During the HALO–(AC)3 campaign, remote sensing and in situ measurements were obtained using instrumentation mounted on three research aircraft: High Altitude and LOng Range Research Aircraft HALO;, Polar 5, and Polar 6 . Here, we briefly describe those instruments that measure the parameters crucial for this study.

All applied instruments were introduced by . HALO and Polar 5 were equipped with remote sensing instruments and dropsonde launch facilities, while Polar 6 performed in situ measurements . We focus on observations from the Polar 5 aircraft, which mainly operated remote sensing instruments such as a radar, lidar, microwave radiometer, and imaging spectrometer.

Polar 5 was equipped with a digital RGB camera (Nikon D5) with a 180° fish-eye lens for measuring the directional distribution of upward radiance over the lower hemisphere. The images are used for characterizing the horizontal structure of cloud tops and surface conditions every 10 s . For flight altitudes at 1000 m above cloud top, the pixel size in cloud altitude is about 3.30 m in the image center. The swath, covering 80° of the field of view (FOV), amounts to 1680 m.

Polar 5 and HALO launched dropsondes during flights to measure vertical profiles of air temperature, humidity, pressure, and the horizontal wind vector . HALO released the dropsondes from an altitude of approximately 10 km, while Polar 5 typically deployed them from around 3 km, effectively covering the ABL from both altitudes. The dropsonde measurements have a vertical resolution of 5 m within the altitude range below 1000 m and are of the Vaisala RD41 type, which have an uncertainty of 0.2 K for temperature measurements and 3 % for relative air humidity. The horizontal wind speed accuracy is approximately 0.5 ms-1, with a resolution of 0.01 ms-1. This accuracy is consistent across wind speeds ranging from 0 to 200 ms-1, leveraging Global Positioning System (GPS) technology for wind component measurements . It is shown by that the dropsondes released from HALO and Polar 5 complement each other. While dropsondes launched from HALO provide a broader coverage, the Polar 5 dropsondes improve the spatial resolution, particularly in the Marginal sea Ice Zone (MIZ).

Broadband irradiances, both upward and downward, are measured on Polar 5 using pairs of CMP22 pyranometers and CGR4 pyrgeometers . The pyranometers are sensitive for wavelengths ranging from 0.2 to 3.6 µm, while the pyrgeometers cover the thermal-infrared wavelengths between 4.5–42 µm. These measurements are taken at a frequency of 20 Hz, with a sensor uncertainty below 3 % . To correct for the misalignment of the irradiance sensor with respect to a horizontal reference plane during flight concerning downward direct solar irradiance, correction methods from and are utilized.

Broadband solar (Fnet,sol) and thermal-infrared (Fnet,TIR) net irradiances are calculated from the upward and downward irradiances: 1Fnet,sol=Fsol↓-Fsol↑,2Fnet,TIR=FTIR↓-FTIR↑.

To mitigate the dependence of Fnet,sol on solar zenith angle, which dominates the variation of Fnet,sol, a normalization is applied by calculating the cloud albedo: 3α=1-Fnet,solFsol↓, where α denotes the albedo, representing the fraction of incident solar radiation that is reflected.

On Polar 5, thermal-infrared radiance in the nadir direction (field of view 2.3°) is measured using a KT19 infrared pyrometer (model KT19.85 II). The KT19 measurements, with a sampling frequency of 20 Hz, are part of the broadband radiometer dataset . Data are provided as brightness temperatures corresponding to the spectral range of the radiometer, covering a narrow wavelength band between 9.6–11.5 µm. These measurements are also used to estimate the sea surface temperature, leveraging the high temporal resolution and spectral sensitivity of the KT19.

The Airborne Mobile Aerosol Lidar (AMALi) on Polar 5 provides profiles of backscatter ratio at two wavelengths (532 and 1064 nm). The data are used to determine the vertical structure of cloud layers, with cloud top altitude as a key measured parameter, having an accuracy of roughly 7 m .

The Humidity And Temperature PROfiler HATPRO; is part of the instrumentation aboard Polar 5. HATPRO provides brightness temperatures across 14 channels, with half of these channels vertically polarized at the water vapor absorption line at 22.24 GHz (K-band), and the other half are horizontally polarized around the oxygen absorption complex at 60 GHz V-band;. The processing method for the data obtained from HATPRO is described in detail by . To retrieve the liquid water path (LWP) over the ocean, differences in brightness temperatures between cloud-free and cloudy conditions are utilized following a regression method outlined by . This method ensures an absolute accuracy of below 30 gm-2 with a sensitivity below 5 gm-2 .

The Polar 6 aircraft was equipped with a range of in situ instruments measuring aerosol and cloud particle properties, as well as trace gas and aerosol particle chemical composition. For this study, we focus exclusively on measurements from the Cloud Imaging Probe (CIP) and the Precipitation Imaging Probe PIP;. These instruments, when used in combination, provide cloud particle size distributions for diameters ranging from 15 µm to 6.4 mm, with resolutions of 15 and 100 µm, respectively. Additionally, both the CIP and PIP capture two-dimensional shadow images of the sampled particles to identify particle phase and shape .

During the HALO–(AC)3 campaign, on 4 April 2022, a distinct CAO was sampled with Polar 5 by a series of overpasses, which were aligned perpendicular to the main wind direction. During this research flight, the dropsonde (DS) location (79.1218° N, 3.0574° E) in the center of the CAO (marked with DS in Fig. ) was passed four times at approximately 40 min intervals. Each time Polar 5 passed this DS location, a dropsonde was launched to capture vertical profiles of air temperature, humidity, and wind. In addition, HALO launched a dropsonde at the DS location 2 min after the final dropsonde from Polar 5. This repeated observation allows for a detailed analysis of the temporal evolution of the cloud streets into isotropic cloud structures over an extended period. Waypoint 1 (WP1) and Waypoint 2 (WP2) in Fig.  were selected based on the flight track to define the section where Polar 5 and Polar 6 repeatedly flew back and forth to observe the temporal evolution of cloud properties. While some dropsondes were deployed near WP1 and WP2 (as marked in Fig. ), these were not included in our analysis. Instead, we focus on the dropsondes released at the DS location, where the cloud transitions were most clearly observed.

On 4 April 2022, the weather conditions in the Fram Strait were part of a broader cold period following a significant shift from warm conditions in late March. This period, characterized by CAOs, began on 21 March 2022 and lasted until 12 April 2022 . To illustrate the flight operations on that day, Fig.  shows the flight track of Polar 5 along with the locations where dropsondes were deployed.

The cloud fraction decreases from 0.84 to 0.73 with an increasing ICS (Fig. a), which is expected since a high ICS indicates more-intense roll clouds with more-pronounced cloud-free areas. This trend is likely due to the cloud organization in linear structures, which creates cloud-free zones between the cloud streets. This reduces the overall cloud fraction, which is particular important because cloud fraction plays an important role in the radiation budget . Larger cloud-free regions allow for more solar radiation to reach the surface, while a higher cloud fraction reflects more solar radiation and therefore leads to a cooling of the surface during polar day.

Cloud top height slightly increases from 330 to 390 m with a decreasing ICS (Fig. b). This trend is likely due to less shear and stronger buoyancy associated with a lower ICS, which potentially allows clouds to develop vertically and reach higher altitudes. Wind shear in organized cloud street structures inhibits vertical motion and tends to CAO cloud growth, while more isotropic clouds can experience greater convective activity, which results in higher cloud tops.

ICS decreases with increasing distance to the sea ice edge (Fig. c), because the air masses sampled further from the sea ice had more time to evolve over the ocean . This means that isotropic cloud patterns occur in our study more frequently with increasing distance to the sea ice edge. The spatial distribution of these cloud patterns affects the Arctic radiative energy budget. This spatial variation shows that their radiative impacts are not uniform across different regions, which highlights the importance of accurately modeling cloud structures in CAOs.

The LWP does not show a clear trend with ICS across all passes, although passes 1 and 4 show higher LWP with lower ICS (Fig. d). However, focusing on pass 1 and 4, it shows that the highest LWP values are reached for small ICS. This is plausible, because the lower ICS indicates thicker clouds, which contain more liquid water. These observations suggest that the changes in cloud structure not only affect the cloud macrophysical properties, like cloud top height and cloud fraction, but also influence the microphysical characteristics of the clouds, such as the droplet size distribution (see also Sect. ) and LWP.

All in all, the changes in cloud street structure and the associated consequences on cloud fraction and cloud top height reveal the influence of cloud organization on the Arctic radiation budget close to the surface. As cloud street structures transition to more isotropic cloud patterns, changing cloud macrophysical and microphysical properties affect the energy exchange between the atmosphere and the surface.

In situ cloud measurements of microphysical properties from Polar 6 are available for passes 2 and 4 of the case study, providing detailed observations of cloud particle distributions at low altitudes. Polar 5 and Polar 6 were well colocated during the flight. Polar 5 flew slightly behind Polar 6 to enable dropsonde deployments without affecting the in situ measurements from Polar 6. As a result, the measurements from both aircraft are directly comparable. The in situ data were sampled at an altitude of approximately 200 m (see Fig. a), well below cloud top, which was about 400 m.

Figure b presents the number size distributions of cloud particles in the range from 100 to 6000 µm averaged for passes 2 and 4. It is evident that during pass 4 the particle concentration was higher across almost the entire size range. The higher particle concentration of pass 4 along the flight leg is the result of the higher ABL and higher cloud top heights, which supports the growth of cloud particles. These characteristics suggest deeper clouds, where larger ice particles can form and precipitate. In addition, the presence of more large particles during pass 4 might also indicate aggregation processes, where smaller ice crystals collide and stick together, forming larger ice particles, which might precipitate. This is a typical process for the development of Arctic precipitation , especially in cold-air outbreak conditions where ice-phase processes dominate .

In addition, we investigated the occurrence of riming for pass 2 and pass 4, which describes the accretion of supercooled liquid water onto ice particles. We calculated the normalized rime mass M of ice particles, which is defined as the rime mass divided by the mass of a spherical graupel particle with equal particle size . To derive M, we used the in situ method from , which is based on in situ observations of particle shape. Only a subset of particles can be used to derive M (particle diameters must be larger than 210 µm for CIP and larger than 1400 µm for PIP), because small particles have round shapes due to imager resolution. For a detailed description of the method and its limitations, we refer the reader to . We consider particles with M<0.01 to be unrimed due to their nearly identical scattering properties to particles with M=0 .

We identified a distinction in the riming characteristics between both passes along the flight leg (see Fig. c). During pass 2, where the cloud streets are visible, we see that riming is present with a higher median normalized rime mass value of M=0.017. Compared with that, we see during pass 4 (isotropic cloud pattern) a negligible M (0.008), which confirms the absence of riming. Regarding , the lack of riming in pass 4 indicates conditions with less favorable dynamics for riming to occur. We assume that the differences in the riming characteristics correlate with the appearance of cloud streets and isotropic cloud patterns. Cloud streets are typically associated with stronger vertical shear at cloud top, while the transition to isotropic cloud patterns coincides with a reduction in wind shear, suggesting a shift toward buoyancy-driven convection. These isotropic cloud patterns contain lower turbulence (lower TKE), which likely reduces riming processes and therefore leads to a lower M.

Figure a reveals a strong correlation between ICS and cloud fraction. It is known that α increases monotonically with cloud fraction . Therefore, a relationship between ICS and α is expected. This correlation is evident in Fig. a, where α increases linearly with cloud fraction. Consistent with this, Fnet,TIR and cloud fraction show a similar trend, although with higher uncertainty (Fig. b).

Interestingly, a linear trend between α and ICS is not visible in Fig. c. Here, an increase in α with increasing ICS is noticeable between α=0.35 and α=0.45. For higher α values, ICS tends to lower values. The colors in Fig. c indicate the distance to sea ice, which suggests the presence of two different isotropic cloud regimes: one with a higher α (over the open water) and one with a lower α (near the ice). This shows that the albedo of the isotropic clouds does not depend directly on ICS. It seems that, depending on the development state, the isotropic cloud patterns (with a low ICS) can have either low or high α. This theory is also applicable to Fnet,TIR in Fig. d. The absence of a clear linear trend between α and ICS in Fig. c highlights the complexity of the radiative energy budget during the cloud transition process. The observed nonlinear relationship suggests that other factors might play a role in shaping the cloud patterns and their corresponding radiative properties. It shows that despite having similar ICS values, isotropic clouds can exhibit a wide range of albedo values, driven by other factors like their varying microphysical properties and developmental states .

To estimate whether the reduction in ABL wind speed is the primary driver of changes in the cloud street structure, we analyzed the stability parameter and roll predictor -H/L mentioned in the Introduction (Sect. ). Herein, L is the Obukhov length describing the effect of buoyancy and vertical wind shear on turbulence in the surface layer of the ABL: 5L=-θ0κg(w′u′‾)03/2(w′θ′‾)0, with θ0 a reference potential temperature in the surface layer, g the gravitational acceleration, κ=0.4 the von Kármán constant, and (w′u′‾)0 and (w′θ′‾)0 the mean near surface turbulent fluxes of momentum and temperature, respectively. To estimate this length scale with the dropsonde measurements, we followed the approach in and calculated the fluxes with the bulk aerodynamic formulas: 6(w′u′‾)0=CDU902,7(w′θ′‾)0=CHΔθasU90. Herein, U90 is the 90 m wind speed, Δθas the potential temperature difference between the air at 90 m height and the water surface, and CD and CH the dimensionless drag and heat transfer coefficients. Dropsonde data for U90 and air temperature were taken from the 90 m level to ensure reliability, given the 5 m vertical resolution of the measurements (as detailed in Sect. ) and to minimize uncertainties associated with the lowest few meters above the surface. Thus, the stability parameter -H/L can be written in the form 8-H/L=Hκgθ0CHΔθasU90CDU9023/2. Similarly to , we assumed the drag and transfer coefficients to be equal as CD=CH=1,3×10-3.

Table  compares the cloud street index Ics and -H/L during the Polar 5 flight on 4 April 2022 at different times at the DS location marked in Fig. . -H/L was calculated with Eq. () using Polar 5 dropsondes (see Fig. ), and the sea surface temperature was estimated with KT19 measurements. We used the values H=400m, θ0=260K (which also equal the temperature of the mixed layer as the 90 m temperature), and Δθas=12.5K for a sea surface temperature of 272.5 K. Although these values are almost constant during the period studied, the values for U90 vary in time and therefore are given in Table  for different times. Clear signals of cloud streets appeared in the cloud street index Ics from 11:07 UTC to 11:48 UTC with values for -H/L less than 15. From 12:35 UTC, a breakup in cloud streets can be identified by a significant decrease in Ics from values around 0.7 to values around 0.2, accompanied by an increasing stability parameter (-H/L≥15). The breakup can also be seen by eye in the camera images in Fig. a–d. This behavior fits to the discussion on the critical value of around 15 for -H/L in , mentioned in Sect. . According to and the discussion in Sect. , the cloud streets observed at position DS between 11:07–11:48 UTC can be attributed to free rolls, which appear due to a pure self organization of the flow. In other words, upstream of position DS surface heterogeneities in the sea ice distribution are not sufficient to force roll convection; otherwise, cloud streets should also be observed for larger values of -H/L as in , where cloud streets for values even larger than 200 were reported. We would like to mention that the critical value of 15 for -H/L should not be understood as a switch for free rolls or no free rolls. With increasing values from about 15, the pattern of free rolls can be expected to become more and more unclear.

Beside -H/L, the Richardson number Ri is often discussed in the context of boundary layer rolls in observations. It goes back to an early theoretical work of , where with a perturbation analysis of a stratified Ekman boundary layer it was found that boundary layer rolls are most likely when there exists in the vertical wind profile of the cross-roll component an inflection point in the upper part of the ABL, and the local Richardson number (Ri) near the inflection point is below a critical value of 0.25. This so-called inflection point instability could not be confirmed under moderate convective conditions by several numerical studies e.g.. Regardless, in several experimental studies Ri was linked with different bulk approaches to roll development. Some of these approaches are summarized in . In contrast to , all of these approaches calculate Ri with values of temperature and wind speed over the entire ABL rather than locally at the inflection point. Therefore, it cannot be expected that the theoretical critical value of 0.25 from can be used for the bulk approach in general. pointed out that critical values of Ri for roll appearance might need to be adapted to the bulk approach. We also tested some approaches on Ri mentioned in and found that the values of Ri were very sensitive to the approach. The parameter -H/L, originally suggested as roll predictor in one of the first LES studies by , seems to be more robust here. It should be borne in mind that all these parameters are defined for idealized profiles of wind and temperature.

We conclude that, in the present case, most likely the reduction in wind speed is responsible for breakup of cloud streets, because other parameters appear to vary less than wind speed. We are aware that the wind speed measured by the dropsondes has a large scattering. Nevertheless, the reduction in wind speed during the breakup can be seen in the entire ABL and not only at 90 m height. Although based on a small sample size, these airborne observations provide the first direct in situ confirmation, to our knowledge, of the theoretical link between decreasing wind speed and the transition from cloud streets to isotropic cloud patterns, as previously suggested by modeling studies (e.g. ).

Figure a–d show images captured by the fish-eye camera, which enables a comprehensive view of the temporal changes in the cloud streets. Alongside these images, Fig. e–h present the vertical profiles of potential temperature, θ; relative humidity, RH; wind direction; and wind speed from the dropsonde launches at the DS location.

The fish-eye camera images clearly show that the cloud structure at the DS location changes over time (Fig. a–d). Starting with a distinct cloud street formation (Fig. a), the structure gradually dissolves into a scattered, isotropic cloud pattern, visible in Fig. c and d. By applying the previously defined parameter, ICS, higher values of 0.67 and 0.69 are obtained for Fig. a and b, respectively, and lower values of 0.23 and 0.21 are obtained for Fig. c and d.

To investigate the atmospheric changes causing these structural alterations, the vertical profiles from the dropsondes launched at the DS location are shown in Fig. e–h. These profiles indicate that θ, RH, and the wind direction remain rather constant over the 2 h period between the first and fourth pass along the flight leg. The cloud top height of 390 m matches the beginning of the inversion layer height. In comparison to other CAO events, which was discussed by and , these cloud tops seem low, which is typical for a weaker CAO. However, in Fig. h a noticeable decrease in wind speed is seen, with a wind speed at cloud top approximately 4 ms-1 lower during the last overpass (13:14 UTC with Polar 5 and 13:16 UTC with HALO) compared to the first overpass (11:07 UTC).

This reduction in wind speed seems to be the reason for the transition of the cloud structure, changing from distinct cloud streets to an isotropic cloud pattern.

The spatially highly resolved Copernicus Arctic Regional Reanalysis (CARRA) data result from the HARMONIE-AROME non-hydrostatic regional numerical weather prediction model , which are available for two domains that overlap in the vicinity of Svalbard . CARRA-West covers Greenland, while CARRA-East covers Svalbard and northern Scandinavia. The temporal resolution of CARRA data is 3 h with a horizontal grid resolution of 2.5 km on 65 vertical model levels (from 15 m up to about 26 km) and 20 vertical model levels below 1000 m altitude.

In Sect. , we elaborated that a decrease in wind speed below 12 ms-1 leads to a breakup of the cloud street structures. To identify how the wind speed changed spatially, we present in Fig.  the wind speed field for four different times, at an altitude of 90 m, in the vicinity of the flight track. It is visible that at the DS location (marked by DS) the wind speed decreases between 09:00 UTC (Fig. a) and 18:00 UTC (Fig. d). In addition, it is noticeable that the Lee region, southwest of Svalbard with wind speeds below 8 ms-1, is shifting further west.

To identify how the vertical wind speed in CARRA data compares to the measured dropsonde profiles, we show in Fig. i the altitude profiles of CARRA at the closest grid point to the DS location (approximately 360 m distance). It is visible that between 12:00–15:00 UTC the wind speed at cloud top decreases from about 11.5 to 10 ms-1. Although this change is less compared to the dropsonde profiles (13–8 ms-1) in Fig. h, the reanalysis data also show a decrease through the whole ABL.