A Space-Based Analysis of North Atlantic Snowfall and Clouds during Marine Cold-Air Outbreaks

Marine cold-air outbreaks (MCAO), the advection of cold air over relatively warmer open ocean, occur frequently at high latitudes where cold air originates from land and sea ice. Recent research shows that the cloud regimes associated with MCAOs are difficult to parameterize due to the complexity of their impacts on surface energy balance, ocean-atmosphere heat exchange, and precipitation processes. To examine how MCAO conditions impact snowfall rates seasonally in the North Atlantic, we combine ECMWF ERA5 data with snowfall and cloud observations retrieved from the CloudSat satellite’s radar from 2007-2010. We first define an M index using ERA5 data, where M θ_SST - θ₈₅₀, and M > 0 indicates MCAO conditions (i.e., low-level instability). We use the M indices to isolate MCAO conditions (M > 0) from non-MCAO conditions (M ≤ 0) over the North Atlantic and examine CloudSat 2C-SNOW-PROFILE surface snowfall and 2B-CLDCLASS-LIDAR cloud retrievals. We find that most snowfall in the Atlantic occurs during MCAO conditions, and the highest mean snowfall rates and highest frequency are located closest to cold air origins where MCAO initiation occurs. Mean cloud top height from 2B-CLDCLASS-LIDAR is lower (< 3 km) when MCAO conditions are present and there is a higher (lower) frequency of stratocumulus (nimbostratus) clouds during these snowfall events. Additionally, cloud top heights (retrieved from CloudSat) increase as a function of increasing M values (derived from ERA5 reanalysis products) during MCAO conditions (M > 0). Using two independent datasets, we show that spaceborne retrievals can identify familiar characteristics of MCAOs and provide observations with greater spatial and temporal coverage than ground-based observations of MCAOs.