Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections

74 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Reduced global warming from CMIP6 projections when weighting models by performance and independence (2020, methodology): “…of ClimWIP in weighting CMIP6 global mean temperature change and the effect of the different diagnostic combinations, we apply a perfect model test (Abramowitz and Bishop, 2015; Boé and Terray, 2015; Sanderson et al., 2017; Knutti et al., 2017b; Herger et al., 2018a,b; Abramowitz et al., 2019).”
Skill and independence weighting for multi-model assessments (2016, methodology): “…reduces the mean bias of the combined model product (a 30 % reduction in root mean square difference (RMSE) compared to a simple multi-model mean; Abramowitz and Bishop, 2015), there remain some issues of interpretation for the transformed ensemble members, which can no longer be directly…”
Weighting climate model ensembles for mean and variance estimates (2015, methodology): “We note that Abramowitz and Bishop (2014) show an application of the approach to precipitation, and that Potempski and Galmarini (2009) may offer an approach to allow weighting several variables at once.”
A climate model projection weighting scheme accounting for performance and interdependence (2017, methodology): “Most methods in this category consider performance but ignore model dependence, and those which do are technically challenging [Abramowitz and Bishop, 2015; Sanderson et al., 2015a].”