Experimental determination of the lidar ratio for cirrus and polar stratospheric clouds at Dome C, Antarctica, using a Young inversion

Cairo, Francesco; Di Liberto, Luca; Bracci, Alessandro; Snels, Marcel

doi:10.5194/egusphere-2026-652

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https://doi.org/10.5194/egusphere-2026-652

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30 Mar 2026

| 30 Mar 2026

Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Experimental determination of the lidar ratio for cirrus and polar stratospheric clouds at Dome C, Antarctica, using a Young inversion

Francesco Cairo, Luca Di Liberto, Alessandro Bracci, and Marcel Snels

Abstract. We present three years (2022–2024) of polarisation lidar observations of polar stratospheric clouds (PSCs) and tropospheric cirrus above Concordia Station (Dome C, Antarctica). Layer-mean lidar ratios (LR) at 532 nm are retrieved using the Young inversion method applied to an elastic backscatter and depolarisation (Rayleigh) lidar. The measurements are classified in the (1 − 1/R, δ_T) phase space, allowing us to separate supercooled ternary solution (STS), nitric-acid trihydrate (NAT) and ice PSC, as well as upper-tropospheric cirrus.

To quantify the impact of the Young assumptions, we analyse both the full set of cloud detections and a Young–optimized subset of clouds that satisfy stricter homogeneity conditions above and below the cloud layer. The comparison between these two datasets allows us to separate the effective climatological variability of lidar ratio values from those retrieved under idealised conditions that strictly satisfy the Young inversion assumptions. For PSCs, the full dataset yields optically weighted median LR values (25–75 percentiles) of 38 (31–52) sr for STS, 50 (37–73) sr for NAT, and 52 (40–65) sr for ice PSC. For cirrus, the median LR is 49 (34–52) sr. These values are consistent with microphysical expectations and with previous groundbased and spaceborne lidar studies.

The Young–optimized subset yields 41 (31–61) sr for STS, 61 (38–78) sr for NAT, 38 (32–38) sr for the few remaining ice PSC, and 41 (31–41) sr for cirrus although for this latter case the number of observations is not statistically significant. The subset thus provides a conservative estimate of the accuracy of the results from the full dataset which can capture the full range of cloud variability.

These values provide a physically consistent reference for PSC and cirrus retrievals over Dome C and can be used in radiative-transfer modelling and satellite-lidar validation.

Received: 03 Feb 2026 – Discussion started: 30 Mar 2026

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Francesco Cairo, Luca Di Liberto, Alessandro Bracci, and Marcel Snels

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'Comment on egusphere-2026-652', Anonymous Referee #1, 18 Apr 2026 reply
This paper analyzes data collected over a 3-y period by a ground-based elastic lidar located at Dome-C. In particular, the authors derived lidar ratio (i.e., the ratio of the extinction to backscatter coefficients) from this dataset, processed the data to provide a weighted single value for each identified cloud, and then analyzed these cloud observations as a function of 4 different cloud types to characterize the median lidar ratio (LR) for each type.
I found the paper well written, the methodology well explained, and the results intriguing. I have a few questions and points that I believe would strengthen the paper before it be accepted for publication.
Ultimately, from the 144 measurement days that spanned the 3-y period, there were less than 400 individual clouds that were analyzed (Table 1). And line 219 states that “each detected cloud layer represents one independent realization for the lidar-ratio retrieval.” However, there seems to be many, many more points that are displayed in the 2-d histograms in the figures. I can only conclude they are showing individual profiles that were measured in each of these clouds in those histograms, which brings up questions about the temporal correlations within a cloud (i.e., these are not independent samples). The authors need to be more clear about this sampling, and the impact of possible temporal correlation on their analyses.

The authors reported (line 135) that there are ancillary radiosondes and model output that provided temperature and pressure, from which ancillary molecular backscattering coefficients could be computed. However, these temperature data were not used in any of their analyses, either to select the height of the tropopause (I do recognize they performed a sensitivity test here, but why not use the actual value?), to evaluate some of their hypotheses (e.g., line 340 when they suggest the difficulty of isolating pure ice layers from mixed-phase structures; knowing if the temperature is above/below -40 Celcius would be useful, or e.g., line 353 when they discuss small vs large NAT crystals), or analyzing the LR values (LR does not depend on height like Fig 4 or Fig 8, but it can be related to temperature which helps constrain moisture).

Line 466 suggests that analyzing the subset of Young-optimized retrievals results in only a modest shift in median LR. However, for LS_ice, the value went from 52 sr to 32 sr; this is not modest. And given there are only 9 vs 2 clouds of ice PSC (Table 1), it seems these statistics are extremely uncertain (especially due to temporal autocorrelation; see first comment). Please provide more discussion here.

Line 479 leads to an intriguing thought: if the Young criteria can be used to identify cloud inhomogeneity, then it seems that it would be useful to indicate that fraction of each observed cloud type that could be considered homogeneous. This could be added to Table 1. But more than just adding an additional column to that table, please see if there is supporting literature that supports the ‘estimated fraction that are homogeneous’ for each class.

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Citation: https://doi.org/10.5194/egusphere-2026-652-RC1

Francesco Cairo, Luca Di Liberto, Alessandro Bracci, and Marcel Snels

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Short summary

Polar stratospheric clouds influence Antarctic ozone depletion, but their optical properties are still uncertain. Using three years of ground-based lidar observations at Dome C and the Young inversion method, we retrieve lidar ratios for different PSC types, revealing clear differences between cloud classes and a systematic increase of lidar ratio with altitude for NAT clouds.


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