We introduce the optimized dynamic mode decomposition algorithm for constructing an adaptive and computationally efficient reduced order model and forecasting tool for global atmospheric chemistry dynamics. By exploiting a low-dimensional set of global spatio-temporal modes, interpretable characterizations of the underlying spatial and temporal scales can be computed. Forecasting is also achieved with a linear model that uses a linear superposition of the dominant spatio-temporal features. The DMD method is demonstrated on three months of global chemistry dynamics data, showing its significant performance in computational speed and interpretability. We show that the presented decomposition method successfully extracts known major features of atmospheric chemistry, such as summertime surface pollution and biomass burning activities. Moreover, the DMD algorithm allows for rapid reconstruction of the underlying linear model, which can then easily accommodate non-stationary data and changes in the dynamics.

翻译：我们提出优化动态模式分解算法，用于构建适用于全球大气化学动力学的自适应、高效降阶模型及预测工具。通过利用低维全局时空模态集合，可计算潜在时空尺度的可解释性表征。基于主导时空特征的线性叠加，该线性模型亦能实现预测功能。基于三个月全球化学动力学数据的验证表明，DMD方法在计算速度与可解释性方面展现出显著性能。研究显示，本文提出的分解方法成功提取了大气化学中已知的主要特征，如夏季地表污染与生物质燃烧活动。此外，DMD算法支持底层线性模型的快速重构，从而能够便捷地适应非平稳数据及动力学变化。