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The book in general is easy to read, but some sections require some basic knowledge in math, economics and biology, though you may skip the excessively technical paragraphs, jump to the end of argument and still understand the general idea. The first three chapters present a very good summary of the history, philosophy and development of science, starting with the Greeks. A real crash course for those not familiar with these subjects and a necessary background to better understand the main topics being discussed in the book: The science of prediction in the fields of climate, health, and economics, and what I consider a very objective critic of simulation models and other techniques used for predictions on these fields. The book includes technical appendices, notes, a glossary and a full bibliography, so you can do follow-up or check the facts by yourself.

The explanation on Chapter 3 on the subject of complex systems is short but outstanding, allowing the layman to understand the basics without the confusion of the math involved. This explanation is fundamental for understanding the limitations of the science of prediction in non-linear systems, such as climate and economics, particularly because it makes clear that these models are not based simple on mathematical relationships reflecting cause-and-effect explanations, like Newton's laws. In astronomy you can calculate where the moon is going to be tomorrow at 5 am, this book makes crystal clear that complex systems are not like that, they are incomputable. And even if existing models can be twisted to fit past data, they cannot predict the future, as it is the case with many models in Economics and the climate simulation models supporting the consensus theory of Global Warming. It is well known that economists have developed models that can explain past external shocks, recessions, commodity booms, Dutch Disease, etc, so they understand conceptually what happened then, but they not predict the future, because neither the economy nor the climate are constrained to follow past behavior. The conditions of the initial variables are not the same, history does not repeat. Technically speaking, positive and negative feedbacks, and multiple feedbacks between the variable result in the inherent unpredictability of complex systems. For example, all 18 models used by the UN's IPCC 2007 Report cannot account for the clouds feedbacks, which may result in much lower temperatures than predicted or much higher (look for in the web for Chapter 8 of the UN's Climate Report, to check by yourself this and other important simulation limitations). As Mr. Orrell explained after showing how badly the OECD predicted GDP growth for the G7 countries from 1986 to 1998, "...Consensus between an ensemble of different models is no guarantor of accuracy: economic models agree with one another far more often than they do with the real economy" (pp 243). This statement is valid for climate models too.

Particularly Chapter 6, on economic predictions, is very interesting, but I will only comment on climate predictions, because so much is being discussed in the media and echoed by famous politicians and even Nobel laureates, with total disregard of basic scientific principles, and an absolute absence of scientific criticism or critical rationalism, as Karl Popper called it. Chapter 4, on climate forecast or prediction, begins with a brief but comprehensive summary of the history of meteorology and climate forecasting, which is important to understand the limitations of modern long-term predictions. As remembered to us by Mr. Orrell, Copernicus and Darwin hold publication of their works because they were afraid of the consequences, since their theories were against the scientific consensus of their times. Unfortunately, most of the environmental movement is blocking any serious discussion of the science behind the theory explaining the causes of Global Warming (the incomplete science has to do with the cause and effect relationship, not with the indisputable fact that most of the world is getting warm), which is based mainly on climate simulation models and assumptions about the state of affairs of the world for the next 100 years. And if you dare to contradict them, you become a heretic, since most environmentalists are acting as if defending a dogma. Not to mention that science must be politically neutral, as quite rightly cited by Mr. Orrell (pp. 107).

Since I do not want to spoil the contents of the book, let me just say that this book is a welcomed light of hope in the middle of the media and political frenzy regarding the real causes of Global Warming. We should be doing real science instead of politicizing science, and as Mr. Orrell recommends at the end of the book, "Apollo's arrow cannot fly to the future or protect us from plague, but it may serve as a compass, point out dangers, and help us navigate an unpredictable world", of course this is possible, if climate scientist stop playing politics and doing the science as they should, objectively and apolitically. Finally, someone has the courage to clearly explain what's wrong with the science behind the consensus theory explaining the Global Warming, as well as in other scientific and social fields.

The final chapter, "Consulting the Cristal Ball" is a must reading. Mr. Orrell presents quite a collection of ideas, scenarios, predictions, and concerns regarding how things will look in the year 2100, together with a box with some great predictions from the past. Just try to image how anyone would have made a reliable prediction of today in the year 1900 (such as cars, airplanes, television, CDs, iPods, computers, atomic bombs, you name it).

If you are serious about understanding the science behind Global Warming, this book is a must. Read it and as previously mentioned, it is worthwhile to search the web for the IPCC 2007 Report, Chapter 8, which presents the evaluation of the simulation models used by the U.N. and their present limitations. You will see that Mr. Orrell is right on the money, there are plenty of positive and negative feedbacks that these models can not replicate and other "anomalies" pending sound explanation. A highly recommended reading for follow-up is Marcel Leroux's "Global Warming - Myth or Reality?: The Erring Ways of Climatology" (too bad this is an expensive book!). We are in need of good old objective science.

Finally, after reading this book it becomes clear that climate simulation models lack any real explanatory power and are incapable to make any reliable predictions, so it seems appropriate to close with a quotation from the best known (Nobel Prize) advocate of the manmade GW based on climate simulation models:

"I have learned that, beyond death and taxes, there is at least one absolutely indisputable fact. Not only does human-caused global warming exist, but it is also growing more and more dangerous, and at a pace that has now made it a planetary emergency". Al Gore, "An Inconvenient Truth: The Planetary Emergency of Global Warming and What We Can Do About It", 2006.

For years most of us have been hearing about "models" of just about everything from weather and hurricane predication to the stock market. Since the advent of the desktop computer, almost everyone seems to have a model of something or other. For those of us who are not modelers, it all sees so cut and dried. Orrell, however, definitely pops the bubble on the works.

The author points out that many of the models, contrary to what most of us understand, are designed to "predict" the past. The presumed variables involved in a particular phenomenon are put into the form of an equation, numbers are estimated for each, and the equation fed to a computer. The problem is that both the variables chosen and the numbers selected to define them are arbitrary and subject to the estimates made by the modeler. I first ran into this when I was taking a structural geology course for my BS in geology. The text went into an elaborate description of a river's output and presented an equation that was said to help estimate the watershed that fed into the river. After looking over all the letters in the equation and what they represented, I realized that most if not all of the variable were almost impossible to measure directly in any accurate way!

Orrell indicates that this is the case with nearly all models. Furthermore he notes that in order to test the results of the model, it is compared to the past to see how well it "predicts" what has occurred. Then the model is tweaked to make it come closer to what was actually seen in the past to "fix" it, on the assumption that the future will be the same as the past.

Most of us would probably accept this as a plausible method of approach, but the author notes that, while events may seem similar, history is perforce not repeatable. He also notes that because life and the earth itself are complex systems in a state of dynamic equilibrium, they are inherently changeable. The estimates of the variables might be anything, and the outcome will change as the estimates do. Because of the complexity of systems in a constant state of change, the interactions of all the variables in the system are inherently beyond our calculation. Thus the more detailed the model, the more subject to error is becomes.

When I first read the author's comments on the weather and other phenomena, I was certain that he was among the nay-sayers over global warming and waited for his objections to the current trends aimed at correcting human impact on the environment. Not so. The author states emphatically that global warming, while the specific outcome is not predictable, is obviously occurring. I suppose it's difficult to ignore a missing glacier. What he does note is that our romance with science and data manipulation has encouraged us to be over optimistic about our ability to control nature. We place too great a trust in models and what their designer's tell us they mean and take mistaken courses of action with respect to climate change, epidemic diseases, and the economy. The entire book is intended to alert and caution the general public about grand claims.

A superb book.

A book that everyone should read. The paperback is finally out; so if cost is a factor you can still read it. There may be more math than most would like but there is an attempt to keep it to the minimum with notes for those who want the details. If your are worried about global warming, do investing, or wonder about your DNA, then this book will be interesting.

Underwhelming. This is a book from someone in dynamical systems discussing chaos in settings to which dynamical systems theory may or may not apply. It is also a typical modern book where 50 pages of material is written and then expanded to 500 pages by adding (possibly through internet searches) background material in great detail on every proper noun in the book. The main point of the author is that model error is also important and he mentions work he has done suggesting it is very important. He does not mention contrary work that has given data suggesting errors in initial conditions contribute 80+% to total uncertainty in weather forecasting. The topics are such that (in my opinion) some precision is required to make sense while the book avoids it studiously.