Cloud radiative effects significantly influence the radiative budget of the planet at high latitudes (Kay and Gettelman, 2009; Schweiger et al., 2008; Morrison et al., 2012), particularly beyond the Arctic Circle, and contribute to the well-known “Arctic amplification” phenomenon (Serreze and Barry, 2011; Liu and Key, 2014; Wendisch et al., 2019). In particular, clouds have impacted sea ice and glaciers which have been melting for several years in connection with climate change (Serreze et al., 2009; Hansen et al., 2010; Koenigk et al., 2013).

Arctic clouds exhibit a robust annual cycle with maximum cloudiness in fall and minimum cloudiness in winter (Yu et al., 2019). The annual cloud fraction amounts to about 80 %, with predominant low-level clouds up to 70 % of the time from spring to autumn (Curry and Ebert, 1992). Based on observations from spaceborne LITE (Lidar In-space Technology Experiment), GLAS (Geoscience Laser Altimeter System), and CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) lidar, Berthier et al. (2008) showed that those low-level stratiform clouds represent between 30 % and 40 % of the cloud cover. As they are located in air masses below the 0 ∘C isotherm, these clouds are very often composed of ice crystals. The presence of supercooled liquid water droplets at the top of these clouds has nevertheless often been reported (Morrison et al., 2012; Shupe et al., 2008). Their subgrid-scale treatment is usually underestimated by physical parameterizations used in the climate models (Klaus et al., 2016; Taylor et al., 2019). This leads to a wrong representation of surface radiative fluxes, both in the solar and infrared spectra (Harrington et al., 1999; Klein et al., 2009; Maillard et al., 2021; Koenigk et al., 2013; Di Biagio et al., 2021). Reliable observations of microphysical and optical properties of ice particles, in particular of their ice crystal effective sizes, are critical for the evaluation of cloud parameterizations and for determining how clouds impact radiation (Zhang et al., 1996). Harrington and Olsson (2001) showed changes of up to 80 W m-2 due to a variation in the effective radius of ice particles. Accurately representing ice clouds in models is therefore necessary to realistically simulate the evolution of the Arctic surface energy budget.

The accuracy of cloud observations has been greatly improved in recent decades by the widespread use of lidar observations so as to better understand and predict the earth system climate (e.g., Hoffmann et al., 2009). Since the precursory work of Platt (1977), ground-based lidar observations have made it possible to better characterize the vertical distribution of cloud structures (Sassen, 1991). Coupled with infrared radiometric observations and even radar measurements, microphysical properties, such as the effective radius of ice crystals, are now accessible (Kalesse et al., 2016). With aircraft and satellite-based instrumentation, microphysical properties are now rendered at a larger spatial scale (Delanoë and Hogan, 2010; Chazette et al., 2022; Liu et al., 2012; Lampert et al., 2009). However, the velocity of those instrumented platforms, along with their temporal resolution, prevent us from sampling clouds at a spatial resolution which would be adequate to properly highlight their internal structure. Indeed, the resulting data provide cloud properties typically averaged over 5 km for satellites and over 1 km for airborne platforms, which may be insufficient to study cloud processes at a microphysical scale. Examining cloud structures with a high horizontal and vertical resolutions is, however, possible via ground-based lidars. Such instruments are unable to match the level of detail from aircraft in situ measurements or the spatial coverage of satellites, but they are fittingly positioned to capture the diurnal variability with high temporal and vertical resolutions (Shupe et al., 2011). This remains a challenge, as it requires that lidar measurements penetrate through the cloud with low attenuation. Semi-transparent clouds with small optical thicknesses (≲2) are therefore accessible to lidar measurements.

Due to the growing interest of the climate science community in clouds over the Arctic region, numerous experiments have been set up recently, for instance, the ACLOUD (Arctic Cloud Observations Using airborne measurements during polar Day (Ehrlich et al., 2019)) and the PASCAL (Physical feedback of Arctic PBL, Sea ice, Cloud And AerosoL (Wendisch et al., 2019)), both in 2017; AFLUX (Airborne measurements of radiative and turbulent FLUXes of energy and momentum in the Arctic boundary layer (Mech et al., 2022)) in 2019; or MOSAiC (Multidisciplinary Drifting Observatory for the Study of Arctic Climate (Shupe et al., 2022)) in 2019–2020. In this respect, we conducted Raman lidar observations at 355 nm during the PARCS (Pollution in the ARCtic System) field experiment (Chazette et al., 2018; Totems et al., 2019) in May 2016 at Hammerfest (Norway, over 70∘ N) in order to better constrain the estimate of ice crystal properties in thin cloud vertical structures. The high vertical resolution of the lidar allowed us to highlight the presence of supercooled water pockets embedded into ice clouds and to trace the vertical profiles of the optical properties of semi-transparent clouds. It was then possible to derive the hydrometeor size profiles taking into account the multiple scattering effects.

The lidar system and associated theory are presented in Sect. 2. Section 3 describes the methodology used to retrieve the effective radii of ice crystals and ice water content from the lidar measurements. The optical properties of the ice crystals accessible to the Raman lidar measurements are given in Sect. 4 and their associated effective radius in Sect. 5. Finally, the conclusions summarizing the findings are provided in Sect. 6.

In order to assess the vertical profiles of the ice crystal effective radius (reff) and ice water content (IWC), we use a Mie code assuming ice particles are all spherical (Sect. 4.3). Ice crystals are generally not spherical. Nevertheless, for the clouds sampled in this study, the ICDR is ∼10 % (Sect. 4.3), far from the values associated with highly non-spherical crystals whose ICDR is between 30 % and 50 %. The complex index of refraction of ice is taken from the database established by Warren and Brandt (2008) from wavelengths between 44 nm and 2 µm at 266 K. The refractive index of ice crystals has been interpolated against the logarithm of the wavelength and assessed to be equal to 1.324 at the wavelength of the lidar (355 nm), with a negligible imaginary part.

Cloud ice crystal size distributions dNdr are commonly represented by generalized Gamma distribution (Stephens et al., 1990) as in microphysical schemes most widely used (Milbrandt and Yau, 2005; Morrison and Gettelman, 2008; Thompson et al., 2008): 12dNdr=2(1+μi)⋅N0i⋅rμi⋅e-2⋅λi⋅r, where r is the radius of the ice crystal and dN the number concentration of ice crystals between r and r+dr. The intercept, slope, and shape parameters of the size distribution are N0i, λi, and μi, respectively. The normalized size distribution n(r) of the ice crystals can also be written equivalently as a function of r and the effective radius reff (Hansen and Travis, 1974): 13nr=1NtdNdr=1(νeff⋅reff)1νeff-2⋅Γ1νeff-2⋅r1νeff-3⋅e-rνeff⋅reff, where νeff is the effective variance of the distribution and Γ the Euler gamma function. The total number of ice crystals is symbolized by Nt.

Here, ice crystals are assumed to be spheres. In this paper, to evaluate the sensitivity of the effective variance νeff of ice crystal size distribution on cloud optical properties, we test two different values. First, the shape parameter μi is assumed to be zero (Marshall–Palmer distribution) as in many well-used two-moment microphysical schemes (Morrison et al., 2005; Thompson et al., 2008). The effective variance νeff is therefore νeff=1μi+3=1/3. Second, we consider a smaller value of the effective variance (νeff=0.2), corresponding to a larger shape parameter (μi=1νeff-3=2), which reduces the backscattering, then the LR, for the smallest particles.

The flow chart in Fig. 1 shows the process used to assess reff and IWC. Lidar-derived LR and IWC are compared with those calculated by a Mie code to retrieve the microphysical parameters of the ice crystals. Naturally, this calculation is carried out in altitude ranges where only ice crystals are present, i.e., with significant VDRs (>2), avoiding pockets of liquid water.

Effective radius. The extinction and backscattering cross-sections are calculated using a Mie code as a function of the ice crystal radius in the range 1–200 µm. For a series of reff values ranging from 5 to 40 µm, the extinction and backscatter coefficients are then derived by integrating their respective cross sections over the size distribution n(r), enabling the retrieval of LR for each value of reff. By definition, LR is independent of the assumed value of the total number of ice crystals Nt. Figure 2 shows the variation of LR as the function of reff following Mie calculation for νeff equal to 0.20 and 0.33. LR turns out to be a monotonically decreasing function of reff. By minimizing the discrepancies between the theoretical and measured values of LR, the effective radius of ice crystals can then unequivocally be determined for the considered size distribution range.

The parameters of the size distribution are thus derived as 14λi=12νeff⋅reffN0i=Nt⋅λiμi+1Γ(μi+1), where Nt is the total number of ice crystals obtained such that the theoretical extinction coefficient matches its observed counterpart.

Ice water content. After assessing the concentration of ice crystals by comparing the cloud extinction coefficients derived from the lidar measurement and the Mie code, the IWC is obtained using 15IWC=π6⋅ρi⋅N0iΓ(μi+4)λiμi+4, where ρi=920 kg m-3 is the density of pure ice.

The procedure is repeated at each altitude sampled, enabling us to derive vertical profiles of reff and IWC for each case study.

The vertical profiles of the effective radius reff and ice water content calculated as in Sect. 3 are shown in Fig. 8. Two values of νeff are considered (0.33 and 0.20) to evaluate potential ranges of reff and IWC (Fig. 8a, c, e, g). The effective radius reff of ice crystals presents rather small values, between 7 and 25 µm. The corresponding IWC is lower than 8 mg m-3, as it corresponds to small ice crystals and semi-transparent clouds. It can be noted that the value of νeff has little influence on reff and IWC because the backscatter phase functions do not change significantly (Fig. 2). The uncertainties on reff and IWC (Fig. 8b, d, f, h) are mainly due to the shot noise of the detection and have been calculated using a statistical error propagation. They are at most 2 µm and 0.65 mg m-3, respectively. The highest uncertainties are generally found at higher altitudes, where the lidar signal is most attenuated.

The values of reff are in the lowest range of those reported in the literature for the Arctic region by Mioche et al. (2017). They have analyzed the vertical distribution of microphysical properties, in most cases stratiform mixed-phase clouds in the boundary layer, using in situ measurements from four airborne spring campaigns in the European Arctic between 2004 and 2010. For these clouds of different nature in the southwestern regime, they showed that the ice phase dominates the microphysical properties with mean values of ∼25 µm and less than 25 mg m-3 for effective radius and IWC, respectively. Their analysis also revealed that large values of the liquid water content and high concentrations of small droplets may be linked to polluted situations and air mass origins from the south, leading to the lower values of ice crystal size and IWC ∼10 mg m-3. Our results are therefore consistent with airborne measurements on the same cloud types. Note that during PARCS, measurements were carried out over the coast of northern Norway, where the influence of local sources of pollution was not negligible (gas flaring from the Melkoya processing facility, presence of shipping activities close to Hammerfest, and transport of anthropogenic pollution from Russia) and when a plume containing biomass burning aerosols from huge forest fires in Canada reached Scandinavia (Chazette et al., 2018). The small values of ice crystal sizes and IWC found in our study may be explained by such polluted situations in comparison with clouds sampled in more pristine conditions in the high Arctic.

Similarly, McFarquhar et al. (2007) investigated the microphysical properties of single-layer stratus clouds over Barrow and Oliktok points in Alaska as part of the M-PACE (Mixed-Phase Arctic Cloud Experiment (Verlinde et al., 2007) campaign in fall 2004. They found that the effective radius of ice crystals was 25.2±3.9 µm and nearly independent of the normalized cloud altitude. Korolev and Isaac (2003) investigated mixed-phase clouds associated with frontal systems. They found that the mean volume radius of particles in ice clouds varied between 10 and 17.5 µm. Between -50 and -30 ∘C where all cloud particles were presumably ice, the effective radius was found to be about 7 µm which is of similar magnitude to our retrievals despite higher temperatures. They argued that IWC in glaciated clouds decreased with decreasing temperature, from about 100 mg m-3 at -5 ∘C to 20 mg m-3 at -35 ∘C. Our observations shown in Fig. 7 do not show any evidence of such behavior for clouds encountered during PARCS. The vertical profile of IWC cannot be explained solely by the temperature values but may also be ascribed to the history of the air masses.

Thin ice clouds were sampled by ground-based lidar in late spring 2016 over the Hammerfest area in northern Norway. In the presence of semi-transparent clouds with an optical thickness lower than 2 at the wavelength of 355 nm, ground-based lidar measurements allow us to differentiate the contributions of ice crystals and liquid water pockets embedded in the cloud. The clouds are located just above the atmospheric boundary layer, with a cloud base between 0.8 and 1.2 km a.m.s.l. where the temperature is below the 0 ∘C isotherm. The inversion of the lidar profiles shows a modest level of depolarization, of the order of 10 %, with a negligible multiple scattering coefficient (<0.95 at the cloud top), suggesting that sampled ice crystals are small and of rather spherical shape. This agrees with Mie computations determining effective radii between ∼7 and 25 µm. The ice water contents are found to be lower than 8 mg m-3. Such small values may be ascribed to more polluted situations compared with pristine conditions in the high Arctic. It is worth noting that the uncertainties on the effective radius and ice water content are on average of 2 µm and 0.65 mg m-3, respectively, allowing us to follow their evolution in the cloud, as long as the lidar signal is not too attenuated.

The use of vibrational Raman measurements to constrain the elastic lidar equation allows us to remove ambiguities on the restitution of optical properties of semi-transparent ice clouds. It is an alternative approach to airborne measurements limited by the ability to sample the clouds over long periods of time. It is complementary to approaches proposing the coupling of lidar and radar measurements at 95 GHz. Its limitation is mainly on the ability of the lidar to sample the cloud layer along the line of sight and on the assumptions of sphericity of ice crystals, which have been justified by the observed values of the depolarization ratio. Our approach can nevertheless be easily extended to clouds containing non-spherical ice crystals if we consider the appropriate phase functions, which can be obtained, for instance, using T-matrix approaches applied on specific shape functions.