Abstract. The representation of aerosol-cloud interaction (ACI) processes in climate models, although long studied, still remains the source of high uncertainty. Very often there is a mismatch between the scale of observations used for ACI quantification and the ACI process itself. This can be mitigated by using the observations from groundbased remote sensing instruments. In this paper we presented a direct application of the aerosol-cloud interaction monitoring technique (ACI monitoring). ACI monitoring is based on the standardised Cloudnet data stream, which provides measurements from ground-based remote sensing instruments working in synergy. For the data set collected at the CESAR Observatory in the Netherlands we calculate ACI metrics. We specifically use attenuated backscatter coefficient (ATB) for the characterisation of the aerosol properties and cloud droplet effective radius (r e ) and number concentration (N d ) for the characterisation of the cloud properties. We calculate two metrics: ACI r = ln(r e )/ln(ATB) and ACI N = ln(N d )/ln(ATB). The calculated values of ACI r range from 0.001 to 0.085, which correspond to the values reported in previous studies. We also evaluated the impact of the vertical Doppler velocity and liquid water path (LWP) on ACI metrics. The values of ACI r were highest for LWP values between 60 and 105 g m −2 . For higher LWP other processes, such as collision and coalescence, seem to be dominant and obscure the ACI processes. We also saw that the values of ACI r are higher when only data points located in the updraught regime are considered. The method presented in this study allow for monitoring ACI daily and further aggregating daily data into bigger data sets.
Abstract. A method for continuous observation of aerosol–cloud interactions with ground-based remote sensing instruments is presented. The main goal of this method is to enable the monitoring of cloud microphysical changes due to the changing aerosol concentration. We use high resolution measurements from lidar, radar and radiometer which allow to collect and compare data continuously. This method is based on a standardised data format from Cloudnet and can be implemented at any observatory where the Cloudnet data set is available. Two example study cases were chosen from the Atmospheric Radiation Measurement (ARM) Program deployment at Graciosa Island, Azores, Portugal in 2009 to present the method. We show the Pearson Product–Moment Correlation Coefficient, r, and the Coefficient of Determination, r2 for data divided into bins of LWP, each of 10 g m−2. We explain why the commonly used way of quantity aerosol cloud interactions by use of an ACI index (ACIr,τ = dln re,τ/dlnα) is not the best way of quantifying aerosol–cloud interactions.
Abstract.A new method for continuous observation of aerosol-cloud interactions with ground-based remote sensing instruments is presented. The main goal of this method is to enable the monitoring of the change of the cloud droplet size due to the change in the aerosol concentration. We use high-resolution measurements from a lidar, a radar and a radiometer, which allow us to collect and compare data continuously. This method is based on a standardised data format from Cloudnet and can be implemented at any observatory where the Cloudnet data set is available. Two example case studies were chosen from the Atmospheric Radiation Measurement (ARM) Program deployment on Graciosa Island, Azores, Portugal, in 2009 to present the method. We use the cloud droplet effective radius (r e ) to represent cloud microphysical properties and an integrated value of the attenuated backscatter coefficient (ATB) below the cloud to represent the aerosol concentration. All data from each case study are divided into bins of the liquid water path (LWP), each 10 g m −2 wide. For every LWP bin we present the correlation coefficient between ln r e and ln ATB, as well as ACI r (defined as ACI r = −d ln r e /d ln ATB, change in cloud droplet effective radius with aerosol concentration). Obtained values of ACI r are in the range 0.01-0.1. We show that ground-based remote sensing instruments used in synergy can efficiently and continuously monitor aerosol-cloud interactions.
Abstract. The interactions between aerosols and clouds are among the least understood climatic processes and were studied over Ascension Island. A ground-based UV polarization lidar was deployed on Ascension Island, which is located in the stratocumulus-to-cumulus transition zone of the southeastern Atlantic Ocean, to infer cloud droplet sizes and droplet number density near the cloud base of marine boundary layer cumulus clouds. The aerosol–cloud interaction (ACI) due to the presence of smoke from the African continent was determined during the monsoonal dry season. In September 2016, a cloud droplet number density ACIN of 0.3 ± 0.21 and a cloud effective radius ACIr of 0.18 ± 0.06 were found, due to the presence of smoke in and under the clouds. Smaller droplets near the cloud base makes them more susceptible to evaporation, and smoke in the marine boundary layer over the southeastern Atlantic Ocean will likely accelerate the stratocumulus-to-cumulus transition. The lidar retrievals were tested against more traditional radar–radiometer measurements and shown to be robust and at least as accurate as the lidar–radiometer measurements. The lidar estimates of the cloud effective radius are consistent with previous studies of cloud base droplet sizes. The lidar has the large advantage of retrieving both cloud and aerosol properties using a single instrument.
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