Extracting valuable information from a multimodel ensemble Similarly, number of RCMs are used to generate fine scales features from GCM coarse resolution.

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Presentation transcript:

Extracting valuable information from a multimodel ensemble Similarly, number of RCMs are used to generate fine scales features from GCM coarse resolution outputs. Over the last few years, several teams around the world used as much GCMs to generate an important amount of global climate simulations (projections). Both global and regional solutions are subject to various sources of uncertainties. The resulting spread between climate solutions is mainly due to : internal variability (due to chaotic nature of models) intermodel variability (conception differences between models) socio-economical projections uncertainty (GES scenario, land-use) By Martin Leduc and René Laprise

Initial conditions and external forcings GCM integration RCM integration ° °°°°°°°° °°°°°°°°°°°°°°°° Each climate projection might be seen as one probability over a wide ensemble of possible states. Uncertainties cascade through model chain lead to a multiplication of the climate solutions (…)

Multimodel ensemble GCM1GCM2…GCMm RCM1ENS1……… RCM2………… …………… RCMn………ENSk k = m x n (dimension) A MME can be used to estimate the climate projection uncertainty related to the choice in between : For a particular emission scenario :  Emission scenarios (external forcing)  GCM flavors  RCM flavors  RCM ensemble members (IC perturbated)

- characterizing a multi-RCM ensemble generated from multi- GCM forcings. - studying the uncertainties cascade through the model chain. - applying / evaluating few techniques of multimodel linear regressions to extracting valuable information from ensembles. - exploring the problematic to calculate a probability density function for climate projections from a multimodel distribution of solutions. References: - D é qu é, M., et al., 2007: An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Clim. Change, doi: /s x. - Giorgi, F., and L.O. Mearns, 2002: Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the reliability ensemble averaging (REA) method. J. Clim., 15, 1141 – R ä is ä nen, J., and T.N. Palmer, 2001: A probability and decision-model analysis of a multi-model ensemble of climate change simulations. J. Clim., 14, 3212 – Further investigations :