Research Highlights
May 2008
Adding Chemistry to the Relationship
Scientists everywhere have a more accurate tool for understanding climate change predictions
This map, generated using the Weather Research and Forecasting model, shows the effect of different pollutants in the air over Houston, Texas. Plumes of smoke from refineries and a power plant react with the moisture in the air, producing new chemicals that absorb and reflect sunlight differently. Colors high on the scale, such as red, indicate regions of cooling compared to other regions with colors lower on the scale. Enlarged View
Results: Scientists at Pacific Northwest National Laboratory (PNNL) have enhanced a computer model that is used worldwide in predicting climate change. The improved Weather Research and Forecasting (WRF) model, released in April 2008, now includes important climate feedback processes, such as interactions among man-made or natural trace gases and particulates, water in clouds, and sunlight, simultaneously with changes in meteorology. In contrast with global climate models that cover the entire planet, the flexible grid structure in WRF also permits simulations on smaller scales suitable for understanding effects of climate change at regional and local levels.
WRF is publicly available and is being used by hundreds of researchers worldwide. Preliminary versions have been used by scientists in Chile, India, England, and Germany as well as several institutions in the United States including at PNNL, the National Oceanic and Atmospheric Administration (NOAA), the National Center for Atmospheric Research (NCAR), Princeton University, and North Carolina State University. For the U.S. Department of Energy's (DOE's) Office of Science programs, PNNL scientists are using WRF to obtain a better understanding of how atmospheric processes affect airborne particulate evolution in areas ranging from meters to hundreds of meters and times from minutes to months.
Why it Matters: Climate change can affect crops, wildlife, diseases and the habitability of our lands; knowing what to expect will help us know how best to prepare. The interaction of aerosols with clouds has been one of the greatest uncertainties in climate change prediction and is large enough to make a substantial difference.
Chemical reactions in clouds can affect how much sunlight is absorbed by the earth or reflected back into space. Heat from the sun affects air circulation, which can in turn affect the chemical reactions. Coupling chemistry and meteorology in the model increases the realism of the resulting prediction.
Methods: The PNNL team added to the model important climate feedback processes such as the effect of airborne particles on shortwave radiation and photochemical production rates as well as chemical interactions between water in clouds and airborne particles.
What's Next: Scientists at PNNL continue to make the model even more robust. For example, they are adding to the model new ways of accounting for the organic chemicals produced by reactions between clouds and pollutants and the chemical interactions of airborne particles with ice in clouds.
Acknowledgments: PNNL is transforming the Nation's ability to predict climate change and its impacts. The meteorological physics and overall software architecture were developed by NCAR with funding from the National Science Foundation. The air quality model was developed by NOAA. Dr. Jerome Fast, Dr. William Gustafson, Jr., Elaine Chapman, Dr. James Barnard, Richard Easter, Dr. Rahul Zaveri, and Dr. Steven Ghan of PNNL expanded the chemistry in the code and added climate processes, with support from the PNNL Laboratory Directed Research and Development (LDRD) program. The model is also being used to study particulate evolution associated with field experiment measurements collected by the Atmospheric Sciences Program of the DOE Office of Biological and Environmental Research.
Citations: Fast JD, WI Gustafson Jr., RC Easter, RA Zaveri, JC Barnard, EG Chapman, and GA Grell. 2006. "Evolution of ozone, particulates, and aerosol direct forcing in an urban area using a new fully-coupled meteorology, chemistry, and aerosol model." J. Geophys. Res., 111:D21305, doi:10.1029/2005JD006721.
Gustafson, W. I., E. G. Chapman, S. J. Ghan, R. C. Easter, and J. D. Fast. 2007. "Impact on modeled cloud characteristics due to simplified treatment of uniform cloud condensation nuclei during NEAQS 2004." Geophys. Res. Lett., 34, L19809, doi:10.1029/2007GL0300321.