An Adaptive Auto‐Reduction Solver for Speeding Up Integration of Chemical Kinetics in Atmospheric Chemistry Models: Implementation and Evaluation in the Kinetic Pre‐Processor (KPP) Version 3.0.0. (3rd February 2023)

• Record Type: Journal Article
• Authors: Lin, Haipeng; Long, Michael S.; Sander, Rolf; Sandu, Adrian; Yantosca, Robert M.; Estrada, Lucas A.; Shen, Lu; Jacob, Daniel J.
• Is Part Of: Journal of advances in modeling earth systems. Volume 15:Number 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-03
• Subjects: chemical kinetics -- atmospheric chemical mechanism -- chemical solvers -- adaptive mechanism reduction -- chemical transport modeling; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2022MS003293
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Kinetic integration of large and stiff chemical mechanisms is a computational bottleneck in models of atmospheric chemistry. It requires implicit solution of the coupled system of kinetic differential equations with time‐consuming construction and inversion of the Jacobian matrix. We present here a new version of the Kinetic Pre‐Processor (KPP 3.0.0) for fast integration of chemical kinetics featuring a range of improvements over previous versions in performance, diagnostics, versatility, and community openness. KPP 3.0.0 includes a new adaptive auto‐reduction solver to decrease the size of any mechanism locally and on the fly under conditions where full complexity is not needed, by partitioning species as "fast" or "slow" based on their local production and loss rates. Previous implementations of this adaptive solver suffered from excessive overhead in the repeated construction of the local Jacobian matrix or were hard‐wired to specific mechanisms. Here we retain the general applicability of the method to any mechanism and avoid overhead by using pre‐computed Jacobian matrix terms for the full mechanism and cropping the matrix locally to remove the slow species with no change in memory allocation. We apply this adaptive solver within KPP 3.0.0 to the GEOS‐Chem global 3‐D model of atmospheric chemistry and demonstrate a 32% reduction in solver time while maintaining a mean error lower than 1% for key species in the troposphere. Plain Language Summary: Calculating chemical evolution in global atmospheric chemistry models is computationally expensive because the chemical mechanisms typically include hundreds of species to account for all conditions from urban to remote. However, the full chemical complexity is not needed under most conditions. Here we have developed an adaptive auto‐reduction chemical solver that reduces any mechanism on the fly depending on local conditions and without significant computational overhead. We apply this adaptive solver as an option in a new version 3.0.0 of the Kinetic Pre‐Processor (KPP) chemical solver software package that also includes a number of updates relative to previous versions. The adaptive solver achieves a 32% reduction in solver time in a global model simulation while incurring less than 1% average errors for key species. Key Points: An updated version 3.0.0 of the Kinetic Pre‐Processor (KPP) integrator of chemical kinetics for atmospheric models has been developed KPP 3.0.0 features an adaptive solver option for reducing chemical mechanisms locally and on the fly where full complexity is not needed The adaptive solver implemented in the global GEOS‐Chem model shows a 32% speedup with errors less than 1% for key tropospheric species