Ocean Mixed Layer and its Interaction with Sea Surface Temperature: An Observational and Model Simulation Study

• Main Author: BYJU POOKKANDY
• Format: Thesis
• Language: unknown
• Published: Monash University 2017
• Subjects: Uncategorized; Pacific; North Atlantic
• Online Access: https://dx.doi.org/10.4225/03/59cae76a0559e; https://bridges.monash.edu/articles/thesis/Ocean_Mixed_Layer_and_its_Interaction_with_Sea_Surface_Temperature_An_Observational_and_Model_Simulation_Study/5445637

Understanding mixed layer (ML) processes are a necessary prerequisite for interpreting global climate and its variability. The thickness of the ML modulates its heat capacity and controls the evolution of sea surface temperature (SST) and their variability. Hence, mean and seasonality of ocean mixed layer depth (MLD) are explored, along with the relative role of net heat flux (NHF), wind-stress and freshwater flux on MLD variability and the variability of SST associated with this. The study utilises ocean reanalysis datasets, CMIP5 model and a single-column ML ocean model. The single-column model is coupled to an atmospheric general circulation model (ACCESS-KPP) to analyse the MLD and the atmospheric forcing terms. This coupled framework (ACCESS-KPP) is computationally less expensive and allows insight into the limit with which MLD characteristics can be simulated by local air-sea interactions. The annual-mean MLD in the open-ocean regions generally follows the heat and wind forcing. The annual-mean MLD characteristics are found to be strongly related to the magnitude of the annual-mean wind field, while the relative seasonal cycle strength of MLD mostly follows the seasonality in heating. However, there are a few regions in which neither of the atmospheric forces seems to match the annual mean or seasonal cycle characteristics of the MLD, indicating that the relationship is more complex. The study, based on the ocean reanalysis and ACCESS-KPP model datasets, suggests that oceanic processes independent of the local atmospheric forcing contribute to the stratification of the upper layer and hence, influence MLD significantly. The relationship between the anomalies of MLD and atmospheric forces tends to become stronger when the atmospheric forces lead approximately by a month during the fall and winter. This is consistent with a larger inertia of the MLD when it is deeper. Further, the seasonally changing MLD produces re-emergence of SST anomalies (SSTA) from one winter to the following. The study detects the possible areas of re-emergence, and I found that recurrence of SSTA not only appears in the midlatitudes but also indicates at some part of the tropics. In addition to the locations identified in the previous studies, I have found a region in the central-eastern South Pacific, where the re-emergence signals are stronger compared to the other regions. The results reveal that re-emergence in the midlatitude oceans is associated with the seasonal change in MLD, while the recurrence in the tropics is related to recurring atmospheric heat flux. The CMIP5 and ACCESS-KPP models simulate the re-emergence signals consistent with the observations. In contrast, CMIP5 models show uncertainty in exhibiting the areas of SSTA re-emergence in the North Atlantic, despite the occurrence of significant seasonal variation in MLD. The factors that further influence the re-emergence mechanism are investigated using the KPP single column ML model forced by stochastic atmospheric forces at one grid point. The experiments suggest that the effects of the anomalous mixed layer for re-emergence are of secondary importance compared to the seasonal cycle of MLD. Further, the damping of SST anomalies by the heat flux feedback reduces the strength of the re-emergence signature. The study also found that the re-emergence process associated with MLD dynamics can influence the spectral variance of SSTA on inter-annual to multi-decadal timescales.