Climate model dependence and the replicate Earth paradigm

By C. Bishop, Gab Abramowitz. Climate Dynamics, 2013

197 citations recorded by the source corpus.

Multi-model ensembles are commonly used in climate prediction to create a set of independent estimates, and so better gauge the likelihood of particular outcomes and better quantify prediction uncertainty. Yet researchers share literature, datasets and model code—to what extent do different simulations constitute independent estimates? What is the relationship between model performance and independence? We show that error correlation provides a natural empirical basis for defining model dependence and derive a weighting strategy that accounts for dependence in experiments where the multi-model mean would otherwise be used. We introduce the “replicate Earth” ensemble interpretation framework, based on theoretically derived statistical relationships between ensembles of perfect models (replicate Earths) and observations. We transform an ensemble of (imperfect) climate projections into an ensemble whose mean and variance have the same statistical relationship to observations as an ensemble of replicate Earths. The approach can be used with multi-model ensembles that have varying numbers of simulations from different models, accounting for model dependence. We use HadCRUT3 data and the CMIP3 models to show that in out of sample tests, the transformed ensemble has an ensemble mean with significantly lower error and much flatter rank frequency histograms than the original ensemble.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/b4af20acc79743a293ddf53fc3f4375a2cfce308

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Citations in the corpus

Weighting climate model ensembles for mean and variance estimates (2015, methodology): “As illustrated by Bishop and Abramowitz (2013), weighting model simulations for independence is somewhat analogous to removing model simulations from the ensemble—if two models are dependent, their simulations contain very similar information.”
A Representative Democracy to Reduce Interdependency in a Multimodel Ensemble (2015, methodology): “In previous studies like Masson and Knutti (2011), the intermodel distances were calculated without the PCA stage, simply calculating distances in the space deﬁned by the anomaly matrices D X 20c and D X 21c .”
Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections (2015, methodology): “Since CMIP ensembles conceptually represent both types of uncertainty, it is perhaps unsurprising that they have been found to be overdispersive (Annan and Hargreaves 2010; Yokohata et al. 2012; Bishop and Abramowitz 2013).”
Skill and independence weighting for multi-model assessments (2016, methodology): “Hence, the use of statistical meta-models as in Bishop and Abramowitz (2013) would not be manageable because each individual application would have to be reconsidered in terms of the paradigm, where the details of statistical significance, model independence and individual model interpretation are…”
An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles (2020, methodology): “In the absence of knowledge of model origin and development, independent entities could instead be defined using statistical properties of model outputs (Masson and Knutti, 2011; Bishop and Abramowitz, 2013).”
Impact of climate change on wheat flowering time in eastern Australia (2015, methodology): “More uncertainty can come from the GCMs themselves (different spatial resolution), statistical downscaled method (bias correction), and the independence of simulated different GCMs (Bishop and Abramowitz, 2013; Evans et al., 2013); these uncertainties were not considered when using multi-model ensem”