In situ observations of wintertime low‐altitude clouds over the Southern Ocean

Abstract

Cloud droplet concentration (Nd), effective radius (reff) and liquid water content (LWC) measured by a DMT CAPS and an SEA WCM‐2000 of wintertime low‐altitude clouds over the Southern Ocean (SO) are presented for 20 flights taken over 3 years (June–October, 2013–2015). Such clouds have been reported to have the lowest Nd on record (10–40 cm−3) from the Southern Ocean Cloud Experiment (SOCEX I) field campaign in 1993. Of the total 20 357 one‐second records spent in cloud, 38.5% were found to contain ice crystals, primarily in mixed‐phase clouds (36.7%). Ice was observed at some point during 19 of the 20 missions. The droplet spectra and temperature range suggest these clouds were often ideal for the Hallett–Mossop ice multiplication process. The average Nd and reff for liquid clouds were 28 (±30) cm−3 and 12.5 (±2.9) µm, which are consistent with those from SOCEX I. Forty‐nine percent of all liquid cloud samples were observed to be drizzling with an average drizzle rate of 0.733 mm h−1. As drizzle samples were commonly in the neighbourhood of mixed‐phase or non‐drizzling clouds, it was rare to observe solid patches of drizzle of greater than 10 s. On average, drizzling clouds had lower Nd and greater reff and LWC than those of non‐drizzling clouds. Distinct observations of non‐drizzling clouds with relatively high Nd (∼89 cm−3), small reff (∼8.5 µm) and low LWC (∼0.173 g kg−1) were noted for two flights. An initial examination of the local environment and synoptic meteorology for these flights failed to identify any particular forcing that may have led to these unique microphysical properties, although these were the only observations of closed mesoscale cellular convection. This research highlights that greater variability exists in the microphysics of wintertime clouds over the SO, when a wider range of synoptic meteorology is investigated.

A Comparison of Cloud Microphysical Properties Derived From MODIS and CALIPSO With In Situ Measurements Over the Wintertime Southern Ocean

“…The mean r eff biases for the 1.6‐, 2.1‐, and 3.7‐μm channels are 5.7, 4.6, and 4.3 μm, respectively (Table ). Taking a 20% CAS sizing error into account (Ahn et al, ), the minimum mean r eff bias should be ~ 2.7 μm (at 3.7‐μm channel). Because the mean CM_SPI of this flight is small (12 and 15 for band 1 and band 2), the expected CM_SPI‐induced bias should be negligible (according to Painemal et al ()).…”

“…More recently, Ahn et al (, hereafter A17) detailed the in situ observations of 20 flights made over the SO to the west and south of Tasmania during the winter season (June–October) over a 3‐year period (2013–2015) to investigate the low‐altitude cloud microphysical properties. Consistent with the early findings of SOCEX I, A17 found that the pristine liquid‐phase clouds commonly have very low cloud droplet number concentration, N d (10–40 cm −3 ), and large effective radius, r eff (11–16 μm), with an exception when overcast closed mesoscale cellular convection (MCC; Wood & Hartmann, ) was encountered.…”

Evidence of a Diurnal Cycle in Precipitation over the Southern Ocean as Observed at Macquarie Island

“…The work in [6] employed precipitation records from Macquarie Island to estimate that only ∼60% of the precipitation was associated with frontal passages. Presumably, the remaining precipitation comes from the shallow boundary layer clouds commonly found between fronts [11].…”

Characteristics of the Marine Atmospheric Boundary Layer Over the Southern Ocean in Response to the Synoptic Forcing

“…Further, large uncertainties exist in the estimated amount of precipitation over the SO (Behrangi et al, ), which also has the potential to contribute to the regional biases. The ubiquitous boundary layer clouds over the SO (Huang, Siems, Manton, Hande, & Haynes, ; Mace et al, ; Muhlbauer et al, ) have been linked to both the incoming solar radiation bias (Bodas‐Salcedo et al, ) and, more recently, to frequent drizzle/light precipitation (Ahn et al, ; Huang et al, ). In order to better understand potential biases in the boundary layer clouds, it is necessary to understand if the boundary layer structure is properly represented in the widely used climate data sets.…”