Seasonal Variation in the Effective Depth of Air-Sea Interaction
Event: 2022 Ocean Surface Topography Science Team Meeting
Session: Science II: Large Scale Ocean Circulation Variability and Change
Presentation type: Type Forum only
Contribution: PDF file
Sea surface temperature (SST) variability is controlled both by ocean processes such as advection, Ekman transport, and mixing, and by surface heat flux driven by atmospheric variations. Quantifying the relative strength of ocean and atmospheric forcing from observations previously has been explored by analyzing local heat budgets. Here, we use an observationally constrained local metric that quantifies the relative influence of ocean and atmospheric forcing from surface observations using a feedback framework. We use monthly satellite observations from 1993-2019 to examine the lagged-correlation relationships between SST, sea surface height (SSH) and surface turbulent heat flux (Q) to define SST-Q and SSH-Q feedbacks, which estimate the strength of atmospheric feedback to SST anomalies and to upper ocean heat content. We then define an effective depth of air-sea interaction (H), which describes the depth of the ocean that participates in the exchange of heat with the atmosphere. We also examine the ratio of the effective depth to the maximum mixed-layer depth (R) to estimate the renewal rate of the mixed-layer heat content from interior ocean processes relative to that from atmospherically driven surface fluxes. We find large values of H and R in regions with strong ocean currents such as the Gulf Stream, the Kuroshio Current, and the Antarctic Circumpolar Current, while interior subtropical regions exhibit small values. We finally study the seasonal dependence of R and find that wintertime R exhibits similar results to the year round R, while in summer, the results are not robust within the midlatitude western boundary currents. This analysis will improve our understanding of the relationship between upper ocean transport processes and surface heat flux variability and enhance our predictive understanding of climate variability.