Towards a Marine Stratus Climatology on Drizzle Occurrence from CALIPSO
Marine stratus are a predominant feature of our planet with the annual mean coverage exceeding 20%. They strongly reflect sunlight, yet exert only a modest effect on outgoing infrared radiation, providing a significant net cooling to the Earth’s radiative balance. Their formation is coupled to boundary layer circulations that are driven, in part, by cloud top radiative cooling and evaporative cooling from precipitation in downdrafts. Understanding how these cloud systems evolve as the climate changes is a key question that requires additional information on their lifecycle and microphysical properties to accurately represent their behavior in global circulation models. From a large-scale perspective, insight into the microphysical properties of marine stratus at cloud top can be realized through estimates of the effective radius (Re) of the droplet size distributions derived from MODIS observations. Estimates on the occurrence of rain/drizzle are available from CloudSat. Together these observations indicate that precipitation frequently occurs in clouds with higher cloud top Re. This relationship is consistent with the well documented shift in cloud top droplet size distributions towards fewer, yet larger droplets prior the onset of precipitation. Here we report on a new and complementary set observations from the CALIPSO mission. The approach derives an extinction-to-backscatter ratio (Sc, also known as the cloud lidar ratio) using an established relationship that depends on observations of the lidar attenuated backscatter and volume depolarization ratio within the cloud. Because Sc is strongly and inversely related to Re, a change in the derived Sc from higher to lower values corresponds to a change in the droplet size distribution as seen by MODIS. This change in the lidar signals at cloud top clearly identifies clouds that are capable of precipitation. The presentation provides a brief overview of the approach for deriving Sc and compares CALIOP-derived Sc with observations from other techniques. CALIOP classifications of drizzling clouds, based on the retrieved values Sc, are compared to independent, collocated assessments of drizzle occurrence reported in the standard CloudSat data products. Regional and seasonal comparisons highlight the strengths and weaknesses of the two sensors. A machine learning approach that combines information from both CALIOP and CloudSat showcases possible improvements in the global identification of scenes likely to contain rain-bearing clouds.