The general goal of this work package is to quantify the capability of the regional climate model COSMO-CLM to provide reliable decadal forecasts for Europe, and to determine how far and why the forecasts vary on decadal time scales. Our aim is thus to uncover the processes underlying this predictability. With this aim, we analyse the predictive skill of the MiKlip hindcasts and identify the physical processes that lead to this predictive skill over Europe.
For this task, we follow two different routes: the first one concentrates on the statistics of the regional predictive skill depending on the large-scale weather-types or phases of the multi-decadal variability. The second one focuses on large climate anomalies related to an enhanced occurrence of extreme events like heat waves, droughts, floods and windstorms in the target region because of their high impact and importance to the society. Both approaches complement each other.
Three groups (Deutscher Wetterdienst (DWD), University of Cologne, and Karlsruhe Institute of Technology (KIT)) cooperate in work package C2-WP2. It is subdivided in four tasks with two thematic aspects: the conditional evaluation of the hindcasts and the analysis of large climate anomalies.
T1 Conditional evaluation of the hindcasts
The hindcasts will be stratified with respect to their weather conditions or state of multi-annual and multi-decadal oscillation (Atlantic multi-decadal Variability (AMV), North Atlantic Oscillation (NAO), etc.), to identify conditions with higher or lower forecast skill. First analysis of stratified hindcasts uncovered conditions with diverging forecast skill in Europe.
The stratified data will be statistically analysed with respect to the predictive skill in Europe. The correlation of precursors and climate elements to be predicted for each weather type will be determined. From the resulting correlations for the different classes (weather condition, states of the oscillations), conditions with higher or lower predictability can be deduced. Conditional climatologies depending on weather conditions will be contrasted against the mean climatology.
T2 Predictability of large climate anomalies
In a first step examples of potentially relevant events like distinct climate shifts from the 90s (warming of the Atlantic sub-polar gyre region) to the 2000s hiatus or other European large climate anomalies, e.g., like the series of heat waves in 2003 and 2006, the large flooding in 2002, the peak of windstorm activity in the early 1990s, or the anomaly of extreme precipitation events will be selected. The results from the conditional evaluation will be used to augment the selected cases with other exceptional conditions. A proper weather typing approach is selected and applied for each large climate anomaly.
Further, the mechanisms behind the predictive skill will be in focus. The correlation between the precursor defining the weather type and the climate elements to be predicted will be analysed to deduce the extent to what the large climate anomalies can be predicted or (partly) attributed to the multi-decadal variability pattern in the Arctic and North Atlantic climate system. In addition the signal leading to the large climate anomalies will be traced back in order to determine how far ahead the large climate anomaly can be detected.
D1a Methods for investigating the stratified hindcasts
D1b Results of the stratified evaluations
D2a Method for investigating the large climate anomalies
D2b Results of the large climate anomaly analysis
A suitable objective method of weather pattern classification is needed for the stratified evaluation as well as for the analysis of mechanisms and process chains behind the large climate anomalies (see example in Fig. 1 for the series of extreme storms in 1990 – Daria, Vivian, Wiebke). An appropriate weather type analysis for Europe is the Hess and Brezowsky Grosswetterlagen (GWL) classification. So far, an objective GWL classification has been applied to ERA40/ERA-Interim (Fig. 1) and the full available ensemble of the MiKLip decadal prediction system (baseline0, baseline1, prototype, historical runs).
Deutscher Wetterdienst - Climate and Environment Consultancy
Dr. Barbara Früh
+49 (0)69 8062-2968
Institute for Geophysics and Meteorology, University of Cologne
Dr. Mark Reyers
+49 (0)221 470-7302
Institute for Meteorology and Climate Research (IMK-TRO) Karlsruhe Institute of Technology (KIT)
+49 (0)721 608-28467