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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - ACRoNNIM (Aerosol and Climate Response to NH3 in the NMMB/BSC Inter-Scale Model)

Teaser

Summary

Multiphase chemical processes are important components of the atmospheric system, with significant but complex effects on air quality and Earthâ€™s climate. The goal of the ACRoNNIM project is to investigate the effect of interactions between gas-phase ammonia and organic molecules within aerosol particles on aerosol mass, composition, and optical properties. Due to the climate, health and visibility effects of particulate matter, inclusion of this multiphase chemistry into regional and global weather and climate models is timely. Complicating these efforts are the large number of species and oxidation pathways involved, their seasonally, spatially, and diurnally varying importance, and the complex physical processes controlling their phase partitioning. Regional and global chemical transport models (CTMs) generally under-predict organic aerosol mass in the atmosphere compared to field measurements, suggesting the presence of as-yet unidentified sources or unaccounted for physical processes.

A common approach to treating multiphase chemistry in large-scale atmospheric models is to split individual process across a collection of â€˜modulesâ€™ that treat, for example, gas-phase chemistry, the partitioning of inorganic species and acidâ€“base chemistry, the partitioning of organic species to the condensed phase, aqueous chemistry in cloud droplets, etc. Progress towards the goal of investigating the multiphase NH3â€“organics system using results from a recently completed field campaign and a mechanism developed in collaboration with researchers performing laboratory experiments on this system required a rethinking of this approach to treating multiphase chemical systems in atmospheric models. This effort led to the development of the Chemistry Across Multiple Phases (CAMP) framework, which answers several key questions in atmospheric modelling, including:

â€¢ How do you describe multiphase atmospheric chemical systems in code in a way that is independent of how a host model treats aerosol systems (e.g., size bins, modes, single particles, etc.)?
â€¢ How do you allow a multiphase chemical system to be solved as a single system, thus avoiding artifacts related to operator splitting?
â€¢ How do you facilitate the rapid transfer of multiphase chemical knowledge from laboratory results to atmospheric models?

CAMP makes significant progress towards answering these questions in an innovative way.

Work performed

Preliminary results from a field campaign in northeastern Spain conducted by collaborators at IDAEA-CSIC suggest a potential correlation between gas-phase ammonia and aerosol absorption, which may indicate an important role for the interaction of aerosol-phase ammonium and organics forming brown carbon in this region. Work performed as part of the ACRoNNIM project in collaboration with researchers at the University of California, Irvine, USA has yielded a preliminary scheme for the NH3â€“organics system that involves the surface reaction of gas-phase ammonia with aerosol-phase organics. The final implementation of this scheme in the MONARCH chemical weather prediction system and comparison with field measurement results is scheduled as part of two follow-up projects.

An important result of the ACRoNNIM project is the development of the CAMP framework. Using modern approaches to software design, CAMP treats comprehensive multiphase chemical systems as a single system and is readily configured at runtime without the significant development efforts typically required to incorporate new multiphase processes into models. It is applicable to a wide variety of models, and has been developed as part of the publicly available PartMC science library (https://github.com/compdyn/partmc) licensed under the GNU General Public License version 2.0. Two papers describing the CAMP framework and its initial deployment in the MONARCH model are in preparation.

Final results

An innovative approach and design allow CAMP to be incorporated into a wide variety of atmospheric models, from box models to global models, to treat multiphase chemical systems without modification of the source code. It is fully runtime configurable, allowing complete chemical mechanisms to be described in a set of input files that are processed during model initialization. It is also compatible with a wide variety of aerosol representations used in atmospheric models (bins, modes, single particles, etc.). To our knowledge no such framework currently exists for atmospheric chemical systems. These goals are accomplished using modern software design patterns, including an object-oriented approach to describing chemical systems in models, the use of JSON descriptions of chemical systems and model configuration, and a full suite of unit tests. Although these are commonly used in commercial software design, they are greatly underutilized in atmospheric science applications. Thus, we consider this framework to make an important contribution to the state-of-the-art for treating multiphase chemical systems in atmospheric models.

The CAMP framework is also designed to facilitate the rapid deployment of new chemical processes to large-scale atmospheric models, and to be applicable to a wide range of users, from atmospheric climate and weather modelers to industry to researchers performing laboratory experiments. Once fully implemented, CAMP will allow experimentalists with little or no software development experience to build human-readable mechanisms describing new chemical processes that they can test in a box model running CAMP to simulate their laboratory results. Once their mechanism is complete, they can provide the same input files to modelers using large-scale atmospheric models running CAMP to rapidly assess the impact of these new mechanisms at the regional or global scale. This will dramatically reduce the current development time involved in incorporating new multiphase chemical processes in different models, thus increasing the capacity for innovation in atmospheric modeling.