1Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
2Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven, Germany
Received: 14 Jul 2015 – Discussion started: 13 Oct 2015
Abstract. Regional decadal predictions have emerged in the past few years as a research field with high application potential, especially for extremes like heat and drought periods. However, up to now the prediction skill of decadal hindcasts, as evaluated with standard methods, is moderate and for extreme values even rarely investigated. In this study, we use hindcast data from a regional climate model (CCLM) for eight regions in Europe and quantify the skill of the model alternatively by constructing time-evolving climate networks and use the network correlation threshold (link strength) as a predictor for heat periods. We show that the skill of the network measure to estimate the low-frequency dynamics of heat periods is superior for decadal predictions with respect to the typical approach of using a fixed temperature threshold for estimating the number of heat periods in Europe.
Revised: 20 Jul 2016 – Accepted: 21 Jul 2016 – Published: 24 Aug 2016
Weimer, M., Mieruch, S., Schädler, G., and Kottmeier, C.: A new estimator of heat periods for decadal climate predictions – a complex network approach, Nonlin. Processes Geophys., 23, 307-317, doi:10.5194/npg-23-307-2016, 2016.