Studying Cloud Transformations in Cold Air Outbreaks using Large-Eddy Simulations Exclusively Driven by HALO-(AC)³ Campaign Data

Abstract

Cold air outbreaks (CAOs) are a key component of the Arctic climate system, featuring intense convective cloud fields embedded in cold, dry air masses over relatively warm surfaces. Large-Eddy Simulation (LES) is a technique often used to investigate CAOs at high spatial and temporal resolutions, resolving the intricate processes involved and providing a wealth of virtual data. A complication with LES studies of CAOs is the typical absence of suitable observational data to fully constrain the simulations, and thus anchor them in reality. This study aims to use observational data from the recent airborn HALO-(AC)³ campaign in the Atlantic sector of the Arctic to drive LES experiments exclusively with observations. To this purpose data from Research Flights 10 and 11 are used, which probed a weak CAO in the Fram Strait on 29 and 30 March 2022. A Lagrangian model framework is adopted, making use of observations along the two-day low-level trajectory that stretched from close to the North Pole to the sea-ice free area Southwest of Svalbard. HALO observations are integrated into the reanalysis-based model forcing in an incremental way, yielding a suite of forcing datasets. These observational data consist of vertical soundings of thermodynamic state, pressure gradients, mesoscale divergence and advective tendencies, as well as surface properties to act as boundary conditions. The LES code incorporates advanced representations for mixed-phase microphysical processes and radiative transfer, to allow a realistic representation of clouds and turbulence in the transforming low-level airmass. LES results obtained with this setup are evaluated against independent HALO datasets on clouds and other boundary-layer properties. Inter-comparing the suite of LES runs with different forcing datasets elucidates the impacts of individual forcing components on the air mass transition and associated cloud evolution.