Book contents
- The Climate Demon
- Reviews
- The Climate Demon
- Copyright page
- Dedication
- Contents
- Figures
- Preface
- Acknowledgments
- Introduction
- Part I The Past
- Part II The Present
- 8 Occam’s Razor
- 9 Constraining Climate
- 10 Tuning Climate
- 11 Occam’s Beard
- 12 The Hansen Paradox
- 13 The Rumsfeld Matrix
- 14 Lost in Translation
- 15 Taking Climate Models Seriously, Not Literally
- Part III The Future
- Glossary
- Notes
- Select Bibliography
- References
- Index
15 - Taking Climate Models Seriously, Not Literally
from Part II - The Present
Published online by Cambridge University Press: 02 November 2021
- The Climate Demon
- Reviews
- The Climate Demon
- Copyright page
- Dedication
- Contents
- Figures
- Preface
- Acknowledgments
- Introduction
- Part I The Past
- Part II The Present
- 8 Occam’s Razor
- 9 Constraining Climate
- 10 Tuning Climate
- 11 Occam’s Beard
- 12 The Hansen Paradox
- 13 The Rumsfeld Matrix
- 14 Lost in Translation
- 15 Taking Climate Models Seriously, Not Literally
- Part III The Future
- Glossary
- Notes
- Select Bibliography
- References
- Index
Summary
The media often lends more credence to dramatic predictions from individual climate models than does the climate science community as a whole. Models are metaphors of reality; they should be taken seriously, not literally. Predictions made by simple climate models need to be confirmed using more comprehensive climate models. Deep uncertainty surrounds estimates of climate metrics like climate sensitivity, meaning that the error bars presented with these metrics may have their own unquantifiable error bars. It is important to make the distinction between precision and accuracy when evaluating uncertainty estimates of climate parameters. Overconfidence in numerical predictions of extreme climate scenarios may lead to a “doomist” belief that a climate catastrophe is inevitable and that there is nothing we can do to prevent it.
Keywords
- Type
- Chapter
- Information
- The Climate DemonPast, Present, and Future of Climate Prediction, pp. 233 - 250Publisher: Cambridge University PressPrint publication year: 2021