english  | Home | Sitemap | KIT

DecReg - Regional Decadal Predictability (Europe)

DecReg - Regional Decadal Predictability (Europe)
Ansprechpartner:

Dr. G. Schädler, Dr. S. Mieruch

Links:
Projektgruppe:

IMK-TRO

Förderung:

BMBF

Projekt Members:

Institut für Meteorologie und Klimaforschung,
Karlsruher Institut für Technologie (KIT)

Dr. Gerd Schädler
Dr. Sebastian Mieruch
Prof. Dr. Christoph Kottmeier


Institut für Physik der Atmosphäre,
Universität Mainz

Dr. Astrid Kerkweg
Klaus Pankatz


Institut für Atmosphäre und Umwelt
Goethe Universität Frankfurt/Main

Prof. Dr. Bodo Ahrens
Dr. Steffen Kothe


Deutscher Wetterdienst
Dr. Peter Bissolli
Dr. Sven Brinkmann

Short description

DecReg is a joint project including the Institute for Meteorology and Climate Research of the Karlsruhe Institute of Technology (KIT), the Institute for Physics of the Atmosphere of the University of Mainz, the Institute for Atmosphere and Environment of the Goethe University Frankfurt/Main and the German Weather Service (DWD). It is coordinated by the Institute for Meteorology and Climate Research of KIT. DecReg studies the feasability of decadal regional climate predictions. This is achieved by downscaling global climate model simulations using the regional climate model CCLM.

 

Aims

DecReg (Regional Decadal Predictability, C-18 in Module C of MiKlip) aims to assess the feasibility, the added value and the uncertainty range of decadal regional climate forecasts as well as the spatial and temporal variation of the predictive potential. For that purpose, predictive decadal hindcasts (1-10 years) for Europe are generated in high resolution with a regional climate model (here CCLM) using global model predictions (MPI-ESM) for the past decades. The ensemble results are used to derive statistics for comparison with observations and to estimate uncertainty ranges. The ensembles are mainly generated using different model setups and initializations, as well as different ways to feed in the driving data. It is expected that high resolution together with ensemble simulations will make it possible to reliably capture the statistics of extremes, like heavy precipitation and droughts. Other results will include time series of precipitation, temperature and wind which can be used for impact studies. Coordination and cooperation with the projects LACEPS, Regio Predict and REDCLIP as well as with Module E is intended.

 

The contributions of the partners are as follows: University of Mainz: coupling between global and regional model; DWD: provision of quality assessed gridded observation data; University of Frankfurt and KIT: impact of land surfaces, regional ensemble generation and analysis; KIT: project coordination.

 

The following goals were achieved:

  • generation of reference simulations
  • provision of high resolution gridded observation data for Europe
  • analysis of the influence of different factors affecting the predictive potential of regional decadal prognoses: coupling between the global and the regional model, spatial resolution, parameterisations used, modelling and initialisation of soil moisture and soil temperature
  • generation and analysis of high resolution hindcast ensembles for Central Europe, estimation of the uncertainty of the prognoses and of the dependence of the predictability on region and season, using suitable metrics
  • validation of the ensembles using gridded observations and reference simulations, including extreme events
  • provision of sample prognoses for impact studies and pre-operational implementation of the methods.

 

Results can be found in Mieruch et al. (2013) and in Weimer et al. (2016), where a complex network approach has been used to identify heat periods.

 

Publications:

S. Mieruch, H. Feldmann, G. Schädler, C.-J. Lenz, S. Kothe and C. Kottmeier: The regional MiKlip decadal  forecast ensemble for Europe. Geophys. Model Develop., 6, 5711–5745, 2013

M. Weimer, S. Mieruch, G. Schädler and C. Kottmeier: A new estimator of heat periods for decadal climate predictions – a complex network approach. Nonlin. Processes Geophys., 23, 307–317, 2016