- The document discusses how giant icebergs can influence marine productivity in the Southern Ocean by providing an important source of iron-rich terrigenous material to surrounding waters. Field studies have shown that iceberg-delivered iron leads to increased primary production and a more developed food chain ecosystem. - Remote sensing of chlorophyll concentrations revealed a 92% likelihood of higher chlorophyll values, up to 3 times background levels, within 30 days and up to 860km from giant icebergs. This suggests icebergs fertilize large areas of ocean waters. - The melting and dispersion of iron-rich debris from icebergs supports a rich pelagic ecosystem, with higher concentrations of phytoplankton,

1. The influence of giant icebergs on the marine productivity of the Southern Ocean
Student Registration Number: 130115429
MSc in Polar and Alpine Change
The University of Sheffield
September, 2014

2. 1
Abstract
Phytoplankton activity is known to be a crucial biological process of the ocean
that can significantly influence biodiversity, human food supply and climate. Few field
studies in the Southern Ocean have investigated the influence of giant icebergs on the
primary production as an important source of iron enriched terrigenous material to the
surrounding waters. Iceberg delivered iron has proved to be followed by an increase in
primary production and pelagic fauna through a rich developed food chain ecosystem
that allows the conversion of phytoplankton to particulate organic carbon that is
eventually exported to the deep sea. However, the magnitude and extent of this effect
around giant icebergs had so far been poorly determined due to the logistic limitations
related to field surveys. This research assessed the impact of giant icebergs on
chlorophyll concentrations using remote sensing chl a concentration colour images. The
results showed a 92% likelihood of increased chlorophyll concentrations after a giant
iceberg’s passage, with chlorophyll values approximately 3 times higher up to 30 days
when compared to background concentration. The mean value from the highest
chlorophyll concentration spot associated with an iceberg effect plume was of 2.84
mg.m-3
roughly 10 times higher than background value. The mean extent value of the
effect plume was ~860Km with the highest concentration values found at a distance 20
to 200 km from the iceberg. Background chlorophyll concentrations were higher in
summer than spring, and significant differences associated with the iceberg calving
sectors in Antarctica were also found. In light of predictions of an increased rate of
iceberg production in the coming centuries due to evidence of a potential collapse of
West Antarctica driven by global warming, the atmospheric carbon dioxide
sequestration promoted by giant icebergs represents a non-negligible negative feedback
to human-caused forcing to the global weather system.

3. 2
1. Introduction
Over the past decades global warming has dramatically impacted the Polar
Regions, leading to the discharge of thousands of icebergs every year associated with
the retreating of glaciers and ice shelves around Antarctica. Ranging from small ice
fragments to large tabular shaped structures that can reach up to 300km long, most
icebergs measure between 60 - 2,200 m in length and 150 - 550 m in thickness (Smith et
al, 2007; NASA, 2013). While the principal source of information regarding small
icebergs around the continent is still gathered from ship observations, giant icebergs
(longer than 18.5 km) are continuously tracked through satellite sensors by the National
Ice Center and the Brigham Young University Centre for Remote Sensing (figure 1)
(Silva, 2005; BYU, 2014).
Large icebergs are originated by major calving events especially in the ice
shelves of the Weddell, Ross and Bellingshausen Seas (Rigot, et al., 2008) and every
year, 30 to 40 giant icebergs are simultaneously found in the Southern ocean (Bigg,
2014). Eventually, the Antarctic anti-clockwise Costal Current and the barotropic
northward flow in the Weddell Sea entrain large numbers of these massive bergs from
different areas around the continent forming a region known as the Iceberg Alley (figure
1) along the East coast of the Antarctic Peninsula in the Weddell Sea (Smith et al.,
2012). The pronounced warming of the West Antarctica in the last decades, where
increased occurrence of large icebergs was observed, has recently drawn the attention of
the scientific community to their potential effects on ocean physicochemical properties.
This changes in the water properties can impact important processes like phytoplankton

4. 3
activity, bottom water formation and seasonal sea-ice extent and therefore contribute to
regulate the planet climate (Arrigo et al., 2002; Jongma et al., 2009).
Figure 1: Left: illustration of the Antarctica iceberg Alley. Source: Smith et al, 2013 (left). Middle:
satellite track of giant icebergs from the BYU since 1978. Right: map showing the four sectors of the
Antarctica continent used by the US National Ice Centre for the icebergs nomenclature. Source:
BYU, 2014.
1.1 The influence of icebergs in the surrounding pelagic ecosystem
Despite being rich in nutrients, Southern Ocean waters are distinguished by low
chlorophyll concentrations mainly because phytoplankton is limited by iron availability
in much area of this region (Boyd et al., 2000; Coale et al., 2004). Among the essential
micronutrients required by phytoplankton growth in the Southern Ocean, iron
availability is usually the responsible to limit primary production (de Barr et al., 1995;
Holm-Hansen and Hewes, 2005). Takeda et al. (1998) demonstrated through incubation
experiments with diatoms cultures in the Southern Ocean waters that twice as much
carbon can be absorbed during iron-induced phytoplankton blooming in the Southern
Ocean.
A
B C
D

5. 4
Iron is supplied to the ocean mainly by sediments, wind dust and glacial ice
sources. Although dust from deserted areas is a very important source to the world
oceans due to its large spatial distribution, its contribution to the total amount of this
mineral input to the ocean represents only 0.3% (Wadley, 2014). Fixed local sources
such as continental shelves or islands represent 89% of the iron input leading to the high
productivity commonly observed in coastal areas. The remaining input is addressed to
glacial ice (11%). In the Southern Ocean dust derived iron represents 15% of the total
input while over 80% of the productivity comes from local, mostly combined sediment
(~75%) and glacial sources such as sea-ice melting (~11%) and free-drifting iceberg (~6
-10 %) (Wadley, 2014).
Field researches have shown that icebergs are in fact an important source of Fe
once they incorporate significant amount of terrigenous material through glacial
processes as rock bed erosion and dust accumulation (Shaw et al. 2011). The utilisation
of this micronutrient by the sea water is believed to be facilitated due to the unique
conditions icebergs can offer and it has been investigated as an efficient system of
carbon sedimentation and sequestration through phytoplankton primary production
enrichment and zooplankton grazing (Smith et al., 2010, 2012).
First studies focused on the Antarctica ice pack have shown that the edge of the
season pack ice is associated with phytoplankton blooms that sustained a complex
ecosystem of marine organisms (Smith and Nelson, 1986; Arrigo, 2002). This increase
in primary production were attributed to the release of iron and entrapped cells from the
ice pack in the melting season as well as due to the stability created by the meltwater in
the surrounding sea water (Sedwick et al., 2000 cited by Smith et al., 2013). In the same

6. 5
way, it was questionable if the freshwater input associated with the drift of the icebergs
could create a continuous upwelling gradient of water rich in nutrients into the surface
(Smith et al., 2012). Other scattered studies also pointed out to the potential of icebergs
as a biological enriched environment in different trophic levels (Ainley et al., 1984;
Ribic et al., 1991; de Baar et al., 1995; Kaufmann et al., 1995; Stone, 2003). On the
other hand, evidence from experiment of artificial fertilisation have showed that
although waters of the Southern Ocean had increased rates of primary production and
downdraw of CO2 after been fertilised with iron, the final export of carbon from the
surface waters to the deep sea in this short-term experiment was still questionable (Boyd
et al., 2004; Coale et al., 2004).
Recently, two giant free-drifting tabular icebergs in the Northwest Weddell Sea
were targeted in important studies to assess their impact on the chemical and biological
characteristics of the surrounding area: A-52 in 2005 (with 30.8 km2
in area) and C-18a
(110 km2
) in 2009 (Smith et al., 2010). Their results showed that the concentration of
chlorophyll a found around the icebergs was comparable to the increased amount
observed in the edge of ice pack or during iron enrichment experiment (Smith et al.,
2007). These studies also confirmed that under natural iron fertilisation by icebergs,
increased phytoplankton biomass is eventually sequestered to the depths along the food
chain through grazing and faecal material export (see figure 1) at higher rates than the
surrounding non fertilised areas (Smith et al., 2010).

7. 6
Figure 2: Physical and biological processes within an iceberg ecosystem. Source: Smith et al., 2013
To confirm the hypothesis of iron fertilisation promoted by giant icebergs and its
potential extent, Smith et al. (2007) used excess of short-lived 224Ra measurement (3.7
days half-life isotope originated from the decay of 228Th associated with terrigenous
particles) as a detection method to access the source of the water terrigenous material.
An enrichment of excess 224Ra could be found in the surface water next to both
icebergs down to 10m depth with a decreasing rate as the distance from the icebergs
increased, suggesting a rapid melting and dispersion of the entrained detrital material.
These samples also showed lower salinity when compared to the surrounding waters,
reinforcing a melt water source for the excess 224Ra. In 2009 new samples of surface
water were collected around iceberg C-18a and the results from excess 224Ra
measurements showed an input of terrigenous material up to 3 times greater than the
estimates of Aeolian dust input to the Southern Ocean (Shaw et al., 2011b). The team
calculated that taking into account the current rates of iceberg production the
terrigenous material input is estimated to be approximately 90 x 106
tons.yr-1
, a total Fe
input of 3 x 106
tons.yr-1
or 4 - 40 x 104
tons.yr-1
of Fe in ferrihydrite, potentially

8. 7
bioavailable. Lin et al. (2010) also measured concentrations of dissolved Fe through
injection chemiluminescence in the surrounding waters of several icebergs in the
Weddell and Scotia Seas during 2008 and 2009 and found that surface dissolved Fe
concentrations associated with low salinity waters were 60% higher at stations near the
icebergs.
The bioavailability of the Fe and other trace metals associated with the iceberg-
borne terrigenous material were also evaluated with samples collected from W-86
through phytoplankton culturing experiments using Thalassiosira weissflogii. This
specie of phytoplankton comprises a critical component of the export production in the
Southern Ocean and is commonly used in studies of Fe availability due to its demands
of the dissolved molecular form of Fe. A positive and faster cell growth rate of the
Diatoms was observed in the culture with iceberg terrigenous material when compared
to the control reinforcing that the mechanisms of iron dissolution and consequently
bioavailability to the cells could naturally occur in the Southern Ocean (Smith et al.,
2007).
In association with this significant enrichment of potentially bioavailable
terrigenous material, Smith et al. (2007) could observe increased concentrations of
phytoplankton biomass, micro, macrozooplankton and micronekton community (i.e.
Antarctic krill) and pelagic seabirds in a radial distance of around 3.7 kilometers from
each iceberg. The majority of the phytoplankton biomass associated with icebergs W-86
and A-52 and C-18a consisted of healthy diatoms cells (Smith et al., 2007; Cefarelli et
al., 2010; Vernet et al., 2010) while macrozooplankton and micronekton were most
composed of Antarctic krill (Euphausia superba) and salps (Salpa thompsoni)

9. 8
(Kaufmann et al., 2011). Higher total zooplankton biomass was observed at 0.4 km
from C-18a, with intermediate biomass at 9 km declining to a minimum background
value at 18 km away (Vernet et al., 2010); and out to a radius of approximately 3.7 km
around iceberg W-86 (Smith et al., 2007). No trends of direct influence from iceberg C-
18a in the bacterioplankton characteristics were verified, however, differences in cell
abundance, heterotrophic production, and community structure were observed in the
bacterioplankton around C-18a when compared to smaller iceberg (Murray et al., 2011).
Ruhl et al. (2011) verified that the population of seabirds within 0.5 km distance from
icebergs was up to six times greater than further away with most common species the
Antarctic fulmar (Fulmarus glacialoides), cape petrel (Daption capense), and Wilson’s
storm petrel (Oceanites oceanicus). Occasional sightings of penguins, eels, whales and
fur seals have also been observed around free-drifting icebergs (Smith et al., 2012).
In summary, giant icebergs can sustain a rich ecosystem where pelagic
consumers can thrive and enhance carbon export due to the micronutrients input
promoted by these massive structures. Nevertheless, the physical mechanism of
meltwater release and dispersion, vertical mixing and sea water column disturbance
caused by a giant iceberg track, crucial to promote such fertilisation, are still poorly
understood.
1.2 Physical processes associated with the iceberg`s passage
Although icebergs can promote the growth of phytoplankton as evidenced in
field studies, the ultimate effect of the iceberg on the ocean water and consequently in

10. 9
the pelagic ecosystem also depends on the physicochemical characteristics of the water
column of which the iceberg is passing through (Vernet et al., 2010). The interactions
between the iceberg melting dynamics and the background conditions which can
determine a following biological enrichment or depletion in the pelagic zone is still not
completely clarified (Smith et al., 2007; Schwarz and Schodlok, 2009).
In general, the colder melt water exerts negative temperature buoyancy and
positive salinity buoyancy due to the lower density of the fresh melt water when
compared to the ocean water. Positive net buoyancy can lead to the formation of a melt
water lens highly permeable to sun light, crucial to the surface phytoplankton (Schwarz
and Schodlok, 2009). In the Southern Ocean, phytoplankton blooms occur during
spring and summer when sunlight increases, water mixing reduces and the mixed layer
is saturated with nutrients brought to the mixed layer from the previous winter storms.
The mixing and upwelling of warmer water from the Circumpolar Deep Current could
also heat the cold winter ocean surface, reducing the formation of sea ice and increasing
the stability of the water column in benefit to phytoplankton growth during spring
(Jenkins, 1999 cited by Silva, 2006). In contrast, highly stagnant water with a strong
stratification in the end of the summer season due to warmer surface waters could lead
to less phytoplankton, since stratification prevents mixing of the ocean layers and
nutrients supply (Behrenfeld, et al., 2009).
The turbulent mixing caused by an iceberg`s passage potentially enables the
transport and exchange of energy, macro and micro nutrients, water of different density
and phytoplankton cells throughout the ocean layers (Lancelot et al., 1993 cited by
Schwarz and Schodlok, 2009). This process can occur both by the mechanical

11. 10
disturbance of the ocean water column promoted by the iceberg`s wake (which strength
is determined by the shape of the iceberg’s keel and its speed) and by advection of the
iceberg basal melt water that creates a density gradient upwelling of nutrient supplying
the ocean mixed layer with water from the pycnocline (Smith et al., 2007; Schwarz and
Schodlok, 2009; Helly et al., 2011; Vernet et al., 2011). In addition to the turbulent
mixing, Gordon et al. (2011) addressed the double-diffusive mixing as the melt water
dispersion process responsible for the horizontal transfer of the iron enriched meltwater
away from the iceberg in the seasonal thermocline enabling the fertilization of a much
greater area.
Taking the above process into account, the deep keel of a giant iceberg could
therefore promote mechanical turbulence and upwelling bringing essential and limited
micronutrient (especially iron) and macro-nutrients such as nitrate, phosphate and
silicate from below the pycnocline to the surface that would also be beneficial in a
shallow impoverished mixed layer (Dafner et al., 2003). In the absence of a strong
windy condition, the melt water could spread over a very large area promoting the
formation of a stable lens of iron-enriched low salinity water allowing high level of
solar irradiation income that could help the phytoplankton growth (Helly et al., 2011).
On the other hand, the passage of a giant iceberg through a well stratified water column
with high phytoplankton biomass concentration in the ocean surface could disturb the
stability of this water layer, diluting and mixing the phytoplankton population cells by
mechanical process and pushing them downward the water column away from the
mixed layer (Schwarz and Schodlok, 2009).

12. 11
Field ship based measurements of the ocean water column proprieties around C-
18a showed that the iceberg sidewall melting and surface ablation produced fresh melt
water above the seasonal pycnocline, diluting and chilling the surface mixed layer water
to a depth of 50m (Helly et al., 2011). The survey also found evidence of potential
disturbance of the water column by mechanical mixing up to 250m depth and disruption
of the Weddell Deep Water to depths up to 1500 m, suggesting the presence of the basal
melt water advection and wake turbulence. Physicochemical properties disturbance
could be observed up to 23 days after the iceberg passage (Helly et al., 2011)
reinforcing the potential implications of the iceberg`s wake to the local phytoplankton
community and ecology.
1.3 The role of icebergs in the carbon cycle
The ocean-atmosphere carbon exchange is driven by physicochemical reactions
between the ocean surface and the atmosphere and by biological processes within the
ocean mixed layer (Gregg et al., 2003). Globally, every year phytoplankton absorbs
about 10 gigatonnes of carbon dioxide from the atmosphere through photosynthesis
from which 5- 15% is eventually sequestrated to the deep ocean through different
biological processes (see figure 3), a scale equivalent to the world forests` uptake.
Increases in atmospheric CO2 levels can interfere in the phytoplankton activity
enhancing the biological carbon absorption in the Southern Ocean (Tordell et al., 2008).
In the same way, natural and anthropogenic driven changes in the ocean primary
productivity can lead to significant variations in the atmospheric carbon dioxide

13. 12
concentrations which will consequently affect global surface temperatures in a feedback
response mechanism (Behrenfeld et al., 2009; Racault et al., 2012; Giering et al., 2014).
Figure 3: Above: global ocean map showing the ocean-atmosphere annual average CO2 exchange
(flux in mol.m-2
.yr-1
). Source: IPCC, 2007. Below: global ocean map of the average chlorophyll
concentration (mg/m2
) from July 2002 to May, 2010. Higher concentrations of phytoplankton
(yellow) are observed in high latitudes and in zones of upwelling (in the equator and in the coastline)
and lower levels occur in oceans scarce in nutrient (dark blue). Source: NASA, 2014. The
comparison of both maps shows a strong correlation between phytoplankton activity (represented by
the chlorophyll concentration) and ocean CO2 uptake revealing the important influence of these
microorganisms in the ocean carbon absorption from the atmosphere.
During previous glacial periods, an increase of the primary production is thought
to have been driven by ocean fertilisation through aeolian input of terrigenous dust (well
represented in Antarctic ice cores) due to stronger wind patterns which might also have
been responsible for half of the previous glacial CO2 sequestration (Watson et al., 2000;
Rothlisberger, 2004). Recently, new evidence indicates icebergs detrital material could
also have contributed as a significant source of iron to the Southern Ocean reinforcing
the hypothesis that the different rate of iceberg production during glacial and inter-

14. 13
glacial periods might have influenced the phytoplankton activity and thus the
atmosphere composition in a significant way (Shaw et al., 2011a, 2011b; Smith et al.,
2012).
In the last decade, some attempts have been undertaken to estimate the role of
Antarctic icebergs in the local and global carbon budget. In order to verify the scale of
the drawdown and eventually sequestration of CO2 through the carbon export flux
caused by the enrichment of the pelagic ecosystems surrounding giant icebergs, Smith
et al. (2010) used Lagrangian Sediment Traps (LST) deployed beneath C-18a to collect
sinking particles at a depth of 600 m during its track through the Northwest Weddell
Sea in March and April 2009. The mass flux of organic carbon associated with iceberg
C-18a were two times higher as at the control site, 74 km further in water free of
icebergs. The mean organic carbon flux associated with iceberg C-18a fertilisation was
5.6mg Corgm-2
.d-1
compared to 2.5 mg Corgm-2
.d-1
from background values. Smith et al.
(2010) estimated that the area of enrichment from C-18a and five similar size icebergs
identified in the surrounding area by satellite images would export 122.4 tons Corg day-1
.
Sinking material was enriched in diatoms, fish and crustacean faecal pellets and detrital
aggregates with mineral grains from bacterial activity, suggesting increased grazing as
the most important mechanism of carbon export through organic material sedimentation
(Smith, et al., 2010, 2012; Vernet et al., 2010).
Based on Smith et al., (2010) result value of carbon flux, Bigg (2014) calculated
that icebergs could contribute with almost 20% of the net 1.6 GtC yr-1
positive
imbalance (net uptake) of the world ocean carbon exchange (IPCC, 2013). This new
estimate could be achieved by multiplying the additional carbon export related

15. 14
exclusively to the presence of the iceberg C-18a (3.1 mgCorgm-2
.d-1
), the influence area
assessed around it (2826 km2
) and the number or summer days in the Southern Ocean
(half of the year in polar regions), leading to a total carb export of 1.58 Mt.yr-1
for the
study area (30km radium from C-18c). Bigg (2014) extrapolated this value to the whole
Southern Ocean by comparing the total area of C-18a (~ 60 km3
) with the Antarctic ice
flux of 2250 km3
.y-1
(Rignot et al., 2011) and considering a mean iceberg lifetime of 5
years (Silva, 2006).
Another complementary approach for the role of iceberg production in the
carbon cycle came with an iron cycling model run by Wadley et al. (2014) based on the
assumptions of light and iron limitation of primary production in an eddy resolving
ocean general circulation model. Since changes in the atmospheric dust and sediment
flux in response to global warming is unlikely to be of the same proportion of the flux
of iron from increased iceberg production, a sensitivity test was run keeping the other
sources of iron to the ocean constant and assuming a 50% efficiency of carbon export to
the deep ocean through ocean fertilisation (Forster et al., 2007 cited by Wadley et al.,
2014). The model showed that a doubling of the iceberg iron input would result in an
additional carbon sequestration of 0.22 x 1015
g.C.y-1
, roughly 3% current anthropogenic
emissions (Wadley et al., 2014).
Although the increase of CO2 uptake and sedimentation by primary production
in the Southern Ocean can impact the global carbon cycle significantly, recent models
have indicated that the increase of global surface warming caused by enhanced
greenhouse gas forcing can change not only the ocean circulation and its chemical
absorption rate of CO2 from the atmosphere, but also the phytoplankton activity itself

16. 15
(Tordell et al., 2008). Primary production might decline in a warmer Earth due to
higher ocean water column stratification and less vertical mixing to recycle nutrients
from deep waters (Gregg, 2003; Behrenfeld et al., 2006; Tordell et al., 2008). The
understanding of the real magnitude of these accomplished processes and their
interactions are primordial to assess the importance of a climate change driven increase
of iceberg production to the global carbon budget and as a negative feedback to global
warming.

17. 16
2. Rationale
In spite of important evidences, previous small scale studies showed a wide
range of results regarding the area of a giant iceberg influence and have not yet proven
that icebergs definitely have a marked impact on the primary production in a bigger
spatial and temporal scale. Limitations for field researches are many, from logistic to
funding and shipboard surveys can only cover a limited area simultaneously (Helly,
2011). Iceberg modelling has also many implications, such as the lack of very accurate
input data and pre-existing data to validate them (Schwarz and Schodlok, 2009). These
limitations upgrade the importance of remote sensing analyses for this purpose and
inspired a study conducted by Schwarz and Schodlok (2009) using iceberg tracking
satellite data during 1999 to 2004 to assess the large scale impact of drifting medium-
sized icebergs in the primary production in the Weddell Sea. Although their results
showed a 33% higher probability of phytoplankton biomass increase in the wake of
medium icebergs compared to open waters biomass background variability, no study
has yet been undergone to access the impact of giant icebergs in the primary production
through satellite images in the Southern Ocean. It is known by now that these massive
structures can hold enormous amounts of bioavailable iron and a freshwater bulk of the
same proportion to the amount carried by all the population of smaller icebergs, thus
they can influence the surrounding water biochemical properties in a much larger scale
(Jacobs et al., 1992 cited by Silva et al., 2006). Besides, the influence of a giant
icebergs’ keel in the ocean water stratification and stability was only empirically studied.
Facing the lack of information this Master research aims to evaluate the impact
of giant free-drifting icebergs in the primary productivity of the Southern Ocean by
analysing existing satellite data. This remote sensing approach potentially enables the

18. 17
assessment, not only of the magnitude (maximum effect), longevity and timing of the
Southern Ocean response to giant icebergs fertilisation but also its response sensibility
to different seasons of the year and to the different iceberg origins around Antarctica.
Finally, a comparison analysis of the effects of giant icebergs in the Southern Ocean
primary production in relation to medium-sized icebergs could also be drawn.

19. 18
3. Methodology
To assess the influence of Antarctic giant icebergs on the physical and
biogeochemical characteristics of Southern Ocean waters, two different existing
satellite-derived data sources – The Brigham Young University Center for Remote
Sensing Iceberg Track database and the NASA Ocean Colour Images - were analysed
and compared.
3.1 Iceberg track database
Radar scatterometers are a satellite-borne instrument which were originally
designed to measure winds over the ocean, but have been proved to be very useful in
several different kinds of land and ice studies. A scatterometer emits microwave energy
pulses and captures the returned energy that depends on the roughness and electrical
properties of the surface. Glacial ice typically returns very high radar backscatter values
that are distinguishable from the much lower values from sea ice allowing a ready
visualisation of the iceberg in the scatterometer images (Long, 2002).
Satellite data revealing the occurrence and location of Antarctic giant icebergs
have been monitored by the Brigham Young University Center (BYU) for Remote
Sensing Iceberg Monitoring Site and the U.S. National Ice Center (NIC) since 1978.
The BYU image database has been obtained from six different space borne
scatterometer instruments on board different satellite missions (some still in use, others
not) as part of the NASA Scatterometer Climate Record Pathfinder (SCP) project

20. 19
(NASA, 2014; BYU, 2014). The scatterometers` data sets are useful to identify and
track icebergs through resolution enhancement performed by the BYU's Scatterometer
Image reconstruction (SIR) and SIR Filtering (SIRF) algorithms, which provide daily
image time series with geographic coordinate position for different icebergs in ASCII
text file (BYU, 2014). The initial position for each iceberg is obtained from a position
reported by the National Ice Center webpage or by spotting a moving iceberg in a time
sequence of scatterometer images.
This research analysed free-drifting giant iceberg paths tracked from 2003 to
2013 through QuikSCAT (QSCAT), ESA Advaced Scatterometer (ASCAT) and
Oceansat-2 scatterometer (OSCAT) scatterometer, the latter still in operation. For this
research purpose, a giant iceberg is defined as being longer than 18.5 km in its longest
axis according to the NIC’s definition. The selected icebergs’ tracks were chosen
according to their origin, path behaviour and location throughout the Southern Ocean.
Grounded icebergs, icebergs transited throughout the surrounding Antarctic sea-ice, and
very winding paths were not considered.
3.2 Chlorophyll Ocean colour images
Parallel analysis of the Southern Ocean along the areas where the selected
icebergs transited during the same period was carried out using NASA satellite ocean
colour images that derive the concentration of phytoplankton in the ocean through the
quantification of its colour. This is possible as the ocean colours vary with the
chlorophyll concentration in the water, giving a greener tone to the water when more

21. 20
phytoplankton is present. Satellite-acquired ocean colour data have been an important
tool for measuring the ocean phytoplankton abundance on a global scale, bringing
important information on the oceans’ role in the global carbon cycle (NASA, 2014).
The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and
Aqua satellites is a key instrument for the acquisition of colour images (see
Supplementary Material 1). Terra and Aqua’s orbit around the Earth are synchronised
so that the entire Earth’s surface can be scanned every 1 to 2 days. Also commonly used
for the same purpose, the SeaWiFS instrument is made of an optical scanner and an
electronics module sensor designed to obtain global ocean biological data and
was launch on board SeaStar spacecraft on 1997 (NASA, 2014).
All the surface chlorophyll a maps used in this research were obtained from
Level 1 and 2 (time and geo-referenced instrument data at full resolution) Moderate
Resolution Imaging Spectroradiometer (MODIS) images from the NASA OceanColour
website. The ocean colour images were exported from the NASA OceanColour website
and then analysed using the freely available SeaDAS software v7.0.2.
3.3 Impact of giant icebergs’ passage in the primary productivity of the Southern
Ocean
An initial qualitative analysis of a potential effect of the presence of a giant
iceberg in the surrounding chlorophyll concentration was undertaken by comparing
chlorophyll colour images in different locations where a giant iceberg has passed with
background values. This initial approach, despite its subjectivity, revealed a distinct

22. 21
correlation between the iceberg`s track and a plume of increased chlorophyll
concentration along its path in almost all the images analysed (figures 4 and 5 below)
and could endorse the following quantitative methodology.
Figure 4: Iceberg C-19c (white arrow) on 26/02/2013 as it moves north from the day before position
(GCP1). The image enables a clear association of the iceberg with the plume of increased
chlorophyll concentration coloured here in hot tones (green – red). The image also shows the
influence of the ocean currents that can drive the plume ahead beyond the iceberg current position.
As the plume moves away from the iceberg location, physical oceanic mechanisms start to act,
dissipating the clear track of the plume and spreading it throughout a larger area. Source: SeaDAS
software v7.0.2. scale: 1.2:1.
Figure 5: Iceberg A-22a (left) on 30/10/2006 and B-15f (right) on 11/01/2013. The images show the
surrounding plume of high chlorophyll concentration (yellow, red and grey) associated with the

23. 22
icebergs where the boundaries of the plume are clearly delimited contrasting with the very low
background values represented by purple and blue colours. It is worth to note the buffer area created
around both icebergs of around 5- 10 km where low chlorophyll values dominate. Source: SeaDAS
software v7.0.2. Scale: 1.2:1 (left); 1.9:1 (right).
3.3.1 Estimating the effect magnitude of free-drifting icebergs in the Southern
Ocean surface chlorophyll concentration
In order to assess the impact of a giant iceberg’ s passage on the surface
chlorophyll a concentration, three sets of satellite-derived chlorophyll colour images at
different times were analysed for a unique chosen position occupied by the iceberg
along its track (defined by geographic coordinate). For every position along the known
iceberg track, the first set was obtained from a one-month period 20 days prior to the
iceberg passage while the second set was obtained from a week period immediately
after the iceberg`s passage. The positions (associated with a specific date) were
randomly chosen on cloudless days during the summer months of the Southern Ocean.
The mean values of the thirty-day period before the iceberg passage and the successive
seven-day period immediately after its passage generated the chli
before and the chli
after
values respectively. An additional sequence of seven successive days’ chlorophyll
images (third set) was obtained three weeks after the iceberg’s passage (from the 23th
to
the 30th
day following its passage), aiming to assess the temporal response (timing and
longevity) of the ocean productivity to a potential iceberg fertilisation.
Therefore, the change in surface chl a after the iceberg track was given by the
difference of these two values:

24. 23
∆chl(1
st
or 2
nd
) = chlafter (1
st
or 2
nd
) - chlbefore (1)
(1
st
or 2
nd
) indicates the first or second sequence of seven-day images
(immediately after and 3 weeks after the iceberg’s passage respectively)
The mean chlorophyll concentration value analysed in the three data sets were
obtained and exported from a circle area with approximately 15 km radius centred in the
iceberg geographic coordinate using the geometry mask tool from SeaDAS software.
The twenty-day gap between the set of values before the date of the iceberg
passage for the chosen position was established after the qualitative analysis of the
images which had showed that the plume of increased chlorophyll associated with the
iceberg generally advances beyond its current position due to sea currents. Because of
this fact, if chlorophyll measurement were done at a period just immediately before the
chosen position date, a higher [chl a] could be found in case the plume were driven
ahead of the iceberg`s current geographic coordinate position along its path not
reflecting therefore the real background value (not influenced by the iceberg). The
unwished problem of that choice is the long time gap between the “before” and “after”
data set. Since it is expected that the background values might increase from the
beginning of spring towards summer due to the increased sea ice melt and higher
radiation income, the difference between spring and summer values (as well as data
from previous studies) were assessed in order to verify any contamination of the results.
In order to evaluate the difference in ocean primary productivity response to
icebergs originating from different areas of the Antarctica continent, icebergs from each

25. 24
of the four different sectors of Antarctica (from which the iceberg`s nomenclature is
derived) A, B, C and D were analysed. Finally, once the chlorophyll images were
obtained and evaluated during the Southern Ocean summer season (Oct – April), the
variability of the Southern Ocean’s response to giant icebergs’ fertilisation between
spring and summer could also be assessed.
The sample size was calculated considering the analysis of repeated measures
since it is expected that the values taken over time are correlated. To estimate the
correlation between measurements taken at the same location, the Pearson correlation
coefficient was used and calculated between the two measurements performed by
Schwarz & Schodlok (2009), which resulted in a value of 0.210. Thus, following the
recommendations of Vonesh & Schork (1986), assuming a minimum correlation of 0.2
between groups, 80% power, type I error of 5% and a minimum difference of 1 standard
deviation to be detected among groups, it was estimated that 19 different positions were
needed in an iceberg track for each of the four sector of the Southern Ocean, giving a
total of 76 points to be observed.
The result values were evaluated to answer the null hypothesis that ‘the passage
of a giant iceberg does not have a significant impact on the surface chlorophyll
concentration’. Firstly, normality tests were applied and the normal distribution of the
samples was refused. The concentrations then were transformed logarithmically for the
comparisons. After that, linear mixed effects models (Pinheiro, C. and D. Bates) were
used to test the differences along time and between groups, including the interaction
effects between them. The correlation structure was assumed to be autoregressive of 1st
order. To compare groups in a single moment an analysis of variance was performed

26. 25
Kutner, M., 1996). Multiple comparisons were performed and corrected according to
Bonferroni’s method and significance level was set at 5%. Statistical analyses were
performed with R version 3.1.0 (R Core Team, 2014).
3.3.2 Size extent of effect plume and maximum chlorophyll concentration
Whereas the iceberg passage generated a clear and delimitated plume with
defined boundaries of increased chlorophyll effect associated to it, the fertilised area
were measured using the “ruler” tool from the SeaDas image software. The mean plume
extent value of different images from each iceberg analysed could therefore be
estimated.
In the same way, for the images which a correlation between the iceberg transit
and increased chlorophyll values could be clearly observed, the highest chlorophyll
concentration within the plume was obtained regardless the current position of the
iceberg. The distance of the area of highest chlorophyll concentration to the iceberg
position were then determined as less or more than 50 km away from the iceberg.
3.3.3 Estimating the [chl a] profile versus distance and the background values.
A selection of 20 colour images where a clear and delimitated plume of
increased chlorophyll effect could be visually associated with the iceberg was chosen to
draw a chlorophyll concentration profile in relation to the iceberg distance. A random

27. 26
line was drawn from the iceberg border toward the background value outside the effect
plume, crossing the plume in its longest axis as demonstrated below (figure 6). Along
the line, chlorophyll concentrations were obtained from 0, 3, 5, 10, 20, 40, 60, 80, 100,
200, 400, 600, 800, 1000 km distance from the iceberg and a general profile graph could
be generated.
Figure 6: a straight line was drawn in the image above from iceberg B-15f on the 4th
of March, 2003
in order to obtain the chlorophyll profile versus distance. Within the line that contemplates the
longer extent of the plume from the iceberg, chlorophyll concentrations were obtained for different
distance down to background chlorophyll values. Source: SeaDAS software v7.0.2. Scale: 1.9:1.
Finally, background values from the first data set used to estimate the iceberg
effect could be also analysed and compared to verify a potential difference between
spring and summer chlorophyll concentration values.
3.4 Limitations
The limitations of the methodology could be grouped to three different types:
methodological, instrumental errors and false positive results.

28. 27
Regarding the methodology design, the results obtained by the current
methodology should take into account the influence of other factors that might affect the
chlorophyll concentration in the area, such as: small untracked icebergs, phytoplankton
growth dynamics and natural seasonal variability, turbulent mixing and grazing pressure.
A small degree of subjectivity in the analysis of the iceberg plume extent must also be
taking into account. The amount of images obtained were also limited due to, among
other factors, the high degree of cloudiness in the Southern Ocean (especially at high
degrees south) and the limited number of sun lit months from which the images could
be analysed. It is also important to consider the proximity of most of the positions
obtained to the South Georgia Island located in the end route of the Iceberg Alley that is
an important source of sediment material to the surrounding ocean.
Another limitation is intrinsic related to the remote sensing method used in this
research. Although MODIS measurement has proved to follow trends of field-based
measurements, previous study has showed that MODIS tends to overestimate
chlorophyll a concentrations that are low but, overall, showed 27% error accuracy for
surface layer measurements in depths > 20m, close to the instrument 35% target error
(Bierman et al., 2009).
Third, deep chlorophyll concentrations could occasionally be disturbed with the
passage of an iceberg with a loss of the “undetectable” biomass from the pynocline,
occasionally replaced by a new superficial community in the melt water lens that could
also prevent the deeper population from receiving light, resulting therefore in a possible

29. 28
false positive result of net biomass gain due to the iceberg passage (Holm-Hansen et al.,
2005; Schwarz and Schodlok, 2009).
Some of these factors will be discussed ahead and in spite of these limitations,
remote sensing is the only available method to estimate large scale chlorophyll a.

30. 29
4. Results and Discussion
4.1 Chlorophyll concentration in the wake of a giant iceberg
Chlorophyll concentrations from satellite-derived images before a giant
iceberg`s passage were analysed for 65 positions within 17 giant iceberg track from
different origin sites (see table 1). Chlorophyll concentration mean values were obtained
at 63 positions for the seven-day period post iceberg and 47 values for the seven-day
period three weeks after the iceberg passage (see table 1). The number of positions
obtained for icebergs from A, B, C and D sectors were 22, 16, 15 and 10 respectively.
The reduced number of positions from icebergs originated from the B, C and D sector
was due to the limited number of useful iceberg track data from that area.
The original data were described as mean, median, standard deviation, quartiles
and minimum and maximum values (see table 4). The values were incompatible with
the normal distribution, according to the Shapiro-Wilk test and were logarithmically
transformed for comparisons. The three time sets were compared using a linear mixed
effects model considering the fixed effect of time and the correlation between
measurements taken in the same place and at different times (R version 3.1.0).
There was an significant increase in chlorophyll concentration (p<0.001) after
the iceberg`s passage of the order of around 3 times, with a mean value of 0.307mg.m-3
before, 0.923 mg.m-3
after one week and 1.126 mg.m-3
after 30 days. Multiple
comparisons corrected by Bonferroni showed differences before and after 6 days (p=
0.001) and between before and 30 days (p<0.001). The comparison between one week

31. 30
and 30 days after showed no significance (p=0.839) and, therefore, there is not enough
evidence to claim that the chlorophyll concentration increases after 30 days post iceberg
transit compared to one week. Nevertheless, the relatively similar mean value between
the two sets of data after the iceberg`s transit can evidence a long lasting effect (of at
least a month period) of the iceberg to the surrounding ocean primary production.
Graph 1: Chlorophyll concentration associated with giant iceberg's passage
Despite the lack of difference between the 2 time periods after the iceberg`s
transit, the same not necessarily states that the maximum phytoplankton activity in
response to the physical and chemical conditions promoted by the iceberg was
immediately achieved after its transit and kept levelled out. In fact, the absence of a
significant difference could be a result of a higher “standard deviation” observed after
30 days. Partly of this higher variability in the chlorophyll concentration found after 30
days can be explained by the higher chance of ocean currents taking the effect plume to
locations away from the original position where the iceberg was located in a specific
date (there is more time for this natural process to occur), while most of the effect

32. 31
plume during the seven-day period following the iceberg`s transit tended to be still
found around the iceberg location.
Despite the greater variability mentioned above and its potential interference in
the result significance, another analysis from this research support a probable increase
trend in the phytoplankton activity days after the iceberg`s passage - the highest
chlorophyll concentration spot in the effect plume associated with the iceberg showed
that around 75% of those values were found at distances further than 50 km away,
occurring predominantly at distances around 80 - 100km from the iceberg. Considering
the estimated giant iceberg velocity in the area of around 0.5 km.h-1
observed from
previous studies (Smith et a, 2007; Schwarz and Schodlok, 2009) and the mean highest
surface current velocity for the Scotia Sea of around 1m.s-1
during regular summers
(Integrated Climate Data Center, 2014) even in an hypothetical situation where iceberg
and ocean surface currents from waters left behind it run in opposite directions, those
areas of maximum [chl a] would represent the phytoplankton response of at least 3-7
days from the date of the iceberg passage. Besides, the highest chlorophyll values from
the two post iceberg`s passage datasets were in fact found at positions after 30 days post
iceberg`s transit, showing a delayed maximum response. Additional evidence from
previous field study show that ocean waters physical properties can be altered by an
giant iceberg over to 23 days (Helly et al., 2011) and that chl a concentrations increases
significantly from 15% (Helly et al., 2011) to 30% (Vernet et al., 2010) ten days after
the iceberg’s passage adding substantial confidence to a late maximum response of the
phytoplankton to the iceberg`s passage and its sustainability through time. This late
increase in phytoplankton activity could be due to in situ growth, the removal of the

33. 32
grazing pressure associated with the iceberg or to advection of deeper communities of
phytoplankton to the surface water (Kaufmann et al., 2011).
Finally, it must be also considered that a constant natural dilution of the
chlorophyll concentration throughout greater extent areas of the ocean surface as
turbulent mixing acts and meltwater spreads out could also influence the chlorophyll
concentration after 30 days in a considerable magnitude adding complexity to establish
the timing for an ideal physical conditions for a maximum blooming associated with the
transit of an iceberg.
4.2 Fertilising effect according to the icebergs origin and background values
When the three sets of data were analysed taking into account the different areas
from where the icebergs were calved in the Antarctic continent, the results showed a
significant time effect (p<0.001), giving evidence of changes in the magnitude of
concentrations over time also for each group individually (see graph below). Less
expected, effect of site was also significant (p<0.001), indicating that there are
differences in the magnitude of the observed values between sites, for both times post
iceberg transit. In order to identify which sites have differed, a pairwise comparison
corrected by Boferroni method was performed. These comparisons showed significant
differences between sites A and B (p = 0.009) and between sites A and C (0.003). One
plausible explanation for this difference is based on the different distance that the
icebergs have transited from their calving area. Since most of the chlorophyll images
analysis were undertaken in the same region of the South Atlantic, it seems reasonable

34. 33
that the further the iceberg origin, the less iron-source terriginous material (ice-rafted
ground detritus that had been carried by the iceberg due to the downwards dragging
from its origin glacier) will still remain on the berg due to the basal melting. As
concluded by Gordon et al. (2011), basal melting contributes with similar amount of
freshwater to the upper ocean near giant icebergs as sidewall melting, an indication of
the potential relation of the loss of entrapped iron source material from the base of
icebergs with the locations of higher fertilisation rates throughout its path. Considering
most icebergs follow the same anti-clockwise route direction around the coast of the
Antarctica continent, icebergs from the B and C sector will have more time to lose their
base material when arriving at the South Atlantic area (Scotia Sea) than icebergs from A
and D sector, in accordance with the results obtained here. A complementary approach
to a better understanding of the potential difference between the effect caused by
icebergs calved from different locations could be obtained with further detailed
fieldwork and iceberg melting models.
Graph 2: chlorophyll concentration versus iceberg calving origin

35. 34
The analysis from the mixed effects model including effects of time, group and
interaction showed no significance, ie, there is not enough evidence that the effect after
6 and 30 days differs between sites (p = 0.199). In other words, the time did not have a
statistically different influence in the effect direction of the groups and therefore, the
opposite effect observed between the areas seen in graph 2 (increase in A and B and
decrease in D) is not of a considerable concern.
The mean value of the background concentration (set of values before the
iceberg passage) according to the season of the year was 0.27 mg.m-3
in the spring
(standard deviation 0.17) and 0.34 mg.m-3
(standard deviation 0.15) in summer. The t-
test applied to the logarithmically transformed data showed a value of p=0.011,
indicating that concentration in summer was higher than in the spring. In spring,
concentration ranged from 0.12 to 0.80 mg.m-3
, while in the summer it varied from 0.15
to 0.88 mg.m-3
. The difference obtained could also be expected given that a previous
study from Schwarz and Schodlok (2009) analysing 690,444 backgrounds values using
the same satellite chlorophyll images has shown that background chlorophyll
concentration naturally increases each month from October to February, decreasing as
autumn begins. This increase along the months can be explained by the continuous sea
ice retreat leading to sea lens formation and increase of light availability towards
summer in favour of phytoplankton growth (Smith and Comiso, 2008 cited by Schwarz
and Schodlok, 2009).
Additionally, Schwarz and Schodlok`s (2009) monthly comparison analysis of
the influence of medium icebergs during summer seasons in Antarctica showed a

36. 35
variable response of chlorophyll concentration after the iceberg`s transit (with higher
positive ∆ chl a in November, positive but lower ∆ chl a in Dec and Jan, and a negative
∆ chl a in February). The Author also demonstrated that the sign of the effect
(positive/negative) promoted by a medium iceberg on the chlorophyll might also be
influenced by the initial chlorophyll concentrations (higher in December) suggesting
that when an iceberg crosses a well-developed phytoplankton bloom during summer
(December), its immediate impact can be a reduction in surface chlorophyll
concentration (Schwarz and Schodlok, 2009). Because of the larger gap between pre
and post values in this study and smaller number of observations, a monthly analysis
could not be done, but the same effect was not observed in this research when
measuring the influence of initial chlorophyll values (lower and higher) separately for
spring and summer (p=0.334). To evaluate concentrations with time taking into account
a potential influence of the season, a mixed model including effects of time, season and
the interaction between site and season was also used (see graph 3 below) and showed
no significance, ie, there is no evidence that the direction of change after 7 or 30 days
differ between seasons (p=0.913). There is evidence of changes in the magnitude of
concentrations over time for both seasons (<0.001) but without significant difference of
magnitude between them (p = 0.442).

37. 36
Graph 3: chlorophyll concentration versus season
Taking into account the magnitude of the positive influence of giant icebergs on
the outcomes observed here (both in spring and summer) it can be proposed that the
increase in the chlorophyll concentration promoted by giant icebergs might not suffer as
significant an influence from natural variability as when considering smaller icebergs.
This hypothesis is based on the fact that although the significant difference of
background values between these seasons indicates a natural increase in the values as
time goes from the start of spring into the high summer, the subtle difference in the
mean background values between these two periods here obtained (∆ chl a 0.07) as well
as the monthly background variability observed by Schwarz and Schodlok (2009) - ∆chl
a 0.006 in Nov, ∆chl a 0.009 in Dec and ∆chl a 0.005 in January - does not represent a
major variability against the much greater positive values observed after a giant
iceberg`s passage. Thus, the potential influence of the 1-2 month time difference
between pre and post iceberg chlorophyll measurement in the results (part of this
methodology design) can apparently be neglected.

38. 37
Finally, the likelihood of increased chlorophyll concentrations immediately after
the giant iceberg passage was 92% in this study against 68% from Schwarz and
Schodlok (2009) who analysed medium icebergs. At the same time, a mean chlorophyll
concentration increase of 3 times was observed a week after a giant iceberg passage
regardless of the month while the increase obtained for medium icebergs (Schwarz and
Schodlok, 2009) was of 2 times order (only for the months of Nov. to Jan.). The above
results indicate a stronger influence of giant iceberg in the Southern Ocean primary
production when compared to smaller ones. It is suggested here that giant icebergs
promote a greater disturbance of the sub-surface water by mixing the physically highly
stratified water column and replenishing the mixed layer with nutrients.
4.3 Maximum chlorophyll concentration associated with the plume
Some of the colour images analysed could reveal a delimited plume of effect
associated with the giant iceberg`s transit and these values were clearly distinguishable
from the background ones in the region assessed. In general, the chlorophyll
concentration inside this plume was not uniform and by determining the areas of higher
concentration within each iceberg plume across the images, another set of maximum
values could be obtained (see last column of table 1). The mean value from the highest
chlorophyll concentration associated with an iceberg effect plume was of 2.841 mg.m-3
,
approximately 10 times higher than the background value. This value is consistent with
an increase in iron concentration from background values, normally set below <0.3 nM
in the upper surface mixed layer of the Southern Atlantic Ocean (Klunder et al., 2011;

39. 38
Raiswell and Canfield, 2012) to levels above the saturation for phytoplankton growth,
as demonstrated by artificial ocean fertilisation experiment (Boyd et al., 2001; Gervais
et al., 2002).
The max concentration from sectors A and D were significantly higher than C
(p=0.002 e p=0.017), according to the ANOVA model applied to logarithmically
transformed maximum values. As it could be expected, the results of maximum
concentration versus iceberg sector origin were totally consistent with the mean values
from the positions within their track obtained for each of them. The areas that showed a
lower mean value for the chlorophyll concentration (B and C) also showed a lower
maximum concentration value in the plume (1.579 and 1.330mg.m-3
respectively) while
A and D had the highest maximum concentration values (3.351 and 3.965mg.m-3
respectively) as well as the higher means. The analysis of max values obtained here
reinforces that the icebergs originated from sectors A and D had a stronger fertilisation
power in the South Atlantic as it has already been considered.
4.4 Increased chlorophyll plume extent
For the same images where a clear and delimited plume of effect associated with
the giant iceberg could be observed, 30 measurements of the plume extent were
obtained from 12 different icebergs (see table 3). The mean value of the plume size,
represented by the chl a concentration from all the different sized giant icebergs
analysed, was 859.5 Km (small error in the accuracy of this value should be considered
due to the inherent subjectivity in stablishing the exact boundary edges for some

40. 39
plumes). This new estimation is much larger than previous measurements of
phytoplankton biomass and chlorophyll concentrations effect extent from previous
studies: Smith et al. (2007) showed increased phytoplankton biomass up to 3.7 km
around iceberg W-86, while Vernet et al. (2010) observed increased chl a concentration
up to 18 km. The results here do, however, concur with Helly et al. (2011) and Bigg
(2014) work that suggest a much larger area of influence promoted by giant icebergs.
Besides, as emphasised by Vernet et al., their own results were probably underestimated
due to many limitations of measurements addressed to logistical difficulties in field
sampling (iceberg drift, high-frequency motion and water masses) that might have led to
increased variability on their results. Therefore, the great area of hundreds of kilometers
of influence observed in this research can evidence the particular advantage of satellite
images to demonstrate the accumulated effect of free-drifting icebergs over large areas
of open waters during its path.
The vast area affected by the influence of giant iceberg observed can be
explained by the iceberg meltwater input physical processes. While the deeper keel of a
giant iceberg can promote the release of meltwater below the thermocline inducing
vertical transport of potentially nutrient-rich water to the surface by turbulent upwelling,
sidewall melting has been proven to have the potential to enrich the thermocline in
micronutrients through horizontal double diffusive process over a much large area
extent without diluting planktonic populations (Gordon et al., 2011).
It is worth noting that most measures were obtained from at the Scotia Sea and
at locations above 60 degrees south; areas that corresponded to the end of the icebergs’
route path and therefore, the sizes of the icebergs has changed from their original

41. 40
calving site. Although a higher size area of the iceberg could at first suggest a greater
plume extent at higher latitudes, other factors like rate of melting, insolation and
proximity to nutrient enriched water sources and sea ice pack can have a major
influence in modulating the magnitude and consequently the extent of the plume further
south.
4.5 Chlorophyll profile versus distance from iceberg
The analysis of the effect plume related to the giant icebergs enabled the
construction of a generic curve of chlorophyll concentration versus distance (graph 4).
The values to make the graphs below were obtained from 20 images (see table 2) from
11 different icebergs. It is important to emphasize that this curve represents an overall
estimate since the size and origin of the giant iceberg, the season and location of its
track and other natural variables were not considered to build the graph.
Graph 4: Chlorophyll profile versus distance from iceberg. Left: the chlorophyll concentration
curve along 1000km distance shows a sharp increase of concentration up to around 100km from the
iceberg following by a decrease heading background values at around 800km distance. Right:
chlorophyll concentration curve in the first 20km showing lower concentrations in the vicinity of a
giant iceberg.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0 100 200 300 400 500 600 700 800 900 1000
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0 5 10 15 20km km

42. 41
The chl a concentration curve shows that the highest concentration values were
found at a distance from 20 to 200 km from the iceberg. In general, the icebergs
analysed by the colour images to build this graph were not in the tip of the plume,
therefore, it is expected the extent of effect from the iceberg to the background value to
be slightly smaller than the mean extent of the whole plume mentioned before. It can
also be noticed that the chlorophyll concentration in the close vicinity of the iceberg
(especially in the first 5 km) is considerably lower (graph on the right) and
progressively increases with the distance from the iceberg. This trend has been
previously observed in the field (Smith et al., 2007; Vernet et al., 2010) as the
continuous increase in productivity around the iceberg caused by the melt water and
micronutrient release will also attract large numbers of zooplankton, such as krill and
salps which graze phytoplankton capping its production through a top-down control
until a new ecological balance is achieved (Sherlock et al., 2011; Smith et al., 2013). In
accordance with the high rates of sedimentation found around C-18a (Smith et al., 2011;
Shaw et al., 2011) it is expected that the ecosystem around giant icebergs to be enriched
at high trophic levels such as zooplankton and birds and impoverished in
bacterioplankton and phytoplankton communities. (Helly et al., 2010). Therefore, chl a
accumulation is expected to occur at waters left behind by the iceberg where the
phytoplankton growth enhanced by turbulent mixing and Fe addition can offset the loss
rates caused by grazers` top-down pressure exclusively associated with the icebergs
(Vernet et al., 2010).
Much closer to the iceberg, even lower chlorophyll concentrations were
observed and are also in accordance to field data around W-86 where reduced

43. 42
phytoplankton biomass was observed at distances less than 0.2 km from the iceberg
(Smith et al., 2007) and reduced chlorophyll concentration less than 1.0 Km around
C18a (Vernet et al., 2010; Helly et al., 2010). Although vertical nutrient transport can
stimulate primary production, the initial effect of a large input of both deeper water and
melt water (that lack these biological populations) around the iceberg is possibly to be a
dilution of phytoplankton (Vernet et al, 2011). This shows that the vertical convection
of melt water released by the sub water basal surface of the iceberg to sustain the
phytoplankton growth will only be noticeable in the wake of the iceberg when this
process will have had time to take place (Gordon et al., 2011; Vernet et al, 2010). This
physical process is also responsible to promote the growth of larger phytoplankton cells
(diatoms) in the vicinity of the iceberg which usually dominates nutrient enriched
productive environments (Vernet et al, 2010; Smith et al., 2007). Likewise, ship-based
measurements showed that if the melt water is produced by a giant iceberg above the
seasonal pycnocline (sidewall melting and calving ablation) it dilutes the surface mixed
layer water around the iceberg as it passes through (Helly et al., 2011). The impact of a
lower incidence of sun light caused by the iceberg shade on the surrounding waters of a
giant iceberg might also contribute to the much lower values immediately adjacent to
the berg.
4.6 Assessing the role of giant icebergs to the carbon cycle and further
considerations
This research results reinforce previous evidence showing that the continuous
presence of giant icebergs throughout the Southern Ocean represents an important

44. 43
mechanism for carbon sequestration from the atmosphere into the deep ocean.
Accounting for the Antarctic ice flux of 2250 km3
.yr-1
(Rignot et al, 2011) and
neglecting the Northern Hemisphere iceberg driven carbon export due to its much lower
ice flux and longevity (Bigg, 2014), a recalculation of Bigg`s iceberg-driven carbon
export estimative considering a more realistic but still conservative area of the iceberg
influence of at least 200km radius reveals a potential 44% contribution of icebergs
fertilisation in the CO2 net positive imbalance (uptake) from the ocean.
The much smaller but still potential bacterial production in glacial ice
(heterotrophic production) can also add complexity to the net carbon budget within
drifting icebergs. The release of dissolved organic carbon (DOC) by the glacial ice
melting has shown to stimulate bacterial activity in cryoconite holes present in glacial
ice in Antarctica, which will eventually be partly oxidized via bacterial respiration to
CO2 and released into the atmosphere (Anesio et al., 2010). However, the role of this
process within free-drifting icebergs` ecosystem is still unknown. Nevertheless,
although refined calculations with additional fieldwork, modelling and remote sensing
data are still required to assess the accurate net carbon budget related to iceberg, the
body of new evidences brought by this research reveals unprecedented information
about the relevance of the giant icebergs in the ocean-atmosphere carbon exchange.
Another important consideration is regarding the amplifying effect from the
Southern Ocean continental and island shelves as well as areas of natural upwelling in
the iceberg fertilization impact and carbon downdraw through a continuously additional
source of iron to the ocean water (Bigg, 2014; Smith et al, 2013). The South Georgia
Island (~50o
S, 40o
W) would have a particular impact in this potentialising effect once

45. 44
most of the icebergs tracks (and the ones analysed in this research) end up in that area
(see image 7). Muench et al. (2002) also showed the Powell Basin area of the Weddell
Sea is one of a high heat flux that could contribute to a higher rate of iceberg melting in
the area that is expected to be transported into the Scotia Sea. Silva et al. (2006) also
demonstrated that an estimated 35% of giant icebergs’ mass is exported above 63
degrees S compared to 3% for smaller bergs, as a consequence of their greater longevity.
These findings upgrade the importance of giant icebergs as carriers of melt water and
micronutrient to this important primary production region.
Figure 7: satellite (above) and satellite colour derived (below) image of icebergs C-19c and B-15f
around South Georgia Island capture by MODIS on 29.09.2012 in an unusual cloudless day showing

46. 45
the fertilization effect of both giant icebergs and the South Georgia Island in the surrounding area.
Source: NASA, 2014. Scale: 1.3:1.

47. 46
5. Conclusion
Over the past decade global warming has been associated with an unprecedented
retreating of glaciers and ice shelves in West Antarctica that are eventually constrained
in the Iceberg Alley along the East coast of the Antarctic Peninsula in the Weddell Sea
(Rignot et al., 2011; NASA, 2013). Field studies in the Southern Ocean have observed
that giant icebergs are important sources of sea fertilisation by delivering a significant
amount of iron-source terrigenous material to surrounding sea water that is followed by
an increase in the phytoplankton biomass and by a rich food chain around them. This
ecosystem represents an efficient process of carbon sequestration through the
conversion of phytoplankton biomass to particulate organic carbon that is eventually
exported to the deep ocean as faecal material (Smith, et al., 2010; 2012; Shaw et al.,
2010). This research brings a new insight of the influence of these giant masses of ice in
the primary production of the Southern Ocean. The impact and extent of giant icebergs
on the phytoplankton activity could be analysed and quantified through satellite
chlorophyll colour images and compared with the impact of medium icebergs. Based on
the above measurements, a new estimate of the giant icebergs contribution to the carbon
cycle could be estimated.
The results from this research reveal a significant positive contribution of giant
icebergs to the Southern Ocean productivity of greater incidence and magnitude than the
effect of medium icebergs. The extent area of ocean water fertilised by a giant iceberg
could also be shown to be much greater than previous field research estimations and can
be compared to estuaries that supply nutrients to coastal regions. Therefore, it is
suggested here that the physical properties and the nutrient enrichment brought by the

48. 47
giant iceberg to the southern ocean water in favour to phytoplankton growth are of
greater influence than any possible negative disturbance to a pre-existing phytoplankton
community caused by the iceberg transit turbulent mixing. The important fertilisation
promoted by giant icebergs to the Southern Ocean represents not only thrives of the
surrounding pelagic zone but also a significant contribution to the ocean CO2 uptake.
These findings also reinforce the hypothesis that the different rate of iceberg production
during glacial and inter-glacial periods might have influenced the phytoplankton
activity and thus the atmosphere composition and climate in a significant way. The
influence of the freshwater budget released from giant icebergs might also have
impacted the Southern Ocean deep water formation and cannot be ignored in a future
climatologic perspective.
In light of the predictions of an increased rate of iceberg production in the next
centuries due to evidences of a potential collapse of West Antarctica driven by global
warming, the atmospheric carbon dioxide sequestration represents a non-negligible
negative feedback to human-caused forcing to the global weather system, justifying the
logistic and budget costs of further field surveys. Finally, awareness must be raised
about the impact of global warming in response to higher atmospheric greenhouse gas
concentration in the phytoplankton activity as the ocean surface warms favouring
increased water column stratification with less vertical mixing and nutrients recycling
from deep waters. In a more speculative way, as climate crisis develop and mitigation
projects like artificial ocean fertilization becomes politically attractive despite
controversies over its effectiveness for carbon sequestration, further researches about
the primary productivity response and longevity related this kind of human interference
as well as its ecological impacts in the pelagic ecosystem are required.

49. 48
6. Acknowledgments
I thank Grant Bigg for bringing the iceberg debate to my attention and for his
support during the whole period of this research. In particular, I thank Elivane Victor for
the assistance with the statistical analyses. This work was only possible due to NASA
maintenance of their colour image website and due to the efforts of the National Ice
Center and Brigham Young University that has been tracking giant icebergs for the last
decades and providing public access to their database. The currant knowledge of the
topic approached in this work is attributed, among other important ones, to the field
survey around giant icebergs done by Smith et al., 2005, 2009 and remote sensing
analysis work by Schwarz and Schodlok, 2009.

50. 49
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8. Tables
Table 1: chlorophyll concentration datasets
iceberg day latitude longitude [Chl a] before
No.
obs.
[Chl a] 7 days
after
No.
Obs
[Chl a] 30
days after
No.
Obs
Max [chl a]
value
a22a 28/10/2006 -585986 -456423 0.14136 1 1.89388 1 1.52288 1 2.1824
a22a 28/11/2006 -57492 -416698 0.34574 2 2.35475 1 1.35161 1 3.35675
a22a 05/02/2007 -545488 -344006 0.33813 1 0.62695 1 - - 3.19493
a22a 15/03/2007 -526871 -427707 0.87822 2 0.35915 2 - - 2.09073
a34a 15/11/2004 -53.9027 -33.8084 0.33222 2 0.83558 1 0.81751 1 6.67451
a34a 15/12/2004 -52.1784 -31.7132 0.30813 3 0.89188 1 1.78033 2 4.72366
a34a 20/12/2004 -52.1599 -30.9337 0.25694 3 0.82064 2 1.47858 2 3.08586
a34a 05/01/2005 52.5374 -28.8347 0.24179 2 0.80247 1 0.62123 1 2.0605
a34a 12/01/2005 -53.2068 -27.0466 0.46442 1 0.80736 1 - - 1.38094
a34a 22/01/2005 -53.7513 -23.9785 0.32116 1 1.43188 1 - - 2.15383
a34a 27/01/2005 -52.7007 -23.7285 0.42508 1 0.71091 1 0.43505 1 1.20445
a34a 27/02/2005 -51.1638 -20.6876 0.31585 1 0.92117 1 - - 2.03833
a38a 20/10/2003 -56.4158 -41.9121 0.28737 3 3.73841 1 - - 5.34654
a38a 20/11/2003 -56.5192 -33.7628 0.79970 2 1.40688 1 1.83680 1 7.63345
a38a 27/11/2003 -55.5968 -32.4021 0.32530 2 2.44273 1 - - 4.36566
a43a 20/11/2004 -53.6944 -33.0866 0.15222 1 - - 0.75384 1 -
a43a 15/12/2004 -52.1784 -31.7132 0.34388 2 0.95517 1 1.78848 3 2.35645
a43a 22/12/2004 -52.2891 -30.5607 0.28980 3 - - 1.54909 1 -
a52 28/12/2005 -60.6932 -53.2555 0.43807 2 1.27054 1 11.06217 2 -
a52 20/01/2006 -60.7847 -53.1402 0.24792 1 1.59850 1 - - 4.21333
a52 12/02/2006 -58.3672 -49.6094 0.46652 1 2.04675 1 0.66037 1 -
a43f 03/10/2008 -50.4974 -39.3975 0.22310 2 1.23372 1 1.37878 1 2.25677
a43f 21/11/2008 -50.5411 -30.4490 0.69421 1 0.52629 1 0.78895 1 -
a43f 18/12/2008 -50.0866 -28.3198 0.35634 1 0.59301 2 0.47848 2 -
b15b 15/12/2012 -57.1183 -22.3864 0.50120 2 0.91253 1 0.85312 1 -
b15b 08/01/2013 -56.4880 -20.3801 0.54800 1 1.01386 1 1.94083 1 -
b15f 25/09/2012 -55.5023 -34.3530 0.13327 1 0.35371 1 0.30231 1 -
b15f 18/10/2012 -54.1289 -35.3834 0.20355 2 0.24941 3 0.21473 1 -
b15f 18/12/2012 -51.7975 -38.8036 0.53003 2 1.15913 2 3.46400 3 -
b15f 25/02/2013 -50.4700 -30.3208 0.56370 3 1.05543 1 0.33080 1 -
b15f 02/03/2013 -50.2533 -30.3144 0.54254 3 0.95131 2 0.61369 1 2.50345
b15f 01/04/2013 -51.2030 -27.1902 0.45035 1 0.43821 1 - - -
b15j 23/09/2011 -57.4869 -160.2252 0.13916 1 0.14697 3 0.13524 2 -
b15j 10/11/2011 -55.3475 -159.2000 0.13412 1 0.14840 1 - - -
b15j 26/11/2011 -55.8095 -157.8272 0.14423 1 0.31184 2 0.57138 1 0.86934
b15j 29/11/2011 -55.5676 -157.4271 0.18552 1 0.30803 1 - - -
b15j 16/12/2011 -53.5579 -154.2062 0.13520 1 0.64075 1 - - -
b15j 04/01/2012 -54.1199 -146.3361 0.15691 1 0.84264 3 0.12986 1 -
b15j1 26/11/2011 -55.9494 -158.6119 0.16883 1 0.31357 2 - - -

55. 54
b15j1 15/12/2011 -54.3503 -155.1178 0.23929 1 0.47182 1 0.26266 2 1.36433
c19a 05/01/2008 -62.5997 -167.0699 0.31774 1 0.43822 1 0.61329 1 0.87213
c19a 12/01/2008 -62.5717 -166.4437 0.23621 1 0.38480 1 0.34665 1 2.39619
c19a 30/01/2008 -62.6616 -165.9821 0.21788 1 0.31247 1 0.57891 1 0.8471
c19a 09/02/2008 -62.6864 -164.6939 0.31799 1 0.30601 1 0.79329 1 1.35407
c19a 05/03/2008 -62.3823 -162.3218 0.30444 1 0.54026 1 - - 1.05312
c19a 25/12/2008 -58.8650 -116.0686 0.23092 1 0.47000 1 - - 0.98248
c19a 20/03/2009 -59.5224 -99.0411 0.15278 2 0.61146 1 2.35651 1 2.35651
c19c 31/10/2012 -50.3656 -34.6671 0.48906 2 0.42965 1 0.58617 2 -
c19c 25/12/2012 -48.2276 -18.9964 0.33914 1 0.55179 1 - - 1.72564
c19c 24/01/2013 -46.3072 -15.7317 0.28921 2 0.42716 1 0.28734 2 -
c19c 28/02/2013 -45.3633 -5.0798 0.20704 1 0.33830 1 0.16296 1 -
c19c 28/03/2013 -44.5605 -0.2632 0.19003 1 1.54086 3 0.33133 2 -
c26 25/10/2010 -61.4326 157.0739 0.11874 1 0.26989 1 0.61778 1 0.84664
c26 09/11/2010 -61.4689 157.1715 0.14914 1 0.55986 1 1.00291 2 0.78564
c28a 28/02/2013 -61.2277 84.6726 0.42185 1 2.08841 1 0.36146 1 1.40754
d17 19/11/2004 -59.4976 -36.5706 0.12856 1 0.91120 1 - - 2.73579
d17 20/01/2005 -58.3897 -32.8410 0.28883 1 4.49000 1 1.90873 1 6.93297
d17a 22/11/2004 -53.8577 -27.8458 0.14851 1 0.82349 1 0.61500 1 1.49733
d17a 01/12/2004 -54.0285 -25.3905 0.15605 1 1.26462 1 0.59304 1 1.78711
d17a 12/01/2005 -52.0532 -19.7138 0.31958 1 0.65367 1 0.86270 1 2.0304
d18 31/10/2008 -55.7810 -29.8413 0.14265 1 0.32786 1 - - 0.81683
d18 12/11/2008 -55.1063 -29.7995 0.21108 2 0.42490 1 1.83292 2 1.35896
d18 02/12/2008 -54.9674 -26.9457 0.35237 1 0.79929 1 0.65914 1 13.91601
d18 24/12/2008 -57.3649 -23.9908 0.20671 1 0.68812 2 0.92162 1 4.76763
d18 12/01/2009 -57.5550 -20.6693 0.27386 2 0.88452 1 0.37798 2 3.80485

56. 55
Table 2: chlorophyll concentration versus iceberg distance
iceberg day 0km 3km 5km 10km 20km 40km 60km 80km 100km 200km 300km 400km 600km 800km 1000km
a22a 30/10/2006 0.30060 0.34850 0.69000 0.58000 0.76000 0.82000 0.85970 1.08000 0.89000 - 0.45000 - 0.14000 0.14000
c19a 16/01/2008 0.19200 0.19390 0.24400 0.30170 0.26540 0.31490 0.31620 0.27240 - 0.50710 0.63830 0.44290 0.13110 - -
unknown 26/02/2013 0.36460 0.49160 0.61430 0.74280 1.44890 1.99580 1.11050 1.09540 1.54410 2.26040 0.50922 0.22060 - - -
c19a 01/02/2008 0.18660 0.23240 0.24570 0.26470 0.35690 0.43480 0.27490 0.38090 0.39200 0.40770 0.31640 0.33870 0.25580 0.16800 0.16800
a22a 02/11/2006 0.26930 0.39480 0.41280 0.48900 0.57710 0.87120 2.17790 1.77790 1.78220 - 0.17740 0.17740 0.17740 - -
a34a 06/12/2004 0.17740 0.17940 0.18510 0.19470 0.25660 0.68670 - - - - 1.47930 1.65440 2.00300 0.23800 0.23800
b15f 24/12/2012 0.56520 0.92230 0.98230 3.94840 4.29800 8.30100 8.00230 5.58080 3.64670 1.36780 1.04980 - 0.44550 0.41200
c19a 11/02/2008 - - - - 0.45280 1.24170 1.36950 0.89520 0.86140 0.75620 0.59400 0.44330 0.16820 - -
c26 08/12/2010 0.53570 1.08840 1.31500 1.83790 1.95730 1.67220 1.36340 1.38000 1.23050 0.98290 0.20700 - - - -
b15j1 19/12/2011 - - - - 0.25650 0.25590 0.39230 0.67070 0.57660 1.18950 0.39820 0.70320 0.12100 - -
b15f 04/03/2013 0.38290 0.51250 0.56520 0.49380 0.86910 0.80460 1.06710 0.81140 0.89220 0.32660 0.25050 - - - -
c19a 01/03/2013 - - - - 0.61990 0.78310 0.58700 0.45520 0.33060 0.81330 0.35590 0.14920 0.14920 - -
b15f 11/01/2013 0.59400 0.77170 1.30830 2.79220 3.25140 3.87510 8.01110 10.29540 10.99580 2.80870 1.24330 0.19070 0.19070 - -
d18 27/12/2008 0.20780 0.36690 0.49620 0.68810 0.72080 1.05430 1.09650 1.10080 1.03130 0.82060 1.22760 0.49530 0.22480 - -
c19a 11/03/2008 0.21410 0.24800 0.24730 0.34930 0.51120 0.54410 0.68260 0.63340 0.75980 1.47860 0.99650 0.39100 0.16860 - -
c19a 10/02/2008 0.22230 0.22940 0.27080 0.29080 0.30680 0.38480 0.41310 0.45310 0.54190 0.26240 0.27390 0.26860 0.26540 0.16610 -
a52 22/01/2006 0.60360 0.63000 0.64840 0.78510 1.93770 3.89490 1.21290 1.38060 2.60370 1.25380 1.21100 0.12590 - - -
d17a 15/01/2005 - - - - 1.16000 0.92210 0.73380 0.95120 1.38010 0.93880 1.52010 0.99680 0.40280 0.14300 -
c19c 22/12/2012 0.34040 0.39860 0.43530 0.52080 0.65720 0.67650 0.76220 0.84530 0.86500 0.73640 0.52630 0.34040 0.15860 - -
a43f 04/10/2008 0.39200 0.58170 0.66410 0.71930 0.95030 1.17990 0.93460 0.88130 1.22150 2.28870 1.72270 1.37300 0.16390 - -
MEAN 0.34678 0.47438 0.58280 0.93741 1.08070 153568 1.65093 1.62847 1.75252 1.12938 0.75737 0.51946 0.32288 0.21118 0.203

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Table 3: Extent of increased chlorophyll plume
Table 4: Statistical parameters for chlorophyll concentration
Chlr_before Chlr_after7 Chlr_after30
Mean 0.307 0.923 1.126
Standard deviation 0.162 0.790 1.634
Lower 0.119 0.147 0.130
Percentile 25 0.186 0.427 0.378
Median 0.289 0.711 0.659
higher 0.352 1.055 1.479
Maximum 0.878 4.490 11.062
N 65 63 47
Table 5: Comparisons between icebergs from sectors A, B, C and D performed by a
model analysis of variance with one factor (ANOVA)
SITE
p
A B C D
Mean 3.351 1.579 1.330 3.965
0.002
Standard deviation 1.808 0.838 0.594 3.962
Lower 1.204 0.869 0.786 0.817
Percentile 25 2.091 0.869 0.847 1.497
Median 2.721 1.364 1.053 2.383
Percentile 75 4.366 2.503 1.726 4.768
Higher 7.633 2.503 2.396 13.916
N 18 3 11 10
iceberg Extent (km)
No
obs.
a22a 767 3
A34a 1092 6
A52 450 1
A43f 800 1
B15j 1150 2
B15j1 879 2
B15f 506 3
C26 1050 2
C19a 1025 6
D17a 1300 1
D18 725 2
D17 570 1
MEAN 859,5

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9. Supplementary Material
Supplementary Material 1: MODIS technical information:
Orbit: 705 km, 10:30 a.m. descending node (Terra) or 1:30 p.m. ascending node (Aqua), sun-
synchronous, near-polar, circular
Scan Rate: 20.3 rpm, cross track
Swath Dimensions: 2330 km (cross track) by 10 km (along track at nadir)
Telescope: 17.78 cm diam. off-axis, afocal (collimated), with intermediate field stop
Size: 1.0 x 1.6 x 1.0 m
Weight: 228.7 kg
Power: 162.5 W (single orbit average)
Data Rate: 10.6 Mbps (peak daytime); 6.1 Mbps (orbital average)
Quantization: 12 bits
Spatial Resolution: 250 m (bands 1-2)
500 m (bands 3-7)
1000 m (bands 8-36)
Design Life: 5 years
Primary Use Band Bandwidth1
Spectral
Radiance2
Required
SNR3
Land/Cloud/Aerosols
Boundaries
1 620 - 670 21.8 128
2 841 - 876 24.7 201
Land/Cloud/Aerosols
Properties
3 459 - 479 35.3 243
4 545 - 565 29.0 228
5 1230 - 1250 5.4 74
6 1628 - 1652 7.3 275
7 2105 - 2155 1.0 110
Ocean Color/
Phytoplankton/
Biogeochemistry
8 405 - 420 44.9 880
9 438 - 448 41.9 838
10 483 - 493 32.1 802
11 526 - 536 27.9 754

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12 546 - 556 21.0 750
13 662 - 672 9.5 910
14 673 - 683 8.7 1087
15 743 - 753 10.2 586
16 862 - 877 6.2 516
Atmospheric
Water Vapor
17 890 - 920 10.0 167
18 931 - 941 3.6 57
19 915 - 965 15.0 250
Surface/Cloud
Temperature
20 3.660 - 3.840 0.45(300K) 0.05
21 3.929 - 3.989 2.38(335K) 2.00
22 3.929 - 3.989 0.67(300K) 0.07
23 4.020 - 4.080 0.79(300K) 0.07
Atmospheric
Temperature
24 4.433 - 4.498 0.17(250K) 0.25
25 4.482 - 4.549 0.59(275K) 0.25
Cirrus Clouds
Water Vapor
26 1.360 - 1.390 6.00 150(SNR)
27 6.535 - 6.895 1.16(240K) 0.25
28 7.175 - 7.475 2.18(250K) 0.25
Cloud Properties 29 8.400 - 8.700 9.58(300K) 0.05
Ozone 30 9.580 - 9.880 3.69(250K) 0.25
Surface/Cloud
Temperature
31 10.780 - 11.280 9.55(300K) 0.05
32 11.770 - 12.270 8.94(300K) 0.05
Cloud Top
Altitude
33 13.185 - 13.485 4.52(260K) 0.25
34 13.485 - 13.785 3.76(250K) 0.25
35 13.785 - 14.085 3.11(240K) 0.25
36 14.085 - 14.385 2.08(220K) 0.35
1
Bands 1 to 19 are in nm; Bands 20 to 36 are in µm
2
Spectral Radiance values are (W/m2
-µm-sr)
3
SNR = Signal-to-noise ratio
4
NE(delta)T = Noise-equivalent temperature difference
Note: Performance goal is 30-40% better than required
Source: NASA, 2014

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Supplementary Material 2: SeaWiFS technical information:
Instrument Bands
Band Wavelength
1 402-422 nm
2 433-453 nm
3 480-500 nm
4 500-520 nm
5 545-565 nm
6 660-680 nm
7 745-785 nm
8 845-885 nm
Mission Characteristics
Orbit Type Sun Synchronous at 705 km
Equator Crossing Noon +20 min, descending
Orbital Period 99 minutes
Swath Width 2,801 km LAC/HRPT (58.3 degrees)
Swath Width 1,502 km GAC (45 degrees)
Spatial Resolution 1.1 km LAC, 4.5 km GAC
Real-Time Data Rate 665 kbps
Revisit Time 1 day
Digitization 10 bits
Source: NASA, 2014