An Aerosol Climatology and Implications for Clouds at a Remote Marine Site: Case Study Over Bermuda

Abstract

Aerosol characteristics and aerosol–cloud interactions remain uncertain in remote marine regions. We use over a decade of data (2000–2012) from the NASA AErosol RObotic NETwork, aerosol and wet deposition samples, satellite remote sensors, and models to examine aerosol and cloud droplet number characteristics at a representative open ocean site (Bermuda) over the Western North Atlantic Ocean (WNAO). Annual mean values were as follows: aerosol optical depth (AOD) = 0.12, Ångström Exponent (440/870 nm) = 0.95, fine mode fraction = 0.51, asymmetry factor = 0.72 (440 nm) and 0.68 (1020 nm), and Aqua‐MODIS cloud droplet number concentrations = 51.3 cm−3. The winter season (December–February) was characterized by high sea salt optical thickness and the highest aerosol extinction in the lowest 2 km. Extensive precipitation over the WNAO in winter helps contribute to the low FMFs in winter (∼0.40–0.50) even though air trajectories often originate over North America. Spring and summer had more pronounced influence from sulfate, dust, organic carbon, and black carbon. Volume size distributions were bimodal with a dominant coarse mode (effective radii: 1.85–2.09 µm) and less pronounced fine mode (0.14–0.16 µm), with variability in the coarse mode likely due to different characteristic sizes for transported dust (smaller) versus regional sea salt (larger). Extreme pollution events highlight the sensitivity of this site to long‐range transport of urban emissions, dust, and smoke. Differing annual cycles are identified between AOD and cloud droplet number concentrations, motivating a deeper look into aerosol–cloud interactions at this site.

Biomass Burning Over the United States East Coast and Western North Atlantic Ocean: Implications for Clouds and Air Quality

“…Jaffe et al ( 2020) also concluded based on satellite data that fires are mainly found in the southeast U.S. during DJF as compared to the rest of the U.S. as a result of prescribed burning. Similar to SON, Bermuda appears to be most impacted by areas of high BC north of North Carolina consistent with characteristic transport trajectories in those months (Aldhaif et al, 2021;Painemal et al, 2021). A noteworthy and consistent result across all seasons is that the influence of BB is evident offshore across the WNAO.…”

Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors

“…Bermuda stands out as having the highest AOD fractional contributions in the PBL in DJF (72 %) and SON (69 %) and among the highest seasonal total AODs in those two seasons (0.14 in DJF and 0.10 in SON) assisted in large part by sea salt ( Fig. 3 ) ( Aldhaif et al, 2021 ), coincident with high seasonal wind speeds ( Corral et al, 2021 ).…”

“…For instance, there is some level of interdependency between input variables. To reduce unwanted consequences of correlated features, the interpre-tation of the results was done with the aid of accumulated local effect (ALE) plots, which are specifically designed to be unbiased to the correlated input variables (Apley and Zhu, 2020). ALE plots illustrate the influence of input variables on the response parameter in ML models.…”

“…The steps in Eq. (2) can be summarized as follows (Molnar, 2019;Apley and Zhu, 2020): (i) the average change in the model's prediction is calculated using the conditional distribution of features; (ii) the average change will then be accumulated by integrating it over feature s; and (iii) a constant will be subtracted to vertically center (i.e., the average of ALE becomes zero) the ALE plot. The aforementioned steps, although seemingly complex, assure the avoidance of undesired extrapolation (especially an issue for correlated variables) occurring in alternative approaches such as partial dependence (PD) plots.…”

Extreme Aerosol Events at Mesa Verde, Colorado: Implications for Air Quality Management

“…We used the Global Data Assimilation System (GDAS) for simulations at 0.5 • × 0.5 • spatial resolution along with the "model vertical velocity method" to drive vertical transport. We used the ending point at the IMPROVE station for simulations and a 0.5 km ending altitude as that level has been used successfully in several other studies examining surface layer air quality [41][42][43].…”