the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Jul 2025
Long range transport of Canadian Wildfire smoke to Europe in Fall 2023: aerosol properties and spectral features of smoke particles
Abstract. Canadian wildfires in Fall of 2023 were unique regarding the observed aerosol properties using remote sensing and in situ measurements for both short-range and long-range transported plumes across North America and Europe. One of the highlights was the observation of special concave spectral curvature in AOD having maxima at higher wavelengths than the minimum measured wavelength which led to negative values of Ångström exponent in spectral ranges below 500 nm. Along with this, large accumulation mode size distributions with volume median diameters reaching about 800 nm were observed. Another unique observation was the non-monotonic spectral curvature in single scattering albedo (SSA). For most of the stations, SSA increased in the UV-Visible region and/or further remaining either constant or decreasing at longer wavelengths as observed from retrievals from column integrated (AOD) from remote sensing and coefficients from in situ measurements. Additionally, two stations in Canada and one in Europe were found to have a well-defined peak in AOD at 500 nm. These Canadian stations also displayed a non-monotonic spectral SSA with maxima at 675 nm, while the high altitude stations of Europe showed monotonically increasing SSA. Finally, a much higher (approximately 5 times) UV absorption than visible absorption indicated the presence of brown carbon and/or tar balls, which have a strong spectral dependence in imaginary refractive index. The SSA concave spectral curvature denotes the mix of black carbon and non-absorbing particulate matter and influence of particle size, while the AOD concave spectral curvature is attributed to particle size.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
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Status: final response (author comments only)
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CC1: 'Comment on Masoom et al. (2025) egusphere-2025-2755', Thomas Eck, 07 Aug 2025
- AC1: 'Reply on CC1', Akriti Masoom, 23 Aug 2025
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RC1: 'Comment on egusphere-2025-2755', Anonymous Referee #1, 11 Aug 2025
In the manuscript “Long-range transport of Canadian Wildfire smoke to Europe in Fall 2023: aerosol properties and spectral features of smoke particles”, Masoom et al., present a multi-instrument study of a long-range smoke transport event originating from Canadian wildfires in late September–early October 2023. Using ground-based column (AERONET, PFR, spectroradiometers) and in-situ (JFJ) measurements, lidar/ceilometer profiles, satellite imagery (MODIS) and HYSPLIT back-trajectories, they document an unusual concave spectral AOD (AOD peaking near ~500 nm) and associated optical/microphysical signatures: very large accumulation-mode volume median diameters (VMDs up to ~800 nm), high UV absorption (AAE ≈ 2), and non-monotonic single-scattering albedo (SSA) spectra. They use Mie calculations to show that the AOD curvature is primarily a scattering/size effect given the retrieved refractive indices. The observations are interesting and important for understanding extreme wildfire aerosol radiative effects and long-range plume evolution. Also, the work is timely, given increasing wildfire activity under climate change, and provides valuable observational evidence of an infrequently occurring and intriguing smoke event. However, several key aspects require clarification, deeper mechanistic explanations, and improved discussion of uncertainties. Below, I outline major and minor concerns :
General Comments:
- The manuscript attributes the large particle sizes (~800 nm) to 'low dilution' and 'high VOC concentrations,' but would benefit from more quantitative analysis of growth pathways (coagulation, condensation, SOA formation). The non-monotonic SSA spectral curvature may additionally reflect evolving aerosol mixing states during aging, as demonstrated in other environments with complex aerosol mixtures. Recent studies of biomass burning and urban aerosols in South/Southeast Asia (Tiwari et al., 2023,2025; Liu J et al., 2024) have revealed similar particle sizes occurring through secondary processes under favorable aging conditions. The resulting core-shell mixing states produce comparable non-monotonic SSA spectral curvature patterns through coating-dependent absorption enhancement. These studies provide valuable mechanistic frameworks that could help test whether similar growth pathways operate in boreal wildfire plumes, interpret the unique DAV/JFJ SSA behavior through mixing-state analogs and strengthen the global context by showing these processes transcend source regions. Incorporating such cross-comparisons would demonstrate how localized observations fit into broader patterns of aerosol evolution. These works also make clear that while such a non-monotonic SSA spectra is infrequent, it has been observed in previous work in other parts of the world, and also associated with urban pollution plumes as well as fire pollution plumes.
- The merging of APS and Fidas size distributions assumes an effective density of 1.6 g cm⁻³ to convert aerodynamic diameter to mobility diameter. Given the extreme accumulation-mode sizes observed (VMD ~800 nm), how sensitive are the merged distributions to this density assumption, particularly for particles approaching 1 μm where dynamic shape factors may become significant? Were any cross-validation measurements performed (e.g., with aerodynamic aerosol classifiers or DMA-CPMA systems) to confirm the accuracy of the merged size distribution in this unusual size regime? Could the near-absence of coarse mode particles (>1 μm) in the merged data potentially reflect artifacts in the APS-Fidas integration method for such large accumulation-mode particles?
- The Mie calculations assume homogeneous spheres with a single refractive index from AERONET inversions. While this simplifies the analysis, it does not explicitly represent coated BC or other heterogeneous morphologies that are expected in aged wildfire smoke. These fires will have a mixture of fresh (irregularly shaped BC), and also rapidly aged BC (coated by non absorbing aerosols, like sulfate or nitrate). If such particles were present, core–shell or externally mixed Mie model could produce different spectral responses, potentially affecting the interpretation of the AOD curvature. It will be difficult for the authors to analyze for both homogenous spheres, fractals and irregular (using DDA, T-matrix approach), but it should be mentioned in the discussion, where the method may not be fully capture these effects, although they still present a formidable representation for the aged aerosols.
- The manuscript contains high-quality instrumentation with calibration statements (e.g., QASUME and PFR uncertainty statements), but the uncertainty bounds are not consistently propagated into the main conclusions, provide quantified uncertainties for (a) spectral AOD at key wavelengths, (b) AERONET inversion outputs used (VSD, RI, SSA, AAOD), and (c) in-situ scattering/absorption coefficients. Discuss whether the observed concave spectral curvature (AOD peak at ≈500 nm) is significant given measurement and retrieval uncertainties. Include error bars / shading on the key plots or an appendix table with uncertainty ranges.
- The MODIS/MERRA-2 vs. AERONET comparisons in Appendix Table A3 should be interpreted with caution, since satellite algorithms are generally based on only 1 or 2 wavebands radiance inversion, while MERRA-2’s spectral and microphysical properties are model-driven and constrained primarily by satellite column AOD at limited wavelengths with simplified microphysics. These approaches are not as likely to represent the peak around 500nm in observed AOD in-situ, and likely will not be captured in these datasets. While such comparisons are useful to show whether the event is represented in the reanalysis, they do not constitute independent validation of the aerosol size distribution, composition, or unusual spectral curvature observed. Several recent studies have shown that MERRA-2 and CAMS can underestimate absorbing aerosol loadings, that they miss key hotspot regions and underestimate BC by a substantial margin (Li et al., 2023; Liu Z et al., 2024). Including a brief discussion of these known limitations supporting the argument with similar findings elsewhere (Liu Z et al., 2024; Tiwari et al., 2025) would help contextualize the differences observed here and prevent over-interpretation of the agreement or disagreement with MERRA-2.
Specific Comments:
- The absence of coarse mode despite large accumulation sizes is puzzling!! Could this indicate measurement limitations in detecting super-micron particles? Was the APS merging process validated for such extreme distributions?
- Could you clarify what configuration was adpated for Mie model simulation, do we assume, core-shell, external, BC fractal morphology? Have you tested core-shell mixing would modify your Mie results? The refractive indices are derived from AERONET? And are used for Mie model (Figure 9)
- Does the plume's vertical evolution suggest lofting mechanisms beyond advection? How does the Fall seasonality potentially differentiate this from summer smoke events?
- Were ACSM composition data used to constrain the AERONET inversions? How does the 500 nm AOD peak impact AERONET's size distribution retrieval accuracy?
- Clarify “smoke age” definition— Table 1 and its use (how days were assigned) should include the assumptions and uncertainties in age estimates (transport time ranges).
- Figure 6: Label station codes in subplots for clarity.
- Figure 9: Include error bars for AERONET retrievals (SSA/RI uncertainties)
References:
Li, W., Wang, Y., Yi, Z., Guo, B., Chen, W., Che, H., & Zhang, X. (2023). Evaluation of MERRA-2 and CAMS reanalysis for black carbon aerosol in China. Environmental Pollution, 343, 123182. https://doi.org/10.1016/j.envpol.2023.123182
Liu, J., Cohen, J. B., Tiwari, P., Liu, Z., Yim, S. H., Gupta, P., & Qin, K. (2024). New top-down estimation of daily mass and number column density of black carbon driven by OMI and AERONET observations. Remote Sensing of Environment, 315, 114436. https://doi.org/10.1016/j.rse.2024.114436
Liu, Z., Cohen, J.B., Wang, S. et al. Remotely sensed BC columns over rapidly changing Western China show significant decreases in mass and inconsistent changes in number, size, and mixing properties due to policy actions. npj Clim Atmos Sci 7, 124 (2024). https://doi.org/10.1038/s41612-024-00663-9
Tiwari, P., Cohen, J.B., Wang, X. et al. Radiative forcing bias calculation based on COSMO (Core-Shell Mie model Optimization) and AERONET data. npj Clim Atmos Sci 6, 193 (2023). https://doi.org/10.1038/s41612-023-00520-1
Tiwari, P., Cohen, J.B., Lu, L. et al. Multi-platform observations and constraints reveal overlooked urban sources of black carbon in Xuzhou and Dhaka. Commun Earth Environ 6, 38 (2025). https://doi.org/10.1038/s43247-025-02012-x
Citation: https://doi.org/10.5194/egusphere-2025-2755-RC1 -
RC2: 'Comment on egusphere-2025-2755', Anonymous Referee #2, 12 Aug 2025
General comments:
This study exploits multi-site, ground-based remote sensing instrumentation to provide a systematic analysis of the long-range transport, microphysical and optical properties of smoke from the 2023 Canadian wildfires that reached Europe in autumn. Uncommon spectral signatures of both AOD and SSA were identified across the ultraviolet-to-shortwave-infrared range (300–2200 nm), revealing previously unreported wavelength-dependent features in the aging smoke plume over Europe. The topic is highly relevant to ongoing extreme wildfire events within the context of global climate change and offers valuable insights for regional air-quality and climate-feedback studies. However, there are still some problems that need to be solved. Minor revisions are recommended before final acceptance.
Specific Comments:
1. Section 2 Data and Methodology, 2.1-2.4, The current section is poorly structured: it alternates between aerosol-property-based and instrument-platform–based sub-divisions, making the narrative difficult to follow.
2. MODIS DT provide AOD over ocean and land, DB provides AOD over ocean, so for this study, which AOD is used here? What is the uncertainty of the dataset?
3. In the section 3.1.3, please give definition of SAE, AAE and SSA AE with the equation.
4. Line 373, there are many stations only has one time stamp, how can say “all stations showed a greatly shifted peak …”, for other station, the shift is also not obviously.
5. Section3.3 satellite observation and model evaluation are ancillary checks on the event. However, they are scarcely mentioned in the Abstract and Conclusions. The necessity of this section therefore needs to be further clarified.
Technical correction:
- Figure 5, numbering in the figure caption and the citations within the text are inconsistent. Please verify and update accordingly.
- Line134, refractive indices (RI) à refractive index, which is shown in the title 3.1.3
- Line152, what is QASUME?
- Line91, PM2.5 to PM2.5
- Line 250-251, change the order, 30 September first then 01 October
- Figure 2, Please use the same y-axis legend for both panels (a) and (b).
- Figure 4, what does the shadow means?
- Line 339, what is MSC?
Citation: https://doi.org/10.5194/egusphere-2025-2755-RC2 -
RC3: 'Comment on egusphere-2025-2755', Anonymous Referee #3, 18 Aug 2025
This manuscript by Masoom et al. addresses an important and timely topic, presenting observational evidence of Canadian wildfire smoke transported across North America and Europe in fall 2023. The study highlights several unique aerosol characteristics, including concave spectral curvature in AOD, non-monotonic SSA, unusually large accumulation-mode particle sizes, and strong UV absorption indicative of brown carbon and/or tar balls. While the observations are compelling, several aspects of the manuscript require clarification or elaboration to strengthen scientific rigor and readability.
General comments:
- The observed concave spectral curvature in AOD and SSA is very interesting. A more detailed discussion on the underlying physical mechanisms such as particle size distribution, mixing state, and the presence of absorbing organic aerosols (e.g., brown carbon) would enhance the manuscript's depth.
- General suggestion/curiosity: it would be useful to compare the observed spectral features and particle properties with past wildfire smoke transport events at the same site to highlight what makes the 2023 episode unique. The authors also mention that seasonal factors may play a role; it is therefore important to compare these observations with past events.
Specific comments:
L82: -> et al. (2024)
L92: PM2.5 -> PM2.5
L137-L139: why not Level 2 AERONET data with improved quality?
Section 2: Since multiple aerosol data sources are used, it would strengthen the manuscript to discuss the uncertainties associated with each dataset (e.g., remote sensing retrievals, in situ measurements) and how these uncertainties might propagate into the subsequent analysis. This would provide readers with a clearer understanding of the confidence in the results.
Section 2 Data and Methodology: Consider renaming the section to simply “Data”, as this may more accurately reflect the content.
Figure A1e: the label (e) appears to be blocked, please readjust it.
Section 3.1.3: what about the AERONET derived SD, SSA and CRI?
Specific comment on Section 3.1.2 – “Anomalous spectral aerosol optical properties at Davos and Jungfraujoch”:
Please consider to include a comparison with prior biomass burning events (2004–2022) to better contextualize the observed AOD values exceeding 0.5 and the spectral peak around 500 nm. The authors should expand the discussion on possible causes of this unusual spectral curvature, including particle size distribution, mixing state, and the presence of absorbing organics such as brown carbon. Providing the temporal evolution of spectral AOD at sub-daily resolution would help assess whether the observed anomaly is transient or sustained.
Specific comment on Section 3.2.1 – “High aerosol loading and unique spectral AOD curvature”:
I would suggest to provide a more detailed discussion of the physical mechanisms underlying the observed AOD curvature across different stations. The link between the observed shift in the volume size distribution (VSD) fine-mode peak (~800 nm) and the spectral curvature in AOD should be clarified, including potential retrieval limitations under high aerosol loading. Furthermore, the criteria used to define “high AOD days” and spectral curvature events should be quantified.
Citation: https://doi.org/10.5194/egusphere-2025-2755-RC3
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This is an interesting paper with useful observations of rare smoke aerosol characteristics. I have a few relatively minor comments with the most significant pertaining to the Abstract (see below).
Abstract: The 2023 biomass burning season in Canada exceeded 5 months duration, and the observations focused on in this manuscript occurred during a one-week interval at the end of this very long biomass burning season. It needs to be emphasized that the other 5 months of smoke observations were very different from these and that makes it quite notable. Additionally, the word 'unique' occurs twice in this abstract and it should be replaced by 'very rare' in both instances. The authors have made it very clear in the Conclusions section that these aerosol characteristics are not unique since other observations of these very unusual AOD spectra (maximum at 500 nm), size distributions and spectral single scattering albedo characteristics were documented by Eck et al. (2023) from forest fire smoke originating in California and Oregon in September 2020.
Lines 136-137: Since the calibration uncertainty of the PFRs AOD are given in the paragraph below the uncertainty of the AERONET AOD data should also be mentioned here. The AERONET field instruments are inter-calibrated versus Mauna Loa and Izana Langley calibrated reference instruments resulting in AOD uncertainty at optical airmass 1 of ~0.01 in the visible and near infrared increasing to ~0.02 in the UV wavelengths. (Eck et al. (1999)).
Lines 490-492: This is incorrect. It seems that 'AOD' should have been 'SSA' since the topic of this section and sentences is spectral absorption, and therefore spectral AOD makes little sense here.
Line 609: This sentence is also incorrect. Eck et al (2023) attributed the concave spectral shape of AOD curvature to the large size radius of sub-micron particles and narrow width of this fine mode distribution. This was even modeled by Mie simulations by Eck et al (2023), see Figure 12. Maybe you mistakenly meant concave spectral "SSA" spectral curvature instead of "AOD" curvature here?
Lines 611-612: This switching back to AOD spectral curvature while discussing SSA spectral curvature in this short paragraph is confusing. Please consider revising in order to clarify here.
Line 613: I suggest adding something like: “plus Canada forest fire smoke transported to Scotland in September 1950 (Wilson, 1951).”