Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 52 |
| Fachzeitschrift | Synthese |
| Jahrgang | 200 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - 28 Feb. 2022 |
Abstract
Climate scientists often apply statistical tools to a set of different estimates generated by an “ensemble” of models. In this paper, I argue that the resulting inferences are justified in the same way as any other statistical inference: what must be demonstrated is that the statistical model that licenses the inferences accurately represents the probabilistic relationship between data and target. This view of statistical practice is appropriately termed “model-based,” and I examine the use of statistics in climate fingerprinting to show how the difficulties that climate scientists encounter in applying statistics to ensemble-generated data are the practical difficulties of normal statistical practice. The upshot is that whether the application of statistics to ensemble-generated data yields trustworthy results should be expected to vary from case to case.
ASJC Scopus Sachgebiete
- Sozialwissenschaften (insg.)
- Allgemeine Sozialwissenschaften
- Geisteswissenschaftliche Fächer (insg.)
- Philosophie
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in: Synthese, Jahrgang 200, Nr. 1, 52, 28.02.2022.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - When is an Ensemble like a Sample? ‘Model-Based’ Inferences in Climate Modeling
AU - Dethier, Corey Nathaniel
N1 - Funding Information: Open Access funding enabled and organized by Projekt DEAL. Funding for this paper was provided by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project 254954344/GRK2073
PY - 2022/2/28
Y1 - 2022/2/28
N2 - Climate scientists often apply statistical tools to a set of different estimates generated by an “ensemble” of models. In this paper, I argue that the resulting inferences are justified in the same way as any other statistical inference: what must be demonstrated is that the statistical model that licenses the inferences accurately represents the probabilistic relationship between data and target. This view of statistical practice is appropriately termed “model-based,” and I examine the use of statistics in climate fingerprinting to show how the difficulties that climate scientists encounter in applying statistics to ensemble-generated data are the practical difficulties of normal statistical practice. The upshot is that whether the application of statistics to ensemble-generated data yields trustworthy results should be expected to vary from case to case.
AB - Climate scientists often apply statistical tools to a set of different estimates generated by an “ensemble” of models. In this paper, I argue that the resulting inferences are justified in the same way as any other statistical inference: what must be demonstrated is that the statistical model that licenses the inferences accurately represents the probabilistic relationship between data and target. This view of statistical practice is appropriately termed “model-based,” and I examine the use of statistics in climate fingerprinting to show how the difficulties that climate scientists encounter in applying statistics to ensemble-generated data are the practical difficulties of normal statistical practice. The upshot is that whether the application of statistics to ensemble-generated data yields trustworthy results should be expected to vary from case to case.
KW - Climate models
KW - Ensemble methods
KW - Model-based
KW - Statistics
UR - http://www.scopus.com/inward/record.url?scp=85125898439&partnerID=8YFLogxK
U2 - 10.1007/s11229-022-03477-5
DO - 10.1007/s11229-022-03477-5
M3 - Article
VL - 200
JO - Synthese
JF - Synthese
SN - 0039-7857
IS - 1
M1 - 52
ER -