Paul N. Edwards


On his book A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming

Cover Interview of March 07, 2011

In a nutshell

The first decade of the 21st century has been the hottest on record. And NASA recently registered 2010 as the hottest year since instrumental observations began. On the planetary scale, the heat just keeps on rising.

But this January, snow fell on 49 of the 50 United States.

And yet, this rare event is not strange.  You can understand it in the context of global warming.  It’s the overall warming of our atmosphere that may be causing colder and snowier winters in the United States—by adding humidity to the air, and by changing global circulation patterns.

How can we possibly know these things? Earth’s atmosphere is 50 billion cubic kilometers of swirling air and moisture, always in motion, whipped into complex, turbulent patterns by solar heat and planetary rotation. Tracking climate change means tracking what’s happening to all that air over long periods—from years to decades to centuries.

A Vast Machine traces the history of weather and climate science as a global knowledge infrastructure.

To study anything on a planetary scale, you have to make global data: collect measurements from everywhere, catalog them, store them, render them accessible for analysis.

Organized international weather observation began in the 1850s and grew rapidly into a near-global system, interrupted only briefly by the two world wars. It’s a remarkable story of long-term international cooperation, leading to a colossal kluge: a global communication system for weather and climate data, cobbled together from telegraphy, fax, shortwave radio, postal mail, computer networks, and half a dozen other media.

Making global data is hard enough—but it’s only the first step. The second, even harder step is to make data global.

Standards, instruments, recording and reporting practices differ, around the world and over time. You have to process noisy information, cope with incomplete coverage, and blend different types of measurements into a uniform whole, a data image of the entire planet.

To take just one example, most satellite instruments measure radiances at the top of the atmosphere. To combine that information with readings from ground stations and weather balloons, scientists have to translate those radiances into the variables that govern atmospheric behavior, such as temperature, pressure, and humidity. That requires complex data modeling.

Thus virtually everything we know about weather and climate depends fundamentally on computer models.