A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming Review

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A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming ReviewUnderstanding how we know about climate, and even what it means to know about climate and climate change, is essential if we are to have an informed debate. This is far and away the best book I have read on the infrastructure behind our knowledge of climate change, how that infrastructure developed, and how the infrastructure shapes our understanding.
The story begins in the 1600s as systematic collection of weather data began (at least in the modern period, other cultures such as the Chinese have older records and it would be interesting to unearth these, although the data normalization issues would be extreme). It picks up speed in the 19th C with global trade and then the telegraph. The more data collected, and the more data is exchanged, the more important it becomes to normalize data for comparison. Normalization requires some form of data model, a theory that makes the data meaningful. Indeed, this is Edwards point, all data about weather and climate only becomes meaningful in the context of a model (this is of course generally true).
Work accelerated during WW2 and then exploded in the 50s and 60s as computers became more available. The role played by John Von Neumann in this is fascinating, as is the nugget that his second wife Klara Von Neumann taught early weather scientists how to program (there is a whole hidden history of the role of woman in developing computer programming that needs to be written - or if you know of one please add it to the comments of this review or tweet it to me @StevenForth).
Edwards also introduces some useful concepts such as Data Friction and Computational Friction. I think my company can apply these in its own work, so for me this has been a very practical text.
Modern models of climate are complex and are growing more so. They have to be to integrate data from multiple sources. One of the main lines of evidence for climate change is that data from many different sources are converging to suggest that climate change is a real and accelerating phenomena. One can meaningfully ask if this convergence is an artifact of the models, although this appears unlikely given the diversity of the data and models. But Edwards shows that it is idiotic to claim that the data and the models can be meaningfully separated. This is true in all science and not just climate science. A theory is a model to normalize and integrate data and to uncover and make meaningful relations between disparate data. That these models are now expressed numerically in computations, rather than as differential equations or sentences in a human language or drawings is one of the major shifts of the information age. It will be interesting to dig deeper into the formal relations between these diffferent modeling languages.A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming OverviewThe science behind global warming, and its history: how scientistslearned to understand the atmosphere, to measure it, to trace its past, and to modelits future.

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Introduction To Three-dimensional Climate Modeling Review

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Introduction To Three-dimensional Climate Modeling ReviewWhile this isn't a complete introduction, and while it may inevitably be slightly out of date, this is nonetheless the best introduction to climate modeling I've found. It presents the primary methods, and discusses the accomplishments and shortcomings of the field honestly (if at times a bit defensively).
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.Introduction To Three-dimensional Climate Modeling OverviewThis book provides an introduction to the development of three-dimensional climate models, including their four major components: atmosphere, ocean, land/vegetation, and sea ice. The fundamental processes in each component and the interactions among them are explained using basic scientific principles, and elements of the numerical methods used in solving the model equations are also provided. The authors show how the theory and models grew historically and how well they are able to account for known aspects of the climate system. This book is written so that a reader who is only vaguely aware of climate models will be able to gain an understanding of what the models are attempting to simulate, how the models are constructed, what the models have succeeded in simulating, and how the models are being used. Examples illustrating the use of the models to simulate aspects of the current climate system are followed by examples illustrating the application of the models to important scientific areas such as understanding paleoclimates, the last millennium, the El Nino/Southern Oscillation, and the effects of increasing greenhouse gas concentrations on future climate change. The book is appropriate for scientists, graduate students, and upper-level undergraduates and can be used as a textbook or for self study and reference. The authors have considerably updated the book from the first edition by adding descriptions of many techniques and results developed since the mid-1980s.

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Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models Review

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Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models ReviewI am so thankful that this book was written. I am a graduate student working on mesoscale meteorology models and good summaries of the many physics options was hard to find. This book fills that gap. It covers parameterization of land surface, soil & vegetation, water, surface layer & planetary boundary layer, convection, microphysics, radiation, clouds, and gravity waves. It is extremely well written (I read it cover to cover). It gives qualitative evaluations along with the necessary equations. The author reviews the basic science behind each type of parameterization at the beginning of each chapter, which avoids having to look back at other books for this information. The variables are defined after EACH equation so no having to look back to know what a letter stands for (every book should do this!). He also avoids unnecessary acronyms. He gives practical advices, such as at what resolution you should run convective vs. microphysics parameterizations. He also includes information on parameterizations for climate models.I highly recommend this book for anyone running NWP models.Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models OverviewNumerical weather prediction models play an increasingly important role in meteorology, both in short- and medium-range forecasting and global climate change studies. The most important components of any numerical weather prediction model are the subgrid-scale parameterization schemes, and the analysis and understanding of these schemes is a key aspect of numerical weather prediction. This book provides in-depth explorations of the most commonly used types of parameterization schemes that influence both short-range weather forecasts and global climate models. Several parameterizations are summarised and compared, followed by a discussion of their limitations. Review questions at the end of each chapter enable readers to monitor their understanding of the topics covered, and solutions are available to instructors at www.cambridge.org/9780521865401. This will be an essential reference for academic researchers, meteorologists, weather forecasters, and graduate students interested in numerical weather prediction and its use in weather forecasting.

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