The ice-nucleating activity of Arctic sea surface microlayer samples and marine algal cultures
Bibliographic record
Abstract
Abstract. In recent years, sea spray as well as the biological material it contains has received increased attention as a source of ice-nucleating particles (INPs). Such INPs may play a role in remote marine regions, where other sources of INPs are scarce or absent. In the Arctic, these INPs can influence water–ice partitioning in low-level clouds and thereby the cloud lifetime, with consequences for the surface energy budget, sea ice formation and melt, and climate. Marine aerosol is of a diverse nature, so identifying sources of INPs is challenging. One fraction of marine bioaerosol (phytoplankton and their exudates) has been a particular focus of marine INP research. In our study we attempt to address three main questions. Firstly, we compare the ice-nucleating ability of two common phytoplankton species with Arctic seawater microlayer samples using the same instrumentation to see if these phytoplankton species produce ice-nucleating material with sufficient activity to account for the ice nucleation observed in Arctic microlayer samples. We present the first measurements of the ice-nucleating ability of two predominant phytoplankton species: Melosira arctica, a common Arctic diatom species, and Skeletonema marinoi, a ubiquitous diatom species across oceans worldwide. To determine the potential effect of nutrient conditions and characteristics of the algal culture, such as the amount of organic carbon associated with algal cells, on the ice nucleation activity, Skeletonema marinoi was grown under different nutrient regimes. From comparison of the ice nucleation data of the algal cultures to those obtained from a range of sea surface microlayer (SML) samples obtained during three different field expeditions to the Arctic (ACCACIA, NETCARE, and ASCOS), we found that they were not as ice active as the investigated microlayer samples, although these diatoms do produce ice-nucleating material. Secondly, to improve our understanding of local Arctic marine sources as atmospheric INPs we applied two aerosolization techniques to analyse the ice-nucleating ability of aerosolized microlayer and algal samples. The aerosols were generated either by direct nebulization of the undiluted bulk solutions or by the addition of the samples to a sea spray simulation chamber filled with artificial seawater. The latter method generates aerosol particles using a plunging jet to mimic the process of oceanic wave breaking. We observed that the aerosols produced using this approach can be ice active, indicating that the ice-nucleating material in seawater can indeed transfer to the aerosol phase. Thirdly, we attempted to measure ice nucleation activity across the entire temperature range relevant for mixed-phase clouds using a suite of ice nucleation measurement techniques – an expansion cloud chamber, a continuous-flow diffusion chamber, and a cold stage. In order to compare the measurements made using the different instruments, we have normalized the data in relation to the mass of salt present in the nascent sea spray aerosol. At temperatures above 248 K some of the SML samples were very effective at nucleating ice, but there was substantial variability between the different samples. In contrast, there was much less variability between samples below 248 K. We discuss our results in the context of aerosol–cloud interactions in the Arctic with a focus on furthering our understanding of which INP types may be important in the Arctic atmosphere.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".