Abstract. We used principal component analysis (PCA) to derive climate indices that describe the main spatial features of the climate in the Baltic states (Estonia, Latvia, and Lithuania). Monthly mean temperature and total precipitation values derived from the ensemble of bias-corrected regional climate models (RCMs) were used. Principal components were derived for the years . The first three components describe 92 % of the variance in the initial data and were chosen as climate indices in further analysis. Spatial patterns of these indices and their correlation with the initial variables were analyzed, and it was detected (based on correlation coefficient between principal components and initial variables) that higher values in each index corresponded to locations with (1) less distinct seasonality, (2) warmer climate, and (3) wetter climate. In addition, for the pattern of the first index, the impact of the Baltic Sea (distance to coast) was apparent; for the second, latitude and elevation were apparent, and for the third elevation was apparent. The loadings from the chosen principal components were further used to calculate the values of the climate indices for the years 2071-2100. An overall increase was found for all three indices with minimal changes in their spatial pattern.

Abstract. We used principal component analysis (PCA) to derive climate indices that describe the main spatial features of the climate in the Baltic States (Estonia, Latvia and Lithuania). Monthly mean temperature and total precipitation values derived from the ensemble of bias-corrected regional climate models (RCM) were used. Principal components were derived for years 1961–1990. The first three components describe 92 % of the variance of the initial data and were chosen as climate indices in further analysis. Spatial patterns of these indices and their correlation with the initial variables were analyzed and it was observed that higher values of each index corresponded to: (1) less distinct seasonality, (2) warmer and (3) wetter climate. The loadings from the chosen principal components were then further used to calculate values of the climate indices for years 2071–2100. Overall increase was found for all three indices with minimal changes in their spatial pattern.

<p>Marine debris and pollution of the sea is well recognized problem. Knowledge of the potential destination and time of arrival for buoyant or nearly buoyant contaminants as, for example, microplastics, is necessary for effective policy planning.</p><p>This work analyzes characteristics of buoyant objects in the Baltic Sea using simulations of Lagrangian particle movement. Simulations are based on current and wind model data. Initially particles are regularly distributed (spaced 5 km) over the Baltic Sea and a new simulation and particle release is started every day over a period of 10 years – years 2008-2017. It is assumed that upon reaching the coast particle gets washed out on the coast.</p><p>The aim of this work was to acquire following 3 drift characteristics for possible buoyant object movement in the Baltic Sea:</p><ol><li>How many days does it take from different regions of the Sea to reach the coast, what regions (clusters) can be identified that share similar behavior for different seasons;</li> <li>Which coastal regions are most at risk – which regions get particles washed out the most;</li> <li>What are main pathways for the particles – which sea regions affect which coastal regions the most.</li> </ol><p>As the distributions of floating time and location are non-normal then the methods of Symbolic Data Analysis (SDA) were used. To be more exact, statistics from each sea point or coastal segment was described by empirical distribution function (histogram) and differences/similarities were calculated using squared Wasserstein distance. The simulations cover multiple seasons – therefore the difference between seasons is also examined for each of 3 drift characteristics.</p><p>Part of the research is supported from the Latvian Academy of Sciences, project lzp-2018/1-0162 DRIMO - DRIft MOdelling for pollution reduction and safety in the Baltic Sea, 2018 - 2021.</p>

Thank you for the review and the comments. We found the input from the comments very useful and they will be taken into account in revised version of the manuscript.Responses to each comment are in the attached file.Please also note the supplement to this comment: http://www.earth-syst-dynam-discuss.net/esd-2017-34/esd-2017-34-AC1-supplement.pdfInteractive comment on Earth Syst. Dynam. Discuss.,

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