The use of the multi-model ensemble in probabilistic climate projections

By C. Tebaldi, Reto Knutti. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2007

1,946 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/cd933c1917011cea4511f304c0239a2524d45b82

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Citations in the corpus

Adding complex trophic interactions to a size-spectral plankton model: emergent diversity patterns and limits on predictability (2011, background): “…it makes possible a new kind of ensemble forecasting with analogs in weather and climate modeling (Bracco et al., 2004; Di Lorenzo et al., 2010; Tebaldi and Knutti, 2007), in which without any manipulation of parameter values, initial conditions are varied slightly among multiple realizations…”
Bayesian estimation of local signal and noise in multimodel simulations of climate change (2010, methodology): “tion in recent years [e.g., Meehl et al., 1997, 2005; Covey et al., 2003; Giorgi and Mearns, 2002; Furrer et al., 2007a, 2007b; Santer et al., 2007; Tebaldi and Knutti, 2007] has focused on methods for separating a statistically significant climate “signal” from the background “noise” of the substan”
The physics of climate variability and climate change (2019, background): “Mean and spread have been taken as the, possibly weighted, first two moments of the models’ outputs for the metric under study (Tebaldi and Knutti, 2007); see Fig.”
Development of New Ensemble Methods Based on the Performance Skills of Regional Climate Models over South Korea (2012, background): “In some previous works, assigning different weightings for the ensemble members on the basis of each member’s performance has been suggested as a way to reduce unwanted uncertainty in climate model projections (Giorgi and Mearns 2002; Murphy et al. 2004; Tebaldi and Knutti 2007; Weigel et al. 2010).”
Seasonal cycle of precipitation over major river basins in South and Southeast Asia: a review of the CMIP5 climate models data for present climate and future climate projections (2015, background): “This is due to the fact that in these models some of the crucial fine scale hydro-climatic processes that occur on a variety of spatio-temporal scales are not explicitly resolved but represented only through parameterization schemes (May, 2002; Hagemann et al., 2006; Tebaldi and Knutti, 2007).”
Climate model dependence and the replicate Earth paradigm (2013, background): “…100 groups share data sets, parametrisations and even sections of model code, so there are reasons to 101 suspect that the assumption of statistical independence of every climate prediction may not be 102 appropriate (Tebaldi and Knutti, 2007; Jewson and Hawkins, 2009; Knutti et al, 2010a).”
A climate model projection weighting scheme accounting for performance and interdependence (2017, background): “…such as in the Coupled Model Intercomparison Projects (CMIP) [Eyring et al., 2016], but each group making different but plausible choices regarding which processes are included and how they are represented [Parker, 2006; Tebaldi and Knutti, 2007; Knutti, 2008; Sanderson and Knutti, 2012].”
Spatial Analysis to Quantify Numerical Model Bias and Dependence (2008, background): “Tebaldi and Knutti (2007) have provided a more thorough review, and Smith et al. (2007) have provided more references.”
Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates (2011, methodology): “…of GCMs reﬂects availability, and may thus not sample the full range of uncertainty or guarantee independence of models (Meehl et al. , 2007; Tebaldi & Knutti, 2007; Knutti et al. , 2008), but the large number of models included is expected to minimize potential biases by model choice…”
Crop Model Improvement Reduces the Uncertainty of the Response to Temperature of Multi-Model Ensembles (2017, neutral): “There was no explicit criterion of inclusion, so this would be an “ensemble of opportunity” as deﬁned in the climate model community (Tebaldi and Knutti, 2007) All of the models have been described in publications and are currently in use.”
Reduced global warming from CMIP6 projections when weighting models by performance and independence (2020, methodology): “The CMIP MMEs in particular have not been designed with the aim of including only independent models and are, therefore, sometimes referred to as “ensembles of opportunity” (e.g., Tebaldi and Knutti, 2007), incorporating as many models as possible.”
Probabilistic Seasonal Forecasts in the North American Multimodel Ensemble: A Baseline Skill Assessment (2016, background): “…National Oceanic and Atmospheric Administration (NOAA) and other forecasting centers are now issued in a probabilistic format, providing the end user with quantitative information about forecast uncertainty and allowing formore informed risk assessment and decision making (Tebaldi and Knutti 2007).”
Detecting change in UK extreme precipitation using results from the climateprediction.net BBC climate change experiment (2010, background): “However, although it intuitively makes sense to trust, and thus weigh, the better models more, it is difficult to objectively quantify model skill and therefore derive model weights (Tebaldi and Knutti 2007).”