NSF Org:	OCE Division Of Ocean Sciences
Recipient:	REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE
Initial Amendment Date:	July 29, 2021
Latest Amendment Date:	July 29, 2021
Award Number:	2123204
Award Instrument:	Standard Grant
Program Manager:	Baris Uz bmuz@nsf.gov  (703)292-4557 OCE  Division Of Ocean Sciences GEO  Directorate for Geosciences
Start Date:	September 1, 2021
End Date:	August 31, 2025 (Estimated)
Total Intended Award Amount:	$366,086.00
Total Awarded Amount to Date:	$366,086.00
Funds Obligated to Date:	FY 2021 = $366,086.00
History of Investigator:	Alexis Kaminski (Principal Investigator) kaminski@berkeley.edu
Recipient Sponsored Research Office:	University of California-Berkeley 1608 4TH ST STE 201 BERKELEY CA  US  94710-1749 (510)643-3891
Sponsor Congressional District:	12
Primary Place of Performance:	University of California-Berkeley 6175 Etcheverry Hall Berkeley CA  US  94720-1740
Primary Place of Performance Congressional District:	12
Unique Entity Identifier (UEI):	GS3YEVSS12N6
Parent UEI:
NSF Program(s):	PHYSICAL OCEANOGRAPHY
Primary Program Source:	010V2122DB R&RA ARP Act DEFC V
Program Reference Code(s):	102Z
Program Element Code(s):	161000
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.050

ABSTRACT

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

This project will examine entrainment processes in the ocean transition layer (TL), the vertical region between the lower part of the turbulent near-surface mixed layer and the stably stratified ocean interior. The project will use a novel combination of approaches, including further analysis of existing high-resolution profile data through the upper ocean layers, high-resolution modeling of entrainment and internal wave processes in the transition layer, and machine learning techniques. These processes help determine ocean mixed layer depth and temperature and ultimately mediate air-sea exchange effects on the global ocean, yet they remain poorly understood or represented in numerical model parameterizations. Outcomes will be directly relevant to improving physical and biogeochemical ocean models. Additionally, the methods developed will be applicable to interpreting and analyzing data from a variety of geophysical flows, and the analysis scripts will be made publicly available. The work will support an early career investigator, the training of a graduate student, contributions to local outreach and educational programs, and will form the basis for a project for a graduate student in the WHOI summer program in Geophysical Fluid Dynamics.

A suite of high-resolution direct numerical simulations will be generated covering a range of expected TL mechanisms, including Kelvin-Helmholtz and Holmboe instabilities and interfacial waves. Using standard fluid dynamical analyses, including characterization of the linear instabilities and a detailed analysis of the flow energetics, this comprehensive library of flow fields will be used to determine how well a given stratified mixing event can be characterized from limited measurements. The simulations will then be used as training data for a neural network-based flow classification method, allowing for input profiles of temperature and velocity to be classified in terms of the underlying waves or instabilities. After the classification method is validated using simulation data, it will be applied to the observations, allowing for identification of the relevant mechanisms driving entrainment in the TL. Knowledge of the mixing associated with each mechanism can thus be used to describe the mixing efficiency and turbulent fluxes in the observational record.

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.

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