







































Biological and Physical 
Interactions at Local Ocean Scales: 

Coupled Systems
 Victoria Boatwright, Baylor Fox Kemper, PhD.

Volume One 
Edition One 
February 2021 

 
GEORGETOWN SCIENTIFIC
RESEARCH JOURNAL

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https://doi.org/10.48091/DNPR7287 

Biological and Physical Interactions at Local 

Ocean Scales: Coupled Systems 

Victoria Boatwright1 and Baylor Fox-Kemper2

1 Department of Physics, Georgetown University, Washington DC, USA 
2 Department of Earth, Environmental, and Planetary Sciences, Brown University, Providence RI, USA 

E-mail: vb405@georgetown.edu
Abstract 
Physical and biogeochemical processes that influence primary production set Earth’s carbon and heat 
budgets. While these processes have long been the focus of research, high resolution models to investigate 
local phenomena have only recently been developed, and two-way coupling between oceanic physics and 
biology is only recently getting attention due to computational power. With these new developments, it is 
possible to study the mechanisms through which these processes interact at both global and regional scales 
to shape Earth’s climate, which is the goal of this paper. This paper introduces oceanic physical 
phenomena at submesoscales to global scales – like mixed layer depth and turbulent structures – and the 
relationship of smaller scale events with biological factors. It discusses the implications of these 
relationships for primary production. After an introductory explanation of turbulence, primarily in the 
form of eddies and fronts, and the effects of internal instability and surface forcing, this paper emphasizes 
the contributions of those phenomena (turbulence, internal instability, and surface forcing) to vertical 
velocities and the influence of vertical transport on biology. Next, it introduces biogeochemical feedbacks, 
concerning both large scale population dynamics and increased absorption of radiation at the 
submesoscale, to consider their impacts on physical dynamics and regional climates. Finally, the paper 
compiles equations of irradiance and variables of significance, suggesting terms that could produce 
meaningful responses to variations in phytoplankton populations. The paper highlights the importance of 
understanding physical-biogeochemical relationships and suggests directions for future research, 
particularly areas related to global warming or abrupt climate change.  

Keywords: turbulence, primary production, phytoplankton, submesoscale

1. Introduction
Oceans occupy 70% of the Earth’s surface,�

accounting for the sequestration of 48% of carbon 
emissions, and its surface dwellers are responsible 
for roughly half of the atmospheric oxygen1. Marine 
primary producers are an integral component of the 
global carbon cycle, oxygen production, and marine 

ecosystems. Considering how marine primary 
production (the base of the food chain, organisms 
that synthesize organic compounds from carbon 
dioxide) accounts for twice the amount of carbon 
fixation performed by the open ocean (90% of the 
ocean surface), it is important to understand the 
factors promoting the growth and abundance of 

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marine producers2. The key marine primary 
producers are phytoplankton, which include a range 
of species with various characteristics, such as 
surface floaters, neutrally buoyant species, species 
dependent on iron for nitrogen fixing, and those 
that are N2-fixing3. While physical phenomena in 
the ocean are known to provide nutrients, light, and 
heat to plankton populations, the relationship 
between these physical phenomena and the 
abundance of marine life is still uncertain for two 
reasons. First, many of the interactions that impact 
biological abundance and spatial variability occur on 
the mesoscale (roughly 100km or less) or even the 
submesoscale (often characterized as 0.1-10km 
scale), depths at which resolution is currently an 
insurmountable computational cost to resolve, 
especially in two-way coupling schemes4.  Second, 
the field is just beginning to understand the coupled 
feedback mechanisms of biogeochemical influences 
on physics. Coupled schemes are used in 
computational models to resolve the complex 
interactions between boundaries or systems; for 
example, ocean-atmosphere coupling was one of the 
first cases of relating two previously independent 
systems through heat flows, wind stress, and surface 
exchanges of molecules like carbon and oxygen5. In 
this paper, we demonstrate coupling between ocean 
physics (e.g. turbulence) and marine 
biogeochemistry (e.g. phytoplankton populations), 
which can be one-way (physics impacting biology, 
the more common approach to ocean models today) 
or two-way (physics impacts biology and biology 
influences physics). In doing so, this paper seeks to 
explain various biogeochemical-biological-physical 
feedbacks and their spatial scales as well as 
determine what parameterizations (variables or 
equations that can represent complex, often small-
scale or unresolvable, interactions) can add to global 
or regional models in understanding the carbon 
cycle and heat exchanges. The discussion section 
details the impacts of global climate change and 
warming on submesoscale and mesoscale 

phenomena, identifying gaps in knowledge and 
potential consequences.  
2. Methods

This review compiles studies that measure mixed
layer depths, phytoplankton concentrations, 
mesoscale to submesoscale phenomena, ocean 
temperatures, and other physical events. 
Observational data in this paper are derived from 
several methods, including long-term hydrographic 
time-series, satellite imaging showing ocean color 
and sea surface height, in-situ measurements by 
Argo floats or ship-based measurements, and ocean 
reanalysis combining historical and computational 
models with observations. Ocean color indicates 
levels of chlorophyll-a, which is a proxy 
measurement for phytoplankton concentrations. 
Sea surface height is a proxy for eddies in the ocean, 
as cyclonic eddies tend to decrease surface height 
while anticyclonic eddies increase surface height. 
Models are used to determine variables of 
importance and predict future outcomes. This paper 
uses models across scales: local scales though the 
Large Eddy Simulation (LES), regional scales 
though the General Ocean Turbulence Model 
(GOTM) and Regional Ocean Modelling System 
(ROMS), and global scales though the Community 
Earth System Model (CESM) and MIT’s General 
Circulation Model (MITgcm).  
3. Findings

All complex dynamics discussed subsequently
depend first on the fluid dynamics of the world’s 
oceans. The Earth is a sphere covered in fluids, 
rotating about an axis. The winds in the atmosphere 
circulate about the globe and respond to pressure 
differentials, adding a shearing force to the ocean 
surface, which is then forced along with the wind’s 
direction. Tidal currents and water density fluxes 
can also influence the direction of ocean surface 
currents. Next, we add continents and islands, and 

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therefore coastlines, to the Earth.  Due to 
hydrostatic pressure, ocean fluids will need to 
deflect in different directions in three dimensions as 
they cannot pile up on themselves along the 
shorelines. Since the fluids of the Earth’s oceans are 
in a rotating frame, the fictitious Coriolis force is 
present in the Earth inertial frame, where the 
Coriolis force will deflect masses according to the 
cross product in  

𝑓𝑓 = −2𝑚𝑚(𝜴𝜴	𝑥𝑥	𝒗𝒗), (1)
where 𝜴𝜴 is Earth’s rotation vector and 𝒗𝒗 is the
velocity of the mass of interest, or, broadly, to the 
right in the Northern Hemisphere and to the left in 
the Southern Hemisphere. This configuration will 
lead to an overall picture of surface currents, which 
circulate into gyres in the oceans, or smaller 
circulations in gulfs and bays. At smaller scales, 
surface currents will tend to circulate clockwise in 
the Northern Hemisphere and counterclockwise in 
the Southern Hemisphere. Incorporating density, 
masses of water with characteristic temperature and 
salinity (or isopycnals) will tend to sink or float (cold 
and high density waters will sink, whereas warm 
and less dense waters will float).  This causes 
thermohaline circulation, which consists of deep 

waters with high temperature and salinity fluxes 
over their depth, allowing the flow of bottom waters 
to be circulated back into the surface currents over 
timescales of roughly a thousand years6. These 
differences in density and temperature can cause 
stratification, where two masses of water with 
different characteristics form an interface at which 
they have limited exchange. As deeper, colder 
waters often carry more nutrients due to sinking, 
this stratification is especially important when 
considering marine organisms as their resources will 
depend on the upwelling of these colder, nutrient-
rich waters. 
 With a background of the global currents, 
smaller scale phenomena add to the complexity of 
large scale circulations and ocean gyres. Mesoscale 
and submesoscale interactions create turbulence 
(the state of fluid motion that is chaotic and 
unsteady) in the form of eddies (smaller vortices), 
mixed layer restratification, fronts, and other 
instabilities7-8. Figure 1 shows a global model of 
simulated submesoscale interactions with a 2km 
resolution, revealing this turbulence across the 
globe. 

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Figure 1. Visualization of global submesoscale phenomena and configuration, including eddies, front and 
filaments. a) Satellite image of cyanobacteria bloom using ocean color detecting chlorophyll-a 
concentrations as a proxy for bloom location. Shapes here indicate mesoscale and submesoscale eddies and 
fronts strongly affect biology. (Taken August 11, 2015, from NASA 
(https://landsat.visibleearth.nasa.gov/view.php?id=86449). b) Observation taken on October 7, 1984 with 
ship tracks labeled A and B. c, d) Simulated ocean turbulence at roughly 2km resolution, expanded at local 
regions. Red indicates upwelling, or cyclonic eddies, and blue indicates downwelling, or anticyclonic 
eddies. c) shows simulation on March 1, 2012 (Northern Hemisphere winter/Southern Hemisphere 
summer) whereas d) shows simulation on September 1, 2012 (Northern Hemisphere summer/Southern 
Hemisphere winter). Taken from Su et. al, 20189. 

 The mixed layer plays an important role in these 
interactions, as it absorbs and responds to the 
interactions at the surface. The mixed layer begins 
at the surface layer of the ocean, at the air-sea 
boundary, and continues throughout the region 
where temperature and salinity remain relatively 
uniform due to intense mixing of the upper ocean 
layer; once the temperature and salinity change 
drastically, the mixed layer ends, at which point the 
mixed layer is stratified from denser regions below. 
The mixed layer depth depends on both top mixing 
and bottom mixing10. Top mixing is driven by wind 
shear, waves, and buoyancy fluxes, while bottom 

mixing is driven by large turbulent eddies that mix 
denser fluid from below and shear instabilities that 
thicken the buoyancy interface and allow for mixing 
from turbulent eddies. Often, models and common 
perception indicate that warmer ocean surfaces yield 
shallower mixed layers and more stratified water 
beneath11. However, observational studies 
demonstrate that this relationship is more complex; 
stabilizing or destabilizing buoyancy forces may be 
the driver in regional mixed layer depth, as heating 
(cooling) can cause stabilizing (destabilizing) 
buoyancy forces leading to stratification (convective 
mixing and a deepening of the mixed layer). 

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Overall, observational studies of global patterns in 
mixed layer depths show a broad correlation 
between surface temperature and mixed layer depth, 
as shown in Figure 2, although a comprehensive 
understanding of mechanisms shaping mixed layer 
depth is still under active research. With a 

foundational knowledge of how physical 
phenomena form currents and circulation patterns, 
the study next investigates relationships at smaller 
scales and their potential impact on biology.  

Figure 2. Global mixed layer depth climatology defined A) by temperature and B) by density. Criteria was 
based on computational inspection of profiles and time series data. Grid boxes are 2° and smoothing was 
used to fill missing data. A) Mixed layer depth for individual profiles is determined using deviation from 
near-surface temperature at 10m depth. A deviation of 0.2° C was used to mark the end of the mixed layer. 
B) Mixed layer depth is determined using deviation from near-surface density at 10m depth. Density
criterion is a difference of 0.03 kg/m3 from near-surface density, indicating the measurement is outside of
well-mixed region. Figures adapted from de Boyer Montégut et. al, 200412

To localize biologically productive regions in 
the ocean, the basic needs of phytoplankton 
populations must first be identified. We know 
phytoplankton (marine plants that perform 
photosynthesis) growth depends on light and 
nutrients. As most phytoplankton have limited 
control over their motion, populations 
predominantly flow with currents, and so physical 
phenomena dictate whether they have access to 
their basic needs. A critical disjunction between 
nutrient and light availability prevails; while 
sunlight is abundant at the surface and 
photosynthetically available radiation decreases 
exponentially with depth, nutrients are increasingly 

abundant in deep waters due to their sinking 
tendencies and storage in more dense waters. 
Similarly, while sunlight is abundant in subtropical 
regions, these regions tend to be depleted of 
nutrients as they tend to have shallow mixed layers 
that do not reach to the nutrients stored below. On 
the other hand, subpolar regions have deep mixed 
layers that can incorporate nutrients from colder, 
deeper waters (often reincorporated to the surface 
from convective mixing from the bottom), but a lack 
of sufficient light to support yearlong growth. This 
spatial consequence is shown in Figure 313. For this 
reason, upwelling regions (where cold, nutrient-
dense water is pumped to the surface from deeper 

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waters by the topography of continental shelves) 
tend to be highly productive with increased levels of 

phytoplankton abundance compared to 
surrounding areas. 

Figure 3. Observational measurements of global primary production. Satellite data from NOAA’s Aqua 
MODIS on Chlorophyll a, incident visible surface irradiance, and sea surface temperature. Scale from 0 
to 800 mg C/(m2day), excluding extreme highs from scale for visualization. Observations from A) January 
16, 2019 (Southern Hemisphere summer) and B) July 16, 2019 (Northern Hemisphere summer). Graphed 
by author. 
 Here, it will be noted that this broad confluence 
of light and nutrients is not the only effect on 
localization of primary production. Zooplankton 
grazing, diversity within phytoplankton species 
allowing for optimizations in different 
environments, and other factors will influence the 
net growth of phytoplankton populations. With the 
general importance of vertical transport in mind, 
the study turns to investigate small scale 
phenomena that can promote vertical velocities and 
fluxes of isopycnals. Focusing in on turbulence at 
the mesoscale and submesoscale, there are a number 
of mechanisms that impact vertical velocities in a 
more substantial way than that of global, large scale 
circulations. Internal instabilities and surface 
forcing enhance or suppress submesoscale 
dynamics8. Eddies can form at scales of 0.1-100km, 
created from anomalies in temperature and salinity, 

and carry rotational kinetic energy that can 
transport heat, salt, carbon, and nutrients in the 
horizontal and vertical planes14. Eddies also tend to 
stratify the mixed layer, which has been shown to 
initiate blooms in subpolar regions by keeping 
phytoplankton populations in upper regions with 
increased light exposure15. Fronts, produced from 
horizontal gradients of buoyancy (or variations in 
static pressure causing drag and lift forces), can form 
boundaries between isopycnals. These cause a cross-
front ageostrophic secondary circulation to reach 
thermal wind balance, producing vertical velocities, 
as shown in Figure 4. The vertical velocities of 
fronts may support the transport of nutrients up 
into the euphotic zone where phytoplankton can 
access them, but they can also drive phytoplankton 
down away from the sunlight into lower levels 
without photosynthetically available radiation13. 

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Figure 4. Depiction of submesoscale surface front configuration and the vertical velocities in 
frontogenesis. The along-front flow is mostly geostrophic, while the secondary circulation (in bold black 
and red arrows) is ageostrophic and acts to balance the system into thermal wind balance. Figure adapted 
from Figure 5 in McWilliams 201616. 
 However, the impact of either of these 
outcomes depends on frontal depth; while a deep 
front can access the nutricline (nutrient-rich layer 
often beneath the mixed layer) and upwell nutrients, 
a shallow front may not reach past the mixed layer 
and therefore have minimal effect to primary 
production near the surface. Further potential 
impact for submesoscale processes on 
phytoplankton populations and spatial distribution 
is suggested by the similarity in timescale. 
Phytoplankton variability develops over the course 
of days, aligning well with the submesoscale 
timescales that last roughly days. This is illustrated 
by the mathematical relationship between timescale 
and length scale, given the timescale approximation 
of: 

! = "!
# 	 , (2) 

where U is advective velocity, typically 0.1m/s, and 
! for submesoscale events is typically 1km8. In

subtropical regions with shallow mixed layers, these 
local phenomena could be the only contribution of 
vertical velocities, yielding variability in regions 
further from the coast and allowing primary 
production. Nevertheless, the effect of 
submesoscale influences are up for debate. Some 
studies argue that the stirring and redistribution of 
the water column on the submesoscale level may not 
have a significant effect on global phytoplankton 
budgets and dynamics due to fewer submesoscale 
interactions in subtropical regions compared to 
subpolar regions, which already have sufficient 
nutrients in the mixed layer13. Evaluating the 
regions with shallow mixed layers where mesoscale 
and submesoscale interactions have potential to 
change phytoplankton populations should be 
incorporated into regional models through 
physical-biological coupling. This will optimize 
predictions of carbon cycling and of higher trophic 
marine populations that depend on primary 
production for regional fisheries.  

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 After investigating physical mechanisms that 
affect biology, it is critical to understand how 
biological factors can have an influence on oceanic 
physics and at what scale. Light can have impacts 
on ocean fluids by increasing sea surface 
temperature (SST) and affecting energy fluxes. In 
clear water, the shortwave radiation absorbed into 
the upper ocean is uniform, depending on  

( ) = , ( ) 

where  is the albedo-corrected surface radiation, z
is the depth, and  is the attenuation length, or a
manipulation of the absorption coefficient of 
seawater17. However, waters are not all crystal clear, 
and varying levels of clarity must be accounted for. 
Jerlov defined five types of oceanic water clarity in 
1968, ranging from clear to murkier, dirtier waters, 
which have been named Jerlov types18. 
Incorporating these different Jerlov types affects the 
irradiance into the ocean, which changes the 
equation to become:  

( ) = (1 − ) ( ) , ( )

in which the first term concerns the red part of the 
light spectrum, the second term concerns the blue-
green part,  is a dimensionless weighting 
parameter, and  and  are attenuation lengths.
All of these parameters will change depending on 
the Jerlov type19. The ( ) term represents
bioturbidity, which depends on phytoplankton 
concentrations and detritus (dead particulate 
organic matter) concentration, as  

( ) = − ( ) ( ) 	 , ( ) 

where  is the attenuation constant for shelf
shading, P is phytoplankton concentrations, i is the 
index of different plankton species, and D is the 
detritus concentration20. Equation 5 illustrates the 
additional absorption provided by biological 

presence, the importance of which is shown by the 
impact of the irradiance curve on physical factors:  

− 	 	 	 = , ( ) 

where T denotes temperature,  represents
molecular diffusivity of heat,	 	is the vertical
component of velocity,  is the specific heat of
seawater, and  is the reference density of
seawater19. Equation 6 highlights the 
aforementioned relationship between light 
(irradiance) and temperature in the ocean. An 
increase in absorbed radiation into the ocean by 
biological presence impacts the potential heat 
storage in the ocean, which in turn affects 
temperature differentials and triggers physical 
responses of turbulence or other events. We can 
make simple calculations to show the magnitude of 
the effect of biology using Jerlov types as proxies for 
high biological presence: We can calculate the effect 
of a typical level of solar radiation (500 W/m2) in 
different Jerlov water types, the murkier water 
(Jerlov Type III) representing regions with high 
phytoplankton abundance and the clear water 
(Jerlov Type I) void of organisms. In a simple 
scheme without mixing, we use Equations 4 and 6 
to evaluate the change in temperature over the 
depth in different Jerlov types. In this basic scheme 
where we assume the water stays in place, the 
magnitude of the warming across our mixed layer in 
Type III is greater by roughly 0.5 °C over time 
compared to Type I (Figure 5). However, the extent 
of this impact remains unclear as ocean waters do in 
fact mix; there is clearly missing information, as 
Havg in Figure 5 has a greater change in 
temperature, which shows the change in 
temperature in a well-mixed water column using the 
equation:  

= , ( ) 

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where H is the mixed layer depth. The increased 
temperature change in the mixed layer scheme 

shows that our simple calculation is only the 
beginning of the story.

Figure 5. Change in temperature between the surface and bottom of the mixed layer for different Jerlov 
Types. Equation 4 was used across 100m depth from the surface to evaluate irradiance over ocean depth. 
Equation 6 was used to convert irradiance to temperature change over the timeframe of a week. Mixed 
layer depth was set to 40m depth arbitrarily and Equation 7 was used to calculate the temperature change. 
The difference between Type III (proxy for high biological presence) and Type I (clear water) after 7 days 
is 0.48 °C.

To fully understand the magnitude of 
biology’s impact of heat storage in the ocean, these 
equations must be incorporated into small-scale 
models or parameterized into larger or global 
models. Using two-way coupled models between 
physics and biology, the potential physical 
outcomes of these additional temperature 
differentials could be detected. Studies have shown 
that an increased abundance of surface marine 
organisms like phytoplankton or other floaters 
causes both the surface albedo (amount of light that 
is reflected back from Earth) and absorption to be 
increased while momentum input from shear stress 
from surface winds causes it to be decreased20. 
Furthermore, research has indicated that for 
surfacing floating phytoplankton species, marine 
populations’ effects of increasing absorption and 
reducing wind drag would outweigh the effects of 
an altered albedo. In regions with shallow mixed 

layer depth, it could be especially important to 
parameterize this biogeochemical-physical 
relationship at frontal events, as a front often 
develops higher biological concentrations on the 
side of higher density. This could theoretically lead 
to differential heating, changing the dynamics of 
the front and affecting feedback loops21. This 
phenomenon could have implications for marine 
ecosystems, as fisheries have long known that 
higher trophic marine organisms like fish 
populations, whales, and seabirds congregate near 
oceanic fronts, yet this occurrence still lacks 
comprehensive understanding in its relationship to 
physical oceanography21. Biogeochemical-physical 
coupling could help inform regional scales of heat 
capacity, as it has already been shown that including 
submesoscale interactions significantly and 
consistently increase upward heat transport and 
warms the sea surface up to 0.3° C at a global scale9. 

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Incorporating irradiance parameterizations into 
climate models could affect regional carbon budgets 
by changing positive feedback loops of plankton 
populations and other unknown physical 
responses20. 
4. Discussion

With an understanding of various physical and
biogeochemical processes that could be 
parameterized, this study discussed the potential 
outcomes and open questions for investigation. As 
mentioned, mesoscale and submesoscale processes 
have potential to impact primary production which 
has serious implications for higher trophic 
ecosystem health. This in turn influences human 
economies through fisheries, tourism, and other 
ecosystem services. Potential heat storage in the 
ocean, and the coupled effects of phytoplankton 
growth in relation to temperature changes, is also a 
relevant area of  study that requires further research. 
In terms of biological studies, future research must 
aim to study different species and diversity within 
phytoplankton populations, as abrupt climate 
changes could have tremendous effects on 
biodiversity, food chain dynamics, and spatial 
distributions and variations across a global scale.  

Considering the looming threat of global 
warming, it is critical to investigate how these 
processes will respond to increased sea surface 
temperatures (SST) and higher levels of 
anthropogenic carbon. Some studies indicate that 
higher SST will lead to a more stratified ocean, 
decreasing nutrient fluxes from the nutricline or 
shallowing the mixed layer into more subpolar 
regions22. However, more frequent and stronger 
storms would increase turbulence in the ocean, 
providing vertical fluxes and nutrients23. While 
global and regional models are equipped to make 
predictions on specific changes in turbulence and 
local circulations, incorporating biological coupling 
into these models and understanding the various 

outcomes in relation to primary production require 
further research until they can be wellunderstood.   
Acknowledgements 
The author wishes to thank Baylor Fox-Kemper 
and Leah Johnson for their research collaborative 
methods and inspiring this research project. The 
author acknowledges the helpful advice from 
Thomas Cronin, Deven Malone, Chris Scholz, and 
Joseph Feldblum. Particular gratitude goes to 
William Boatwright and Louisa Boatwright for 
encouraging and supporting the author’s studies. 
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