This project will dramatically improve the representation of shallow clouds in the Department of Energy’s (DOE’s) Accelerated Climate Modeling for Energy (ACME) model. ACME is a new global model that will forecast climate over the coming decades. These types of models are sensitive to how well they simulate low-lying clouds, which reflect sunlight and hence have a large impact on Earth's radiative balance and temperature. Indeed, how best to represent shallow cloud cover in global climate models is one of the largest sources of uncertainty in climate forecasts.  The CM4 project is designed to fill this gap by bringing together the requisite experts in observations, theory, and modeling to solve it.

The CM4 project is a collaboration between scientists at the Lawrence Berkeley National Laboratory (LBNL), the Brookhaven National Laboratory (BNL), the University of Washington, and the University of Texas at Austin, and is led by Principal Investigator David Romps at LBNL. This collaboration includes several observational scientists who are working on a suite of new meteorological instruments being deployed at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) site, which will open new windows into the behavior of shallow clouds. These new observations will be used to refine the existing ACME algorithm for representing shallow clouds and develop new ones; several of the project’s scientists are experts in this area. Finally, atmospheric scientists, computer scientists, and applied mathematicians will assist in the fine-tuning and evaluation of these schemes in the ACME model. By taking advantage of the newest ARM observations, the latest developments in cloud modeling, theory and parameterization, and state-of-the-art techniques for diagnosis and validation, the CM4 project aims to provide ACME with the world’s most accurate and physically based schemes for shallow clouds.