Climate model genealogy

By David Masson, Reto Knutti. 2011

345 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/c9d35a820e3b627104a3f8d8128908a77cf4e3dd

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Citations in the corpus

Statistical problems in the probabilistic prediction of climate change (2012, background): “Some climate models are close to one another in this tree, whereas others are rather unrelated with few common components (Masson and Knutti, 2011).”
A climate model projection weighting scheme accounting for performance and interdependence (2017, background): “On (a), many models duplicate ideas, or even use large parts of the code of others, so at best are providing little additional information and at worst are biasing the result [Annan and Hargreaves, 2011; Masson and Knutti, 2011a; Pennell and Reichler, 2011; Knutti et al., 2013].”
Decision Making under Deep Uncertainty (2019, neutral): “(2010). How do you adapt in an uncertain world? Lessons from the Thames Estuary 2100 project.World Resources Report, Washington DC. Available online at http://www. worldresourcesreport.org, pp. 16. Smet, K. (2017). Engineering options: A proactive planning approach for aging water resource infrastru”
Separating signal and noise in atmospheric temperature changes: The importance of timescale (2011, background): “This assumption is almost certainly unjustified [Masson and Knutti, 2011].”
Simulations for CMIP6 With the AWI Climate Model AWI‐CM‐1‐1 (2020, methodology): “Many models that participated in CMIP3 and CMIP5 have common descent and share ideas and code with each other (Knutti et al., 2013; Masson & Knutti, 2011).”
High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections (2023, background): “Even though the Model Subset selection did not involve assessing precipitation trends, the magnitude of the best estimate change and uncertainty were also reduced for mean precipitation when compared to All Models (Fig.”
Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles (2011, background): “these issues are illustrated in recent analyses of multi‐model and perturbed parameter ensembles [Masson and Knutti, 2011].”
Weighting climate model ensembles for mean and variance estimates (2015, background): “We already know that the structures of climate models are somewhat dependent (Masson and Knutti 2011).”
Skill and independence weighting for multi-model assessments (2016, background): “Various studies (Knutti et al., 2013; Masson and Knutti, 2011; Sanderson et al., 2015; Pennell and Reichler, 2011) have pointed out that the ensemble contains demonstrable inter-dependence, where similarities in the spatial biases in model simulations correspond well to expected relationships, which”
Climate model dependence and the replicate Earth paradigm (2013, background): “116 117 Intuitively, one may want to define model independence in terms of shared model structure or 118 parametrisations (as though we were using evolutionary cladistics for species classification; in 119 which case shared genetic history implies dependence, e.g. Masson and Knutti, 2011).”
A Representative Democracy to Reduce Interdependency in a Multimodel Ensemble (2015, methodology): “However, we show in this study (e.g., Masson and Knutti 2011) that many of those similarities can be identiﬁed also through a simple analysis of model output.”
Reduced global warming from CMIP6 projections when weighting models by performance and independence (2020, methodology): “This approach is conceptually similar to the work of Masson and Knutti (2011) and Knutti et al. (2013) and follows their example of showing similarity as model “family trees”.”
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).”
Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe (2013, background): “Although theQUMP ensemble members have slightly different climates, they are all produced by an AOGCM with the same structure, which may limit the differences between the members (e.g. Masson and Knutti, 2011).”