Abstract
Abstract. The major computational bottleneck in atmospheric chemistry models is the numerical integration of the stiff coupled system of kinetic equations describing the chemical evolution of the system as defined by the model chemical mechanism (typically over 100 coupled species). We present an adaptive method to greatly reduce the computational cost of that numerical integration in global 3-D models while maintaining high accuracy. Most of the atmosphere does not in fact require solving for the full chemical complexity of the mechanism, so considerable simplification is possible if one can recognize the dynamic continuum of chemical complexity required across the atmospheric domain. We do this by constructing a limited set of reduced chemical mechanisms (chemical regimes) to cover the range of atmospheric conditions and then pick locally and on the fly which mechanism to use for a given grid box and time step on the basis of computed production and loss rates for individual species. Application to the GEOS-Chem global 3-D model for oxidant–aerosol chemistry in the troposphere and stratosphere (full mechanism of 228 species) is presented. We show that 20 chemical regimes can largely encompass the range of conditions encountered in the model. Results from a 2-year GEOS-Chem simulation shows that our method can reduce the computational cost of chemical integration by 30 %–40 % while maintaining accuracy better than 1 % and with no error growth. Our method retains the full complexity of the original chemical mechanism where it is needed, provides the same model output diagnostics (species production and loss rates, reaction rates) as the full mechanism, and can accommodate changes in the chemical mechanism or in model resolution without having to reconstruct the chemical regimes.
Highlights
Accurate representation of atmospheric chemistry is of central importance for air quality and Earth system models (National Research Council, 2016), but it is computationally expensive
We start with the objective identification of a limited number of chemical regimes that encompass the range of atmospheric conditions encountered in the model
Instead of building a local chemical mechanism subset at every time step as in Santillana et al (2010), we greatly improve the computational efficiency by preselecting a limited number (M) of chemical mechanism subsets for which we pre-define the Jacobian matrix in Kinetic PreProcessor 2.2.4 (KPP)
Summary
Accurate representation of atmospheric chemistry is of central importance for air quality and Earth system models (National Research Council, 2016), but it is computationally expensive. Santillana et al (2010) combined these ideas in an adaptive algorithm for 3-D models that determines locally at each time step (“on the fly”) which species in the chemical mechanism need to be solved in the coupled implicit system This was done by computing the local production (Pi) and loss rates (Li) for all species at the beginning of the time step. We start with the objective identification of a limited number of chemical regimes that encompass the range of atmospheric conditions encountered in the model These regimes are defined by the subset of fast species from the full mechanism that need to be considered in the coupled system, and we pre-code the Jacobian matrix and its inverse for each. Our method can be adapted to any mechanism and model, retains the complexity of the full mechanism where it is needed, and preserves full diagnostic information on chemical evolution (such as reaction rates and production and loss of individual species)
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