Deep ocean turbulence and tracer transport

The flows of climatically important tracers—like heat and carbon—into the deep ocean shape Earth's climate change trajectory; it is thus important to understand these flows deeply enough that they can be adequately represented in global ocean models. The largest of these flows can be "resolved" by a numerical ocean model and emerge naturally from solving the equations of fluid mechanics on a discretized grid, while those that occur on scales smaller than a grid cell need to be added in separately using "parameterizations". Parameterization is the process of distilling the essence of a physical process into a simple formula or algorithm that only depends on the "resolved" environment, thus giving us a self-consistent way of closing the model equations without leaving out any important physics. The process of parameterization is complicated by the turbulent nature of oceanic flows, which links fluctuations at the smallest scales (a few cm) to global currents (10,000 km) and makes the ocean inherently chaotic and unpredictable. Developing a parameterization requires both theoretical understanding of the process (or at least its statistics) and a training data set for calibration; in-situ observations and high-resolution simulations are both commonly used. While many of the most important surface mixed layer processes are now fairly well understood and have been successfully parameterized, the equivalent processes in the bottom mixed layer remain relatively mysterious.

I develop theoretical models of deep ocean transport processes and test these ideas with available data and quasi-realistic simulations. I use these theories, simulations, and observations to improve theoretical understanding of the global ocean circulation, develop new parameterizations for climate models, and inform observational campaigns (such as the ongoing Bottom Layer Turbulence and Abyssal Recipes program).

A movie showing how a turbulent field of eddies stirs an blob of purple tracer around a narrow and hilly deep ocean canyon The movie shows that the blob of tracer, initially just a few km in diameter, gets diffused across an area of about 60km by 60km over the course of 200 days.
The turbulent three-dimensional transport of a spherical blob of tracer in a deep ocean canyon. This simulation mimics a real ocean expedition in which 100 kg of Sulfur Hexafluoride was injected 4000 meters below the ocean surface in the Brazil Basin and was traced out over the course of several years [Source: Drake et al., in press at JPO]

Overturning circulation theory

At the high latitudes of the Arctic and Antarctic, cold, salty, and hence dense surface waters sink to fill the dark abyss of the deep ocean. Since seawater mass is conserved in the ocean, we know that these deep waters must return to the surface elsewhere. It is convenient to split up this global overturning circulation into two vertically stacked (and partially connected) cells with different dynamics: a wind-driven adiabatic (along density surfaces) upper cell and a mixing-driven diabatic (across density surfaces) lower cell. In the upper cell, dense waters form in the North Atlantic during winter storms and flow south along density surfaces towards the Southern Ocean, where their upwelling by a "residual circulation" along density surfaces is the result of a competition between winds and turbulent eddies. These newly upwelled waters are then lightened by an influx of freshwater from melting sea ice, and transported back northwards to close the upper cell. In the lower cell, on the other hand, the bottom waters that form off the coast of Antarctica are so dense that they do not outcrop to the surface anywhere else in the ocean, and thus must upwell "diabatically", i.e. by some process that changes their density. Oceanographers originally theorized that these dense abyssal waters upwelled by mixing vigorously with lighter waters. This theory was later challenged by observations: the turbulent mixing measured in the open ocean was too weak to drive the required upwelling. Eventually, observations revealed that turbulence levels increased dramatically near the jagged hills lining the sea floor, where we now know powerful internal waves break and mix up the water column. These new observations seem paradoxical at first: since the density of seawater also increases with depth, the logical conclusion was that deep water mixes preferentially with denser waters and thus the mixing results in sinking, not upwelling! This paradox is resolved by considering what happens right at the bottom of the ocean, where the mixing runs into the seafloor and causes a thin but vigorous burst of upwelling (see schematic to the right).

I use geophysical theory and numerical simulations to provide insights into the dynamical processes described above and the global-scale circulations that emerge when they are combined.

A schematic of the mixing-driven overturning circulation: a depth-longitude view of a typical ocean basin with a contentinental slope on the left and a rugged mid ocean ridge on the right. Mixing-driven flow along the flank of the mid-ocean ridge drives a dipole of up- and down-welling flows which combine to equal the meridional overturning circulation.
A schematic of how bottom-enhanced turbulence above the rough topography of the mid-ocean ridge drives up– and down–welling mixing layer flows. The net effect of these abyssal mixing layer flows is to transform dense bottom waters into lighter deep waters, a key part of the large-scale meridional overturning circulation that keeps the global ocean moving. [Source: Drake et al. (2020), JPO]

Lagrangian analysis of geophysical flows

While geophysical flows are typically viewed from a fixed frame of reference (Eulerian perspective), it is often desirable to view geophysical flows from a frame of reference that follows the flow (Lagrangian perspective). The Lagrangian approach is particularly useful for determining how seawater (or tracers carried by seawater) flows from one region to another or is transformed over time. Lagrangian analysis can be applied either to inherently Lagrangian observations (i.e. surface drifters or Argo floats) or to velocity field output from Ocean General Circulation Models. Applications of Lagrangian analysis include tracking of heat, salt, carbon, nutrients, larvae, microplastics, icebergs, oil droplets, etc.

In my past research, I have used Lagrangian analysis to detangle the complicated three-dimensional pathways of the Meridional Overturning Circulation and quantify their volume transports and timescales. I am currently developing a theoretical framework for Lagrangian tracer budgets to make these Lagrangian methods more quantitatively useful in physical oceanography and climate dynamics applications.

Particle trajectories of Circumpolar Deep Water (CDW)
Lagrangian trajectories of virtual water parcels in a coupled climate model show upwelling of water from the horizon at a depth of 2000 meters (purple) at 30 degrees South to the surface of the ocean (yellow) around Antarctica [Source: Drake et al. (2018), GRL].

Insights from simple climate models

My preferred scientific approach is to develop the simplest possible model that can be used to answer a given scientific question. The scientific problem that I am most passionate about– and which connects all of my research interests– is the long-term (10–10,000 year) evolution of Earth's climate. On these long timescales, the exchange of heat and carbon between the atmosphere and the ocean, and their distribution within the deep ocean, is a crucial control on Earth's climate. In the context of ongoing anthropogenic climate change, humans emit greenhouse gases, which weakens the atmosphere's ability to cool itself by radiating heat to space and causes the surface to warm. The atmosphere and surface of the planet responds to this warming effect (or "forcing"), causing a self-amplification of the warming in the net (a "positive feedback"). Thankfully, about 30% of this potential global warming is delayed for centuries as the ocean takes up much of the excess heat. These three key processes: greenhouse gas radiative forcing, radiative feedbacks, and ocean heat uptake, form the three key parameters in a widely used zero-dimensional "energy balance model" of Earth's climate. This extremely simple model can be tuned to more complicated "general circulation models" to yield remarkably accurate projections of global warming (see "Evaluating historical climate models" below) and form the basis of many climate-economic models of climate change (see my extremely simple one in the schematic on the right).

I am interested in the dynamical processes that determine the rate of ocean heat (and carbon) uptake in these conceptual energy balance models, how these processes might change over time, and any implications on long-term climate policy. I plan to continue developing my extremely simple climate-economic optimization model, ClimateMARGO.jl, to explore the dependence of climate policy decisions on ocean and climate science.

A cartoony schematic showing the causal chain of climate damages, and how four different interventions that can stop it: greenhouse gas emissions (which can be mitigated) cause CO2 concentrations to accumulate in the atmosphere (which can be reversed by carbon dioxide removal); rising greenhouse gas concentrations increase the radiative forcing, which warms the atmosphere (which can be cooled by solar radiation Geoengineering); a warming planet causes a myriad of changes (some of which can be adapated to), leading to climate damages.
Schematic of the causal chain from greenhouse gas emissions to climate damages, including the unique effects of four climate controls: emissions Mitigation, carbon dioxide Removal, Geoengineering by solar radiation management, and Adaptation. Climate controls yield benefits in terms of avoided climate damages, which are balanced against control deployment costs. [Source: Drake et al. (2021), ERL]