the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 May 2026
Marine Organic Aerosols Reflect Ecosystem Variability from Phytoplankton Functional Types to Micronekton
Abstract. Marine organic aerosols remain a major source of uncertainty in aerosol cloud–climate interactions, in part because marine ecosystem structure and biological drivers are often represented in overly simplified terms, typically reduced to bulk chlorophyll‑a. Here, a full year of high-resolution aerosol mass spectrometry measurements at Mace Head (west coast of Ireland) is combined with HYSPLIT air-masses exposure metrics and gap-free phytoplankton functional type (PFT) fields to explore influences on primary marine organic aerosol (PMOA) and methane sulphonic acid (MSA). During the spring-summer diatom climax, PMOA correlates with dominant bloom taxa (R=0.65-0.70) and micronekton (R=0.55), with rapid 1-3 day responses and secondary maxima at ~25 days, consistent with early labile release and later lysis/grazing. During that same phase, MSA also showed a lagged responses to both PFT and micronekton reflecting delayed DMS production and oxidation. However, comparable phytoplankton air-mass exposure in the late summer of that same year (i.e. early depletion phase) did not reproduce such high correlations, with time-scale analyses indicating weakened coupling at warmer sea-surface temperatures despite moderately stronger winds. These results imply that structured ecosystem composition and physical forcing both contribute to cross-basin seasonal differences in marine organic aerosols formation. This motivates future research vessel campaigns and mesocosm experiments to explicitly manipulate PFT interactions and air-sea physics.
- Preprint
(1777 KB) - Metadata XML
-
Supplement
(3455 KB) - BibTeX
- EndNote
Status: open (until 08 Jul 2026)
- RC1: 'Comment on egusphere-2026-2745', Anonymous Referee #1, 18 Jun 2026 reply
-
RC2: 'Comment on egusphere-2026-2745', Anonymous Referee #2, 01 Jul 2026
reply
The manuscript deals with primary marine organic aerosol formation and its association with plankton abundance and biodiversity in the eastern North Atlantic. By leveraging new comprehensive oceanographic datasets on phytoplankton types and micronecton, and by integrating the analysis of air-sea interfacial physical processes with a detailed investigation of the potentially relevant ecological processes in the surface ocean, this study clearly provides advances in current research on aerosol-biosphere interactions. The scientific approach is rather straightforward, while the discussion of the results is sometimes hard to follow, especially for an “atmospheric” readership. The paper provides convincing evidence of the complexity of the potential biological processes involved. At the same time, the reader may get the impression that this complexity is such that it is not actually constrained by the data. For instance, (line 384) an increase of viscosity is associated with a slower degradation of surfactants and with a higher degree of lipid supersaturation, both stimulating PMOA production; however, a higher viscosity would decrease the buoyancy of small bubbles and their resurfacing. I understand the Authors’ efforts to support the statistical analysis with plausible mechanisms, however, when contrasting effects are expected, this should be acknowledged. Alternatively, the Authors should simplify the discussion when too speculative or based on ad hoc hypotheses.
I have one major comments on the results. In Section 3.3, it is highlighted that “During the spring-summer climax bloom, PMOA closely tracks recent biological activity”, as clearly witnessed by the data shown in Fig. 2A, while in Section 3.4, the Authors observed that “For primary marine organic aerosol (PMOA) during the spring-summer climax phase, PMOA-PFT cross entropy shows significant lags between PMOA and several PFT” with lag values up to 25 days or more (Fig. 4). Reasons for this lagged association were found in procaryote-diatoms interactions or other trophic-level processes. These findings discussed in Section 3.4 contrast with the observations done in Section 3.3. I wonder whether the lagged association peaking at > 20 days might reflect the succession of two blooms (and two PMOA peaks) during the spring-summer maximum (Fig. 2A): the second PMOA peak would positively correlate with the first algal bloom with a lagged association of > 20 days. This is however only apparent, because if not, the second algal bloom would lead to a third PMOA peak at the end of July which did not take place in fact (Fig. S13).
The Conclusions section does not provide a fair account of the main findings of this study because too many statements about the ecological processed driving the variability in PMOA are just unsupported and too speculative. For instance, the simple finding that “During the spring-summer climax, diatoms, dinoflagellates, chlorophytes, and cryptophytes all covaried along with PMOA” is discussed in terms of the diverse PMOA precursors that can be produced by the specific PFTs leading the Authors to conclude that a cumulative contribution from all major PFTs to PMOA in this period of the year is “expected” (line 475). Surprisingly, the Authors neglect the most obvious explanation: all PFTs covary with PMOA because they first covary between each other. If so (can a table be provided?), it remains unclear whether PMOA are driven mainly from some individual PFTs rather than from their ensemble.
The paragraph discussing the lagged response of PMOA during August 2018 and 2009 in contrast with the closer coupling found in spring- early summer (lines 500 – 507) is clear and useful, but again I wonder if the indicative mood is appropriate in sentences like “cooler SST raised viscosity and stabilised gel-like surfactants, higher significant wave heights and a markedly larger wave Reynolds number sustained breaking waves and film drops so the same PFT pool was transferred to the atmosphere far more efficiently”. Are any data about the physical state of gel-like surfactants and on film drops fluxes available in this study?
Specific comments:
Figure 2A. The colour palette does not allow to clearly associate the lines in the plot with the individual PFTs.
Figure S12: what do “A”, “B”, “C” stand for?
Citation: https://doi.org/10.5194/egusphere-2026-2745-RC2
Interactive computing environment
Marine Organic Aerosol Reflects Ecosystem Variability from Phytoplankton Functional Types to Micronekton Emmanuel Chevassus https://zenodo.org/records/20155093
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 91 | 21 | 9 | 121 | 10 | 7 | 7 |
- HTML: 91
- PDF: 21
- XML: 9
- Total: 121
- Supplement: 10
- BibTeX: 7
- EndNote: 7
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
General Assessment
This manuscript presents a comprehensive year-long study combining high-resolution aerosol mass spectrometry with gap-free phytoplankton functional type (PFT) data and HYSPLIT trajectory analysis at the Mace Head station. The work demonstrates that PFT-specific linkages to PMOA and MSA are more nuanced than bulk chlorophyll-a approaches, with distinct lag structures and seasonal shifts in the dominant PFT-aerosol couplings. The methodology is sophisticated, combining PMF source apportionment, cross-entropy lag analysis, and wavelet coherence.
The study makes a valuable contribution to understanding marine aerosol sources, but several methodological and interpretive aspects require clarification before publication.
The following issues should be clarified to improve the manuscript further:
1. PMF Source Apportionment
Q1a: The PMOA factor was constrained using reference spectra from Ovadnevaite et al. (2011a) with a-values ranging from 0.1 to 0.5. How sensitive are your PMOA concentrations to the choice of a-value? You mention that 3-5 factor solutions showed unrealistic behavior, but what about varying a-values within the 6-factor solution? A sensitivity analysis should be presented or referenced in the supplement.
Q1b: The PMOA mass spectrum shows 55% oxygenated carbons and 39% aliphatic fragments. How does this fingerprint compare to the reference spectrum you used for constraint? Is there evidence of significant deviation that might indicate mixing with other sources?
Q1c: You excluded pollution events using MAAP/eBC data. What was the threshold for flagging pollution events, and what percentage of data were excluded? This is critical for ensuring the "clean marine" signature you claim.
2. PFT Data and Exposure
Q2a: The AIGD-PFT dataset has 4 km resolution, but your HYSPLIT trajectories use a 20 km radius for exposure calculations. How do you account for the spatial scale mismatch between PFT patchiness (kilometer-scale features noted on line 252) and trajectory averaging?
Q2b: You filtered trajectories with endpoints below 850 hPa and boundary layer heights below 50 m. What fraction of total trajectories were excluded by these filters?
Q2c: The exposure calculation assumes aerosols are well-mixed within the boundary layer. Did you test the sensitivity to using a different vertical mixing scheme or decay rate for organic aerosols during transport? This is particularly relevant for the 72-hour back trajectories, where loss processes could be significant.
3. Cross-Entropy and Wavelet Analysis
Q3a: Cross-entropy was used instead of conventional cross-correlation to avoid "spurious lag structure under non-stationary conditions." Did you test both methods and compare results? Figure S4 shows the PMOA-eBC comparison failing cross-entropy, but this doesn't demonstrate that cross-entropy is superior—only that it's more conservative. A direct comparison table would strengthen your methodological choice.
Q3b: The wavelet coherence analysis interprets arrows pointing "upward" as phytoplankton leading PMOA. However, the wavelet phase interpretation depends on the convention used in the biwavelet package. Please clarify whether upward arrows correspond to PFT leading PMOA or the reverse, and verify this against known biological timelines.
Q3c: For the wavelet analysis, you state that "coherence patches exhibiting rapidly rotating or opposing phase arrows within the same significant region were not interpreted." What percentage of significant coherence regions were excluded by this criterion? This should be quantified.
4. PFT Temporal Dynamics
Q4a: The changepoint analysis shows diatoms entering climax on March 16 and persisting until July 17. However, Figure 2A shows PMOA peaking sharply in early July. Is there a lag between maximum PFT exposure and maximum PMOA? The cross-entropy analysis suggests 2-25 day lags, but the visual inspection of Figure 2A suggests the PMOA peak might lag the PFT peak. Please clarify.
Q4b: Table S1 shows diatoms dominating all Longhurst provinces (19.4-65.5%). Given this dominance, why do Prochlorococcus (which are minor contributors) show significant lagged associations with MSA at 30 days? This seems counterintuitive—please discuss whether this is a statistical artifact or a genuine biogeochemical signal.
5. PMOA-PFT Correlations
Q5a: The correlations during the climax phase (R=0.65-0.70 for diatoms, dinoflagellates, cryptophytes, chlorophytes) are strong. Have you accounted for multiple testing? With 8 PFTs and multiple time lags, the chance of spurious correlations is non-negligible. Please clarify whether p-values were corrected (e.g., FDR or Bonferroni).
Q5b: Haptophytes show weak correlation (R=0.30) despite being known DMSP producers. Could this be because haptophytes in the North Atlantic are dominated by non-coccolithophorid species that produce less DMSP? Or is this a seasonal effect (haptophyte bloom later in the year)?
Q5c: The comparison with "August 2009" is interesting but raises questions. August 2009 data used the same instrumentation and PMF methodology? If not, inter-annual differences in PMOA concentrations (one order of magnitude higher) could reflect instrumental changes rather than physical forcing. Please confirm that the same AMS calibration and PMF constraints were applied.
6. Lag Estimates
Q6a: Cross-entropy shows PMOA lags for diatoms at 2-25 days. How do you distinguish between "early labile release" (2-3 days) and "lysis/grazing" (25 days) given the continuous nature of the signal? Do you have independent evidence (e.g., nutrient data, viral abundance, zooplankton biomass) to support this mechanistic interpretation?
Q6b: For MSA, you report lags at 4, 9, and 30 days. The 30-day lag for Prochlorococcus seems extraordinarily long. Given typical phytoplankton turnover times of days to weeks, a 30-day lag might reflect seasonal trends rather than causal coupling. Did you test for spurious correlation due to shared seasonality (e.g., detrending the data)?
7. Physical Forcing
Q7a: You attribute the August 2009 PMOA enhancement to stronger winds (6.83 m/s median) and cooler SST. However, August 2009 also had higher micronekton biomass (11.2 gC/m² vs. 5.6 gC/m² in 2018). Could the PMOA difference be primarily biological rather than physical? A partial correlation or variance partitioning analysis would help separate biological vs. physical drivers.
Q7b: The wave Reynolds number is used as a proxy for bubble-mediated aerosol production. Did you test alternative parameterizations (e.g., whitecap fraction, bubble plume penetration depth) to see if the conclusions are robust? The Reynolds number depends on assumptions about seawater viscosity and salinity—were these varied in sensitivity tests?
7. Discussion & Interpretation
Q7a: The statement "diatoms, dinoflagellates, chlorophytes, and cryptophytes all covaried along with PMOA" (line 474-475) is supported by correlation analysis. But correlation does not establish causation. Could the PMOA-PFT correlations simply reflect shared seasonal forcing (e.g., light, temperature, nutrients) rather than direct biological emission? Please discuss how you rule out common environmental drivers.
Q7b: You invoke "viral lysis" as a mechanism for PMOA release (lines 507-508). Do you have any independent evidence for viral activity during the sampled periods? Without viral abundance data, this remains speculative. Consider tempering this interpretation or citing literature from the same region.
Q7c: The discussion of "homeoviscous adaptation" (line 399-401) linking colder SST to increased lipid unsaturation is interesting but speculative for your specific dataset. Did you measure lipid composition in seawater or aerosol? If not, this should be presented as a hypothesis rather than a conclusion.
Q7d: You note that "not all biological activity is represented (e.g., viruses, archaea, fungi)" (line 518). How significant are these omissions? Recent work suggests archaea and fungi can be important in sea spray aerosol formation. Please quantify or discuss the potential contribution of these missing groups.
Q7e: The conclusion states that "this study provides observational evidence that higher trophic levels contribute directly to PMOA" (line 499). Given that micronekton are not primary producers but consumers, is the correlation with micronekton simply tracking the same seasonal signal as phytoplankton? A partial correlation controlling for total PFT biomass would help establish whether micronekton add explanatory power beyond phytoplankton.
8. Technical/Editorial Issues
Q8.1: There are duplicate author affiliations and repeated text in the author list (lines 5-8 appear duplicated). Please correct.
Q8.2: The abbreviation "SPM" (suspended particulate matter) is introduced but not used in the main analysis. Was SPM considered? If not, remove from methods.
Q8.3: Several references in the text are incomplete or incorrectly formatted (e.g., "Daey and Wakeham_1986" in line 433, "Alberne et al., 2024" in references has no initials). Please check all references against the EGU reference style.
Q8.4: The manuscript uses both "micronekton" and "microenokton" (typo on line 165). Please standardize spelling throughout.
Q8.5: Figure numbers: Figure S5, S6 etc. are referenced but the supplement file was not provided in the review. Please ensure all supplementary figures are properly labeled and referenced.