Title
Insights in global SOA distributions and radiative forcing from multigenerational chemistry and photolysis processes using the Energy Exascale Earth System Model
Authors
Manish Shrivastava (PNNL), Sijia Lou (PNNL), Richard Easter (PNNL), Hailong Wang (PNNL), Philip Rasch (PNNL), Po-Lun Ma (PNNL), Alla Zelenyuk (PNNL), Yang Yang (PNNL), John Shilling (PNNL), Johannes Schneider (Max Planck), Christiane Schulz (Max Planck), Pedro Campuzano-Jost (CU Boulder), Jose Jimenez (CU Boulder), Qi Zhang (UC Davis), Scot Martin (Harvard), Virendra Ghate (ANL), Manvendra Dubey (LANL), Philip Cameron Smith (LLNL)
Abstract
Secondary organic aerosols (SOA) are large contributors to fine particle mass loadings and number concentrations, and interact with clouds and radiation. Several processes affect the formation, chemical transformation and removal of SOA in the atmosphere. These processes govern the horizontal, vertical and temporal distributions of fine particles and their ability to act as cloud condensation nuclei (CCN). Global models that use simplified treatments of SOA often do not capture the dynamics of SOA formation. Here, we conduct simulations using the Energy Exascale EarthSystem Model (E3SM) global model with a detailed treatment of SOA to investigate how SOA distributions respond to some of the important but uncertain processes. Our primary findings are as follows:
(1) The branching ratio between fragmentation and functionalization that governs the multigenerational aging of gas-phase SOA precursors greatly impacts SOA formation and its long-range transport. Decreasing fragmentation with an increase in functionalization reactions results in a stronger source of SOA.
(2) Both a strong source (i.e. strong functionalization) and a strong sink of SOA (i.e. particle-phase photolysis) are needed to explain vertical organic aerosol (OA) profiles measured by aircraft during several field campaigns (DOE Green Ocean Amazon (GoAmazon2014/5), Atmospheric Tomography Mission (Atom) 2016, and the NASA Arctic Research of the composition of the Troposphere from Aircraft and Satellite (ARCTAS) 2008). While SOA treatments that do not include photolysis also agree with surface-based OA measurements e.g. with Aerosol Mass Spectrometer (AMS) and IMPROVE network at several locations, these treatments overpredict SOA at middle and upper troposphere. A strong sink of SOA, like photolysis, is needed to explain OA loadings at higher altitudes where wet removal sinks are small.
(3) To account for recent field studies that suggest no increase in net OA formation over and downwind biomass burning regions, we also test a simple SOA treatment that increases primary organic aerosol (POA) from biomass burning and anthropogenic emissions near source region and converts POA to SOA with an aging timescale of 1 day. This simple treatment without explicit multigenerational aging of SOA precursors performs surprisingly well in simulating OA loadings near the surface as measured by AMS and IMPROVE network. However, the simple treatment also overestimates OA loadings in middle and upper troposphere compared to aircraft measurements, especially during the dry biomass burning season of GoAmazon2014/5, and the wintertime Atom2 field campaign flights over equatorial ocean and North America. The model configuration that includes moderate 50% fragmentation and photolysis performs much better than the simple treatment in these regions, and performs as well as the simple treatment in other regions.
(4) Differences in SOA treatments greatly affect the direct radiative forcing of aerosols ranging from -0.67 W m-2 (50% fragmentation and photolysis) to -2.1 W m-2 (50% fragmentation without photolysis). Notably, most SOA formulations predict similar global indirect forcing of SOA calculated as the difference in cloud forcing between present-day and pre-industrial simulations likely due to cancellations of errors in SOA formulations. However, the simple treatment is an anomaly and predicts 20% differences in global indirect forcing compared to explicit SOA formulations.
Compared to the default E3SMv1 model, our explicit SOA treatment with fragmentation and photolysis agrees much better with oxygenated organic aerosol (OOA) and IMPROVE OC measurements at multiple surface locations. In addition, our new formulation agrees much better with aircraft-based OA measurements in the middle and upper troposphere compared to E3SMv1, which likely overestimated OA concentrations at high altitudes.
Our results provide important insights about how different SOA processes likely impact aerosol distributions and radiative forcing.