Coastal Region ◽  
Optimal Interpolation ◽  
The Baltic Sea ◽  
Spatial Coverage ◽  
The Baltic

<p>The rise and fall of the sea surface in the coastal region is observed closely by two different sources: tide gauges measure the relative sea level anomaly at the coast at high temporal resolution (minutes or hours) and satellite altimeters measure the absolute sea surface height of the open ocean along tracks multiple times a day. However, these daily tracks are scattered across the Baltic Sea with each track being repeated at a lower temporal resolution (days). Due to the inverse relationship between spatial and temporal coverage of the satellite altimetry data, gridded satellite altimetry products often prioritize spatial coverage over temporal resolution, thus filtering out the high sea level variability. In other words, the satellite data, and especially averaged products, often miss the daily sea level variability, such as storm surges, which is most important for all societies in the coastal region. To compensate for the sparse spatial coverage from satellite altimetry, we here present an experimental product developed as part of the ESA project Baltic+SEAL:  on a 3-day scale, the DMI Optimal Interpolation (DMI-OI) method is combined with error statistics from a storm surge model as well as 3-day averages from both tide gauge observations and satellite altimetry tracks to generate a gridded sea level anomaly product for the Baltic Sea for year 2017. The product captures the overall temporal evolution of the sea level changes well for most areas with an average RMSE wrt. tide gauge observations of 17.2 cm and a maximum of 34.2 cm. Thus, the 3-day mean gridded product shows potential as an alternative to monthly altimetry products, although further work is needed.</p>

scholarly journals Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea

Ocean Science ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 525-541 ◽  
Author(s):  
Ye Liu ◽  
Weiwei Fu
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Baltic Sea ◽  
Sea Surface ◽  
The Baltic Sea ◽  
Dependent Observations ◽  
The Baltic ◽  
The Impact

Abstract. We assess the impact of assimilating the satellite sea surface temperature (SST) data on the Baltic forecast, particularly on the forecast of ocean variables related to SST. For this purpose, a multivariable data assimilation (DA) system has been developed based on a Nordic version of the Nucleus for European Modelling of the Ocean (NEMO-Nordic). We use Kalman-type filtering to assimilate the observations in the coastal regions. Further, a low-rank approximation of the stationary background error covariance metrics is used at the analysis steps. High-resolution SST from the Ocean and Sea Ice Satellite Application Facility (OSISAF) is assimilated to verify the performance of the DA system. The assimilation run shows very stable improvements of the model simulation as compared with both independent and dependent observations. The SST prediction of NEMO-Nordic is significantly enhanced by the DA forecast. Temperatures are also closer to observations in the DA forecast than the model results in the water above 100 m in the Baltic Sea. In the deeper layers, salinity is also slightly improved. In addition, we find that sea level anomaly (SLA) is improved with the SST assimilation. Comparisons with independent tide gauge data show that the overall root mean square error (RMSE) is reduced by 1.8 % and the overall correlation coefficient is slightly increased. Moreover, the sea-ice concentration forecast is improved considerably in the Baltic Proper, the Gulf of Finland and the Bothnian Sea during the sea-ice formation period, respectively.

scholarly journals Nemo-Nordic 2.0: Operational marine forecast model for the Baltic Sea

2021 ◽  
Author(s):  
Tuomas Kärnä ◽  
Patrik Ljungemyr ◽  
Saeed Falahat ◽  
Ida Ringgaard ◽  
Lars Axell ◽  
...  
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Sea Level ◽  
Ocean Circulation ◽  
Circulation Model ◽  
Forecast Model ◽  
The Baltic Sea ◽  
Baltic Basin ◽  
The North ◽  
The Baltic

Abstract. This paper describes Nemo-Nordic 2.0, an operational marine forecast model for the Baltic Sea. The model is based on the NEMO (Nucleus for European Modelling of the Ocean) circulation model and the previous Nemo-Nordic 1.0 configuration by Hordoir et al. [Geosci. Model Dev., 12, 363–386, 2019]. The most notable updates include the switch from NEMO version 3.6 to 4.0, updated model bathymetry and revised bottom friction formulation. The model domain covers the Baltic and the North Seas with approximately 1 nautical mile resolution. Vertical grid resolution has been increased from 3 to 1 m in the surface layer. In addition, the numerical solver configuration has been revised to reduce artificial mixing to improve the representation of inflow events. Sea-ice is modeled with the SI3 model instead of LIM3. The model is validated against sea level, water temperature and salinity observations, as well as Baltic Sea ice chart data for a two-year hindcast simulation. Sea level root mean square deviation (RMSD) is typically within 10 cm throughout the Baltic basin. Seasonal sea surface temperature variation is well captured, although the model exhibits a negative bias of approximately −0.5 °C. Salinity RMSD is typically below 1.5 g/kg. The model captures the 2014 Major Baltic Inflow event and its propagation to the Gotland Deep. The skill analysis demonstrates that Nemo-Nordic 2.0 can reproduce the hydrographic features of the Baltic Sea.

Impacts of Climate Change on the Ecosystem of the Baltic Sea

2019 ◽  
Author(s):  
Markku Viitasalo
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
Primary Production ◽  
Baltic Sea ◽  
Benthic Communities ◽  
Trophic Levels ◽  
Sea Surface ◽  
The Baltic Sea ◽  
Top Predators ◽  
The Baltic

Climate change influences the Baltic Sea ecosystem via its effects on oceanography and biogeochemistry. Sea surface temperature has been projected to increase by 2 to 4 °C until 2100 due to global warming; the changes will be more significant in the northern areas and less so in the south. The warming up will also diminish the annual sea ice cover by 57% to 71%, and ice season will be one to three months shorter than in the early 21st century, depending on latitude. A significant decrease in sea surface salinity has been projected because of an increase in rainfall and decrease of saline inflows into the Baltic Sea. The increasing surface flow has, in turn, been projected to increase leaching of nutrients from the soil to the watershed and eventually into the Baltic Sea. Also, acidification of the seawater and sea-level rise have been predicted. Increasing seawater temperature speeds up metabolic processes and increases growth rates of many secondary producers. Species associated with sea ice, from salt brine microbes to seals, will suffer. Due to the specific salinity tolerances, species’ geographical ranges may shift by tens or hundreds of kilometres with decreasing salinity. A decrease in pH will slow down calcification of bivalve shells, and higher temperatures also alleviate establishment of non-indigenous species originating from more southern sea areas. Many uncertainties still remain in predicting the couplings between atmosphere, oceanography and ecosystem. Especially projections of many oceanographic parameters, such as wind speeds and directions, the mean salinity level, and density stratification, are still ambiguous. Also, the effects of simultaneous changes in multiple environmental factors on species with variable preferences to temperature, salinity, and nutrient conditions are difficult to project. There is, however, enough evidence to claim that due to increasing runoff of nutrients from land and warming up of water, primary production and sedimentation of organic matter will increase; this will probably enhance anoxia and release of phosphorus from sediments. Such changes may keep the Baltic Sea in an eutrophicated state for a long time, unless strong measures to decrease nutrient runoff from land are taken. Changes in the pelagic and benthic communities are anticipated. Benthic communities will change from marine to relatively more euryhaline communities and will suffer from hypoxic events. The projected temperature increase and salinity decline will contribute to maintain the pelagic ecosystem of the Central Baltic and the Gulf of Finland in a state dominated by cyanobacteria, flagellates, small-sized zooplankton and sprat, instead of diatoms, large marine copepods, herring, and cod. Effects vary from area to area, however. In particular the Bothnian Sea, where hypoxia is less common and rivers carry a lot of dissolved organic carbon, primary production will probably not increase as much as in the other basins. The coupled oceanography-biogeochemistry ecosystem models have greatly advanced our understanding of the effects of climate change on marine ecosystems. Also, studies on climate associated “regime shifts” and cascading effects from top predators to plankton have been fundamental for understanding of the response of the Baltic Sea ecosystem to anthropogenic and climatic stress. In the future, modeling efforts should be focusing on coupling of biogeochemical processes and lower trophic levels to the top predators. Also, fine resolution species distribution models should be developed and combined with 3-D modelling, to describe how the species and communities are responding to climate-induced changes in environmental variables.

scholarly journals Absolute Baltic Sea Level Trends in the Satellite Altimetry Era: A  Revisit

2021 ◽  
Author(s):  
Julius Oelsmann ◽  
Keyword(s):  
Baltic Sea ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Altimetry Data ◽  
The South ◽  
The Baltic Sea ◽  
The North ◽  
Sea Level Anomalies ◽  
South West ◽  
The Baltic

<p>For sea level studies, coastal adaptation, and planning for future sea level scenarios, regional responses require regionally-tailored sea level information. Global sea level products from satellite altimeter missions are now available through the European Space Agency’s (ESA) Climate Change Initiative Sea Level Project (SL_cci). However, these global datasets are not entirely appropriate for supporting regional actions. Particularly for the Baltic Sea region, complications such as coastal complexity and sea-ice restrain our ability to exploit radar altimetry data.</p><p>This presentation highlights the benefits and opportunities offered by such regionalised advances, through an examination by the ESA-funded Baltic SEAL project (http://balticseal.eu/). We present the challenges faced, and solutions implemented, to develop new dedicated along-track and gridded sea level datasets for Baltic Sea stakeholders, spanning the years 1995-2019. Advances in waveform classification and altimetry echo-fitting, expansion of echo-fitting to a wide range of altimetry missions (including Delay-Doppler altimeters), and Baltic-focused multi-mission cross calibration, enable all altimetry missions’ data to be integrated into a final gridded product.</p><p>This gridded product, and a range of altimetry datasets, offer new insights into the Baltic Sea’s mean sea level and its variability during 1995-2019. Here, we focus on the analysis of sea level trends in the region using both tide gauge and altimetry data. The Baltic SEAL absolute sea level trend at the coast better aligns with information from the in-situ stations, when compared to current global products. The rise in sea level is statistically significant in the region of study and higher in winter than in summer. A gradient of over 3 mm/yr in sea level rise is observed, with sea levels in the north and east of the basin rising more than in the south-west. Part of this gradient (about 1 mm/yr) is directly explained by a regression analysis of the wind contribution on the sea level time series. A sub-basin analysis comparing the northernmost part (Bay of Bothnia) with the south-west reveals that the differences in winter sea level anomalies are related to different phases of the North-Atlantic Oscillation (0.71 correlation coefficient). Sea level anomalies are higher in the Bay of Bothnia when winter wind forcing pushes waters through Ekman transport from the south-west towards east and north.</p><p>The study also demonstrates the maturity of enhanced satellite altimetry products to support local sea level studies in areas characterised by complex coastlines or sea-ice coverage. The processing chain used in this study can be exported to other regions, in particular to test the applicability in regions affected by larger ocean tides. We promote further exploitation and identification of further synergies with other efforts focused on relevant oceanic variables for societal applications.</p>

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