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RIWER203 hydrometeorological scenarios

6600 hydrometeorological projections were produced for the 240 years of the 1860 - 2100 period with 11 climate experiments of the EU ENSEMBLE project, different versions of 3 Statistical Downscaling Models (SDM : analog, dsclim et d2gen) (Lafaysse et al. 2014) , different hydrological models (Cequeau, Mordor, SIM) (Hingray et al. 2013) , and different representations of the optimal management strategy of the multipurpose Serre Ponçon water Reservoir (François, 2013) .

Meteorological scenarios are available upon request. Contact : benoit.hingray (at) ujf-grenoble.fr

The QEANOVA framework for partionning and quantifying uncertainty sources

Matlab code of QEANOVA and test dataset :

A major current challenge is the quantification of uncertainties associated to climate projections from multimember multimodel ensembles (MM2E). Challenges are more precisely :

  • estimating total uncertainty,
  • identifying and quantifying the main uncertainty sources and check for the possibility to reduce them,
  • estimating the significance of expected changes with respect to the internal variability of models.

This analysis is not straightforward especially when the internal variability of modelling chains is important when compared to their climate response and / or when the MM2E is unbalanced (not the same number of members for each simulation chain).

The QEANOVA framework is a simple and robust framework for the partitioning of the different components of internal variability and model uncertainty in an unbalanced MM2E of climate projections obtained for a suite of regional downscaling models (RDMs) and global climate models (GCMs). It is based on the quasi-ergodic assumption for transient climate simulations. Model uncertainty components are estimated from the noise-free-signals of the different modeling chains using a two-way ANOVA framework. The residuals from the noise-free-signals are used to estimate the large and small scale internal variability components associated with each considered GCM/SDM configuration. This framework makes it possible to take into account all members available from any climate ensemble of opportunity. It especially allows estimating :

  • the different components of model uncertainty (GCM, SDM, hydrological model),
  • the different components of the internal variability of modelling chains (large and small scale),
  • the significance of changes and the time of emergence of significant changes if any.

QEANOVA was applied for RIWER2030 projections for different projection lead times and different hydrometeorological variables (Lafaysse et al. 2014) . QEANOVA can be easily adapted for MM2E similar to the RIWER2030 MM2E (cf. Hingray and Saïd, 2014 ).

Associated Publications : Cadre théorique   I   Application

Matlab code of QEANOVA and test dataset. Data are data used for illustration in (Hingray and Saïd, 2014) .

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