References »
Machine learning and the quest for objectivity in climate model parameterization. Climatic Change. 2023
Calibration/tuning, Expert judgement, Machine Learning, Values
Cites (12)
Citations in the corpus, listed by decreasing publication date.
Can Machines Learn How Clouds Work? The Epistemic Implications of Machine Learning Methods in Climate Science. Philosophy of Science. 2021
Understanding climate phenomena with data-driven models. Studies in History and Philosophy of Science Part A. 2020
Understanding climate change with statistical downscaling and machine learning. Synthese. 2020
Machine Learning, Regional climate modelling, Understanding & explanation
Model Evaluation: An Adequacy-for-Purpose View. Philosophy of Science. 2020
Practice and philosophy of climate model tuning across six US modeling centers. Geoscientific Model Development. 2017
Calibration/tuning, Confirmation & evaluation, Ensemble methods
How to develop climate models? The “gamble” of improving climate model parameterizations. In Cultures of Prediction in Atmospheric and Climate Science. 2017
The Art and Science of Climate Model Tuning. Bulletin of the American Meteorological Society. 2017
Calibration/tuning, Confirmation & evaluation, Parameterization
Model robustness as a confirmatory virtue: The case of climate science. Studies in History and Philosophy of Science Part A. 2015
Distinguishing between legitimate and illegitimate values in climate modeling. European Journal for Philosophy of Science. 2015
Hybrid Models, Climate Models, and Inference to the Best Explanation. The British Journal for the Philosophy of Science. 2013
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
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