Insights on sources and formation mechanisms of liquid-bearing clouds over MOSAiC examined from a Lagrangian framework

Abstract

Understanding Arctic stratiform liquid-bearing cloud life cycles and properly representing these life cycles in models is crucial for evaluations of cloud feedbacks as well as the faithfulness of climate projections for this rapidly warming region. Examination of cloud life cycles typically requires analyses of cloud evolution and origins on short time scales, on the order of hours to several days. Measurements from the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provide a unique view of the current state of the central Arctic over an annual cycle. Here, we use the MOSAiC radiosonde measurements to detect liquid-bearing cloud layers over full atmospheric columns and to examine the cloud-generating air masses’ properties. We perform 5-day (120 h) back-trajectory calculations for every detected cloud and cluster them using a unique set of variables extracted from these trajectories informed by ERA5 reanalysis data. This clustering method enables us to separate between the air mass source regions such as ice-covered Arctic and midlatitude open water. We find that moisture intrusions into the central Arctic typically result in multilayer liquid-bearing cloud structures and that more than half of multilayer profiles include overlying liquid-bearing clouds originating in different types of air masses. Finally, we conclude that Arctic cloud formation via prolonged radiative cooling of elevated stable subsaturated air masses circulating over the Arctic can occur frequently (up to 20% of detected clouds in the sounding data set) and may lead to a significant impact of ensuing clouds on the surface energy budget, including net surface warming in some cases.

ARMTRAJ: A Set of Multi-Purpose Trajectory Datasets Augmenting the Atmospheric Radiation Measurement (ARM) User Facility Measurements

“…Set radiosonde samples as "cloud" if RH values exceed 96%. This threshold value considers the radiosonde vendor's uncertainty (Holdridge, 2020) and is consistent with previous comparisons to cloud layer detections based on other instruments (e.g., Silber and Shupe, 2022;Silber et al, 2020, Fig. S1;Stanford et al, 2023, Appendix D).…”

“…For example, a comprehensive understanding of cloud lifecycles often necessitates knowledge about the hysteresis and origin of cloudy airmasses. Trajectory analyses support studies focused on warm, mixed-phase, and cold clouds, from low to high latitudes (e.g., Christensen et al, 2020;Ilotoviz et al, 2021;Mohrmann et al, 2019;Silber and Shupe, 2022;Svensson et al, 2023;Wernli et al, 2016). Back-trajectories can inform on potential cloud formation mechanisms (e.g., Silber and Shupe, 2022;Svensson et al, 2023), be used to evaluate the influence of airmass intrusions on cloud evolution (e.g., Christensen et al, 2020;Ilotoviz et al, 2021;Mohrmann et al, 2019), and generally support process understanding through modeling studies by providing boundary conditions and observationally-based benchmarks (e.g., Neggers et al, 2019;Silber et al, 2019;Tornow et al, 2022).…”

Investigating the development of clouds within marine cold-air outbreaks

“…MCAO events may start at a lower N d due to a potentially different origin of the air relative to non-MCAO events; the air may have travelled over ice for longer and is therefore cooler and cleaner than the non-MCAO air. Previous work has found that some air masses can travel over the sea ice for extended periods before reaching the ocean (Silber and Shupe, 2022); however, a full investigation is beyond the scope of this study. Previous work has found a similarly steep decline in N d concentration following an MCAO (Abel et al, 2017;Sanchez et al, 2022).…”

Arctic Cloud‐Base Ice Precipitation Properties Retrieved Using Bayesian Inference

“…Because the model requires knowledge of the atmospheric thermodynamic state, KAZR and HSRL measurements are taken from 15‐min windows starting at local radiosonde release times. This type of sounding‐constrained analysis retains high correspondence between variables measured using the different instruments (e.g., Silber, Fridlind, et al., 2020; Silber & Shupe, 2022, their Figure S1) and enables the examination of temperature‐dependent effects on ice formation and growth.…”