This study integrates machine learning and particle‐resolved aerosol simulations to develop emulators that predict submicron aerosol mixing state indices from the Earth system model (ESM) simulations. The emulators predict aerosol mixing state using only quantities that are predicted by the ESM, including bulk aerosol species concentrations, which do not by themselves carry mixing state information. We used PartMC‐MOSAIC as the particle‐resolved model and NCAR's CESM as the ESM. We trained emulators for three different mixing state indices for submicron aerosol in terms of chemical species abundance (χ_a), the mixing of optically absorbing and nonabsorbing species (χ_o), and the mixing of hygroscopic and nonhygroscopic species (χ_h). Our global mixing state maps show considerable spatial and seasonal variability unique to each mixing state index. Seasonal averages varied spatially between 13% and 94% for χ_a, between 38% and 94% for χ_o, and between 20% and 87% for χ_h with global annual averages of 67%, 68%, and 56%, respectively. High values in one index can be consistent with low values in another index depending on the grouping of species and their relative abundance, meaning that each mixing state index captures different aspects of the population mixing state. Although a direct validation with observational data has not been possible yet, our results are consistent with mixing state index values derived from ambient observations. This work is a prototypical example of using machine learning emulators to add information to ESM simulations.

中文翻译：

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使用机器学习和地球系统建模在全球范围内估算亚微米气溶胶混合状态

这项研究将机器学习与颗粒解析气溶胶仿真相结合，以开发可从地球系统模型（ESM）仿真预测亚微米气溶胶混合状态指数的仿真器。仿真器仅使用ESM预测的数量来预测气溶胶混合状态，包括散装的气溶胶物种浓度，而这些数量本身并不携带混合状态信息。我们使用PartMC‐MOSAIC作为粒子解析模型，并使用NCAR的CESM作为ESM。我们在化学物种丰富度方面（训练的模拟器了三个不同的混合状态指数为亚微米的气溶胶χ_一），光学地吸收和不吸收的物质（混合χ _Ø）和吸湿性以及不吸潮物质的混合（χ _{^ h}）。我们的全球混合状态图显示了每个混合状态指标所独有的相当大的空间和季节变化。季节性平均13％和94％之间空间上变化的χ_一个，38％和94％之间χ _Ò，20％和87％之间χ _ħ全球年平均分别为67％，68％和56％。一个索引中的高值可能与另一索引中的低值一致，这取决于物种的分组及其相对丰度，这意味着每个混合状态索引都捕获了种群混合状态的不同方面。尽管尚无法用观测数据进行直接验证，但我们的结果与从环境观测得到的混合状态指数值一致。这项工作是使用机器学习仿真器向ESM仿真中添加信息的典型示例。