the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Mar 2025
Saharan dust transport event characterization in the Mediterranean atmosphere using 21 years of in-situ observations
Abstract. The Mediterranean Basin is regularly affected by atmospheric dust transport from the Saharan desert. These recurring events have strong implications for the Earth’s energy budget, cloud formation processes, human health, and solar energy production. Monte Cimone, with 2165 m a.s.l, is an ideal platform to investigate dust outbreaks in Mediterranean Europe. In this study, we present 21 years (2003–2023) of dust transport event identification, derived from continuous measurements of the aerosol optical size distribution coupled with backward trajectories. Throughout all the years investigated, the fraction of dust transport days remained constant at values between 15 % and 20 % without any detectable trend. This absent trend was also observed in the particulate matter concentration. The annual cycle of dust transport days was characterized by two peaks from May to August and in October and November with values up to 20 %. A similar annual cycle was reflected in the particulate matter concentration with the highest concentrations is summer and the lowest in winter. Grouping consecutive dust transport days into dust transport events revealed that in the winter months a typical event had a duration of one or two days, whereas in the summer months dust transport events lasted longer (three or more days). The 21 years of measurements presented in this study will set a baseline to assess future dust transport scenarios. Furthermore, they can be used to validate dust forecast models to increase the accuracy of predicting atmospheric dust transport towards the Mediterranean Basin.
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Status: open (until 15 May 2025)
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RC1: 'Comment on egusphere-2025-1278', Anonymous Referee #1, 29 Apr 2025
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General comment
The manuscript by Vogel et al. (2025) present the analysis of 21 years of optical particle counter (OPC) in-situ observations at the remote Monte Cimone site as an extension of the work by Duchi et al. (2016). The authors analyse the OPC data to provide an estimate of the trend for the annual fractions of dust transport days (DTDs) and PMcoarse, describe the PMcoarse variability compared to background values (enhancement factor) and investigate the correlation between dust events and PMcoarse concentrations.
From a general point of view, although the paper is well written, the analysis could benefit from further refinement and inclusion of additional data sources (i.e. satellite data or reanalysis) and a more accurate and clear presentation of the results (e.g. discussion of measurement uncertainties), which I see as the added value of this work compared to Duchi et al. (2016). I would recommend considering publication once the comments have been adequately addressed.
Specific comments
- This work appears to be an extension of Duchi et al. (2016). How do the results of your analysis compare quantitatively with those presented in that study? This comparison is missing in the paper. Which is the added value compared to that study? Please discuss.
- I would see the added value extending this analysis to include spatially-resolved data such as satellite data and/or reanalysis, especially considering that Monte Cimone is located at 2165 m a.s.l., where aerosol optical depth can provide additional insights into dust episodes. OPC in-situ observations alone may not be sufficient to provide a robust estimate of trends in DTD/DTE and to set a "milestone in DTE identification".
- What happens at your analysis if you neglect the data before the 2008 (i.e. after the inlet in the line has been heated)? I rather see a decreasing trend from 2008 to 2023 (Fig. 9) in PMcoarse and a more increasing trend in DTDs (Fig. 2). Please detail and consider adding supporting information.
- You present a dataset without discussing the uncertainties in the measurements/plots. This aspect must be better clarified and taken into account in the statistical analysis/plots avoid limiting the analysis of dataset variability to percentile-based metrics only. How significant is your trend estimation considering the uncertainties you have in the measurements?
- L.96 the FLEXTRA back trajectory configuration is missing. A section (i.e. meteorological inputs, configuration, model description) should be incorporated into the methods, with details on the configuration of the back-trajectories, given that it represents a major factor in determining the DTD. Why do you decrease the 10 days from Duchi et al. (2016) back trajectory to 7 days? Is there any reason? Please explain.
Minor Comments
- I would rather suggest adding a section in the introduction on aerosol optical depth as a proxy for dust outbreaks, as it represents a more informative parameter compared to surface PM measurements.
- Please consider adding a few sentences describing the structure of the paper at the end of the introduction.
L.37 Please add a reference
L.38 if dust particles remain in the upper layers there will be no increase in the surface PM concentration.
L.76-77 the GRIMM 1.108 starts at 0.3 µm and not at 0.25 µm with the 780 nm operating wavelength
L.81 Please include the number of bins that you consider for the “coarse” mode.
L.92-103 I suggest introducing a list of items instead of the text to identify the different steps of the algorithm.
L.102 how many months are available in 21 years of data?
L.105 Why are back trajectories missing? Due to missing meteorological data?
L.108-110 “consecutive days”, how many consecutive days do you consider? I see it in Figure 1 but it should be written also here.
L.117 Please add more details, which is the average particle density you obtain?
L.138 “user-defined alpha value”, which is?
L.168 replace “merging” by “grouping” and “years” by “DTD yearly values”.
L.168-170 here it could be very interesting to compare with the aerosol optical depth. Your results are consistent with the seasonal cycle in the aerosol optical depth climatology observed for the Po Valley in (Di Antonio et al, 2023) using satellite data.
https://acp.copernicus.org/articles/23/12455/2023/
L.199-200 which is the uncertainty linked to this value?
L.226 Background concentrations are also expected to show diurnal variability in summer compared to winter.
L.218-239 What is the key message then here? What do we learn with the seasonal cycle? It is not very clear to me.
L.242-243 this should go in the methods
L.279 the median is always below the WMO threshold.
L.286-87 I would avoid to make such a suggestion based on the analysis of a single point data source.
L.297-301 I do not fully agree with this statement. On the one hand, dust transport over the Mediterranean basin is generally favoured during the summer months due to the development of a deeper planetary boundary layer (PBL) over the Sahara, on the other hand, it is also strongly influenced by synoptic-scale weather patterns that facilitate such transport. I would rather say that DTE appears to be more closely associated with the persistence of high-pressure systems over the region, rather than directly linked to PBL development over the Sahara. Given the considerable distance from the source areas, the observations primarily reflect atmospheric transport processes rather than continuous Saharan emissions. It may be helpful to investigate reanalysis data to assess whether the occurrence of longer DTEs is associated with more specific stable atmospheric conditions (looking at geopotential height for example).
L.314 aerosol or dust optical depth?
L.314 what do you mean with “reanalysis data from satellites”?
Technical comments
Methods and results can be presented in a clearer way:
- Could you kindly list the different key processes that the dataset has undergone (i.e. Sec. 2.2, 2.3, 2.4) rather than describing them in a block of text?"
- You can summarize major results (such as the average conditions for background/non-background) in tables.
L.40 replace “mineral aerosol” by “dust particles”
L.40 PM “surface” values
Figures: Adding markers to the line can improve the clarity of the plot.
L.263 add space
Citation: https://doi.org/10.5194/egusphere-2025-1278-RC1
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