Download PDF A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming (Infrastructures)

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Ovshinsky, a brilliant, self-taught inventor whose technology innovations continue to shape our world. Share your thoughts with other customers. Write a customer review. There was a problem filtering reviews right now. Please try again later. However, mathematical treatment of fluid dynamics is very complex. The advent of computers has greatly enhanced weather prediction and analysis, but even today in meteorology five to six days forecasts are at best are only eighty to eighty five percent accurate. Forecasting years ahead, as in climatology are, in my estimation, only educated guesses.

We simply don't have a comprehensive enough data base, in climatology, for dependable forecast analysis even with computer models. Edwards presents his case admirably, I am not convinced we are ready to forecast climate with any degree of accuracy now or in the foreseeable future.

Understanding 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.

  • A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming?
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  • 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 s 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.

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    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. As a "seasoned" climatologist, reading this book was like visiting an old friend, and meeting all the children and descendants.

    His book, as he puts it, presents "an historical account of climate science as a global knowledge infrastructure". I was impressed by Edwards' use of recent papers by Peterson, Karl, Easterling, well blended with older works references by Smagarenski, Sagen, Manabe. And of course his good friend, the late Schneider is there throughout.

    Edwards makes the point that it is through models that we revamp our knowledge about climate, whether these are simulation models based on physical theory, reanalysis models that blend observations with forecast simulations into uniform global data, or data analysis models that produce coherent data from heterogeneous, time-varying information. Climate knowledge works like historians work; there is always more to learn about the past. The book suffered a bit, I think, from not using Zillman's excellent and short!

    I especially enjoyed the description of reanalysis chapter 12 , the summary of preth century observations, and best of all, the Greek derivation of climate - klima - from "inclination" - the slant of the suns rays with latitude with the tilt of the earth. Best of all, I see that Edwards has a degree in "Science, Technology, and Society" - exactly the course of study my son is following - along with an engineering degree.

    Paul N. Edwards

    If Edwards' book is any guide, this is certainly what we need more of. I'd be remiss if I didn't add that Edwards includes useful, dispassionate, historical references to S. Fred Singer's questionable work in fighting the science of climate change, acid raid, and the ozone hole! Plus ca change, plus c'est la meme chose". See all 17 reviews. Most recent customer reviews. Published on April 12, Published on November 3, Published on September 16, Published on June 23, Published on May 19, Meteorology, and specifically Numerical Weather Prediction NWP , played a key role in the early development of computers, with John von Neumann taking a prominent role; one of the first things run on ENIAC, in , was a retrospective forecast.

    Edwards provides a brief introduction to general circulation models GCMs and how they work before recounting their early history. Norman Phillips was the first to run a computerized GCM, in , using a machine with 1kB of memory and 2kB of drum storage, but others soon followed.

    Edwards gives a brief history of four of the major research groups in the US. Gilbert Plass applied GCMs to the effects of carbon dioxide, introducing "doubling the concentration" as a paradigmatic modeling experiment, and it was quickly realised that water vapour feedback was critical. One early project was the International Geophysical Year — in which data was still distributed through micro-photographs of printed report forms. The WMO's early existence was intertwined with Cold War geopolitics, with military involvement particularly notable in expensive upper atmosphere science, in atomic fallout monitoring for test ban treaties, and in the use of weather and climate observations as cover for spying.

    For political as well as technical reasons, it was implemented as a kind of internetwork, coupling autonomous systems national weather services which retained different internal protocols and standards. Collecting global data is one problem; there is also the challenge of automating its processing and what Edwards calls "making data global", or generating the regular grid data needed for modeling.

    With observations poorly matching the needs of GCMs, gaps have to be filled by interpolation or by using the previous forecast.

    A Vast Machine (Paul Edwards) - book review

    There are also challenges involved with using low quality data sources where model analysis sometimes leads to instrumentation corrections. They link thousands of heterogeneous, imperfect, incomplete data sources, render them commensurable, check them against one another, and interpolate in time and space to produce a uniform global data image. In my terms, they make data global. There were problems using satellite imagery at first, with photographs and volume measurements hard to translate into the grid and point measurements of NWP.

    Models were built to apply theory, not test it — what Edwards calls "reproductionism" instead of reductionism. Data is not neutral, but is collected for particular purposes. One response has been infrastructural inversion, going back to old data and reinterpreting it, in a process of "continual self-interrogation". An example is the determination of what kind of temperature recording systems specific ships used, so that their logs can be readjusted individually. This is coupled with "reanalysis", going back and rerunning weather models over long periods using all the data available, including those that were delayed too long to be useful for forecasting.

    Sub-grid processes in GCMs are handled by simple models with ad hoc parameters, and these models need to be tuned, creating a trade-off between more parameters — with greater realism but risks of over-parametrisation — and fewer — producing "why aren't you incorporating cosmic radiation? There are serious epistemological issues over "verification" and "validation" of models; performing multiple runs with parameter variation is one approach to getting a grip on uncertainties. It is, as Edwards puts it, models "all the way down": Returning to the politics of atmospheric and simulation studies, Edwards traces the history of concerns about carbon dioxide driven global warming from the s down to the present.

    Edwards also touches here on nuclear winter and ozone layer concerns. A final chapter "Signal and Noise" considers a range of topics:

    A Vast Machine Computer Models Climate Data and the Politics of Global Warming Infrastructures