Heat Waves

A percentile based definition of heat waves (HW) is used to calculate HW days using daily maximum temperatures (cf. Fig. 1 left). Temporal and spatial characteristics of the HWs across Europe were analysed to determine their duration, location and intensity.

A large year-to-year variability were found for the spatial distribution of the HW, which often occurred in cohesive patterns across wide areas, indicating a dependence on circulation anomalies (CA). Figure 1 right shows the correlation of the 4-year mean values of the HW-days between the recalibrated regional baseline1 (b1) hindcasts and the E-OBS observations. For Central and Southern Europe high correlations between the hindcast and observations were found.

To analyse the connection between the HW index and the CAs monthly sums of HW days are used to calculate correlations between them and the monthly indices of the teleconnection patterns (defined after Barnston and Livezey, 1987) e.g. the North Atlantic Oscillation (NAO), the Pacific Transition (PT), the East Atlantic (EA), the Polar (POL) or the Scandinavian pattern (SCAND) for Europe and different PRUDENCE regions (Fig. 2; cf. Fig. 1 work package C3-WP3 for the location of the PRUDENCE regions).

To capture the different phases of the CAs, the indices were separated into positive and negative phases. Correlations of up to r ≤ ±0.5 for certain regions and phases have been found with large variations for regions and index phase. Partly vice versa signals occurred between In the different phases of the indices the signals partly switch their sign, but sometimes the correlations do not react symmetric in opposite phases.

Thus, only for certain regions and patterns clear dependences between LCAs and HW days are found.

Heavy Precipitation

In this section, heavy precipitation events, as an example for large climate anomalies, will be discussed concerning the link to teleconnection patterns and their predictability.

First of all we analysed how the baseline1 hindcast ensemble represents different teleconnection patterns. Focussing on Europe, we choose the North Atlantic Oscillation (NAO), the East Atlantic (EA), the East Atlantic/Western Russia (EAWR) and the Scandinavian Pattern (SCAND) as defined by Barnston and Livezey (1987). We found that in the baseline1 hindcasts the first lead-years provide positive scores concerning time-correlation, mean square errors and probabilistic forecast-skill. Concerning large climate anomalies, we defined a percentile based index, which captures heavy precipitation events potentially causing floods. This index consists of field-means over the main European river catchments (from west to east: Rhine, Elbe, Oder, Vistula and Danube, c.f. Fig. 3) and 7 day running means of daily precipitation data.

Combining this index with the chosen teleconnection patterns, we found a clear relation between heavy precipitation events in winter and teleconnection patterns which changes over the catchments respectively over longitudes (c.f. Fig. 4).

The index is positively correlated to the three Atlantic patterns (EA, EAWR, NAO) for the western river catchments, but anti-correlated for the eastern. For the Scandinavian pattern, which is basically a southward shifted NAO pattern, the correlation trend between east and west switches its sign. From this we conclude that in the positive phase of the three Atlantic patterns, the potential for heavy precipitation events is increased in the Western catchments. In the Eastern catchments it is higher for the positive phase of the Scandinavian pattern. For the intermediate catchment of the Oder no clear tendency was found.

The long-term variability of extreme indices and their teleconnection to the Atlantic Multi-decadal Oscillation index (AMO) has been studied using the centennial downscaling experiments from work package C2-WP3, which cover the period since 1900. Several precipitation indices indicate a higher intensity in AMO+ phases. These findings still have to be evaluated carefully, since few reliable observational data are available before the mid of the 20th century.

Additionally, the regionalised and recalibrated (method developed within MiKlip Module E RECALIBRATION by Pasternack et. al 2017) baseline1 hindcast ensemble was analysed in terms of high percentiles of 7 day running mean precipitation. We found large areas of positive skill respectively correlation over Europe, again for the early lead-years (c.f. Fig. 5).

Overall we found that the teleconnection pattern provide an indicator for heavy precipitation events potentially causing floods, which changes from West to East. Moreover the high percentiles of precipitation shows positive skill for the early lead-years.

Wind storms

Wind storms and phases with increased storm activity for several European regions (PRUDENCE) are analysed using a meteorological storm severity index (MI). This index depends on the exceedance of the 98^th percentile of daily maximum surface wind speeds and the affected area. Within this work package, we focus on the number of MI days per extended winter season (ONDJFM) and its correlation with different teleconnection patterns. For CCLM-ERA, results show that the MI (and therefore the storm activity over Europe) is related to the occurrence of certain teleconnection patterns (Fig. 6).