C2-WP1 - Added value of regionalisation 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 regionalisation. 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 conducted at the Karlsruhe Institute of Technology (KIT). It is subdivided in two thematic aspects:

  • Development/application of methodologies to derive user-relevant variables (in cooperation with IPRODUCTS and SUPPORT from Module D)

  • Quantification of the predictive skill and the added value of downscaling for these variables (in cooperation with C2-WP2).

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.
 

Deliverables

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. These variables are related to temperature extremes, wind extremes, heavy precipitation and agronomy (e.g. windstorm losses, crop productivity or heat waves).

Regarding windstorm losses, a collaboration with an insurance company has been established in cooperation with GERICS (Module D). Focus is given to forecasts of wind gusts potentially causing losses (gusts > 17.2m/s) for 1-5 years ahead. Especially the change in the number of days per year where wind gusts exceed this threshold is analysed. A “forecast” for 2012-2016 for the preop ensemble is linked to insurance data (Fig. 1). Since the present calibration is based on absolute wind speeds, we currently develop a bias correction and calibration method, together with Module E.

 

 

Fig. 1: Number of days per year with gust speeds > 17.2m/s for 2012-2016 compared to 1970-2010 for CCLM-ERA, for the ensemble mean of regionalised preop, for the ensemble median of regionalised preop and for the standard deviation.

Regarding temperature extremes, we developed an index based on the exceedance of the 90th percentile of daily maximum temperature (Tmax) to identify heat waves for different European regions (see also C2-WP2). The results show a predictive skill for heat waves (e.g. for mean and maximum heat wave temperature) for the first years after initialisation.
Additionally, we analysed the predictive skill of several climate indices (ETCCDI) that are related to temperature extremes in the baseline1 ensemble. These indices include heating degree days, frost days and daily maximum summer temperature. All of these indices show high predictive skill for most of Europe, especially for lead years 2-5 (Fig. 2). The skill patterns are spatially robust, with higher skill scores for indices related to warm temperatures.

Fig. 2: Anomaly correlation coefficient (ACC; as calculated with MurCSS) for lead years 2-5 for the baseline1 ensemble for heating degree days, frost days and the daily maximum summer temperature.

Regarding heavy precipitation, events that may cause floods in large European river catchments (Rhine, Elbe, Oder, Danube, Vistula) are analysed (see also C2-WP2). With this aim, we developed an index (based on 7day-running-mean precipitation and percentile exceedance) to derive flood events directly from regional precipitation data.

For agronomy, we investigate a climate index, which characterises the length of the growing season. The growing season starts when six consecutive days have temperatures above 5°C and ends when six days are below 5°C. For large parts of Europe, this index exhibits high positive skill scores in the baseline1 ensemble for lead years 2-5 (Fig. 3). Additionally, the productivity of several crops (wine, olives, fruit) is analysed using an index, which is based on precipitation, minimum/maximum temperature and soil parameters. First results show a high predictability in the baseline1 ensemble for nearly all lead times.

Overall, we found predictive skill over Europe on the decadal scale for several variables far beyond yearly mean temperatures. The results might be relevant for users outside the scientific community.

Fig. 3: Anomaly correlation coefficient (ACC; as calculated with MurCSS) for lead years 2-5 for the baseline1 ensemble for the growing season length.

Contact

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

Institute for Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology (KIT)
Julia Mömken
julia.moemken(at)nospamkit.edu
+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