Relationship Between Winter Downwelling Conditions and Summer Hypoxia
Severity Along the Oregon Coast
By:
Helen Marie Walters
An Undergraduate Thesis Submitted to
Oregon State University
In partial fulfillment of
the requirements for the
degree of
Baccalaureate of Science in BioResource Research,
Sustainable Ecosystems
Presented May 28, 2014
Commencement June 14, 2014
2
Acknowledgements:
I would like to express my appreciation to:
Dr. Yvette Spitz
Dr. Harold Batchelder
Dr. Katharine Field
Brandy Cervantes
Wanda Crannell
Center for Coastal Marginal Observation and Prediction
E.R. Jackman Scholarship Fund
Friends and Family
1
Abstract
Hypoxia is a naturally occurring phenomenon that happens seasonally off the Oregon
coast. As a consequence, there has been a great deal of research focused around analyzing the
strength and duration of the hypoxic event during the summer season. This paper, however, takes
an innovative approach by looking at the preceding winter seasons to determine the impact initial
conditions of an oncoming summer season have on the hypoxic event. The methodology
presented here has two facets: 1. Analysis of the atmospheric conditions during winter; and 2.
Multiple model simulations with various atmospheric forcing. While the atmospheric condition
analysis does not lead to any conclusive correlation between the winter wind stress and summer
dissolved oxygen levels most likely due to scarcity of data, the results of the model simulations
indicate otherwise. The simulated ocean conditions using atmospheric forcing from three
contrasting years, while using the climatological biochemical and physical initial conditions in
January show that the winter conditions could lead to low levels of dissolved oxygen on the
Oregon shelf in the pre-upwelling season. When the biological components of the model are
removed in order to isolate the impact of biological versus physical forcing, model predictions
differ only minimally; this result validates, to some degree, the idea that winter/early spring
atmospheric conditions impact the strength of the ensuing summer hypoxia.
2
1. Introduction
The presence of dissolved oxygen (DO) in the ocean is one of the many factors essential
to the survival of marine organisms. The amount of DO found in the ocean is a result of
atmospheric exchange and oceanic physical and biological processes (Pierce et al., 2012). The
delicate balance of the oceanic ecosystem can be disrupted when DO levels fluctuate slightly,
especially if they drop below the level necessary for most marine organisms to survive. This
level of DO deficiency (O2 < 1.4 ml l-1) is known as hypoxia.
Hypoxia may be caused by either anthropogenic influences or natural processes. Both
anthropogenic and natural hypoxia can be detrimental to the natural environment by adding
stress to the organisms residing within the hypoxic zone. Anthropogenic, or human-caused,
hypoxia is often a result of treated, nutrient (mainly nitrate and phosphorous) rich sewage water
being released into rivers or fertilizer run-off from farms draining into nearby rivers. Diaz and
Rosenberg (2008) showed that most hypoxic coastal zones are near areas with a large human
footprint and have increased in size exponentially since the 1960’s. The Gulf of Mexico is one
example. The highly populated areas near the Mississippi River cause the Gulf of Mexico to
receive excess amounts of nitrate and phosphorous, and at times develop hypoxic waters (Zhang
et al., 2010).
Hypoxia as a result of natural processes is a phenomenon often seen in summer or early
fall along coasts with eastern boundary currents (Rabalais et al., 2009). In these locations, the
coast is east of the open ocean. In the summer the wind blows equatorwards, which results in
advection of surface water away from the coast, allowing cold, nutrient rich, and DO deficient
water to rise upwards (or upwell) on the shelf. During the winter, however, the wind blows
3
mainly polewards, pushing surface water towards the coast and resulting in the surface
oxygenated water being downwelled (or sinking). Thus, during the winter season, DO levels in
the water are regenerated through wind-induced mixing.
In naturally occurring hypoxic locations, the portion of the water column which have low
oxygen levels is called the oxygen minimum zone (OMZ). When the OMZ encompasses a
greater portion of the water column than normal and/or reaches onto the shelf, organisms living
within this zone may react poorly. Upon the onset of hypoxia, these organisms, customary to
oxygen rich habitats, may either be unable to survive the low DO levels or unable to carry out
basic biological functions. The latter impact may also ultimately result in mortality.
Over the last two decades, hypoxic events have received much attention and generated
research efforts in part due to the wide-scale impacts levied upon the ocean ecosystem. Many
properties of hypoxia have been previously examined; these include the extent, severity,
geographic location, and impacts on higher tropic levels including shellfish, benthic organisms,
and numerous species of fish (Grantham et al., 2004, Chan et al., 2008).
A study conducted in the California Current System, which ranges from northern
California to Washington, showed that the spreading of near-bottom low-oxygen waters has
large impacts on higher trophic levels, particularly, those with higher DO requirements such as
benthic communities (Bograd et al., 2008). Another study stated: ‘Mass mortality of benthos and
fish over large areas due to hypoxia have been reported’ and ‘sensitive species have been
permanently or periodically removed in many places’ (Wu, 2002). Declining population size of
certain marine organisms is a cause for concern. The ocean has a delicate balance and changes to
even a single species may potentially have detrimental effects to the oceanic ecosystem at large
(Barth et al., 2007).
4
Not only do hypoxic events impact the mortality rates of certain organisms, but they also
impact the seasonal cycle of benthic organisms. Grantham et al. (2004) noted a severe seasonal
decrease in the crab population off the coast of Newport in 2002. This abnormality was attributed
to decreased DO levels in the water. Benthic organisms, being less mobile, are unable to escape
the coastal shelf, leaving them more susceptible to the low oxygen waters (Villas et al., 2012).
Even though benthic organisms are more affected by hypoxic conditions due to their immobility,
if the onset of hypoxia is rapid, certain fish may be trapped in the low DO area as well. Chan et
al. (2007) references a submersible-based survey that reported in August of 2006 a ‘complete
absence of all fish from rocky reefs that normally serve as habitats for diverse rockfish.’ These
scenarios show various negative effects on oceanic organisms due to anomalous hypoxic events.
Naturally occurring hypoxia often exhibits a seasonal cycle, and such is the case off the
Oregon coast. The lowest DO concentrations occur during the summer due to upwelling and are
widespread on the shelf at the end of the summer (August-September) (Huyer et al., 2007). This
pattern repeats annually with variation in its strength and duration (Pierce et al., 2012) and is
what gives the Oregon coast seasonal DO patterns.
Much research has been centered on the summer time period when the hypoxia off the
Oregon coast is most pronounced due to seasonal upwelling. Pierce et al. (2012) used
observations along the Newport Hydrographic Line (44.65N) to describe the spatial and
temporal evolution in DO across the shelf during the summer. In their study, correlations
between the upwelling index and DO concentrations as well as chlorophyll-a levels can explain
much about the seasonal cycle of summer hypoxia. The causes of the inter-annual variability of
the severity in summer hypoxia remains, however, largely unexplained.
5
While there are fewer observations during the winter downwelling season, year to year
variability in ocean conditions during the downwelling period (fall-winter) and early spring
transition season may impact the severity of the hypoxia during the following summer. If
downwelling favorable winds are weak during fall-winter, there will be less oxygenation of the
bottom water at the shelf break. Also, if during the spring transition period, upwelling favorable
winds dominate, then one might expect a greater subsequent incident of summer hypoxia. The
persistence of upwelling favorable winds during the spring transition period does not allow for
adequate mixing of oxygen through the water column and leaves the water less oxygenated at the
beginning of the summer season.
Unique spring conditions immediately preceding the 2002 upwelling season resulted in
wide spread summer hypoxia on the Oregon shelf that began earlier than on average during the
period between 2000 and 2008 (Koch et al., in preparation). The DO concentrations at an
offshore location along the Newport transect in spring 2002 was significantly lower than the
climatological DO. Modeled hypoxic conditions on the shelf occurred much earlier with the in
situ 2002 initial conditions and the severity was greater (more than 50% of the shelf with bottom
hypoxic water) than when climatological offshore DO concentrations were considered.
The objective of this study was to analyze the prior winter conditions in relation to the
summer-fall hypoxic conditions along the Newport Hydrographic Line (NH) (Figure 1). The NH
line has been sampled extensively over the last few decades. Analysis of the downwelling
condition (alongshore wind stress strength) and DO content during the winter and early spring
seasons in relation to the ensuing hypoxia event will be discussed.
6
Figure 1: Newport Hydrographic Line (NH) with 50,
200, and 2000 meter isobaths and the NH stations
labeled (Pierce et al., 2006).
2. Data and Methods
In order to address the role that the preceding fall/winter season plays in the level of
hypoxia during the summer upwelling season, we took a twofold approach:
1. Analysis of the wind forcing from late fall (beginning of the downwelling season) to
the spring/summer (beginning of upwelling season) in relation to the oxygen
conditions on the central Oregon shelf at the beginning of the spring transition/early
summer season (upwelling season) for six years (1998-2003).
2. Model simulations of the physical and biogeochemical dynamics, including oxygen
concentrations, of the shelf ocean using January – June atmospheric forcing from
three contrasting years.
7
1. Analysis of atmospheric conditions and impact on spring water column oxygen.
Multiple variables including alongshore wind stress, dissolved oxygen (DO), and
chlorophyll-a pigment were examined in an analysis of the impact of atmospheric conditions on
initial summer oxygen conditions.
Alongshore wind stress determines the amount of upwelled (cold, oxygen deficient,
nutrient rich) or downwelled (warmer, oxygen rich, nutrient poor) water on the shelf. Stronger
and prolonged upwelling favorable wind during the winter means lower bottom DO level on the
shelf at the start of the summer season. Similar to the study of Pierce et al. (2006) that
established the timing of onset and the strength of the upwelling season, the onset, duration, and
strength of the downwelling season were determined using the wind stress from
http://damp.coas.oregonstate.edu/windstress/. The wind stress data were derived from observed
winds at Newport, Oregon, using the Large and Pond method (1981). The diurnal variations
were removed with low-pass filtering (Pierce et al., 2006). More specifically, duration and
spring transition dates (see Section 3) were determined by analyzing when the wind stress shifted
from consistently downwelling favorable to consistently upwelling favorable. Cumulative wind
stresses since January 1 of each year were calculated to analyze the impact of the strength of the
downwelling season on oxygen. In this study, alongshore wind stress (referred to as wind stress
in the remaining of the paper) is positive when the wind is blowing polewards (downwelling
favorable) and negative when blowing equatorwards (upwelling favorable).
The Newport Hydrographic Line (NH line), located at 44.65N off the central Oregon
coast, is one of the few locations in the northeast Pacific with a long record of DO measurements
(spanning 1960-2012) (Pierce et al., 2006). Although the data collection were conducted for over
50 years, there is not a complete time series. The Next Ten Years of Oceanography (TENOC)
8
program collected data from 1961-1971. There was then a 26-year period of few oceanographic
observations until the Global Ocean Ecosystems Dynamics (GLOBEC) Long Term Observation
Program (LTOP) sampled from 1997-2004. More recent years since 2004 have been relatively
well sampled by several research programs and autonomous vehicles such as gliders.
Figure 2: Time series of DO at station NH-5 at
(50 m depth). Linear regression lines with 95%
confidence intervals are shown (Pierce et al.,
2006).
While there does appear to be a decline in DO over the years 1961-2004 (Figure 2) (Chan
et al., 2008 and Pierce et al., 2006), it is difficult to state confidently that there is a downward
trend because 26 years of missing data is a long time span to interpolate over. In addition,
instrumentation used for collection changed during the period between TENOC and GLOBECLTOP collection. Because of these issues, our analysis focused on the latest period of sampling
conducted by GLOBEC. DO concentrations were obtained from CTD (ConductivityTemperature-Depth) casts from 1999-2003 and reported in the LTOP database (see the W9904B,
9
W0004B, W0103B, W0204A, and W0304A reports on the NEP GLOBEC-LTOP website:
http://nepglobec.bco-dmo.org/reports/ccs_cruises/ccs_cr_rpts.html).
Chlorophyll-a concentrations and DO data analyzed from discrete depth Niskin bottles on
the March/April GLOBEC cruises, were used to characterize the pre-upwelling conditions during
1999-2003.
Due to the temporal limitations of GLOBEC-LTOP cruises (conducted 4-7 times a year),
chlorophyll-a pigment data derived from satellite imagery were used to provide a more regularly
available dataset spanning winter to summer seasons. Chlorophyll-a pigment was used as a proxy
for bottom consumed DO (calculation and justification in Section 3 and Appendix C). 9 km
spatial resolution and 8-day composite surface chlorophyll-a field data were obtained from the
Sea-viewing Wide Field of View Sensor (SeaWiFS) satellite ocean color database
(http://oceancolor.gsfc.nasa.gov/). Dr. Ricardo Letelier (Oregon State University, College of
Earth, Ocean, and Atmospheric Sciences) further supplied isolated Oregon coast SeaWiFS data
gridded to 4 km resolution and composited at 7-day intervals. These two SeaWiFS datasets
differed slightly due to resolution, algorithms used in processing, and number of days in
composite. See Appendix B for a discussion about these two different imagery sources. The 4
km resolution and 7-day composite SeaWiFS imagery were preferred to the 9 km resolution
because the increased resolution limited the amount of interpolation between pixels when
comparing with the LTOP observations at the cruise stations along the NH line.
2. Model simulations of winter-spring oxygen condition and the impact of initial conditions
on the summer oxygen concentration.
10
The model used to simulate different spring scenarios was a coupled biological-physical
model based on the NAPZD (nitrate, ammonium, phytoplankton, zooplankton, and detritus)
biological model from Spitz et al. (2005) and the Regional Ocean Modeling System (ROMS) 3D
coastal ocean physical model (www.myroms.org). DO and chlorophyll-a state variables and
equations representing the processes that alter these fields were added to the NAPZD model to
directly simulate DO and chlorophyll-a for comparison to observations. Equations and
parameters for the NAPZDO model are given in Appendix A. Figure 3 shows the basic
interactions of the six major biological components of the model (oxygen, nitrate, phytoplankton,
ammonium, zooplankton, and detritus). In addition, arrows show oxygen is gained and lost in the
system.
Figure 3: Schematic of NAPZDO model including the six major biologic components
(oxygen, nitrate, phytoplankton, ammonium, zooplankton, and detritus). Dissolved
oxygen gain and loss processes are represented by dark red and blue arrows,
respectively. Flow chart adapted from Spitz et al., 2005.
11
The model was run in a similar fashion to Spitz et al. (2005) from January to the end of
June (years 2000-2002). The model domain extended alongshore 634 km from 41.7o N to about
47.3o N and offshore 250 km. The model domain included three open boundaries located on the
north, south and west of the domain and is periodic in the alongshore direction. The benefit of
running a periodic channel model (no remote forcing) was that there were no boundary
conditions needed to run the model, allowing the focus to be solely on the impact of the
atmospheric forcing on the bottom DO levels.
The model uses realistic continental shelf
bathymetry with 10 m minimum and 1200 m maximum depths. The vertical resolution is 45
levels with higher resolution at the surface and bottom. The horizontal resolution is a rectangular
grid with 1 km at the coast increasing gradually to about 6 km off shore and 1 to 2 km in N-S
directions.
The atmospheric forcing is from the Coupled Ocean/Atmosphere Mesoscale Prediction
System (COAMPS) daily predictions and is spatially uniform over the model domain (Hodur,
1997). The initial conditions for biology were obtained from climatological profiles (1997-2003)
85 miles offshore of Newport and imposed uniformly over the domain. In order to assess the
impact of atmospheric forcing on DO concentrations at the end of the beginning of the upwelling
season, the initial conditions (January 1) for the biological components were held constant for all
three simulations whereas the atmospheric forcing varied by year. The circulation model started
from rest in January and the first 22 days were considered a spin-up period (the time allowing the
model to acclimate).
12
3. Results and Discussion
1. Analysis of atmospheric conditions and impact on spring water column oxygen.
Analysis of the wind stress from 1998-1999 to 2003-2004 seasons showed large interannual variability in the length and termination date of the winter season (Table 1). Some years
had a very short downwelling period, such as 2002-2003 that had consistently downwelling
favorable winds for 166 days and an upwelling season beginning on April 20th. Other years such
as 1999-2000 had a much longer winter downwelling season (237 days) and late starting
upwelling season (June 13th).
Year
Length of Winter
Downwelling Season (days)
End Day of Downwelling Season
1998-1999
1999-2000
2000-2001
2001-2002
2002-2003
2003-2004
174
237
200
191
166
207
29-Mar
13-Jun
1-May
17-May
20-Apr
21-Apr
Table 1: Length of winter downwelling season and ending day of the downwelling season
calculated from wind stress (http://damp.coas.oregonstate.edu/windstress/).
In addition to the inter-annual variability of the length of the downwelling season shown
in Table 1, there were differences in the cumulative strength of downwelling (wind stress) during
the same period (Figure 4). For example, 1998-1999 had a relatively short downwelling season
but strong northward winds and a large cumulative downwelling (Figure 4). Conversely, 20002001, while displaying a downwelling season of average duration, had relatively weak northward
winds and the least downwelling favorable cumulative wind stress. We could then expect that the
13
winter-spring conditions of 2000-2001 would set the stage for a potentially more severe summer
hypoxic season.
Figure 4: Cumulative alongshore wind stress during the downwelling
seasons (1998-2004), beginning on the first day of September of the
beginning year. Annual variations of the length and strength of the
downwelling season are shown. For example, 98-99 had a much stronger
downwelling favorable wind stress but the downwelling season was much
shorter in duration than in 99-00.
As a consequence of the wind stress inter-annual variability, the water column oxygen
levels fluctuate inter-annually. Using the NH line data, the depths of the 6, 4, and 2 ml l-1 DO
were analyzed and examined for relationships to downwelling period wind stress. In agreement
with the cumulative wind stress analysis, the 2 ml l-1 DO isocline was 25-30 m deeper in the
water column at NH 25 in April 1999 than in 2000 or 2002. However, this relationship between
water DO in April and wind stress during downwelling season was not strong, possibly due to
lack of data (only one sample taken in March/April of four years).
14
NH25 Oxygen Content Newport April Cruise
Year
Depth
1999
0
2001
2003
O2-6 ml/l
50
O2-4 ml/l
100
O2-2 ml/l
150
200
250
Figure 5: Depth of the 6, 4 and 2 ml l-1 oxygen levels at NH 25 and during the
1999-2003 April GLOBEC-LTOP cruises. Note: no data is shown in April
2001 because there was no GLOBEC cruise during this time period.
In order to examine the relationship between wind stress and DO for a longer time span,
SeaWiFS surface chlorophyll-a data were used to estimate spring time (January- end of March)
bottom DO consumption. The goodness of fit between satellite derived chlorophyll-a and in situ
GLOBEC-LTOP chlorophyll-a is discussed in Appendix C. Since about 10% per day of the
phytoplankton biomass is removed as detritus that sinks to the bottom, the 8-day composite
SeaWiFS chlorophyll-a were used and this study assumed that chlorophyll-a is converted in
detritus and then into consumed DO at the bottom using the equation:
Consumed O2 (ml l-1) = Total Chl (mg m-3)* 0.0247
(1)
Table 2 shows the chlorophyll-a pigment and consumed DO levels for years 1999-2004.
This table showed the inter-annual variability that is displayed in surface chlorophyll-a data and
by extension consumed DO levels.
15
Chlorophyll-a Pigment
(mg m-3)
Consumed Dissolved
Oxygen (ml l-1)
Chlorophyll-a Pigment
(mg m-3)
Consumed Dissolved
Oxygen (ml l-1)
1999
2000
2001 2002
January 1 – End of March
11.8
18.7
27.5
33.6
2003
2004
18.8
17.9
0.3
0.5
0.4
January 1 – End of May
44.0
35.4
44.1
77.0
37.3
33.0
1.1
0.9
0.8
0.5
0.9
0.7
1.1
0.9
1.8
Table 2: SeaWiFS surface chlorophyll-a and equivalent dissolved oxygen (Equation 1) for years
1999-2004, January 1 – End of March and January 1 – May 30. Chlorophyll-a data were
interpolated to the NH stations (25 35 45 65 and 85) and summed over all stations and over time
for each year.
To examine the effect of the downwelling season on the consumed bottom oxygen, we
considered two periods, January to the end of March and January through end of May. The first
period encompasses most of the downwelling seasons from 1999-2003, except for 1999-2000.
We, therefore, considered a longer period, which encompassed the downwelling season for all
the five years. The consumed dissolved oxygen levels throughout all five of the years show
trends that are consistent with the cumulative wind stress. The lowest level of January- end of
March consumed bottom DO is found in 1999 followed by the level in 2000 and 2003,
corresponding to the strongest cumulative wind stress (1998-1999, 1999-2000, 2002-2003).
However, Table 2 shows that even by the end of March, 2002 corresponded to the largest
consumed DO, which is not consistent with the trend in the cumulative wind stress. This could
be due phytoplankton biomass and therefore detritus production during intermittent upwelling
persistent periods to the spring transition. This analysis showed that by early June 2002, the
bottom oxygen level could be much lower than at the same time in other years. This would lead
to potentially more severe hypoxia in the summer 2002, as this water with less oxygen will be
16
upwelling on the shelf. This is consistent with the report of Grantham et al. (2004) that severe
hypoxia in 2002 led to extensive mortality of Dungeness crab.
Table 2 provided insight into the inter-annual variation in chlorophyll-a pigment and
potential oxygen consumption. While these data were representative of the surface chlorophyll-a
levels, they provide no information about the vertical profile of chlorophyll-a. Looking at the
depth profiles of chlorophyll-a during the GLOBEC-LTOP April cruise (the spring transition
time), we found similar inter-annual variability (Figure 6), 2002 chlorophyll-a levels during the
April cruise were much higher than any of the other years and display a strong subsurface
maximum. This observation gives us some confidence that SeaWiFS data are a useful indicator
of bottom oxygen consumption.
1999
2000
2001
2002
2003
Figure 6: Chlorophyll-a profiles (mg m-3) at NH 25 and 35.
SeaWiFS satellite chlorophyll-a and wind stress across 1998-2003 showed large interannual variability and significant correlations between the two variables (cumulative wind stress
and cumulative chlorophyll) was found until April (Figure 7). For example, in 1999 there were
high cumulative wind stress levels (~7 N m-2) until April but moderate cumulative chlorophyll-a
17
levels (~15 mg m-3) at that time. In a year with very low cumulative wind stress levels such as
2001 (cumulative wind stress of ~1 N m-2) there was a higher level of cumulative chlorophyll-a
than in 1999 (~30 mg m-3). These correlations while existing are however weak as noted when
comparing 2001 and 2002.
Figure 7: Cumulative wind stress (N m-2) (Barth et al., 2006). Cumulative SeaWiFS,
Oregon gridded, 4 km, 7-day composite, chlorophyll-a (mg m-3) (Ricardo Letelier,
personal communication). Both cumulative wind stress and cumulative chlorophyll-a
are shown in relation to days (152 days, January-May). The cumulative chlorophylla data were calculated by taking an aggregation of the NH 25, 35, 45, 65, and 85
data points. Linear interpolation between the nearest pixels was done when data are
unavailable. These data range from 1998-2003. Wind data were 36 hour filtered.
One factor that may have contributed to the lack of a strong relationship between wind
stress and DO (displayed using chlorophyll-a proxy) was the scarcity of data. Discussion of data
scarcity and additional factors that contributed to a lack of strong correlation between cumulative
wind stress and cumulative chlorophyll-a are in Appendix C.
18
2. Model simulations of winter-spring oxygen condition and the impact of initial conditions
on the summer oxygen concentration: 2000-2002
Model simulations can be useful in evaluating the conditions under which low bottom
oxygen conditions occurred. Model simulations were conducted of January- June for 2000, 2001,
and 2002 to assess the relationship between winter atmospheric forcing conditions, bottom
oxygen conditions and subsequent summer hypoxic events. The model simulations were run in a
N-S periodic channel (no remote forcing) as described in section 2 with atmospheric forcing
(wind stress and heat flux). The validity of the modeled fields was assessed by comparing
contours of modeled DO to contours of DO from the corresponding GLOBEC cruises (Figure 8).
Reasonable agreement between the modeled and in situ DO is found for all years. Only
results for 2000 are displayed in Figure a-b. Mixing of surface oxygen was slightly deeper in the
simulations but the upwelling (shown by the green line: 2.5 ml l-1 isocline) was well represented.
There was sufficient agreement in the patterns displayed by the in situ data and the modeled
oxygen to use the simulation to assess the oxygen spring condition (Figure 8, April 11,
2000,2001,2002, panels a,c,d) on the shelf and its inter-annual variability. 2000 had higher levels
of DO on the shelf opposed to 2001 and 2002. 2002 showed the lowest bottom DO on the shelf,
as the 2.5 ml l-1 was found at a location less than 100 m deep.
Time series of bottom DO at NH 10 and NH 25 (Figure 9) show similar results with
highest bottom DO in 2000 and lower DO in 2001 and 2002. While there was a slight gain of
bottom oxygen during winter/spring 2000, the bottom DO levels steadily declined to reach close
to 2 ml l-1 in 2001 and 2002. The short period of strong downwelling during late April in 2001
and 2002 (Figure 10) was not enough to replenish the DO levels and they remained low (around
2 ml l-1). In 2000 even though there was a short period of upwelling (beginning of April) there
19
was a quick recovery and the bottom DO levels were replenished to 6 ml l-1 by May, 2000. The
difference in bottom oxygen conditions at the end of the spring season could be a major factor
contributing to the differences in summer hypoxia of these three years.
Model
2000
a.
GLOBEC
2000
b.
Model
2001
c.
Model
2002
d.
Figure 8: a. Contours of dissolved oxygen (DO) on April 11, 2000 with 2.5 ml l-1 (green) and
3.5 ml l-1 (red) isolines highlighted. b. Contours of DO from April 11, 2000 GLOBEC-LTOP
cruise to show relationship of modeled DO with in situ data. c. Contours of modeled DO on
April 11, 2001. d. Contours of modeled DO on April 11, 2002. All the sections are from the
Newport Line.
20
2000
2001
2002
Figure 9: NH 10 (first panel) and NH 25 (second panel) bottom DO
levels are shown for years 2000, 2001, and 2002.
The inner-annually variability of the winter/spring wind stress can explain the interannual variability in bottom oxygen. In 2000 the January-March wind stress was, on average,
downwelling favorable, opposed to 2001 and 2002 with on average, upwelling favorable winter
winds (Table 3). The standard deviation for all three years was very high (4-11 times the mean)
which is expect when there is substantially high frequency reversal of wind duration between
upwelling and downwelling favorable winds during the winter season (Figure 10).
21
.
Figure 10: COAMPS Alongshore wind stress used to force model simulations for years 2000,
2001, 2002. The units are in N/m2 and the duration from January to the end of June, marking the
period of the winter season, spring transition, and beginning of the summer bloom. Blue shading
shows upwelling favorable wind stress and red shows downwelling favorable wind stress.
22
Alongshore wind stress
mean (January 1- April
1) (N/ m-2)
Year
Standard
Deviation
2000
0.0299
0.1204
2001
-0.025
0.1504
2002
-0.0128
0.1436
Table 3: Mean and standard deviation of alongshore wind stress
2000-2002. A positive mean shows downwelling favorable wind and
negative mean shows upwelling favorable.
The model simulations showed less chlorophyll-a in April 2000 off the coast of Newport
than in either 2001 or 2002 (Figure 11). Lower chlorophyll-a level means less phytoplankton
biomass and perhaps less detritus and reduced prevalence of low bottom oxygen. To assess the
impact of winter/spring phytoplankton biomass on the winter/spring bottom oxygen, we repeat
the 2002 model simulations but with biological oxygen production and consumption processes
deactivated in the model. If the phytoplankton biomass and levels of detritus were important in
determining bottom oxygen concentration, then the simulation without biological processes
should have produced greatly different results from the previous simulation.
23
Figure 11: Modeled chlorophyll-a concentrations along
the NH transect on April 11 for each of 2000 (first panel),
2001(second panel), and 2002 (third panel).
24
It is clear that the simulation with and without biological processes have nearly identical
DO through the beginning of April (Figure 12). In early April the two simulations began to
deviate from each other, which indicates that the phytoplankton biomass, detritus, and oxygen
interaction became more important in determining bottom DO. This coincides with the start of
the upwelling season when the upwelled nutrients and increasing light allow phytoplankton
bloom. The similarities from January to April in the two runs (titled ‘2002’ and ‘No Biology
2002’ in Figure 12) indicate that the intensity of bottom hypoxia in this periodic channel was
largely determined by the winter atmospheric forcing and ocean circulation that established the
initial (e.g. April) nutrient and DO conditions just prior to the spring transition.
2002
No Biology 2002
Figure 12: Bottom oxygen 2002 at NH 10 (near shore) and NH25 (offshore)
with (red) and without biological forcing for oxygen (black.)
25
4. Conclusion
A combination of data analysis and model simulations were used to examine the
relationship between alongshore winter wind stress, winter/spring water oxygen, and ensuring
summer hypoxic events along the Oregon coast. Wind stress, oxygen, and chlorophyll-a were
analyzed for the time period 1998-2003. We found a weak, non-significant, correlation between
winter wind stress (strength and duration of downwelling), chlorophyll-a concentrations, and
oxygen at the end of the winter/spring season. The lack of a strong relationship might have been
due to sparse availability of data on DO and chlorophyll-a.
The NAPZDO model simulations for 2000-2002 with different atmospheric forcing but
identical January initial biological and oxygen conditions reveal inter-annual differences in
winter/spring oxygen levels and chlorophyll-a. In year 2000, substantially higher downwelling
winds during winter/spring reduced chlorophyll-a and resulted in higher bottom DO. During
winter/spring of both 2001 and 2002, wind stresses were more variable with periods of upwelling
favorable winds interspersed with periods of downwelling favorable winds and the mean wind
over the winter/spring period being slightly upwelling favorable. In response, chlorophyll-a
concentrations were higher and bottom DO reduced. The atmospheric forcing conditions during
the winter/spring season had an impact on the initial oxygen condition for the following summer.
This winter/spring preconditioning could have a large impact on the prevalence and intensity of
summer hypoxia.
When a model simulation was conducted without the biological forcing for oxygen, there
was minimal difference until April (compared to the simulation with all biological forcing
included). This comparison indicates that summer initial conditions were almost entirely
controlled by winter/spring physics rather than biological processes. Without biological forcing,
26
the bottom DO developed similarly to a simulation that included biological forcing. This
suggests that the initial conditions of oxygen and the wind forcing that controls the extent of low
oxygen water across the shelf may be one of the key factors controlling the development of
summer hypoxia.
27
References
Barth, J.A., B.A. Menge, J. Lubchenco, F. Chan, J.M. Bane, A.R. Kirincich, M.A. McManus,
K.J. Nielsen, S.D. Pierce, and L. Washburn, 2007. Delayed Upwelling Alters Nearshore
Coastal Ocean-Ecosystems in the Northern California Current. Proceedings of the National
Academy of Sciences of the United States of America, 104.10, 3719-724.
Bograd, S.J., C.G. Castro, E.Di Lorenzo, D.M. Palacios, H. Bailey, W. Gilly, and F.P. Chavez,
2008. Oxygen declines and the shoaling of the hypoxic boundary in the California Current.
Geophysical Research Letters, 35, L12607, DOI: 10.1029/2008GL034185
Chan, F., J.A. Barth, J. Lubchenco, A. Kirincich, H. Weeks, W.T. Peterson, and B.A. Menge,
2007. Emergence of Anoxia in the California Current Large Marine Ecosystem. Science
Magazine, 319.5865, 920, DOI: 10.1126/science.1149016.
Diaz R.J. and R. Rosenberg, 2008. Spreading Dead Zones and Consequences for Marine
Ecosystems, Science, 321, 926 (2008); DOI: 10.1126/science.1156401
Garcia, H.E. and L.I. Gordon, 1992. Oxygen solubility in seawater—Better fitting equations:
Limnology and Oceanography, vol. 37, no. 6, p. 1307-1312.
Grantham, B.A., F. Chan, K.J. Nielsen, D.S. Fox, J.A. Barth, A. Huyer, J. Lubchenco, and B.A.
Menge, 2004. Upwelling-driven Nearshore Hypoxia Signals Ecosystem and Oceanographic
Changes in the Northeast Pacific. Nature Publishing Group, 29, 749-54.
Hodur, R. M.,1997. The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale
Prediction System (COAMPS), Monthly Weather Review, 125, 1414–1430.
Huyer, A., P.A. Wheeler, T. Strub, R.L. Smith, R. Letelier, and M. Kosro, 2007. The Newport
line off Oregon – Studies in the North East Pacific. ScienceDirect; Progress in Oceanography,
75, 126-160. Doi:10.1016/j.pocean.2007.08.003
Koch, A., Y.H. Spitz, and H.P. Batchelder, 2010. Hypoxic events on the Oregon shelf: analysis
of the physical and biological forcing. In preparation.
Large, W.G. and S. Pond, 1981. Open ocean momentum flux measurements in moderate to
strong winds. Journal Physical Oceanography, 11, 324-481.
Pierce, S.D., J.A. Barth, K.R. Shearman, and A.Y. Erofeev, 2012. Declining Oxygen in the
Northeast Pacific. American Meteorological Society, 170th ser., 10.11 495-501.
Pierce, S.D., J.A. Barth, R.E. Thomas, and G.W. Fleischer, 2006. Anomalously warm July 2005
in the northern California Current: Historical context and the significance of cumulative wind
stress. Geophysical Research Letter, 33, L22S04, doi:10.1029/2006GL027149
28
Rabalais, N.N., R.J. Diaz, L.A. Levin, R.E. Turner, D. Gilbert, and J. Zhang, 2009. Dynamics
and Distribution of Natural and Human-caused Coastal Hypoxia. Biogeosciences Discuss., 6,
9359–9453.
Wu, R.S.S., 2002. Hypoxia: from molecular responses to ecosystem responses. Marine Pollution
Bulletin, 45.1-12, 35-45.
Spitz, Y.H., J.S. Allen, and J. Gan, 2005. Modeling of Ecosystem Processes on the Oregon Shelf
during the 2001 Summer Upwelling. Journal of Geophysical Research, 110, C10S17, 1-21.
Villnas, A., J. Norkko, K. Lukkari, and A. Norkko, 2012. Consequences of Increasing Hypoxic
Disturbances on Benthic Communities and Ecosystem Functioning. PLoS,
doi:10.1371/journal.pone.0044920
Wanninkhof, R, 1992. Relationship between wind speed and gas exchange over the ocean.
Journal of Geophysical Research, 97, 7373–7382.
Zhang, J., D. Gilbert, A.J. Gooday, L. Levin, S.W.A. Naqvi, J.J. Middelburg, M. Scranton, W.
Ekau, A. Pena, B. Dewitte, T. Oguz, P.M.S. Monteiro, E. Urban, N.N. Rabalais, V. Ittekkot,
W.M. Kemp, O. Ulloa, R. Elmgren, E. Escobar-Briones, and A.K. Van der Plas, 2010. Natural
and Human-induced Hypoxia and Consequences for Coastal Areas: Synthesis and Future
Development. Biogeoscienes, DOI: 1-.5194/bg-7-1443-2010.
Appendix A
The equations for the six-component nitrogen-based ecosystem model (Figure 3), including
nitrate (NO3), ammonium (NH4), phytoplankton (P), zooplankton (Z), detritus (D) and dissolved
oxygen (O2), are as follows:
 NO3

NO3
  NH4  Vm f(I)
e NH 4 P,
t
K u  NO3

(1)

 NH4 
NH4
P,
  D  Z  NH4  Vm f(I)
t
K u  NH4 
(2)

 NO3
P
NH 4 
P  Rm 1  e  P Z  P,
 Vm f(I)
e NH 4 
t
K

NO
K

NH
 u
3
u
4 
(3)
Z
 Rm 1   1  e  P Z  Z,
t
(4)
D
D
 Rm1  e  P Z   D  P  wd
,
t
z
(5)
 NO3

O2
NH4
 Vm f(I)
e NH 4 rO 2:NO 3 
rO 2:NH 4 P
t
K u  NH4
K u  NO3

2NH4  rO 2:NH 4 Z   rO 2:NH 4 D  Qge (O2sat - O2 )
(6)




t denotes time and z denotes the vertical coordinate. The parameters are defined in Table A1. The
light limitation f(I) is given by
f (I) 
I
Vm2   2I 2
with I(z,t)  I0 exp( kw z  k p

z
0
P(z' )dz' ) and H0  z  0 so that the positive depth is –z.

The gas exchange is defined as


30
Qge  KO2 /zn ,
where zn is the height of the top cell. The gas transfer coefficient is

KO 2  0.31u2 660 /Sc ,

where u is the average wind speed (m/s) and K is in [cm/h]. The Schmidt number Sc is calculated

after Wanninkhof (1992) as
Sc 1953.4 128*T 3.9918*T2 0.050091*T3,
where T is the temperature in OC. The saturation concentration of oxygen is defined after Garcia

and Gordon (1992) as
O2sat  e A 1000 / 22 .9316
and with
A  2.00907 3.22014* Ts  4.05010* Ts 2  4.94457* Ts 3  0.256847* Ts 4  3.88767* Ts 5
 S * (0.00624523 0.00737614* Ts  0.0103410* Ts 2  0.00817083* Ts 3 )
 4.88682107 * S 2
where Ts  ln((298.15  T)/TK ) and TK denotes the absolute temperature in Kelvin.

 Table A1. Model Parameters
Parameter
Light attenuation due to sea water, m-1
Light attenuation by phytoplankton, 10-3 m-2 (mmol N)-1
Initial slope of P-I curve, d-1 (W m-2)-1
Phytoplankton maximum uptake rate, d-1
Half-saturation for phytoplankton NO3 uptake, mmol Nm-3
Half-saturation for phytoplankton NH4 uptake, mmol Nm-3
NH4 inhibition parameter, (mmol N m-3)-1
NH4 oxidation coefficient, d-1
Detritus decomposition rate, d-1
Table A1. Model Parameters (continued)
Symbol
kw
kp

Vm
Ku
Ku



Value
0.067
9.5
0.025
1.5
1.0
1.0
1.46
0.25
0.1
31
Phytoplankton specific morality rate, d-1
Zooplankton specific excretion, mortality rate, d-1
Zooplankton maximum grazing rate, d-1
Ivlev constant, mmol N m-3
Fraction of zooplankton grazing egested, %
Detritus sinking rate, m d-1
Oxygen to nitrate ratio, mmol O2/mmol N
Oxygen to ammonium ratio, mmol O2/mmol N


Rm

g
wd
r02:NO3
r02:NH4
0.1
0.1
0.6
0.15
30
8.0
8.625
6.625
32
Appendix B
Satellite chlorophyll-a data were obtained for two different spatial and temporal
resolutions in order to carry out this study. When comparing the two satellite data sets (NASA
Goddard Space Center 9 km chlorophyll-a imagery and Oregon coast 4 km chlorophyll-a
imagery data from Dr. Ricardo Letelier, Oregon State University) there were noticeable
differences. The Oregon coast gridded 4 km 7-day composite SeaWiFS data set (Figure B1)
appeared to have much higher chlorophyll-a levels compared to the 9 km 8-day composite
SeaWiFS data set.
Figure B1: SeaWiFS 4 km and 9 km resolution imagery in 2000.
Cumulative chlorophyll shown in micrograms/liter. Total
chlorophyll-a were calculated by summing the values at NH 25,
35, 45, 65, and 85. Satellite chlorophyll-a values were first
interpolated to the station and then summed in order to compute
the total chlorophyll-a along the NH line
These differences can be attributed to various factors, including the gridding resolution,
the algorithms used to convert ocean color to chlorophyll-a, and the time period over which
composite chlorophyll-a data were calculated. The 9 km data set was created to examine large
33
scale patterns of chlorophyll-a. Large spatial analysis involves a lot of data and very high
resolution was not necessary. However, when looking at a single transect (in this study the
Newport Hydrographic Line), the 4 km resolution satellite product enables more discrete
comparison of the smaller-scale patterns of chlorophyll-a that are typically on continental shelves
and better comparison with the GLOBEC observations along the NH line. Another factor that
sets these two datasets apart even though they were obtained from the same satellite was the
number of days in the composite. The 4 km resolution was compiled into a 7-day composite
whereas the 9 km resolution was compiled into an 8-day composite. While phytoplankton
decorrelation timescales are approximately 6-8 days over the Oregon shelf (Spitz et al,, 2005),
the 7-day composite image might not capture the same portion of the phytoplankton bloom as the
8-day composite image.
Over the years, algorithms to convert ocean color to chlorophyll-a have been refined,
especially for coastal waters. The coastal waters contain a large pool of colored dissolved
organic matter (CDOM), which is difficult to remove from the chlorophyll-a signal. Newer
algorithms are more successful in doing so. We, then, expected that the 9km chlorophyll-a values
would be somewhat lower than the 4km chlorophyll-a values as it uses a more recent, and
presumably improved algorithm.
34
Appendix C
Phytoplankton biomass production in the upper water column, where photosynthesis
occurs, increases DO levels. This upper portion of the water column has oxygen being mixed
into the water and leaving the water through atmospheric exchange. The decaying phytoplankton
biomass (detritus) sinks to the bottom of the ocean. Decomposition of phytoplankton biomass
consumes oxygen and causes DO levels near bottom to decrease. We assumed that
phytoplankton biomass was immediately converted to detritus that sinks rapidly to the bottom
and consumes oxygen. Thus, this approach provides a maximum estimate of bottom oxygen
consumption due to organic decomposition.
Since we used SeaWiFS surface chlorophyll-a as a proxy for phytoplankton biomass for
our calculation of oxygen consumption, it was imperative to validate satellite measurements
against in situ observations from GLOBEC-LTOP. Total remotely sensed chlorophyll-a
concentration and GLOBEC-LTOP April cruise total chlorophyll-a along the NH line at the
GLOBEC-LTOP stations (25, 35, 45, 65, and 85 miles) were compared (Figure C1). While in
good agreement in general, differences between the two computed total chlorophyll-a values can
be noted and explained as follows.
35
Figure C1: Total SeaWiFS chlorophyll-a data, 7-day composite (solid line)
compared with total GLOBEC-LTOP surface chlorophyll-a (dots) for 1998-2003
and along the NH line. Total chlorophyll-a were calculated by summing the values
at NH 25, 35, 45, 65, and 85. Satellite chlorophyll-a values were first interpolated
to the station and then summed in order to compute the total chlorophyll-a along
the NH line
Satellite data are 7-day composite data while in situ observations are for a specific day at
a specific time of the day. Therefore, the 7-day composite data might not capture the same phase
of the bloom as the in situ measurements. An illustration of this is given by looking when the
GLOBEC-LTOP April 2002 cruise took place with respect to the 7-day period of the composite
image. The cruise took place on April 5th while the 7-day composite covered the period of April
2st through April 8th. The 2002 wind stress (Figure C2) shows that the cruise data were collected
at the end of a strong upwelling event while the satellite composite image covered the core of the
upwelling period. Since there is a time lag of about 7 days (Spitz et al., 2005) between wind
stress and phytoplankton bloom, one would expect that the in situ chlorophyll-a value would be
higher than the 7-day composite value for the equivalent period, which is in agreement with what
is displayed in Figure C1.
36
Figure C2: Red dot depicts the date of the GLOBECLTOP cruise (April 5th). Blue line is the wind stress
with a 10 day low-pass filter applied from February to
June. Green dashed lines are SeaWiFS 7-day
composite (April 2nd – April 8th).
In addition to the difference in sampling period, high cloud cover prevalent in coastal
Oregon impacts satellite observation. When there were no data (pixel) available, linear
interpolation was conducted between pixels, which might lead to under- or over-estimation of
chlorophyll-a with respect to in situ concentrations. The percent of missing data per year varies
from 28% (1999) to 57% (1998) (Table C1).
37
Year
Average Percent of Missing SeaWiFS Data
1998
1999
2000
2001
2002
2003
57
28
35
51
48
40
Table C1: Average percentage of missing SeaWiFS data for
five GLOBEC-LTOP stations (NH 25, 35, 45, 65 and 85) over
the first 152 days of each year.