Performance of Sentinel-3A SAR Altimetry Retrackers: The SAMOSA Coastal Sea Surface Heights for the Baltic Sea

Marginal seas are a dynamic and still to large extent uncertain component of the global carbon cycle. The large temporal and spatial variations of sea-surface partial pressure of carbon dioxide (pCO2) in these areas are driven by multiple complex mechanisms. In this study, we analyzed the variable importance for the sea surface pCO2 estimation in the Baltic Sea and derived monthly pCO2 maps for the marginal sea during the period of July 2002–October 2011. We used variables obtained from remote sensing images and numerical models. The random forest algorithm was employed to construct regression models for pCO2 estimation and produce the importance of different input variables. The study found that photosynthetically available radiation (PAR) was the most important variable for the pCO2 estimation across the entire Baltic Sea, followed by sea surface temperature (SST), absorption of colored dissolved organic matter (aCDOM), and mixed layer depth (MLD). Interestingly, Chlorophyll-a concentration (Chl-a) and the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd_490nm) showed relatively low importance for the pCO2 estimation. This was mainly attributed to the high correlation of Chl-a and Kd_490nm to other pCO2-relevant variables (e.g., aCDOM), particularly in the summer months. In addition, the variables’ importance for pCO2 estimation varied between seasons and sub-basins. For example, the importance of aCDOM were large in the Gulf of Finland but marginal in other sub-basins. The model for pCO2 estimate in the entire Baltic Sea explained 63% of the variation and had a root of mean squared error (RMSE) of 47.8 µatm. The pCO2 maps derived with this model displayed realistic seasonal variations and spatial features of sea surface pCO2 in the Baltic Sea. The spatially and seasonally varying variables’ importance for the pCO2 estimation shed light on the heterogeneities in the biogeochemical and physical processes driving the carbon cycling in the Baltic Sea and can serve as an important basis for future pCO2 estimation in marginal seas using remote sensing techniques. The pCO2 maps derived in this study provided a robust benchmark for understanding the spatiotemporal patterns of CO2 air-sea exchange in the Baltic Sea.

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Variational assimilation of observation data in the mathematical model of the Baltic Sea dynamics

Russian Journal of Numerical Analysis and Mathematical Modelling ◽

10.1515/rnam-2015-0018 ◽

2015 ◽

Vol 30 (4) ◽

Cited By ~ 5

Author(s):

Valeriy I. Agoshkov ◽

Eugene I. Parmuzin ◽

Vladimir B. Zalesny ◽

Victor P. Shutyaev ◽

Natalia B. Zakharova ◽

...

Keyword(s):

Mathematical Model ◽

Heat Flux ◽

Baltic Sea ◽

Satellite Observation ◽

Sea Surface ◽

Observation Data ◽

The Baltic Sea ◽

Variational Assimilation ◽

The Baltic ◽

The Mathematical Model

AbstractA mathematical model of the dynamics of the Baltic Sea is considered. A problem of variational assimilation of sea surface temperature (SST) data is formulated and studied. Based on variational assimilation of satellite observation data, an algorithm solving the inverse problem of heat flux restoration on the interface of two media is proposed. The results of numerical experiments reconstructing the heat flux functions in the problem of variational assimilation of SST observation data are presented. The influence of SST assimilation on other hydrodynamic parameters of the model is considered.

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Changing seasonality of the Baltic Sea

Biogeosciences ◽

10.5194/bg-13-1009-2016 ◽

2016 ◽

Vol 13 (4) ◽

pp. 1009-1018 ◽

Cited By ~ 34

Author(s):

Mati Kahru ◽

Ragnar Elmgren ◽

Oleg P. Savchuk

Keyword(s):

Baltic Sea ◽

Chlorophyll Concentration ◽

Ecological Status ◽

Sea Surface ◽

Seasonal Cycles ◽

The Baltic Sea ◽

Several Variables ◽

Satellite Sensors ◽

The Baltic ◽

Increasing Trends

Abstract. Changes in the phenology of physical and ecological variables associated with climate change are likely to have significant effect on many aspects of the Baltic ecosystem. We apply a set of phenological indicators to multiple environmental variables measured by satellite sensors for 17–36 years to detect possible changes in the seasonality in the Baltic Sea environment. We detect significant temporal changes, such as earlier start of the summer season and prolongation of the productive season, in several variables ranging from basic physical drivers to ecological status indicators. While increasing trends in the absolute values of variables like sea-surface temperature (SST), diffuse attenuation of light (Ked490) and satellite-detected chlorophyll concentration (CHL) are detectable, the corresponding changes in their seasonal cycles are more dramatic. For example, the cumulative sum of 30 000 W m−2 of surface incoming shortwave irradiance (SIS) was reached 23 days earlier in 2014 compared to the beginning of the time series in 1983. The period of the year with SST of at least 17 °C has almost doubled (from 29 days in 1982 to 56 days in 2014), and the period with Ked490 over 0.4 m−1 has increased from about 60 days in 1998 to 240 days in 2013 – i.e., quadrupled. The period with satellite-estimated CHL of at least 3 mg m−3 has doubled from approximately 110 days in 1998 to 220 days in 2013. While the timing of both the phytoplankton spring and summer blooms have advanced, the annual CHL maximum that in the 1980s corresponded to the spring diatom bloom in May has now shifted to the summer cyanobacteria bloom in July.

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Nemo-Nordic 2.0: Updated Baltic Sea model based on NEMO 4.0

10.5194/egusphere-egu21-8728 ◽

2021 ◽

Author(s):

Tuomas Kärnä ◽

Ida Ringgaard ◽

Vasily Korabel ◽

Adam Nord ◽

Patrik Ljungemyr ◽

...

Keyword(s):

Baltic Sea ◽

Horizontal Resolution ◽

Sea Surface ◽

Small Scale ◽

The Baltic Sea ◽

Inflow Event ◽

Arkona Basin ◽

The Baltic ◽

Marine Environment Monitoring ◽

Monitoring Service

<p>We present Nemo-Nordic 2.0, the latest version of the operational marine forecasting model for the Baltic Sea used and developed in the Baltic Monitoring Forecasting Centre (BAL MFC) under the Copernicus Marine Environment Monitoring Service (CMEMS). The most notable differences between Nemo-Nordic 2.0 and its predecessor Nemo-Nordic 1.0 are the switch from NEMO 3.6 to NEMO 4.0 and an increase in horizontal resolution from 2 to 1 nautical mile. In addition, the model's bathymetry and bottom friction formulation have been updated. The model configuration was specially tuned to represent Major Baltic Inflow events. Focusing on a 2-year validation period from October 1, 2014, covering one Major Baltic Inflow event, Nemo-Nordic 2.0 simulates Sea Surface Height (SSH) well: centralized Root-Mean-Square Deviation (CRMSD) is within 10 cm for most stations outside the Inner Danish Waters. CRMSD is higher at some stations where small-scale topographical features cannot be correctly resolved. SSH variability tends to be overestimated in the Baltic Sea and underestimated in the Inner Danish Waters. Nemo-Nordic 2.0 represents Sea Surface Temperature (SST) and Salinity (SSS) well, although there is a negative bias around -0.5&#176;C in SST. The 2014 Major Baltic Inflow event is well reproduced. The simulated salt pulse agrees well with observations in the Arkona basin and progresses into the Gotland basin in 3 to 4 months.</p>

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

Earth System Dynamics ◽

10.5194/esd-8-1031-2017 ◽

2017 ◽

Vol 8 (4) ◽

pp. 1031-1046 ◽

Cited By ~ 3

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.

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