Search

An accurate determination of location and amount of liquid water in clouds is crucial for precipitation formation, cloud lifetime, and cloud radiative effects. Most remote-sensing retrievals, such as Cloudnet use lidar measurements to infer the location of liquid cloud droplets from measurements. However, lidar observations are of very limited use for optically thick or multilayer mixed-phase clouds (MPC) where they usually underestimate the presence of liquid water due to full signal attenuation, leading to large biases in simulated radiative fluxes.At the same time, general circulation models largely overestimate the downwelling shortwave radiation at the bottom of the atmosphere especially in the Southern Ocean regions. We argue that, in order to reduce this shortwave radiation bias in models, we first need better observational-based retrievals for supercooled-liquid detection that can be used for model validation. For this purpose, the machine-learning-based retrieval VOODOO is used to capture the extent of liquid layers over the complete vertical range of the clouds.To conceptualize the latter, a case study from the DACAPO-PESO campaign in Punta Arenas, Chili (53.13° S, 70.88° W) was investigated in detail by performing a radiative closures study. The shortwave cloud radiative effects of multilayer non-precipitating stratiform MPC - with liquid water layers detected by Cloudnet and VOODOO - was determined using a 1-D radiative transfer simulator and validated with downwelling pyranometer observations. The shortwave radiation bias was reduced by a factor of two suggesting that improved liquid-layer detection helps to decrease the shortwave radiation bias in radiative transfer simulations., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

For understanding Arctic climate change, it is critical to quantify and address uncertainties in climate data records on clouds and radiative fluxes derived from long-term passive satellite observations. A unique set of observations collected during the PS106 expedition of the research vessel Polarstern (28 May to 16 July 2017) by the OCEANET facility, is exploited here for this purpose and compared with the CERES SYN1deg ed. 4.1 satellite remote-sensing products. Mean cloud fraction (CF) of 86.7 % for CERES SYN1deg and 76.1 % for OCEANET were found for the entire cruise. The difference of CF between both data sets is due to different spatial resolution and momentary data gaps, which are a result of technical limitations of the set of shipborne instruments. A comparison of radiative fluxes during clear-sky (CS) conditions enables radiative closure (RC) for CERES SYN1deg products by means of independent radiative transfer simulations. Several challenges were encountered to accurately represent clouds in radiative transfer under cloudy conditions, especially for ice-containing clouds and low-level stratus (LLS) clouds. During LLS conditions, the OCEANET retrievals were particularly compromised by the altitude detection limit of 155 m of the cloud radar. Radiative fluxes from CERES SYN1deg show a good agreement with ship observations, having a bias (standard deviation) of −6.0 (14.6) and 23.1 (59.3) W m−2 for the downward longwave (LWD) and shortwave (SWD) fluxes, respectively. Based on CERES SYN1deg products, mean values of the radiation budget and the cloud radiative effect (CRE) were determined for the PS106 cruise track and the central Arctic region (70–90∘ N). For the period of study, the results indicate a strong influence of the SW flux in the radiation budget, which is reduced by clouds leading to a net surface CRE of −8.8 and −9.3 W m−2 along the PS106 cruise and for the entire Arctic, respectively. The similarity of local and regional CRE supports the consideration that the PS106 cloud observations can be representative of Arctic cloudiness during early summer.

Quantifying the role of clouds in the Earth radiation budget is essential for improving our understanding of the drivers and feedbacks of climate change. This holds in particular for the Arctic, the region currently undergoing the most rapid changes. This region, however, also poses significant challenges to remote-sensing retrievals of clouds and radiative fluxes, introducing large uncertainties in current climate data records. In particular, low-level stratiform clouds are common in the Arctic but are, due to their low altitude, challenging to observe and characterize with remote-sensing techniques. The availability of reliable ground-based observations as reference is thus of high importance. In the present study, radiative transfer simulations based on state-of-the-art ground-based remote sensing of clouds are contrasted to surface radiative flux measurements to assess their ability to constrain the cloud radiative effect. Cloud radar, lidar, and microwave radiometer observations from the PS106 cruise in the Arctic marginal sea ice zone in summer 2017 were used to derive cloud micro- and macrophysical properties by means of the instrument synergy approach of Cloudnet. Closure of surface radiative fluxes can only be achieved by a realistic representation of the low-level liquid-containing clouds in the radiative transfer simulations. The original, likely erroneous, representation of these low-level clouds in the radiative transfer simulations let to errors in the cloud radiative effect of 43 W m-2. The present study highlights the importance of jointly improving retrievals for low-level liquid-containing clouds which are frequently encountered in the high Arctic, together with observational capabilities both in terms of cloud remote sensing and radiative flux observations. Concrete suggestions for achieving these goals are provided.

In the Arctic, new particle formation (NPF) and subsequent growth processes are the keys to produce Aitken-mode particles, which under certain conditions can act as cloud condensation nuclei (CCNs). The activation of Aitken-mode particles increases the CCN budget of Arctic low-level clouds and, accordingly, affects Arctic climate forcing. However, the growth mechanism of Aitken-mode particles from NPF into CCN range in the summertime Arctic boundary layer remains a subject of current research. In this combined Arctic cruise field and modeling study, we investigated Aitken-mode particle growth to sizes above 80 nm. A mechanism is suggested that explains how Aitken-mode particles can become CCN without requiring high water vapor supersaturation. Model simulations suggest the formation of semivolatile compounds, such as methanesulfonic acid (MSA) in fog droplets. When the fog droplets evaporate, these compounds repartition from CCNs into the gas phase and into the condensed phase of nonactivated Aitken-mode particles. For MSA, a mass increase factor of 18 is modeled. The postfog redistribution mechanism of semivolatile acidic and basic compounds could explain the observed growth of >20 nm h−1 for 60-nm particles to sizes above 100 nm. Overall, this study implies that the increasing frequency of NPF and fog-related particle processing can affect Arctic cloud properties in the summertime boundary layer.

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 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.

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.

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 modification of an existing cloud property retrieval scheme for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the geostationary Meteosat satellites is described to utilize its high-resolution visible (HRV) channel for increasing the spatial resolution of its physical outputs. This results in products with a nadir spatial resolution of 1×1 km2 compared to the standard 3×3 km2 resolution offered by the narrowband channels. This improvement thus greatly reduces the resolution gap between current geostationary and polar-orbiting meteorological satellite imagers. In the first processing step, cloudiness is determined from the HRV observations by a threshold-based cloud masking algorithm. Subsequently, a linear model that links the 0.6 µm, 0.8 µm, and HRV reflectances provides a physical constraint to incorporate the spatial high-frequency component of the HRV observations into the retrieval of cloud optical depth. The implementation of the method is described, including the ancillary datasets used. It is demonstrated that the omission of high-frequency variations in the cloud-absorbing 1.6 µm channel results in comparatively large uncertainties in the retrieved cloud effective radius, likely due to the mismatch in channel resolutions. A newly developed downscaling scheme for the 1.6 µm reflectance is therefore applied to mitigate the effects of this scale mismatch. Benefits of the increased spatial resolution of the resulting SEVIRI products are demonstrated for three example applications: (i) for a convective cloud field, it is shown that significantly better agreement between the distributions of cloud optical depth retrieved from SEVIRI and from collocated MODIS observations is achieved. (ii) The temporal evolution of cloud properties for a growing convective storm at standard and HRV spatial resolutions are compared, illustrating an improved contrast in growth signatures resulting from the use of the HRV channel. (iii) An example of surface solar irradiance, determined from the retrieved cloud properties, is shown, for which the HRV channel helps to better capture the large spatiotemporal variability induced by convective clouds. These results suggest that incorporating the HRV channel into the retrieval has potential for improving Meteosat-based cloud products for several application domains.

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. PS106 contributed observations for the initiative "Arctic Amplification: Climate Relevant Atmospheric and Surface Processes and Feedback Mechanisms (AC)3" which involves numerous projects aiming on understanding the role of atmospheric and surface processes in the ongoing rapid changes in the Arctic climate. As one of the central facilities of (AC)3, 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 multi-wavelength Raman polarization lidar PollyXT and a 14-channel microwave radiometer HATPRO. 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-stare observations aboard the moving Polarstern. Also, low-level clouds and the presence of fog 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 165m 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 50m and 165m above sea level is presented. In addition, we provide an overview of the data processing chain of the OCEANET observations and demonstrate case studies of aerosol and cloud studies to introduce the capabilities of the dataset. The retrieval of aerosol optical and microphysical properties from the observations of PollyXT is presented by means of observations performed during the ice floe camp. Synergies between the remote sensing instruments and auxiliary observations from aboard Polarstern were analyzed by means of Cloudnet which provides as primary output a target classification mask. This target classification is the basis for value-added products such as liquid- and ice-cloud microphysical properties, cloud dynamics which can in subsequent steps be used as input for the investigation of cloud microphysical processes, radiative transfer calculations, or model evaluation. To this end, new approaches for ice crystal effective radius and eddy dissipation rates have been implemented into Cloudnet.

The role of clouds in recent Arctic warming is not fully understood, including their effects on the shortwave radiation and the surface energy budget. To investigate relevant small-scale processes in detail, an intensive field campaign was conducted during early summer in the central Arctic during the Physical feedbacks of Arctic planetary boundary layer, Sea ice, Cloud and AerosoL (PASCAL) drifting ice floe station. During this campaign, the small-scale spatiotemporal variability of global irradiance was observed for the first time on an ice floe with a dense network of autonomous pyranometers. 15 stations were deployed covering an area of 0.83 km × 1.3 km from June 4–16, 2017. This unique, open-access dataset is described here, and an analysis of the spatiotemporal variability deduced from this dataset is presented for different synoptic conditions. Based on additional observations, 5 typical sky conditions were identified and used to determine the values of the mean and variance of atmospheric global transmittance for these conditions. Overcast conditions were observed 39.6 % of the time predominantly during the first week, with an overall mean transmittance of 0.47. The second-most frequent conditions corresponded to multi-layer clouds (32.4 %) which prevailed in particular during the second week, with a mean transmittance of 0.43. Broken clouds had a mean transmittance of 0.61 and a frequency of occurrence of 22.1 %. Finally, the least frequent sky conditions were thin clouds and cloudless conditions, which both had a mean transmittance of 0.76, and occurrence frequencies of 3.5 % and 2.4 %, respectively. For overcast conditions, lower global irradiance was observed for stations closer to the ice edge, likely attributable to the low surface albedo of dark open water, and a resulting reduction of multiple reflections between the surface and cloud base. Using a wavelet-based multi-resolution analysis, power spectra of the time-series of atmospheric transmittance were compared for single-station and spatially averaged observations, and for different sky conditions. It is shown that both the absolute magnitude and the scale-dependence of variability contains characteristic features for the different sky conditions.

Covering about 30% of the Earth, upper tropospheric clouds play a key role in the climate system by modulating the Earth’s energy budget and heat transport. Infrared Sounders reliably identify cirrus down to an IR optical depth of 0.1. Recently LMD has built global cloud climate data records from AIRS and IASI observations, covering the periods from 2003-2015 and 2008-2015, respectively. Upper tropospheric clouds often form mesoscale systems. Their organization and properties are being studied by (1) distinguishing cloud regimes within 2° × 2° regions and (2) applying a spatial composite technique on adjacent cloud pressures, which estimates the horizontal extent of the mesoscale cloud systems. Convective core, cirrus anvil and thin cirrus of these systems are then distinguished by their emissivity. Compared to other studies of tropical mesoscale convective systems our data include also the thinner anvil parts, which make out about 30% of the area of tropical mesoscale convective systems. Once the horizon...