The dominant modes of blocking frequency variability in the Atlantic-European region are evaluated for the 1871–2010 period. An Empirical Orthogonal Function (EOF) analysis of a two-dimensional blocking indicator field reveals three dominant EOFs, describing about 35% of interannual to multidecadal blocking variability. The first EOF captures an out-of-phase blocking frequency anomaly over Greenland and Western Europe regions. The corresponding principal component time series is strongly correlated with the North Atlantic Oscillation index, but shows also significant correlations with indices of the East Atlantic, Scandinavian and East Atlantic-Western Russia patterns. The second EOF shows a dominant center over the North Sea region as well as a less pronounced center with anomalies of the same sign over southeastern Greenland. The multidecadal variations of this mode of blocking variability are related with a basin wide North Atlantic sea surface temperature anomaly which projects partly on the Atlantic Multidecadal Oscillation (AMO). The third mode is an east–west dipole of blocking frequency anomalies from Scandinavian and southern Greenland regions and shows enhanced variability at ~20 year time scales. The coherent variations of the time coefficients of this pattern with open solar flux suggest a possible solar influence on blocking variability at these time scales. Furthermore the dominant patterns of blocking variability are related with distinct anomaly patterns in the occurrence of extreme low temperature events over Europe at interannual to multidecadal time-scales. AMO as well as the solar signals were detected also in the corresponding extreme low temperature blocking patterns. We argue that multivariate analysis of blocking indicators gives additional information about blocking and related extreme climate phenomena variability and predictability comparative with classical sectorial approach.
Helmholtz Research Programs > PACES II (2014-2018) > TOPIC 3: The earth system from a polar perspective > WP 3.3: From process understanding to enabling climate prediction