An Investigation on the Spatial Dependence of Autoconversion and Accretion in Eastern North Atlantic Boundary Layer Clouds

Principal Investigator(s):
Roger Marchand, University of Washington

In many climate models, boundary layer (that is, low) clouds over the ocean produce precipitation that occurs too frequently and is too light.  This result is likely due to an imbalance between autoconversion (the collision of small cloud droplets to form larger precipitation drops, which initiates precipitation), and accretion (the collection of cloud droplets by falling precipitation drops, which has a large impact on the precipitation rate).  Because these processes affect cloud water path and cloud lifetime, they play a critical role in the Earth radiative balance and aerosol-cloud interactions.  Recent papers have emphasized the importance of small spatial scale variability (scales much smaller than the grid spacing used in current global models), and the spatial correlation of cloud and precipitation as factors that are not adequately represented in model parameterizations of autoconversion and accretion and likely contribute to an imbalance between these processes.

In this project, cloud and precipitation data being collected at the ARM Eastern North Atlantic (ENA) site will be used to evaluate the spatial variability of boundary layer clouds and precipitation, the correlation between them, and the impact on autoconversion and accretion with spatial scale.  The analysis will result in a parameterization for the scale dependence of autoconversion and accretion, and research activities will include detailed modeling to evaluate the parameterization. Advancements in ARM instrumentation and retrievals, including retrievals from the ARM Ka-band zenith pointing Doppler radar and X-band scanning radar will play a key role in the analysis.  In particular, results based on a Doppler Spectra retrieval that simultaneously separates the cloud and precipitation contributions to the total measured Doppler spectra will be critical to determine the (cross) correlation between cloud and precipitating water.