Although it’s easy to calculate the impact of additional greenhouse gasses on the temperature, these simple calculations don’t capture the potential for feedbacks in the system. The easiest feedback to understand is the ice-albedo response. As temperatures rise, ice melts; that ice normally reflects back most of the sunlight that hits it, so its loss leads to increased absorption of sunlight and hence, a further increase in temperature. Ice is hardly the only feedback, however, so researchers use climate models to try to incorporate as many of these feedbacks as possible.
Unfortunately, there’s often disagreement and uncertainty as to how some of the feedbacks operate. In the past week, a couple of papers have come out that address these uncertainties. In one, an author analyzes the impact of clouds on climate, one of the largest uncertainties in current models. In the other paper, the authors argue that past attempts at figuring out the response of plants to climate change have gotten it all wrong.
Planting a feedback
The impact of plants has grabbed attention because of a rather impressive figure in the abstract: if their assumptions about vegetation are correct, then the warming of the land surface will be 0.6°C less than previously expected. Considering the low-end predictions place warming at 2°C at the end of the century, this is a substantial drop.
The result comes from incorporating a number of different assumptions into existing climate models. Although many have argued that plants will respond to additional CO2 as if it were a fertilizer, the authors argue that foliage will quickly run up against a physiological limit. The enzyme that actually incorporates CO2 into plants’ biochemical pathways will quickly run into a wall, limiting what the plants can do with existing leaves.
As a result, the authors argue, plants will respond by growing more leaves, provided they’re not starved for water and nitrogen. And, they argue, lots of existing foliage isn’t, and increases in precipitation are likely to accompany ongoing climate changes. So, if their arguments (they term them a “postulate”) are correct, we should expect plants to expand what they term the “leaf area index.”
If you incorporate these postulates into an existing climate model, then things change pretty significantly. The added foliage increases the absorption of sunlight in many areas, raising temperatures a bit. But that effect is swamped by what happens with water. Some of the increased rain produced by the climate models still ends up as runoff, but a substantial fraction ends up back in the atmosphere, either through evaporation or via transpiration from the plants themselves. This process requires significant energy, which is absorbed from the atmosphere. As a result, temperatures don’t rise nearly as much as expected.
There are a couple of very large assumptions in their model. The leaf density is assumed to increase only where there’s already vegetation, and the vegetation is assumed not to migrate—for example, into formerly ice-covered areas of the Arctic. These are almost certainly significant simplifications; determining whether they end up making the model physically unrealistic, however, will probably have to wait for the rest of the scientific community to evaluate the paper. In the authors’ favor, they state that the patterns of increased runoff that are currently being observed are consistent with their model.
The other thing to note is that the 0.6°C figure from the abstract is only over land, where transpiration is a significant factor; the global figure is only 0.3°C, which is much less significant overall.
A feedback in the clouds
The lone author on the second paper (Texas A&M’s A.E. Dessler) states the problem with clouds pretty simply: “Clouds affect the climate by reflecting incoming solar radiation back to space, which tends to cool the climate, and by trapping outgoing infrared radiation, which tends to warm the climate.” Right now, reflectivity dominates, but it’s possible that rising temperatures will alter the cloud type and altitude in a way that shifts this balance. And, right now at least, there’s substantial uncertainty as to whether that will happen. Figures have been published that range from a continued cooling through reflection to a shift to insulation.
Dessler doesn’t solve the problem for the long-term, but he does nail down short-term variability. Using satellite measurements and the temperature record for the past decade, he obtained the incoming radiative flux, and started subtracting the major other impacts on it: changes in greenhouse gasses, alterations in albedo (the Earth’s reflection of sunlight), etc. What’s left, he argues, is the impact of changes in clouds.
He then plotted the changes in clouds’ impact against the changes in temperature to determine how well they correlate. The resulting scatter plot is pretty noisy, but the trend is fairly small: about a half watt per square meter, with errors that are a bit larger than that. This means that, as a whole, the cloud feedback is probably positive—it enhances the warming. Given the size of the errors, it’s still possible that it it inhibits warming slightly, but the effect would likely be negligible.
It’s important to remember, however, that this is only the short-term contribution during a period of ongoing climate change. Should we ever stabilize our emissions of greenhouse gasses, it’s possible that the clouds will reach an equilibrium that has a different impact. To try to figure this out, the author ran several climate models for both short-term changes and long-term equilibria.
The good news is that a lot of the models seem to get short-term changes that match the data. The bad news is that there’s little correlation between short-term accuracy and the estimated long-term impact of clouds. In other words, knowing a model does clouds right for a decade doesn’t tell us whether it’ll do a good job for a century. “For the problem of long-term climate change, what we really want to determine is the cloud feedback in response to long-term climate change,” Dessler concludes. “Unfortunately, it may be decades before a direct measurement is possible.”
What do we use these models for?
Although the two studies address a similar problem—the impact of feedbacks on the climate—and both rely heavily on climate models, the papers are very different. To an extent, the vegetation paper is a thought experiment. Foliage will be influenced by a number of factors as CO2 and temperatures rise, and these changes will undoubtedly influence the pace of climate change. What the authors have done is make a number of assumptions, some more scientifically plausible than others—and used a model to see what happens.
And that’s a valuable thing. If an untested factor appears to have a large impact in a model, it’s a sign that we should probably look into that factor in the real world. Different models also provide researchers a chance to experiment with valid differences of opinion. Various models handle clouds in distinct ways, and these differences help provide our expectations a sense of the error bars involved.
For the cloud paper, models had nothing to do with the primary result, which was the value of the short-term feedback of clouds. The author, however, used the empirically derived value to test whether models were getting it right. It was more a model-validation effort than a modeling paper. And that’s also essential, since the people behind the few models that weren’t getting the clouds right can go back and try to figure out why.
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This post was submitted by Mudit Agrawal.
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