New Community Atmosphere Model’s Chemistry Scheme Improves Simulation
Atmospheric chemical and aerosol species and their precursors are released by energy combustion and from natural processes. The species affect the atmospheric energy budget and the climate system, so it is important that they are included in climate model simulations. However, because the species typically have short lifetimes (i.e., days to months), it is challenging to capture their spatial and temporal distribution. In this study, partially funded by DOE, the newest version of atmospheric chemistry in the global Community Atmosphere Model version 4 (CAM4), the atmospheric component of the Community Earth System Model (CESM), is described and evaluated. CAM4 offers a variety of configurations for the representation of tropospheric and stratospheric chemistry, wet removal, and online and offline meteorology. Major model biases include a negative bias in the high-latitude carbon monoxide distribution, a positive bias in upper-tropospheric/lower-stratospheric ozone, and a positive bias in summertime surface ozone over the United States and Europe. Aerosol optical depth tends to be underestimated over most regions, with large surface concentration biases for most species, but with good sulfate simulation over the United States. Overall, the model-data comparison indicates that the offline simulation driven by GEOS5 (Goddard Earth Observing System Model, Version 5) meteorological analyses provides the best simulation, possibly due in part to the increased vertical resolution. Ongoing efforts will focus on improving the simulation of chemistry in CAM4 to better understand and project the climate and pollution consequences of various energy pathways.
Lamarque, J.-F., L. K. Emmons, P. G. Hess, D. E. Kinnison, S. Tilmes, F. Vitt, C. L. Heald, E. A. Holland, P. H. Lauritzen, J. Neu, J. J. Orlando, P. J. Rasch, and G. K. Tyndall. 2012. “CAM-Chem: Description and Evaluation of Interactive Atmospheric Chemistry in the Community Earth System Model. Geoscientific Model Development 5, 369-411. DOI: 10.5194/gmd-5-369-2012.