Collaborative Research: Coupled Ocean Mixed Layer Processes Driving Sea Surface Temperature

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This project would investigate the effects of upper ocean mixed layer depth variability on sea surface temperature (SST). The hypothesis is that fast timescale air-sea interactions such as diurnal solar warming, wind gusts and rain showers rectify onto longer timescale seasonal to sub-seasonal SST variability. The project would first develop a conceptual framework to identify these contributions, then metrics from this framework would be analyzed from data collected throughout the tropical and subtropical oceans. These statistics will identify specific ocean-atmosphere coupled processes essential for SST prediction in regional and global circulation models. If successful, this work would improve weather predictions that save lives and property. This proposal will support a female early-career researcher. Johnson will continue her involvement with Mentoring Physical Oceanography Woman to Increase Retention (MPOWIR) as a mentor and contributor, and with workshops to promote field safety for ocean sciences lead by the Consortium for Ocean Leadership (COL). A new method is presented that evaluates the role of high frequency ocean mixed layer variability on seasonal to subseasonal SST anomalies. Using statistics from observations, the project will develop and test a stochastic model that integrates the high frequency co-variability between atmospheric forcing and mixed layer depth to predict SST evolution. Existing observations from moored platforms and profiling instruments that measure coincident air-sea interaction and upper ocean processes will be utilized. Single column mixing models forced by the observational metrics would also be utilized to provide more information about processes that contribute to upper ocean variability. This work will open the possibility for very simple coupled ocean mixed layer models that simulate SST anomalies and their coupling to the atmosphere more accurately than constant-depth ocean mixed layer models. Results will provide a framework for understanding how well coupled models need to resolve ocean mixed layer variability to accurately predict SST anomalies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.