Climate indices for the Baltic states from principal component analysis

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...

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Main Authors: L. Bethere, J. Sennikovs, U. Bethers
Format: Article
Language:English
Published: Copernicus Publications 2017-10-01
Series:Earth System Dynamics
Online Access:https://www.earth-syst-dynam.net/8/951/2017/esd-8-951-2017.pdf
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spelling doaj-c501db9f8a5548859a76f66e1e9f9c172020-11-24T21:42:14ZengCopernicus PublicationsEarth System Dynamics2190-49792190-49872017-10-01895196210.5194/esd-8-951-2017Climate indices for the Baltic states from principal component analysisL. Bethere0J. Sennikovs1U. Bethers2Laboratory for Mathematical Modelling of Environmental and Technological Processes, University of Latvia, Riga LV-1002, LatviaLaboratory for Mathematical Modelling of Environmental and Technological Processes, University of Latvia, Riga LV-1002, LatviaLaboratory for Mathematical Modelling of Environmental and Technological Processes, University of Latvia, Riga LV-1002, LatviaWe 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.https://www.earth-syst-dynam.net/8/951/2017/esd-8-951-2017.pdf
collection DOAJ
language English
format Article
sources DOAJ
author L. Bethere
J. Sennikovs
U. Bethers
spellingShingle L. Bethere
J. Sennikovs
U. Bethers
Climate indices for the Baltic states from principal component analysis
Earth System Dynamics
author_facet L. Bethere
J. Sennikovs
U. Bethers
author_sort L. Bethere
title Climate indices for the Baltic states from principal component analysis
title_short Climate indices for the Baltic states from principal component analysis
title_full Climate indices for the Baltic states from principal component analysis
title_fullStr Climate indices for the Baltic states from principal component analysis
title_full_unstemmed Climate indices for the Baltic states from principal component analysis
title_sort climate indices for the baltic states from principal component analysis
publisher Copernicus Publications
series Earth System Dynamics
issn 2190-4979
2190-4987
publishDate 2017-10-01
description 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.
url https://www.earth-syst-dynam.net/8/951/2017/esd-8-951-2017.pdf
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