Climate predictions - and the computer models behind them - play a key role in shaping public opinion on climate change. Providing an accessible introduction to the history, science, and philosophy of climate modeling, it is for anyone with an interest in climate change and a basic knowledge of science.
Climate predictions - and the computer models behind them - play a key role in shaping public opinion on climate change. Providing an accessible introduction to the history, science, and philosophy of climate modeling, it is for anyone with an interest in climate change and a basic knowledge of science.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
R. Saravanan is Head of the Department of Atmospheric Sciences at Texas A&M University. He is a climate scientist with a background in physics and fluid dynamics and has been a lead researcher using computer models of the climate for over thirty years. He built an open-source simplified climate model from scratch, and has worked on complex models run on the world's most powerful supercomputers. He has worked with scientists at multiple climate modeling centers: the Geophysical Fluid Dynamics Laboratory (GFDL) in Princeton; the UK Universities Global Atmospheric Modelling Programme (UGAMP) in Cambridge; and the National Center for Atmospheric Research (NCAR) in Boulder, Colorado. Saravanan has served on national and international committees on climate science, including the National Research Council (NRC) Committee on the Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, and the Science Steering Committee of the Prediction and Research Moored Array in the Atlantic (PIRATA). He recently helped create the TED-Ed animated short, 'Is the weather actually becoming more extreme?'.
Inhaltsangabe
List of figures Preface Acknowledgements Introduction Part I. The Past: 1. Deducing weather: The dawn of computing 2. Predicting weather: The butterfly and the tornado 3. The greenhouse effect: Goldilocks and the three planets 4. Deducing climate: Smagorinsky's laboratory 5. Predicting climate: Butterflies in the greenhouse 6. The ozone hole: Black swan at the polar dawn 7. Global warming: From gown to town. Part II: The Present: 8. Occam's razor: The reduction to simplicity 9. Constraining climate: A conservative view of modeling 10. Tuning climate: A comedy of compensating errors 11. Occam's beard: The emergence of complexity 12. The Hansen paradox: The red Queen's race of climate modeling 13. The Rumsfeld matrix: Degrees of knowledge 14. Lost in translation 15. Taking climate models seriously, not literally. Part III. The Future: 16. Moore's law: To exascale and beyond 17. Machine learning: The climate imitation game 18. Geoengineering: Reducing the fever 19. Pascal's wager: Hedging our climate bets 20. Moonwalking into the future. Epilogue. Glossary. Selected Bibliography. References. Index. Endnotes.
List of figures Preface Acknowledgements Introduction Part I. The Past: 1. Deducing weather: The dawn of computing 2. Predicting weather: The butterfly and the tornado 3. The greenhouse effect: Goldilocks and the three planets 4. Deducing climate: Smagorinsky's laboratory 5. Predicting climate: Butterflies in the greenhouse 6. The ozone hole: Black swan at the polar dawn 7. Global warming: From gown to town. Part II: The Present: 8. Occam's razor: The reduction to simplicity 9. Constraining climate: A conservative view of modeling 10. Tuning climate: A comedy of compensating errors 11. Occam's beard: The emergence of complexity 12. The Hansen paradox: The red Queen's race of climate modeling 13. The Rumsfeld matrix: Degrees of knowledge 14. Lost in translation 15. Taking climate models seriously, not literally. Part III. The Future: 16. Moore's law: To exascale and beyond 17. Machine learning: The climate imitation game 18. Geoengineering: Reducing the fever 19. Pascal's wager: Hedging our climate bets 20. Moonwalking into the future. Epilogue. Glossary. Selected Bibliography. References. Index. Endnotes.
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