The diversity of model tuning practices in climate science
Date
2016
Authors
Steele, Katie
Werndl, Charlotte
Journal Title
Journal ISSN
Volume Title
Publisher
University of Chicago Press
Abstract
Many examples of calibration in climate science raise no alarms regarding model reliability. We examine one example and show that, in employing classical hypothesis testing, it involves calibrating a base model against data that are also used to confirm the model. This is counter to the ‘intuitive position’ (in favor of use novelty and against double counting). We argue, however, that aspects of the intuitive position are upheld by some methods, in particular, the general cross-validation method. How cross-validation relates to other prominent classical methods such as the Akaike information criterion and Bayesian information criterion is also discussed
Description
Keywords
Citation
Collections
Source
Philosophy of Science
Type
Journal article
Book Title
Entity type
Access Statement
License Rights
DOI
Restricted until
2099-12-31
Downloads
File
Description