Climate model genealogy: Generation CMIP5 and how we got there

By Reto Knutti, David Masson, Andrew Gettelman. 2013

854 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/f6507ca951e61214c9268ce9a43376724b2f9bcf

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Citations in the corpus

Evaluation of CNRM Earth System Model, CNRM‐ESM2‐1: Role of Earth System Processes in Present‐Day and Future Climate (2019, background): “In addition to surface temperature, precipitation is an important geophysical field when tracking model performance (Flato et al., 2013; Knutti et al., 2013).”
Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective (2015, background): “For example, in climate studies, it has been shown that the performance of ensemble models significantly improved when pseudo-diversity was reduced (Knutti et al. 2013).”
The effects of 1.5 and 2 degrees of global warming on Africa in the CORDEX ensemble (2018, background): “There are 36 models in table 1S but many of them are from the same modelling centres and share many components leading to a smaller number of truly independent models (family of models) (Knutti et al2013).”
Evaluating Emergent Constraints on Equilibrium Climate Sensitivity (2018, result): “Correlation with a subset of models provides a weak sense of the robustness of our conclusions; testing on new ensembles would provide a (Pennell and Reichler 2011; Knutti 2010; Knutti et al. 2013, and references therein), so successive CMIP ensembles are not themselves completely independent.”
Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America (2016, methodology): “Fifteen GCMs were selected that represent all major clusters of similar AOGCMs [20].”
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, background): “Models have historically shared code, from parametrization schemes to full components, and tend to have the same limitations associated with resolution (i.e., simplified topography) (Masson and Knutti, 2011; Knutti et al., 2013; Boé, 2018).”
Causes of Higher Climate Sensitivity in CMIP6 Models (2020, background): “Despite the fact that models from different centers may also be related (Knutti et al., 2013; Sanderson et al., 2015), we use this approach because accurately defining model independence is still an area of ongoing research and the required machinery would distract from the focus of this work.”
Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive (2014, background): “Comparisons between models will therefore be more complicated since models will not have the same consistency of scope that existed in CMIP3, although analyses of the genealogy of the multimodel ensemble shed some light on this [Knutti et al., 2013].”
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections (2015, methodology): “The data set contains the outcome of control simulations of the GISS global circulation model [20, 21] and is part of the CMIP5 climate modeling ensemble.”
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).”
Statistical significance of climate sensitivity predictors obtained by data mining (2014, result): “While previous studies have noted the nonindependence of CMIP models [Knutti, 2010; Pennell and Reichler, 2011; Knutti et al., 2013], ours is the first we know of which accounts for all of these complex dependencies in the determination of field significance.”
Ensemble Averaging and Mean Squared Error (2016, background): “However, offsetting biases is an inplausible hypothesis for the current generation of climate simulators, as has been shown empirically in the ‘genealogy’ study of Knutti et al. (2013). Simulators have common biases, which cannot be expected to offset each other over the entire ensemble to give an o”
Skill and independence weighting for multi-model assessments (2016, background): “54 In addition, the models that are present in the archive are not equally skill55 ful in representing the present day or past climate [12, 5].”