Articles | Volume 8, issue 4
https://doi.org/10.5194/esd-8-951-2017
https://doi.org/10.5194/esd-8-951-2017
Research article
 | 
26 Oct 2017
Research article |  | 26 Oct 2017

Climate indices for the Baltic states from principal component analysis

Liga Bethere, Juris Sennikovs, and Uldis Bethers

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 1961–1990. 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.

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Short summary
We define three new climate indices based on monthly mean temperature and total precipitation values that describe the main features of the climate in the Baltic states. Higher values in each index correspond to (1) less distinct seasonality and (2) warmer and (3) wetter climate. It was calculated that in the future all three indices will increase. Such indices summarize and illustrate the spatial features of the Baltic climate, and they can be used in further analysis of climate change impact.
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