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James Girton

Principal Oceanographer

Affiliate Assistant Professor, Oceanography

Email

girton@apl.washington.edu

Phone

206-543-8467

Research Interests

Overflows and Deep-Water Formation, Internal Waves, Mesoscale Eddies, Oceanic Surface and Bottom Boundary Layers, Measurements of Ocean Velocity Through Motionally-Induced Voltages

Biosketch

James Girton's research primarily investigates ocean processes involving small-scale turbulence and mixing and their influence on larger-scale flows. An important part of physical oceanography is the collection of novel datasets to shed new light on important physical processes, and to this end Dr. Girton's research has frequently drawn
upon the widely under-utilized electromagnetic velocity profiling technique developed by Tom Sanford (his Ph.D. advisor and frequent collaborator). Instruments utilizing this technique include the expendable XCP, the full-depth free-falling AVP, and the autonomous long-duration EM-APEX. Each of these instruments has a unique role to
play in the study of phenomena ranging from deep boundary currents and overflows to upper ocean mixing and internal waves.

In addition to being less well-understood elements of ocean physics, many of these phenomena are potentially important for the behavior of the large-scale ocean circulation, particularly the meridional overturning that transports heat to subpolar and polar regions and sequesters atmospheric gases in the deep ocean. Prediction of future climate change by coupled ocean-atmosphere models requires reliable predictions of ocean circulation, so physically-based improvements to parameterizations of mixing, boundary stresses and internal waves in
such models are an ongoing goal.

Department Affiliation

Ocean Physics

Education

B.A. Physics, Swarthmore College, 1993

Ph.D. Oceanography, University of Washington, 2001

Publications

2000-present and while at APL-UW

Autonomous control of marine floats in the presence of dynamic, uncertain ocean currents

Troesch, M., S. Chien, Y. Chao, J. Farrara, J. Girton, and J. Dunlap, "Autonomous control of marine floats in the presence of dynamic, uncertain ocean currents," Rob. Auton. Syst., 108, 100-114, doi:10.1016/j.robot.2018.04.004, 2018.

More Info

1 Oct 2018

A methodology is described for control of vertically profiling floats that uses an imperfect predictive model of ocean currents. In this approach, the floats have control only over their depth. This control authority is combined with an imperfect model of ocean currents to attempt to force the floats to maintain position. First, the impact of model accuracy on the ability to station keep (e.g. maintain X–Y position) using simulated planning and nature (ground-truth in simulation) models is studied. In this study, the impact of batch versus continuous planning is examined. In batch planning the float depth plan is derived for an extended period of time and then executed open loop. In continuous planning the depth plan is updated with the actual position and the remainder of the plan re-planned based on the new information. In these simulation results are shown that (a) active control can significantly improve station keeping with even an imperfect predictive model and (b) continuous planning can mitigate the impact of model inaccuracy. Second, the effect of using heuristic path completion estimators in search are studied. In general, using a more conservative estimator increases search quality but commensurately increases the amount of search and therefore computation time. Third are presented results from an April 2015 deployment in the Pacific Ocean that show that even with an imperfect model of ocean currents, model-based control can enhance float control performance.

When mixed layers are not mixed. Storm-driven mixing and bio-optical vertical gradients in mixed layers of the Southern Ocean

Carranza, M.M., S.T. Gille, P.J.S. Franks, K.S. Johnson, R. Pinkel, and J.B. Girton, "When mixed layers are not mixed. Storm-driven mixing and bio-optical vertical gradients in mixed layers of the Southern Ocean," J. Geophys. Res., 123, 7264-7289, doi:10.1029/2018JC014416, 2018.

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1 Oct 2018

Mixed layers are defined to have homogeneous density, temperature, and salinity. However, bio‐optical profiles may not always be fully homogenized within the mixed layer. The relative timescales of mixing and biological processes determine whether bio‐optical gradients can form within a uniform density mixed layer. Vertical profiles of bio‐optical measurements from biogeochemical Argo floats and elephant seal tags in the Southern Ocean are used to assess biological structure in the upper ocean. Within the hydrographically defined mixed layer, the profiles show significant vertical variance in chlorophyll‐a (Chl‐a) fluorescence and particle optical backscatter. Biological structure is assessed by fitting Chl‐a fluorescence and particle backscatter profiles to functional forms (i.e., Gaussian, sigmoid, exponential, and their combinations). In the Southern Ocean, which characteristically has deep mixed layers, only 40% of nighttime bio‐optical profiles were characterized by a sigmoid, indicating a well‐mixed surface layer. Of the remaining 60% that showed structure, ∼40% had a deep fluorescence maximum below 20‐m depth that correlated with particle backscatter. Furthermore, a significant fraction of these deep fluorescence maxima were found within the mixed layer (20–80%, depending on mixed‐layer depth definition and season). Results suggest that the timescale between mixing events that homogenize the surface layer is often longer than biological timescales of restratification. We hypothesize that periods of quiescence between synoptic storms, which we estimate to be ∼3–5 days (depending on season), allow bio‐optical gradients to develop within mixed layers that remain homogeneous in density.

Development, implementation, and validation of a California coastal ocean modeling, data assimilation, and forecasting system

Chao, Y., and 8 others including J.B. Girton, "Development, implementation, and validation of a California coastal ocean modeling, data assimilation, and forecasting system," Deep Sea Res. II, 151, 49-62, doi:10.1016/j.dsr2.2017.04.013, 2018.

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1 May 2018

A three-dimensional, near real-time data-assimilative modeling system for the California coastal ocean is presented. The system consists of a Regional Ocean Modeling System (ROMS) forced by the North American Mesoscale Forecast System (NAM). The ocean model has a horizontal resolution of approximately three kilometers and utilizes a multi-scale three-dimensional variational (3DVAR) data assimilation methodology. The system is run in near real-time to produce a nowcast every six hours and a 72-hour forecast every day. The performance of this nowcast system is presented using results from a six-year period of 2009–2015.

More Publications

In The News

Ice-diving drones embark on risky Antarctic mission

Scientific American, Mark Harris

To forecast sea level rise, a flotilla of undersea robots must map the unseen bottom of a melting ice shelf — if they are not sunk by it.

6 Dec 2017

Scientists get robots ready to study Antarctic ice shelves from below, with $2M boost from Paul Allen

GeekWire, Alan Boyle

Researchers from the University of Washington and Columbia University are getting ready for an unprecedented months-long campaign to study Antarctica’s ice shelves from the ocean below. Robotic Seagliders and EM-APEX profiling floats will be used to probe the ocean under ice shelves.

6 Nov 2017

Wave Glider surfs across stormy Drake Passage in Antarctica

UW News, Hannah Hickey

The University of Washington sent a robotic surf board to ride the waves collecting data from Antarctica to South America.

20 Sep 2017

More News Items

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