C2-WP1 - Added value of regionalization for user-relevant variables

Project aims

The main goal of this work package is to quantify the decadal predictive skill of extremes of key climate variables (temperature, wind, and precipitation) and of exemplary user-relevant quantities for Central Europe (e.g. wind energy and losses associated with windstorms), which will be selected in collaboration with end users. In particular, we will quantify the added value of downscaling on the predictability of these quantities, which are mostly influenced by local conditions and physical processes on the regional scale. The objective is to develop or update methodologies for the computation of these variables on the global (GCM) and regional (RCM) scale. Since the temporal and spatial resolution of observations and model output data is not generally uniform, and a post-processing of simulated standard variables is necessary, the computation of the user-relevant quantities is not straightforward. Different skill metrics are applied to quantify the added value of regionalization. The developed methods will contribute to the MiKlip II central evaluation system (CES) and to an effective dissemination of the project results to the potential users.   

Project structure

The work package C2-WP1 is subdivided in two thematic aspects: development/application of methodologies to derive user-relevant variables, and quantification of the predictive skill and the added value of downscaling for these variables.

Tasks of the project

T1 Development/application of methods to derive user-relevant quantities
Different and appropriate methodologies are required to derive user-relevant quantities. For this task existing approaches will be further extended or new methodologies will be developed. The methodologies will be applied to standard output of the global and regional decadal prediction system and to observational data sets.

T2 Analysis of decadal predictability and added value of downscaling
The intention of this task is to find and apply suitable skill metrics to assess the forecast accuracy and the reliability of the MiKlip decadal prediction system with respect to climate extremes and user-relevant variables. These metrics are applied to both the global and regional hindcast simulations, to quantify the added value of downscaling.


D1a Methodologies to derive user-relevant variables
D1b Homogeneous time-series of variables on regional and global scale
D2a Suitable skill metrics for accuracy and reliablilty
D2b Summary of predictive skill for extremes and user-relevant variables

Progress so far

Several potentially user-relevant variables have been identified: wind storm losses, heavy precipitation causing floods, wine productivity, and temperature extremes. Regarding wind storm losses, a collaboration with an insurance company has been established in cooperation with GERICS. Focus is given to forecasts of wind gusts potentially causing losses for 1-5 years ahead. The decadal predictability of the number of days per year with wind gusts above a certain threshold is analysed. The results will be linked to insurance data in a next step.
Regarding heavy precipitation, events that may cause floods in large European rivers are analysed. Therefore, a statistical downscaling method based on analogues has been developed. The method has been tested and outperforms CCLM-ERA in some regions (e.g. Eastern Europe). The method is currently applied to the global hindcasts to quantify the decadal predictive skill for all large European river catchments.
In terms of wine productivity, several bioclimatic indices, which can be used to distinguish suitable years for grapevine growing, are analysed: e.g. Hydrothermic Index of Branas, Huglin Heliothermal Index, Dryness Index and Winter Severity Constraint. A bias correction is applied to the basic variables (maximum/minimum/mean daily temperatures and precipitation) before calculating the indices for daily data from ERA and the MiKlip hindcasts.


Figure 1: MSESS for mean Tmax in baseline1 (left) and prototype (ORAs4, right) for lead years 2-5.

Regarding temperature extremes, focus is given to Tmax. A statistical multiple-linear-regression method has been developed. It is validated against E-OBS and then applied to all available hindcast generations (baseline1, prototype and preop). For Germany, a high forecast skill is found for yr2-5 in baseline1, while skill scores are weaker for the prototype (Figure 1). Similar analyses are currently done based on the dynamically downscaled regional MiKlip ensemble. In the future, Tmax will be used for the analysis of heat waves.


Institute for Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology (KIT)
Prof. Dr. Joaquim Pinto
+49 (0)721 608-28467

Institute for Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology (KIT)
Julia Mömken
+49 (0)721 608-22805

Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe

2016 - Tellus A, Vol. 68 (29199)

Moemken, J. | M. Reyers, B. Buldmann, and J.G. Pinto JG

Contrasting interannual and multi-decadal NAO variability

2014 - Clim Dyn., Vol. 45 (1), pp. 539-556

Woollings, T. | C. Franzke, D.L.R. Hodson, B. Dong, E.A. Barnes, C.C. Raible and J.G. Pinto

Development of a wind gust model to estimate gust speeds and their return periods

2014 - Tellus A, Vol. 66 (22905)

Seregina, L.S. | R. Haas, K. Born, and J.G. Pinto

Decadal predictability of regional-scale peak winds over Europe based on MPI-ESM-LR

2014 - Meteorologische Zeitschrift, Vol. 25 No. 6, pp. 739-752

Haas, R. | M. Reyers, and J.G. Pinto