Fig 1.
Study area and the locations of time-series by system categories.
Fig 2.
Results of multivariate RS analysis by the studied systems.
A timing of statistically significant RSs is shown by filled squares with the size of square indicating the relative strength of RS (i.e. the relative number of individual series within a block that exhibited RS around particular year). Here the RS year indicates the first year of new regime. The thickness of line depicts the relatedness of time series within each system (i.e. the average similarity between the clustered profiles of individual time series and the respective block) and thicker line indicates higher similarities among the studied time series. See Data analyses subsection of Material and methods for further details. For the list of used time series, their original temporal resolution and spatial extent see S1 Table. A script on how to execute the analysis under the R environment are given in the S2 Table.
Fig 3.
Potted history for each time series that exhibited RSs in the CP analysis.
Normalized time series are displayed separately for abiotic and biotic elements of atmospheric, terrestrial, bog, lake, river and marine systems. A timing of typical RSs within different subsystems is shown by broken lines. See Data analyses subsection of Material and methods for further details. More detailed descriptions and timing of RSs of each studied time series are shown in S1 Table.
Fig 4.
Relatedness of different components of the regional climate system in terms of the timing and strength of RSs during 1966−2013.
Each value represents an average similarity of all possible pairs of time series between the respective studied systems with higher values indicating higher similarity as measured by the Agresti’s Adjusted Rand index. NAO indices represent global drivers of change, and a wide range of abiotic and biotic time series of atmospheric, terrestrial, bog, lake, river and marine systems are regional responders of such global change. More detailed descriptions of each studied time series within different studied systems are shown in S1 Table.
Fig 5.
Ordination of the individual time series of different components of the regional climate system in terms of the timing and strength of RSs during 1966−2013.
Here the closer distance between time series shows closer resemblances. NAO indices represent the atmospheric circulation driver of change, and a wide range of abiotic and biotic time series of atmospheric, terrestrial, bog, lake, river and marine systems are regional responders of such large-scale change. The coloured polygons depict the range of variability of time series in different studied systems. The codes of time-series are explained in S1 Table.
Table 1.
Robustness analysis of RS detection.
Within each system category a single time series was dropped and full CP analysis based on the reduced block was carried out (leave-one-out or LOO). This was repeated for all single time series. Thus e.g. for the NAO category 51 different multivariate datasets were used, each consisting of 50 time-series. LOO RS repeatability shows the percent of LOO repetitions where a RS was detected within +/-2 years of the RS detected on the full dataset (displayed on Fig 2). LOO relatedness deviation shows the standard deviation of the mean Agresti’s Adjusted Rand index.
Fig 6.
Schematic diagram of the impact of the 1989 RS in atmospheric circulation on abiotic and biotic components of different systems of the Estonian regional climate.