An explicit–implicit numerical approach for atmospheric chemistry–transport modeling

Abstract We present the main features of a new atmospheric chemistry–transport code. The employed concepts satisfy essential requirements of third generation atmospheric–transport models. It is shown that our approach can reduce the computational work load of current chemistry transport models by as much as 70–80%. In this paper, we focus on a new temporal integration scheme which is applied to the spatially discretized transport equations. The spatial discretization is performed on a terrain–following grid which allows on-line coupling to existing mesoscale models. Our time integration scheme is of explicit–implicit type. The horizontal advection is integrated explicitly with a large time step and acts as an artificial source in the coupled implicit integration of all vertical transport processes as well as the chemistry. The second-order BDF method is applied for the implicit integration. The linear algebra core in the LSODE code is replaced by a block Gauss–Seidel iteration. This method exploits the sparsity structure of the chemistry–transport problem. We find that two or three iterations suffice, and therefore make the code even faster than the traditional QSSA method. In contrast to the traditional operator splitting, the new approach is free of a transient phase during each implicit integration step. Together with our non–standard starting procedure, large step sizes are maintained throughout integration, with the exception of sunrise and sunset. Large parts of the code are vectorized by loops about the grid cell dimension. Due to memory limitations, a decomposition of the horizontal grid into rectangular subdomains is implemented. The implicit integration is performed with its own time-step selection for each subdomain. The computational efficiency of our approach is investigated with a realistic scenario in Saxony and is compared to the efficiency obtained by an operator splitting approach in combination with a QSSA solver for the chemistry. The sensitivity of our model to three different mechanisms is discussed briefly. Lastly, a technique is introduced with which chemical reaction mechanisms can be easily incorporated into chemistry–transport models.