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Illuminating the Black Box of Climate
Naomi Lubick

Outside my window, streaky clouds veil a cornflower blue sky. Beneath that layer of haze, a cloud that looks strikingly like a goldfish is swimming past. The sun is shining through, only slightly muted. The goldfish cloud is gone in a few minutes.

Although it looks sunny and warm, I know from the Weather Channel that it’s cold outside — 39 degrees Fahrenheit and it feels like 30 degrees from the winds that blew away the goldfish. Still, chances are good that it’s going to be a beautiful weekend, around 45 degrees Fahrenheit with some clouds and lots of sun. The meteorologists can even tell me that next week, there’s a 30 percent chance it’s going to rain on Monday.

But the forecast for the weather outside my window on a February afternoon 50 years from now or 100 years from now is much more uncertain. While meteorologists have gotten dramatically better at predicting the week’s weather over the past few decades, predicting long-term climate changes with certainty is currently impossible.

That has not stopped climate scientists from trying. They first created general circulation models, also known as global climate models or GCMs, in the 1960s and 1970s, based on weather prediction models. As the name suggests, the models can only give generalized global average temperature changes for Earth.

The wide range of results for varying climate scenarios indicates that global average surface temperatures will rise several degrees Celsius over the next century or so. While the basic principles behind the models have not changed in the past few decades, the scientific understanding and the technological horsepower behind them have improved. Still, the models’ inherent uncertainties have continued to cause debate among scientists and policy-makers about the best course of action to take.

Building a model

Describing Earth’s complex climate system requires complex models, made of computer algorithms that generalize physical characteristics at a scale that covers the entire globe in patches, a few hundred square kilometers apiece. Many of the characteristics the algorithms describe are things a meteorologist might take into account: wind direction, ambient temperature, past behaviors for global and regional weather, ocean currents, clouds and a slew of other aspects.

Jeff Kiehl, a senior scientist in NCAR’s Climate Change Research section, examines changes in ocean circulation from the institute’s coupled global climate model. Improved geologic information and technological power continue to advance such models. Courtesy of NCAR.

“The big climate models are remarkable achievements,” says Richard Alley, a paleoclimatologist at Pennsylvania State University in University Park. “The amount of work that goes into them and how rapidly they do it … are really very impressive,” and carried out with “a lot of skill.” In the past decade, the models’ accuracy has also increased dramatically, Alley and others say.

When comparing the models, however, “there’s quite a bit of difference,” says Eric Barron, dean of the College of Earth and Mineral Sciences at Penn State, who has been watching climate models over the past decade. The leading models from institutions such as the National Center for Atmospheric Research (NCAR) in Boulder, Colo., the Geophysical Fluid Dynamics Laboratory at Princeton, the Hadley Centre in Exeter, United Kingdom, and others, all incorporate particular physical processes differently and focus on different details.

Modelers generally test their hypothetical Earth climates by doubling carbon dioxide instantaneously from present levels, which imbalances the energy in the system. In a feedback loop, the extra greenhouse gas traps thermal radiation that otherwise would escape out to space, as more sunlight keeps coming. “The system is going to do everything it can to get back into balance,” says Jeff Kiehl, a senior scientist in NCAR’s Climate Change Research section. Fundamental physics says that the system has to warm up.

But in the real world, Kiehl points out, increases in the concentration of carbon dioxide take longer. “The buildup of carbon dioxide through the use of fossil fuels causes a gradual warming, which is moderated by the large heat capacity of the oceans,” he says. Including the deep ocean, the system takes about 3,000 years to come into equilibrium — a time scale that is accessible in the geologic record but that, in a model’s world, is “computationally expensive,” Kiehl says.

Thus, by necessity, models are incomplete and simplified in comparison to the real world. Coupled climate models link ocean and atmosphere together, and while most consider changing sea-ice dynamics, others ignore land ice-sheet variability altogether. But the biggest factors in uncertainty in the models’ results come from their large scales in time and space, and from an inability to incorporate small-scale processes that are relatively unknown or cannot be described at the level of detail that would allow modeling both quickly and with more certainty.

That uncertainty comes into play, for example, with ocean eddies, which occur on a scale of about 10 to 200 kilometers and which may be a factor in pumping heat off the equator and determining the path of the Gulf Stream, says Axel Timmerman, a physical oceanographer at the University of Hawaii at Manoa, in Honolulu. Computer power partly restricts the inclusion of eddies in the global models, he says, as well as lack of knowledge: “The equations of circulation are very well-known, but the equations that capture the small-scale processes, such as mixing in the ocean or cloud formation in the atmosphere, are not so well-known.”

Like eddies, cloud processes cannot be “explicitly resolved,” Timmerman says. Rather, their “gross effect” on the atmosphere is captured in 200- by 200-kilometer grid boxes. Clouds can reflect sunlight, much as ice at the poles reflects light, decreasing warming in a region. They also indicate moisture present in a patch of sky. “What we do not know is how the clouds in a greenhouse-warming world operate. Do they operate as sun shields,” Timmerman asks rhetorically, “or are they super-greenhouse factories because of the availability of the most efficient greenhouse gas, water vapor?”

Despite these unknowns, researchers are finding new ways to model large quantities of data more quickly and at reduced cost, while incorporating new geologic information.

Refining the details

To get around the cost problem, some researchers have used systems similar to SETI@home and other distributed-computing projects. Thousands of home computer users program their idle computers to run computations for the project climateprediction.net, which released its first results in January (see story, Geotimes, April 2004, and Web Extra, Feb. 7, 2005). That project is trying to pin down the sensitivity of a modified model originally produced by the Hadley Centre.

As computers get faster and cheaper, modelers expect bigger and better GCMs. Japanese climate scientists are dedicating one of the fastest computers in the world to simulate a global coupled climate system at a resolution of a few kilometers, Timmer-man says, “which will be a major scientific breakthrough.”

Researchers also continue to try to pin down other sources of uncertainty. Tiny bits of particulate matter — for example, from a highway nearby, with cars putting out nitrogen and sulfur oxides, or other far away sources — complicate modeling interactions of clouds and moisture. Such aerosols vary in behavior and concentration: Some particles, such as soot, are very dark and absorb radiation, making the planet warmer. Other particles, such as sulfate, which comes from burning coal, tend to reflect sunlight, cooling the atmosphere. Whereas carbon dioxide and other long-lived gases get uniformly spread, short-lived particulates can clump or scatter, varying across distances.

How those particulates interact with each other and with moisture is “a very knotty problem,” says Drew Shindell, a climatologist at NASA Goddard Institute for Space Studies in New York City. Small molecules may make a large difference in cloud cover, Shindell says. Researchers at NASA Ames in California are studying the indirect effects aerosols might have by changing clouds and water vapor in general, and finding that the impacts may be smaller than thought but still significant. Yet despite how little is known, Shindell says, “we have a lot of information, so that if we are clever, we can attempt to evaluate the models by comparing with the past.”

Scientists continue to elucidate past climate records, turning up some surprises. New data published in the Feb. 10 Nature shows that over the past 2,000 years, Northern Hemisphere temperatures recorded in tree rings and lake sediments fluctuated quite a bit more than earlier studies showed. The variability includes a warmer period similar to the past several decades that occurred about 1,000 years ago — which paints a different picture than the “hockey stick” graph showing relatively steady temperatures that suddenly jumped in the mid-1850s.

Climate scientists have used proxies such as tree rings, ice cores and historical records (blue lines) and thermometers (red lines) to recreate temperature variability in the Northern Hemisphere over the past 1,000 years. The resulting “hockey stick” image has become a flashpoint in the global climate debate, and new data have shown the stick’s handle (blue) to have even more variability than previously thought. Courtesy of IPCC.


Modelers are finding that they can match some climate variability as they understand more about Earth’s processes. “I’m actually very impressed by the progress in the last few years,” Timmermann says. Most models can simulate daily weather “quite realistically,” as well as other climate phenomena — even predicting El Niño events somewhat successfully. “In that sense, I would trust the latest generation models because they capture, for example, the dynamic processes in the Pacific Ocean,” he says.

Paleoclimatic modeling is also getting stronger with improved data. Alley of Penn State says that “paleoclimatic simulations seem to get a lot of things right,” including the freshening of the North Atlantic 8,200 year ago. Geologic data show which regions got wetter and which drier, and experiments with fully coupled ocean-atmospheric models are in “pretty good agreement” with those changes. “There’s no cheating here,” Alley says. “This is a model that was built for other purposes, taken and run for that particular experiment.”

But no model has ever calculated the 100,000-year cycle of ice ages, says Richard Lindzen, a meteorologist and atmospheric scientist at MIT who does not believe that current global warming is primarily due to human influences. “It seems to me you have to have a religious belief in the models” to trust the results, he says. The models can be “forced” to fit the data. And when it comes to modeling clouds, some models differ from each other and from observations by as much as 40 percent, an “error” that is “crucial to the answers they get,” Lindzen says. “What is the answer to this? To clutch at everything we don’t know,” including aerosols and other factors.

To account for disagreements between the models, the Program for Climate Model Diagnosis and Intercomparison at the Lawrence Livermore National Laboratory in California has been comparing GCMs with varying parameters since 1989. Part of their work will be factored into the upcoming Fourth Assessment Report from the Intergovernmental Panel on Climate Change (IPCC), due out in 2006.

The differences in models and scenario ranges highlight their uncertainty a regional level, although scientists may have a “fairly good sense” of a plausible range of global warming, says Barron of Penn State. “You have trouble trusting a prediction for 100 years out, for a particular place, for a particular time, for particular phenomena. If you are asking what severe storms will look like in Washington, D.C., in maybe, 2020 — it’s hopeless,” he says. “There are too many factors that are just unknowable. … I don’t think there are going to be terrific breakthroughs in the next decade that will change that.”

Living with uncertainty

The most recent IPCC assessment set the window of global temperature changes at increases of 1.4 to 5.8 degrees Celsius by 2100. Policy-makers around the world are struggling over what to do, even as the Kyoto Protocol went into effect on Feb. 16.

Even if researchers reduce the uncertainty in climate modeling, to pinpoint temperature changes in a certain place, “you would still not know what to do to cope with such changes,” says Rosina Bierbaum, dean of the School of Natural Resources and Environment at the University of Michigan, Ann Arbor, and former advisor to presidents Bill Clinton and George W. Bush on climate change issues. “I think it’s a red herring that we have to wait for really good regional models” before evaluating societal costs and options for adapting to climate change, she says.

Instead, Beirbaum says, addressing “some crude what-if” scenarios is necessary: Communities should be thinking about how their current problems might be exacerbated by climate changes (see stories in this issue). For example, in the Pacific Northwest, models show snowmelt will be earlier, by several weeks, she says, which could mean increased summer water shortages as spring thaws occur earlier. “You don’t need a lot of really sophisticated modeling to say there’s going to be a big problem here.”

With the accepted projections from climate models not changing much since the 1980s, says Daniel Sarewitz, director of the Consortium for Science, Policy, and Outcomes at Arizona State University, Tempe, uncertainty is “as much a sociological phenomenon as a scientific one,” that reflects politics as well as “a state of mind of the scientists doing the work” and their own “confidence in the science that they are doing.” Unlike deterministic systems, such as flipping a coin, uncertainty in the models is not measurable in nature — even if the models are perfectly valid. “It’s not like you have some instrument and you measure it over and over again,” he says.

In this case, Sarewitz says that meteorologists have the advantage: They make millions of near-term forecasts and can feed their successes and failures immediately back into their own modeling systems, which “allows them to continually improve the science,” Sarewitz says. And, he adds, “it improves the judgment of the scientists.”

Part of that success means that the users — people watching the Weather Channel who want to know what the weather will be in their grandma’s backyard — “get comfortable with the predictions,” Sarewitz says, and their uncertainties. Most people, however, are not comfortable talking about climate change, particularly when trying to relate to global average temperature changes (1 degree Celsius doesn’t sound like much, despite serious effects projected around the world).

“The closer you are to what really governs the way individuals live are the things that are hardest to predict,” Sarewitz says. “We’re going to have to live with a high level of uncertainty probably for a long time, with the knowledge that the planet is warming.”


Lubick is a staff writer for Geotimes.

Links:
"What Makes Good Climates Go Bad?" Geotimes, April 2005
"Climate Policy in an Adapting World," Geotimes, April 2005
"A climate of your own," Geotimes, April 2004
"Virtual climate experiment's results," Geotimes Web Extra, Feb. 7, 2005
www.climateprediction.net
SETI@home

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