Chaotic variability of the North Atlantic Subtropical Mode Water

• Main Authors: Narinc, Olivier, Thierry, Penduff, Guillaume, Maze, Stéphanie, Leroux
• Other Authors: Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Observatoire des Sciences de l'Univers de Grenoble (Fédération OSUG)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP), Université Grenoble Alpes (UGA)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2022
• Subjects: [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography; North Atlantic
• Online Access: https://hal.science/hal-04549998; https://doi.org/10.5194/egusphere-egu22-11847

International audience Following the recommendations of CMIP6, some climate models have for the first time started using a resolution of 1/4° for their oceanic component. This is significant, as it means that large eddies are resolved (so-called eddy-permitting models), introducing chaotic variability in oceanic models. Observational studies of the North Atlantic Subtropical Mode Water (STMW) have found that not all of its variability can be explained by atmospheric variability. The STMW is a water mass formed by ventilation over the winter and is the most abundant T,S class of water in the surface North Atlantic. Consequently it plays a key role in air-sea exchanges over the basin. These elements have motivated the present model investigation of the STMW's ocean-driven (intrinsic) chaotic variability using a NEMO-based, 1/4°, 50-member ensemble simulation of the Northern Atlantic ocean. Using this dataset, six STMW-wide integrated variables are defined and analysed: total volume, and averaged potential vorticity, depth, temperature, salinity and density. The model solution is assessed against the ARMOR3D ocean reanalysis, based on in situ data collected from ARGO floats and satellite observations. The water mass' chaotic variability is estimated from the time-averaged ensemble standard deviation, and is compared to the total variability estimated from the ensemble mean of the temporal standard deviations of all members. Initial results show that chaotic variability is significant for STMW properties at interannual timescales, representing almost half of the total variability of its average temperature. A spectral analysis indicates that chaotic variability remains significant at longer timescales. This suggests that as climate models move towards finer spatial resolution in the ocean, oceanic chaos can be expected to introduce more variability at interannual and longer timescales. This study also highlights the necessity of a good parametrisation of this oceanic chaos in non-eddying ocean models.