Berlin-Workshop on Bias Correction in Climate Studies

Oct. 4th - Oct. 6th, 2016, Berlin-Dahlem

Climate projections, decadal and seasonal predictions generally exhibit systematic deviations from observations. Removing those biases is typically the first step towards useful climate projections or decadal/seasonal forecasts and thus a prerequisite for subsequent use, e.g. in impact studies for climate services. Challenges are not only posed by highly skewed variables such as precipitation but also by time dependent biases (drifts) or physical and spatial consistency.

The goal of this workshop is to review the state of the art of current methods and challenges for statistical bias correction for climate projections, decadal and seasonal predictions and the application for various impact studies and climate services. Hence, the workshop aims to bring together climate modelers, developers of bias correction (BC) methods and users of such methods to cover the whole range of questions raised by biases in climate models.

Topics covered can be related to (non-exhaustive list):

  • Development of bias correction methods
  • multivariate (spatial or multi-variables) methods
  • stochastic vs. deterministic methods
  • bias correction and ensemble generation
  • Seasonal to decadal
  • time and lead time dependent bias (drift) correction
  • ensemble calibration
  • Applications of bias corrections methods for impact modeling and climate services
  • e.g. agronomy, hydrology, ecology, renewable energy, etc.
  • Further problems such as
  • Identification of biases in GCMs or RCMs
  • Impacts of bias correction on trends and variability of climate simulations

The number of participants will be limited to about 80 to ease exchanges and discussions. The scientific committee will therefore perform a selection of oral and poster contributions. The workshops starts Tuesday, Oct. 4th at noon and ends Thursday, Oct. 6th in the evening. Registration will be soon open and announced here.

This workshop is financially supported by:

  •  Freie Universität Berlin within the Excellence Initiative of the German Research Foundation

  • the "Statistical Regionalization Models Intercomparison and hydrological impacts Project" (StaRMIP) via the French National Research Agency (ANR)