Abstract
Abstract. This paper discusses the implementation and performance of an array of gas-phase chemistry solvers for the state-of-the-science GEOS-Chem global chemical transport model. The implementation is based on the Kinetic PreProcessor (KPP). Two perl parsers automatically generate the needed interfaces between GEOS-Chem and KPP, and allow access to the chemical simulation code without any additional programming effort. This work illustrates the potential of KPP to positively impact global chemical transport modeling by providing additional functionality as follows. (1) The user can select a highly efficient numerical integration method from an array of solvers available in the KPP library. (2) KPP offers a wide variety of user options for studies that involve changing the chemical mechanism (e.g., a set of additional reactions is automatically translated into efficient code and incorporated into a modified global model). (3) This work provides access to tangent linear, continuous adjoint, and discrete adjoint chemical models, with applications to sensitivity analysis and data assimilation.
Highlights
GEOS-Chem is a state-of-the-science global 3-D model of atmospheric composition driven by assimilated meteorological observations from the Goddard Earth Observing System (GEOS) of the NASA Global Modeling Assimilation Office (GMAO)
(2) Kinetic PreProcessor (KPP) offers a wide variety of user options for studies that involve changing the chemical mechanism
The widely used state-of-the-science GEOS-Chem model has been added the capability of using KPP to build the gasphase chemistry simulation code
Summary
GEOS-Chem (http://www-as.harvard.edu/chemistry/trop/ geos/index.html) is a state-of-the-science global 3-D model of atmospheric composition driven by assimilated meteorological observations from the Goddard Earth Observing System (GEOS) of the NASA Global Modeling Assimilation Office (GMAO). The GEOS-Chem native chemistry solver is the Sparse Matrix Vectorized GEAR II (SMVGEARII) code which implements backward differentiation formulas and efficiently solves first-order ordinary differential equations with initial value boundary conditions in large grid-domains (Jacobson and Turco, 1994; Jacobson, 1998). These sparse matrix operations reduce the CPU time associated with LUdecomposition and make the solver very efficient. Given the push for running GEOS-Chem at progressively finer resolutions, there is a continual need for efficient implementation of sophisticated numerical methods.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
