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Introduction To Three-dimensional Climate Modeling 2nd Revised ed. Edition
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- ISBN-101891389351
- ISBN-13978-1891389351
- Edition2nd Revised ed.
- PublisherUniversity Science Books
- Publication dateMay 16, 2005
- LanguageEnglish
- Dimensions7 x 1 x 10 inches
- Print length368 pages
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- Publisher : University Science Books; 2nd Revised ed. edition (May 16, 2005)
- Language : English
- Hardcover : 368 pages
- ISBN-10 : 1891389351
- ISBN-13 : 978-1891389351
- Item Weight : 2.08 pounds
- Dimensions : 7 x 1 x 10 inches
- Best Sellers Rank: #2,350,687 in Books (See Top 100 in Books)
- #1,428 in Rivers in Earth Science
- #1,469 in Weather (Books)
- #2,408 in Climatology
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There are some problems. The first problem is that climate is not three dimensional. (It is bizarre to discuss climate without the preeminence of the temporal dimension.) Of course the authors are aware of the importance of time. However, there are bizarre omissions in the presentation of the theoretical work with relation to the underlying physical reasons for climate change. Superficial dynamics equations give you no insight into the actual history of Earth's climate any more than they yield the actual state of an ocean wave breaking upon a shore at any specific moment in time. All you get is a general abstraction.
There are good reasons for this omission. The physical understanding of the history of Earth's climate is still locked in controversy due to the scarcity of good verifiable data. That means the physical understanding of Earth's climate is still evolving.
Secondly, the book fails as an introduction into climate modeling. The transition from theoretical continuous simplifications to actual discrete model is simply not adequately covered. The background theory on discretization of continuous differential equations does not bridge the gap necessary to understand climate models.
Again there are good reasons for this omission. I believe neither author has actually written a climate model. Thus they do not discuss the fundamentals of climate models. Instead they discuss differential equations. The distinction is a knowledge of what can be modeled and what can not with existing technologies. Thus, they omit the all important defining assumptions behind the model plots presented in the final section of the book.
A well known maxim applies to this work. There is a very small number of people who actually write climate models. There is a bigger client community that just runs their model and talks about what it produced. This book has ample examples of the client products. What is obvious is that the authors, as most clients, do not know what defines those products. They do not know what is actually used and what is not. They do not know what limits the usefulness of those products. And they omit the discussion of important facets of those models that is only known to the climate model authors. That may sound like a negative comment but it is true of most scientific publication in this area. As such it is a state of the art.
In summary, if you want to know what goes into a model then ask somebody who built one. Don't ask the manager or PI that is five levels above him. They will tell you a lot about everything except what you want to know.
There are clear limitations to climate modeling. For anyone with training in more mainstream Artificial Intelligence techniques, it's very uncomfortable to "test on the training data," which is exactly what climate modelers must do (i.e., they must run their models on the recent past, and if their models don't perform well on it, they will be tweaked until they do--effectively "cheating" because fidelity to the recent past is obviously no indication of predictive power if you tweak the model specifically to work on the recent past). Much of the uncertainty comes from sub-grid interactions that must be parameterized. For example, the formation of clouds is still an area of great uncertainty, and yet has an enormous effect on the climate. The authors recognize this issue, and identify it as a field where future research should focus.
On the other hand, climate modeling has been extremely useful as an inspiration to the imagination, and in working out the logical implications of what we currently believe to be true (both through those things that can be tested in a lab, like the absorption spectrum of carbon dioxide, or those that need to be parameterized, like cloud formation). We can't predict the future, but models allow us to get a sense of what may happen.
The authors discuss all of this, and illustrate it throughout. Of course, as scientists who have spent their lives in climate modeling, they are as supportive of climate modeling as one might expect. But they are also good scientists, and therefore open about the shortcomings.
In short, I'd recommend this book to anyone who wants an introduction to climate modeling. It's a fascinating and important subject that more people should know more about.


