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MODIS true color images from the NASA Worldview application (https://worldview.earthdata.nasa.gov, last access: 5 October 2019) on (a) 25 May 2017 during a cold air outbreak and on (c) 2 June 2017 during a warm air advection. Zooms into the regions delimited by black squares are shown in (b) and (c). The measurements location (79.5 • N, 9.5 • E on 25 May and 79.2 • N, 10.7 • E on 2 June) is indicated by the green section of the flight track of Polar 5 (orange). The areas extracted from the LESs are indicated by the dashed red rectangle. The dashed-dotted blue on 2 June line indicates the location of the SID-3 measurements.
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The combination of downward-looking airborne lidar, radar, microwave, and imaging spectrometer measurements was exploited to characterize the vertical and small-scale (down to 10 m) horizontal distribution of the thermodynamic phase of low-level Arctic mixed-layer clouds. Two cloud cases observed in a cold air outbreak and a warm air advection even...
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... cloud cases observed on 25 May, during the cold air outbreak, and on 2 June 2017, during the warm air advection, were analyzed in detail. Figure 2 displays the corresponding MODerate resolution Imaging Spectroradiometer (MODIS) true color images showing the clouds on both days. Figure 3 illustrates the combined measurements of MiRAC and AMALi for the 1 min sequence acquired over open ocean for the two cloud cases. ...
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... which are typically larger and strongly absorb radiation at 1625 nm wavelength, bias the retrieval of both quantities towards higher values ( Riedi et al., 2010). The particle size distribution observed by the SID-3 (Schnaiter and Järvinen, 2019) deployed in Polar 6 between 09:25 and 09:35 UTC in the vicinity of the AISA Hawk measurements (Fig. 2) revealed that, for the observed cloud, the particles at cloud top present effective radii of approximately 10 µm. Overall, 75 % of the AISA Hawk measurements on 2 June retrieved an effective radii larger than this value ( Fig. 6g and h). The small-scale variability in the cloud properties shows that the largest deviation in the ...
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... the two cloud cases of 25 May and 2 June, two regions of 21 km × 11 km enclosing the corresponding aircraft measurements were simulated by ICON-LEM (Fig. 2). The resulting cloud profiles are shown in Fig. 9a-c and e-g. The profiles of ice fraction IF(z) shown in Fig. 9b and f are calculated, in correspondence to Eq. (7), ...
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... 3D radiative transfer simulations are used to estimate this 3D radiative effect and analyze whether the observed correlation between R 1240 , I s , and LWP are caused by shadows or by inhomogeneous distributions of the cloud thermodynamic phase. Figure A2 presents 3D simulations of two idealized stratiform cloud decks with a constant TWP of 100 g m −2 . Figure A2a represents a liquid water cloud with an inhomogeneous cloud top height (50 m lower cloud top in the center of the cloud field). ...
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... A2 presents 3D simulations of two idealized stratiform cloud decks with a constant TWP of 100 g m −2 . Figure A2a represents a liquid water cloud with an inhomogeneous cloud top height (50 m lower cloud top in the center of the cloud field). For a SZA of 57 • , similar to the measurement on 2 June, the dip on the cloud top casts a shadow that gets imprinted on R 1240 (Fig. A2c), I s (Fig. A2e), and the retrieved LWP (Fig. A2g). ...
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... of two idealized stratiform cloud decks with a constant TWP of 100 g m −2 . Figure A2a represents a liquid water cloud with an inhomogeneous cloud top height (50 m lower cloud top in the center of the cloud field). For a SZA of 57 • , similar to the measurement on 2 June, the dip on the cloud top casts a shadow that gets imprinted on R 1240 (Fig. A2c), I s (Fig. A2e), and the retrieved LWP (Fig. A2g). Whereas in the shaded region R 1240 decreases on average by 35 % with respect to the nonshaded region, I s increases on average by 20 %. These opposite effects result in an almost-constant LWP, which does not show a signature of the cloud dip. Figure A2b shows a pure liquid water cloud ...
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... stratiform cloud decks with a constant TWP of 100 g m −2 . Figure A2a represents a liquid water cloud with an inhomogeneous cloud top height (50 m lower cloud top in the center of the cloud field). For a SZA of 57 • , similar to the measurement on 2 June, the dip on the cloud top casts a shadow that gets imprinted on R 1240 (Fig. A2c), I s (Fig. A2e), and the retrieved LWP (Fig. A2g). Whereas in the shaded region R 1240 decreases on average by 35 % with respect to the nonshaded region, I s increases on average by 20 %. These opposite effects result in an almost-constant LWP, which does not show a signature of the cloud dip. Figure A2b shows a pure liquid water cloud with a constant ...
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... a constant TWP of 100 g m −2 . Figure A2a represents a liquid water cloud with an inhomogeneous cloud top height (50 m lower cloud top in the center of the cloud field). For a SZA of 57 • , similar to the measurement on 2 June, the dip on the cloud top casts a shadow that gets imprinted on R 1240 (Fig. A2c), I s (Fig. A2e), and the retrieved LWP (Fig. A2g). Whereas in the shaded region R 1240 decreases on average by 35 % with respect to the nonshaded region, I s increases on average by 20 %. These opposite effects result in an almost-constant LWP, which does not show a signature of the cloud dip. Figure A2b shows a pure liquid water cloud with a constant cloud top height and an embedded ...
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... opposite effects result in an almost-constant LWP, which does not show a signature of the cloud dip. Figure A2b shows a pure liquid water cloud with a constant cloud top height and an embedded mixed-phase region of 150 m horizontal extent. The TWP is kept always constant at 100 g m −2 (i.e., the pure-phase region considers a LWP of 100 g m −2 ; the mixed-phase region considers a LWP of 60 g m −2 and a IWP of 40 g m −2 ). ...
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... considers a LWP of 100 g m −2 ; the mixed-phase region considers a LWP of 60 g m −2 and a IWP of 40 g m −2 ). The liquid water droplets have an r eff of 10 µm, and the ice crystals have an r eff of 60 µm. The inhomogeneous phase distribution obviously biases the retrieved cloud top properties and the calculated phase index. In this case, R 1240 (Fig. A2d) decreases by 34 % in the mixed-phase region compared to the pure-phase region, and I s increases by 58 %. However, contrasting the shaded case, the presence of ice crystals leads to a significant increase in LWP by 36 ...
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... I s , and LWP is crucial to interpret the observations of AISA Hawk. Only a simultaneous increase in I s and LWP when R 1240 decreases is indicative of mixed-phase regions. Although we cannot completely discard shading artifacts on the 2 June case study, the observed increment of I s and LWP in regions of low R 1240 agrees with the simulations in Fig. A2d, f, and h and supports the hypothesis of mixed-phase on this day. E. Ruiz-Donoso et al.: Small-scale structure of thermodynamic phase in Arctic mixed-phase ...
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The directional reflection of solar radiation by the Arctic Ocean is dominated by two main surface types: sea ice (often snow-covered) and ice-free (open) ocean. However, in the transitional marginal sea ice zone (MIZ), the reflection properties of both surface types are mixed, which might cause uncertainties in the results of retrieval methods of...
Citations
... Differences in T B for clear-sky and cloudy situations are used to retrieve LWP over ocean via a regression approach(Ruiz- Donoso et al., 2020;Maherndl et al., 2024).Lidar measurement of backscattered intensities at 532 nm (parallel and perpendicular polarized) and 355 nm (not polarized;Stachlewska et al., 2010) are used to derive cloud top height (CTH) during HALO-(AC) 3(Mech et al., 2022a;Schirmacher et al., 2023;Maherndl et al., 2024;Mech et al., 2024b).140 Cloud particle observations obtained with a variety of cloud probes cover a size range from 2 µm to about 2 cm for IMPACTS and 2.8 µm to 6.4 mm for HALO-(AC) 3 . ...
Mixed-phase clouds (MPC) are a key component of the Earth's climate system. Observations show that ice water content (IWC) is not distributed homogeneously in MPC. Instead, high IWC tends to occur in clusters. However, it is not sufficiently understood, which ice crystal formation and growth processes play a dominant role in IWC clustering. One important ice growth process is riming, which occurs when liquid water droplets freeze onto ice crystals upon contact. Here, airborne measurements of MPC in mid- and high-latitudes are used to study spatial variability of ice clusters and investigate how this variability is linked to riming. We use data from the IMPACTS (mid-latitudes) and the HALO-(AC)³ (high-latitudes) aircraft campaigns, where closely spatially and temporally collocated cloud radar and in situ measurements were collected. Ice cluster scales and IWC variability are quantified using pair correlation functions. By comparing IWC calculations accounting for riming to IWC calculations neglecting riming, we single out the influence of riming. During all analyzed flight segments, riming is responsible for 66 % and 63 % of total IWC during IMPACTS and HALO-(AC)³, respectively. In mid-latitude MPC, riming does not significantly change IWC cluster scales, but increases the probability of clusters occurrence. This enhancement occurs at similar scales as liquid water content variability. In cold air outbreak MPC observed during HALO-(AC)³, riming impacts IWC clustering at two distinctive scales. First, riming enhances the probability of IWC clusters at spatial scales below 2 km, which corresponds to the wavelength of the roll cloud updraft and circulation features. Second, riming leads to additional IWC clustering at spatial scales of 3–5 km. We find that the presence of mesoscale updraft features leads to enhanced occurrences of riming and therefore additional IWC clustering. An increased liquid water path might increase the effect, but is not a necessary criterion. These results help to improve our understanding of how riming is linked to IWC variability and can be used to evaluate and constrain models of MPC.
... The passive channel observes brightness temperatures (T B) primarily influenced by emission of liquid clouds and the surface. Differences in T B for clear-sky and cloudy situations are used to retrieve LW P over ocean via a regression approach (Ruiz-Donoso et al., 2020). Its sensitivity is below 5 g m −2 and the absolute accuracy below 30 g m −2 ...
Marine cold air outbreaks (MCAOs) strongly affect the Arctic water cycle and, thus, climate through large-scale air mass transformations. The description of air mass transformations is still challenging partly because previous observations do not resolve fine scales, particularly for the initial development of a MCAO, and lack information about cloud microphysical properties. Therefore, we focus on the crucial initial development within the first 200 km over open water of two MCAO events with different strengths observed during the HALO-(AC)3 campaign. Based on unique sampling of high-resolution airborne remote sensing and in-situ measurements, the development of the boundary layer, formation of clouds, onset of precipitation, and riming are studied. For this purpose, we establish a novel approach, solely based on radar reflectivity measurements, to detect roll circulation that forms cloud streets. The two MCAO events observed in April 2022 just three days apart occurred under relatively similar thermodynamic conditions. However, for the first event, colder airmasses from the central Arctic led to a marine cold air outbreak index twice that high as for the second event. Thus, the two cases exhibit different properties of clouds, riming, and roll circulations though the width of the roll circulation is similar. For the stronger MCAO, cloud tops are higher, more liquid-topped clouds exist, the liquid layer at cloud top is wider, and the liquid water path, mean radar reflectivity, precipitation rate, and occurrence are increased. These parameters evolve with distance over open water, as seen by, e.g., boundary layer deepening and cloud top height rising. Generally, cloud streets form after traveling 15 km over open water. After 20 km, this formation enhances cloud cover to just below 100 %. After around 30 km, precipitation forms, though for the weaker event, the development of precipitation is shifted to larger distances. For the stronger event, we detect riming for cloud temperatures below -20 °C. The variability of rime mass has the same horizontal scales as the roll circulation implying the importance of roll circulation on precipitation. The detailed observations of the two MCAO events could serve as a valuable reference for future model intercomparison studies.
... The brightness temperature (T B ) is also measured under a tilted angle of 25°backward to nadir. From this observation, the liquid water path (LWP) is estimated over open ocean only with a temporal resolution of 1 s, as described in Ruiz-Donoso et al. (2020). The retrieval takes profiles of nearby dropsondes to calculate T B as a function of LWP measurements from simulations with the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA; . ...
Riming is a key precipitation formation process in mixed-phase clouds which efficiently converts cloud liquid to ice water. Here, we present two methods to quantify riming of ice particles from airborne observations with the normalized rime mass, which is the ratio of rime mass to the mass of a size-equivalent spherical graupel particle. We use data obtained during the HALO-(AC)3 aircraft campaign, where two aircraft collected radar and in situ measurements that were closely spatially and temporally collocated over the Fram Strait west of Svalbard in spring 2022. The first method is based on an inverse optimal estimation algorithm for the retrieval of the normalized rime mass from a closure between cloud radar and in situ measurements during these collocated flight segments (combined method). The second method relies on in situ observations only, relating the normalized rime mass to optical particle shape measurements (in situ method). We find good agreement between both methods during collocated flight segments with median normalized rime masses of 0.024 and 0.021 (mean values of 0.035 and 0.033) for the combined and in situ method, respectively. Assuming that particles with a normalized rime mass smaller than 0.01 are unrimed, we obtain average rimed fractions of 88 % and 87 % over all collocated flight segments. Although in situ measurement volumes are in the range of a few cubic centimeters and are therefore much smaller than the radar volume (about 45 m footprint diameter at an altitude of 500 m above ground, with a vertical resolution of 5 m), we assume they are representative of the radar volume. When this assumption is not met due to less homogeneous conditions, discrepancies between the two methods result. We show the performance of the methods in a case study of a collocated segment of cold-air outbreak conditions and compare normalized rime mass results with meteorological and cloud parameters. We find that higher normalized rime masses correlate with streaks of higher radar reflectivity. The methods presented improve our ability to quantify riming from aircraft observations.
... A warmer surface and a higher moisture content in the air correspond to higher and more developed low-level clouds, but to a reduction in high and mid-level clouds and, consequently, to a decrease in the total cloud top height. This relationship is partially confirmed in the study by Ref. [64]. Clouds under such conditions are predominantly composed of liquid droplet particles, which explains the negative correlation coefficients with the cloud phase parameter (Table 3). ...
The Arctic experiences remarkable changes in environmental parameters that affect fluctuations in the surface energy budget, including radiation and sensible and latent heat fluxes. Cold air masses and cloud transformations during marine cold air outbreaks (MCAOs) substantially influence the radiative fluxes, thereby shaping the link between large-scale dynamics, sea ice conditions, and the surface energy budget. In this study, we investigate various cloud characteristics during intense MCAOs over the Barents Sea from 2000 to 2018 using satellite data. We identify 72 intense MCAO events that propagated southward using reanalysis data of the surface temperature and potential temperature at the 800 hPa level. We investigate the macro- and microphysical parameters and radiative properties of clouds within selected MCAOs, their dependence on sea ice concentration, and their initial air mass properties using satellite data. A significant increase in low-level clouds near the ice edge (up to +25% anomalies) and a smooth transition to upper-level clouds is revealed. The total cloud top height during intense MCAOs is generally 500–700 m lower than under neutral conditions. MCAOs induce a positive net cloud radiative effect, which peaks at +20 W m−2 (100 km from the ice edge) and gradually decreases towards the continent (−2.3 W m−2 per 100 km). Our study provides evidence for the importance of changes in the cloud radiative effect within MCAOs, which should be accurately simulated in regional and global climate models.
... This bias was traced back to the cloud condensation nuclei activation in the microphysical scheme. For a specific cloud case observed during ACLOUD, Ruiz-Donoso et al. (2020) investigated the thermodynamic phase of mixed-phase clouds as modeled by ICON large-eddy simulations and found that measured spectral radiances reveal an underestimation of the modeled ice crystal number concentration. Jäkel et al. (2019b) used ACLOUD observations to analyze the performance of the sea ice 100 albedo scheme used in a regional coupled climate model and found an underestimation of the variability of the sea ice albedo caused by a biased surface albedo parameterization dependence on surface temperature. ...
... The low-level clouds observed during ACLOUD are mostly of mixed-phase character although dominated by liquid droplets 320 (Ruiz-Donoso et al., 2020;Klingebiel et al., 2023). To test the relevance of the representation of ice crystals in the IFS to the cloud-reflected solar irradiance, no direct observations are available from ACLOUD. ...
The simulations of upward and downward irradiances by the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts are compared to broadband solar irradiance measurements from the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign. For this purpose, offline radiative transfer simulations with the ecRad radiation scheme using the operational IFS output were performed. The simulations of the downward solar irradiance agree within the measurement uncertainty. However, the IFS underestimates the reflected solar irradiances above sea ice significantly by −35 Wm−2. Above open ocean, the agreement is closer with an overestimation of 29 Wm−2. A sensitivity study using measured surface and cloud properties is performed with ecRad to quantify the contributions of the surface albedo, cloud fraction, ice and liquid water path and cloud droplet number concentration to the observed bias. It shows that the IFS sea ice albedo climatology underestimates the observed sea ice albedo, causing more than 50 % of the bias. Considering the higher variability of in situ observations in the parameterization of the cloud droplet number concentration leads to a smaller bias of −27 Wm−2 above sea ice and a larger bias of 48 Wm−2 above open ocean by increasing the range from 36–69 cm−3 to 36–200 cm−3. Above sea ice, realistic surface albedos, cloud droplet number concentrations and liquid water paths contribute most to a bias improvement. Above open ocean, realistic cloud fractions and liquid water paths are most important to reduce the model-observation differences.
... AisaEagle and AisaHawk are across-track push-broom imaging spectrometers with a field of view (FOV) of 36 • , which is spatially divided into 1024 (AisaEagle) and 384 (AisaHawk) pixels. These instruments measure the upward radiance with 20 Hz temporal resolution, covering collectively a wavelength range from 400 to 2500 nm (Schäfer et al., 2013;Ruiz-Donoso et al., 2020). The radiance measurements have a spectral, temporal, and spatial dimension. ...
A melting snow layer on Arctic sea ice, as a composition of ice, liquid water, and air, supplies meltwater that may trigger the formation of melt ponds. As a result, surface reflection properties are altered during the melting season and thereby may change the surface energy budget. To study these processes, sea ice surface reflection properties were derived from airborne measurements using imaging spectrometers. The data were collected over the closed and marginal Arctic sea ice zone north of Svalbard in May–June 2017. A retrieval approach based on different absorption indices of pure ice and liquid water in the near-infrared spectral range was applied to the campaign data. The technique enabled us to retrieve the spatial distribution of the liquid water fraction of a snow layer and the effective radius of snow grains. For observations from three research flights, liquid water fractions between 6.5 % and 17.3 % and snow grain sizes between 129 and 414 µm were derived. In addition, the melt pond depth was retrieved based on an existing approach that isolates the dependence of a melt pond reflection spectrum on the pond depth by eliminating the reflection contribution of the pond ice bottom. The application of the approach to several case studies revealed a high variability of melt pond depth, with maximum depths of 0.33 m. The results were discussed considering uncertainties arising from the airborne reflection measurements, the setup of radiative transfer simulations, and the retrieval method itself. Overall, the presented retrieval methods show the potential and the limitations of airborne measurements with imaging spectrometers to map the transition phase of the Arctic sea ice surface, examining the snow layer composition and melt pond depth.
... The processes which affect the MCAO cloud evolution are fundamentally time-dependent, and knowledge of these process rates is essential for improving their representation in climate models (Pithan et al., 2018). Previous studies have used models (e.g Tornow et al., 2021), in situ or airborne measurements (Hartmann et al., 1997;Young et al., 2016;Abel et al., 2017;Lloyd et al., 2018;Ruiz-Donoso et al., 2020;Geerts et al., 2022), and satellites (Wu and Ovchinnikov, 2022) to investigate the factors influencing cloud property development during the course of an outbreak. However, these were typically based on a relatively small set of examples of MCAO events. ...
Marine cold-air outbreaks are important parts of the high-latitude climate system and are characterised by strong surface fluxes generated by the air–sea temperature gradient. These fluxes promote cloud formation, which can be identified in satellite imagery by the distinct transformation of stratiform cloud “streets” into a broken field of cumuliform clouds downwind of the outbreak. This evolution in cloud morphology changes the radiative properties of the cloud and therefore is of importance to the surface energy budget. While the drivers of stratocumulus-to-cumulus transitions, such as aerosols or the sea surface temperature gradient, have been extensively studied for subtropical clouds, the factors influencing transitions at higher latitudes are relatively poorly understood. This work uses reanalysis data to create a set of composite trajectories of cold-air outbreaks moving off the Arctic ice edge and co-locates these trajectories with satellite data to generate a unique view of liquid-dominated cloud development within cold-air outbreaks. The results of this analysis show that clouds embedded in cold-air outbreaks have distinctive properties relative to clouds following other trajectories in the region. The initial strength of the outbreak shows a lasting effect on cloud properties, with differences between clouds in strong and weak events visible over 30 h after the air has left the ice edge. However, while the strength (measured by the magnitude of the marine cold-air outbreak index) of the outbreak affects the magnitude of cloud properties, it does not affect the timing of the transition to cumuliform clouds or the top-of-atmosphere albedo. In contrast, the initial aerosol conditions do not strongly affect the magnitude of the cloud properties but are correlated to cloud break-up, leading to an enhanced cooling effect in clouds moving through high-aerosol conditions due to delayed break-up. Both the aerosol environment and the strength and frequency of marine cold-air outbreaks are expected to change in the future Arctic, and these results provide insight into how these changes will affect the radiative properties of the clouds. These results also highlight the need for information about present-day aerosol sources at the ice edge to correctly model cloud development.
... Hence, their representation in numerical weather prediction and climate models remains challenging (Morrison et al., 2011;Bock et al., 2020). In the Arctic the micro-and macrophysical properties of clouds are strongly affected by seasonal changes in meteorological weather situations such as atmospheric rivers, warm air intrusions, cold air outbreaks, or Arctic cyclones, as well as small-scale temperature and humidity fluctuations (McFarquhar et al., 2007;Mioche et al., 2017;Ruiz-Donoso et al., 2020;. Turbulent fluxes and moisture transport are affected by the presence of sea ice (Lüpkes et al., 2011;Vihma et al., 2014;Wendisch et al., 2019;Elvidge et al., 2021;Schmale et al., 2021;Michaelis and Lüpkes, 2022). ...
Airborne in situ cloud measurements were carried out over the northern Fram Strait between Green-land and Svalbard in spring 2019 and summer 2020. In total, 811 min of low-level cloud observations were performed during 20 research flights above the sea ice and the open Arctic ocean with the Polar 5 research aircraft of the Alfred Wegener Institute. Here, we combine the comprehensive in situ cloud data to investigate the distributions of particle number concentration N, effective diameter D eff , and cloud water content CWC (liquid and ice) of Arctic clouds below 500 m altitude, measured at latitudes between 76 and 83 • N. We developed a method to quantitatively derive the occurrence probability of their thermodynamic phase from the combination of microphysical cloud probe and Polar Nephelometer data. Finally, we assess changes in cloud microphysics and cloud phase related to ambient meteorological conditions in spring and summer and address effects of the sea ice and open-ocean surface conditions. We find median N from 0.2 to 51.7 cm −3 and about 2 orders of magnitude higher N for mainly liquid clouds in summer compared to ice and mixed-phase clouds measured in spring. A southerly flow from the sea ice in cold air outbreaks dominates cloud formation processes at temperatures mostly below −10 • C in spring, while northerly warm air intrusions favor the formation of liquid clouds at warmer temperatures in summer. Our results show slightly higher N in clouds over the sea ice compared to the open ocean, indicating enhanced cloud formation processes over the sea ice. The median CWC is higher in summer (0.16 g m −3) than in spring (0.06 g m −3), as this is dominated by the available atmospheric water content and the temperatures at cloud formation level. We find large differences in the particle sizes in spring and summer and an impact of the surface conditions, which modifies the heat and moisture fluxes in the boundary layer. By combining microphysical cloud data with thermodynamic phase information from the Polar Nephelometer, we find mixed-phase clouds to be the dominant thermodynamic cloud phase in spring, with a frequency of occurrence of 61 % over the sea ice and 66 % over the ocean. Pure ice clouds exist almost exclusively over the open ocean in spring, and in summer the cloud particles are most likely in the liquid water state. Published by Copernicus Publications on behalf of the European Geosciences Union. 7258 M. Moser et al.: Microphysical and thermodynamic phase analyses of Arctic low-level clouds The comprehensive low-level cloud data set will help us to better understand the role of clouds and their thermodynamic phase in the Arctic radiation budget and to assess the performance of global climate models in a region of the world with the strongest anthropogenic climate change.
... AisaEagle AisaHawk AisaEagle and AisaHawk are across track pushbroom imaging spectrometers with a field of view (FOV) of 36 • , which is spatially divided into 1024 (AisaEagle) and 384 (AisaHawk) pixels. These instruments measure the upward radiance with 20 Hz temporal resolution, covering collectively a wavelength range from 400 nm to 2500 nm (Schäfer et al., 2013;Ehrlich et al., 2019;Ruiz-Donoso et al., 2020). The radiance measurements have a spectral, temporal, and spatial dimension. ...
A melting snow layer on Arctic sea ice, as a composition of ice, liquid water, and air, supplies meltwater that may trigger the formation of melt ponds. As a result, surface reflection properties are altered during the melting season and thereby may change the surface energy budget. To study these processes, sea ice surface reflection properties were derived from airborne measurements using imaging spectrometers. The data were collected over the closed and marginal Arctic sea ice zone north of Svalbard in May/June 2017. A retrieval approach based on different absorption indices of pure ice and liquid water in the near-infrared spectral range was applied to the campaign data. The technique enables to retrieve the spatial distribution of the liquid water fraction of a snow layer and the effective radius of snow grains. For observations from three research flights liquid water fractions between 8.7 % and 15.6 % and snow grain sizes between 115 μm and 378 μm were derived. In addition, the melt pond depth was retrieved based on an existing approach that isolates the dependence of a melt pond reflectance spectrum on the pond depth by eliminating the reflection contribution of the pond ice bottom. The application of the approach to several case studies revealed a high variability of melt pond depth with maximum depths of 0.33 m. The results were discussed considering uncertainties arising from the reflectance measurements, the setup of radiative transfer simulations, and the retrieval method itself. Overall, the presented retrieval methods show the potential and the limitations of airborne measurements with imaging spectrometers to map the transition phase of the Arctic sea ice surface, examining the snow layer composition and melt pond depth.
... LES can supplement the observational data record and act as a virtual research laboratory (e.g., Ovchinnikov et al., 2014). At the same time, independent measurements of mixed-phase cloud properties can be used to evaluate the simulations Kretzschmar et al., 2020;Ruiz-Donoso et al., 2020). This approach has led to demonstrable progress in understanding Arctic clouds. ...
Springtime Arctic mixed-phase convection over open water in the Fram Strait as observed during the recent ACLOUD (Arctic CLoud Observations Using airborne measurements during polar Day) field campaign is simulated at turbulence-resolving resolutions. The first objective is to assess the skill of large-eddy simulation (LES) in reproducing the observed mixed-phase convection. The second goal is to then use the model to investigate how aerosol modulates the way in which turbulent mixing and clouds transform the low-level air mass. The focus lies on the low-level thermal structure and lapse rate, the heating efficiency of turbulent entrainment, and the low-level energy budget. A composite case is constructed based on data collected by two research aircraft on 18 June 2017. Simulations are evaluated against independent datasets, showing that the observed thermodynamic, cloudy, and turbulent states are well reproduced. Sensitivity tests on cloud condensation nuclei (CCN) concentration are then performed, covering a broad range between pristine polar and polluted continental values. We find a significant response in the resolved mixed-phase convection, which is in line with previous LES studies. An increased CCN substantially enhances the depth of convection and liquid cloud amount, accompanied by reduced surface precipitation. Initializing with the in situ CCN data yields the best agreement with the cloud and turbulence observations, a result that prioritizes its measurement during field campaigns for supporting high-resolution modeling efforts. A deeper analysis reveals that CCN significantly increases the efficiency of radiatively driven entrainment in warming the boundary layer. The marked strengthening of the thermal inversion plays a key role in this effect. The low-level heat budget shifts from surface driven to radiatively driven. This response is accompanied by a substantial reduction in the surface energy budget, featuring a weakened flow of solar radiation into the ocean. Results are interpreted in the context of air–sea interactions, air mass transformations, and climate feedbacks at high latitudes.
