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A dataset of microphysical cloud parameters from optically thin clouds, retrieved from infrared spectral radiances measured in summer 2017 in the Arctic, is presented. Measurements were performed using a mobile Fourier-transform infrared (FTIR) spectrometer which was carried by RV Polarstern. The dataset contains retrieved optical depths and effective radii of ice and liquid water, from which the liquid water path and ice water path are calculated. The water paths and the effective radii retrieved from the FTIR measurements are compared with derived quantities from a combined cloud radar, lidar and microwave radiometer measurement synergy retrieval, called Cloudnet. The purpose of this comparison is to benchmark the infrared retrieval data against the established Cloudnet retrieval. For the liquid water path, the data correlate, showing a mean bias of 2.48 g m−2 and a root-mean-square error of 10.43 g m−2. It follows that the infrared retrieval is able to determine the liquid water path. Although liquid water path retrievals from the Cloudnet retrieval data come with an uncertainty of at least 20 g m−2, a root-mean-square error of 9.48 g m−2 for clouds with a liquid water path of at most 20 g m−2 is found. This indicates that the liquid water paths, especially of thin clouds, of the Cloudnet retrieval can be determined with higher accuracy than expected. Apart from this, the dataset of microphysical cloud properties presented here allows researchers to perform calculations of the cloud radiative effects when the Cloudnet data from the campaign are not available, which was the case from 22 July 2017 until 19 August 2017. The dataset is published at PANGAEA (https://doi.org/10.1594/PANGAEA.933829, Richter et al., 2021).

Distinct events of warm and moist air intrusions (WAIs) from mid-latitudes have pronounced impacts on the Arctic climate system. We present a detailed analysis of a record-breaking WAI observed during the MOSAiC expedition in mid-April 2020. By combining Eulerian with Lagrangian frameworks and using simulations across different scales, we investigate aspects of air mass transformations via cloud processes and quantify related surface impacts. The WAI is characterized by two distinct pathways, Siberian and Atlantic. A moist static energy transport across the Arctic Circle above the climatological 90th percentile is found. Observations at research vessel Polarstern show a transition from radiatively clear to cloudy state with significant precipitation and a positive surface energy balance (SEB), i.e., surface warming. WAI air parcels reach Polarstern first near the tropopause, and only 1–2 days later at lower altitudes. In the 5 days prior to the event, latent heat release during cloud formation triggers maximum diabatic heating rates in excess of 20 K d-1. For some poleward drifting air parcels, this facilitates strong ascent by up to 9 km. Based on model experiments, we explore the role of two key cloud-determining factors. First, we test the role moisture availability by reducing lateral moisture inflow during the WAI by 30%. This does not significantly affect the liquid water path, and therefore the SEB, in the central Arctic. The cause are counteracting mechanisms of cloud formation and precipitation along the trajectory. Second, we test the impact of increasing Cloud Condensation Nuclei concentrations from 10 to 1,000 cm-3 (pristine Arctic to highly polluted), which enhances cloud water content. Resulting stronger longwave cooling at cloud top makes entrainment more efficient and deepens the atmospheric boundary layer. Finally, we show the strongly positive effect of the WAI on the SEB. This is mainly driven by turbulent heat fluxes over the ocean, but radiation over sea ice. The WAI also contributes a large fraction to precipitation in the Arctic, reaching 30% of total precipitation in a 9-day period at the MOSAiC site. However, measured precipitation varies substantially between different platforms. Therefore, estimates of total precipitation are subject to considerable observational uncertainty.

This study presents the micro- and macrophysical cloud properties as a function of their surface coupling state with the sea ice during the wintertime of the MOSAiC field experiment. Cloud properties such as cloud base height, liquid- and ice water content have been previously found to have statistically distinguished features under the presence of sea ice leads (characterized by sea ice concentration, SIC) along downwind direction from the central observatory RV Polarstern. Those findings are mainly in an increase of liquid water content, and favored occurrence of low level clouds as contrasted to situations when the clouds are thermodynamically decoupled. The present contribution is an update considering two recent developments in the liquid detection in clouds and in the detection of sea ice leads. First, radar and lidar-based cloud droplet detection approaches like Cloudnet (Illingworth et al. 2007, Tukiainen et al. 2020) using Arctic wintertime observations and applied to measurements by the Atmospheric Radiation Measurement mobile facility (ARM) instrumental suite on-board the RV Polarstern during MOSAiC. Secondly, we explore a new sea ice lead fraction product based on sea ice divergence. Sea ice divergence is estimated from sequential images of space-borne synthetic aperture radar with a spatial resolution of 700 m. The lead divergence product, being independent of cloud coverage, offers the unique advantage to detect opening leads at high spatial resolution. Statistics for the wintertime cloud properties based on the coupling state with the sea ice concentration and sea ice lead fraction will be presented as an approach to study Arctic clouds and their interaction with sea ice.

The microwave radiometers HATPRO (Humidity and Temperature Profiler) and MiRAC-P (Microwave Radiometer for Arctic Clouds - Passive) continuously measured radiation emitted from the atmosphere throughout the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition on board the research vessel Polarstern. From the measured brightness temperatures, we have retrieved atmospheric variables using statistical methods in a temporal resolution of 1 s covering October 2019 to October 2020. The integrated water vapour (IWV) is derived individually from both radiometers. In addition, we present the liquid water path (LWP), temperature and absolute humidity profiles from HATPRO. To prove the quality and to estimate uncertainty, the data sets are compared to radiosonde measurements from Polarstern. The comparison shows an extremely good agreement for IWV, with standard deviations of 0.08–0.19 kg m−2 (0.39–1.47 kg m−2) in dry (moist) situations. The derived profiles of temperature and humidity denote uncertainties of 0.7–1.8 K and 0.6–0.45 gm−3 in 0–2 km altitude.

From 25 May to 21 July 2017, the research vessel Polarstern performed the cruise PS106 to the high Arctic in the region north and northeast of Svalbard. The mobile remote-sensing platform OCEANET was deployed aboard Polarstern. Within a single container, OCEANET houses state-of-the-art remote-sensing equipment, including a multiwavelength Raman polarization lidar PollyXT and a 14-channel microwave radiometer HATPRO (Humidity And Temperature PROfiler). For the cruise PS106, the measurements were supplemented by a motion-stabilized 35 GHz cloud radar Mira-35. This paper describes the treatment of technical challenges which were immanent during the deployment of OCEANET in the high Arctic. This includes the description of the motion stabilization of the cloud radar Mira-35 to ensure vertical-pointing observations aboard the moving Polarstern as well as the applied correction of the vessels heave rate to provide valid Doppler velocities. The correction ensured a leveling accuracy of ±0.5∘ during transits through the ice and an ice floe camp. The applied heave correction reduced the signal induced by the vertical movement of the cloud radar in the PSD of the Doppler velocity by a factor of 15. Low-level clouds, in addition, frequently prevented a continuous analysis of cloud conditions from synergies of lidar and radar within Cloudnet, because the technically determined lowest detection height of Mira-35 was 165 m above sea level. To overcome this obstacle, an approach for identification of the cloud presence solely based on data from the near-field receiver of PollyXT at heights from 50 m and 165 m above sea level is presented. We found low-level stratus clouds, which were below the lowest detection range of most automatic ground-based remote-sensing instruments during 25 % of the observation time. We present case studies of aerosol and cloud studies to introduce the capabilities of the data set. In addition, new approaches for ice crystal effective radius and eddy dissipation rates from cloud radar measurements and the retrieval of aerosol optical and microphysical properties from the observations of PollyXT are introduced.

The tethered balloon-borne measurement system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was deployed over the Arctic sea ice for 4 weeks in summer 2020 as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition. Using BELUGA, vertical profiles of dynamic, thermodynamic, aerosol particle, cloud, radiation, and turbulence properties were measured from the ground up to a height of 1,500 m. BELUGA was operated during an anomalously warm period with frequent liquid water clouds and variable sea ice conditions. Three case studies of liquid water phase, single-layer clouds observed on 3 days (July 13, 23, and 24, 2020) are discussed to show the potential of the collected data set to comprehensively investigate cloud properties determining cloud evolution in the inner Arctic over sea ice. Simulated back-trajectories show that the observed clouds have evolved within 3 different air masses (“aged Arctic,” “advected over sea ice,” and “advected over open ocean”), which left distinct fingerprints in the cloud properties. Strong cloud top radiative cooling rates agree with simulated results of previous studies. The weak warming at cloud base is mostly driven by the vertical temperature profile between the surface and cloud base. In-cloud turbulence induced by the cloud top cooling was similar in strength compared to former studies. From the extent of the mixing layer, it is speculated that the overall cloud cooling is stronger and thus faster in the warm oceanic air mass. Larger aerosol particle number concentrations and larger sizes were observed in the air mass advected over the sea ice and in the air mass advected over the open ocean.

Wasserdampf trägt als Treibhausgas zum Strahlungsbudget der Atmosphäre bei und ist im atmosphärischen Energietransport, bei Wolkenprozessen und der Niederschlagsbildung von Bedeutung. Die Kenntnis der vertikalen Wasserdampfprofile in der Arktis ist ein wichtiger Beitrag zum Verständnis des arktischen Klimasystems und seines Wandels. Im Rahmen der MOSAiC-Kampagne wurden vom Oktober 2019 bis Oktober 2020 über ein Jahr verschiedenste klimarelevante Parameter gemessen. Mit dem Raman-Lidar PollyXT konnten erstmals nördlich von 85°N vertikal hochaufgelöste Profile des atmosphärischen Wasserdampfes aufgenommen werden. Die Dunkelheit der Polarnacht und niedrige Sonnenstände ermöglichten kontinuierliche Messungen des Wasserdampfes von Oktober 2019 bis März 2020. Die Kalibrierung der Raman-Lidar-Daten erfolgt mit Radiosondenprofilen oder dem integrierten Wasserdampf eines Mikrowellenradiometers.Die gemessenen Absolutwerte des Wasserdampfmischungsverhältnisses in der Arktis sind sehr gering, die vertikale Verteilung ist jedoch hoch variabel und die relative Feuchte erreicht aufgrund der tiefen Temperaturen bodennah häufig Werte nahe 100%. Die vertikale Struktur des Wasserdampfes und der verschiedenen in unterschiedlichen Höhen gemessenen Feuchteschichten lässt auf unterschiedliche Quellen des Wasserdampfes schließen. Zum einen gibt es lokale Quellen wie Verdunstung und Kondensation, die vor allem bodennah auftreten und zum anderen wird im Bereich der freien Troposphäre Wasserdampf aus entfernteren Regionen herantransportiert. Die Stärke des Transports wird dabei hauptsächlich von der allgemeinen Zirkulation in der Atmosphäre bestimmt, welche in der Arktis durch die Arktische Oszillation (AO) beschrieben werden kann. Mit Hilfe des AO Indexes lassen sich positive Phasen (AO>0) mit einem starken Polar Vortex und wenig meridionalem Transport und negative Phasen (AOdeutliche Unterschiede in der Vertikalstruktur und der Gesamtmenge des Wasserdampfes für die beiden Phasen. Während der negativen Phase der arktischen Oszillation werden mehrere zeitlich sehr variable Wasserdampfschichten beobachtet. Bei positivem AO Index ist dagegen nur eine homogene Schicht erkennbar und die Werte des Wasserdampfmischungsverhältnisses sind deutlich geringer. Zudem lassen sich in beiden Phasen Zusammenhänge zwischen Wasserdampf- und Temperaturprofilen erkennen. In der Höhe von Feuchteinversionen treten zum Beispiel häufig auch Temperaturinversionen auf. Mit der Untersuchung weiterer Fallbeispiele soll die vertikale Struktur des Wasserdampfes in der Atmosphäre, deren zeitliche Veränderung und der Zusammenhang zur Arktischen Oszillation weiter analysiert werden.

Water vapor profiles with high vertical and temporal resolution were determined by use of the Raman lidar PollyXT within the MOSAiC campaign in the Arctic during the winter time 2019 – 2020. These measurements need a calibration. Usually, radiosonde data are utilized to calibrate the lidar data by the profile or the linear fit method, respectively. The radiosonde is drifting with the wind; thus, it is often measuring different atmospheric volumes compared to the lidar observations. The period 5-7 February 2020 is used to demonstrate the results. The correlation coefficient of the linear fit between the radiosonde and the lidar data varies with the different atmospheric conditions. The calibration results from the profile method coincide with those of the linear fit method, but the selection of the appropriate calibration setup is not straightforward. The varying correlation of the calibration results is attributed to the partly too low data-variability of the water vapor mixing ratio in the respective heights. Moreover, the drift of the radiosondes with the wind and hence measurements of atmospheric volumes with lateral distances will have decreased the correlation between the lidar and the radiosonde measurements. During MOSAiC a microwave radiometer was collocated close to the lidar. This system was measuring the same atmospheric vertical column. Its product, the integrated water vapor, might be useful for the calibration of the lidar. Hence, the contribution will analyze the error of the lidar retrieved water vapor mixing ratio that includes the calibration with the radiosonde data and the microwave radiometer product.

The observation of low-level stratocumulus cloud decks in the Arctic poses challenges to ground-based remote sensing. These clouds frequently occur during summer below the lowest range gate of common zenith-pointing cloud radar instruments, like the KAZR and the Mira-35. In addition, the optical thickness of these low-level clouds often do cause a complete attenuation of the lidar beam. For remote-sensing instrument synergy retrievals, as Cloudnet (Illingworth, 2007) or ARSCL (Active Remote Sensing of Clouds, Shupe, 2007), liquid-water detection in clouds is usually based on lidar backscatter. Thus, a complete attenuation can cause misclassification of mixed-phase clouds as pure-ice clouds. Moreover, the missing cloud radar information makes it difficult to derive the cloud microphysical properties, as most common retrievals are based on cloud radar reflectivity. A new low-level stratus detection mask (Griesche, 2020) was used to detect these clouds. The liquid-water cloud microphysical properties were derived by a simple but effective analysis of the liquid-water path. This approach was applied to remote-sensing data from a shipborne expedition performed in the Arctic summer 2017. The values calculated by applying the adjusted method improve the results of radiative transfer simulations yielding the determination of radiative closure. Illingworth et al. (2007). “Cloudnet”. BAMS. Shupe (2007). “A ground-based multisensor cloud phase classifier”. GRL. Griesche et al. (2020). “Application of the shipborne remote sensing supersite OCEANET for profiling of Arctic aerosols and clouds during Polarstern cruise PS106”. AMT.

Während MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) wurden verschiedene Aerosol- und Wolkentypen mit einem Mehrwellenlängen-Polarisations-Raman-Lidar (Polly-XT) der OCEANET-Atmosphere-Plattform und mit dem KAZR-Wolkenradar der ARM (Atmospheric Radiation Measurement user facility) an Bord des Eisbrechers POLARSTERN beobachtet. Im Winterhalbjahr (2019/20) wurden dafür in der zentralen Arktis regelmäßig Aerosole in Oberflächennähe bis in 4-6 km Höhe (arktischer Dunst) und in der oberen Troposphäre und unteren Stratosphäre (Waldbrandrauch, bis in 18 km Höhe) beobachtet. Neu entwickelte Methoden der Fernerkundung ermöglichen die Bestimmung der Konzentrationen von Wolkenkondensationskernen (CCNC), der Wolkentröpfchenanzahl (CDNC), der eiskeimbildenden Partikel (INPC) und sogar, mit Hilfe von Dopplerradarbeobachtungen, der Eiskristallzahl (ICNC). Gleichzeitig sind Profile der relativen Luftfeuchtigkeit und der Temperatur aus Raman-Lidar, Mikrowellen-Radiometer und Radiosondierungen verfügbar. Mit Hilfe dieses einzigartigen Datensatzes präsentieren wir eine Aerosol-Wolkenschlussstudie, in der wir zeigen, dass CCNC und CDNC sowie INPC und ICNC miteinander verknüpft werden können. Die Ergebnisse können verwendet werden, um zu testen, welche CCN- und INP-Parametrisierungen (aus idealisierten Labormessungen) im arktischen Regime am besten zutreffen. In Anlehnung an diese Methoden werden im zukünftigen Projekt SCiAMO (Smoke Cirrus interaction in the Arctic during MOSAiC) etwa 65 beobachtete Zirren im Hinblick auf Eisnukleationsprozesse in Abhängigkeit vom Auftreten von Rauchpartikeln in der Winter- und Sommersaison analysiert und verglichen.

The highlight of our multiwavelength polarization Raman lidar measurements during the 1-year MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the Arctic Ocean ice from October 2019 to May 2020 was the detection of a persistent, 10 km deep aerosol layer in the upper troposphere and lower stratosphere (UTLS) with clear and unambiguous wild-fire smoke signatures. The smoke is supposed to originate from extraordinarily intense and long-lasting wildfires in central and eastern Siberia in July and August 2019 and may have reached the tropopause layer by the self-lifting process.Temporally almost parallelly, record-breaking wildfires accompanied by unprecedentedly strong pyroconvection were raging in the south-eastern part of Australia in late December 2019 and early January 2020. These fires injected huge amounts of biomass-burning smoke into the stratosphere where the smoke particles became distributed over the entire southern hemispheric in the UTLS regime from 10-30 km to even 35 km height. The stratospheric smoke layer was monitored with our Raman lidar in Punta Arenas (53.2°S, 70.9°W, Chile, southern South America) for two years.The fact that these two events in both hemispheres coincided with record-breaking ozone hole events in both hemispheres in the respective spring seasons motivated us to discuss a potential impact of the smoke particles on the strong ozone depletion. The discussion is based on the overlapping height ranges of the smoke particles, polar stratospheric clouds, and the ozone hole regions. It is well known that strong ozone reduction is linked to the development of a strong and long-lasting polar vortex, which favours increased PSC formation. In these clouds, active chlorine components are produced via heterogeneous chemical processes on the surface of the PSC particles. Finally, the chlorine species destroy ozone molecules in the spring season. However, there are two pathways to influence ozone depletion by aerosol pollution. The particles can influence the evolution of PSCs and specifically their microphysical properties (number concentration and size distribution), and on the other hand, the particles can be directly involved in heterogeneous chemical processes by increasing the particle surface area available to convert nonreactive chlorine components into reactive forms. A third (indirect) impact of smoke, when well distributed over large parts of the Northern or Southern hemispheres, is via the influence on large-scale atmospheric dynamics.We will show our long-term smoke lidar observations in the central Arctic and in Punta Arenas as well as ozone profile measurements during the ozone-depletion seasons. Based on these aerosol and ozone profile data we will discuss the potential interaction between smoke and ozone.

A dataset of microphysical cloud parameters from optically thin clouds, retrieved from infrared spectral radiances measured in summer 2017 in the Arctic, is presented. Measurements were conducted using a mobile Fourier-transform infrared (FTIR) spectrometer which was carried by the RV Polarstern. This dataset contains retrieved optical depths and effective radii of ice and water, from which the liquid water path and ice water path are calculated. These water paths and the effective radii are compared with derived quantities from a combined cloud radar, lidar and microwave radiometer measurement synergy retrieval, called Cloudnet. Comparing the liquid water paths from the infrared retrieval and Cloudnet shows significant correlations with a standard deviation of 8.60 g · m−2. Although liquid water path retrievals from microwave radiometer data come with a uncertainty of at least 20 g · m−2, a significant correlation and a standard deviation of 5.32 g · m−2 between the results of clouds with a liquid water path of at most 20 g · m−2 retrieved from infrared spectra and results from Cloudnet can be seen. Therefore, despite its large uncertainty, the comparison with data retrieved from infrared spectra shows that optically thin clouds of the measurement campaign in summer 2017 can be observed well using microwave radiometers within the Cloudnet framework. Apart from this, the dataset of microphysical cloud properties presented here allows to perform calculations of the cloud radiative effects, when the Cloudnet data from the campaign are not available, which was from the 22nd July 2017 until the 19th August 2017. The dataset is published at Pangaea (Richter et al., 2021).

A consortium of polar scientists combined observational forces in a field campaign of unprecedented complexity to uncover the secrets of clouds and their role in Arctic amplification. Two research aircraft, an icebreaker research vessel, an ice-floe camp including an instrumented tethered balloon, and a permanent ground-based measurement station were employed in this endeavour. Clouds play an important role in Arctic amplification. This term represents the recently observed enhanced warming of the Arctic relative to the global increase of near-surface air temperature. However, there are still important knowledge gaps regarding the interplay between Arctic clouds and aerosol particles, surface properties, as well as turbulent and radiative fluxes that inhibit accurate model simulations of clouds in the Arctic climate system. In an attempt to resolve this so-called Arctic cloud puzzle, two comprehensive and closely coordinated field studies were conducted: the ”Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD)” aircraft campaign, and the ”Physical feedbacks of Arctic boundary layer, Sea ice, Cloud and AerosoL (PASCAL)” ice breaker expedition. Both observational studies were performed in the framework of the German ”ArctiC Amplification:Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3” project. They took place in the vicinity of Svalbard (Norway) in May and June 2017. ACLOUD and PASCAL explored four pieces of the Arctic cloud puzzle: Cloud properties, aerosol impact on clouds, atmospheric radiation, and turbulent-dynamical processes. The two instrumented Polar 5 and Polar 6 aircraft, the icebreaker research vessel (RV) Polarstern, an ice-floe camp including an instrumented tethered balloon, and the permanent, ground-based measurement station at Ny-Ålesund (Svalbard) were employed to observe Arctic low and mid-level, mixed-phase clouds, and to investigate related atmospheric and surface processes. The Polar 5 aircraft served as a remote sensing observatory examining the clouds from above by downward-looking sensors; the Polar 6 aircraft operated as a flying in-situ measurement laboratory sampling inside and below the clouds. Most of the collocated Polar 5/6 flights were conducted either above RV Polarstern or over the Ny-Ålesund station, both of which monitored the clouds from below using similar but upward-looking remote sensing techniques as the Polar 5 aircraft. Several of the flights were carried out underneath collocated satellite tracks. The paper motivates the scientific objectives of the ACLOUD/PASCAL observations and describes the measured quantities, retrieved parameters, and the applied complementary instrumentation. Furthermore, it discusses selected measurement results, and poses critical research questions to be answered in future papers analyzing the data from the two field campaigns.

The Arctic is currently experiencing a more rapid warming compared to the rest of theworld. This phenomenon, known as Arctic Amplification, is the result of several processes.Within the Collaborative Research Centre on Arctic Amplification: Climate Relevant Atmosphericand Surface Processes and Feedback Mechanisms (AC)3, our research focuseson the influence of water vapour, the strongest greenhouse gas. The collection of dataabout water vapour is essential to understand its impact on Arctic Amplification. Overthe past decades, a positive trend in integrated water vapour in the Arctic has beenidentified using radiosondes and reanalyses for certain regions and seasons. However, inconsistentmagnitudes of the moistening trend in the reanalyses cause the need of a morethorough investigation. While radiosondes offer precise measurements of thermodynamic(temperature and humidity) profiles, they fail to capture the variability of water vapourbecause of the low sampling rate (two to four sondes per day) and spatial coverage. Toobtain a more complete picture of water vapour variability, remote sensing instruments(satellite- and ground-based) are used. Microwave radiometers (MWRs) onboard polarorbiting satellites allow the coverage of the entire Arctic but suffer from uncertaintiesrelated to surface emission. Observations at the surface gathered during the Multidisciplinarydrifting Observatory for the Study of Arctic Climate (MOSAiC) campaign canserve as reference measurements in the central Arctic for the assessment of water vapourproducts from reanalyses, models and satellite retrievals.In this study, we give a first insight into the variability of integrated water vapour (IWV),liquid water path (LWP) and thermodynamic profiles retrieved from two ground-basedMWRs onboard the research vessel Polarstern throughout the MOSAiC campaign. Thefirst radiometer is a standard low frequency HATPRO system and the other one is thehigh-frequency MiRAC-P, which is particularly suited for low water vapour contents. Theretrieved quantities are compared with radiosonde measurements. A first analysis revealsthat the IWV is very well captured by the MWR measurements. Over the observationperiod (October 2019 - October 2020), a large variety of meteorological conditions occurred.Besides the considerable seasonal cycle, which is especially interesting because ofthe contrast between polar night and polar day, several synoptic events contribute to thevariety of conditions, which will be highlighted as well.We gratefully acknowledge the funding by the Deutsche Forschungsgemeinschaft (DFG, German ResearchFoundation) — Project 268020496 — TRR 172, within the Transregional Collaborative Research Center"Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms(AC)3". Data used in this manuscript was produced as part of the international Multidisciplinary driftingObservatory for the Study of the Arctic Climate (MOSAiC) with the tag MOSAiC20192020 and thePolarstern expedition AWI_PS122_00.

Clouds influence the shortwave (SW) and longwave (LW) radiative fluxes, thereby affecting the radiative budget by enhancing or diminishing the heat budget at the surface (SFC), at the top of the atmosphere (TOA), and through the atmosphere. In the Arctic, their complexity enhances due to their intrinsic interactions with several physical processes and feedback mechanisms.With the aim to further investigate the Arctic system, the project (AC)³ (Arctic Amplification: Climate Relevant Atmospheric and SurfaCe Processes and Feedback Mechanisms) established two major field campaigns in summer of 2017. Both performed in situ and remote sensing observations over the ocean with PS106 and in the air with ACLOUD (Macke and Flores, 2018, Wendisch et al., 2019). The observations collected during PS106 are considered to investigate the effects and influence of clouds in the radiation budget for the summer central Arctic.The PS106 expedition took place aboard the German research vessel Polarstern which was equipped with active and passive remote sensing instrumentation (Griesche et al., 2020). The synergistic operation of this instrumentation was used to derive macro and microphysical properties of clouds by applying the Cloudnet algorithm. These retrievals together with vertical profiles of temperature and relative humidity are used as input to the Rapid Radiative Transfer Model for GCM applications (RRTMG). The results of the broadband SW and LW radiative simulations along with hourly satellite products from Clouds and the Earth’s Radiant Energy System (CERES) Synoptic 1-degree Ed.4. are compared to ship-borne observations indicating a better agreement for single-level liquid clouds than for more challenging sky conditions. The results of the comparison bring sufficient information to discuss a radiative closure assessment for selected case studies and for the entire PS106 expedition. Based on these results the cloud radiative effect (CRE) is calculated indicating a net effect of -8.1 W/m².The study is extended by applying this methodology to the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). Preliminary results will be presented for the first leg which will allow a direct comparison of the contrasting properties of cloud radiative effects during summer and winter season.ReferencesGriesche, H. J., and coauthors. (2020): Application of the shipborne remote sensing supersite OCEANETfor profiling of Arctic aerosols and clouds during Polarstern cruise PS106, Atmos. Meas. Tech., 13,5335–5358, https://doi.org/10.5194/amt-13-5335-2020Macke, A. and Flores, H. (2018): The Expeditions PS106/1 and 2 of the Research Vessel POLARSTERNto the Arctic Ocean in 2017 , Berichte zur Polar- und Meeresforschung = Reports on polar and marineresearch, Bremerhaven, Alfred Wegener Institute for Polar and Marine Research, 719 , 171 p.http://hdl.handle.net/10013/epic.4ff2b0cd-1b2f-4444-a97f-0cd9f1d917abWendisch, M., and coauthors. (2019): The Arctic Cloud Puzzle: Using ACLOUD/PASCAL MultiplatformObservations to Unravel the Role of Clouds and Aerosol Particles in Arctic Amplification. Bull. Amer.Meteor. Soc., 100, 841–871, https://doi.org/10.1175/BAMS-D-18-0072.1

The MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, lasting from September 2019 to October 2020, was the largest Arctic research initiative in history. The goal of the expedition was to take the closest look ever at the Arctic as the epicenter of global warming and to gain fundamental insights that are key to better understand global climate change. We continuously operated a multiwavelength aerosol/cloud Raman lidar aboard the icebreaker Polarstern, drifting through the Arctic Ocean trapped in the ice from October to May, and monitored aerosol and cloud layers in the Central Arctic up to 30 km height at latitudes mostly > 85°N. The lidar was integrated in a complex remote sensing infrastructure aboard Polarstern. A polarization Raman lidar is designed to separate the main continental aerosol components (mineral dust, wildfire smoke, anthropogenic haze, volcanic aerosol). Furthermore, the Polarstern lidar enabled us to study the impact of these different basic aerosol types on the evolution of Arctic mixed-phase and ice clouds. The most impressive and unprecedented observation was the detection of a persistent, 10 km deep aerosol layer of aged wildfire smoke over the North Pole region between 8 and 18 km height from October 2019 until the beginning of May 2020. The wildfire smoke layers originated from severe and huge fires in Siberia, Alaska, and western North America in 2019 and may have contained mineral dust injected into the atmosphere over the hot fire places together with the smoke. We will present the main MOSAiC findings including a study of a long-lasting mixed-phase cloud layer evolving in Arctic haze (at heights below 6 km) and the role of mineral dust in the Arctic haze mixture to trigger heterogeneous ice formation. Furthermore, we present a case study developing in the smoke-dominated layer around 10 km height.

During the one-year MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition the German icebreaker Polarstern drifted through the Arctic Ocean ice from October 2019 to May 2020, mainly at latitudes between 85° N and 88.5° N. A multiwavelength polarization Raman lidar was operated aboard the research vessel and continuously monitored aerosol and cloud layers up to 30 km height. The highlight of the lidar measurements was the detection of a persistent, 10 km deep wildfire smoke layer in the upper troposphere and lower stratosphere (UTLS) from about 7–8 km to 17–18 km height. The smoke layer was present throughout the winter half year until the polar vortex, the strongest of the last 40 years, collapsed in late April 2020. The smoke originated from major fire events, especially from extraordinarily intense and long-lasting Siberian fires in July and August 2019. In this article, we summarize the main findings of our seven-month smoke observations and characterize the aerosol properties and decay of the stratospheric perturbation in terms of geometrical, optical, and microphysical properties. The UTLS aerosol optical thickness (AOT) at 532 nm ranged from 0.05–0.12 in October–November 2019 and was of the order of 0.03–0.06 during the central winter months (December–February). As an unambiguous sign of the dominance of smoke, the particle extinction-to-backscatter ratio (lidar ratio) at 355 nm was found to be much lower than the respective 532 nm lidar ratio. Mean values were 55 sr (355 nm) and 85 sr (532 nm). We further present a review of previous height resolved Arctic aerosol observations (remote sensing) in our study. For the first time, a coherent and representative view on the aerosol layering features in the Central Arctic from the surface up to 27 km height during the winter half year is presented. Finally, a potential impact of the wildfire smoke aerosol on the record-breaking ozone depletion over the Arctic in the spring of 2020 is discussed based on smoke, ozone, and polar stratospheric cloud observations.

An advanced multiwavelength polarization Raman lidar was operated aboard the icebreaker Polarstern during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, lasting from September 2019 to October 2020, to contiuously monitor aerosol and cloud layers in the Central Arctic up to 30 km height at latitudes mostly between 85° N and 88.5° N. The lidar was integrated in a complex remote sensing infrastructure aboard Polarstern. Modern aerosol lidar methods and new lidar techniques and concepts to explore aerosol-cloud interaction were applied for the first time in the Central Arctic. Aim of the introductory article is to provide an overview of the observational spectrum of the lidar products for representative measurement cases. Highlight of the lidar measurements was the detection of a 10 km deep wildfire smoke layer over the North Pole area from, on average, 7 km to 17 km height with an aerosol optical thickness (AOT) at 532 nm around 0.1 (in October–November 2019) and 0.05 from December to mid of March 2020. The wildfire smoke was trapped within the extraordinarily strong polar vortex and remained detectable until the beginning of May 2020. Arctic haze was also monitored and characterized in terms of backscatter, extinction, and extinction-to-backscatter ratio at 355 and 532 nm. High lidar ratios from 60–100 sr in lofted mixed haze and smoke plumes are indicative for the presence of strongly light-absorbing fine-mode particles. The AOT at 532 nm was of the order of 0.025 for the tropospheric haze layers. In addition, so-called cloud closure experiments were applied to Arctic mixed-phase cloud and cirrus observations. The good match between cloud condensation nucleus concentration (CCNC) and cloud droplet number concentration (CDNC) and, on the other hand, between ice-nucleating particle concentration (INPC) and ice crystal number concentration (ICNC) indicated a clear influence of aerosol particles on the evolution of the cloud systems. CDNC was mostly between 20 and 100 cm−3 in the liquid-water dominated cloud top layer. ICNC was of the order of 0.1–1 L−1. The study of the impact of wildfire smoke particles on cirrus formation revealed that heterogeneous ice formation with smoke particles (organic aerosol particles) as INPs may have prevailed. ICNC values of 10–40 L−1 were clearly below ICNC levels that would indicate homogeneous freezing.

In the Arctic summer of 2017 (June, 1st to July, 16th) measurements with the multiwavelength polarization lidar PollyXT-OCEANET, 35-GHz cloud radar of the OCEANET platform, and radiosonde measurements were conducted during cruise PS106 of the research vessel Polarstern around Svalbard. In the scope of the presented study, the influence of cloud height and surface coupling on the probability of clouds to contain and form ice is investigated. The analyzed data set shows a significant impact of the surface-coupling state on the probability of ice formation. Surface-coupled clouds, identified by a quasi-constant potential temperature profile from the surface up to liquid layer base, in the same cloud-top temperature range contain ice more frequent than decoupled clouds by a factor of up to 5 for cloud-top intervals between −7.5 and −5 °C (169 vs. 31 profiles). These findings provide evidence that heterogeneous ice formation in Arctic mixed-phase clouds occurs by a factor of 2–5 more likely when the cloud layer is coupled to the surface. In turn, for cloud-top temperatures below −15 °C, the frequency of ice-containing cloud profiles for coupled and decoupled conditions approached the respective curve for the Central-European site of Leipzig, Germany (51° N, 12° E). This provides further evidence that the free-tropospheric ice nucleating particles (INP) reservoir over the Arctic is controlled by continental aerosol. One possible explanation for the observation is that turbulent mixing of the air below surface-coupled clouds allows ice particles, acting as seeds for ice multiplication, or marine aerosols, acting as INP, to be transported into the cloud layer more efficiently than in the case of decoupled conditions. This hypothesis is corroborated by recent in-situ measurements of INP in the Arctic, of which much higher concentrations were found in the surface-coupled atmosphere in close vicinity to the ice shore. Using lidar measurements we also found evidence for enhanced INP number concentrations (INPC) within surface-coupled cloud-free air masses. The INPC have been estimated based on particle backscatter profiles, published freezing spectra of biogenic INP and existing parameterizations.

Infrared spectral radiances of optically thin clouds show high sensitivity to changes of the microphysical cloud parameters. Therefore, measurements of infrared spectral radiance of clouds in the spectral range from 770.9 cm−1 to 1163.4 cm−1 using a mobile Fourier Transform Infrared spectrometer were performed on the German research vessel Polarstern in the Arctic in summer 2017. A new retrieval for microphysical cloud parameters of optically thin clouds called Total Cloud Water retrieval, designed to retrieve cloud water optical depth τcw, total effective droplet radius rtotal and condensed water path CWP from infrared spectral radiances without the incorporation of spectral radiances in the far-infrared below 600cm−1, has been developed for application on radiances from the measurement campaign. Validation is performed against derived quantities from a combined cloud radar, lidar and microwave radiometer measurement synergy retrieval, called Cloudnet, performed by the Leibnitz Institute for Trospheric Research. Applied to spectral radiances of synthetic testcases, Total Cloud Water retrieval shows a high ability to retrieve τcw with a correlation of |r| = 0.98, as well as to retrieve CWP with |r| = 0.95 and rtotal with |r| = 0.86. Using the dataset from the campaign, a comparison between CWP from Total Cloud Water retrieval and Cloudnet was performed and showed a correlation of |r| = 0.81. In conclusion, the comparison to artificial clouds and the validation using Cloudnet showed that Total Cloud Water retrieval is able to retrieve the condensed water path from clouds for optically thin clouds and makes it a useful complementation for thin clouds to existing microwave-based measurements.

Clouds are frequently composed of more than one particle population even at the smallest scales. Cloud radar observations frequently contain information on multiple particle species in the observation volume when there are distinct peaks in the Doppler spectrum. Multi-peaked situations are not taken into account by established algorithms, which only use moments of the Doppler spectrum. In this study, we propose a new algorithm that recursively represents the subpeaks as nodes in a binary tree. Using this tree data structure to represent the peaks of a Doppler spectrum, it is possible to drop all a priori assumptions on the number and arrangement of subpeaks. The approach is rigid, unambiguous and can provide a basis for advanced analysis methods. The applicability is briefly demonstrated in two case studies, in which the tree structure was used to investigate particle populations in Arctic multilayered mixed-phase clouds, which were observed during the research vessel Polarstern expedition PS106 and the Atmospheric Radiation Measurement Program BAECC campaign.