A framework for aerosol-cloud interactions monitoring

Clouds have an important role in the climate system: their ability to scatter solar radiation and absorb/emit thermal IR radiation makes them a significant regulator of the Earth's radiation budget. According to the latest report published by the Intergovernmental Panel on Climate Change (IPCC), aerosols and their interactions with clouds continue to contribute to the largest uncertainty of the total radiative forcing estimate. The first aerosol indirect effect, or albedo effect, has a well established underlying physical basis. Increasing amount of aerosols results in an elevated number of nuclei for cloud droplet formation, thus the concentration of cloud droplets can be higher but the cloud droplet effective radius becomes smaller. The resulting clouds are brighter which leads to a higher cloud albedo. This happens if all other parameters, mainly the amount of available liquid water, are kept constant. A broad range of strategies has been used to study the first aerosol indirect effect. Some observation strategies have used remote sensing instruments at specific sites, sometimes combining them with in situ measurements. Others have analysed airborne data from short-lived field campaigns. Further, many approaches have focused on satellite remote sensing to examine the aerosol effect on a global scale. The wide scope of methods and scales used makes it difficult to quantitatively compare result from different studies with precision.

In this paper, we propose a method of aerosol-cloud interaction monitoring based on widely available remote sensing instruments and, thus, easily applicable at many different observatories. This method provides fast and easy way of identifying cases where a change in the aerosol environment causes a change in the cloud. In this scheme we attempt to use (as far as possible) the observed signal from lidar and radar. For an aerosol proxy we propose to use the attenuated backscatter (sensitive to aerosol concentration) and to obtain information about changes in the cloud we use the radar reflectivity factor (sensitive to cloud droplet size and concentration). We supplement this observations with Liquid Water Path (LWP) from radiometer, which provides further information on the amount of liquid water present in the cloud. Assuming a positive dependence between the number concentration of cloud droplets and the number concentration of aerosol we expect that an increase of the attenuated backscatter coefficient will correspond to a small increase of the radar reflectivity factor (due to the increase of cloud droplets concentration). However, the slope of this correlation will vary. In the analysed case studies we can observe a systematic change in the slope of the relation between the attenuated backscatter coefficient and the radar reflectivity factor. A number of factors, such as meteorology or cloud drop microphysical properties, can influence changes in a cloud. For that reason we put a constraint on the LWP. We divide the data by the bins defined by the value of LWP (every bin consists of values varying by the maximum of 5 g/m²). This limitation ensures that the variability in the cloud will be primarily due to changes in microphysical properties associated with the variation in aerosols. Further, we limit the cases only to non-precipitating, low-level stratiform and stratocumulus clouds with no drizzle present (by putting a constraint on the value of the radar reflectivity factor - we only process data if the Z < -20dBZ). In next step we will divide processed data into aerosol activation zones. This will be done by identifying updrafts, representing activation zones, and downdrafts close to the cloud base with the Doppler velocity.

Although this method is based on a synergy of remote sensing instruments, we use widely available systems for a quick and efficient evaluation of the aerosol influence on the cloud. The main advantages of this scheme are: it is a simple method, we use direct observables from widely spread remote sensing instruments; there is no assumptions about the microphysical properties of clouds; this method can be easily implemented at different observatories; it is less restrictive in the selection of study cases than many microphysical properties retrieval algorithms; it can (and should) be complemented by microphysical properties for the further analysis of the data. We plan to implement this framework over the cloud profiling sites of the ACTRIS network in Europe to enable monitoring of the aerosol-cloud interaction close to real-time. We believe that obtaining data in the same format over multiple regions will allow for more studies of this phenomena and will result in a better understanding of the interactions between aerosols and clouds.