Climate indices for the Baltic states from principal component analysis

Abstract

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.

Climate zones in Iran

“…Then, a data matrix of order n × p was developed, where n is the number of synoptic stations (139) and p (720) is monthly precipitation and temperature during a 30‐year period (1991–2020) (2 × 12 × 30 = 720). Based on the following equation, a typical PCA is applied to a p × p covariance (or the correlation) matrix to obtain the covariance matrix S (Bethere et al, 2017), $$$$ S={\left(n-1\right)}^{-1}\ {\mathrm{X}}^{\mathrm{T}}X, $$$$ where X is the sample covariance matrix associated with the dataset and X T denotes its transpose. We can find eigenvectors ( e i , i = 1, …, 720) and corresponding eigenvalues ( λ i , i = 1, …, 720) by the following equation (Bethere et al, 2017): $$$$ Se=\lambda e, $$$$ where e is an eigenvector and λ is the corresponding eigenvalue of the covariance matrix S. We obtained non‐correlated linear combinations of the initial climatic variables using the following equation (Bethere et al, 2017): $$$$ {Y}_i={X}_i\ {e}_i\ i=1,\dots, 720, $$$$ where λ i represents the variance of each principal component Y i , and e i defines each PC called loadings.…”

“…where e is an eigenvector and λ is the corresponding eigenvalue of the covariance matrix S. We obtained noncorrelated linear combinations of the initial climatic variables using the following equation (Bethere et al, 2017):…”

“…Then, a data matrix of order n  p was developed, where n is the number of synoptic stations (139) and p (720) is monthly precipitation and temperature during a 30-year period (1991-2020) (2  12  30 = 720). Based on the following equation, a typical PCA is applied to a p  p covariance (or the correlation) matrix to obtain the covariance matrix S (Bethere et al, 2017),…”

Effect of hydroclimatological teleconnections on the watershed‐scale drought predictability in the southeastern United States

“…In order to address the inherent nonlinearity in the relationship of the climate indices and hydrologic variables, several studies have used nonlinear approaches like mutual information (Knuth et al, 2014;Yoon and Lee, 2016), cross-wavelet analysis (Labat, 2010;Agarwal et al, 2017) or PC analysis (Bethere et al, 2017), etc. However, the objective of this study is only to highlight the spatial variability in the strength of interrelationship between watershed-scale drought and climate indices; the inferences are derived from linear correlation analysis and R-square of the least-square model fit between hydrologic variables and DIs.…”

PCA analysis of wind direction climate in the baltic states

“…Applications of PCA in studies of wind climatology include classifying and investigating wind field patterns over the USA (Klink and Willmott, 1989) and the Iberian Peninsula (Pedro et al, 2009) and grouping observation stations based on wind gust patterns (Jungo et al, 2002). In more recent studies PCA has been used as a dimensional reduction tool in artificial neural network based weather forecasts (Mezaache et al, 2016) and used for climate model data for the eastern Baltic region to derive climate indices based on monthly mean temperature and precipitation (Bethere et al, 2017).…”