Engineering PapersSearch

DOE OSTI · 2586675

Shoreline wave breaking strongly enhances the coastal sea spray aerosol population: Climate and air quality implications

Abstract

Sea spray aerosol (SSA) emission is a major source of atmospheric aerosols, influencing global climate and coastal air quality. Much of our knowledge about SSA derives from coastal observations near shorelines, but whether and when these observations accurately represent open oceans remain unclear. Here, we show that strong nearshore SSA production during high-wave periods greatly enhances downwind cloud condensation nuclei (CCN) and aerosol mass concentrations. Strong shoreline wave breaking is widespread globally, and swell waves, which are decoupled from local winds, play a dominant role in many coastal regions. Therefore, extrapolating results based on coastal measurements to open oceans may significantly overestimate SSA concentration and its contribution to CCN and, by extension, the impact of SSA on clouds and climate. Additionally, the strong enhancement of coastal aerosol population by shoreline wave breaking and its environmental impact on coastal communities cannot be captured by current regional models, which do not parameterize nearshore SSA generation using wave energy or completely neglect it.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhou, Shengqian [Washington University in St. Louis, MO (United States)] (ORCID:0000000308460127), Salter, Matthew [Stockholm University (Sweden)] (ORCID:0000000306453265), Bertram, Timothy [University of Wisconsin, Madison, WI (United States)] (ORCID:0000000230267588), Azevedo, Eduardo Brito [University of the Azores (Portugal)] (ORCID:0000000151727742), Reis, Francisco [Environmental Observatory of the Azores (Portugal)] (ORCID:0009000339175959), Wang, Jian [Washington University in St. Louis, MO (United States)] (ORCID:0000000228154170). 2025-08-27. Shoreline wave breaking strongly enhances the coastal sea spray aerosol population: Climate and air quality implications. https://doi.org/10.1126/sciadv.adw0343

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES