A151-0004
Cloud structure of the Arctic cyclone

Monday, 14 December 2020
Poster
Zheng Liu, Applied Physics Laboratory University of Washington, Polar Science Center, Seattle, WA, United States and Axel J B Schweiger, University of Washington Seattle Campus, Seattle, WA, United States
Abstract:
One of the challenges in improving the predictability of Arctic cyclones (ACs hereinafter) is the fact that they can be distinct from cyclones in lower latitudes. Cyclones over the Arctic Ocean sometimes exhibit an equivalent barotropic structure with positive vorticity from the surface all the way to the lower stratosphere and little vertical tilt. The transition processes from a typical baroclinic extratropical cyclone to an equivalent barotropic cyclone are critical for the long lifetime of ACs over the Arctic Ocean. The development of ACs is associated closely with clouds and cloud processes. The presence of low-level clouds is well correlated with the static stability of the lower troposphere, whereas high clouds are important for the intensification of tropospheric polar vortices and ACs. The synoptic conditions associated with cyclone events is closely related to the spatial and vertical distribution of clouds. Previous studies have shown that although atmospheric reanalyses capture the link between ACs, synoptic conditions, and the vertical and spatial distribution of clouds qualitatively, substantial biases in cloud amount and their seasonal variability remain. Such biases imply that models are deficient in representing cloud–AC interactions on shorter timescales. In this study, we use the satellite observations of clouds from CloudSat, CALIPSO, and ICESat-2 to construct the cloud structure of the ACs. We examine the variability of the AC characteristics, such as their thermodynamic and dynamic structure, with AC types and the connection with the cloud structure in the ACs. The representation of the cloud-AC interactions in ERA-Interim is investigated. The result of this study is the first step of our efforts to understand the role of the cloud-AC interactions on the predictability of ACs.