A Good Climate for Looking at Clouds

How much do we know about clouds and the effects they have on climate change? It’s a lingering source of uncertainty, with as many questions as answers. No wonder the National Science Foundation calls them “The Wild Card of Climate Change” on its new website about the effect of clouds in climate.
The site is good place to start thinking about this complicated issue. The NSF page features videos of cloud experts like David Randall of Colorado State University and AMS President Peggy LeMone of NCAR, as well as a slide show, animations, articles, and other educational material that address some of most salient cloud/climate questions, such as: Will clouds help speed or slow climate change? Why is cloud behavior so difficult to predict? And how are scientists learning to project the behavior of clouds?
The impression one gets from the website about the progress of the science in this area may vary depending on your point of view, but Randall, for one, sounds about as optimistic as you can get. In his video, he admits that optimism is a job requirement for climate modelers, but in his assessment, “We’re not in the infant stages of understanding [clouds] any more; we’re in first or second grade, and on the way to adolescence.” His hope for solving their role in climate and representing cloud effects in climate modeling rests in part on better computers and in part on the numerous bright people entering the field now, ready to overshadow the work of their mentors.
The AMS Annual Meeting in Seattle will be a good occasion to dig deeper at the roots of Randall’s optimism and sample some of the emerging solutions to the cloud/climate relationship. For example, Andrei Sokolov and Erwan Monier of MIT will discuss the influence that adjusting cloud feedback has on climate sensitivity  (Wednesday, 26 January, 11:30 a.m. in Climate Variability and Change). Basically, they’re using small adjustments to the cloud cover used to calculate surface radiation in a model to create a suite of results–an ensemble. The range of results better reflects the sensitivity of climate observed in the 20th century better than some other methods of creating ensembles, such as adjusting the model physics.
Randall says in his video that early predictions about climate change are already coming to pass and this leads to optimism that more predictions will verify well in the coming years as we scrutinize climate more and more closely. This of course presupposes sustained efforts to observe and verify. Laying the groundwork for this task–and for thus better climate models–are Stuart Evans (University of Washington) and colleagues in a study they are presenting in Seattle. According to their abstract, “Improving cloud parameterizations in large scale models hinges on understanding the statistical connection between large scale dynamics and the cloud fields they produce.” Their study focuses on the relationship between synoptic-scale dynamic patterns and cloud properties (Monday, 24 January, 11 a.m. in Climate Variability and Change). Evans et al. dig through 13 years of cloud vertical radar profiles from the US Southern Plains site of the DOE ARM program and relate it to atmospheric “states”, thus providing a metric for evaluating how well climate models relate cloudiness to radiation and other surface properties.
While Evans and colleagues use upward looking remote sensing, Joao Teixeira (JPL/Cal Tech) and coauthors look down at boundary layer cloudiness from above–using satellites. They expect to show how new methodologies with satellite data can improve the way low level clouds are parameterized in climate models (Thursday, 27 January, 9:30 a.m., in Climate Variability and Change). A recent workshop at Cal Tech on space-based studies of this problem stated:

Clouds in the boundary layer, the lowermost region of the atmosphere adjacent to the Earth’s surface, are known to play the key role in climate feedbacks that lead to these large uncertainties. Yet current climate models remain far from realistically representing the cloudy boundary layer, as they are limited by the inability to adequately represent the small-scale physical processes associated with turbulence, convection and clouds.

The lack of realism of the models at this low level is compounded by the lack of global observing of what goes on underneath the critical low-level cloud cover–hence the effort of Teixeira et al. (and others) to “leverage” satellite observing, with its global reach, to improve understanding of low level thermodynamics in the name of improving climate simulations.

From the new NSF web page on clouds and climate, this picture shows a series of mature thunderstorms in southern Brazil. Photo credit: Image Science & Analysis Laboratory, NASA Johnson Space Center

Climatology: Inverting the Infrastructure

Atmospheric science may not seem like a particularly subversive job, but from an information science perspective, it involves continually dismantling the infrastructure that it requires to survive. At least that’s the way Paul Edwards, Associate Professor of Information at the University of Michigan described climatology, and one other sister science, in an interesting hour-long interview on the radio show, “Against the Grain” last week. (Full audio is also available  for download.)
In the interview Edwards describes how the weather observing and forecasting infrastructure works (skip to about the 29 minute mark if that’s familiar), then notes that climatology is the art of undoing all that:

To know anything about the climate of the world as a whole we have to look back at all those old [weather] records. …But then you need to know about how reliable those are. [Climate scientists] unpack all those old records and study them, scrutinize them and find out how they were made and what might be wrong with them–how they compare with each other, how they need to be adjusted, and all kinds of other things–in order to try to get a more precise and definitive record of the history of weather since records have been kept. That’s what I call infrastructural inversion. They take the weather infrastructure and they flip it on its head. They look at its guts.

In his book, The Vast Machine: Computer, Models, Climate Data, and the Politics of Global Warming, Edwards points out that people don’t realize how much of this unpacking—and with it multiple layers of numerical modeling–is necessary to turn observations into usable, consistent data for analysis and (ultimately) numerical weather and climate predictions. The relationship between data and models is complicated:

In all data there are modeled aspects, and in all models, there are lots of data. Now that sounds like it might be something specific to [climate] science, but …in any study of anything large in scope, you’ll find the same thing.

In part because of this “complicated relationship” between observations and models, there’s a lot of misunderstanding about what scientists mean when they talk about “uncertainty” in technical terms rather than in the colloquial sense of “not knowing”. Says Edwards,

We will always keep on finding out more about how people learned about the weather in the past and will always find ways to fix it a little bit. It doesn’t mean [the climate record] will change randomly or in some really unexpected way. That’s very unlikely at this point. It means that it will bounce around within a range…and that range gets narrower and narrower. Our knowledge is getting better. It’s just that we’ll never fix on a single exact set of numbers that describes the history of weather.

Climatology is not alone in this perpetual unpacking of infrastructure. Economists seem like they know all about what’s going on today with their indexes, Gross Domestic Products, inflation rates, and money supply numbers. That’s like meteorology. But to put together an accurate history of the economy, they have to do a huge amount of modeling and historical research to piece together incongruous sources from different countries.

There is a thing called national income accounting that has been standardized by the United Nations. It wasn’t really applied very universally until after the Cold War….Just to find out the GDP of nations you have to compare apples and oranges and find out what the differences are.

And to go back as recently as the 1930s?

You would have to do the same things the climate scientists have to do…invert the infrastructure.