Abstract
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 (χa), the mixing of optically absorbing and nonabsorbing species (χo), and the mixing of hygroscopic and nonhygroscopic species (χh). 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 χa, between 38% and 94% for χo, and between 20% and 87% for χh 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.
| Original language | English |
|---|---|
| Article number | e2020EA001500 |
| Journal | Earth and Space Science |
| Volume | 8 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 2021 |
Funding
We thank Valérie Gros, Laurent Poulain, and Robert Healy for sharing the observational data from MEGAPOLI campaign for the emulator validation. We would like to acknowledge high-performance computing support from Cheyenne (https://doi.org/10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The CESM project is supported primarily by the National Science Foundation. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This research was supported in part by an appointment to the Oak Ridge National Laboratory ASTRO Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education. We also acknowledge funding from DOE grant DE-SC0019192 and NSF grant AGS-1254428. This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) the State of Illinois, and as of December, 2019, the National Geospatial-Intelligence Agency. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. Louisa Emmons is thanked for thoughtful comments on the CESM2 simulations and the manuscript. We thank AWS for providing AWS Cloud Credits for Research. We thank Valérie Gros, Laurent Poulain, and Robert Healy for sharing the observational data from MEGAPOLI campaign for the emulator validation. We would like to acknowledge high‐performance computing support from Cheyenne ( https://doi.org/10.5065/D6RX99HX ) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The CESM project is supported primarily by the National Science Foundation. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE‐AC05‐00OR22725. This research was supported in part by an appointment to the Oak Ridge National Laboratory ASTRO Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education. We also acknowledge funding from DOE grant DE‐SC0019192 and NSF grant AGS‐1254428. This research is part of the Blue Waters sustained‐petascale computing project, which is supported by the National Science Foundation (awards OCI‐0725070 and ACI‐1238993) the State of Illinois, and as of December, 2019, the National Geospatial‐Intelligence Agency. Blue Waters is a joint effort of the University of Illinois at Urbana‐Champaign and its National Center for Supercomputing Applications. Louisa Emmons is thanked for thoughtful comments on the CESM2 simulations and the manuscript. We thank AWS for providing AWS Cloud Credits for Research.