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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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 |
work_keys_str_mv |
AT lbethere climateindicesforthebalticstatesfromprincipalcomponentanalysis AT jsennikovs climateindicesforthebalticstatesfromprincipalcomponentanalysis AT ubethers climateindicesforthebalticstatesfromprincipalcomponentanalysis |
_version_ |
1725918112825475072 |