How can you predict global warming if you can't predict rain?

The team, led by Martin Hoerling, took on the study when NOAA announced in January that 2006 had been nearly 2 degrees F. warmer than normal. NOAA couldn't say why. It may have been El Niño (a periodic warming of the eastern Pacific that has profound climatic effects); it may have been a rise in greenhouse gases.

How models pointed to CO2

Dr. Hoerling's team looked at historical data and calculated that the 2006 increase was unlikely to have occurred through natural fluctuations alone. When they looked at temperature data from 10 previous El Niño years, they found that average temperatures over the US had not changed or had cooled slightly. Could El Niño-like conditions cool the US? They ran a climate model, and found the answer was yes. So if El Niño was unlikely to have caused 2006's warming, did greenhouse gases? Using the modeling data the Intergovernmental Panel on Climate Change used in its latest report, the team found that more than half the increase could be attributed to greenhouse gases.

Scientists have used a similar approach to implicate greenhouse-gas emissions from human activities in warming over the past 30 years. In short, the only way to reproduce late 20th-century warming is to include the growth in greenhouse gases. Modeling results are not the only line of evidence, researchers say. But in combination with other lines of evidence, the case becomes more persuasive.

But why should we trust climate models any more than a three-week weather forecast? Roger Pielke Sr., a research scientist at the University of Colorado at Boulder who focuses on land-atmosphere interactions, notes that climate forecasting is more complicated than weather forecasting. Far more processes are involved.

Keith Dixon, who managed the GFDL's contribution to recent IPCC modeling efforts, adds that climate predicting and weather forecasting are much different, as are the measures of success.

The accuracy of a weather forecast depends largely on the quality of the twice-daily global atmospheric measurements used. Weather conditions trigger weather-forecast models, and such conditions are far more susceptible to "the flapping of the butterfly's wings," Dr. Dixon says. He's referring to well-established ideas about how small-scale, chaotic features can grow over time and affect weather at great distances. That's why today's weather forecasts don't have much use beyond two weeks.

Looking at climate over decades or a century or more, "we're dealing more with boundary forcings" external to the climate system, Dixon says – solar radiation, aerosols, changes in atmospheric CO2. "The key is how the model will respond to changes in these forcing agents," and not whether it will rain in New York on Tuesday.

Global climate models can't predict the next El Niño or volcanic eruption, both of which affect climate. The critical point for climate models, Dixon continues, is to reproduce the climate's random variability over time in a realistic way.

Models can also be checked against their ability to reproduce global temperature trends since the mid-1800s, when the Industrial Revolution began in earnest. The better the models do, the more confidence modelers have in their handiwork.

Regional projections not as good

But if modelers feel confident about their virtual climates on a global scale, confidence falls as global models are drafted to provide regional projections. And it's these narrowly confined projections that policymakers demand.

Hendrik Tennekes, former director of the Royal Netherlands Meteorological Society, has argued that while he agrees with the IPCC that rising CO2 levels are affecting global temperatures, once models begin focusing on regional projections, they break down. Much of that has to do with how they handle poorly understood features of the climate system such as the Arctic Oscillation, which affects rainfall patterns below the Arctic Circle.

Overinterpreting models can also be a problem, says Tom Delworth, who heads the climate dynamics and prediction group at GFDL.

"The more you know about models," he says, "the better you realize how important it is to make sure that you're using the model for the class of problems it was built to answer. Sometimes people try to use models to answer questions they weren't designed to answer."

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