The aim of the paper was to confirm the proposition that the classical SST algorithms MCSST and NLSST originally prepared for AVHRR data could also be used for Meteosat/SEVIRI data with satisfactory accuracy in the mid-latitude region, where the spatial resolution is about 7 × 7 km. The research was performed in the southern Baltic Sea (between 13• E 53 • N and 21• E 58 • N). Data were collected in all the seasons of 2007. The coefficients were found by means of regression analysis. SSTs determined on the basis of AVHRR data were used in the regression analysis instead of in situ data. A set of paired AVHRR and SEVIRI images spaced no more than 8 minutes apart were compared. The results show that the method is capable of producing sea surface temperatures with a statistical error (standard deviation) of 1• C.

This chapter describes observed changes in sea level and wind waves in the Baltic Sea basin over the past 200 years and the main climate drivers of this change. The datasets available for studying these are described in detail. Recent climate change and land uplift are causing changes in sea level. Relative sea level is falling by 8.2 mm year −1 in the Gulf of Bothnia and slightly rising in parts of the southern Baltic Sea. Absolute sea level (ASL) is rising by 1.3-1.8 mm year −1 , which is within the range of recent global estimates. The 30-year trends of

2D sea level trend and variability fields of the Baltic Sea were reconstructed based on statistical modeling of monthly tide gauge observations, and model reanalysis as a reference. The reconstruction included both absolute and relative sea level (RSL) in 11 km resolution over the period 1900-2014. The reconstructed monthly sea level had an average correlation of 96% and root mean square error of 3.8 cm with 56 tide gauges independent of the statistical model. The statistical reconstruction of sea level was based on multiple linear regression and took land deformation information into account. An assessment of the quality of an open ocean altimetry product (ESA Sea Level CCI ECV, hereafter "the CCI") in this regional sea was performed by validating the variability against the reconstruction as an independent source of sea level information. The validation allowed us to determine how close to the coast the CCI can be considered reliable. The CCI matched reconstructed sea level variability with correlation above 90% and root-mean-square (RMS) difference below 6 cm in the southern and open part of the Baltic Proper. However, areas with seasonal sea ice and areas of high natural variability need special treatment. The reconstructed RSL change, which is important for coastal communities, was found to be dominated by isostatic land movements. This pattern was confirmed by independent observations and the values were provided along the entire coastline of the Baltic Sea. The area averaged absolute sea level change for the Baltic Sea was 1.3 ± 0.3 mm/yr for the 20th century, which was slightly below the global mean for the same period. Considering the relative shortness of the satellite era, natural variability made trend estimation sensitive to the selected data period, but the linear trends derived from the reconstruction (3.4 ± 0.7 mm/yr for 1993-2014) fitted with those of the CCI (4.0 ± 1.4 mm/yr for 1993-2015) and with global mean estimates within the limits of uncertainty.

This study presents an overview of the Estonian sea level data set obtained from the coastal tide gauges over the period 1842–2005. Variations in the time‐series of annual mean sea level, maxima and minima, as well as standard deviations are investigated and their relationships with variations in the North Atlantic Oscillation index are studied. After correcting the sea level series to spatially varying land uplift rates the series display increasing (1.5–2.7 mm yr−1) trends, which in case of Pärnu tide gauge evidently exceed the global sea level rise rate. The increase is larger in winter, which is in accordance with similar seasonal structures of the NAO index trends. The rise in mean sea level, standard deviations and particularly in maxima (3.5–11.2 mm yr−1) could be explained by the local response to the changing regional wind climate. Due to its windward location the sea level variations in the semi‐enclosed study area are sensitive to the ongoing intensification of cyclones and prevailing westwinds. In case of the Pärnu Bay, the statistical fit of both the frequency distributions of hourly data and the maximum values distributions for 1923–2005 are inconsistent with the two highest storm surge values of 253 and 275 cm.

The physical and optical properties of an atmospheric aerosol mixture depend on a number of factors. The relative humidity influences the size of hydroscopic particles and the effective radius of an aerosol mixture. In consequence, values of the aerosol extinction, the aerosol optical thickness and theÅngström coefficient are modified. A similar effect is observed when the aerosol composition changes. A higher content of small aerosol particles causes the effective radius of an aerosol mixture to decrease and theÅngström coefficient to increase. Both effects are analysed in this paper. The parameters of the size distribution and the type of components used to represent natural atmospheric aerosol mixtures are based on experimental data. The main components are sea-salts (SSA), anthropogenic salts (WS, e.g. NH 4 HSO 4 , NH 4 NO 3 , (NH 4 ) 2 SO 4

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The aim of this paper is to assess the potential oil spill related ecological risk for the southern Gulf of Finland coastal waters using the Bayesian Belief Network (BBN) methodology. The BBN prior probabilities were obtained from knowledge on spatial variability in the sensitivity of coastal ecosystem of the southern Gulf of Finland. The sensitivity data represented the three different ecosystem elements: the EU Habitat Directive Annex 1 habitats and associated habitat forming species, the EU Birds Directive Annex 1 birds and seals. Information on bird, seal and habitat layers were integrated into a single measure of ecosystem sensitivity. For this purpose the maximum value of different layers was calculated in each raster cell. The scenario modelling results showed that the western Gulf of Finland could be considered as an area of the highest ecological risk for the all seasons.