Precipitation is an essential component of the Arctic climate system as part of the hydrological cycle, linking the atmosphere and cryosphere. Much of the Arctic precipitation sublimates or evaporates before it reaches the ground due to dry sub-cloud layers. The processes are thus controlling the input of the surface mass balance.We use long-term atmospheric observations at Ny-Ålesund, Svalbard, with vertically-pointing cloud radars and backscattering lidars to identify and quantify atmospheric sublimation/evaporation. Radar observation-based sub-cloud precipitation profiles are studied by employing a virga detection tool, the so-called Virga-Sniffer (Kalesse-Los et al., 2023). The quantification of the sublimation/evaporation is based on sub-cloud vertical gradients of radar moments. First statistical results of precipitation thermodynamical phase, virga depth, and full sublimation/evaporation altitude above ground will be shown.We will show investigations on wind direction dependence on virga statistics. Air masses advected from the Arctic Ocean are more humid and lead to more precipitation reaching the ground and thus less virga. Air masses advected over Ny-Ålesund from Easterly directions are often characterized by low-humidity subcloud layers leading to more evaporation/ sublimation and hence a higher fraction of virga. Furthermore, the occurrence frequency of virga and surface precipitation observed during different weather regimes such as cyclones, fronts, and atmospheric rivers is contrasted.This work was supported by the DFG funded Transregio-project TRR 172 “Arctic Amplification (AC)3“.Refernces:Kalesse-Los, H., Kötsche, A., Foth, A., Röttenbacher, J., Vogl, T., and Witthuhn, J.: The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations, Atmos. Meas. Tech., 16, 1683–1704, https://doi.org/10.5194/amt-16-1683-2023, 2023.

Abstract. Clouds over the tropical Atlantic remain under-represented in ground-based observational studies, despite their key role in the regional radiative and hydrological budgets. This observational gap contributes to substantial uncertainties in the representation of these clouds in climate models. The recent BOWTIE (German: Beobachtung von Ozean und Wolken – Das Trans ITCZ Experiment) campaign aimed to address this deficiency through a six-week, ship-borne measurement campaign across the tropical Atlantic, spanning from Cape Verde to Barbados. This study investigates the influence of Saharan Air Layers (SALs) on cloud properties using vertically pointing remote sensing observations collected during the research cruise. Cloud types are classified and combined with Cloudnet-derived microphysical products to assess both macrophysical and microphysical cloud characteristics. The analysis reveals a shift in the cloud characteristics toward less convective regimes during SAL presence, accompanied by changes in cloud base height (CBH) and cloud top height (CTH). Microphysically, the suppression of convection associated with SAL leads to a greater vertical separation of cloud layers, meaning that droplet and ice effective radii (DER and IER) differ across layers. However, hydrometeor populations across all observed layers are comparable to those observed in marine cloud regimes reported in previous studies. Overall, the thermodynamic structure of the SAL emerges as the dominant driver of the observed changes in cloud properties. These findings highlight the importance of accounting for SAL influences in understanding cloud development over the tropical Atlantic and improving their representation in atmospheric models.

The far infrared region plays a critical role in the Earth's greenhouse effect. Measurements across this range are limited and previous studies have found it challenging to achieve radiative closure between different instruments across the mid-far infrared. This study compares high-resolution downwelling spectra measured from the surface in the Arctic to radiative transfer simulations run with atmospheric profiles from a radiosonde, microwave radiometer, and the 5th ECMWF atmospheric reanalysis. The radiosonde-driven simulation generates the closest agreement to the radiance observations. The HATPRO-driven simulation matches within its uncertainty across the entire range. However, it has a larger uncertainty envelope than the other sources. There is a bias in all three simulations associated with underestimated humidity. Our results reiterate the sensitivity of the far infrared to the humidity profile. This suggests that the wider deployment of spectrometers capable of measuring across this region, in parallel with efforts to improve knowledge of far infrared water vapour spectroscopy, could markedly improve our ability to routinely characterise polar environments.

This paper explores whether it is possible to achieve consistency between ground-based infrared radiance measurements made in the presence of cirrus, co-located in-situ aircraft measurements of the cirrus microphysics, and ancillary ground-based remote sensing. Specifically we use spectrally resolved radiances covering the range 400-1500 cm(-1), in-situ measurements of cirrus particle sizes and habits, backscatter ceilometer observations of cloud vertical structure and microwave inferred temperature and humidity profiles to investigate whether we can obtain consistency between the derived cloud properties and atmospheric state from these independent sources of data. The primary focus of this study is on the sensitivity of the retrieved cloud particle size to the assumed crystal habit. Excellent consistency between the retrieved cloud parameters is achieved both with the ceilometer derived optical depth and the size distribution measured by the aircraft by assuming the crystal habit to be comprised of bullet rosettes. The averaged values of the effective diameter and optical depth obtained from radiometric measurements are (26.5 +/- 1.8) mu m and (0.12 +/- 0.01) in comparison with the values derived from in-situ and ceilometer measurements equal to (31.5 +/- 5.0) mu m and (0.13 +/- 0.01), respectively. Furthermore, we show that the radiance information contained within the far-infrared (wavenumbers < 650 cm(-1)) spectrum is critical to achieving this level of agreement with the in-situ aircraft observations. The results emphasize why it is vital to expand the current limited database of measurements encompassing the far-infrared spectrum, particularly in the presence of cirrus, to explore whether this finding holds over a wider range of conditions.

The highly sensitive Ka-band cloud radar at the Barbados Cloud Observatory (BCO) frequently reveals radar reflectivity signals below −50 dBZ within the convective sub-cloud layer. These so-called haze echoes are signals from hygroscopically grown sea salt aerosols. Within the Cloudnet target classification scheme, haze echoes are generally misclassified as precipitation (target class: “drizzle or rain”). We present a technique to discriminate between “drizzle or rain” and sea salt aerosols in Cloudnet that is applicable to marine Cloudnet sites. The method is based on deriving heuristic probability functions utilizing a combination of cloud radar reflectivity factor, radar mean Doppler velocity, and the ceilometer attenuated backscatter coefficient. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements at the BCO. The results are validated against the amount of precipitation detected by the Virga-Sniffer tool. We analyze data for measurements at BCO covering 2 years (July 2021–July 2023). A first-ever statistical analysis of the Cloudnet target classification product including the new “haze echo” target over 2 years at the BCO is presented. In the atmospheric column above the BCO, “drizzle or rain” is on average more frequent during the dry season compared to the wet season due to the higher occurrence of warm clouds contributing to the amount of precipitation. Haze echoes are identified about 4 times more often during the dry season compared to the wet season. The frequency of occurrence of “drizzle or rain” in Cloudnet caused by misclassified haze echoes is overestimated by up to 16 %. Supported by the Cloudnet statistics and the results obtained from the Virga-Sniffer tool, 48 % of detected warm clouds in the dry and wet season precipitate. The proportion of precipitation evaporating fully before reaching the ground (virga) is higher during the dry season.

Usually, microwave radiometer observations have to be discarded during rain. The radomes of the receiver antenna get wet, which hampers accurate measurements since the retrieval algorithms to derive atmospheric quantities are not trained for rain events. The reason for the latter is, that the raindrops dominate the microwave signal compared to the weaker signal from atmospheric gases. To account for this, radiative transfer simulations need to include the electromagnetic properties of rain, which usually requires more complicated and expensive simulations. In this work, the performance of newly developed microwave radiometer retrievals that are not based on rain simulations is evaluated to assess how they work during rain events. It is shown that it is possible to retrieve low-level temperature profiles during rain by omitting certain frequencies and zenith observations. Retrievals with various combinations of elevation angles and frequencies are evaluated. We show that retrievals based on scanning mode observations with angles below 30 degrees without zenith observation and only the less transparent upper four HATPRO microwave radiometer frequencies of the V-band (54.94, 56.66, 57.3, 58 GHz) provide the best results. An analysis of the calculated degrees of freedom of the signal shows that the retrieval of temperature profiles up to 3 km for no rain, 1.5 km for light to moderate rain, and 1 km for very heavy rain is driven by the HATPRO observation and not by climatology. Finally, the performance of the temperature profile retrieval is explained using a case study in Lindenberg, Germany, and evaluated with temperature profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF) model for different rainfall intensities. The results show that the higher the rainfall rate, the larger the deviation of the retrieved microwave radiometer temperature profile from the ECMWF model output. The proposed retrievals for temperature profiles up to at least 1.5 km for rain rates below 0.5 and below 2.5 mmh-1 have uncertainties of less than 1 and 2 K, respectively, compared to ECMWF model output profiles.

Climate extremes are on the rise. Impacts of extreme climate and weather events on ecosystem services and ultimately human well-being can be partially attenuated by the organismic, structural, and functional diversity of the affected land surface. However, the ongoing transformation of terrestrial ecosystems through intensified exploitation and management may put this buffering capacity at risk. Here, we summarize the evidence that reductions in biodiversity can destabilize the functioning of ecosystems facing climate extremes. We then explore if impaired ecosystem functioning could, in turn, exacerbate climate extremes. We argue that only a comprehensive approach, incorporating both ecological and hydrometeorological perspectives, enables us to understand and predict the entire feedback system between altered biodiversity and climate extremes. This ambition, however, requires a reformulation of current research priorities to emphasize the bidirectional effects that link ecology and atmospheric processes.

During the EUREC4A field study, microwave radiometric measurements were performed at Barbados Cloud Observatory (BCO) and aboard RV Meteor and RV Maria S Merian in the downstream winter trades of the North Atlantic. We present retrieved integrated water vapor (IWV), liquid water path (LWP), and temperature and humidity profiles as a unified, quality-controlled, multi-site data set on a 3 s temporal resolution for a core period between 19 January and 14 February 2020 in which all instruments were operational. Multi-channel radiometric measurements were performed at BCO and aboard RV Meteor between 22 and 31 GHz (K-band) and from 51 to 58 GHz (V-band). Combined radar–radiometer measurements of a W-band Doppler radar with a single-channel radiometer instrument were conducted at 89 GHz aboard RV Meteor and RV Maria S Merian. We present a novel retrieval method to retrieve LWP from single-channel 89 GHz measurements, evaluate retrieved quantities with independent measurements, and analyze retrieval uncertainties by site and instrument intercomparison. Mean IWV conditions of 31.8 kg m−2 match independent radiosoundings at BCO with a root-mean-square difference of 1.1 kg m−2. Mean LWP conditions in confidently liquid cloudy, non-precipitating conditions ranged between 63.1 g m−2 at BCO and 46.8 g m−2 aboard RV Maria S Merian. Aboard the ships, 90 % of LWP was below 120 g m−2 with a 30 % uncertainty for LWP of 50 g m−2. Up to 20 % of confidently liquid cloudy profiles ranged below the LWP detection limit due to optically thin clouds. The data set comprises of processed raw data (Level 1), full quality-controlled post-processed instrument data (Level 2), a unified temporal resolution (Level 3), and a ready-to-use multi-site time series of IWV and LWP (Level 4), available to the public via AERIS (https://doi.org/10.25326/454##v2.0; Schnitt et al., 2023a). The data set complements the airborne LWP measurements conducted during EUREC4A and provides a unique benchmark tool for satellite evaluation and model–observation studies.

Continuous long-term ground-based remote-sensing observations combined with vertically pointing cloud radar and ceilometer measurements are well suited for identifying precipitation evaporation fall streaks (so-called virga). Here we introduce the functionality and workflow of a new open-source tool, the Virga-Sniffer, which was developed within the framework of RV Meteor observations during the ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte (EUREC4A) field experiment in January–February 2020 in the tropical western Atlantic. The Virga-Sniffer Python package is highly modular and configurable and can be applied to multilayer cloud situations. In the simplest approach, it detects virga from time–height fields of cloud radar reflectivity and time series of ceilometer cloud base height. In addition, optional parameters like lifting condensation level, a surface rain flag, and time–height fields of cloud radar mean Doppler velocity can be added to refine virga event identifications. The netCDF-output files consist of Boolean flags of virga and cloud detection, as well as base and top heights and depth for the detected clouds and virga. The sensitivity of the Virga-Sniffer results to different settings is explored (in the Appendix). The performance of the Virga-Sniffer was assessed by comparing its results to the CloudNet target classification resulting from using the CloudNet processing chain. A total of 86 % of pixels identified as virga correspond to CloudNet target classifications of precipitation. The remaining 14 % of virga pixels correspond to CloudNet target classifications of aerosols and insects (about 10 %), cloud droplets (about 2 %), or clear sky (2 %). Some discrepancies of the virga identification and the CloudNet target classification can be attributed to temporal smoothing that was applied. Additionally, it was found that CloudNet mostly classified aerosols and insects at virga edges, which points to a misclassification caused by CloudNet internal thresholds. For the RV Meteor observations in the downstream winter trades during EUREC4A, about 42 % of all detected clouds with bases below the trade inversion were found to produce precipitation that fully evaporates before reaching the ground. A proportion of 56 % of the detected virga originated from trade wind cumuli. Virga with depths less than 0.2 km most frequently occurred from shallow clouds with depths less than 0.5 km, while virga depths larger than 1 km were mainly associated with clouds of larger depths, ranging between 0.5 and 1 km. The presented results substantiate the importance of complete low-level precipitation evaporation in the downstream winter trades. Possible applications of the Virga-Sniffer within the framework of EUREC4A include detailed studies of precipitation evaporation with a focus on cold pools or cloud organization or distinguishing moist processes based on water vapor isotopic observations. However, we envision extended use of the Virga-Sniffer for other cloud regimes or scientific foci as well.

In mixed-phase clouds, the variable mass ratio between liquid water and ice as well as the spatial distribution within the cloud plays an important role in cloud lifetime, precipitation processes, and the radiation budget. Data sets of vertically pointing Doppler cloud radars and lidars provide insights into cloud properties at high temporal and spatial resolution. Cloud radars are able to penetrate multiple liquid layers and can potentially be used to expand the identification of cloud phase to the entire vertical column beyond the lidar signal attenuation height, by exploiting morphological features in cloud radar Doppler spectra that relate to the existence of supercooled liquid. We present VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn), a retrieval based on deep convolutional neural networks (CNNs) mapping radar Doppler spectra to the probability of the presence of cloud droplets (CD). The training of the CNN was realized using the Cloudnet processing suite as supervisor. Once trained, VOODOO yields the probability for CD directly at Cloudnet grid resolution. Long-term predictions of 18 months in total from two mid-latitudinal locations, i.e., Punta Arenas, Chile (53.1∘ S, 70.9∘ W), in the Southern Hemisphere and Leipzig, Germany (51.3∘ N, 12.4∘ E), in the Northern Hemisphere, are evaluated. Temporal and spatial agreement in cloud-droplet-bearing pixels is found for the Cloudnet classification to the VOODOO prediction. Two suitable case studies were selected, where stratiform, multi-layer, and deep mixed-phase clouds were observed. Performance analysis of VOODOO via classification-evaluating metrics reveals precision > 0.7, recall ≈ 0.7, and accuracy ≈ 0.8. Additionally, independent measurements of liquid water path (LWP) retrieved by a collocated microwave radiometer (MWR) are correlated to the adiabatic LWP, which is estimated using the temporal and spatial locations of cloud droplets from VOODOO and Cloudnet in connection with a cloud parcel model. This comparison resulted in stronger correlation for VOODOO (≈ 0.45) compared to Cloudnet (≈ 0.22) and indicates the availability of VOODOO to identify CD beyond lidar attenuation. Furthermore, the long-term statistics for 18 months of observations are presented, analyzing the performance as a function of MWR–LWP and confirming VOODOO's ability to identify cloud droplets reliably for clouds with LWP > 100 g m−2. The influence of turbulence on the predictive performance of VOODOO was also analyzed and found to be minor. A synergy of the novel approach VOODOO and Cloudnet would complement each other perfectly and is planned to be incorporated into the Cloudnet algorithm chain in the near future.

We present retrievals of tropospheric and stratospheric height profiles of particle mass, volume, surface area, and number concentrations in the case of wildfire smoke layers as well as estimates of smoke-related cloud condensation nuclei (CCN) and ice-nucleating particle (INP) concentrations from backscatter lidar measurements on the ground and in space. Conversion factors used to convert the optical measurements into microphysical properties play a central role in the data analysis, in addition to estimates of the smoke extinction-to-backscatter ratios required to obtain smoke extinction coefficients. The set of needed conversion parameters for wildfire smoke is derived from AERONET observations of major smoke events, e.g., in western Canada in August 2017, California in September 2020, and southeastern Australia in January–February 2020 as well as from AERONET long-term observations of smoke in the Amazon region, southern Africa, and Southeast Asia. The new smoke analysis scheme is applied to CALIPSO observations of tropospheric smoke plumes over the United States in September 2020 and to ground-based lidar observation in Punta Arenas, in southern Chile, in aged Australian smoke layers in the stratosphere in January 2020. These case studies show the potential of spaceborne and ground-based lidars to document large-scale and long-lasting wildfire smoke events in detail and thus to provide valuable information for climate, cloud, and air chemistry modeling efforts performed to investigate the role of wildfire smoke in the atmospheric system.

In this paper, we present two micro rain radar-based approaches to discriminate between stratiform and convective precipitation. One is based on probability density functions (PDFs) in combination with a confidence function, and the other one is an artificial neural network (ANN) classification. Both methods use the maximum radar reflectivity per profile, the maximum of the observed mean Doppler velocity per profile and the maximum of the temporal standard deviation (±15 min) of the observed mean Doppler velocity per profile from a micro rain radar (MRR). Training and testing of the algorithms were performed using a 2-year data set from the Jülich Observatory for Cloud Evolution (JOYCE). Both methods agree well, giving similar results. However, the results of the ANN are more decisive since it is also able to distinguish an inconclusive class, in turn making the stratiform and convective classes more reliable.

Die ständige Weiterentwicklung und Verbesserung der Wetter- und Klimamodelle stellt die atmosphärische Fernerkundung vor eine große Herausforderung. Für die Evaluierung der Modelle werden immer besser aufgelöste Messungen und Methoden benötigt. Herkömmliche Ansätze scheitern hier vor allem an kontinuierlichen Beobachtungen der Temperatur und Feuchte bei allen Wetterbedingungen und insbesondere bei Regen. Ein Radar-Windprofiler (RWP) ist allerdings auch bei solchen atmosphärischen Bedingungen in der Lage Vertikalinformationen der Temperatur- und Feuchtegradienten zu beobachten. Das Signal ist direkt proportional zu starken Änderungen in der Temperatur und der Feuchte. Hier lernen künstliche neuronale Netzwerke die Zusammenhänge aus dem RWP Dopplerspektrum und zeitgleichen atmosphärischen Profilen von Radiosonden. Es werden zwei unterschiedliche Ansätze aus dem maschinellen Lernen vorgestellt. Einmal als Vorwärtsmodell (multilayer perceptron) um das RWP Signal aus Radiosonden zu generieren und einmal andersherum, also aus RWP-Dopplerspektren Temperatur- und Feuchteprofile zu erzeugen (convolutional neural network). Die langen Zeitreihen an Daten (seit 2010) und die Kombination an sich ergänzenden Messinstrumenten, insbesondere mit dem 482 MHz Windprofiler am Meteorologischen Observatorium Lindenberg – Richard Aßmann Observatorium (MOL-RAO), sind einzigartig. Daraus kann eine kontinuierliche Zeitreihe von Temperatur- und Feuchteprofilen mit bisher nicht erreichter Genauigkeit innerhalb und oberhalb von Wolken und insbesondere bei Niederschlag erstellt werden. Erste Ergebnisse bei wolkenlosen Fällen sind vielversprechend. In naher Zukunft können die Algorithmen als Vorwärtsmodelle für variationelle Methoden (optimale Schätzung) verwendet werden.

The southern midlatitudes and Sub-Antarctica are a key region for the Earth’s climate and a source for uncertainties in climate modelling. The low concentration of ice nucleating particles is considered to diminish the efficiency of heterogeneous ice formation. Climate models underestimate the supercooled liquid water content which causes shortwave radiation biases. The project DACAPO-PESO (Dynamics, Aerosol, Cloud And Precipitation Observations in the Pristine Environment of the Southern Ocean) which is being conducted in Punta Arenas (53°S, 71°W), Chile from Nov 2018 – March 2020 is endorsed by YOPP (Year of Polar Prediction) and fills an observational gap in the Southern Oceans, for which to date hardly any combined observations of lidar, cloud radar and microwave radiometer are available. During that field experiment, LACROS (Leipzig Aerosol and Cloud Remote Observations System) of Leibniz Institute for Tropospheric Research (TROPOS) which comprises numerous remote sensing instruments, including multi-wavelength polarization Raman lidar, cloud radars, microwave radiometer, radiation sensors among others is deployed. From March 2019 onwards, additionally in-situ observations of the INP and cloud condensation nuclei properties were collected by TROPOS on a 623m high mountain 10 km upwind of the LACROS site. Meso-scale numerical modeling will provide support for interpretation of the results. The presentation will be dedicated to 1. a) provide an overview of the setup of DACAPO-PESO 2. b) show case studies of how instrument synergies are used to characterize aerosol-cloud-interaction processes in the pristine atmosphere over Punta Arenas and 3. c) show a case study of an Atmospheric River event which was also observed in Antarctica.

The study discusses the two algorithms PDF and ANN for classifying convective and stratiform precipitation profiles based on MRR data. The authors utilizes the maximum reflectivity, mean Doppler velocity and maximum deviation in velocity within +/15 min. But there have been a numerous studies on this topic using various ANN based algorithms (e.g., Ghada et al., 2019, doi:10.3390/atmos10050251; Jergensen

The southern midlatitudes and Sub-Antarctica are a key region for the Earth’s climate and a source for uncertainties in climate modelling. The low concentration of ice nucleating particles is considered to diminish the efficiency of heterogeneous ice formation. Climate models underestimate the supercooled liquid water content which causes shortwave radiation biases. The project DACAPO-PESO (Dynamics, Aerosol, Cloud And Precipitation Observations in the Pristine Environment of the Southern Ocean) which is being conducted in Punta Arenas (53°S, 71°W), Chile from Nov 2018 – March 2020 is endorsed by YOPP (Year of Polar Prediction) and fills an observational gap in the Southern Oceans, for which to date hardly any combined observations of lidar, cloud radar and microwave radiometer are available. During that field experiment, LACROS (Leipzig Aerosol and Cloud Remote Observations System) of Leibniz Institute for Tropospheric Research (TROPOS) which comprises numerous remote sensing instruments, including multi-wavelength polarization Raman lidar, cloud radars, microwave radiometer, radiation sensors among others is deployed. From March 2019 onwards, additionally in-situ observations of the INP and cloud condensation nuclei properties were collected by TROPOS on a 623m high mountain 10 km upwind of the LACROS site. Meso-scale numerical modeling will provide support for interpretation of the results. The presentation will be dedicated to 1. a) provide an overview of the setup of DACAPO-PESO 2. b) show case studies of how instrument synergies are used to characterize aerosol-cloud-interaction processes in the pristine atmosphere over Punta Arenas and 3. c) show a case study of an Atmospheric River event which was also observed in Antarctica.

We present particle optical properties of stratospheric smoke layers observed with multiwavelength polarization Raman lidar over Punta Arenas (53.2∘ S, 70.9∘ W), Chile, at the southernmost tip of South America in January 2020. The smoke originated from the record-breaking bushfires in Australia. The stratospheric aerosol optical thickness reached values up to 0.85 at 532 nm in mid-January 2020. The main goal of this rapid communication letter is to provide first stratospheric measurements of smoke extinction-to-backscatter ratios (lidar ratios) and particle linear depolarization ratios at 355 and 532 nm wavelengths. These aerosol parameters are important input parameters in the analysis of spaceborne CALIPSO and Aeolus lidar observations of the Australian smoke spreading over large parts of the Southern Hemisphere in January and February 2020 up to heights of around 30 km. Lidar and depolarization ratios, simultaneously measured at 355 and 532 nm, are of key importance regarding the homogenization of the overall Aeolus (355 nm wavelength) and CALIPSO (532 nm wavelength) lidar data sets documenting the spread of the smoke and the decay of the stratospheric perturbation, which will be observable over the entire year of 2020. We found typical values and spectral dependencies of the lidar ratio and linear depolarization ratio for aged stratospheric smoke. At 355 nm, the lidar ratio and depolarization ratio ranged from 53 to 97 sr (mean 71 sr) and 0.2 to 0.26 (mean 0.23), respectively. At 532 nm, the lidar ratios were higher (75–112 sr, mean 97 sr) and the depolarization ratios were lower with values of 0.14–0.22 (mean 0.18). The determined depolarization ratios for aged Australian smoke are in very good agreement with respective ones for aged Canadian smoke, observed with lidar in stratospheric smoke layers over central Europe in the summer of 2017. The much higher 532 nm lidar ratios, however, indicate stronger absorption by the Australian smoke particles.

Within this publication, lidar observations of the vertical aerosol distribution above Punta Arenas, Chile (53.2∘ S and 70.9∘ W), which have been performed with the Raman lidar PollyXT from December 2009 to April 2010, are presented. Pristine marine aerosol conditions related to the prevailing westerly circulation dominated the measurements. Lofted aerosol layers could only be observed eight times during the whole measurement period. Two case studies are presented showing long-range transport of smoke from biomass burning in Australia and regionally transported dust from the Patagonian Desert, respectively. The aerosol sources are identified by trajectory analyses with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and FLEXible PARTicle dispersion model (FLEXPART). However, seven of the eight analysed cases with lofted layers show an aerosol optical thickness of less than 0.05. From the lidar observations, a mean planetary boundary layer (PBL) top height of 1150 ± 350 m was determined. An analysis of particle backscatter coefficients confirms that the majority of the aerosol is attributed to the PBL, while the free troposphere is characterized by a very low background aerosol concentration. The ground-based lidar observations at 532 and 1064 nm are supplemented by the Aerosol Robotic Network (AERONET) Sun photometers and the space-borne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The averaged aerosol optical thickness (AOT) determined by CALIOP was 0.02 ± 0.01 in Punta Arenas from 2009 to 2010.