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Title: Do Indian and Pacific Ocean carbon isotopes reflect a change in the biological
pump during the last deglaciation (~19,000-‐11,000 years ago)?
Authors: Aaron Rachels1, Emma Gleeman1, Andreas Schmittner2
Affiliations: 1Brown University; Department of Earth, Environmental, and Planetary Sciences;
Providence, Rhode Island 2Oregon State University; College of Earth, Oceanic, and Atmospheric Sciences;
Corvallis, Oregon
Abstract:
The biological pump is a significant control on atmospheric carbon dioxide
levels due to its ability to sequester carbon in the deep ocean. δ13C measured from
the carbonate shells of benthic foraminifera serves as a proxy for biologic pump
efficiency, so analyzing these carbon isotope ratios from sediment cores can provide
information about the pump’s efficiency in the past. Here we compile and analyze
δ13C data from the Pacific and Indian Ocean basins during the last deglaciation
(19,500 to 10,500 years ago), when atmospheric carbon dioxide levels increased
about 80 ppm. Hundreds of published results and unpublished data were assembled
into a database and selected for records of at least millennial time resolution. The
resulting 59 records were analyzed in 8 regions, divided based on depth
(intermediate [< 2 km] and deep [> 2 km]), and geographic location (North-‐,
Equatorial-‐, and South-‐Pacific and Indian Ocean). Overall, in both basins, δ13C
increased throughout the deglaciation, indicating that the deglacial biological pump
became more inefficient.
Introduction:
During the last deglaciation, atmospheric carbon dioxide increased by about
80 ppm (Marcott et al., 2014). However, the mechanisms for this increase are not
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well understood. Schmittner and Lund (2015) using compiled δ13C data from the
Pacific and Indian ocean basins and modeling suggest that a shutdown of the
Atlantic Meridional Overturning Circulation (AMOC) could have altered the
efficiency of the biological pump as one potential mechanism that could explain the
initial CO2 rise from the late Last Glacial Maximum (20,000 years ago) to the
Heinrich Stadial event 1 (HS1; 18-‐15,000 years ago). However, only a few
datapoints from these ocean basins were included in their data compilation.
The biologic pump is an important control on atmospheric carbon dioxide
levels because it enables sequestration of carbon at ocean depths. Understanding
the interplay between biological pump efficiency, ocean circulation, and
atmospheric carbon dioxide levels during the last deglaciation would both enable a
more comprehensive understanding of how changes in global paleoclimate lead to
modern-‐day climate and provide a more solid foundation for post-‐Last Glacial
Maximum (LGM) paleoclimate work in the future.
Carbon isotopes are usually reported as δ13C, equal to !"!!"! !"#$%&!"!!"! !"#$%#&%
− 1 ∗
1000 ‰, which is a ratio of the two stable isotopes of carbon normalized to a
standard. It can be used as a proxy for biologic efficiency due to photosynthetic
fractionation. The global mean of δ13C in oceanic surface water is about 2‰ while
phytoplankton fractionate the light carbon isotope during photosynthesis at a ratio
of about -‐21‰ (Schmittner et al. 2013). When these phytoplankton die and sink to
depths, they are respired and their carbon is released back into the water, meaning
that δ13C at ocean depths is dependent on the efficiency of photosynthetic activity at
the surface.
In addition to δ13C, many models and proxies of various data types suggest a
biological pump reduction during the last deglaciation. Galbraith and Jaccard (2012)
examine fractionation of nitrogen isotopes to conclude that reduced biologic
efficiency diminished deep-‐ocean carbon storage, while Schmittner and Galbraith
(2008) use paleoclimate modeling to suggest that an AMOC shutdown could result
in a less efficient biological pump and in turn, a 20-‐30 ppm increase in atmospheric
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CO2 concentrations. If true, this mechanism would be reflected in a δ13C increase in
the Southern, Indian, and Pacific oceans during the early part of the deglaciation
from 19-‐15 kyr BP (Schmittner and Lund, 2015). This inefficient pump could have
been caused through the following mechanism: if downwelling of nutrient-‐poor
NADW decreases, upwelling of nutrient-‐rich water must also decrease at other
locations, hindering biologic efficiency. As a result, concentrations of preformed
nutrients (such as phosphate) increase at depth while remineralized nutrient
concentrations decrease; the sum of the two remains constant on short time scales,
as the residence time of phosphate is estimated to be about 20,000 years or greater
(Paytan and McLaughlin, 2007). Furthermore, remineralized phosphate
concentrations and δ13C are anticorrelated due to the preferential fractionation of
light carbon isotopes by plankton during photosynthesis (in which phosphate is also
consumed). Thus, a decrease in biologic pump efficiency should be accompanied by
both a decrease in remineralized phosphate concentrations and an increase in δ13C.
This signal can best be measured in benthic foraminifera in the sedimentary record
that incorporate carbon into their carbonate shells with little to no isotopic
fractionation (Dunbar and Wefer, 1984; Duplessy et al. 1984). Oliver et al. (2010)
provide a synthesis of marine sediment core proxies over the past 150,000 years,
among them δ13C, and these data do indicate an increase in Pacific and Indian δ13C
during the deglaciation. However, here we will attempt to study changes at higher
temporal (millennial) resolution and increase the number of analyzed cores,
resulting in a more comprehensive analysis.
To detect whether a deglacial increase in δ13C does in fact occur, thousands of
data points gathered from a plethora of cores will be analyzed to ensure that any
observed trend is regional, not local. Thus, since δ13C data from many individual
publications exist, a compilation and analysis of them all would be best suited to
answer the research question. As a result, the research goals intersect with a much
larger project, the Ocean Circulation and Carbon Cycling project (OC3). The goal of
this project is to compile the largest existing δ13C dataset on record, synthesizing
both published results and unpublished data. This would provide new evidence,
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using δ13C data from benthic foraminifera, concerning the efficiency of the biological
pump and atmospheric CO2 levels at many different times in many different
locations. Thus, through this project, one control on paleo-‐atmospheric carbon
dioxide concentrations can be more thoroughly understood, but more importantly, a
foundation for future paleoclimate analyses and a baseline OC3 dataset will be
established.
Materials and Methods:
The first task in this project was to determine the best method to not only
address the scientific question at hand, but to form an accessible and simple
database. Future scientists who desire either to add data to the OC3 project or use
its data should have no trouble doing so. The chosen format for such a database was
an Excel spreadsheet; such a format is easy to use and format. Furthermore, should
any analyses of OC3 data require more analytical software, transferring data to
another file type is not difficult.
The spreadsheet itself, available in Supplementary Materials #1, was
structured to contain data from all available sites in one spreadsheet; these can be
found in the “Master” tab. Within this tab, information concerning location, core
depth, age model, age model resolution, foraminifera species, δ13C, δ18O, and
citation/original source are available for each site. This encompasses many types of
filters that could be used to analyze paleoclimatic isotope data. If any additional
data were required for any given site, it would need to be located in the original
publication (provided in the “Author” column).
Another goal for this database, in addition to size and accessibility, was to
make age models easily updatable, so that its scientific rigor could continue to
improve as age models do. To accomplish this, the database was structured so that
each individual core has two tabs: a data tab and an age tab, consistent with a
community wide agreement within OC3. The information in each data tab is
identical to the information contained in the Master tab; the data is just subdivided
by site. Each age tab however contains information about how the age model was
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generated; examples of this are radiocarbon tie-‐lines, δ18O alignment, or simply
source information. These tabs are crucial to the goal of updatable age models;
whenever anybody uses data from a specific site and has an improved age model,
the age model in the data (and Master) tabs should be updated while information on
the changes made should be recorded in the age tab. Through this method, the OC3
database will not become obsolete.
The data compiled into the OC3 database at this time primarily comes from
five sources: Olivier Cartapanis, NOAA, Pangaea, Andreas Schmittner, and Lorranie
Lisiecki. The Cartapanis data was originally in the form of a Matlab database
comprised of data from NOAA and Pangaea submitted prior to 2011. This data, and
the script which extracted the data from Matlab to an Excel spreadsheet form, are
available in Supplementary Materials #2. These data comprise the plurality of the
OC3 database at this time, although much work needs to be done improving and
locating age models. Since the Cartapanis database only had data published up to
2011, the NOAA database was additionally mined for carbon and oxygen isotope
datasets post-‐dating 2011. Age models from these data are generally quite precise
and come from the initial publication source.
The final two data sources came from two paleoclimate researchers, Andreas
Schmittner and Lorraine Lisiecki, who have specifically focused on the transition
after the Last Glacial Maximum. The Lisiecki data also has internally consistent age
models whose δ18O time series have been aligned, making them a valuable tool for
generating age models for other sites in the database. While the combination of all
four data sources did result in some duplicate data sets being added to the OC3
database, whenever a duplicate is found it is deleted. Additionally, if duplicate data
sets were present with different age models, both age models are provided in that
site’s age tab.
Upon the addition of the data from all four sources, the OC3 Master Tab had
112,710 data points spanning 495 different drilling sites (Figure 1). However, to
conduct analyses of the Pacific and Indian ocean basins, Excel’s sort function was
used to extract the applicable data. First, the data was sorted by Age (Column K) and
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all of the data which fell between 10,000 and 20,000 years ago were copied into a
new spreadsheet. Then, the data were sorted by longitude to most easily eliminate
the sites in the Atlantic Ocean. 70°W and 30°E were used as initial cutoffs, but each
site’s location was manually checked to eliminate points in the Gulf of Mexico and
various European seas. Finally, the data were sorted by age resolution and any data
set with a resolution lower than a millennial time scale was eliminated, as that
resolution would be too coarse for these analyses. After this filtration, data from 59
sites remained (Figure 2).
To best analyze these data for global shifts in carbon isotope ratios, averages
were calculated, as the examination of 59 individual time series would have been
needlessly tedious and difficult to succinctly describe. However, different parts of
the ocean basins behave differently due to circulation patterns; there may be
variations with respect to site depth as well. Thus, the data were divided into eight
different regions according to both their geographic location and depth. The
geographic subdivisions were the North Pacific, Equatorial Pacific, South Pacific, and
Indian; each geographic subdivision was further divided into both an intermediate
zone (<2000 meters depth) and a deep zone (>2000 meters depth). Within each
zone, averages of available time series should reflect the widespread change in that
area during the last deglaciation.
To average the time series, each individual site’s data set was first averaged
to 1000 year intervals: 10,500 years ago, 11,500 years ago… 19,500 years ago. As a
result, each individual site was reduced to a ten-‐point data set with identical ages.
Then, the “site-‐specific averages” within a single zone were averaged to form a
“zone-‐specific average.” It is these zone-‐specific averages that should shed light on
the changes in biologic pump efficiency beginning 19,500 years ago.
Results:
The current magnitude of the OC3 database enables analyses with sizable
data densities. However, as shown in Figure 1, the Pacific and Indian Oceans are not
nearly as well sampled as the Atlantic. A potential reason for this is one of the most
reliable genera of foraminifera, cibicoides, does not thrive as well in the more acidic
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bottom water of the Pacific and Indian oceans. Thus, after dividing the applicable
data into zones (Figures 3 and 4), the data density within some of these zones is
relatively low, such as in the Southern and Equatorial Intermediate Pacific.
Conversely, some areas are quite well sampled, such as in the Equatorial Deep
Pacific. Regardless, a goal of OC3 is for the database to be dynamic, not static, so
while at this time the data density within some of these zones is not ideal, they will
only improve with time. Despite sporadic low data density, after calculating the
zonal average time series, a clear trend became apparent; an increase in δ13C
through the deglaciation.
In the Intermediate North Pacific (INP), this trend with an increase of about
0.5 permil is especially apparent at the beginning of the deglaciation from 19.5 to
16.5 ka, before roughly equilibrating by the end of Heinrich-‐Stadial 1 (Figure 5). An
increase in δ13C is also observable in the Deep North Pacific (DNP) (Figure 6)
throughout the entire time series. The magnitude of these changes, both with
respect to the initial change (19,500-‐16,500 years ago) and the total change
(19,500-‐10,500 years ago) is shown in Table 1. The standard deviation of the trends,
calculated as the standard deviation of the trend from all individual records, is also
provided in this figure; in most cases the standard deviation is smaller than the
observed trend, indicating a significant trend. In the future, as more data is added to
the database, standard deviations may decrease further.
In the Intermediate and Deep Equatorial Pacific (IEP, DEP) (Figures 7 and 8,
respectively), the increase in δ13C over the entire deglaciation can again be
observed, although in the IEP most of that increase (~0.2 permil) appears in the
early deglaciation before year 16,000, whereas in the DEP most of the increase of
~0.15 permil occurs late after year 14,000. However, a δ13C decrease in the IEP
occurs from 16,500 to 12,500 years ago, with the largest decrease of 0.1 permil
around year 13,000, contrary to the overall observed increasing trends. This
anomaly could perhaps be due to the low data density within this zone, but a
potential explanation for this will be discussed below.
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The Intermediate and Deep South Pacific (ISP, DSP) (Figures 9 and 10,
respectively) also show a gradual increase in δ13C throughout the majority of the
deglaciation. As shown in Table 1, the magnitude of the change is also particularly
large in these zones (~0.5 to 0.7 permil) and most of that increase happens in the
early part of the deglaciation. Finally, an increase in δ13C is recorded in the
Intermediate and Deep Indian (II, DI) (Figures 11 and 12, respectively) as well,
although the increase is quite small in the II. Similar to the IEP and ISP, a temporary
δ13C decrease is observed in the II, although this increase occurs a couple thousand
years earlier. Again, while this trend could be accurate, the II is another zone with
particularly low data density, meaning the trends observed in a small number of
sites cannot be assumed to be representative of the entire region with the same
degree of confidence.
In summary, while there are a couple of slight deviations from the overall
trend, a basin-‐wide increase in δ13C is apparent in both the Pacific and Indian
oceans. This trend seems to be most extreme in the early part of the deglaciation,
but is mostly continuous throughout particularly in the deep, whereas some of the
intermediate zones show a temporary decrease. All of the time series can be
observed together in Figure 13.
Discussion:
δ13C is a proxy for biologic pump efficiency, so these results directly display
how deglaciation affected it. The data clearly shows basin-‐wide trends to a more
inefficient biological pump; in turn, this would enable less carbon sequestration at
ocean depths, which would likely accelerate deglaciation. This is a potential
explanation for why, in some zones, the biological pump becomes drastically more
inefficient in the first 3,000 years, and then gradually more inefficient throughout
the remaining 7,000.
While the data clearly show the trend throughout, the triggering mechanism
may remain somewhat nebulous. However, as previously outlined, the changes
displayed in these graphs are contemporaneous with the initial ice sheet melt and
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resultant AMOC shutdown/reduction during HS1. Based on known ocean circulation
patterns-‐ the Pacific Ocean receives about half its bottom water from the Atlantic
and half from the Southern Ocean-‐ the biological pump did not just become more
inefficient in the Pacific/Indian Ocean, but globally. Thus, the AMOC shutdown
during HS1 may have acted as the triggering mechanism for continued deglaciation
and biologic inefficiency, while not definitively proven by this data, is certainly
supported.
Two more subtle trends presented in the data, beyond the general global δ13C
increase, merit further examination. First, additionally indicating the likelihood the
AMOC shutdown, the magnitudes of the δ13C increases during the first 2-‐3 thousand
years of the deglaciation align rather closely with the modeled changes presented by
Schmittner and Lund (Figure 15). Figure 14 shows change of about .2-‐.3 for the
majority of the Pacific with a more drastic increase of .56 in the INP, which is
approximately what Figure 15 predicts. As the OC3 database grows and its data
density increase, a contour map based solely on actual data, not modeled, will be
able to be generated and compared more rigorously to the Schmittner/Lund model.
Furthermore, the II, ISP, and IEP actually show a temporary decrease in δ13C
about midway through the deglaciation. This is slightly puzzling, especially since a
similar trend is not observed in surrounding zones, making a relatively local
decrease more difficult to explain. However, one potential explanation may be that
the AMOC vigorously restarted about 14,500 years ago. Such a restart would have
caused opposite trends to those shown in Fig. 15 and Table 1 and may be consistent
with the observed decreases in the II, ISP, and IEP. The data in these three regions
suggest that this could have happened, but data density needs to be significantly
increased to support this with any certainty.
Conclusions: In conclusion, these results are an exciting first step in using the compiled
database to better understand the changes that occurred during the last
deglaciation. These data are some of the most temporally and spatially thorough to
have ever been analyzed during this time frame. However, as is the goal of OC3, the
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density and age models of these data will have to be improved to enable more
rigorous analyses in the future. Furthermore, this paper did not analyze the most
thoroughly sampled basin, the Atlantic, which is the topic of the companion paper
by Gleeman et al. In sum, this analysis is likely just the first of many more analyses
to come as a result of OC3’s goals.
However, to improve the quality of these analyses, the database requires
additional work. The biggest obstacle in this project is age model consistency; since
different scientists applied age models to most of these cores, accurately comparing
them temporally is quite difficult. Thus, they need to be normalized to internally
consistent age models.
Two factors will make this possible: radiocarbon tie-‐lines and alignment of
δ18O. In general, radiocarbon analyses are quite expensive and therefore relatively
scarce. They often help with constraining age models, but in the case of this
database, it is nearly impossible to base entire age-‐models solely upon them. Thus,
alignment of δ18O will have to be the primary mechanism used. The fractionation of
oxygen isotopes is dependent both on ice sheet volume and temperature, both of
which hold relatively constant controls across the “zones” formed in this project.
Thus, within a zone, δ18O time series of different cores should align closely if the
cores’ age models are consistent. Some δ18O alignment was used in this project to
ensure the validity of the data; Figure 15 is an example of the δ18O taken from all the
cores in the DNP region. This figure highlights some of the difficulties facing age
model consistency checks; while these curves may roughly align, the data sets are all
have different resolutions and are from different locations. In the case of these
analyses, cores with egregiously terrible alignment were discarded through a simply
visual check. However, a tool much more advanced than simple spot checks is
necessary. Tuning all age models in the OC3 database to be internally consistent is
vital to the database’s value and growth.
The key to do this will be Match software (Lisiecki and Herbert, 2007), the
purpose of which is to take δ18O time series of different cores and to align their age
models. Note that every single site in the OC3 database has both carbon and oxygen
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isotope data, so it is possible to use this software on every single core. Some of the
OC3 database has already had this alignment applied to it; these sites are
highlighted in a pale green and have a source of “Peterson.” However, the large
majority of this database still needs the Match software application. This will not
only greatly increase the size of data with age models in the database, but improve
the quality of the age models already present as well.
Acknowledgements: I thank the National Science Foundation and Oregon State University REU
program for providing me with the funding and opportunity for this summer research
project. I would also like to thank Olivier Cartapanis, Lorraine Lisiecki, and Caryle
Peterson for their contributions to the OC3 database. I would finally like to thank
Andreas Schmittner not only for his data contributions, but also for his guidance and
advising in the entirety of this project.
References: Dunbar, R. B., & Wefer, G. (1984). Stable isotope fractionation in benthic foraminifera from the Peruvian continental margin. Marine Geology, 59(1), 215-225. Duplessy, Jean-Claude, et al. "13 C record of benthic foraminifera in the last interglacial ocean: Implications for the carbon cycle and the global deep water circulation." Quaternary Research 21.2 (1984): 225-243. Jaccard, S. L., & Galbraith, E. D. (2012). Large climate-driven changes of oceanic oxygen concentrations during the last deglaciation. Nature Geoscience,5(2), 151-156. Lisiecki, L.E., and T.D. Herbert (2007), Automated composite depth scale construction and estimates of sediment core extensions, Paleoceanography, 22, PA4213, doi:10.1029/2006PA001401.
Marcott, Shaun A., et al. "Centennial-scale changes in the global carbon cycle during the last deglaciation." Nature 514.7524 (2014): 616-619. Oliver, K. I., Hoogakker, B. A., Crowhurst, S., Henderson, G. M., Rickaby, R. E. M., Edwards, N. R., & Elderfield, H. (2010). A synthesis of marine sediment core δ 13 C data over the last 150 000 years. Climate of the Past, 6, 645-673.
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Paytan, A., & McLaughlin, K. (2007). The oceanic phosphorus cycle. Chemical Reviews, 107(2), 563-576.
Schmittner, A., & Galbraith, E. D. (2008). Glacial greenhouse-gas fluctuations controlled by ocean circulation changes. Nature, 456(7220), 373-376. Schmittner, A., Gruber, N., Mix, A. C., Key, R. M., Tagliabue, A., & Westberry, T. K. (2013). Biology and air–sea gas exchange controls on the distribution of carbon isotope ratios (δ 13 C) in the ocean. Biogeosciences, 10(9), 5793-5816.
Schmittner, A., & Lund, D. C. (2015). Early deglacial Atlantic overturning decline and its role in atmospheric CO 2 rise inferred from carbon isotopes (δ 13 C).Climate of the Past, 11(2), 135-152. Stern, J. V., & Lisiecki, L. E. (2014). Termination 1 timing in radiocarbon‐dated regional benthic δ18O stacks. Paleoceanography, 29(12), 1127-1142. Figures and Tables:
Figure 1: The above �gure shows the coordinates of every site currently in the OC3 database.
Figure 2: The above �gures shows the coordinates of all the sites with high-resolution data from the Paci�c and Indian oceans during the last deglaciation.
-6000
-5000
-4000
-3000
-2000
-1000
0 -60 -40 -20 0 20 40 60
LatitudeSite D
epth (m)
DSP
ISP
DEP
IEP INP
DNP
Figure 3: The above �gure shows the distribution of remaining sites after the entire OC3 database was �ltered for high-resolution Paci�c data spanning 20,000 to 10,000 years ago.
-3500
-3000
-2500
-2000
-1500
-1000
-500
0 -50 -40 -30 -20 -10 0 10 20
Dep
th (
m)
Latitude
DI
II
Figure 4: The above �gure shows the distribution of remaining sites after the entire OC3 database was �ltered for high-resolution Indian data spanning 20,000 to 10,000 years ago.
Figure 5: The above �gure shows the changes in d13C during the deglaciation in the Intermediate North Paci�c. The increase was initial quite large, before leveling out to a more gradual rate of change.
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000
d13C
Years BP
Intermediate North Pacific
Figure 6: The above �gure shows the changes in d13C during the deglaciation in the Deep North Paci�c. The increase is of relatively small mangitude and gradual.
-1.2
-1.15
-1.1
-1.05
-1
-0.95
-0.9
-0.85
-0.8 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000
d13C
Years BP
Deep North Pacific
Figure 7: The above �gure shows the changes in d13C during the deglaciation in the Deep North Paci�c. The increase is steady until about 15,500 years until beginning to decrease.
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000
d13C
Years BP
Intermediate Equatorial Pacific
Figure 8: The above �gure shows the changes in d13C during the deglaciation in the Deep Equatorial Paci�c. The increase is relatively steady throughout the deglaciation, but takes a couple thousand years to begin.
-0.4
-0.35
-0.3
-0.25
-0.2
-0.15
-0.1 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000
d13C
Years BP
Deep Equatorial Pacific
Figure 9: The above �gure shows the changes in d13C during the deglaciation in the Intermediate South Paci�c. The increase is of greater magnitude initially, but shows a slight increase at about 14,000 years ago (contemporaneous with
the potential AMOC restart).
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000
d13C
Years BP
Intermediate South Pacific
Figure 10: The above �gure shows the changes in d13C during the deglaciation in the Deep South Paci�c. The increase is of greater magnitude initially, before �uctuating much more unpredictably starting at 14,000 years ago.
-1.4
-1.3
-1.2
-1.1
-1
-0.9
-0.8
-0.7
-0.6 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000
d13C
Years BP
Deep South Pacific
Figure 11: The above �gure shows the changes in d13C during the deglaciation in the Intermediate Indian. The increase is very slight for the �rst few thousand years and temporairily increases about 16,000 years ago.
0.2
0.21
0.22
0.23
0.24
0.25
0.26
0.27
0.28 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000
d13C
Years BP
Intermediate Indian
Figure 12: The above �gure shows the changes in d13C during the deglaciation in the Deep Indian. The increase is fairly consistent throughout the deglaciation.
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000
d13C
Years BP
Deep Indian
Figure 13: The above �gure shows the d13C time series from all of the latitudinal/depth zones. While each individual time series may have unique characteristics, the clear trend is an increase in d13C throughout the time period.
-2
-1.5
-1
-0.5
0
0.5
1 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000
d13C
Years BP
All Sites: d13C Time Series (20,000-10,000 Years BP)
North Pacific (I)
North Pacific (D)
Equatorial Pacific (I)
Equatorial Pacific (D)
South Pacific (I)
South Pacific (D)
Indian (I)
Indian (D)
Figure 15: The above �gure is taken from (Schmittner and Lund, 2015). These are modeled changes in each ocean basin 2500 years after the deglaciation. Note the signi�cant increase in the INP is consistent with Figure 5.
Figure 16: The above �gure shows all of the d18O time series from the INP region. As can be seen, the curves are in apprxoaimte alignment, but on a �ner scale these cruves represent part of the important work that lies ahead on the OC3 database.
2
2.5
3
3.5
4
4.5
5
5.5
9000 11000 13000 15000 17000 19000 21000
d18O
Years Before Present
d18O Time Series: Intermediate North Pacific
AHF16832
EW9504-05
HLY0202-51J
EW9504-9
ODP167-101
KT90-921
Changes in d13C
Table 1: The above �gure shows changes in d13C in di�erent ocean “zones”across two di�erent time frames: the entire deglaciation (19,5000-10,500 Years BP) and the very beginning of the deglaciation (19,500-16,500 Years BP).
