If FASTER can be considered a jet that speeds global climate modelers to analyze fast physics processes, its wings are the testbed and associated research.
The testbed integrates two major “fast” components: a single column model (SCM), a roughly 100 kilometer by 100 km column that complements traditional global climate models; and a numerical weather prediction model (NWP). The testbeds allow researchers to evaluate fast physics parameterizations quickly.
Wuyin Lin of Brookhaven National Laboratory oversees the FASTER testbed.
Lin has “strong experience in climate model development and climate simulations,” he says, “with a special interest in understanding the physics of cloud and convective processes and their parameterizations in large-scale models, and cloud-climate feedback in general.”
“I’ve incorporated various computational technologies through previous projects on developing an automated framework for numerical weather prediction and seasonal forecasts based on the National Center for Atmospheric Research Community Atmosphere Model. My research focus is on climate modeling, the impacts of clouds and the uncertainties due to clouds, from regional to global scales.”
Lin says both the SCM and NWP testbeds will have online components and are not computationally intensive, but a high-resolution cloud-resolving platform is in the works that will require supercomputing power. For the SCM, for example, Lin says the online interface provides a convenient platform for parameterization within participating models against well-established case studies. NWP forecast and analysis products are readily available from national centers, so the NWP testbed will be focused on evaluating the available NWP cloud products against long-term data from the Department of Energy’s Atmospheric Radiation Measurement program, instead of the NWP simulation itself.
“The Web framework is designed such that researchers, including those who are not even typical model parameterization developers, can test their original ideas in full climate models quickly and effectively,” Lin says. “To aid in the evaluation and development process, a multi-regime case library will be built to integrate and categorize available high-quality forcing and evaluation data.”
Forcing is atmospheric heating or cooling and moistening or drying induced by large-scale motions. Forcing largely sets the atmospheric environment that may produce clouds and precipitation.
Lin says development of the testbed is moving forward quickly. Currently, researchers have three versions of the NCAR CAM models (CAM3, CAM4 and CAM5) in the testbed and are collaborating with Geophysical Fluid Dynamics Laboratory and Goddard Institute for Space Studies researchers to work their models in.
“The main theme of the testbed is to confront the models with data – quantify performance of the models under a variety of atmospheric conditions, “ he says. “The data include the large-scale forcing data that are needed to drive the models and the detailed cloud property products that are needed for model evaluation.”
Lin says the testbed is not only capable of evaluating model physics but also can evaluate the quality of forcing data, in particular when new forcing data are being developed. The Lawrence Livermore National Laboratory group is expanding its continuous forcing data development beyond the currently available three years of 1999-2001.
FASTER team members are putting in a “tremendous effort,” Lin says, to enable the popular Weather Research and Forecast model (WRF) for improving cloud simulations constrained by observational data. In the FASTER framework, the WRF model will take inputs from the ARM observations to produce detailed cloud-scale dynamic and thermodynamic properties.