THE ARCTIC CLOUD PUZZLE Wendisch, Manfred; Macke, Andreas; Ehrlich, André ...
Bulletin of the American Meteorological Society,
05/2019, Letnik:
100, Številka:
5
Journal Article
Recenzirano
Odprti dostop
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, and 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) 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 (R/V) 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 the R/V 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.
Accurately measuring the turbulent transport of reactive and conservative greenhouse gases, heat, and organic compounds between the surface and the atmosphere is critical for understanding trace gas ...exchange and its response to changes in climate and anthropogenic activities. The relaxed eddy accumulation (REA) method enables measuring the land surface exchange when fast-response sensors are not available, broadening the suite of trace gases that can be investigated. The β factor scales the concentration differences to the flux, and its choice is central to successfully using REA. Deadbands are used to select only certain turbulent motions to compute the flux.
Accurately measuring the turbulent transport of reactive and conservative greenhouse gases, heat, and organic compounds between the surface and the atmosphere is critical for understanding trace gas ...exchange and its response to changes in climate and anthropogenic activities. The relaxed eddy accumulation (REA) method enables measuring the land surface exchange when fast-response sensors are not available, broadening the suite of trace gases that can be investigated. The β factor scales the concentration differences to the flux, and its choice is central to successfully using REA. Deadbands are used to select only certain turbulent motions to compute the flux. This study evaluates a variety of different REA approaches with the goal of formulating recommendations applicable over a wide range of surfaces and meteorological conditions for an optimal choice of the β factor in combination with a suitable deadband.
Observations were collected across three contrasting ecosystems offering stark differences in scalar transport and dynamics: a mid-latitude grassland ecosystem in Europe, a loose gravel surface of the Dry Valleys of Antarctica, and a spruce forest site in the European mid-range mountains.
We tested a total of four different REA models for the β factor: the first two methods, referred to as model 1 and model 2, derive βp based on a proxy p for which high-frequency observations are available (sensible heat Ts). In the first case, a linear deadband is applied, while in the second case, we are using a hyperbolic deadband. The third method, model 3, employs the approach first published by Baker et al. (1992), which computes βw solely based upon the vertical wind statistics. The fourth method, model 4, uses a constant βp, const derived from long-term averaging of the proxy-based βp factor. Each β model was optimized with respect to deadband size before intercomparison.
To our best knowledge, this is the first study intercomparing these different approaches over a range of different sites. With respect to overall REA performance, we found that the βw and constant βp, const performed more robustly than the dynamic proxy-dependent approaches. The latter models still performed well when scalar similarity between the proxy (here Ts) and the scalar of interest (here water vapor) showed strong statistical correlation, i.e., during periods when the distribution and temporal behavior of sources and sinks were similar.
Concerning the sensitivity of the different β factors to atmospheric stability, we observed that βT slightly increased with increasing stability parameter z/L when no deadband is applied, but this trend vanished with increasing deadband size. βw was unrelated to dynamic stability and displayed a generally low variability across all sites, suggesting that βw can be considered a site-independent constant. To explain why the βw approach seems to be insensitive towards changes in atmospheric stability, we separated the contribution of w′ kurtosis to the flux uncertainty. For REA applications without deeper site-specific knowledge of the turbulent transport and degree of scalar similarity, we recommend using either the βp, const or βw models when the uncertainty of the REA flux quantification is not limited by the detection limit of the instrument. For conditions when REA sampling differences are close to the instrument's detection limit, the βp models using a hyperbolic deadband are the recommended choice.
In many types of clouds, multiple hydrometeor populations can be present at the same time and height. Studying the evolution of these different hydrometeors in a time–height perspective can give ...valuable information on cloud particle composition and microphysical growth processes. However, as a prerequisite, the number of different hydrometeor types in a certain cloud volume needs to be quantified. This can be accomplished using cloud radar Doppler velocity spectra from profiling cloud radars if the different hydrometeor types have sufficiently different terminal fall velocities to produce individual Doppler spectrum peaks. Here we present a newly developed supervised machine learning radar Doppler spectra peak-finding algorithm (named PEAKO). In this approach, three adjustable parameters (spectrum smoothing span, prominence threshold, and minimum peak width at half-height) are varied to obtain the set of parameters which yields the best agreement of user-classified and machine-marked peaks. The algorithm was developed for Ka-band ARM zenith-pointing radar (KAZR) observations obtained in thick snowfall systems during the Atmospheric Radiation Measurement Program (ARM) mobile facility AMF2 deployment at Hyytiälä, Finland, during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) field campaign. The performance of PEAKO is evaluated by comparing its results to existing Doppler peak-finding algorithms. The new algorithm consistently identifies Doppler spectra peaks and outperforms other algorithms by reducing noise and increasing temporal and height consistency in detected features. In the future, the PEAKO algorithm will be adapted to other cloud radars and other types of clouds consisting of multiple hydrometeors in the same cloud volume.
Riming, i.e., the accretion and freezing of supercooled liquid water (SLW) on ice particles in mixed-phase clouds, is an important pathway for precipitation formation. Detecting and quantifying ...riming using ground-based cloud radar observations is of great interest; however, approaches based on measurements of the mean Doppler velocity (MDV) are unfeasible in convective and orographically influenced cloud systems. Here, we show how artificial neural networks (ANNs) can be used to predict riming using ground-based, zenith-pointing cloud radar variables as input features. ANNs are a versatile means to extract relations from labeled data sets, which contain input features along with the expected target values. Training data are extracted from a data set acquired during winter 2014 in Finland, containing both Ka- and W-band cloud radar and in situ observations of snowfall by a Precipitation Imaging Package from which the rime mass fraction (FRPIP) is retrieved. ANNs are trained separately either on the Ka-band radar or the W-band radar data set to predict the rime fraction FRANN. We focus on two configurations of input variables. ANN 1 uses the equivalent radar reflectivity factor (Ze), MDV, the width from left to right edge of the spectrum above the noise floor (spectrum edge width – SEW), and the skewness as input features. ANN 2 only uses Ze, SEW, and skewness. The application of these two ANN configurations to case studies from different data sets demonstrates that both are able to predict strong riming (FRANN > 0.7) and yield low values (FRANN ≤ 0.4) for unrimed snow. In general, the predictions of ANN 1 and 2 are very similar, advocating the capability of predicting riming without the use of MDV. The predictions of both ANNs for a wintertime convective cloud fit with coinciding in situ observations extremely well, suggesting the possibility to predict riming even within convective systems. Application of ANN 2 to an orographic case yields high FRANN values coinciding with observations of solid graupel particles at the ground.
In a warming Arctic the increased occurrence of new
particle formation (NPF) is believed to originate from the declining ice
coverage during summertime. Understanding the physico-chemical properties ...of
newly formed particles, as well as mechanisms that control both particle
formation and growth in this pristine environment, is important for
interpreting aerosol–cloud interactions, to which the Arctic climate can be
highly sensitive. In this investigation, we present the analysis of NPF and
growth in the high summer Arctic. The measurements were made on-board
research vessel Polarstern during the PS106 Arctic expedition. Four
distinctive NPF and subsequent particle growth events were observed, during
which particle (diameter in a range 10–50 nm) number concentrations
increased from background values of approx. 40 up to 4000 cm−3. Based
on particle formation and growth rates, as well as hygroscopicity of
nucleation and the Aitken mode particles, we distinguished two different
types of NPF events. First, some NPF events were favored by negative ions,
resulting in more-hygroscopic nucleation mode particles and suggesting
sulfuric acid as a precursor gas. Second, other NPF events resulted in
less-hygroscopic particles, indicating the influence of organic vapors on
particle formation and growth. To test the climatic relevance of NPF and its
influence on the cloud condensation nuclei (CCN) budget in the Arctic, we
applied a zero-dimensional, adiabatic cloud parcel model. At an updraft
velocity of 0.1 m s−1, the particle number size distribution (PNSD)
generated during nucleation processes resulted in an increase in the CCN
number concentration by a factor of 2 to 5 compared to the background CCN
concentrations. This result was confirmed by the directly measured CCN
number concentrations. Although particles did not grow beyond 50 nm in
diameter and the activated fraction of 15–50 nm particles was on average
below 10 %, it could be shown that the sheer number of particles produced
by the nucleation process is enough to significantly influence the
background CCN number concentration. This implies that NPF can be an important
source of CCN in the Arctic. However, more studies should be conducted in
the future to understand mechanisms of NPF, sources of precursor gases and
condensable vapors, as well as the role of the aged nucleation mode
particles in Arctic cloud formation.
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.
Cloud and precipitation processes are still a main source of
uncertainties in numerical weather prediction and climate change
projections. The Priority Programme “Polarimetric Radar Observations meet
...Atmospheric Modelling (PROM)”, funded by the German Research Foundation
(Deutsche Forschungsgemeinschaft, DFG), is guided by the hypothesis that
many uncertainties relate to the lack of observations suitable to challenge
the representation of cloud and precipitation processes in atmospheric
models. Such observations can, however, at present be provided by the
recently installed dual-polarization C-band weather radar network of the
German national meteorological service in synergy with cloud radars and
other instruments at German supersites and similar national networks
increasingly available worldwide. While polarimetric radars potentially
provide valuable in-cloud information on hydrometeor type, quantity,
and microphysical cloud and precipitation processes, and atmospheric models
employ increasingly complex microphysical modules, considerable knowledge
gaps still exist in the interpretation of the observations and in the
optimal microphysics model process formulations. PROM is a coordinated
interdisciplinary effort to increase the use of polarimetric radar
observations in data assimilation, which requires a thorough evaluation and
improvement of parameterizations of moist processes in atmospheric models.
As an overview article of the inter-journal special issue “Fusion of radar
polarimetry and numerical atmospheric modelling towards an improved
understanding of cloud and precipitation processes”, this article outlines
the knowledge achieved in PROM during the past 2 years and gives
perspectives for the next 4 years.
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.
Ice-nucleating particles (INPs) initiate the primary ice formation in clouds at temperatures above ca. −38 ∘C and have an impact on precipitation formation, cloud optical properties, and cloud ...persistence. Despite their roles in both weather and climate, INPs are not well characterized, especially in remote regions such as the Arctic.
We present results from a ship-based campaign to the European Arctic during May to July 2017. We deployed a filter sampler and a continuous-flow diffusion chamber for offline and online INP analyses, respectively. We also investigated the ice nucleation properties of samples from different environmental compartments, i.e., the sea surface microlayer (SML), the bulk seawater (BSW), and fog water.
Concentrations of INPs (NINP) in the air vary between 2 to 3 orders of magnitudes at any particular temperature and are, except for the temperatures above −10 ∘C and below −32 ∘C, lower than in midlatitudes. In these temperature ranges, INP concentrations are the same or even higher than in the midlatitudes.
By heating of the filter samples to 95 ∘C for 1 h, we found a significant reduction in ice nucleation activity, i.e., indications that the INPs active at warmer temperatures are biogenic. At colder temperatures the INP population was likely dominated by mineral dust.
The SML was found to be enriched in INPs compared to the BSW in almost all samples. The enrichment factor (EF) varied mostly between 1 and 10, but EFs as high as 94.97 were also observed. Filtration of the seawater samples with 0.2 µm syringe filters led to a significant reduction in ice activity, indicating the INPs are larger and/or are associated with particles larger than 0.2 µm. A closure study showed that aerosolization of SML and/or seawater alone cannot explain the observed airborne NINP unless significant enrichment of INP by a factor of 105 takes place during the transfer from the ocean surface to the atmosphere.
In the fog water samples with −3.47 ∘C, we observed the highest freezing onset of any sample. A closure study connecting NINP in fog water and the ambient NINP derived from the filter samples shows good agreement of the concentrations in both compartments, which indicates that INPs in the air are likely all activated into fog droplets during fog events.
In a case study, we considered a situation during which the ship was located in the marginal sea ice zone and NINP levels in air and the SML were highest in the temperature range above −10 ∘C. Chlorophyll a measurements by satellite remote sensing point towards the waters in the investigated region being biologically active. Similar slopes in the temperature spectra suggested a connection between the INP populations in the SML and the air. Air mass history had no influence on the observed airborne INP population. Therefore, we conclude that during the case study collected airborne INPs originated from a local biogenic probably marine source.
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