Innovation Mitigates Cloud Problem in Climate and Weather Forecast Models
CIRES scientist’s new framework promises to improve cloud representation, 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, a CIRES scientist working in NOAA’s Chemical Sciences Division.
The new framework, described earlier this year in the AGU’s 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 NOAA’s 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.
Yamaguchi won a CIRES Outstanding Performance Award in 2015 for his work on modeling aerosol-cloud interactions and their impact on climate change.