Skill and independence weighting for multi-model assessments
By B. Sanderson, Michael F. Wehner, Reto Knutti. 2016
213 citations recorded by the source corpus.
Abstract. We present a weighting strategy for use with the CMIP5 multi-model archive in the fourth National Climate Assessment, which considers both skill in the climatological performance of models over North America as well as the inter-dependency of models arising from common parameterizations or tuning practices. The method exploits information relating to the climatological mean state of a number of projection-relevant variables as well as metrics representing long-term statistics of weather extremes. The weights, once computed can be used to simply compute weighted means and significance information from an ensemble containing multiple initial condition members from potentially co-dependent models of varying skill. Two parameters in the algorithm determine the degree to which model climatological skill and model uniqueness are rewarded; these parameters are explored and final values are defended for the assessment. The influence of model weighting on projected temperature and precipitation changes is found to be moderate, partly due to a compensating effect between model skill and uniqueness. However, more aggressive skill weighting and weighting by targeted metrics is found to have a more significant effect on inferred ensemble confidence in future patterns of change for a given projection.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/6914df034a8f23d1e4427ec92f8675627878c25d
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Citations in the corpus
- Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6? (2020, unknown): “Promising approaches to quantify the model interdependency that could be followed include, for example, the studies of Sanderson et al. (2015); Sanderson et al. (2017) and Knutti et al. (2017b).”
- Reduced global warming from CMIP6 projections when weighting models by performance and independence (2020, methodology): “In turn, methods that are based on such a basket of diagnostics have been found to generally lead to weaker constraints (Sanderson et al., 2017; Brunner Earth Syst.”