Investigation of drizzle onset in liquid clouds using ground based active and passive remote sensing instruments

One of the major challenges of climate prediction is a correct representation of the interactions among aerosols, clouds and precipitation. Aerosols have a strong impact on the life cycle of boundary layer clouds, which are known to significantly influence the energy available to the Earth-Atmosphere system. Specifically, drizzle formation in low-level clouds, which has been shown to depend on aerosol concentration (second indirect aerosol effect), determines cloud life time. In models, the transition from liquid cloud to precipitation must be parameterized by the so-called autoconversion process. Different parameterizations of autoconversion have been developed, whereby the corresponding transition rates differ of up to one order of magnitude. Even observations of this microphysical process are very challenging. Satellite observations have been exploited in the past to evaluate different autoconversion schemes but one of the main reasons for the encountered differences between models and observations was the poor representation of the vertical cloud structure in the satellite observations. In this context, ground-based cloud observations present a unique tool to provide observational constraints for model parameterization development by exploiting their highly temporally and spatially resolved profiling capability. In recent years, new ground-based techniques exploiting higher moments of the cloud radar Doppler spectrum (the skewness, in particular) have been successfully applied for the detection of drizzle onset in maritime clouds. In this thesis, a new, extended ground-based dataset for continental liquid clouds is exploited in order to assess the potential for early drizzle detection. For this purpose, ground-based observations of liquid water path and of the cloud radar Doppler moments reflectivity, mean Doppler velocity, spectral width and skewness have been synergetically exploited. It has been found that skewness detects drizzle formation at an earlier stage than the other radar moments. The different observational variables have been used for the development of a drizzle probability index (DI) to improve currently available drizzle classification schemes, i.e. Cloudnet. The DI represents the probability of each cloud radar bin to contain drizzle. In comparison to the Cloudnet classification, case studies show that the DI detects earlier stages of drizzle formation and eliminates falsely detected, inconsistent time-height drizzle structures. However, due to the presence of turbulence, the DI sometimes falsely attribute drizzle to a pixel. In order to understand how turbulence can impact radar Doppler measurements and also in order to optimize the radar measurement settings for the purpose of drizzle detection, sensitivity studies on integration time, spectral resolution and radar antenna beam width have been conducted using raw radar data and a forward radar simulator. It has been found that integration times no longer than 2 seconds should be used for drizzle detection and that the spectral resolution obtained with the fast Fourier transform (FFT) using 256 FFT points resolves the characteristics of the Doppler spectrum with sufficient accuracy. Also, simulations showed that smaller beam widths are beneficial for drizzle detection and that turbulence is responsible for an increase of spectral width and a reduction of observed skewness values. Finally, a microphysical interpretation of the skewness signal is provided by comparing the simulations of drizzle formation from a 1D steady-state binned microphysical model to observations. The forward simulated vertical profiles of skewness based on the modeled cloud drop and drizzle size distributions strongly depend on the applied autoconversion parameterization. A validation of the different schemes indicates that the scheme from Seifert et al., (2010) best matches the observations of reflectivity and skewness. The comparison also suggests that the modeled autoconversion rates tend to produce large drizzle too fast and too early for continental liquid clouds. This first model comparison thus demonstrates that ground-based cloud radar observations, particularly skewness, can be used for testing autoconversion parameterizations. The dataset and the results of this work constitute a unique basis for evaluating model outputs, e.g. in a next step the results of large eddy simulations, and for carrying out additional process studies to refine for example the drizzle detection criterion. Also, this data set could be exploited for future validations of satellite products, e.g. of EarthCARE. This thesis hence shows how ground-based cloud radar observations can be optimally exploited to better understand the autoconversion process and also represents an important step forward in bringing observations of drizzle and modeling together

EUREC4A's Maria S. Merian ship-based cloud and micro rain radar observations of clouds and precipitation

As part of the EUREC4A field campaign, the research vessel Maria S. Merian probed an oceanic region between 6° N and 13.8° N and 51° W to 60° W for approximately 32 days. Trade wind cumulus clouds were sampled in the trade-wind alley region east of Barbados as well as in the transition region between the trades and the intertropical convergence zone, where the ship crossed some mesoscale oceanic eddies. We collected continuous observations of cloud and precipitation profiles at unprecedented vertical resolution (7–10 m in the first 3000 m) and high temporal resolution (1–3 s) using a W-band radar and micro-rain radar (MRR-PRO), installed on an active stabilization platform to reduce the impact of ship motions on the observations. The paper describes the ship motion correction algorithm applied to the Doppler observations to extract corrected hydrometeors vertical velocities and the algorithm created to filter interference patterns in the MRR-PRO observations. Radar reflectivity, mean Doppler velocity, spectral width and skewness for W-band and attenuated reflectivity, mean Doppler velocity and rain rate for MRR-PRO are shown for a case study to demonstrate the potential of the high resolution adopted. As non-standard analysis, we also retrieved and provided liquid water path (LWP) from the 89 GHz passive channel available on the W-band radar system. All datasets and hourly and daily quicklooks are publically available. Data can be accessed and basic variables can be plotted online via the intake catalog of the online book "How to EUREC4A".Postprint (author's final draft

Detection and attribution of aerosol-cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic model

Clouds and aerosols contribute the largest uncertainty to current estimates and interpretations of the Earth’s changing energy budget. Here we use a new-generation large-domain large-eddy model, ICON-LEM (ICOsahedral Non-hydrostatic Large Eddy Model), to simulate the response of clouds to realistic anthropogenic perturbations in aerosols serving as cloud condensation nuclei (CCN). The novelty compared to previous studies is that (i) the LEM is run in weather prediction mode and with fully interactive land surface over a large domain and (ii) a large range of data from various sources are used for the detection and attribution. The aerosol perturbation was chosen as peak-aerosol conditions over Europe in 1985, with more than fivefold more sulfate than in 2013. Observational data from various satellite and ground-based remote sensing instruments are used, aiming at the detection and attribution of this response. The simulation was run for a selected day (2 May 2013) in which a large variety of cloud regimes was present over the selected domain of central Europe. It is first demonstrated that the aerosol fields used in the model are consistent with corresponding satellite aerosol optical depth retrievals for both 1985 (perturbed) and 2013 (reference) conditions. In comparison to retrievals from ground-based lidar for 2013, CCN profiles for the reference conditions were consistent with the observations, while the ones for the 1985 conditions were not. Similarly, the detection and attribution process was successful for droplet number concentrations: the ones simulated for the 2013 conditions were consistent with satellite as well as new ground-based lidar retrievals, while the ones for the 1985 conditions were outside the observational range. For other cloud quantities, including cloud fraction, liquid water path, cloud base altitude and cloud lifetime, the aerosol response was small compared to their natural variability. Also, large uncertainties in satellite and ground-based observations make the detection and attribution difficult for these quantities. An exception to this is the fact that at a large liquid water path value (LWP > 200 g m−2), the control simulation matches the observations, while the perturbed one shows an LWP which is too large. The model simulations allowed for quantifying the radiative forcing due to aerosol–cloud interactions, as well as the adjustments to this forcing. The latter were small compared to the variability and showed overall a small positive radiative effect. The overall effective radiative forcing (ERF) due to aerosol–cloud interactions (ERFaci) in the simulation was dominated thus by the Twomey effect and yielded for this day, region and aerosol perturbation −2.6 W m$^{-2}$. Using general circulation models to scale this to a global-mean present-day vs. pre-industrial ERFaci yields a global ERFaci of −0.8 W m$^{-2}$

Isotopic measurements in water vapor, precipitation, and seawater during EUREC4^4A

n early 2020, an international team set out to investigate trade-wind cumulus clouds and their coupling to the large-scale circulation through the field campaign EUREC$^4$A: ElUcidating the RolE of Clouds-Circulation Coupling in ClimAte. Focused on the western tropical Atlantic near Barbados, EUREC$^4$A deployed a number of innovative observational strategies, including a large network of water isotopic measurements collectively known as EUREC$^4$A-iso, to study the tropical shallow convective environment. The goal of the isotopic measurements was to elucidate processes that regulate the hydroclimate state – for example, by identifying moisture sources, quantifying mixing between atmospheric layers, characterizing the microphysics that influence the formation and persistence of clouds and precipitation, and providing an extra constraint in the evaluation of numerical simulations. During the field experiment, researchers deployed seven water vapor isotopic analyzers on two aircraft, on three ships, and at the Barbados Cloud Observatory (BCO). Precipitation was collected for isotopic analysis at the BCO and from aboard four ships. In addition, three ships collected seawater for isotopic analysis. All told, the in situ data span the period 5 January–22 February 2020 and cover the approximate area 6 to 16° N and 50 to 60° W, with water vapor isotope ratios measured from a few meters above sea level to the mid-free troposphere and seawater samples spanning the ocean surface to several kilometers depth. This paper describes the full EUREC$^4$A isotopic in situ data collection – providing extensive information about sampling strategies and data uncertainties – and also guides readers to complementary remotely sensed water vapor isotope ratios. All field data have been made publicly available even if they are affected by known biases, as is the case for high-altitude aircraft measurements, one of the two BCO ground-based water vapor time series, and select rain and seawater samples from the ships. Publication of these data reflects a desire to promote dialogue around improving water isotope measurement strategies for the future. The remaining, high-quality data create unprecedented opportunities to close water isotopic budgets and evaluate water fluxes and their influence on cloudiness in the trade-wind environment. The full list of dataset DOIs and notes on data quality flags are provided in Table 3 of Sect. 5 (“Data availability”)

A New Criterion to Improve Operational Drizzle Detection with Ground-Based Remote Sensing

Light shallow precipitation in the form of drizzle is one of the mechanisms for liquid water removal, affecting cloud lifetime and boundary layer dynamics and thermodynamics. The early formation of drizzle drops is of particular interest for quantifying aerosol-cloud-precipitation interactions. In models, drizzle initiation is represented by the autoconversion, that is, the conversion of liquid water from a cloud liquid water category (where particle sedimentation is ignored) into a precipitating liquid water category. Various autoconversion parameterizations have been proposed in recent years, but their evaluation is challenging due to the lack of proper observations of drizzle development in the cloud. This work presents a new algorithm for Classification of Drizzle Stages (CLADS). CLADS is based on the skewness of the Ka-band radar Doppler spectrum. Skewness is sensitive to the drizzle growth in the cloud: the observed Gaussian Doppler spectrum has skewness zero when only cloud droplets are present without any significant fall velocity. Defining downward velocities positive, skewness turns positive when embryonic drizzle forms and becomes negative when drizzle starts to dominate the spectrum. CLADS identifies spatially coherent structures of positive, zero, and negative skewness in space and time corresponding to drizzle seeding, drizzle growth/nondrizzle, and drizzle mature, respectively. We test CLADS on case studies from the Julich Observatory for Cloud Evolution Core Facility (JOYCE-CF) and the Barbados Cloud Observatory (BCO) to quantitatively estimate the benefits of CLADS compared to the standard Cloudnet target categorization algorithm. We suggest that CLADS can provide additional observational constraints for understanding the processes related to drizzle formation better

Optimizing observations of drizzle onset with millimeter-wavelength radars

Cloud Doppler radars are increasingly used to study cloud and precipitation microphysical processes. Typical bulk cloud properties such as liquid or ice content are usually derived using the first three standard moments of the radar Doppler spectrum. Recent studies demonstrated the value of higher moments for the reduction of retrieval uncertainties and for providing additional insights into microphysical processes. Large effort has been undertaken, e.g., within the Atmospheric Radiation Measurement (ARM) program to ensure high quality of radar Doppler spectra. However, a systematic approach concerning the accuracy of higher moment estimates and sensitivity to basic radar system settings, such as spectral resolution, integration time and beam width, are still missing. In this study, we present an approach on how to optimize radar settings for radar Doppler spectra moments in the specific context of drizzle detection. The process of drizzle development has shown to be particularly sensitive to higher radar moments such as skewness. We collected radar raw data (I/Q time series) from consecutive zenith-pointing observations for two liquid cloud cases observed at the cloud observatory JOYCE in Germany. The I/Q data allowed us to process Doppler spectra and derive their moments using different spectral resolutions and integration times during identical time intervals. This enabled us to study the sensitivity of the spatiotemporal structure of the derived moments to the different radar settings. The observed signatures were further investigated using a radar Doppler forward model which allowed us to compare observed and simulated sensitivities and also to study the impact of additional hardware-dependent parameters such as antenna beam width. For the observed cloud with drizzle onset we found that longer integration times mainly modify spectral width (S-w) and skewness (S-k), leaving other moments mostly unaffected. An integration time of 2 s seems to be an optimal compromise: both observations and simulations revealed that a 10 s integration time - as it is widely used for European cloud radars - leads to a significant turbulence-induced increase of S-w and reduction of S-k compared to 2 s integration time. This can lead to significantly different microphysical interpretations with respect to drizzle water content and effective radius. A change from 2 s to even shorter integration times (0.4 s) has much smaller effects on S-w and S-k. We also find that spectral resolution has a small impact on the moment estimations, and thus on the microphysical interpretation of the drizzle signal. Even the coarsest spectral resolution studied, 0.08 ms(-1), seems to be appropriate for calculation moments of drizzling clouds. Moreover, simulations provided additional insight into the microphysical interpretation of the skewness signatures observed: in low (high)-turbulence conditions, only drizzle larger than 20 mu m (40 mu m) can generate S-k values above the S-k noise level (in our case 0.4). Higher S-k values are also obtained in simulations when smaller beam widths are adopted

Fast atmospheric response to a cold oceanic mesoscale patch in the north‐western tropical Atlantic

International audienc

Ship- And island-based atmospheric soundings from the 2020 EUREC4A field campaign

To advance the understanding of the interplay among clouds, convection, and circulation, and its role in climate change, the Elucidating the role of clouds-circulation coupling in climate campaign (EUREC4A) and Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) collected measurements in the western tropical Atlantic during January and February 2020. Upper-air radiosondes were launched regularly (usually 4-hourly) from a network consisting of the Barbados Cloud Observatory (BCO) and four ships within 6-16°N, 51-60°W. From 8 January to 19 February, a total of 811 radiosondes measured wind, temperature, and relative humidity. In addition to the ascent, the descent was recorded for 82 % of the soundings. The soundings sampled changes in atmospheric pressure, winds, lifting condensation level, boundary layer depth, and vertical distribution of moisture associated with different ocean surface conditions, synoptic variability, and mesoscale convective organization. Raw (Level 0), quality-controlled 1 s (Level 1), and vertically gridded (Level 2) data in NetCDF (Stephan et al., 2020) are available to the public at AERIS (https://doi.org/10.25326/137 https://doi.org/10.25326/137). The methods of data collection and post-processing for the radiosonde data set are described here.Atmospheric Remote Sensin

EUREC4A Campaign, Cruise No. MSM89, 17. January - 20. February 2020, Bridgetown (Barbados) - Bridgetown (Barbados), The ocean mesoscale component in the EUREC4A++ field study

The MSM89 expedition of Maria S Merian was a contribution to the international research initiative EUREC4A (www.eurec4a.eu). The cruise was carried out in concert with the M161 campaign on RV METEOR (Germany) and the EUREC4A-OA campaign on NO L’ATALANTE (France). Airplane and drone operations as well as well as continuous observations from the ground-based site on the Island of Barbados (BCO) were considered during the MSM89 campaign. Moreover, the cruise was coordinated with ships and Saildrone© operations in the context of the US American ATOMIC project. The overall goal of the EUREC4A field campaign was to collect observational data that will enable research on dynamic and thermodynamic processes in the atmosphere and ocean that will bring the understanding of the role of clouds in the climate system to a new level. MSM89 had its focus on the ocean/atmosphere coupling across ocean mesoscale vortices. For this purpose, both ocean and atmosphere profile measurements were carried out to observe the temporal evolution and spatial heterogeneity of the atmospheric and oceanic boundary layer. Autonomous observing platforms (underwater glider) and a ship towed platform (Cloudkite) augmented the ship-based observations. Incubation experiments were performed to determine Nitrogen fixation rates, the gas exchange for carbon dioxide and oxygen uptake