Predicting the Unpredictable
Climate Modeling Has Limits, but Without It, We’re Underwater
SOURCE: UCAR
Climate modelers work with the data they have and play a role in understanding the complexities of the Earth’s environments. But to adapt to future climate changes, we have to invest in their predictive tools.
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Pity the poor climate modeler. Here’s someone whose contributions are chronically underappreciated, whose methodology is under constant scrutiny and, worse, whose findings are often questioned, if not directly undermined. What’s a modeler to do when it often seems like all his or her work—the entire basis for the discipline, really—gets a bum rap from fellow scientists? Now, at a time when the global community arguably needs more accurate models and data than ever to predict future climate change and weather patterns, it certainly looks as though we should be embracing modelers’ efforts—not denigrating them—and providing them with all the necessary tools to help them improve their output. So what gives?
As a climate modeler, you are always working with the best of what’s available—whether that means the best data, best infrastructure, or best science.
To be fair, “denigrate” might be a little too strong of a word to use to characterize the often-legitimate criticism that has come climate modeling’s way. The critics’ main point of contention? Quite simply that models cannot—and likely never will—accurately represent the whole climate picture. There are simply too many known unknowns and unknown unknowns—pardon the reference—for even the most skilled modeler to wrap his head around. On a more basic level, does anybody really think that a collection of models, let alone a single model, can fully reproduce Earth’s complex inner workings? No, of course not, and that’s a point any climate modeler will readily concede.
As a climate modeler, you are always working with the best of what’s available—whether that means the best data, best infrastructure, or best science. And since all those variables are subject to frequent revision, it’s rare to find a robust model that is able to withstand years of new findings. Scientists often relish poking holes in them, using the results from a recent research expedition, for instance, to undermine a single component—regardless of how well the model otherwise captures the environment. While some of this criticism may seem gratuitous, or even childish at times, it is often done with good reason.
Take a recent study published in the Proceedings of the National Academy of Sciences, whose findings risk invalidating over 60 percent of the so-called “climate envelope” studies. Climate envelope models help predict where species will live under conditions of future climate change by using their current distributions to make up a set of climatic conditions—the “envelope”—that closely approximate their needs.
In the past, these models have come under withering criticism for failing to take into account a number of other factors, such as anthropogenic activity or species-species interactions, that figure as prominently, if not more so, as climate change in influencing species distribution. Despite some of their limitations, the Intergovernmental Panel on Climate Change put its stamp of approval on their use in its 2007 report, noting that they “offer the advantage of assessing climate change impacts on biodiversity quantitatively.” Colin Beale of the United Kingdom’s Macaulay Institute of Land Use Research, the PNAS study’s lead author, found that the models performed no better than a simple roll of the die—pure chance—in approximating several bird species’ natural habitats.
To be clear, Beale’s study would not be the first to take such a dismissive view of climate envelope studies—many scientists argue that they still play an important role in predicting future species abundances, if not their exact distributions—and should therefore be taken with a grain of salt. In other words, the pretext of the study is not to invalidate the findings of the IPCC or to cast doubt on the link between climate change and species. Beale is quick to point out that his study did find a significant relationship between the climate and a third of species—and that he is concerned his findings could be misused as evidence that there is no link between climate change and species extinctions.
Other scientists have been critical of the IPCC for seemingly lending too much credence to models’ predictive abilities. While several existing models, especially the so-called “coupled” models (which consider the atmosphere, oceans, land surface, sea ice, and other physical characteristics in conjunction to project future climate trends), have become advanced enough to yield valuable insights on current and past climate patterns—almost matching the accuracy of conventional atmospheric observations—most fall woefully short when it comes to answering the most important question: What will our future climate look like?
One big problem, some argue, is that many current models suffer from oft-debilitating inconsistencies—in their representations of observed changes in global mean surface temperature or in their range of sensitivities, for example—that could significantly diminish their capability to reduce uncertainties in Earth’s climate dynamics and, thus, to predict future changes. As a result, they suggest that international organizations like the IPCC, which have a lot of clout in the scientific and political communities, tamp down some of their expectations—lest they invest too much credibility in models that could very well turn out to be wrong.
If there’s one issue on which most scientists—modelers included—agree, it’s that climate modelers need more: more research funding, more powerful computers to run their “petascale” models (which can make a whopping 1,000,000,000,000,000 calculations per second), and more time to meet ever-rising expectations. With the IPCC shifting its focus to examining the community and state-level impacts of various climate change scenarios (so as to impart more actionable information for policymakers) for its 2013 report, the pressure is on climate modelers to redouble their efforts to come up with more powerful and accurate global models. The bottom line is that we should by no means abandon modeling, but do need to help improve it.
Already premier research institutions like the Breckenridge, Colorado-based National Center for Atmospheric Research, the originator of one of the most widely used coupled models in the United States, is falling behind on meeting an October 1 deadline to update it. Even legislators who tend to only focus on the short-term—in other words, most of them—should see the wisdom in supporting work that could also lead to better hurricane research, an outcome that would yield immediate, and very tangible, benefits.
Perhaps Science Progress contributing editor Chris Mooney put it best in framing his argument for more research funding by tying together hurricanes, climate change and what he calls the “next” New Orleans:
“Which inevitably brings us to contemplating the future—one in which we will be even more exposed to hurricane risks. While it remains hard to predict precisely what global warming will do to hurricanes, we know that it will raise sea levels, and probably intensify storms on average, not to mention increasing their rainfall rates. No matter how you slice it, then, global warming worsens hurricanes—and, accordingly, hurricane-related insurance costs—which makes it a more-than-legitimate topic to invoke in the context of this year’s hurricane threats and landfalls.”
The impacts of climate change are predictable, but the task will be difficult without the additional resources to build more accurate models and adapt to an altered planet.
Jeremy Jacquot is a graduate student in marine environmental biology at the University of Southern California and is a contributing writer for VentureBeat, DeSmogBlog and TreeHugger.
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