General comments:

This study examines the long-term variability of aerosol optical properties in a boreal forest, categorised by size range. It focuses particularly on the contribution of particles larger than 10 μm, which are usually not considered in aerosol studies as this is the inlet cut-off point. As aerosol optical properties directly influence their radiative effect and larger diameter particles contribute significantly to the AOD, this topic is of great interest for climate modelling parameterisation. Using absorption and scattering measurements coupled with an impactor, the authors investigated the relative contribution of each PM size range to extensive and intensive scattering and absorption parameters. This study's novelty lies in its use of an aerosol classification for PM10, highlighting the significant impact of episodic events such as pollen and dust on optical properties and PM mass. The conclusions provide clear evidence of shifts in the size distribution and composition of aerosols, as well as their seasonality, which are linked to anthropogenic and biogenic emissions. The manuscript is well written and structured. However, several passages are redundant (e.g. the enhanced contribution of dust to the increasing SAE in sections 3.3.2 and 3.3.3), as are some details on the classification matrix (see specific comments). This paper would benefit from being shortened slightly. More importantly, the correction for multiple scattering on the instrument measuring absorption (AE33) was not properly discussed, as it appears to be misunderstood. This is important because it could also explain some discontinuities in the time series from 2018. I consider the manuscript to be publishable after minor revisions and responses to the following points.

Specific comments:

l. 139 : Are you sure that the AE33 adjusts the C_ref value taking into account the aerosol concentrations and the environmental conditions ? Please check here the AE33 manual : home.iiserb.ac.in/~ramyasr/files/Manuals/Manual for AE33 (Aethalometer).pdf . The C value is fixed in the instrument settings, but can be adjusted by the user, and depends on the filter material and type.

l. 218 and Fig S9 : What is the r² value of the linear regression ? Did you keep all the AE data, even the one that were far from the slope?

l. 356 to 363 : If you say that the absorption coefficient decrease is statistically significant, could you provide hypothesis for why absorbing aerosols are less present than in 2010 ? Do you expect that the instrumental differences are a major contributor of this decrease?

from l.398 : AE33 still has a constant C_ref value, depending on the filter tape. Yus-Diez et al. (2021) have shown that this C value is also depending on the SSA measured at the site. One can wonder whether the C value used in the AE33 was appropriate.

Part 3.3.2 : Could you comment on the much higher variability of the SAE after 2018 ? Is there one of the nephelometer wavelengths that shows an abrupt change in the σ_sca ?

l. 486-487 : But then if the MSC decreases because of the lower sulfate mass fraction within PM1, why does the MSC time series has a positive trend ?

l. 517-518 : The multiple scattering effect on the AE filter would increase if the SSA is higher (which is the case, regarding Fig 4), leading to a higher correction factor C, and to even lower σ_abs, so the correction of this parameter can’t really explain the decrease of σ_abs during May, June and July. Related to that, do these boxplots (Fig 3 and 4) look the same before and after 2018 ?

Fig. 6 : Be careful because this classification from Carzola et al. was made with AAE and SAE calculated at the wavelength pairs 462-648 nm and 450-700 nm, respectively, while here the AAE wavelength pair is broader. It would be useful to briefly comment on how this scatterplot looks like if the AAE is calculated between similar wavelengths as Carzola et al. Maybe the dust event will then appear closer to the “Dust-EC mix” area ?

l. 606 to 611 and Table 3 : I am not sure that this is necessary. Several studies have used this classification, and already detailed this. Furthermore, the values used here for the classification matrix are the same as in Carzola et al.  I would suggest moving this table 3 to the Supplementary material.

l. 644-651 : Looking at Fig.6, there is almost no data on the “dust dominated” and “OC/Dust mix” regions, so I wouldn’t highlight an agreement with Laing et al. (2016).

Technical corrections:

l. 89-90 : “two Magee Scientific Aethalometers”

l. 92-93 : please fix the intervals in the parenthesis : “(i.e. ≤ PM1, between PM1 and PM2.5, between PM2.5 and PM10, ≤ PM10, > PM10)”.

l. 106 “aethalometers”

l. 127-128 please fix the citation format.

l. 212 please fix the citation format.

l. 269: “which is used in conversion of σ_abs to BC mass”

l. 277-278 : The composition is not a physical characteristic

l. 319 : “ contribution additional variability” this sentence is strange

Fig 1 and Fig 2: Please provide the meaning of the blue shaded area on panels a and b.

l. 333 : Please provide at least for the first notification the information on the two different values : slope and relative trend.

l.475 : Why “albeit” ? Statistically significant is not contrasting with the beginning of the sentence.

Fig 5 : It would be great to remove the decimal part of the y-axis ticks on panels c and d. Furthermore, it is a bit difficult to see with this representation the contribution of pollen and dust events to Super PM10, as we don’t see on which months occurred these events. Maybe you can add the red and green stars also on panels c and d ?

Reference:

Yus-Díez, J., Bernardoni, V., Močnik, G., Alastuey, A., Ciniglia, D., Ivančič, M., Querol, X., Perez, N., Reche, C., Rigler, M., Vecchi, R., Valentini, S., & Pandolfi, M. (2021). Determination of the multiple-scattering correction factor and its cross-sensitivity to scattering and wavelength dependence for different AE33 Aethalometer filter tapes: A multi-instrumental approach. Atmospheric Measurement Techniques, 14(10), 6335–6355. https://doi.org/10.5194/amt-14-6335-2021

This study provides a valuable long-term analysis of super micron aerosol particles (PM₁–₁₀) in a boreal forest environment, leveraging a 12-year dataset from the SMEAR II station in Hyytiälä, Finland. The paper highlights the critical, yet often underexplored, role of size-segregated coarse PM in aerosol–climate interactions, especially their influence on light scattering and absorption. It focuses on aerosol optical properties and mass, categorized by size range, to understand their variability, seasonality, and episodic significance, particularly in the context of aerosol climate interactions. Notably, the study highlights the often-overlooked contribution of particles larger than 10 µm, which are typically excluded due to instrumental cut-off limitations. By employing both optical instruments and cascade impactor filters, the authors successfully quantify these coarse-mode particles. The Key contributions of the study including the seasonal and long-term trends in PM₁–₁₀ mass along with the optical properties and the episodic events (e.g., pollen bursts and dust intrusions) as dominant sources of variability are interesting. The findings emphasize that coarse-mode particles, though often neglected, play a significant and episodically dominant role in aerosol–radiation interactions in boreal ecosystems. The study strengthens the case for including super micron aerosols in climate models and long-term monitoring networks, particularly in environments prone to biogenic emissions and transboundary dust events.

Comments and corrections:

Lines 39–40: "...particles from episodic sources like wildfires or small-scale wood combustion, which could explain higher concentrations in winter." Wildfires are seasonal (summer), while wood combustion peaks in winter. Grouping them implies wildfires contribute to winter PM, which is inaccurate.

Line 126: for AE31 from October 2010 to December 2017 a single value is valid across all wavelengths for long duration. What uncertainties this may introduce in the long term measurements in this dataset?

Line 139: The Cref value is a user-defined parameter within the AE33 instrument settings. It is not automatically adjusted by the instrument based on aerosol concentrations or environmental conditions. Instead, the Cref value is typically determined through calibration procedures and is influenced by factors such as the filter material and the specific characteristics of the aerosol being measured. This should be corrected in the text and check the values.

Line 225: How does autocorrelation in time series data affect the results of the Mann-Kendall test, and how is this addressed?

Line 227: How do seasonal fluctuations and non-linear trends influence the interpretation of long-term aerosol optical trends?

Line 40:"which could explain higher concentrations in winter." Add “higher coarse particle concentrations in winter."

Line 41-42: "while pollen and spores often originate from more local biogenic emissions that are highly episodic and seasonal." The phrase "more local" can be improved.

Line 43:"Sea salt, though typically associated with marine environments, can occasionally reach boreal forests during strong winds." Could be written as "can occasionally be transported to boreal forests during strong wind events."

Line 45: Replace "over 10 µm" (line 45) with >10 µm for consistency with earlier notation (e.g., line 23: >1 µm).

Line 45–47: "may not be fully captured by standard PM10 measurements, leaving their contributions underrepresented...". Add the reason here “due to cut-off inlets or sampling loss”.

Line 49:"contribute to atmospheric heterogeneity". Specify "spatial and temporal heterogeneity" for more scientific clarity.

Line 51: the reference added here is in non-chronological citation order. Also, Brasseur et al., 2024 is cited in a 2025-dated document. While plausible (if published in early 2024), ensure this reference exists. If not, update to the correct publication year.

Line 57:"potentially leaving a significant fraction of aerosol mass unquantified". Replace with "potentially resulting in a significant underestimation of aerosol mass."

Line 64-68: These line are slightly redundant with earlier statements and too long

Line 86–87: “Thus, SMEAR II represents the typical conditions that may be found in a boreal forest.” Should be replaced by "Therefore, SMEAR II reflects typical boreal forest conditions."

Line 88: “It is a part of the European Aerosols, Clouds, and Trace Gases Research Infrastructure or ACTRIS…” should be replaced by "The station is part of ACTRIS (Aerosols, Clouds, and Trace Gases Research Infrastructure)..."

Line 60: "such as pollen, may escape standard measurements, whereas smaller coarse-mode particles, such as fungal spores and dust, are more likely to be captured. Replace with "Smaller coarse-mode particles like fungal spores and dust are more likely to be captured, whereas larger ones, such as pollen, may escape detection."

Line 77: Define "super-PM₁₀" earlier for clarity:37-48: Provide references for these statements.

Line 88-93: 4 instruments have been used primarily, but only 3 have been covered here.

Line 97: ‘a’ Dp >10 µm

Line 104: is aimed ‘to’ be kept

Line 120: Are there any recent publications post Liousse et al., 1993 which cover this?

Line 149: Use l min-1 for consistency.

Line 237-238: This is repetition of 232-233.

Line 289: Write PM1–10 with subscript.

Line 341: SOA already defined in line 33, so expansion is not needed here.

Line 342: Instead of ‘biogenic VOC’, use BVOC as already defined.

Line 460: OA has not been defined previously.

Line 645: SOA already defined in line 33, so expansion is not needed here.

Line 709: typo after reference link

Line 760: Provided link is not working.

Line 927: Link to reference has not been provided. Pls correct all the incorrect references.

Use subscript formatting for PM size ranges (e.g., PM₁₋₁₀ instead of PM1-10).