The Climate Demon

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?'.

'If you wish to correctly interpret climate modeling results, read The Climate Demon. Saravanan's brilliant and humorous book helps both scientists and the general public objectively understand strengths and limitations of climate predictions.' Samuel Shen, San Diego State University

• 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.