Russ Steele
David Orrell’s insightful book attracted my attention when it was advertised to discuss the pros, cons and uncertainties of prediction science. Given my interest in climate change, I was most interested in reading this book. Part of my quest to understand the limitations of climate modeling, and I was not disappointed.
Orrell has a Ph.D. in mathematics from Oxford and has studied and written extensively about prediction in complex systems. His theory that modeling errors are more responsible for bad weather forecasts rather than chaos was not well received by weather forecasters. They could not longer blame “the butterfly effect.” In this book Orrell looks at more than weather forecasting, and why models of complex systems lead us astray.
Orrell concentrates on three main subjects that seem to be unrelated: the weather, human health and the economy. However, readers soon discover these three subjects share a common connection, the shortcomings of the models used to predicts the future. The author makes his point in non technical language on how modeling errors limit the ability of models to accurately predict the future. This is especially true for long range climate forecasts, which are too crude to model clouds and participation, major contributors to long term climate change.
I found the history of science prediction, going back to the early Greek thinkers, most interesting. Orrell is a friendly and engaging writer, with few passages that need to be reread for understanding, though there are some. This is a complex subject, but the non technical reader can lean quite a bit about how forecasts are made and why they are so often wrong witout getting mired in the technical details. This book should be of interest to everyone who wants to understand modeling, especially science buffs with an interest in weather, human health and economic forecasting. I highly recommend it to the local global warming crowd, it may open their eyes to the limits of Al Gore’s forecasts of climate disaster.
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