Building Confidence in Climate Model Projections: An Analysis of Inferences from Fit. Baumberger, C.; Knutti, R.; and Hirsch Hadorn, G. 8(3):n/a+.
Building Confidence in Climate Model Projections: An Analysis of Inferences from Fit [link]Paper  doi  abstract   bibtex   
Climate model projections are used to inform policy decisions and constitute a major focus of climate research. Confidence in climate projections relies on the adequacy of climate models for those projections. The question of how to argue for the adequacy of models for climate projections has not gotten sufficient attention in the climate modeling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of 'model fit'; that is, how well model results fit observation-based data (empirical accuracy) and agree with other models or model versions (robustness). However, such assessments are largely silent about what those degrees of fit imply for a model's adequacy for projecting future climate. We provide a conceptual framework for discussing the evaluation of the adequacy of models for climate projections. Drawing on literature from philosophy of science and climate science, we discuss the potential and limits of inferences from model fit. We suggest that support of a model by background knowledge is an additional consideration that can be appealed to in arguments for a model's adequacy for long-term projections, and that this should explicitly be spelled out. Empirical accuracy, robustness and support by background knowledge neither individually nor collectively constitute sufficient conditions in a strict sense for a model's adequacy for long-term projections. However, they provide reasons that can be strengthened by additional information and thus contribute to a complex non-deductive argument for the adequacy of a climate model or a family of models for long-term climate projections. WIREs Clim Change 2017, 8:e454. doi: 10.1002/wcc.454 For further resources related to this article, please visit the WIREs website.
@article{baumbergerBuildingConfidenceClimate2017,
  title = {Building Confidence in Climate Model Projections: An Analysis of Inferences from Fit},
  author = {Baumberger, Christoph and Knutti, Reto and Hirsch Hadorn, Gertrude},
  date = {2017-05},
  journaltitle = {WIREs Clim Change},
  volume = {8},
  pages = {n/a+},
  issn = {1757-7780},
  doi = {10.1002/wcc.454},
  url = {https://doi.org/10.1002/wcc.454},
  abstract = {Climate model projections are used to inform policy decisions and constitute a major focus of climate research. Confidence in climate projections relies on the adequacy of climate models for those projections. The question of how to argue for the adequacy of models for climate projections has not gotten sufficient attention in the climate modeling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of 'model fit'; that is, how well model results fit observation-based data (empirical accuracy) and agree with other models or model versions (robustness). However, such assessments are largely silent about what those degrees of fit imply for a model's adequacy for projecting future climate. We provide a conceptual framework for discussing the evaluation of the adequacy of models for climate projections. Drawing on literature from philosophy of science and climate science, we discuss the potential and limits of inferences from model fit. We suggest that support of a model by background knowledge is an additional consideration that can be appealed to in arguments for a model's adequacy for long-term projections, and that this should explicitly be spelled out. Empirical accuracy, robustness and support by background knowledge neither individually nor collectively constitute sufficient conditions in a strict sense for a model's adequacy for long-term projections. However, they provide reasons that can be strengthened by additional information and thus contribute to a complex non-deductive argument for the adequacy of a climate model or a family of models for long-term climate projections. WIREs Clim Change 2017, 8:e454. doi: 10.1002/wcc.454 For further resources related to this article, please visit the WIREs website.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14250908,climate-change,communicating-uncertainty,corroboration,definition,environmental-modelling,epistemology,extrapolation-uncertainty,modelling,scientific-communication,terminology,uncertainty,unknown,validation,verification-vs-corroboration},
  number = {3}
}
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