The end of model democracy?

By Reto Knutti. 2010

508 citations recorded by the source corpus.

Subfields: Political Science and International Relations, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/fb9fe141177e5605574c0f7d9635fb0581f717f1

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Citations in the corpus

Climate model genealogy (2011, background): “…models, supporting earlier claims that the effective number of independent models is smaller than the actual number of models in the multi model ensemble [Pirtle et al., 2010; Pennell and Reichler, 2011; Jun et al., 2008b; Tebaldi and Knutti, 2007; Knutti, 2008; Knutti et al., 2010b; Knutti, 2010].”
An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles (2020, background): “…is weighted is by all members receiving a weight, wIi , of 1. wIi = 1 (1) This equal weighting follows from the assumption that all multi-model ensemble members are independent and equally plausible and is sometimes referred to as a “model democracy” assumption (Knutti et al., 2010a; Knutti, 2010).”
Emulating atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 2: Applications (2011, background): “Obviously not (for a discussion, seeTebaldi and Knutti, 2007; Knutti, 2010; Santer et al., 2009).”
A climate model projection weighting scheme accounting for performance and interdependence (2017, background): “The prevailing approach for dealing with multimodel ensemble results is model democracy [Knutti, 2010].”
Evaluating Emergent Constraints on Equilibrium Climate Sensitivity (2018, result): “Correlation with a subset of models provides a weak sense of the robustness of our conclusions; testing on new ensembles would provide a (Pennell and Reichler 2011; Knutti 2010; Knutti et al. 2013, and references therein), so successive CMIP ensembles are not themselves completely independent.”
Scientific Ambiguity and Climate Policy (2013, unknown): “See e.g. Knutti et al. (2010), Knutti (2010) for discussion. per year.”
Statistical significance of climate sensitivity predictors obtained by data mining (2014, result): “While previous studies have noted the nonindependence of CMIP models [Knutti, 2010; Pennell and Reichler, 2011; Knutti et al., 2013], ours is the first we know of which accounts for all of these complex dependencies in the determination of field significance.”
A Representative Democracy to Reduce Interdependency in a Multimodel Ensemble (2015, methodology): “However, as has been noted before (Knutti 2010), this sample is far from perfect.”
Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques (2014, background): “However, the value of multimodel averages has been questioned (Knutti, 2010), particularly for regional assessments (Knutti et al.”