Abstract

Abstract. A technique for retrieving warm cloud microphysics using synergistic ground based remote sensing instruments is presented. The SYRSOC (SYnergistic Remote Sensing Of Cloud) technique utilises a Ka-band Doppler cloud RADAR, a LIDAR (or ceilometer) and a multichannel microwave radiometer. SYRSOC retrieves the main microphysical parameters such as cloud droplet number concentration (CDNC), droplets effective radius (reff), cloud liquid water content (LWC), and the departure from adiabatic conditions within the cloud. Two retrievals are presented for continental and marine stratocumulus advected over the Mace Head Atmospheric Research Station. Whilst the continental case exhibited high CDCN (N = 382 cm−3; 10th-to-90th percentile [9.4–842.4] cm−3) and small mean effective radius (reff = 4.3; 10th-to-90th percentile [2.9–6.5] μm), the marine case showed low CDNC and large mean effective radius (N = 25 cm−3, 10th-to-90th percentile [1.5–69] cm−3; reff = 28.4 μm, 10th-to-90th percentile [11.2–42.7] μm) as expected since continental air at this location is typically more polluted than marine air. The mean LWC was comparable for the two cases (continental: 0.19 g m−3; marine: 0.16 g m−3) but the 10th–90th percentile range was wider in marine air (continental: 0.11–0.22 g m−3; marine: 0.01–0.38 g m−3). The calculated algorithm uncertainty for the continental and marine case for each variable was, respectively, σN = 161.58 cm−3 and 12.2 cm−3, σreff = 0.86 μm and 5.6 μm, σLWC = 0.03 g m−3 and 0.04 g m−3. The retrieved CDNC are compared to the cloud condensation nuclei concentrations and the best agreement is achieved for a supersaturation of 0.1% in the continental case and between 0.1%–0.75% for the marine stratocumulus. The retrieved reff at the top of the clouds are compared to the MODIS satellite reff: 7 μm (MODIS) vs. 6.2 μm (SYRSOC) and 16.3 μm (MODIS) vs. 17 μm (SYRSOC) for continental and marine cases, respectively. The combined analysis of the CDNC and the reff, for the marine case shows that the drizzle modifies the droplet size distribution and reff especially if compared to reffMOD. The study of the cloud subadiabaticity and the LWC shows the general sub-adiabatic character of both clouds with more pronounced departure from adiabatic conditions in the continental case than in the marine.

Highlights

  • At the global scale clouds increase the reflection of incoming solar radiation from 15 % to 30 % with an overall forcing of about −44 W m−2

  • All microphysical variables are calculated by SYRSOC and shown in two-panel figures for the continental and the marine cases in the following sub-sections

  • The aim of the study is to provide the full cloud microphysics by applying SYRSOC to the synergistic suite of three remote sensors, namely cloud RADAR, LIDAR and microwave radiometer (MWR) installed at the GAW Atmospheric Station of Mace Head, Ireland

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Summary

Introduction

At the global scale clouds increase the reflection of incoming solar radiation from 15 % to 30 % with an overall forcing of about −44 W m−2. The reduced cloud thermal emission below clear-sky values enhances the cloud greenhouse effect by about 31 W m−2 determining a net cooling effect of about 13 W m−2 (Ramanathan et al, 1989). The determination of the global cloud radiative forcing intended as the difference between the radiation budget components for cloudy conditions and clear-sky conditions is a challenging task which remains affected by a large uncertainty. Despite the critical role of this energy mechanism, the balance between cooling and warming effect due to LW and SW net fluxes in cloudy regions remains one of the largest uncertainties when assessing the aerosol indirect effect. The fact that the greenhouse effect due to cloud is orders of magnitude larger than the one that would result from a hundredfold increase in CO2 mixing ratio as well as the fact that hydrometeors size and concentration affect the cloud albedo are amongst the primarily reasons why in the last 50 yr studying cloud microphysics became paramount in order to understand climate changes

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