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Creators/Authors contains: "Mansour, Karam"

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  1. 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. 
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    Free, publicly-accessible full text available June 10, 2026
  2. Abstract. 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 aerosolparticles collected on filter samples by means of two offline instruments:the Dynamic Filter Processing Chamber (DFPC) and the West Texas CryogenicRefrigerator Applied to Freezing Test system (WT-CRAFT) to assesscondensation and immersion freezing, respectively. DFPC measured nINPs for aset of filters collected through two size-segregated inlets: one fortransmitting particulate matter of less than 1 µm (PM1), theother for particles with an aerodynamic diameter of less than 10 µmaerodynamic diameter (PM10). Overall, nINPPM10 measured by DFPC ata water saturation ratio of 1.02 ranged from 3 to 185 m−3 attemperatures (Ts) of −15 to −22 ∘C. On average, the super-micrometer INP (nINPPM10-nINPPM1) accounted forapproximately 20 %–30 % of nINPPM10 in spring, increasing in summer to45 % at −22 ∘C and 65 % at −15 ∘C. This increase in super-micrometer INP fraction towards summer suggests that super-micrometeraerosol particles play an important role as the source of INPs in theArctic. For the same T range, WT-CRAFT measured 1 to 199 m−3. Althoughthe two nINP datasets were in general agreement, a notable nINP offset wasobserved, particularly at −15 ∘C. Interestingly, the results ofboth DFPC and WT-CRAFT measurements did not show a sharp increase in nINPfrom spring to summer. While an increase was observed in a subset of ourdata (WT-CRAFT, between −18 and −21 ∘C), the spring-to-summernINP 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 particleconcentration. In 2018, AF increased from spring to summer. This seasonal AFtrend corresponds to the overall decrease in aerosol concentration towardssummer and a concomitant increase in the contribution of super-micrometer particles. Indeed, the AF of coarse particles resulted markedly higher thanthat 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 isinfluenced by both terrestrial (snow-free land) and marine sources. Ourspatiotemporal analyses of satellite-retrieved chlorophyll a, as well as spatial source attribution, indicate that the maritime INPs at GVB may comefrom the seawaters surrounding the Svalbard archipelago and/or in proximityto Greenland and Iceland during the observation period. Nevertheless,further analyses, performed on larger datasets, would be necessary to reachfirmer and more general conclusions. 
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