by Judith Curry
“It’s a major, major problem right now that we can’t constrain it better,” said Trude Storelvmo, a climate scientist at Yale University. “It’s not given that we’ll be able to constrain it any better. It’s not obvious how we can go about constraining it better.”
It’s a reality that climate scientists have hinted at for several years. In 2010, Kevin Trenberth, a prominent climate modeler,reported that efforts to improve the predictive abilities of climate models would result in greater uncertainty. And this June, a pair of British scientists warned that more, not less, uncertainty is expected in the next U.N. report, presenting a serious public-image problem for scientists.
Some cloud scientists have grown impatient with the need to frame their work through the global warming rate, which is known in scientific circles as climate sensitivity. It’s a reality that Graeme Stephens, the head of climate science at NASA’s Jet Propulsion Lab, railed against in a lecture last year. The error in the sensitivity might not be reduced, but if you look past that, real progress is happening, he said.
Beyond the frame imposed by the political debate, scientists now understand clouds, and their response to air pollution, better than ever. Unknown unknowns are now known unknowns. Looking into the sky from observatories like the Sphinx, or down from new satellites above, new contours are visible. But they are not yet in focus.
Among scientists, Zieger continued, there’s a famous bar chart in the IPCC reports. It depicts the current human influence on the climate, both warming and cooling. Almost all the error comes from aerosols and their influence on clouds.
Solving the aerosol-cloud interaction won’t end the uncertainty in climate sensitivity stemming from the response of clouds to higher temperatures. Aerosols are more a derivative of the larger problem. But the field presents tractable problems, rather than a fundamental question of physics, which is perhaps why it has attracted a lion’s share of cloud work over the past decade.
Look at any photo of the low, flat clouds that run along America’s Pacific coast, and bright cotton-ball streamers appear. These are the tails left by ships, steaming across the ocean and releasing pollution — aerosols — as they go. The higher number of cloud nuclei allows smaller, brighter cloud droplets to form, reflecting more sunlight into space.
It’s called the albedo effect by scientists, or the Twomey effect, after the man who popularized it. (For true wonks, it’s also called the first indirect effect.) There’s little doubt it’s real. Early on, it was easy to add into climate models: Simply make the cloud droplets over aerosol-emitting continents smaller and see what happens. The models have improved recently, getting actual physics into them. It remains the only cloud effect that the IPCC has tried to estimate.
If there’s certainty in aerosol and cloud science, it’s that the albedo effect exists, at least in some cloud systems, said Graham Feingold, a scientist at NOAA’s Earth System Research Lab who has spent the past decade knitting together theories on cloud formation, informed by the surge of data from satellites, radar and aircraft, including the first ground-based observation of the albedo effect.
“Nobody argues with the fact that more particles means smaller droplets,” he said.
But the hot debate now, she added, is over the science’s other pillar — the lifetime effect.
“That’s a controversy right now,” she said. “Is the lifetime effect even real?”
The lifetime effect connects back to the cloud seeding experiments of the 1940s and builds off the albedo effect in a simple way. If more aerosols cause smaller water droplets in clouds, then won’t these droplets collide less, causing less rain and, ultimately, more clouds to persist in the sky? It’s an elegant theory, and one that was picked up by much of the modeling community without question.
That has started to change in recent years, partially due to an influential paper published by NOAA’s Feingold. It’s time to stop looking at clouds as single entities, he said. They are part of a system. When aerosols are added, sure, there may be less rainfall, at first. But that cloud may grow deeper and darker, predisposing it to even more rain than before. In effect, the cloud buffers its response to human pollution.
“One could think about this as the resilience of the cloud system,” Feingold said.
Feingold’s theories echo what has been a deficiency in how climate models rendered aerosols. In the past, the programs simulated how pollution could change a cloud, but not vice versa, said Andreas Muhlbauer, a climate scientist at the University of Washington.
“One thing that’s been neglected is the impact of clouds on aerosols,” he said. This was a topic of much discussion at a global modeling workshop this summer in Poland. “If precipitation kicks in, it can clean out the atmosphere.”
There have been reports this summer out of the newest suite of climate satellites that bolster Feingold’s case, if not necessarily his theory. For the first time, these satellites, collectively known as A-Train, allow scientists to make nearly instantaneous snapshots of rain, aerosols and clouds. Combining A-Train data with a traditional satellite, French researchers found support for the albedo effect, but not much for the lifetime effect. Similarly, a study published in August found evidence for a weaker lifetime effect than estimated by climate models alone, a result echoed by NASA’s Stephens, one of A-Train’s leaders, in remarks earlier this year.
There’s still much more to come from the satellites, Stephens added in his lecture last year.
“Raining clouds are a hell of a lot brighter than nonraining clouds,” he said at the American Geophysical Union’s annual meeting last December. “Precipitation has an important radiative signature that we haven’t really considered in feedbacks.”
Still, as these studies admit, satellites and climate models alone won’t crack the aerosol and cloud conundrum. It remains maddeningly difficult to quantify aerosols on a global level. Each method of study has its drawbacks. The Jungfraujoch captures a cloud only as it passes; a plane catches only a pencil line of its prey. Or take satellites. They may snap only two pictures a day of a cloud.
“Someone once likened this to someone completely unfamiliar with the rules of soccer getting snapshots twice a game,” Feingold said. “And after the fact trying to figure out what the rules of the game are.”
To get at aerosols, scientists are going to have to find a way to unify this data: to link the small scales crucial to cloud dynamics with the large scales of the climate. Much of this is a question of statistics and scale, and gets arcane quickly. But Feingold and Allison McComiskey, his co-worker and a geographer, are getting at the question, developing a method to represent strong but variable aerosol effects at coarse global scales. If it is successful, it could be reproduced by scientists across the planet.
Clearly, it’s going to take some Herculean science to sort it all out, Storelvmo said.
“It may look like we’ve made no progress, but that’s far from being true,” she said. “It’s a huge community working on cloud and aerosol interactions. We’ve made a lot of progress. But it hasn’t resulted in a narrower spread of estimates.”
Yes — more uncertainty. There’s a stumbling point any scientist, or anyone, can reach with clouds and climate change. It seems intractable. It’s tempting to move on. Many do.
“It’s almost a truism that we need to understand clouds better,” Chicago’s Pierrehumbert said. “People have known that clouds are a problem for 40 years. It’s been incremental. It’s not that exciting. It’s frustrating.”
From the second article:
Time and again, clouds have provided the most intractable disagreement among models. Over simulated decades, sometimes the clouds accelerated warming; sometimes they were unchanged; and rarely, they dampened the heat. But most of all, they told their creators that they had failed to solve the problem in a convincing way. And the biggest point of disagreement has been low clouds like those off California’s coast.
“This really is the fundamental problem in climate models,” said Joao Teixeira, deputy director of climate science at NASA’s Jet Propulsion Lab and a collaborator on Lewis’ Spirit project. “How do you represent the clouds?”
There are ways the models do agree. All project that the wispy ice clouds in the upper atmosphere will rise in height, causing more warming, a result seen not just in silicon, but in the real world. But without an accurate handle on low clouds, Teixeira added, it’s impossible to fix on the planet’s exact climate sensitivity, a shorthand used by scientists to test Earth’s warming under instantly doubled CO2 emissions.
Even the primary mechanism of this cloud feedback is in doubt: The change could come from higher surface temperatures, which increase in a laggy, variable way due to the ocean’s heat retention and natural variability, or it could be governed by fast feedbacks, CO2 emissions changing the clouds even before they warm the Earth. Even more likely, both dynamics play together in an irreducibly complex double Dutch.
Still, cloud scientists are game. Using historical records, ground radar, satellite data and advanced supercomputers, these researchers are improving the models and probing for any sign of past change in the clouds. And although this work has not led to a decreased spread in the climate sensitivity, which will remain about the same in next year’s U.N. climate science report, it has been far from for naught.
“The uncertainty in cloud feedbacks hasn’t gone down. It’s sort of stayed the same,” said Chris Bretherton, an atmospheric scientist at the University of Washington. But that’s not the same as stalled progress, he added. “We’re taking what was before uncertainty that lay outside of the models, and putting it into the models.”
These questions pivot especially on marine stratocumulus clouds, sheets of bulbous gray gruel that rest low above a fifth of the Earth’s surface, reflecting the sun. And there are few better examples of those clouds than the transect between Los Angeles and Hawaii, where a remote, eerily stable stretch of stratocumuli slowly dissolves, on approach to coconut trees, into cotton-puff cumulus and open sky.
For decades, scientists have known they’d need to take their cloud tools off the shore and into the ocean, but until last month, those efforts had stalled. Instead, work funded by the Energy Department has been earthbound, based in Oklahoma, or at best on remote islands, where clouds remain shaped by even the dullest contours of the land.
“There are very few measurements over the ocean for any period of time,” Lewis said.
There is an enormous pent-up demand for the data Lewis and his team will find along the Spirit’s path. The project, nicknamed MAGIC, will provide evidence for teams of climate scientists and modelers waiting to test their theories against reality. A successful, stable voyage is crucial. It is one of the best hopes left to help resolve the cloud enigma.
So far, scientists have found no other shortcuts to solve the problem. But, of course, that doesn’t mean they’ve stopped trying to find one.
If only the world had infinite computing power, there wouldn’t be a cloud problem.
When it comes to clouds, there are two types of computer models, and it’s easy to get them mixed up. There are the global climate models familiar to most, which chop the Earth into 100-square-kilometer grids, simulating the planet for years on end. Much less known, however, are the small-scale cloud models that divide the atmosphere down into boxes of 10 meters square, where the computer can begin to capture the chaotic atmospheric turbulence that rules the clouds.
Those cloud models are so demanding, however, that no computer can run them on anywhere close to a global scale. It’s a dream, but not one to be realized soon.
“If I could run a [cloud-resolving] model globally, or even over a huge area, I might be able to get a result I at least didn’t know was wrong,” said Joyce Penner, a longtime modeling expert at the University of Michigan. “But we can’t do that yet.”
Klein also compared these climate models with their decade-old code, using his test bed to get at how well each re-created observed clouds from the satellite record. For a long time, models have been notorious for having sparse clouds, with the ones they did create reflecting far more light than they do in reality. It’s known as the “too few, too bright” bias. And it’s a problem that’s quietly slipping away.
“Clouds are not as bright as they used to be, and they’re getting more of them,” Klein said.
Indeed, his work has hopeful news for modelers: They’re getting better.
“This is clear evidence that work into clouds has gotten better over time,” he said. They still don’t agree on all the feedbacks, but it’s progress. “Even if we’re not fully reducing the uncertainty, we’re certainly making the clouds we have today better.”
At this point, Klein can only speculate why they’ve improved. He doesn’t write the code. He only tests it. Maybe it’s a better representation of the clouds’ microphysics, the fine-scale process of how vapor becomes a cloud, and back again. The models have also refined their vertical resolution — perhaps fewer clouds are rendered 500 meters thick, when they’re really only 100 meters tall. And it could be they’ve better represented how to get at the uncertainty inherent in grids often half covered by clouds.
Despite these improvements, and the vast political pressure created by the U.N. reports to increase their accuracy every five years, the models, including those tested by Klein, have far to go. They don’t even reflect much current science, Teixeira said.
“While we know much more than we did, we haven’t been able to implement that into models in a way that’s satisfactory,” he said. “That’s as much an engineering problem.”
Teixeira talked with his peers about the problem at a recent workshop, and there were no obvious solutions. Around the world, there are maybe 150 people working on serious climate modeling, to be generous, with 20 or 30 of those doing cloud work. Teixeira compares that with one of JPL’s other projects, the most recent Mars rover, which took hundreds of engineers to create, none of whom was under pressure to publish or perish.
“One of the most important things we learned from doing this work,” he said, “is that I think we understand the reasons there’s a lot of uncertainty. … Basically, there are processes that work both ways — ones increasing clouds and ones decreasing clouds.”
For instance, in a warmer climate, the middle and upper atmosphere heat more than the surface. That will strengthen the “lid” on marine clouds, making mixing harder and increasing cloud cover. On the other hand, increased CO2, even before it warms the surface, will trap more heat in the clouds, weakening the process that drives their formation. Any battle like that makes it difficult for models to get it right, though notably all the simulations agreed that there would be no negative feedback.
JC comments: I’ve excerpted a small fraction of these lengthy articles, they are both well worth reading in their entirety. Voosen has done an exceptional job on these two articles, and he provides some genuine insights into the problem.
At the time of the FAR (1990), clouds were identified as the greatest uncertainty. In subsequent years, attention focused on aerosols and how they influenced clouds, with the aerosol indirect effect being hypothesized to have a strong cooling effect (partially canceling out the anthropogenic greenhouse effect). Over the last few years (e.g. Feingold and Stephens), it is becoming increasingly apparent that the aerosol indirect effect is significantly smaller than previously hypothesized (and represented in climate models).
This finding has important implications both for AGW and also cloud research. Without a large aerosol indirect effect to counter the the anthropogenic greenhouse effect, the climate model sensitivity to CO2 seems too large. With respect to cloud research, the attention needs to move away from the more tractable problem of cloud-aerosol microphysical interactions to the cloud-turbulence-dynamics interaction, which is an extremely difficult problem. And I think it is naive to believe that this problem can be solved by more computing power.