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Despite their essential role in the high‐latitude climate, the representation of mixed‐phase clouds is still a challenge for Global Climate Models (GCMs)'s cloud schemes. In this study we propose a methodology for robustly assessing Arctic mixed‐phase cloud properties in a climate model using airborne measurements. We leverage data collected during the RALI‐THINICE airborne campaign that took place near Svalbard in August 2022 to evaluate the simulation of mid‐level clouds associated with Arctic cyclones. Simulations are carried out with the new limited‐area configuration of the ICOLMDZ model which combines the recent icosahedral dynamical core DYNAMICO and the physics of LMDZ, the atmospheric component of the IPSL‐CM Earth System Model. Airborne radar and microphysical probes measurements are then used to evaluate the simulated clouds. A comparison method has been set‐up to guarantee as much as possible the spatiotemporal co‐location between observed and simulated cloud fields. We mostly focus on the representation of ice and liquid in‐cloud contents and on their vertical distribution. Results show that the model overestimates the amount of cloud condensates and exhibits a poor cloud phase spatial distribution, with too much liquid water far from cloud top and too much ice close to it. The downward gradual increase in snowfall flux is also not captured by the model. This in‐depth model evaluation thereby pinpoints priorities for further improvements in the ICOLMDZ cloud scheme. Plain Language Summary: Mixed‐phase clouds, where ice crystals and supercooled liquid droplets coexist at sub‐zero temperatures, are ubiquitous in the polar regions. Albeit essential for the high‐latitude climate, they are still poorly represented by climate models. In this study, original measurements from the RALI‐THINICE airborne campaign, which took place in the Svalbard region in August 2022, are used to assess how the ICOLMDZ atmospheric model represents Arctic mixed phase clouds. A methodology is developed to make robust and co‐located model‐aircraft observation comparisons. In situ cloud probes and onboard radars are then used to evaluate the simulated amounts of liquid water and ice in clouds as well as the snowfall flux in deep clouds associated with the transit of Arctic cyclones. The results of the model evaluation show an overall overestimation of cloud condensates with too much liquid water far from cloud top and too much ice close to it. Key Points: Airborne RALI‐THINICE observations are used to evaluate Arctic clouds in the new limited area version of the ICOLMDZ modelA methodology has been developed to make robust and co‐located model‐observation comparisonsICOLMDZ simulates overly high amounts of liquid and ice in clouds particularly near cloud top [ABSTRACT FROM AUTHOR]

Global warming is amplified in the Arctic. However, numerical models struggle to represent key processes that determine Arctic weather and climate. To collect data that help to constrain the models, the HALO–(AC) 3 aircraft campaign was conducted over the Norwegian and Greenland seas, the Fram Strait, and the central Arctic Ocean in March and April 2022. The campaign focused on one specific challenge posed by the models, namely the reasonable representation of transformations of air masses during their meridional transport into and out of the Arctic via northward moist- and warm-air intrusions (WAIs) and southward marine cold-air outbreaks (CAOs). Observations were made over areas of open ocean, the marginal sea ice zone, and the central Arctic sea ice. Two low-flying and one long-range, high-altitude research aircraft were flown in colocated formation whenever possible. To follow the air mass transformations, a quasi-Lagrangian flight strategy using trajectory calculations was realized, enabling us to sample the same moving-air parcels twice along their trajectories. Seven distinct WAI and 12 CAO cases were probed. From the quasi-Lagrangian measurements, we have quantified the diabatic heating/cooling and moistening/drying of the transported air masses. During CAOs, maximum values of 3 K h -1 warming and 0.3 g kg -1 h -1 moistening were obtained below 1 km altitude. From the observations of WAIs, diabatic cooling rates of up to 0.4 K h -1 and a moisture loss of up to 0.1 g kg -1 h -1 from the ground to about 5.5 km altitude were derived. Furthermore, the development of cloud macrophysical (cloud-top height and horizontal cloud cover) and microphysical (liquid water path, precipitation, and ice index) properties along the southward pathways of the air masses were documented during CAOs, and the moisture budget during a specific WAI event was estimated. In addition, we discuss the statistical frequency of occurrence of the different thermodynamic phases of Arctic low-level clouds, the interaction of Arctic cirrus clouds with sea ice and water vapor, and the characteristics of microphysical and chemical properties of Arctic aerosol particles. Finally, we provide a proof of concept to measure mesoscale divergence and subsidence in the Arctic using data from dropsondes released during the flights. [ABSTRACT FROM AUTHOR]

Mixed-phase clouds are not well represented in climate and weather forecasting models, due to a lack of the key processes controlling their life cycle. Developing methods to study these clouds is therefore essential, despite the complexity of mixed-phase cloud processes and the difficulty of observing two cloud phases simultaneously. We propose in this paper a new method to retrieve the microphysical properties of mixed-phase clouds, ice clouds and supercooled water clouds using airborne or satellite radar and lidar measurements, called VarPy-mix. This new approach extends an existing variational method developed for ice cloud retrieval using lidar, radar and passive radiometers. We assume that the lidar attenuated backscatter β at 532 nm is more sensitive to particle concentration and is consequently mainly sensitive to the presence of supercooled water. In addition, radar reflectivity Z at 95 GHz is sensitive to the size of hydrometeors and hence more sensitive to the presence of ice particles. Consequently, in the mixed phase the supercooled droplets are retrieved with the lidar signal and the ice particles with the radar signal, meaning that the retrievals rely strongly on a priori and error values. This method retrieves simultaneously the visible extinction for ice αice and liquid αliq particles, the ice and liquid water contents IWC and LWC, the effective radius of ice re,ice and liquid re,liq particles, and the ice and liquid number concentrations Nice and Nliq. Moreover, total extinction αtot , total water content (TWC) and total number concentration Ntot can also be estimated. As the retrieval of ice and liquid is different, it is necessary to correctly identify each phase of the cloud. To this end, a cloud-phase classification is used as input to the algorithm and has been adapted for mixed-phase retrieval. The data used in this study are from DARDAR-MASK v2.23 products, based on the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) observations from the CALIPSO and CloudSat satellites, respectively, belonging to the A-Train constellation launched in 2006. Airborne in situ measurements performed on 7 April 2007 during the Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) campaign and collected under the track of CloudSat–CALIPSO are compared with the retrievals of the new algorithm to validate its performance. Visible extinctions, water contents, effective radii and number concentrations derived from in situ measurements and the retrievals showed similar trends and are globally in good agreement. The mean percent error between the retrievals and in situ measurements is 39 % for αliq , 398 % for αice , 49 % for LWC and 75 % for IWC. It is also important to note that temporal and spatial collocations are not perfect, with a maximum spatial shift of 1.68 km and a maximum temporal shift of about 10 min between the two platforms. In addition, the sensitivity of remote sensing and that of in situ measurements is not the same, and in situ measurement uncertainties are between 25 % and 60 %. [ABSTRACT FROM AUTHOR]

The first closure study involving passive microwave and submillimetre measurements of ice clouds with the consideration of oriented particles is presented, using a unique combination of polarised observations from the ISMAR spectral-like radiometer, two radars with frequencies of 35 and 95 GHz , and a variety of in situ instruments. Of particular interest to this study are the large V–H polarised brightness temperature differences measured from ISMAR above a thick frontal ice cloud. Previous studies combining radar and passive submillimetre measurements have not considered polarisation differences. Moreover, they have assumed particle habits a priori. We aim to test whether the large V–H measurements can be simulated successfully by using an atmospheric model consistent with in situ microphysics. An atmospheric model is constructed using information from the in situ measurements, such as the ice water content, the particle size distribution, and the mass and shape of particles, as well as background information obtained from dropsonde profiles. Columnar and dendritic aggregate particle models are generated specifically for this case, and their scattering properties are calculated using the independent monomer approximation under the assumption of horizontal orientation. The scattering properties are used to perform polarised radiative transfer simulations using ARTS to test whether we can successfully simulate the measured large V–H differences. Radar measurements are used to extrapolate the 1-D microphysical profile to derive a time series of particle size distributions which are used to simulate ISMAR brightness temperatures. These simulations are compared to the observations. It is found that particle models that are consistent with in situ microphysics observations are capable of reproducing the brightness temperature depression and polarisation signature measured from ISMAR at the dual-polarised channel of 243 GHz. However, it was required that a proportion of the particles were changed in order to increase the V–H polarised brightness temperature differences. Thus, we incorporated millimetre-sized dendritic crystals, as these particles were observed in the probe imagery. At the second dual-polarised channel of 664 GHz , the brightness temperature depressions were generally simulated at the correct locations; however, the simulated V–H was too large. This work shows that multi-frequency polarisation information could be used to infer realistic particle shapes, orientations, and representations of the split between single crystals and aggregates within the cloud. [ABSTRACT FROM AUTHOR]

Observations collected during the SOuth west FOGs 3D experiment for processes study (SOFOG3D) field campaign are examined to document vertical profile of microphysical and thermodynamic properties of fog layers. In situ measurements collected under a tethered balloon provide 140 vertical profiles of liquid water content (LWC) from an adapted cloud droplet probe (CDP), which allow an exhaustive analysis of the life cycle of 8 thin fogs (thickness < 50 m) and 4 developed layers. We estimate thin-to-thick transition time from remote sensing instruments (microwave radiometer and Doppler cloud radar) and surface measurements, by using thresholds for longwave radiation flux, turbulent kinetic energy, vertical temperature gradient, fog top height and liquid water path (LWP) values. We found that a LWP threshold value of 15 g.m-2S is more suited for the thick fogs sampled at the super-site. CDP data are used to compute the equivalent fog adiabaticity from closure (αclosureeq) and compare to value derived from remote sensing instruments, 2-m height visibility, and an one-column conceptual model of adiabatic continental fog assuming that LWC linearly increases with height. The comparison of αclosureeq shows a large variability that results mainly from the parameterization used to estimate LWC at ground, but their evolution as a function of the fog thickness follows the same trend. We found larger negative values of αclosureeq for thin layers, associated to low LWP values. CDP data reveal that reverse trend of LWC profile (LWC being maximal at the ground and decreasing with altitude) are ubiquitous in optically thin fogs, while quasi-adiabatic features with increasing LWC values with altitude are mainly observed in well-mixed optically thick fogs. We investigate the actual fog adiabaticity and lapse rate fraction by using linear regressions to best fit the vertical profiles of LWC and temperature, respectively. This analysis highligths that reverse LWC profiles, when stable temperature conditions exist during the optically thin phase of fogs, evolve towards quasi-adiabatic features with slightly unstable temperature lapse rate, when fogs become optically thick. We also found that LWC at ground is higher during the thin phase and significantly decreases as the profile is changing from reverse to increasing with height. But this trend could be balanced when collision-coalescence and sedimentation processes redistribute the LWC through the fog layer from the top to the ground. This study provides new insights on the evolution of LWC profile during the fog life cycle, that would help to constrain numerical simulations. [ABSTRACT FROM AUTHOR]

The global warming is amplified in the Arctic. To collect data that help to constrain weather and climate models, which often do not realistically represent the enhanced Arctic warming, the HALO-(AC)³ aircraft campaign was conducted in March and April 2022 over the Norwegian and Greenland Seas, the Fram Strait, and the central Arctic Ocean. Observations were made over areas of open ocean, the marginal sea ice zone, and the central Arctic sea ice. Two low-flying and one long-range, high-altitude research aircraft have been employed. Whenever possible, the three aircraft were flown in collocated formation. The campaign focused on one specific challenge posed by the models: The reasonable representation of transformations of air masses during their meridional transport into (northward by moist and warm air intrusions, WAIs) and out of (southward via marine cold air outbreaks, CAOs) the Arctic. To observe the air mass transformations, a quasi-Lagrangian flight strategy using trajectory calculations was realized enabling to sample the moving air mass parcels twice along their trajectories. Eight distinct WAI and 12 CAO cases were probed extensively. From the quasi-Lagrangian measurements, we have derived the diabatic heating and moistening of the moving air masses during CAOs and WAIs, the development of cloud macrophysical and microphysical properties along the southward pathways of the air masses during CAOs, and the moisture budget of WAIs. As an example result, we have obtained typical values of the surface-driven diabatic heating between 1–3 K h-1 and of the near-surface moistening between 0.05–0.3 g kg-1 h-1 within the lowest about 0.5 km. From the observations of WAIs, a weak diabatic cooling of up to 0.4 K h-1 and a moisture loss of up to 0.1 g kg-1 h-1 from the ground to about 5 km altitude were derived. In addition, we discuss the frequency of occurrence of the different thermodynamic phases of Arctic low-level clouds, the interaction of Arctic cirrus with sea ice, water vapor, and aerosol particles, and the characteristic microphysical and chemical properties of Arctic aerosol particles. Finally, we provide proof of a concept to measure mesoscale divergence and subsidence in the Arctic using data from dropsondes released during circular flight patterns. [ABSTRACT FROM AUTHOR]

Naturally available, stable, and heavy water molecules such as HDO and H 218 O have a lower saturation vapour pressure than the most abundant light water molecule H 216 O; therefore, these heavy water molecules preferentially condense and rain out during cloud formation. Stable water isotope observations thus have the potential to provide information on cloud processes in the trade-wind region, in particular when combined with high-resolution model simulations. In order to evaluate this potential, nested COSMO iso (isotope-enabled Consortium for Small Scale Modelling;) simulations with explicit convection and horizontal grid spacings of 10, 5, and 1 km were carried out in this study over the tropical Atlantic for the time period of the EUREC 4 A (Elucidating the role of clouds-circulation coupling in climate;) field experiment. The comparison to airborne in situ and remote sensing observations shows that the three simulations are able to distinguish between different mesoscale cloud organisation patterns as well as between periods with comparatively high and low rain rates. Cloud fraction and liquid water content show a better agreement with aircraft observations with higher spatial resolution, because they show strong spatial variations on the scale of a few kilometres. A low-level cold-dry bias, including too depleted vapour in the subcloud and cloud layer and too enriched vapour in the free troposphere, is found in all three simulations. Furthermore, the simulated secondary isotope variable d -excess in vapour is overestimated compared to observations. Special attention is given to the cloud base level, which is the formation altitude of shallow cumulus clouds. The temporal variability of the simulated isotope variables at cloud base agrees reasonably well with observations, with correlations of the flight-to-flight data as high as 0.7 for δ2 H and d -excess. A close examination of isotopic characteristics under precipitating clouds, non-precipitating clouds, clear-sky and dry-warm patches at the altitude of cloud base shows that these different environments are represented faithfully in the model with similar frequencies of occurrence, isotope signals, and specific-humidity anomalies as found in the observations. Furthermore, it is shown that the δ2 H of cloud base vapour at the hourly timescale is mainly controlled by mesoscale transport and not by local microphysical processes, while the d -excess is mainly controlled by large-scale drivers. Overall, this evaluation of COSMO iso , including the isotopic characterisation of different cloud base environments, suggests that the simulations can be used for investigating the role of atmospheric circulations on different scales for controlling the formation of shallow cumulus clouds in the trade-wind region, as will be done in part 2 of this study. [ABSTRACT FROM AUTHOR]

Particle attenuated backscatter and depolarization ratio at 532 nm from the WALES (Water Vapor Lidar Experiment in Space) instrument are used in combination with the radar reflectivity from the 35 GHz MIRA35 cloud radar in order to perform an aerosol-cloud target classification on the lidar/radar collocated observations carried out with the High Altitude and LOng-range research aircraft (HALO), in high temporal and spatial resolution. The methodology is applied to the measurements conducted during the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) in fall 2016. Here, we present one case study to show the feasibility and information context of the aerosol-cloud discrimination, which can serve as a good complement to the Cloud target categorization applied already in the measurements. Our results demonstrate that the developed mask is capable to identify complex stratifications with different aerosol and cloud types and even aerosol layers of low signal.

The EarthCARE mission aims to probe the Earth's atmosphere by measuring cloud and aerosol profiles using its active instruments, the Cloud Profiling Radar (CPR) and ATmospheric LIDar (ATLID). The correct identification of hydrometeors and aerosols from atmospheric profiles is an important step in retrieving the properties of clouds, aerosols and precipitation. Ambiguities in the nature of atmospheric targets can be removed using the synergy of collocated radar and lidar measurements, which is based on the complementary spectral response of radar and lidar relative to atmospheric targets present in the profiles. The instruments are sensitive to different parts of the particle size distribution and provide independent but overlapping information in optical and microwave wavelengths. ATLID is sensitive to aerosols and small cloud particles, and CPR is sensitive to large ice particles, snowflakes and raindrops. It is therefore possible to better classify atmospheric targets when collocated radar and lidar measurements exist compared to using a single instrument. The cloud phase, precipitation and aerosol type within the column sampled by the two instruments can then be identified. ATLID-CPR target classification (AC-TC) is the product created for this purpose by combining the ATLID target classification (A-TC) and CPR target classification (C-TC). AC-TC is crucial for the subsequent synergistic retrieval of cloud, aerosol and precipitation properties. AC-TC builds upon previous target classifications using CloudSat and CALIPSO synergy while providing richer target classification using the enhanced capabilities of EarthCARE's instruments, specifically CPR's Doppler velocity measurements to distinguish snow and rimed snow from ice clouds and ATLID's lidar ratio measurements to objectively discriminate between different aerosol species and optically thin ice clouds. In this paper, we first describe how the single-instrument A-TC and C-TC products are derived from ATLID and CPR measurements. Then the AC-TC product, which combines the A-TC and C-TC classifications using a synergistic decision matrix, is presented. Simulated EarthCARE observations based on combined cloud-resolving and aerosol model data are used to test the processors generating the target classifications. Finally, the target classifications are evaluated by quantifying the fractions of ice and snow, liquid clouds, rain, and aerosols in the atmosphere that can be successfully identified by each instrument and their synergy. We show that radar–lidar synergy helps better detect ice and snow, with ATLID detecting radiatively important optically thin cirrus and cloud tops, while CPR penetrates most deep and highly concentrated ice clouds. The detection of rain and drizzle is entirely due to C-TC, while that of liquid clouds and aerosols is due to A-TC. The evaluation also shows that simple assumptions can be made to compensate for when the instruments are obscured by extinction (ATLID) or surface clutter and multiple scattering (CPR); this allows for the recovery of the majority of liquid cloud not detected by the active instruments. [ABSTRACT FROM AUTHOR]

Stable water isotope observations have the potential to provide information on cloud processes in the trade-wind region, in particular when combined with high-resolution model simulations. In order to evaluate this potential, nested convection-resolving COSMOiso simulations with horizontal grid spacings of 10, 5, and 1 km were carried out in this study over the tropical Atlantic for the time period of the EUREC4A field experiment. To keep the conditions in the domain as close as possible to the real meteorology, we applied a spectral nudging of horizontal winds towards reanalysis data. The comparison to airborne in situ and remote sensing observations shows that the three simulations are able to distinguish between different mesoscale cloud organisation patterns as well as between periods with comparably high and low rain rates. Precipitation, cloud fraction and liquid water content are sensitive to the grid spacing. Cloud fraction and liquid water content show a better agreement with aircraft observations with higher spatial resolution. Contrastingly, temperature, humidity, and isotopes in vapour remain fairly unaffected by the model resolution. A low-level cold-dry bias, including too depleted vapour in the subcloud and cloud layer and too enriched vapour in the free troposphere, is found in all three simulations. Furthermore, the simulated secondary isotope variable d-excess in vapour is overestimated compared to observations. Special attention is given to the cloud base level, the formation altitude of shallow cumulus clouds, which are rooted in the thermals of the subcloud layer. The temporal variability of the simulated isotope variables at cloud base agrees reasonably well with observations, with correlations of the flight-to-flight data as high as 0.69 for δ 2H and 0.74 for d-excess. A close examination of different mesoscale cloud base features, including clouds and clear-sky dry-warm patches, and their isotopic characteristics shows that i) these features are represented faithfully in the model with similar frequency of occurrence, isotope signals and specific humidity anomalies as found in the observations (+2 ‰ to +5 ‰ [+2 g kg−1] for precipitating clouds vs. −3 ‰ to −4 ‰ [−2 g kg−1] in dry-warm patches for δ 2H [ q ]) and ii) the δ 2H of cloud base vapour at the hourly time scale is mainly controlled by mesoscale transport and not by local microphysical processes while the d-excess is mainly controlled by large-scale drivers. Overall, this evaluation of COSMOiso, including the isotopic characterisation of cloud base features, suggests that the simulations can be used for investigating the role of atmospheric circulations on different scales for controlling the formation of shallow cumulus clouds in the trade-wind region, as will be done in part 2 of this study. Additionally, we provide explicit recommendations for adaptations of the modelling setup to be tested in future research. [ABSTRACT FROM AUTHOR]

Cloud radars are capable of providing continuous high-resolution observations of clouds and now offer new capabilities within fog layers thanks to the development of frequency-modulated continuous-wave 95 GHz cloud radars. These observations are related to the microphysical properties of clouds. Power law relations in the form of Z=a⋅LWCb are generally used to estimate liquid water content (LWC) profiles. The constants a and b from the power law relation vary with the cloud type and cloud characteristics. Due to the variety of such parameterizations, selecting the most appropriate Z –LWC relation for a continuous cloud system is complicated. Additional information such as liquid water path (LWP) from a co-located microwave radiometer (MWR) is used to scale the LWC of the cloud profile. An algorithm for estimating the LWC of fog and warm clouds using 95 GHz cloud radar–microwave radiometer synergy in a variational framework is presented. This paper also aims to propose an algorithm configuration that retrieves the LWC of clouds and fog using radar reflectivity and a climatology of the power law parameters. To do so, variations in the scaling factor ln⁡a (the logarithm of pre-factor a from power law relation) when MWR observations are available are allowed in each cloud profile to build a climatology of the scaling factor ln⁡a that can be used when MWR observations are not available. The algorithm also accounts for attenuation due to cloud droplets. In this algorithm formulation, the measure of uncertainty in the observations, the forward model, and the a priori information of desired variables acts as weights in the retrieved quantities. These uncertainties in the retrieval are analyzed in the sensitivity analysis of the algorithm. The retrieval algorithm is first tested on a synthetic profile for different perturbations in sensitivity parameters. The sensitivity study has shown that this method is susceptible to LWP information because LWP is point information for the whole cloud column. By further investigating the sensitivity analysis of various biases in LWP information, it was found that it is beneficial to incorporate LWP, even if it is biased, rather than not assimilate any LWP. The algorithm is then implemented in various cloud and fog cases at the SIRTA observatory to estimate LWC and the scaling factor. The scaling factor (ln⁡a) changes for each cloud profile, and the range of ln⁡a is consistent with suggested values in the literature. The validation of such an algorithm is challenging, as we need reference measurements of LWC co-located with the retrieved values. During the SOFOG-3D campaign (southwest of France, October 2019 to March 2020), in situ measurements of LWC were collected in the vicinity of a cloud radar and a microwave radiometer, allowing comparison of retrieved and measured LWC. The comparison demonstrated that the cloud–fog heterogeneity played a key role in the assessment. The proposed synergistic retrieval algorithm is applied to 39 cloud and fog cases at SIRTA, and the behavior of the scaling factor is studied. This statistical analysis of scaling is carried out to develop a radar-only retrieval method. The climatology revealed that the scaling factor can be linked to the maximum reflectivity of the profile. From climatology, the statistical relations for the scaling factor are proposed for fog and clouds. Thanks to the variational framework, a stand-alone radar version of the algorithm is adapted from the synergistic retrieval algorithm, which incorporates the climatology of the scaling factor as a priori information to estimate the LWC of warm clouds. This method allows the LWC estimation using only radar reflectivity and climatology of the scaling factor. [ABSTRACT FROM AUTHOR]