Bytes for Bits: Researchers Develop a New, Efficient Aerosol Module for Climate Models
Thinking small, scientists achieved big impact. Pacific Northwest National Laboratory researchers led a team developing a new computational module to depict tiny atmospheric particles that have a large effect on climate. Coming closer to a realistic depiction of these atmospheric bits, “MAM,” short for Modal Aerosol Module, also achieves a new level of computational efficiency. Developed for the atmospheric component of the Community Earth System Model version 1 (CESM1) used for the Intergovernmental Panel on Climate Change 5th Assessment report, the minimal MAM representation means a big impact for climate modeling.
“MAM balances a realistic treatment of aerosol physiochemical properties and processes and the need for computer efficiency in climate simulations. Because CESM1 is a community climate model, MAM has generated a significant impact in both aerosol and climate communities,” said Dr. Xiaohong Liu, lead author of the study and atmospheric scientist at PNNL.
A major challenge for scientists working with global climate models (GCMs) is how to realistically represent aerosols, and all their complex properties and processes, within the limitations of computational resources. Achieving a minimal representation of aerosol in GCMs to capture the essentials of how aerosols affect the climate is highly desirable.
A research team, led by Liu, developed two MAM versions for the Community Atmospheric Model (CAM5): a complete 7-mode version and a simplified 3-mode version and compared them to observations. First, the complete 7-mode version was developed as a benchmark used in detailed studies. From the 7-mode version, the scientists developed a 3-mode version to use in decade- to century-long climate simulations. By evaluating both versions with a collection of observation data obtained from surface stations, aircraft campaigns, and satellite measurements, the researchers showed that the simplified 3-mode version achieves the goal of a computationally compact representation of aerosol’s effects on climate. Compared to earlier aerosol modules in CAM, MAM is capable of simulating the aerosol size distribution and has a more realistic representation of mixing states between aerosol components. Both features are critical for capturing the aerosol direct and indirect effects on climate.
MAM has been well received by the modeling community. It has been implemented in the Weather Research and Forecasting Model, a major regional model for weather, air quality, and climate studies. MAM was adopted by the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System Model, version 5 (GEOS-5) for NASA’s satellite data assimilation, climate modeling, and air quality studies.
Tiny but mighty. Aerosols are bits of dust, soot, and chemicals in the air that are intensely scrutinized by scientists because they affect climate and weather in so many big ways. These multi-talented specks can absorb or scatter the sun’s energy and change what happens inside clouds. Considering the many complex and sometimes conflicting climate effects, PNNL scientists have developed a new representation to account for aerosols efficiently and accurately within one of the leading global climate models. This scientific advance will help provide a clearer picture of the future climate.
DOE researchers at Pacific Northwest National Laboratory led a team that developed a modal aerosol module (MAM) for the Community Atmospheric Model version 5 (CAM5), the atmospheric component of Community Earth System Model version 1 (CESM1). MAM is capable of simulating the aerosol size distribution and mixing states between different aerosol components, and treating numerous aerosol physical and chemical processes. Two versions of MAM were developed: one complete version with 7 aerosol modes serving as the benchmark and used for the detailed aerosol studies, and a simplified version with 3 aerosol modes used for long-term (decade to century) climate simulations. MAM well simulates the temporal and spatial distributions of aerosol mass, number and size distribution, and aerosol optical depth when evaluated with observations. As a module in CESM1, MAM is being used for the International Panel on Climate Change (IPCC) the 5th Assessment Report (AR5). MAM has also been adopted by other major global and regional models (e.g., NASA GEOS-5, Weather Research Forecast Model). It is widely used by aerosol and climate communities and to date its documentation paper has been frequently cited. Atmospheric aerosol plays an important role in the climate system, and its forcing is one of the largest sources of uncertainties in the climate projection. The challenge for global climate models (GCMs) to realistically represent aerosol has been constrained by the complexities of aerosol properties and processes, and limitations of computer resources. Therefore a minimal representation of aerosol in GCMs that can capture the essentials of aerosol forcing is a substantial achievement.
This research was supported by DOE’s Scientific Discovery through Advanced Computing (SciDAC) program. The Community Earth System Model project is supported by the National Science Foundation and the DOE Biological and Environmental Research. Computing resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation and other agencies. The work was performed by Dr. Xiaohong Liu, Mr. Richard Easter, Drs. Steve Ghan, Rahul Zaveri, Phil Rasch, and Mr. Xiangjun Shi of PNNL; Drs. Jean-Francois Lamarque, Andrew Gettleman, Hugh Morrison, Francis Vitt, Andrew Conley, Sungsu Park, Rich Neale, and Cecile Hannay of the National Center for Atmospheric Research; Dr. Annica Ekman of Stockholm University; Drs. Peter Hess and Natalie Mahowald of Cornell University; Dr. William Collins of Lawrence Berkeley National Laboratory; Michael Iacono of Atmospheric and Environmental Research, Inc.; Dr. Chris Bretherton of the University of Washington; Dr. Mark Flanner of the University of Michigan; and Dr. David Mitchell of the Desert Research Institute. The research used computing resources from the National Energy Research Scientific Computing Center, supported by the Office of Science, DOE.