CMG: Improve the Computational Performance of Global Atmospheric Chemistry Models through Spatial Mechanism Reduction

Harvard University, Cambridge MA

Investigators

Abstract

The ability to model oxidant concentrations (ozone and OH) in the lower atmosphere (troposphere) is central to a wide range of environmental issues. It plays an essential role in addressing issues of air quality, aerosol and acid formation, and global budgets of greenhouse gases. Our fundamental understanding of the chemical factors controlling tropospheric oxidants is fairly well established, but the computational challenge of atmospheric modeling is enormous. Chemical mechanisms include hundreds of coupled chemical species reacting on timescales ranging from milliseconds to many years. The cost of solving the resulting stiff system of coupled differential equations in a global model is such as to prevent simulations of adequate spatial resolution or temporal extent. This computational difficulty hinders general progress in our ability to model atmospheric chemistry and to address related environmental issues. The problem will be exacerbated over the next decade as satellite observations provide vast amounts of data on atmospheric composition. Exploitation of these data will require fast models. Meeting this challenge requires substantial advances in both computational resources and numerical algorithms. The present project will improve numerical algorithms for describing oxidant chemistry in global models through the collaboration of an applied mathematician (Brenner) and an atmospheric chemistry modeler (Jacob). Our central idea is that most of the chemical complexity is confined to a relatively small atmospheric domain (chemical boundary layer) near the continental surface where emissions take place. There is thus the possibility of using a targeted reduction of the chemical mechanism in which a reduced set of reactants is used in the remote atmosphere and the full chemical mechanism is used in the chemical boundary layer. Implementation is complicated by the dynamic nature of the chemical boundary layer, the need to have different definitions of the chemical boundary layer for different chemical species, and the need for a matching procedure to accurately connect the two regimes. We expect that results from this project will significantly enhance the capabilities of global atmospheric chemistry models and in this manner will improve our ability to address a range of important environmental issues.