2017 News & Events

Innovation Mitigates Cloud Problem in Global Climate and Weather Forecast Models

26 July 2017
adapted from the story by CIRES Communications

cloud cover
A CSD-led innovation promises to help scientists better represent clouds in computer models, and the technique could improve weather and climate forecasts. Image: NOAA / CIRES

CSD scientist's new framework promises to improve cloud representation and forecast accuracy.

Anyone with a cell phone camera and kids or dogs knows that resolution is "expensive:" taking lots of very high-resolution photographs and video clips can quickly fill a device.

An analogous resolution challenge in weather and climate modeling has dogged modelers for years: Computationally, it's just too expensive to represent certain clouds in the detail needed to make them behave realistically; yet clouds are critical to accurate weather and climate modeling. Now, a team of CIRES, NOAA and University of Wisconsin-Milwaukee experts has proposed a solution, and in a test, their new clouds even produced credible drizzle.

"Our concept is equivalent to having a camera automatically provide higher resolution in just parts of the photograph, say on a human face but not in the background," said lead author Takanobu Yamaguchi. Tak won a CIRES Outstanding Performance Award in 2015 for his work on modeling aerosol-cloud interactions and their impact on climate change.

The new framework, described earlier this year in the AGU Journal of Advances in Modeling Earth Systems, could improve the way models capture thin, layered clouds and help scientists better understand those clouds' roles in weather patterns and climate change.

In computer models of Earth's climate and weather systems, the atmosphere is divided up into individual "grid boxes," analogous to a digital camera's pixels. And just as with cameras, the resolution of global climate models has improved dramatically as computing power has increased.

For clouds, recent improvements have been somewhat limited to better horizontal resolution. That's great for producing realistic deep, convective clouds such as thunderstorms, but not for shallow and thin layered clouds such as stratocumulus in the lower atmosphere and cirrus in the upper troposphere. For those clouds – they each cover about 30 percent of the globe on average – researchers needed better vertical resolution.

"For operational modeling, increasing vertical resolution over the entire field of the model is just not an option," said CSD researcher Graham Feingold, a co-author of the new paper. "It would require an unaffordable amount of computational power."

So Yamaguchi developed a way to solve the impasse: When his framework anticipates that layered clouds may form in the near future, it creates a vertically high-resolution grid just in those places, and calculates selected atmospheric processes at high resolution. In the paper, an atmospheric model with a prototype of the new framework dramatically improved the representation of stratocumulus clouds, and, more strikingly, the way they produce rain. The team also showed that the new framework is significantly cheaper, computationally, than applying high resolution over the entire depth of the atmosphere.

Takanobu Yamaguchi, Graham Feingold, and Vincent E. Larson, Framework for improvement by vertical enhancement: A simple approach to improve representation of low and high-level clouds in large-scale models, Journal of Advances in Modeling Earth Systems, doi:10.1002/2016MS000815, 2017.


Low and high clouds of shallow extent, especially stratocumulus and even more so for high-level cirrus clouds that reside where vertical resolution is particularly coarse, are poorly represented in large-scale models such as global climate models and weather forecasting models. This adversely affects, among others, estimation of cloud feedbacks for climate prediction and weather forecasts. Here we address vertical resolution as a reason for the failure of these models to adequately represent shallow clouds. We introduce a new methodology, the Framework for Improvement by Vertical Enhancement (FIVE). FIVE computes selected processes on a one-dimensional vertical grid with local high resolution in the boundary layer and near the tropopause. In addition to the host model, variables on the locally high-resolution grid are predicted in parallel so that high-resolution information is retained. By exchanging tendencies with one another, the host model and high-resolution field are always synchronized. The methodology is demonstrated for drizzling stratocumulus capped by a sharp inversion. First, FIVE is applied to a single-column model to identify the cause of biases associated with computing an assigned process at low resolution. Second, a two-dimensional regional model coupled with FIVE is shown to produce results comparable to those performed with high vertical resolution. FIVE is thus expected to represent low clouds more realistically and hence reduce the low-cloud bias in large-scale models. Finally, we propose a number of methods that will be developed and tested to further optimize FIVE.