The acronym for the project Yangang Liu helps lead has a multitude of meanings, he says.
It’s called FASTER, for FAst-physics System TEstbed and Research. Its goal is to characterize and evaluate the “fast physics” phenomena of clouds, aerosols (particles suspended in the atmosphere) and precipitation in current and future global climate models.
A frame from a WRF, or weather research and forecasting model, that shows an area over Oklahoma where the FASTER fast-cloud physics project will be put to the test.
Many physical processes that influence Earth’s climate occur on scales of time and space that are too small to be portrayed by most global climate models. Fast physics denotes all these climatic processes collectively, primarily focusing on processes related to clouds and precipitation. These include cloud microphysics, convection, boundary layer processes, radiation and aerosol-cloud interactions.
“We chose the acronym for its obvious relevance, but also because the project will develop the testbed and perform research and address the fast processes as an interacting system, as they occur in nature,” says Liu, a scientist in the Atmospheric Sciences Division (ASD) at Brookhaven National Laboratory (BNL). “Also, global climate models’ resolution increases over time as computer technology advances, and the fast processes in climate models will actually become faster as a result. And it’s a reminder: We will always try to perform evaluations faster than others.”
FASTER is an elaborate collaboration between atmospheric scientists and climate modelers from BNL, Lawrence Berkeley National Laboratory, NASA, the National Oceanic and Atmospheric Administration and seven other universities and institutions around the world. Many came to BNL, on New York’s Long Island, for two days last November to launch the ambitious Department of Energy (DOE) initiative.
“The kickoff meeting gave us a forum and the opportunity to review some preliminary results, discuss collaborations and lay out items for immediate actions, as well as let some external experts see the extent and scope of the project,” says ASD head Robert McGraw. Kiran Alapaty of DOE’s Atmospheric System Research program is co-manager of the project, and Peter Daum, chairman of the BNL environmental sciences department, rounds out the project management team.
“The DOE anticipates that the outcome from this project will lead to major improvement in climate models, “Alapaty says. “The number of scientists who are participating in this effort is an indication of how important accurate climate change projections are now considered worldwide.”
Because most climate models can’t capture fast physics, the processes must be parameterized, or characterized in a way that models can interpret. Global climate models often differ in how they represent the fast physics driving processes like precipitation and aerosol and cloud formation. Those differences are largely responsible for significant uncertainties in predictions of climate sensitivity and indirect effects from aerosols, Liu says.
“One of the main goals of the fast physics project is to devise parameterizations based on scientific research and observation and bring them to a form suitable for testbed evaluation and use in the climate model,” McGraw says.
Liu adds: “Despite tremendous effort over the past three decades, progress has been frustratingly slow. In particular, representation of cloud-related processes has remained one of the greatest sources of uncertainty in global climate models.”
Two chief reasons advancement has lagged involve observations and compartmentalized research activities. Modeling and parameterization progress relies heavily on comparing models with observations to discern systematic biases and parameterization problems, Liu says. Traditionally, global-scale evaluation of the way climate models represent clouds has involved comparison with observations such as those from the International Satellite Cloud Climatology Project. Satellite observations are unmatched in covering the globe spatially, but they often aren’t well resolved across time and lack vertical details, such as cloud microphysics and liquid water content, that are much needed to address the fast physics problem.
DOE’s Atmospheric Radiation Measurement Program (ARM) was started more than a decade ago to fill this critical need and scientists around the world use its measurements.
ARM data let scientists continuously evaluate whether particular parts of climate models show systematic biases over an extended period and range of conditions, Liu says. “But so far model evaluation using such surface-based observations has relied largely on case studies or intensive observational periods.” Highly detailed data from long-term observational programs such as ARM gathered over the past decade have been underused to evaluate climate model performance, he adds. The FASTER project is driven by the Earth System Modeling Program of DOE’s Office of Biological & Environmental Research.
Liu says understanding fast processes, developing parameterizations for them and examining their effects on climate sensitivity should involve a chain of activities in areas ranging from observation to model evaluation to theoretical understanding of the physical processes and corresponding parameterizations. But following the chain means getting scientists in different areas of expertise to work in concert to overcome their “cultural differences.”
Previous activities “suggest to me a need for a more focused project that capitalizes on all of the developments in both measurements and model evaluation,” Liu says. “FASTER is a comprehensive project that integrates expertise and will use the available continuous observations to perform fast evaluation of global climate model performance. We think that it will greatly advance climate modeling.”
Modeling cloud and precipitation processes is complex because they involve multibody systems, with numerous particles of different sizes and shapes, and occur over a wide range of time and space scales, Liu explains. He calls them “4M complexities” – multibody, multiscale, multitype and multidimension.
FASTER’s first order of business is to evaluate and delineate the individual fast processes. Because fast physics parameterization is a major problem in global climate simulation, any improvement in fast physics will lead to better models and, eventually, a better understanding and prediction of such crucial phenomena as global warming.
“Virtually all of the fast physics processes interact,” Liu says, “so once we get a handle on the individual processes, we want to see how they interact and how to evaluate these interactions.”
FASTER’s testbed aspect integrates two major “fast” components: a single-column model (SCM) testbed that takes advantage of SCM versions of a global climate model; and a numerical weather prediction model (NWP) testbed that capitalizes on routinely reported results from major NWP research centers. The testbeds allow researchers to evaluate fast physics parameterizations quickly. Presently, the researchers are focused on the SCMs of the NASA Goddard Institute for Space Studies, the NOAA Geophysical Fluid Dynamics Laboratory, the National Center for Atmospheric Research and the European Center for Medium-Range Weather Forecast.
Liu says FASTER also involves a suite of higher resolution modeling activities – cloud-resolving models and large eddy simulations, for example – to enhance the SCM and NWP activities. There are plans to have a platform for running the Weather Research and Forecasting model continuously for the period needed to evaluate models.
“A unique aspect of FASTER is that its main objective and task is to evaluate models, which requires not only storage of observational data but also simulation data derived from models of various resolutions,” Liu says. “To put these two streams of data in the same format and at different resolutions in a ready-to-use mode is a huge challenge. Data integration is an essential part of the project, and we will work hard to explore innovative ways for data mining and visualization.”