Biomass-burning smoke properties and its interactions with marine stratocumulus clouds in WRF-CAM5 and southeastern Atlantic field campaigns
Authors: Howes Calvin , Saide Pablo E. , Coe Hugh , Dobracki Amie Nicole , Freitag Steffen , Haywood Jim M. , Howell Steven G. , Gupta Siddhant , Uin Janek , Kacarab Mary , Kuang Chongai , Leung L. Ruby , Nenes Athanasios , McFarquhar Greg , Redemann Jens , Sedlacek Arther J. , Thornhill Kenneth L. , Wong Jenny P. S. , Wood Robert , Wu Huihui , Zhang Yang , Zhang Jianhao , Zuidema Paquita 
Published: June 2023

Abstract. A large part of the uncertainty in climate projections comes from poorly understood or constrained aerosol properties (e.g., particle size, composition, mixing state, aging processes) and aerosol-cloud interactions, as well as the difficulty in remotely sensing them. This is an issue especially in remote regions such as the southeast Atlantic, which exhibits large model spread due to the seasonal coexistence of extensive cloud cover and regional biomass burning smoke. Here we address these gaps by comparing the WRF-CAM5 model to multi-campaign observations (ORACLES, CLARIFY, and LASIC) of the southeastern Atlantic region in August 2017 to evaluate a broad swath of the model’s aerosol properties, processes, and transport, and the degree to which aerosol interactions with clouds are captured. Building on earlier work showing strong performance in model advection and mixing, we find that biomass-burning smoke aerosol size and composition are generally well-captured in the marine free troposphere, except for a likely overprediction of dust in the accumulation mode (7–17 % modeled dust fraction which is not present in the observations). Evaluating smoke aging trends, the model shows a steady increase in aerosol mean diameter and an unchanging composition as smoke ages, deviating from the observed trends that show a rise and subsequent fall in mean diameter over 4–12 days and a decreasing OA : BC ratio beyond 3 days. Both results are likely due to missing processes in the model that remove OA from the particle phase such as photolysis and heterogeneous aerosol chemistry. The observed composition change from the free-troposphere to the marine boundary layer (MBL) is not fully captured in the model, especially the observed enhancement of sulfate from 11 % to 37 % by mean mass fraction in ORACLES, and from 11 % to 26 % in CLARIFY. This points to the importance of properly representing sulfate formation from marine dimethyl sulfide (DMS) emissions and in smoke-free parcels. Additionally, the model does not capture the occurrence of an Aitken mode during clean and medium-smoke conditions in the boundary layer, likely pointing to misrepresentation of new particle formation. The model shows a persistent overprediction of aerosols in the MBL, especially for clean conditions, that multiple pieces of evidence link to weaker aerosol removal in the modeled MBL than reality. This evidence includes the model not representing observed shifts in the aerosol size distribution towards smaller sizes, the model not capturing the relative concentrations of carbon monoxide compared to black carbon, model underprediction of heavy rain events, and little evidence of persistent biases in modeled entrainment. Average below-cloud aerosol activation fraction (NCLD/NAER) remains relatively constant in WRF-CAM5 between field campaigns (~0.65), while it decreases substantially in observations from ORACLES (~0.78) to CLARIFY (~0.5), which could be due to the model misrepresentation of clean aerosol conditions. WRF-CAM5 also overshoots an observed upper limit on liquid cloud droplet concentration around NCLD=400–500 cm-3 observed in both ORACLES and CLARIFY and also overpredicts the spread in NCLD. This could be related to the model often drastically overestimating the strength of boundary layer vertical turbulence by up to a factor of 10 and having a bimodal—rather than the observed unimodal—probability distribution of updraft turbulent kinetic energy. We expect these results to motivate similar evaluations of other modeling systems and promote model development in these critical areas to reduce uncertainties in climate simulations.