References »
Understanding climate phenomena with data-driven models. Studies in History and Philosophy of Science Part A. 2020
Cites (11)
Citations in the corpus, listed by decreasing publication date.
Explicating Objectual Understanding: Taking Degrees Seriously. Journal for General Philosophy of Science. 2019
Applying big data beyond small problems in climate research. Nature Climate Change. 2019
Issues in the theoretical foundations of climate science. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics. 2018
Climate & climate change, Theoretical foundations, Variability
The Diversity of Model Tuning Practices in Climate Science. Philosophy of Science. 2016
Confirmation & evaluation, Confirmational holism
Full textCitesCited byNote
A discussion of what is dubbed the “intuitive position” of calibration and confirmation of climate models. In short, this position states, the tuning of a model directly influences the evaluation of said model, as a result the values used to tune the model cannot be used in the model’s evaluation. Steele and Werndl focus on the diversity of formal calibration methods and how the different perspectives relate to the intuitive position.
Predictivism and old evidence: a critical look at climate model tuning. European Journal for Philosophy of Science. 2015
Calibration/tuning, Confirmation & evaluation, Confirmational holism
Predictivism and old evidence: a critical look at climate model tuning. European Journal for Philosophy of Science. 2015
Simulation and Understanding in the Study of Weather and Climate. Perspectives on Science. 2014
When Climate Models Agree: The Significance of Robust Model Predictions*. Philosophy of Science. 2011
Confirmation & evaluation, Ensemble methods, Robustness, Values
Confirmation and Robustness of Climate Models. Philosophy of Science. 2010
Holism, entrenchment, and the future of climate model pluralism. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics. 2010
Cited by (2)
Cited by these reference in the corpus, listed by decreasing publication date.
Machine learning and the quest for objectivity in climate model parameterization. Climatic Change. 2023
Calibration/tuning, Expert judgement, Machine Learning, Values
Understanding climate change with statistical downscaling and machine learning. Synthese. 2020
Machine Learning, Regional climate modelling, Understanding & explanation