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. Global modeling of atmospheric chemistry is a greatcomputational challenge because of the cost of integrating the kineticequations for chemical mechanisms with typically over 100 coupled species.Here we present an adaptive algorithm to ease this computational bottleneckwith no significant loss in accuracy and apply it to the GEOS-Chem global3-D model for tropospheric and stratospheric chemistry (228 species, 724reactions). Our approach is inspired by unsupervised machine learningclustering techniques and traditional asymptotic analysis ideas. We locallydefine species in the mechanism as fast or slow on the basis of their totalproduction and loss rates, and we solve the coupled kinetic system only forthe fast species assembled in a submechanism of the full mechanism. To avoidcomputational overhead, we first partition the species from the fullmechanism into 13 blocks, using a machine learning approach that analyzesthe chemical linkages between species and their correlated presence as fastor slow in the global model domain. Building on these blocks, we thenpreselect 20 submechanisms, as defined by unique assemblages of the speciesblocks, and then pick locally and on the fly which submechanism to use inthe model based on local chemical conditions. In each submechanism, weisolate slow species and slow reactions from the coupled system of fastspecies to be solved. Because many species in the full mechanism areimportant only in source regions, we find that we can reduce the effectivesize of the mechanism by 70 % globally without sacrificing complexitywhere/when it is needed. The computational cost of the chemical integrationdecreases by 50 % with relative biases smaller than 2 % for importantspecies over 8-year simulations. Changes to the full mechanism includingthe addition of new species can be accommodated by adding these species to therelevant blocks without having to reconstruct the suite of submechanisms.

geoscientific model development

A machine-learning-guided adaptive algorithm to reduce the computational cost of integrating kinetics in global atmospheric chemistry models: application to GEOS-Chem versions 12.0.0 and 12.9.1