TY - JOUR
T1 - Global climate modelling of Saturn's atmosphere, Part V
T2 - Large-scale vortices
AU - Donnelly, Padraig T.
AU - Spiga, Aymeric
AU - Guerlet, Sandrine
AU - James, Matt K.
AU - Bardet, Deborah
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2025/1/1
Y1 - 2025/1/1
N2 - This paper presents an analysis of large-scale vortices in the atmospheres of gas giants, focusing on a detailed study conducted using the Saturn-DYNAMICO global climate model (GCM). Large-scale vortices, a prominent feature of gas giant atmospheres, play a critical role in their atmospheric dynamics. By employing three distinct methods – manual detection, machine learning via artificial neural networks (ANN), and dynamical detection using the Automated Eddy-Detection Algorithm (AMEDA) – we characterise the spatial, temporal, and dynamical properties of these vortices within the Saturn-DYNAMICO GCM. Our findings reveal a consistent production of vortices due to well-resolved eddy-to-mean flow interactions, exhibiting size and intensity distributions broadly in agreement with observational data. However, notable differences in vortex location, size, and concentration highlight the model's limitations and suggest areas for further refinement. The analysis underscores the importance of zonal wind conditions in influencing vortex characteristics and suggests that more accurate modelling of giant planet vortices may require improved representation of moist convection and jet structure. This study not only provides insights into the dynamics of Saturn's atmosphere as simulated by the GCM but also offers a framework for comparing vortex characteristics across observations and models of planetary atmospheres.
AB - This paper presents an analysis of large-scale vortices in the atmospheres of gas giants, focusing on a detailed study conducted using the Saturn-DYNAMICO global climate model (GCM). Large-scale vortices, a prominent feature of gas giant atmospheres, play a critical role in their atmospheric dynamics. By employing three distinct methods – manual detection, machine learning via artificial neural networks (ANN), and dynamical detection using the Automated Eddy-Detection Algorithm (AMEDA) – we characterise the spatial, temporal, and dynamical properties of these vortices within the Saturn-DYNAMICO GCM. Our findings reveal a consistent production of vortices due to well-resolved eddy-to-mean flow interactions, exhibiting size and intensity distributions broadly in agreement with observational data. However, notable differences in vortex location, size, and concentration highlight the model's limitations and suggest areas for further refinement. The analysis underscores the importance of zonal wind conditions in influencing vortex characteristics and suggests that more accurate modelling of giant planet vortices may require improved representation of moist convection and jet structure. This study not only provides insights into the dynamics of Saturn's atmosphere as simulated by the GCM but also offers a framework for comparing vortex characteristics across observations and models of planetary atmospheres.
KW - Atmospheric dynamics
KW - Dynamical detection
KW - Eddy-to-mean flow interactions
KW - Gas giants
KW - Geospatial information systems
KW - Global climate model
KW - Machine learning
KW - Saturn
KW - Vortices
U2 - 10.1016/j.icarus.2024.116302
DO - 10.1016/j.icarus.2024.116302
M3 - Article
AN - SCOPUS:85203665919
SN - 0019-1035
VL - 425
JO - Icarus
JF - Icarus
M1 - 116302
ER -