In this study, we present atmospheric ice-nucleating particle (INP) concentrations from the Gruvebadet (GVB) observatory in Ny-Ålesund (Svalbard). All aerosol particle sampling activities were conducted in April–August 2018. Ambient INP concentrations (nINP) were measured for aerosol particles collected on filter samples by means of two offline instruments: the Dynamic Filter Processing Chamber (DFPC) and the West Texas Cryogenic Refrigerator Applied to Freezing Test system (WT-CRAFT) to assess condensation and immersion freezing, respectively. DFPC measured nINPs for a set of filters collected through two size-segregated inlets: one for transmitting particulate matter of less than 1 µm (PM₁), the other for particles with an aerodynamic diameter of less than 10 µm aerodynamic diameter (PM₁₀). Overall, nINP${}_{{\mathrm{PM}}_{\mathrm{10}}}$ measured by DFPC at a water saturation ratio of 1.02 ranged from 3 to 185 m⁻³ at temperatures (Ts) of −15 to −22 ^∘C. On average, the super-micrometer INP (nINP${}_{{\mathrm{PM}}_{\mathrm{10}}}-n$ INP${}_{{\mathrm{PM}}_{\mathrm{1}}}$ ) accounted for approximately 20 %–30 % of nINP${}_{{\mathrm{PM}}_{\mathrm{10}}}$ in spring, increasing in summer to 45 % at −22 ^∘C and 65 % at −15 ^∘C. This increase in super-micrometer INP fraction towards summer suggests that super-micrometer aerosol particles play an important role as the source of INPs in the Arctic. For the same T range, WT-CRAFT measured 1 to 199 m⁻³. Although the two nINP datasets were in general agreement, a notable nINP offset was observed, particularly at −15 ^∘C. Interestingly, the results of both DFPC and WT-CRAFT measurements did not show a sharp increase in nINP from spring to summer. While an increase was observed in a subset of our data (WT-CRAFT, between −18 and −21 ^∘C), the spring-to-summer nINP enhancement ratios never exceeded a factor of 3. More evident seasonal variability was found, however, in our activated fraction (AF) data, calculated by scaling the measured nINP to the total aerosol particle concentration. In 2018, AF increased from spring to summer. This seasonal AF trend corresponds to the overall decrease in aerosol concentration towards summer and a concomitant increase in the contribution of super-micrometer particles. Indeed, the AF of coarse particles resulted markedly higher than that of sub-micrometer ones (2 orders of magnitude). Analysis of low-traveling back-trajectories and meteorological conditions at GVB matched to our INP data suggests that the summertime INP population is influenced by both terrestrial (snow-free land) and marine sources. Our spatiotemporal analyses of satellite-retrieved chlorophyll a, as well as spatial source attribution, indicate that the maritime INPs at GVB may come from the seawaters surrounding the Svalbard archipelago and/or in proximity to Greenland and Iceland during the observation period. Nevertheless, further analyses, performed on larger datasets, would be necessary to reach firmer and more general conclusions.

As the most ubiquitous natural source of sulfur in the atmosphere, dimethylsulfide (DMS) promotes aerosol formation in marine environments, impacting cloud radiative forcing and precipitation, eventually influencing regional and global climate. In this study, we propose a machine learning predictive algorithm based on Gaussian process regression (GPR) to model the distribution of daily DMS concentrations in the North Atlantic waters over 24 years (1998-2021) at 0.25° × 0.25° spatial resolution. The model was built using DMS observations from cruises, combined with satellite-derived oceanographic data and Copernicus-modelled data. Further comparison was made with the previously employed machine learning methods (i.e., artificial neural network and random forest regression) and the existing empirical DMS algorithms. The proposed GPR outperforms the other methods for predicting DMS, displaying the highest coefficient of determination (R²) value of 0.71 and the least root mean square error (RMSE) of 0.21. Notably, DMS regional patterns are associated with the spatial distribution of phytoplankton biomass and the thickness of the ocean mixed layer, displaying high DMS concentrations above 50°N from June to August. The amplitude, onset, and duration of the DMS annual cycle vary significantly across different regions, as revealed by the k-means++ clustering. Based on the GPR model output, the sea-to-air flux in the North Atlantic from March to September is estimated to be 3.04 Tg S, roughly 44 % lower than the estimates based on extrapolations of in-situ data. The present study demonstrates the effectiveness of a novel method for estimating seawater DMS surface concentration at unprecedented space and time resolutions. As a result, we are able to capture high-frequency spatial and temporal patterns in DMS variability. Better predictions of DMS concentration and derived sea-to-air flux will improve the modeling of biogenic sulfur aerosol concentrations in the atmosphere and reduce aerosol-cloud interaction uncertainties in climate models.

The ways in which marine biological activity affects climate, by modifying aerosol properties, are not completely understood, causing high uncertainties in climate predictions. In this work, in situ measurements of aerosol chemical composition, particle number size distribution, cloud condensation nuclei (CCN), and ice‐nucleating particle (INP) number concentrations are combined with high‐resolution sea surface chlorophyll‐a concentration (CHL) and back‐trajectory data to elucidate the relationship between oceanic biological activity and marine aerosol. The measurements were performed during an intensive field campaign conducted in late summer (August–September) 2015 at the Mace Head Research Station (MHD). At the short time scale (1–2 months) of the experiment, we observed a clear dependency of the main aerosol physicochemical and cloud‐relevant properties on the patterns of biological activity, in specific oceanic regions with a delayed response of about 1–3 weeks. The oceanic region comprised between 47°–57°N and 14°–30°W was identified as the main source of biogenic aerosols during the campaign, with hints of some minor influence of waters up to the Greenland coast. These spatial and temporal relationships demonstrate that the marine biota influences aerosol properties under a variety of features up to the most cloud‐relevant properties. Such dependency of aerosol properties with oceanic biological activity was previously reported over the North Atlantic Ocean only for multiyear data sets, where the correlation may be enhanced by coincident seasonalities. A better knowledge of these short time scale interactions may lead to a significant improvement in understanding the ocean‐atmosphere‐cloud system, with important impacts on climate science.

Oceanic dimethyl sulfide (DMS) is the largest natural source of atmospheric sulfur. DMS is biologically produced in seawater and emitted into the atmosphere, where its oxidation products contribute to aerosol formation with consequences for cloud albedo and the Earth’s radiative budget and climate. Climate model projections of how DMS emissions change with global warming are largely uncertain, even contradictory. Here, we use machine-learning models trained with biome-resolved global observations to simulate seawater DMS concentrations (1850 to 2100) using physico-chemical and biological predictors from eight CMIP6 models. The scatter in current projections is largely reduced, and globally averaged seawater DMS concentrations are predicted to decrease in the coming decades. However, global DMS emissions will increase due to rising surface wind speeds and sea surface temperatures which contradicts the current AR6 assessment that the DMS flux will reduce in the future. Concurrence of increasing DMS emissions and declining anthropogenic sulfur dioxide emissions suggests an increase in the relative importance of DMS to sulfate aerosol formation and its climate cooling impact.

The current understanding of the impact of natural cloud condensation nuclei (CCN) variability on cloud properties in marine air is low, thus contributing to climate prediction uncertainty. By analyzing cloud remote sensing observations (2009–2015) at Mace Head (west coast of Ireland), we show the oceanic biota impact on the microphysical properties of stratiform clouds over the Northeast Atlantic Ocean. During spring to summer (seasons of enhanced oceanic biological activity), clouds typically host a higher number of smaller droplets resulting from increased aerosol number concentration in the CCN relevant‐size range. The induced increase in cloud droplet number concentration (+100%) and decrease in their radius (−14%) are comparable in magnitude to that generated by the advection of anthropogenically influenced air masses over the background marine boundary layer. Cloud water content and albedo respond to marine CCN perturbations with positive adjustments, making clouds brighter as the number of droplets increases. Cloud susceptibility to marine aerosols overlaps with a large variability of cloud macrophysical and optical properties primarily affected by the meteorological conditions. The above findings suggest the existence of a potential feedback mechanism between marine biota and the marine cloud‐climate system.