Factors Controlling Low-Cloud Evolution over the Eastern Subtropical Oceans: A Lagrangian Perspective Using the A-Train Satellites

Abstract

Lagrangian technique is developed to sample satellite data to quantify and understand factors controlling temporal changes in low-cloud properties (cloud cover, areal-mean liquid water path, and droplet concentration). Over 62 000 low-cloud scenes over the eastern subtropical/tropical oceans are sampled using the A-Train satellites. Horizontal wind fields at 925 hPa from the ERA-Interim are used to compute 24-h, two-dimensional, forward, boundary layer trajectories with trajectory locations starting on the CloudSat/CALIPSO track. Cloud properties from MODIS and AMSR-E are sampled at the trajectory start and end points, allowing for direct measurement of the temporal cloud evolution. The importance of various controls (here, boundary layer depth, lower-tropospheric stability, and precipitation) on cloud evolution is evaluated by comparing cloud evolution for different initial values of these controls. Viewing angle biases are removed and cloud anomalies (diurnal and seasonal cycles removed) are used throughout to quantify cloud evolution relative to the climatological-mean evolution. Cloud property anomalies show temporal changes similar to those expected for a stochastic red noise process, with linear relationships between initial anomalies and their mean 24-h changes. This creates a potential bias when comparing the evolutions of sets of trajectories with different initial anomalies; three methods are introduced and evaluated to account for this. Results provide statistically robust observational support for theoretical/modeling studies by showing that low clouds in deep boundary layers and under weak inversions are prone to break up. Precipitation shows a more complex and less statistically significant relationship with cloud breakup. Cloud cover in shallow precipitating boundary layers is more persistent than in deep precipitating boundary layers. Liquid water path and cloud droplet concentration decrease more rapidly for precipitating clouds and in deep boundary layers.