Synopses & Reviews
andlt;Pandgt;Global warming skeptics often fall back on the argument that the scientific case for global warming is all model predictions, nothing but simulation; they warn us that we need to wait for real data, andquot;sound science.andquot; In A Vast Machine Paul Edwards has news for these doubters: without models, there are no data. Today, no collection of signals or observations--even from satellites, which can andquot;seeandquot; the whole planet with a single instrument--becomes global in time and space without passing through a series of data models. Everything we know about the world's climate we know through models. Edwards offers an engaging and innovative history of how scientists learned to understand the atmosphere--to measure it, trace its past, and model its future. Edwards argues that all our knowledge about climate change comes from three kinds of computer models: simulation models of weather and climate; reanalysis models, which recreate climate history from historical weather data; and data models, used to combine and adjust measurements from many different sources. Meteorology creates knowledge through an infrastructure (weather stations and other data platforms) that covers the whole world, making global data. This infrastructure generates information so vast in quantity and so diverse in quality and form that it can be understood only by computer analysis--making data global. Edwards describes the science behind the scientific consensus on climate change, arguing that over the years data and models have converged to create a stable, reliable, and trustworthy basis for the reality of global warming.andlt;/Pandgt;
Review
"A thorough and dispassionate analysis by a historian of science and technology, Paul Edwards' book is well timed. Although written before the University of East Anglia e-mail leak, it anticipates many of the issues raised by the 'climategate' affair. [...] A Vast Machine puts the whole affair into historical context and should be compulsory reading for anyone who now feels empowered to pontificate on how climate science should be done." Myles Allen, Nature
Review
“With this new book, Paul Edwards once again writes the history of technology on a grand scale. Through his investigation of computational science, international governance, and scientific knowledge production, he shows that the very ability to conceptualize a global climate as such is wrapped up in the history of these institutions and their technological infrastructure. In telling this story, Edwards again makes an original contribution to a crowded field.” Greg Downey, University of Wisconsin-Madison
Review
"A Vast Machine is a beautifully written, analytically insightful, and hugely well-informed account of the development and influence of the models and data that are the foundation of our knowledge that the climate is changing and that human beings are making it change." Donald MacKenzie, Professor of Sociology, University of Edinburgh, author of An Engine, Not a Camera
Review
"In philosophical terms, Edwards's focus is epistemological rather than ontological. He explores how we have come to know weather and climate, beginning his inquiry with events from well over a century ago. As with all nonlaboratory science, the challenge of describing — let alone explaining or predicting — the behavior of large and dynamic biophysical systems is formidable. As Edwards rightly says, 'You can't study global systems experimentally; they are too huge and complex.'" Noel Castree, American Scientist (Read the entire )
Synopsis
Global warming skeptics often fall back on the argument that the scientific case for global warming is all model predictions, nothing but simulation; they warn us that we need to wait for real data, sound science. In
A Vast Machine Paul Edwards has news for these skeptics: without models, there are no data. Today, no collection of signals or observations — even from satellites, which can see the whole planet with a single instrument — becomes global in time and space without passing through a series of data models. Everything we know about the world's climate we know through models.
Edwards offers an engaging and innovative history of how scientists learned to understand the atmosphere — to measure it, trace its past, and model its future. Edwards argues that all our knowledge about climate change comes from three kinds of computer models: simulation models of weather and climate; reanalysis models, which recreate climate history from historical weather data; and data models, used to combine and adjust measurements from many different sources. Meteorology creates knowledge through an infrastructure (weather stations and other data platforms) that covers the whole world, making global data. This infrastructure generates information so vast in quantity and so diverse in quality and form that it can be understood only by computer analysis — making data global. Edwards describes the science behind the scientific consensus on climate change, arguing that over the years data and models have converged to create a stable, reliable, and trustworthy basis for establishing the reality of global warming.
Synopsis
The science behind global warming, and its history: how scientists learned to understand the atmosphere, to measure it, to trace its past, and to model its future.
Synopsis
Global warming skeptics often fall back on the argument that the scientific case for global warming is all model predictions, nothing but simulation; they warn us that we need to wait for real data, sound science. In
Synopsis
andlt;Pandgt;The science behind global warming, and its history: how scientists learned to understand the atmosphere, to measure it, to trace its past, and to model its future.andlt;/Pandgt;
About the Author
Paul N. Edwards is Associate Professor in the School of Information at the University of Michigan. He is the author of The Closed World: Computers and the Politics of Discourse in Cold War America (1996) and a coeditor (with Clark Miller) of Changing the Atmosphere: Expert Knowledge and Environmental Governance (2001), both published by the MIT Press.