Ocean Circulation and Ecosystem Dynamics in the Vicinity of the Antarctic Peninsula:
An Assessment Coupling Multiple Remote Sensing, In Situ Observation, and Modeling
PROJECT DESCRIPTION
INTRODUCTION
The Antarctic Peninsula region is influenced by large-scale atmospheric forcing, such as the
El Nino-Southern Oscillation, the Antarctic Circumpolar Wave, and the circumpolar trough of
low pressure, as well as by the Antarctic Circumpolar Current (ACC) and interannual
variability in sea ice extent. Despite relatively modest primary productivity throughout much
of the Southern Ocean (Jacques 1989), waters to the west of the Antarctic Peninsula
historically have supported an unusually high production of Antarctic krill and, thus, have
been a favorable habitat for krill predators (e.g., fish, penguins, seals, and whales) (Marr
1962). Krill have a large interannual variability in recruitment, which has been correlated
with the timing and extent of sea ice formation (Kawaguchi and Satake 1994, Siegel and Loeb
1995). A strong recruitment year, however, may be intervened by 4 - 6 years of moderate or
poor year classes. Dramatic environmental changes are currently taking place along the
Antarctic Peninsula (Vaughan et al. 2003, Smith et al. 1999). The most significant is
warming air temperatures of about 0.1ºC per year during winter over recent decades (King et
al. 2003). Given that krill recruitment is highly variable and possibly linked to sea ice extent,
changing ice conditions may reduce krill populations and, through direct and indirect effects,
upper trophic level populations as well (Fraser and Hofmann 2003). Owing to the remote
location of the Southern Ocean and the difficulty of sampling in sea ice, however, our
knowledge of ocean and ecosystem dynamics remains limited.
As part of the Southern Ocean GLOBEC program, we collected field data during autumn
and winter that indicated a large recruitment of krill occurred during 2001 in waters over the
shelf west of the Antarctic Peninsula (Fig. 1), whereas there was poor recruitment in 2000 and
low recruitment in 2002 (Fig. 2). The combination of environmental conditions that supported
the high krill recruitment in 2001 cannot be deduced from sparse shipboard sampling.
Satellite technology vastly increases the spatio-temporal sampling for these remote regions.
Imagery from the Coastal Zone Color Scanner (CZCS) and SeaWiFS have been successfully
used to investigate the distribution of phytoplankton blooms in different regions of the
Southern Ocean (e.g., Comiso et al. 1990, Sullivan et al. 1993, Arrigo and McClain 1994,
Moore et al. 1999). These data suggest that estimates of primary productivity need to be
revised upwards. In addition, various geophysical ice parameters, sea surface temperature,
cloud cover, and wind velocity are available for detailed investigations of ocean processes in
polar regions at relatively high temporal and spatial resolutions (Comiso 1991). Here, we
propose to use data from a suite of satellite sensors, in situ ocean observations, and an ocean
circulation model to investigate different scales of forcing on ocean circulation and the
relationship between different forcing mechanisms and krill recruitment for a period of 1997
to present in order to improve our ability to predict marine ecosystem response to climate
variability in this dynamic region.
BACKGROUND
The annual advance and retreat of sea ice (>16 million km2) in the Southern Ocean is one
of the most profound changes affecting ecosystems on this planet. Sea ice plays a central role
in the temporal structuring of the ecosystem (Daly and Macaulay 1991) and by sustaining
different communities within sea ice compared with that in the underlying water (Eicken
1992). In addition, sea ice extent and ocean circulation affects ecosystem structure and
biological activity by forming boundaries defining different marine regimes (Nicol et al.
2000). Interannual variability in sea ice is influenced by a number of large-scale processes,
such as the Southern Oscillation (Kwok and Comiso 2002) and the Antarctic Circumpolar
Wave (ACW; White & Peterson 1996), and interactions between them. The ACW may take
the form of a wavenumber-2 perturbation of sea surface temperature (SST), surface air
pressure, sea-surface height, windstress, and sea ice extent, circling eastward around
Antarctica with a period of around 4 years.
In general, sea ice forms during May and June along the Antarctic Peninsula and starts to
retreat near the northern end in August. Most of the Bellingshausen Sea to the west of the
Peninsula, however, experienced a shortening of the sea ice season between 1979-1999
(Parkinson 2002), which was related to the Southern Oscillation index (Kwok and Comiso
2002). Little sea ice was present during 1999 and 2000 along the Peninsula, whereas during
2001 and 2002, sea ice conditions were
extensive in our study area (Fig. 3).
Anomalously high atmospheric pressure
over the south Atlantic and anomalous lows
over the Bellingshausen, also resulted in
exceptionally heavy ice conditions during
summer 2001/2002 in the Weddell Sea, east
of the Antarctic Peninsula (Turner et al.
2002).
The Antarctic Peninsula is the only part
Fig. 3. Color-coded sea ice concentration maps
of the Antarctic where the circumpolar
during winter maximum of sea ice extent. Arrow
trough crosses the continent. This lowshows location of GLOBEC study area.
pressure trough rings the southern
[removed because Comiso is co-I here. Fig.
hemisphere between 60 and 70º S and is a
Needs to be zoomed to show legend]
region of higher cyclogenesis, particularly in
the Bellingshausen Sea (Turner et al. 1998).
The relative position of the circumpolar trough across the Antarctic Peninsula influences the
variability in the seasonal cycle of atmospheric temperature, pressure, wind, precipitation,
cyclonic activity and the sea ice distribution in this region (Smith et al. 1999). Warm winters
along the Antarctic Peninsula have been associated with negative sea surface pressure
anomalies in the Bellingshausen Sea and with positive anomalies for cold winters (Marshall
and King 1998). The Southern Ocean also is characterized by low seawater temperatures,
high nutrient concentrations, and extreme seasonal changes in incoming solar radiation, which
results in highly seasonal primary production. Although much of the Southern Ocean is a
high nutrient-low biomass environment, elevated concentrations of phytoplankton and
zooplankton occur, particularly in coastal regions (Smith et al. 1996).
The Antarctic krill, Euphausia superba, occupies a key role in the Southern
Ocean ecosystem as the major pelagic herbivore and prey for most upper trophic level
predators. It is not known why the waters west of the Antarctic Peninsula support one of the
largest concentrations of krill in the Southern Ocean. A large krill recruitment, however,
depends on several factors: (1) an early and sustained reproduction (i.e., November – March),
(2) survival and high growth rates of larvae during summer [austral summer?], and (3)
survival of larvae overwinter. The correlation between sea ice and recruitment may be due to
the fact that ice edge blooms of phytoplankton support early adult reproduction and provide a
food source for early feeding larvae (Daly and Macaulay 1991), while during winter sea ice
may provide a food source (i.e., sea ice biota) and a haven from predation for overwintering
larval krill (Daly 1990). Along the Antarctic Peninsula, adult krill migrate offshelf into the
eastward flowing ACC during spring to spawn (Siegel 1989), their eggs sink into the
underlying, warmer Upper Circumpolar Deepwater (UCDW) where they hatch, and then the
larvae swim back up to the surface (Hofmann et al. 1992). Most larvae in offshore waters are
probably advected eastward in the ACC towards South Georgia and are likely the source
population for that area (Hofmann et al. 1998). For krill to have a large recruitment on-shelf
and maintain a population along the Peninsula, offshore larvae must advected onto the shelf
and then retained on the shelf until the following spring. Capella et al. (1992) used a model to
demonstrate that surface flow was a primary factor governing the final location of larvae and,
that due to seasonal changes in wind stress fields, favorable conditions for onshore transport
of larvae were more likely early in the spawning period. Offshore larvae occur between 0 –
500 m depth, but the depth of maximum abundance offshelf and on-shelf is in the upper 30 m
(Fig. 4, Daly in press). Recent results indicate that gyres, which could retain larvae and
phytoplankton, occur in several locations on the shelf west of the Peninsula (Klinck ref?).
The small internal Rossby radius of deformation at these high latitudes (ca. 5 –15 km?) results
in narrow currents and relatively small mesoscale eddies and gyres. Thus, our central
hypothesis is: [I move the "SeaWiFS" paragraph in the work plan/method part, as it reads
abrupt here]
The central western Antarctic Peninsula experiences a unique combination of environmental
conditions that contributes to enhanced krill reproduction, growth, winter survivorship and
recruitment. These conditions include early-forming and extensive sea ice coverage during
fall and winter, predictable ice edge blooms both offshelf and on-shelf during spring,
intrusions of Antarctic Circumpolar Current-derived Upper Circumpolar Deep Water onto the
shelf which supplies nutrients that sustain phytoplankton blooms on the shelf, and a sluggish
cyclonic shelf circulation that retains krill larvae. Atmospheric forcing, sea ice distribution,
and oceanographic processes dictate the timing and location of phytoplankton blooms, the
timing and magnitude of krill reproduction, the advective transport of larvae, the
overwintering survival of larvae, and subsequent recruitment to juveniles during spring.
Specifically, our objectives are to
 determine the suite of environmental conditions present during 2001 that would
favorably support krill recruitment in comparison with conditions during other years.
The environmental conditions include timing and extent of sea ice, atmospheric
pressure, wind speed and direction, sea surface temperature, ocean currents, and the
timing, location, and magnitude of chlorophyll concentrations;

determine interannual changes in regional and large-scale oceanographic conditions
and investigate the associated large-scale forcings to develop a better understanding of
how climate variability influences ecosystem dynamics in the Antarctic Peninsula.
To meet these objectives we will:
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Collate and statistically analyze in situ and remotely sensed data, develop
climatologies, and assess anomalies for 1999 – 2003;
Validate ocean color data with in situ chlorophyll concentrations; refine algorithms as
necessary using in situ chlorophyll, CDOM, and transmissometer measurements;
Study the correlation between phytoplankton (concentration, distribution, and timing)
and krill recruitment
Fine-tune and validate an ocean circulation model with satellite derived winds and sea
ice cover;
Use the ocean circulation model to test hypotheses related to sea ice distribution and
krill recruitment and the transport and retention of larval krill on the Antarctic
Peninsula shelf. [We also will investigate the potential impact of a reduction in ice
habitat on krill populations - John I am not sure what more can be done with the model
beyond advection questions - have I overstepped here?]
Estimate the percent of krill larvae advected downstream in the ACC vs. the
proportion advection onto the shelf west of the Antarctic Peninsula under different
environmental conditions.
Investigate how large-scale atmospheric and oceanographic forcings influence
interannual variability in krill recruitment using statistical analyses (what statistical
tests?) of remotely sensed data.
Data sets
Remote sensing
The remote sensors primarily used in this study will include:
 SeaWiFS (1997 - present); ocean color (spectral water-leaving radiance, chlorophyll
concentration, diffuse attenuation)
 MODIS (Terra: 1999 – present; Aqua: 2002 - present); ocean color and sea surface
temperature (for quality control MODIS/Aqua data may be used only)
 AVHRR pathfinder (1985 - present); sea surface temperature

 QuikScat (1999 - present); surface wind
 TOPEX/POSEIDON (T/P, 1996 - present): surface height anomaly from 70oS to 70oN
 EPTOMS (1996 - present); total ozone thickness
 NCEP; blended meteorological data (surface pressure, water vapor, humidity, wind)
with remote and in situ sensors and models.
 Sea ice sensors??????
Historical data from CZCS (1978-1986) and AVHRR (1985 - present) will be used for
decadal comparisons. We recognize that CZCS ocean color data (pigment concentration) may
not be comparable to those obtained from SeaWiFS and MODIS due to differences in sensor
calibration and data processing algorithms, therefore only distribution patterns will be studied.
In situ measurement
Historical and recent in situ data will be obtained from a variety of sources. We have
available to us all of the underway data collected by NSF/Polar Programs vessels, the RV
L.M. Gould and the RVIB N.B. Palmer from their shipboard surface flow-through systems as
well as biogeochemical data and hydrographic data collected at cruise stations as part of the
several field program. These data from the field programs are listed in Table 1. These will be
quality controlled [I think we need to focus on a subset and explain why we need data type X]
and used to fine-tune and validate remote sensing algorithms and circulation models (see
Methods).
Table 1. Catalogue of available in situ data
Measurements
AMLR
Vertical Profiles
Temperature
Oxygen
Depth
PAR
Fluorescence
Transmissometer
1995 - 2003
1995 - 2002
1995 - 2003
1995 - 2003
1995 - 2003
1995 - 2003
Underway data
Sea surface temperature
Sea surface salinity
Fluorescence
Air temperature
Pressure
Air humidity
Wind speed and direction
1999 – 2002
1999 – 2002
1999 – 2002
1999 – 2002
1999 – 2002
1999 – 2002
1999 – 2002
Phytoplankton
Chlorophyll a concentration
PAR
HPLC
POC/PON
Phycoerythrins (PE)
Beam attenuation (transmissometer)
Fluorescence
Up/Downwelling irradiance (Ed + Eu)
Ap + As (coeffcicients)
Seawifs imagery
Species composition
Cell size spectrum
Primary production
1995 – 2003
1999 – 2002
1999 – 2002
1995 – 2002
1999 – 2002
1999 – 2002
1999 – 2002
1999 – 2002
1999 – 2002
1999 – 2002
1995 - 1997
1995 - 1997
1995 - 1997
Photoadaptational state of cells
1996 - 1997
Krill abundance/stage/size
1995 – 2003
METHODS
Ocean color data analyses (timing and magnitude of food availability)
Phytoplankton provides food for all higher trophic levels. Therefore one of our major
efforts will be on quantifying its concentration, distribution, and temporal changes by
combining in situ and remote measurements.
CZCS and SeaWiFS-derived chlorophyll data have been used to document distributions of
phytoplankton in the Polar regions (e.g., Sullivan et al., 1993; Moore et al., 1999), yet these
pioneering studies are limited in the time span, therefore do not provide a comprehensive
image. Our own preliminary study with low-resolution (9-km) SeaWiF chlorophyll data from
1997 to 2003 indicates that spring blooms west of the Antarctic Peninsula start offshelf in the
ACC then progress into coastal waters, where relatively intense blooms occur in different
areas throughout the summer, particularly at the southern end of the Peninsula in the vicinity
of the GLOBEC study site near Marguerite Bay (Fig. 5). During austral summer, the ACC
has low chlorophyll concentrations, while coastal regions and frontal zones are food-rich
environments. Chlorophyll maxima are usually near surface in waters over the continental
shelf and there is a tight coupling between surface chlorophyll and chlorophyll integrated to
depth (Smith et al. 1996). Chlorophyll concentrations near surface are usually > 1.0 mg m-3
between October and April (austral spring – fall) and, on average, about 5 mg m-3 during
austral summer, with maxima up to 40 mg m-3. Prézelin et al. (2000) demonstrated that
diatom blooms primarily occur in areas on the shelf that have topographically-driven
upwelling of UCDW from the meandering southern boundary of the ACC. Seasonal and
interannual changes in the location and strength of the ACC will influence the flushing of
subpycnocline waters on the shelf. Wind-driven upwelling also may occur at some sites;
however, summer winds (predominantly from the north-northeast) generally favor
downwelling conditions. The areas where upwelling of UCDW occurs, nutrients are
replenished near surface permitting sustained phytoplankton production. Elsewhere on the
shelf, blooms may be dominated by prymnesiophytes, pelagophytes, or cryptophytes in
regions influenced by glacial melt. Intrusions of UCDW were observed in the GLOBEC
study site where a deep canyon intersected the shelf break (Klinck et al. in press?), and large
blooms were dominated by the diatoms, Chaetoceros criophilum and Synedra antarctica, and
phaeodarian radiolarians throughout Marguerite Bay (Daly in press).
A preliminary comparison between SeaWiFS- and ship-derived chlorophyll values (Fig. X
bottom panel) shows that SeaWiFS (Version 4 processing) tends to underestimate chlorophyll
at low concentrations, but overestimate it in higher concentrations. This is consistent with
findings from the Arctic region (Cota et al., 2004), but different from results obtained by
Moore et al. (1999), where SeaWiFS data from earlier version of processing. Certainly, the
comparison is not strict as each SeaWiFS pixel used here is about 99 km2 and the ship
sampling may not be concurrent (up to one month difference). Yet, the availability of larger
in situ dataset, as well as higher-resolution SeaWiFS data (daily and weekly), makes it
possible for an extensive comparison, based on which a regional algorithm may be developed
and validated with independent in situ measurement.
Ocean color estimates from satellite are sub to uncertainties, especially in the high-latitude
regions for a number a reasons, one of which is lack of reliable optical measurements in these
remote regions. Although the relative distribution and the temporal changes of the distribution
rather than the absolute chlorophyll concentration are more important in our study, it is
critical to fine-tune the algorithms to produce at least consistent, if not accurate (this is our
ultimate goal, though), time-series of chlorophyll distributions. Otherwise it is unknown if a
distribution pattern and its temporal change are real, due to several factors. For example,
another important water constituent is colored dissolved organic matter (CDOM, also called
Gelbstoff), which is often interpreted as chlorophyll because they both strongly absorb blue
light and the band-ratio OC4 bio-optical algorithm (O'Reilly et al., 2000) does not distinguish
the two. Also, the spectral water-leaving radiance data (Lw()) from SeaWiFS or MODIS in
this region have never been validated in this region due to lack of reliable in situ Lw()
measurements. However, starting with the satellite-derived Lw() and assuming that they are
consistently derived, a regional bio-optical inversion algorithm may be derived with the in
situ chlorophyll and other measurement. Specifically, we plan to
 collect in situ data (historical and ongoing) that are related to ocean color (see Data
Sets), for example chlorophyll, CDOM, transmission/attenuation, and light profiles
 obtain Lw() and other data products (chlorophyll, attenuation) from SeaWiFS and
MODIS at higher spatial (4- and 1-km) and temporal (daily to weekly) resolutions
from NASA DAAC and GSFC
 validate the satellite parameters by comparing the concurrent matching pairs from
satellite and in situ measurements (e.g., Hu et al., 2003), and if necessary (very
likely, as indicated by Fig. X) develop and fine-tune a regional algorithm by using
the satellite Lw() and in situ parameters, similar to the approach used by Kahru
and Mitchell (2001)
This effort is of particular importance to NASA, as both SeaWiFS and MODIS show
unacceptable bias in high latitude waters (e.g., Cota et al., 2004) even after several rounds of
data reprocessing. Meanwhile the southern ocean plays a significant role in the global carbon
cycle, but it is at present difficult to quantify the uncertainties associated with the satellite
estimates in a systematic fashion, primarily due to lack effort in consolidating all related in
situ data collected in recent years (see Data Sets) with satellite data. Our studies will address
these issues and ultimately improve global carbon cycle models, which depend critically on
reliable chlorophyll estimates.
Sea ice analyses
Ocean circulation model
SIGNIFICANCE OF RESEARCH
PROJECT COORDINATION AND TIMELINE
Kendra Daly - primary contact, organization, in situ data, supervise grad student, co-supervise
post-doc
Chuanmin Hu - responsible for all remote sensing products except sea ice, co-supervise postdoc
Joey Comiso - analysis of sea ice products, coordinate with Chunamin on other satellite data
and with John on model
John Klinck - model development, validation, hypothesis testing, supervise grad student, work
with USF post-doc?
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