EPSL-10546; No of Pages 14
Earth and Planetary Science Letters xxx (2010) xxx–xxx
Contents lists available at ScienceDirect
Earth and Planetary Science Letters
j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / e p s l
Progress toward a multi-basin calibration for quantifying deep sea calcite
preservation in the tropical/subtropical world ocean
Figen Mekik ⁎, Nathan Noll, Mary Russo
Grand Valley State University, Department of Geology, Allendale, MI 49507, United States
a r t i c l e
i n f o
Article history:
Received 16 April 2010
Received in revised form 16 August 2010
Accepted 17 August 2010
Available online xxxx
Editor: P. DeMenocal
Keywords:
deep sea
calcite preservation
multi-basin calibration
a b s t r a c t
The development of a quantitative and multi-basin deep sea calcite preservation proxy is important for a
better understanding of the marine carbonate system, both in modern sediments and in down core work. We
present CaCO3 preservation data from 89 core top samples in tropical and subtropical areas of the Pacific,
Atlantic and Indian Oceans. We observe that the Globorotalia menardii Fragmentation Index (MFI) has
potential to trace deep sea CaCO3 dissolution quantitatively across the tropical/subtropical world ocean. This
is evident in the robust relationship between MFI and bottom water calcite under-saturation in six depth
saturation states. We also found a strong relationship
transects with markedly different bottom water CO2−
3
between MFI and modeled estimates of percent CaCO3 dissolved at our core top locations. Corroboration for
MFI's dissolution trend in the subtropics comes from the concurrent drop in size-normalized shell weight of
under-saturation, and the dissolution driven
Globorotalia truncatulinoides specimens with increasing CO2−
3
drop in Mg/Ca measurements from tests of three foraminifer species: G. truncatulinoides, Globigerina
conglobatus and Globorotalia hirsuta. MFI's core top calibration is further corroborated by the fragmentation
trend of foraminifers that prefer higher latitudes like Globigerina bulloides, G. truncatulinoides and
Neogloboquadrina pachyderma. Our observations suggest that MFI is coming closer to meeting the criteria
for an ideal calcite dissolution proxy with world-wide applicability.
© 2010 Elsevier B.V. All rights reserved.
1. Introduction
Estimating the amount of calcite preserved in deep sea sediments
requires developing a reliable, accurate and multi-basin proxy. Such
a proxy, or a set of proxies well correlated with one another, is
crucial for understanding the marine carbonate system, both in
modern sediments and in down core work, because the dissolution
of carbonates in deep sea sediments is an integral part of the global
ocean's buffering mechanism, and its ability to regulate atmospheric
pCO2 over millennia (Broecker, 1971; Archer and Maier-Reimer,
1994; Archer et al., 2000). Other factors governing the marine
carbonate system include the influx of ions into the ocean as
weathering products from land, air–sea gas exchange, the marine
biological pump, and the flux of organic carbon and calcite to the
seabed.
Deep sea CaCO3 dissolution is driven mainly by three factors:
[1] the degree of calcite under-saturation of bottom waters (we will
2+
] is generally
refer to this parameter as [CO2−
3 ] hereafter because [Ca
]
is
variable
and generally
uniform throughout the ocean but [CO2−
3
decreases with increasing water depth), [2] the amount of organic
carbon raining down into the sediments, and [3] the amount of calcite
⁎ Corresponding author. Tel.: +1 616 331 3020.
E-mail address: mekikf@gvsu.edu (F. Mekik).
flux reaching the sediments. Bottom water [CO2−
3 ] is often expressed
2−
2−
as ΔCO2−
3 which is the difference between [CO3 ] in situ and [CO3 ] at
the calcite saturation horizon. The rate of CaCO3 dissolution in
of bottom waters and organic
sediments is sensitive to both ΔCO2−
3
carbon fluxes; while percent CaCO3 dissolved within sediments is
determined by the dissolution rate and CaCO3 flux (e.g. Berger, 1970).
of bottom waters and/or increasing organic
Decreasing the ΔCO2−
3
carbon flux to the seabed drives the carbonate system toward
dissolving CaCO3 in the sediments; and increasing CaCO3 flux acts as
a buffer to drive the carbonate system toward preserving CaCO3.
Therefore, the ratio of organic carbon flux to CaCO3 flux (rain ratio) is
an important parameter in quantifying calcite dissolution in deep sea
sediments (Archer and Maier-Reimer, 1994).
Quantifying deep sea CaCO3 dissolution, however, has been a
challenging problem for oceanographers for decades (e.g. Arrhenius,
1952; Berger, 1973; Broecker, 1982; Archer and Maier-Reimer,
1994; Mekik et al., 2002; Mekik and Raterink, 2008). The challenges
mostly stem from influences of other oceanographic parameters on
available CaCO3 dissolution proxies, uncertainties in accurately
estimating bottom water [CO2−
3 ] for specific locations, and difficulties in incorporating the ratio of organic carbon to CaCO3 flux in
calculations of percent CaCO3 dissolved due to the paucity of deep
sea sediment trap data. The latter problem yields proxies that are not
reliable in regions where surface ocean productivity is high and the
rain ratio is variable. Also, Henrich et al. (2003) observe that many
0012-821X/$ – see front matter © 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.epsl.2010.08.024
Please cite this article as: Mekik, F., et al., Progress toward a multi-basin calibration for quantifying deep sea calcite preservation in the
tropical/subtropical world ocean, Earth Planet. Sci. Lett. (2010), doi:10.1016/j.epsl.2010.08.024
2
F. Mekik et al. / Earth and Planetary Science Letters xxx (2010) xxx–xxx
qualitative dissolution proxies have limited applicability outside
their calibration regions.
Most CaCO3 dissolution indicators use tests of foraminifers. Some
approaches include comparing the number of fragmented foraminifer
tests to the number of whole tests in a sediment sample aliquot (e.g.
Peterson and Prell, 1985a,b; Le and Shackleton, 1992, Mekik et al.,
2002; Loubere and Chellappa, 2008); measuring loss in sizenormalized whole foraminifer shell weight (SNSW) as a result of
dissolution in the sediments (Lohmann, 1995, Broecker and Clark,
2001a,b); and/or measuring changes in the Mg/Ca of foraminifer tests
through dissolution (Brown and Elderfield, 1996, Rosenthal et al.,
2000; Dekens et al., 2002; Rosenthal and Lohmann, 2002; Mekik and
François, 2006; Mekik et al., 2007a). However each approach comes
with its caveats. Not every species of foraminifers bear tests that
fragment quantifiably with increasing dissolution; foraminifer test
thickness and therefore weight can be influenced by the [CO2−
3 ] of
foraminifer habitat waters (e.g. Bijma et al., 1999; Barker and
Elderfield, 2002; Naik and Naidu, 2007; Mekik and Raterink, 2008);
and the Mg/Ca in foraminifer shells is strongly influenced by the
temperature (e.g. Dekens et al., 2002; Anand et al., 2003) and even
salinity (Kisakürek, 2008) of foraminifer habitat waters.
Quantifying bottom water [CO2−
3 ] is difficult for individual core top
locations because extrapolations need to be made from bottle data
amassed through oceanic expeditions with limited ocean floor
coverage, such as GEOSECS (Levitus, 1982; Levitus et al., 1993 ) or
GLODAP (Key et al., 2004; Sabine et al., 2005).
While most proxies calibrate dissolution-related changes in
foraminifer shells to the [CO2−
3 ] of bottom waters (e. g. Broecker
and Clark, 2001a,b; Dekens et al., 2002; Marchitto et al., 2005), it is
not the [CO2−
3 ] of bottom waters alone that drives CaCO3 dissolution
in sediments. Organic carbon rain to the seabed drives respiratory
CaCO3 dissolution (Emerson and Bender, 1981) independently of
the [CO2−
3 ] of bottom waters. Most CaCO3 dissolution proxies are
not calibrated to account for this, except for one: the Globorotalia
menardii Fragmentation Index (MFI). In defining MFI, Mekik et al.
(2002) related the fragmentation trend of G. menardii shells to
model-derived estimates of percent CaCO3 dissolved in deep sea
sediments by taking into account the [CO2−
3 ] of bottom waters,
respiratory CaCO3 dissolution within sediments driven by fluxes of
organic carbon to the seabed (Emerson and Bender, 1981) and
CaCO3 fluxes to the seafloor.
Mekik et al. (2002) provide a detailed summary of the historical
development of deep sea CaCO3 dissolution proxies. Mekik and
Raterink (2008) list some qualities the ideal deep sea CaCO3
preservation proxy would be expected to have which include being
[1] without biological/ecological bias, [2] calibrated against an
independent and quantitative estimate of percent CaCO3 dissolved,
[3] reliably applicable in areas with strong gradients in the deep sea
fluxes of organic carbon and CaCO3, and [4] unaffected by variable
surface ocean conditions like temperature, [CO2−
3 ], and nutrient
availability.
Mekik and François (2006) reported Mg/Ca and Mg/Sr from shells
of Pulleniatina obliquiloculata and G. menardii which independently
corroborate MFI's dissolution trend on the Ontong-Java Plateau.
Mekik et al. (2002, 2007b) expanded MFI's applicability to core top
samples in the eastern equatorial Pacific (EEP) where both surface
ocean productivity and the rain ratio are highly variable. Mekik and
Raterink (2008) showed with core tops in the EEP that MFI is
unaffected by surface ocean parameters like temperature, [CO2−
3 ], and
nutrient availability. And lastly, MFI has been used to correct for
CaCO3 dissolution in a variety of paleoceanographic applications. For
230
example, MFI-based % dissolved estimates were used to correct Thnormalized carbonate accumulation rates for CaCO3 dissolution in
both core top (Mekik et al., 2007b), and down core work (Loubere et
al., 2004; Richaud et al., 2007). Also, Mekik et al. (2007a) used MFIbased % CaCO3 dissolved estimates in core tops to correct for
dissolution effects on the Mg/Ca of foraminifer shells which is used
in estimating sea surface temperature.
However, MFI's calibration is limited to the tropical Pacific, and
though its dissolution trend has been corroborated by other
independent proxies (Mekik and François, 2006), this corroboration
also has been limited to the western equatorial Pacific. We present
evidence herein that extends MFI's calibration into the tropics and
subtropics of the Atlantic and Indian Oceans.
Our research questions are:
[1] How well does MFI trace deep sea CaCO3 dissolution outside
the tropical Pacific, and into the tropical and subtropical
Atlantic and Indian Oceans?
[2] Can the fragmentation trend of foraminifer species other than
G. menardii be used to trace CaCO3 dissolution in deep sea
sediments; and if yes, the fragmentation trend of shells from
which species are most reliable?
[3] Can MFI's applicability in the subtropics be corroborated by
independent CaCO3 dissolution indicators like the decrease in
Mg/Ca if foraminifer tests or the drop in SNSW with increasing
dissolution?
[4] Can MFI's core top calibration be correlated with the dissolution trend of foraminifers that prefer higher latitudes (like
Globigerina bulloides, Neogloboquadrina pachyderma and Globorotalia truncatulinoides)?
2. The Globorotalia menardii Fragmentation Index (MFI)
MFI is defined as the ratio of the number of damaged specimens to
the number of whole plus damaged specimens of G. menardii within a
sediment aliquot (Eq. (1)).
D = ðD + W Þ
ð1Þ
where D is the number of damaged specimens and W is the number of
undamaged, whole shells within a sediment aliquot.
MFI is based on Ku and Oba's (1978) laboratory experiments
demonstrating quantifiable dissolution damage in G. menardii shells
with increasing dissolution. G. menardii has a distinct shell morphology which distinguishes it and its fragments from other species, even
from those under the same genus, Globorotalia. G. menardii tests have
a uniquely low trochospire on both the spiral and umbilical sides of
the test (shell looks almost flat), a smooth shell wall with small pores,
triangularly shaped chambers on the umbilical side, and a prominent
keel (Fig. 1). The keel is a calcareous growth like a thick, non-hollow
rim or bar at the outer edge of the shell.
Fig. 1. Shell morphology of Globorotalia menardii and categories of shell fragmentation
used in the G. menardii Fragmentation Index (MFI).
Please cite this article as: Mekik, F., et al., Progress toward a multi-basin calibration for quantifying deep sea calcite preservation in the
tropical/subtropical world ocean, Earth Planet. Sci. Lett. (2010), doi:10.1016/j.epsl.2010.08.024
F. Mekik et al. / Earth and Planetary Science Letters xxx (2010) xxx–xxx
D in Eq. (1) is calculated with Eq. (2) which includes four
categories of damaged shells: specimens with holes, specimens with
greater than half of the test intact, specimens with less than half of the
test intact, and keels (Fig. 1).
D = ð# with holesÞ + ð# N half Þ + ð# b half = 3Þ + ð # of keels = 5Þ
ð2Þ
Even shells with very small holes fall under the category of
“specimens with holes” (Eq. (2)) because the G. menardii test is
smooth with small pores not easily visible under a standard stereo
microscope. Also, the rims of the apertures are smooth in undamaged
specimens. Therefore, “holes” are unlikely to be confused with pores
or even apertures in G. menardii specimens. It is easy to distinguish G.
menardii fragments that are greater than half from those less than half
because the chamber arrangement of G. menardii (Fig. 1) makes it
unlikely for the shell to fragment into two pieces of exactly the same
size. In such a case, however, the shell is counted under the “greater
than half intact” category. The keels of Globorotalia tumida shells are
also well developed like G. menardii keels. While this may lead to
some confusion during counting, keels are the least weighted category
in calculating D (Eq. (2)) and have a minimal effect in calculating MFI
(Eq. (1)). Nonetheless, confusion of keels between G. tumida and G.
menardii are sometimes unavoidable. This likely leads to some scatter
in the trend of our MFI data when compared to other parameters of
the marine carbonate system, like ΔCO2−
3 .
Mekik et al. (2002) used the biogeochemical model Muds_constcal
(Archer et al., 2002) to calculate percent CaCO3 dissolved at specific
core top locations in the tropical Pacific Ocean for calibrating the MFI
transfer function (see Mekik et al., 2002 for the details of MFI's
calibration). Both bottom water ΔCO2−
3 and organic carbon and calcite
fluxes reaching the sediments were included in the model (Archer et
al., 2002) and in calculations of percent CaCO3 dissolved (Mekik et al.,
2002).
The MFI transfer function (Mekik et al., 2002) is the relationship
between the fragmentation trend of G. menardii shells in core tops on
the Ontong-Java Plateau and East Pacific Rise and model-derived
estimates of percent CaCO3 dissolved (R2 = 0.88) for the same core
top locations (Eq. (3)).
2
% Calcite Dissolved = −5:111 + ðMFI*160:491Þ– MFI *79:636
ð3Þ
In summary, G. menardii tests provide a quantifiable fragmentation trend with increasing dissolution. MFI is the only dissolution
proxy calibrated against model-derived estimates of percent CaCO3
dissolved per sample location; and MFI uses a species whose
fragments are easy to identify. However, MFI's applicability in core
tops outside the tropical Pacific has not yet been explored. That is
our goal herein.
3. Materials and methods
3.1. Surface sediment samples
We present MFI data from core top samples closely clustered
within six general regions (Fig. 2A): Ontong-Java Plateau (OJP; 22
samples; 1900–4441 m), East Pacific Rise (EPR; 16 samples; 3049–
4060 m), Ceara Rise (CEA; 5 samples; 3711–4569 m), Rio Grande Rise
(RIO; 28 samples; 1562–4427 m), Seychelles/Mascarene Ridge (SEY;
8 samples; 3470–4051 m) and the 90 East Ridge (90E; 10
samples;3076–4091 m). All of our core tops (0–4 cm) are from
gravity cores in order to minimize loss of the most modern sediments.
For the purposes of an initial attempt at a multi-basin calibration
for MFI, we chose our core top locations in regions that are far from
upwelling zones where there are strong gradients to both organic
carbon and CaCO3 fluxes reaching the seabed. This is important for
minimizing the effect of variations in the rain ratio on CaCO3
3
dissolution and for isolating bottom water [CO2−
3 ] as the dominant
driver of calcite dissolution among our samples.
Detailed information describing procedures for data generation,
modeling and ascertaining that our core tops are modern are provided
as on-line Supplementary material. All core information, new data
presented herein and data used in modeling are also available in online Supplementary tables.
3.2. Calculating deep sea carbonate ion saturation
We used two different methods for estimating bottom water CO2−
3
under-saturation: [1] Broecker and Clark's (2001a) [CO2−
3 ]*, and [2]
with the software package called Ocean Data
calculations of ΔCO2−
3
View version 3.4.2 (Schlitzer, 2008) and bottle data from GLODAP
(Key et al., 2004; Sabine et al., 2005).
Broecker and Clark (2001a) defined [CO2−
3 ]* as a depth-normal2−
ized bottom water [CO2−
3 ] indicator based on [CO 3 ] values
2−
extrapolated from GEOSECS data. They define [CO3 ]* with Eq. (4):
h
2−
CO3
i
h
i
2−
* = CO3 + 20 ð4−zÞ
ð4Þ
2−
where [CO2−
3 ]* represents depth normalized [CO3 ] and z is water
depth in kilometers.
Ocean Data View calculates [CO2−
3 ]in situ and Ωcalcite (which is the
saturation state of calcite in seawater) for each core top location as
functions of [1] water depth or pressure, [2] temperature, [3]
salinity, [4] total inorganic carbon, [5] total alkalinity, [6] phosphate
and [7] silicate. We calculated ΔCO23 for our core top locations by
first estimating [CO2−
3 ] at saturation using Eq. (5) and then
with Eq. (6):
calculating ΔCO2−
3
h
2−
CO3
i
2−
ΔCO3
saturation
h
i
2−
= CO3
h
i
2−
= CO3
insitu
insitu
= Ωcalcite
h
i
2−
− CO3
saturation
ð5Þ
ð6Þ
Figure 2B and C illustrates the relationship between [CO2−
3 ] and
and [CO2−
water depth for our core tops for both ΔCO2−
3
3 ]* derived
from GLODAP data. It is clear that each depth transect has a unique
trend between [CO2−
3 ] of bottom waters and water depth. The
and [CO2−
correlation between ΔCO2−
3
3 ]* for our core tops is strong
2
(R = 0.91).
3.3. Data generation and reproducibility
We followed methods outlined in Mekik et al. (2002) for
generating data for MFI and % calcite in sediments. Similarly to MFI,
we used Eq. (1) to determine a fragmentation ratio for each species. D
was calculated for G. truncatulinoides specimens the same way it was
calculated for MFI (Eq. (2)). The keels for G. truncatulinoides are less
well developed and therefore easy to distinguish from G. menardii
keels even among fragments. For the other species which have
rounder, more bulbous morphologies and no keels, D was calculated
with Eq. (2) without the category for keels.
We followed procedures described in Lohmann (1995) and
Broecker and Clark (2001a) for SNSW measurements. Mekik and
Raterink (2008) provide a detailed discussion of error and reproducibility in SNSW measurements.
Figure 3A shows duplicated MFI data for the same sediment
samples generated by three researchers in addition to Mekik. The
reproducibility of MFI data appears robust. We see some scatter at the
lower end of the MFI range which is likely due to noise generated from
using different sample splits between the two researchers (even if
they are nominally from the core top). Also, in some samples from the
RIO transect, total counts of G. menardii fell below 100 in both Mekik's
and Chellappa's data which potentially causes some of this
Please cite this article as: Mekik, F., et al., Progress toward a multi-basin calibration for quantifying deep sea calcite preservation in the
tropical/subtropical world ocean, Earth Planet. Sci. Lett. (2010), doi:10.1016/j.epsl.2010.08.024
4
F. Mekik et al. / Earth and Planetary Science Letters xxx (2010) xxx–xxx
Fig. 2. A. Core top transect locations plotted over map of bottom water ΔCO2−
3 estimates from Archer's (1996a) database. Ocean Data View software (Schlitzer, 2008) was used
in making the map. 89 total core top samples from 6 geographically restricted depth-transects: Ontong-Java Plateau (22 samples; 1900–4441 m), East Pacific Rise (16 samples
3049–4060 m), Ceara Rise (5 samples, 3711–4569 m), Rio Grande Rise (28 samples, 1562–4427 m), Seychelles/Mascarene Ridge (8 samples 3470–4051 m) and 90-East Ridge
under-saturation shown as ΔCO2−
in Panel B and [CO2−
(10 samples, 3076–4091 m). Water depth plotted against CO2−
3
3
3 ]* in Panel C. Both parameters were calculated using
GLODAP bottle data for bottom water [CO2−
3 ]. See text for details. EPR: East Pacific Rise, OJP: Ontong-Java Plateau; RIO: Rio Grande Rise; CEA: Ceara Rise; SEY: Seychelles/
Mascarene Plateau; 90E: 90-East Ridge.
disagreement. Figure 3B shows MFI reproducibility when the same
researcher (Mekik) counted G. menardii fragments from the samples
on SEY and 90E, but from different splits. Again agreement between
the two counts is strong. The reproducibility error calculated from all
duplicate MFI counts (Figs. 3A and B) is ±0.04 MFI units.
MFI reproducibility is good as long as each operator can indentify
G. menardii shells and their fragments correctly and can distinguish
this species from others in the sediment. As discussed earlier,
confusion of keels from G. menardii tests with those from G. tumida
may be unavoidable; but the agreement between the duplicate MFI
data (Fig. 3) demonstrates that this confusion does not create
significant uncertainty in reproducibility of MFI data.
3.4. Modeling percent CaCO3 dissolved for each core top location
We used the biogeochemical model Muds_constcal (Archer et
al., 2002) to calculate percent CaCO3 dissolved for each core top
location following the same procedures as Mekik et al. (2002)
wherein MFI was originally calibrated for the tropical Pacific.
Muds_constcal is a model of pore water pH and redox chemistry
that allows estimating CaCO3 dissolution rates for specific locations
on the seafloor. The model is driven by the fluxes of organic carbon
and CaCO3 to the seabed and uses the chemistry of the overlying
water as a boundary condition (Archer et al., 2002). The dissolution
rates Muds_constcal calculates were corrroborated (see Archer et
al., 2002 for details) with benthic flux data from the California
continental slope (Berelson et al., 1996) and microelectrode data
from the Ceara Rise and the Ontong-Java Plateau (Hales and
Emerson, 1996, 1997). For details of the chemical formulations and
rate constants used in the model see Archer et al. (2002) and
Mekik et al. (2002).
Muds_constcal requires the input of bottom water oxygen content,
nitrate, silica, alkalinity, ΔCO2−
3 , water depth, organic carbon flux and
percent calcite in bulk sediment to output a calcite dissolution rate.
Please cite this article as: Mekik, F., et al., Progress toward a multi-basin calibration for quantifying deep sea calcite preservation in the
tropical/subtropical world ocean, Earth Planet. Sci. Lett. (2010), doi:10.1016/j.epsl.2010.08.024
F. Mekik et al. / Earth and Planetary Science Letters xxx (2010) xxx–xxx
Fig. 3. A. Comparison of Mekik's counts used for calculating MFI with those from other
researchers on the same sediment samples. Chellappa's data is for a subset of our RIO
samples but from different splits and were published in Loubere and Chellappa (2008).
Russo used the same sample splits as Mekik. Richaud's data are from MW91-9 38BC and
were published in Richaud et al. (2007). R value shown applies for data points along the
whole range of MFI and not any individual researcher's data. B. Reproducibility of MFI
by the same researcher (Mekik) but different splits of samples from the Seychelles/
Mascarene Plateau (SEY) and 90-East Ridge (90E).
We used Eq. (7) to calculate percent CaCO3 dissolved for each of our
core top locations.
% CaCO3 dissolved = ðCaCO3 dissolution rate = CaCO3 fluxÞ*100
ð7Þ
3.4.1. Data for modeling
All ΔCO23, sedimentary % CaCO3 and MFI data, as well as CaCO3
dissolution rates calculated by Muds_constcal are listed as on-line
Supplementary material.
Although MFI's original calibration was made with Archer's
values, we prefer to use ΔCO2−
estimated from
(1996a) ΔCO2−
3
3
GLODAP bottle data for expanding MFI's calibration because Archer's
(1996a) extrapolations are based on smoothed bathymetry which can
estimates; and because GLODAP
negatively affect accuracy of ΔCO2−
3
makes use of a much larger dataset that is more recent and better
calibrated than that of Archer (1996a) (de Menocal, pers. comm.,
2009). We extrapolated all other chemical data for each core top
location using GLODAP bottle data and Ocean Data View.
3.4.2. Organic carbon flux estimates
We estimated organic carbon flux for each sample location using
the surface ocean productivity compilations of Behrenfeld and
5
Falkowski (1997) and the relationship for degradation of organic
carbon with water depth of Berger et al. (1987) to maintain
consistency with both MFI's original calibration transfer function in
the tropical Pacific (Mekik et al., 2002) and among all core tops herein.
As Friedrichs et al. (2009) carefully illustrated, however, most
depth integrated satellite ocean color based models of surface ocean
productivity, such as that of Behrenfeld and Falkowski (1997),
significantly underestimate primary production by a factor of two.
For this reason we compared our estimates of organic carbon flux with
those from deep sea sediment traps near our transect sites where such
data are available. Unfortunately, published deep sediment trap data
in areas of low surface ocean productivity are few.
Supplementary Tables 5 and 6 list the values we used for organic
carbon flux, calcite flux and molar rain ratios in model calculations for
each transect and corroborating sediment accumulation rate and deep
sediment trap data for these parameters. Supplementary Table 6 also
lists the dissolution rates calculated for each core top location with
Muds_constcal and those measured in benthic flux chambers and
microelectrode studies in sediment pore waters for nearby regions
where available.
Data from deep sediment traps on the slopes of the Rio Grande Rise
in the Vema Basin show organic carbon fluxes of 3–10 μmol/cm2/yr
(Gardner et al., 1997). For the Ceara Rise, Martin and Sayles (1996)
estimated an average rain ratio of 1.1 from pore water microelectrode
data. But for parts on the Ceara Rise near the water depths for CEA,
their rain ratio estimate is 0.65 which is also in keeping with Archer's
(1996b) estimates of the rain ratio for this region. This rain ratio
230
combined with the calcite flux of 5–10 μmol/cm2/yr from Thnormalized carbonate accumulation rate estimates of François et al.
(1990) and deep sediment trap work (~10 μmol/cm2/yr, from 13.5°N
and 54°W, close to the Ceara Rise, Honjo et al., 1982) yield organic
carbon flux estimates of 3–7 μmol/cm2/yr. These sediment trap and
pore water data corroborate the values of organic carbon flux of 5.8–
9.2 μmol/cm2/yr that we calculated for RIO and CEA from Behrenfeld
and Falkowski's (1997) primary productivity data. Also, our dissolution rate estimates calculated by Muds_constcal for CEA (1.2–
5.6 μmol/cm2/yr ) are corroborated by dissolution rate estimates of
2–9.1 μmol/cm2/yr from benthic flux chamber (Berelson et al., 2007)
and pore water microelectrode data (Hales and Emerson, 1996, 1997)
for the Ceara Rise.
Dymond and Lyle (1993) estimated 7.5–16 μmol/cm2/yr of organic
carbon flux from sediment traps and deep sea sediments in the eastern
equatorial Pacific. This corroborates estimates of organic carbon
reaching the seabed that Mekik et al. (2002) used and that we use
herein (from Behrenfeld and Falkowski's (1997) surface ocean
productivity data) for our EPR transect (10.5–14.3 μmol/cm2/yr). For
the OJP transect, there is no deep sea sediment trap work documenting
organic carbon fluxes from nearby, but Berelson et al. (1997) report
organic carbon flux estimates of 7–20 μmol/cm2/yr from deep sediment
trap data in the central equatorial Pacific at 140° W. This corroborates
the organic carbon flux estimates** Mekik et al. (2002) used for both
the OJP and EPR transects (7.8–11.8 μmol/cm2/yr and 10.5–14.3 μmol/
cm2/yr, respectively). While 140°W is far from both OJP and EPR, all
three regions (EPR, OJP and equatorial 140°W ) are open ocean settings
in the tropical Pacific far removed from areas of high surface ocean
productivity. Furthermore, Berelson et al.'s (1997) estimates are close to
the estimates by Dymond and Lyle (1993) for the EPR transect alone
(7.5–16 μmol/cm2/yr). So it is plausible to remotely corroborate the
estimates calculated from surface ocean productivity data for the OJP
(7.8–11.8 μmol/cm2/yr) with sediment trap data from the central
equatorial Pacific (Berelson et al., 1997). Furthermore, our dissolution
rates calculated by Muds_constcal for the EPR and OJP (8.9–13.4 and
3.2–13.8 μmol/cm2/yr, respectively) are corroborated by dissolution
rates of 7.7 to 21.5 μmol/cm2/yr for the EPR from benthic flux chamber
data (Berelson et al., 2007) and 3.5 to 6 μmol/cm2/yr from pore water
microelectrode data (Hales and Emerson, 1996).
Please cite this article as: Mekik, F., et al., Progress toward a multi-basin calibration for quantifying deep sea calcite preservation in the
tropical/subtropical world ocean, Earth Planet. Sci. Lett. (2010), doi:10.1016/j.epsl.2010.08.024
6
F. Mekik et al. / Earth and Planetary Science Letters xxx (2010) xxx–xxx
Corroboration for organic carbon rain estimates from Behrenfeld
and Falkowski's (1997) data for transects in the Indian Ocean is made
difficult by the lack of any deep sediment trap work or sediment
accumulation rate studies near SEY or 90E (Fig. 2A). All deep sediment
trap data for the Indian Ocean has been focused on the Bay of Bengal
and Gulf of Arabia where organic carbon fluxes (15–30 μmol/cm2/yr)
and rain ratios (0.8–1.8) are high (Ittekkot et al., 1991; Haake et al.,
1993; Ramaswamy and Nair, 1994; Ramaswamy and Gaye, 2006). So,
we are unable to constrain the uncertainties resulting from using
Behrenfeld and Falkowski's (1997) surface ocean productivity
estimates to calculate organic carbon fluxes on SEY and 90E (5–
8.8 μmol/cm2/yr and 5.8–8.8 μmol/cm2/yr, respectively).
3.4.3. CaCO3 Flux Estimates
Sediment accumulation rate and deep sediment trap data for
estimating CaCO3 fluxes are also few. Mekik et al. (2002) used
sediment flux data from Berger and Killingley (1982) to estimate
CaCO3 fluxes for OJP samples (6–14 μmol/cm2/yr) and the work of
McMurty et al. (1981) for the EPR transect (19–21 μmol/cm2/yr).
Kawahata et al. (2000) report CaCO3 fluxes of 5–20 μmol/cm2/yr for
the OJP from their deep sediment traps which corroborates the
estimates from Berger and Killingley (1982). This data coupled with
our organic carbon estimates for OJP and EPR generates molar rain
ratios of ~0.6 for the Pacific transects which are comparable to the
average range of molar rain ratios presented in deep sediment trap
data (0.4–0.8) for the tropical Pacific by Berelson et al. (1997,
RR = 0.6), Dymond and Lyle (1993, RR = 0.45–0.6), Kawahata et al.
(2000, RR = 0.4–0.6, specifically for OJP), and Walsh et al. (1988,
RR = 0.77) and which are also corroborated by modeled estimates of
the rain ratio by Archer (1996b).
For both RIO and CEA transects, organic carbon rain estimates
derived from surface ocean productivity data by Behrenfeld and
Falkowski (1997) divided by a rain ratio of 0.5–0.65 yield CaCO3 fluxes
(9–14 and 9–20 μmol/cm2/yr for RIO and CEA respectively) which
approximate well the CaCO3 flux estimates near our transects (7–
20 μmol/cm2/yr from deep sediment traps in Gardner et al., 1997 for
RIO; and from carbonate accumulation rate data in François et al, 1990
and Milliman, 1993 for CEA).
The lack of sediment trap data from areas near our core tops in the
Indian Ocean is also problematic for estimating CaCO3 flux. Based on
deep sediment trap work north of the equator in the Indian Ocean
(Ramaswamy and Nair, 1994; Ramaswamy and Gaye, 2006; Ittekkot
et al., 1991; Haake et al., 1993) CaCO3 fluxes are regionally more
uniform and fall in a tighter range (15–20 μmol/cm2/yr) than organic
carbon flux. These estimates are also in keeping with those of Berelson
et al. (1997) for the open ocean CaCO3 flux in the central equatorial
Pacific (~20 μmol/cm2/yr) and those of Milliman (1993) for the global
open ocean (15–20 μmol/cm2/yr). So we used CaCO3 flux values from
sediment traps nearest to our transects (18 μmol/cm2/yr for SEY after
Ramaswamy and Gaye, 2006; and 16 μmol/cm2/yr for 90E after
Ittekkot et al., 1991). These CaCO3 flux estimates together with
organic carbon flux estimates from surface ocean primary productivity data (Behrenfeld and Falkowski, 1997) yield rain ratios of 0.3–0.4
for SEY and 90E which is consistent with modeled estimates of the
rain ratio for this region (Archer, 1996b).
3.4.4. Modeling Sensitivities
A sensitivity analysis of our modeling to address concerns about
the effects of uncertainties in organic carbon flux and rain ratio
estimates on calculations of % CaCO3 dissolved is shown in Figure 4. At
low rain ratios even a fourfold difference in organic carbon flux yields
only a 2–3% change in % CaCO3 dissolved, when all other model input
parameters are kept constant (Fig. 4A). However, at higher rain ratios,
such as 1, a fourfold difference in organic carbon flux yields a
difference as high as 15% in estimates of percent CaCO3 dissolved. Also
changing the rain ratio has a more profound effect on estimating %
Fig. 4. A. Model sensitivity in calculating percent calcite dissolved with varying organic
carbon fluxes and rain ratios. Percent calcite dissolved was calculated using the
(= 0 μmol/
Muds_constcal sediment diagenesis model and constant values for ΔCO2−
3
kg) and percent calcite in bulk sediment (75%) in all model runs for this panel. B. Model
sensitivity in calculating percent dissolved at constant values of ΔCO2−
3 (= 0 μmol/kg),
org carbon flux (= 10 μmol/cm2/yr), but varying percent calcite in bulk sediment.
CaCO3 dissolved than changing organic carbon flux while keeping the
rain ratio constant. A difference in the rain ratio of ±0.1 units results
in uncertainties of ±6% in estimating % CaCO3 dissolved (Fig. 4 A).
Our core top locations are chosen from regions where the rain ratio
does not exceed 0.6–0.7. At these low rain ratios, even assuming
fourfold uncertainties on organic carbon flux estimates (based on
errors reported by Friedrichs et al. (2009) of about a two-fold error in
primary productivity estimates and about a two-fold error in
estimating degradation of organic carbon with water depth after
Berger et al., 1987), our resulting modeling error margin for % CaCO3
dissolved is ~ 10–12% dissolved. This is similar to the error margin
Mekik et al. (2002) calculated for their modeling results when
calibrating the MFI transfer function for the tropical Pacific.
Another input parameter that needs to be constrained in
Muds_constcal is the % of CaCO3 in bulk sediment. We have published
data for our EPR and OJP transects from the work of Mekik et al.
(2002). But we were able to generate new % CaCO3 data for only a
subset of our RIO samples because we do not have unprocessed
sample material remaining from our other core tops. For these
samples we used the average % CaCO3 estimate from our core tops in
our model calculations which is 75% and which is also in keeping with
an average % CaCO3 value from sediments in the tropical/subtropical
deep seas (Milliman, 1993). Figure 4B illustrates the model's
sensitivity to % CaCO3 in sediments when calculating CaCO3
dissolution rates and percent CaCO3 dissolved while all other model
input parameters are kept constant. Note that for samples with 60% or
Please cite this article as: Mekik, F., et al., Progress toward a multi-basin calibration for quantifying deep sea calcite preservation in the
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F. Mekik et al. / Earth and Planetary Science Letters xxx (2010) xxx–xxx
greater CaCO3, the model calculates % CaCO3 dissolved within about
2–3% variation, even at high rain ratios (Fig. 4B).
4. Results
MFI from our six regions, far removed from one another by both
geography and bottom water chemistry (Fig. 2), has a robust
(Fig. 5). Figure 5B through D
relationship with bottom water ΔCO2−
3
in each ocean basin,
shows the relationship between MFI and ΔCO2−
3
and this relationship is strong in the Pacific and Atlantic transects
(R2 = 0.9 and 0.82 respectively). While MFI data from our transect on
the 90 East Ridge in the Indian Ocean seems to have a modest
(R2 = 0.73), MFI data from the SEY transect
relationship with ΔCO2−
3
shows no relationship with ΔCO2−
3 .
MFI data plotted against modeled estimates of percent CaCO3
dissolved for each core top (Fig. 6) yields a good relationship
(R2 = 0.8). We find the strongest relationship in our Pacific and
Atlantic transects (R2 = 0.87 and 0.84, respectively) and hardly any
relationship in SEY in the Indian Ocean but a modest one on 90E
(R2 = 0.69).
Figure 7A–C shows fragmentation data for other foraminifer
species in our EPR transect. While the fragmentation trend of
7
of
Neogloboquadrina dutertrei and N. pachyderma follow ΔCO2−
3
bottom waters moderately well (R2 = 0.69 and 0.64 respectively,
Fig. 7A), the fragmentation trends of P. obliquiloculata (Fig. 7A),
Globigerina sacculifer and Globigerina ruber (Fig. 7B) have no
relationship with ΔCO2−
3 . MFI and the fragmentation trend of N.
dutertrei have the strongest relationship (R2 = 0.87) (Fig. 7C).
Figure 7D and E shows the fragmentation ratio of five foraminifer
species on RIO, including G. menardii, plotted against ΔCO2−
3 . Similarly
to the EPR, P. obliquiloculata fragmentation has no discernable
on RIO. While Neogloboquadrina species
relationship with ΔCO2−
3
decreases, their
show a clear increase in fragmentation as ΔCO2−
3
relationships are modest. Figure 7E shows that both Globorotalia
and the
species have a strong quadratic relationship with ΔCO2−
3
fragmentation trends of these two species follow each other well
(R2 = 0.8 for a linear correlation between their respective fragmentation trends). The fragmentation trends of N. pachyderma and G.
truncatulinoides have strong relationships with MFI (Fig. 7F; R2 = 0.86
and 0.8, respectively).
Figure 8A–C compares Mg/Ca data from three planktonic foraminifer species and MFI on RIO. Surface ocean annual average temperature over RIO is ~ 20–21 °C (Locarnini et al., 2006). Thus, the
temperature effect on foraminifer Mg/Ca is not enough to obscure
Fig. 5. A. MFI plotted against ΔCO2−
in μmol/kg estimated using bottom water [CO2–
3
3 ] values from GLODAP bottle data. EPR: East Pacific Rise, OJP: Ontong-Java Plateau; RIO: Rio
Grande Rise; CEA: Ceara Rise; SEY: Seychelles/Mascarene Plateau; 90E: 90-East Ridge. For samples for which we have MFI data with repeated counts, an average value of the multiple
2−
counts are shown. B. MFI plotted against ΔCO3 in μmol/kg estimated using GLODAP bottle data for the Pacific core tops only (OJP and EPR). Color coding of data symbols same as in
Panel A. C. MFI plotted against ΔCO2−
3 in μmol/kg estimated using GLODAP bottle data for the Atlantic core tops only (CEA and RIO). Color coding of data symbols same as in Panel A.
in μmol/kg estimated using GLODAP bottle data for the Indian core tops only (SEY and 90E). Color coding of data symbols same as in Panel A. (For
D. MFI plotted against ΔCO2−
3
interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Mekik, F., et al., Progress toward a multi-basin calibration for quantifying deep sea calcite preservation in the
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8
F. Mekik et al. / Earth and Planetary Science Letters xxx (2010) xxx–xxx
Fig. 6. A. MFI plotted against modeled estimates of percent calcite dissolved. EPR: East Pacific Rise, OJP: Ontong-Java Plateau; RIO: Rio Grande Rise; CEA: Ceara Rise; SEY: Seychelles/
Mascarene Plateau; 90E: 90-East Ridge. See text for modeling details and calculations of percent calcite dissolved. For samples for which we have MFI data with repeated counts, an
average value of the multiple counts are shown. B. MFI plotted against modeled estimates of percent calcite dissolved for the Pacific core tops only (OJP and EPR). Color coding of data
symbols same as in Panel A. C. MFI plotted against modeled estimates of percent calcite dissolved for the Atlantic core tops only (CEA and RIO). Color coding of data symbols same as
in Panel A. D. MFI plotted against modeled estimates of percent calcite dissolved for the Indian core tops only (SEY and 90E). Color coding of data symbols same as in Panel A. (For
interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
the dissolution signal of this proxy here. The relationship between
Mg/Ca of all three species and ΔCO2−
3 is modest but clearly decreases
as bottom water [CO2−
3 ] drops. The relationship of Mg/Ca in G.
truncatulinoides and Globigerina conglobatus with both MFI and MFIbased % CaCO3 dissolved is moderate to good (Fig. 8B and C) where
Mg/Ca steadily drops with increasing fragmentation and dissolution.
Percent dissolved values here were calculated using the new MFI
transfer function for the Atlantic Ocean as depicted in Figure 6C.
G. truncatulinoides SNSW in RIO samples follow MFI's trend against
well (Fig. 8D and E), though the larger size range (400–
ΔCO2−
3
500 μm) follows this trend more closely. Both Mg/Ca and SNSW data
for two size ranges of Globorotalia inflata (Fig. 8F) shows no clearly
on RIO.
discernable relationship with ΔCO2−
3
5. Discussion
5.1. Progress toward a multi-basin calibration
Finding a reliable and quantitative CaCO3 dissolution proxy is a
crucial part of understanding the marine carbonate system, both in
core tops and in paleoceanographic work. New MFI data and MFIbased transfer functions for % CaCO3 dissolved from the tropical and
subtropical Atlantic and Indian core tops (CEA, RIO, SEY and 90E)
(Figs. 5 and 6) expand MFI's calibration range beyond the tropical
Pacific Ocean (OJP and EPR) with robust relationships between [CO2−
3 ]
of bottom waters and MFI as well as modeled estimates of percent
CaCO3 dissolved and MFI.
Please cite this article as: Mekik, F., et al., Progress toward a multi-basin calibration for quantifying deep sea calcite preservation in the
tropical/subtropical world ocean, Earth Planet. Sci. Lett. (2010), doi:10.1016/j.epsl.2010.08.024
F. Mekik et al. / Earth and Planetary Science Letters xxx (2010) xxx–xxx
9
Fig. 7. Panel A: Fragmentation data for Neogloboquadrina dutertrei, Neogloboquadrina pachyderma and Pulleniatina obliquiloculata plotted against ΔCO2−
from the East Pacific Rise
3
(3049–4060 m). Panel B: Fragmentation data for Globigerina sacculifer and Globigerina ruber plotted against ΔCO2−
from the East Pacific Rise.Panel C: MFI plotted against
3
fragmentation data of N. dutertrei and N. pachyderma from the East Pacific Rise.Panel D: Fragmentation data for N. dutertrei, N. pachyderma and P. obliquiloculata plotted against
2−
ΔCO2−
3 from the Rio Grande Rise (1562–4427 m)Panel E. Fragmentation data for G. menardii and Globorotalia truncatulinoides plotted against ΔCO3 from the Rio Grande Rise.Panel
F. MFI plotted against fragmentation data of N. dutertrei, N. pachyderma and G. truncatulinoides from the Rio Grande Rise.See text for explanation of calculating fragmentation ratio for
each species. For samples for which we have MFI data with repeated counts, an average value of the multiple counts are shown.
In many of our transects, sediment accumulation rate and deep
sediment trap data corroborate values for organic carbon flux, CaCO3
flux and rain ratios we used in modeling % CaCO3 dissolved.
Furthermore, CaCO3 dissolution rates calculated with Muds_constcal
for our core tops are corroborated by CaCO3 dissolution rate estimates
from benthic flux chamber data and microelectrode measurements
from pore waters for OJP and EPR (Berelson et al., 2007; Hales and
Emerson, 1996), and CEA (Berelson et al., 2007; Hales and Emerson,
1997; Martin and Sayles, 1996). Unfortunately, direct measurements
of calcite dissolution rates from sediment pore waters are few.
Nevertheless, the good agreement between pore water CaCO3
dissolution rate measurements and model-derived CaCO3 dissolution
Please cite this article as: Mekik, F., et al., Progress toward a multi-basin calibration for quantifying deep sea calcite preservation in the
tropical/subtropical world ocean, Earth Planet. Sci. Lett. (2010), doi:10.1016/j.epsl.2010.08.024
10
F. Mekik et al. / Earth and Planetary Science Letters xxx (2010) xxx–xxx
Fig. 8. Panel A. Mg/Ca data for Globorotalia truncatulinoides, Globigerina conglobatus and Globorotalia hirsuta plotted against ΔCO2−
Panel B. Mg/Ca data for G. truncatulinoides, G.
3
conglobatus and G. hirsuta plotted against MFI. Panel C. Mg/Ca data for G. truncatulinoides, G. conglobatus and G. hirsuta plotted against MFI-based % calcite dissolved using equation for
2
the Atlantic transects (% Dissolved = (−90.342 MFI ) + (178.1 MFI) − 11.998. All samples are from core tops on the Rio Grande Rise (RIO; 1562–4427 m). Panels D and E: Sizeplotted over MFI vs. ΔCO2−
normalized shell weight (SNSW) of G. truncatulinoides vs. ΔCO2−
3
3 . Panel A shows SNSW data for shells of small diameter ranging between 400 and
500 μm, and Panel B shows weight data for shells of small diameter ranging between 80 and 100 μm. Panel F: Mg/Ca and SNSW data for two size fractions for Globorotalia inflata
2−
plotted against ΔCO2−
3 . ΔCO3 estimates are derived from GLODAP bottle data. All samples are from core tops on the Rio Grande Rise (RIO; 1562–4427 m). MFI values shown are an
average of Loubere and Chellappa's (2008) MFI data and those presented herein where core top samples overlap.
rates for these three transects supports our choices of rain ratios and
organic carbon and CaCO3 fluxes used in modeling % CaCO3 dissolved.
While this gives us some confidence in the MFI transfer function for
calculating % CaCO3 dissolved in the sediments (Fig. 6), there are
several layers of uncertainty that are intrinsic to calculations of %
CaCO3 dissolved using both the model and MFI.
First, uncertainties in our estimates of organic carbon and CaCO3
fluxes as well as % CaCO3 in sediments lead to ±10–15% error in
Please cite this article as: Mekik, F., et al., Progress toward a multi-basin calibration for quantifying deep sea calcite preservation in the
tropical/subtropical world ocean, Earth Planet. Sci. Lett. (2010), doi:10.1016/j.epsl.2010.08.024
F. Mekik et al. / Earth and Planetary Science Letters xxx (2010) xxx–xxx
Muds_constcal's calculations of % CaCO3 dissolved for specific
locations on the seafloor (Fig. 4). Second, while the reproducibility
of MFI data among various researchers is robust (Fig. 3), the error
margin here is ±0.04 MFI units. Using the transfer function in
Figure 6A, this uncertainty in measuring MFI yields ±1% error in
estimating % CaCO3 dissolved. Third, after adding four transects to
MFI's calibration (CEA, RIO, SEY and 90E), we find that MFI
of bottom waters with a predictive error of
approximates ΔCO2−
3
estimates from GLODAP data
±10 μmol/kg using the ΔCO 2−
3
(Fig. 5A); and that the average predictive error of our new MFI
transfer function for all our core tops in is ±7% calcite dissolved
(Fig. 6A).
Modeling errors are notably affected by errors in estimating
sediment fluxes and rain ratios. However, our transects are from open
ocean settings, far from high productivity zones, where both rain
ratios and organic carbon fluxes are small. Also our samples are
clustered close to one another within each transect which leads to
small variations of organic carbon flux among samples from the same
transect. But, the strong depth gradient in each transect results in a
among samples within each transect. So the
large gradient to ΔCO2−
3
. Loubere
main driver of calcite dissolution in our samples is ΔCO2−
3
and Chellappa (2008) calculated through modeling experiments that
only 6% of the dissolution rate calculated by Muds_constcal for their
RIO samples is attributable to organic carbon flux, and more than 90%
under-saturation with water
is attributable to the increase in CO2−
3
depth. They also found that changes in organic carbon flux with water
depth have only a minor influence on dissolution rate.
Our Indian Ocean transects show weak relationships between MFI
and modeled estimates of % CaCO3 dissolved. The
and ΔCO2−
3
modeling in this region is strongly hampered by the lack of deep
sediment trap data to accurately constrain organic carbon and CaCO3
rain over SEY and 90E. While MFI data from the 90E transect have
and modeled estimates
moderately strong relationships with ΔCO2−
3
of % CaCO3 dissolved, there is no discernable relationship between MFI
or % CaCO3 dissolved on SEY (Figs. 5D and 6D). However,
and ΔCO2−
3
the range of variation for ΔCO2−
3 among SEY samples (~10 μmol/kg) is
within the predictive error of ΔCO2−
3 by MFI. Therefore, expanding the
range of samples from the Indian Ocean is
water depth and ΔCO2−
3
necessary to better calibrate the MFI transfer function for this basin.
Thus, despite uncertainties in modeling and data reproducibility,
the MFI transfer function can be used to quantify the direction and
relative magnitude in changes of percent CaCO3 dissolved. MFI
remains the only available calcite dissolution proxy that is applicable
in regions where the rain ratio is an important factor driving calcite
dissolution in deep sea sediments because the MFI transfer function
(Fig. 6A) is calibrated against both bottom water ΔCO2−
3 and the rain
ratio reaching the seabed at any one location on the deep seafloor
(herein, Mekik et al., 2002, 2007b). This allows the MFI transfer
function to be used as a quantitative bulk sediment calcite dissolution
proxy regardless of whether the main driver of calcite dissolution is
or both. And, while several studies (Mekik et
high rain ratios, ΔCO2−
3
al., 2002, 2007b; Mekik and Raterink, 2008) have demonstrated MFI's
applicability in the EEP, future studies from upwelling regions in the
Atlantic and Indian Oceans are necessary to improve MFI's multi-basin
core top calibration.
5.2. Interpreting foraminifer fragmentation
We chose quadratic equations to describe our data when
comparing MFI with ΔCO2−
3 (Fig. 5) and modeled estimates of percent
CaCO3 dissolved (Fig. 6) because doing so provided the best fit to the
trend in our data. While this is simply an empirical approach, it is not
wholly unsupported by theory. Morse (1978) showed that the N62 μm
fraction in carbonate sediment dissolved at only ~10% the rate that
modeling predicted by the relative surface areas of the grains. Morse
(1978) argued that this may be caused by two factors: [1] foraminifer
11
tests are “closed chambers” the interior of which may contain water
which quickly equilibrates with the CaCO3 of the shell. This would
impede dissolution inside the test until the shell is breached or
broken. Since the N63 μm fraction is composed mostly of foraminifer
tests, this idea is reasonable. And, [2] the b63 μm fraction contains
grains where the surface to volume ratio is high which tends to
increases the dissolution rate of CaCO3. Therefore, extent of CaCO3
dissolution changes the rate of CaCO3 dissolution and decreases with
decreasing surface to volume ratio (= increasing grain size; Morse,
1978).
G. menardii shells have smooth, thin chamber walls that have very
small pores. Thus, it is arguable that they may act like a “closed
chamber” containing waters that are equilibrated with CaCO3 which
hinder the dissolution of the shell from the inside out. Once the shell
breaks or holes appear on it, corrosive waters entering the test may
start dissolving the shell at a faster rate. Following on Morse's (1978)
second argument, once the shells begin to fragment, and as these
fragments dissolve and become smaller, the surface to volume ratio of
the broken pieces of G. menardii will increase. This would lead to the
increase of the dissolution rate of the shell. Based on this reasoning, it
is plausible for G. menardii fragmentation to have an exponential or
and CaCO3 dissolution; and
quadratic relationship with ΔCO2−
3
quadratic relationships fit the trends in our data in Figures 5 and 6
more closely than exponential ones.
Morse's (1978) considerations for the progression of test dissolution may also explain why tests of all species of planktonic
foraminifers do not fragment in the same manner. Figure 7B shows
that G. sacculifer and G. ruber tests have no relationship with ΔCO2−
3 of
bottom waters. These two species have tests with multiple large
apertures and large pores. Therefore, it is possible that their tests do
not act as a “closed chamber” trapping calcite-friendly waters within.
Also, a porous test provides more surface area compared to its volume.
Higher surface to volume ratios would increase the dissolution rate of
these shells. It is also the authors' observation that in sediment
samples that have visibly experienced high CaCO3 dissolution, like
those containing many fragmented foraminifers and G. menardii keels,
G. ruber and G. sacculifer specimens are usually absent, even in regions
where they are abundant in surface waters. All these observations
could explain the fast dissolution of their tests and the lack of an easily
quantifiable relationship between G. ruber and G. sacculifer fragments
. Another complication here is that
and bottom water ΔCO2−
3
fragments of G. ruber may be easily confused with those of G.
sacculifer which likely also contributes to the scatter in our data
(Fig. 7B).
Likewise, N. dutertrei and N. pachyderma species have similar
morphologies to one another which potentially contribute to
mistaken identification of fragments during counting. P. obliquiloculata tests have a very distinct morphology which hinders confusion
with other species or fragments of other species. Nonetheless, we find
no relationship between P. obliquiloculata fragmentation and ΔCO2−
3
of bottom waters, neither on EPR (Fig. 7A) nor on RIO (Fig. 7D). P.
obliquiloculata tests are even smoother than G. menardii tests with
extremely small pores. One theory could be that P. obliquiloculata
shells act as a very robust “closed chamber” and the shell dissolves
from outside in until a certain point is reached beyond which the shell
breaks apart randomly. Naturally, laboratory experiments are necessary to observe if this theory is verified.
Different planktonic foraminifer species have different fragmentation trends with increasing CaCO3 dissolution (Fig. 7). Also fluxes of
individual foraminifer species vary with location and time (Schiebel,
2002; Kawahata et al., 2002). So simply using a “whole assemblage”
of
fragmentation ratio for quantifying CaCO3 dissolution or ΔCO2−
3
bottom waters is unreliable in both core top and down core work.
And, unlike other foraminifer species, G. menardii test fragmentation
has been shown to have a quantitative relationship with increasing
dissolution in lab experiments (Ku and Oba, 1978).
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F. Mekik et al. / Earth and Planetary Science Letters xxx (2010) xxx–xxx
5.3. MFI in the subtropics
Foraminifer fragmentation, Mg/Ca and SNSW data from RIO independently corroborate MFI's dissolution trend in the subtropics. Although
G. menardii generally prefer tropical waters, there are a sufficient
number of G. menardii tests and their fragments in the subtropical
South Atlantic to make MFI a useful dissolution indicator there.
Loubere and Chellappa (2008) used the fragmentation trend of
G. trunctulinoides, G. bulloides and G. inflata (in two subgroups A and B)
in order to trace CaCO3 dissolution in subtropical and higher latitude
seas, specifically on RIO. We used some of the same core top samples
as they did. They reported the percent of undamaged specimens of G.
bulloides and G. inflata A and B within each sediment aliquot. Figure 9A
of bottom waters
and B shows their data plotted against ΔCO2−
3
(estimated from GLODAP) and against MFI (Fig. 9C). While the
fragmentation trend of G. bulloides has a remarkable relationship
2
with ΔCO2−
3 (R = 0.85), its relationship with MFI in the same samples
is less robust (R2 = 0.6).
Loubere and Chellappa (2008) used the percent of undamaged
individuals to quantify the fragmentation trend of G. truncatulinoides
specimens also. Figure 9C shows strong correlations between the
fraction of undamaged G. truncatulinoides tests and MFI (R2 = 0.8) and
(R2 = 0.73). Furthermore,
between that and GLODAP-based ΔCO2−
3
the decrease in Mg/Ca in G. truncatulinoides tests in RIO samples
(Fig. 9D) correlates well with both the increase in G. truncatulinoides
fragmentation in our data as well as the decrease in % undamaged
G. truncatulinoides tests in Loubere and Chellappa's (2008) work,
R2 = 0.9 and 0.73 respectively (Fig. 9D). These observations provide
further independent corroboration for MFI's applicability in the
subtropics and the potential for correlating MFI's dissolution trend
with foraminifers of higher latitudes like G. bulloides, G. truncatulinoides,
G. inflata and N. pachyderma.
5.4. Multi-basin dissolution trends: MFI and SNSW
Lastly, we compare MFI's multi-basin trend with that of Broecker
and Clark's (2001a) global calibration of SNSW. Figure 10 shows our
core top MFI data plotted against [CO2−
3 ]* as defined by Broecker and
Clark (2001a), Eq. (4) and derived from GLODAP data. We also plotted
Broecker and Clark's (2001a) SNSW data for N. dutertrei and P.
obliquiloculata tests (both from 355–415 μm size fraction) onto this
graph using the [CO2−
3 ]* estimates Broecker and Clark (2001a)
published for their core tops. While the SNSW data show more
scatter than MFI, the trend in SNSW data fits MFI's trend well. The
range of [CO2−
3 ]* for core tops from which we generated MFI data is
slightly larger (by ~ 20 μmol/kg) than that for SNSW.
The influence of surface ocean [CO2−
3 ] on SNSW data was
demonstrated by Barker and Elderfield (2002), Naik and Naidu
Fig. 9. Panels A and B: Loubere and Chellappa's (2008) percent undamaged data for Globigerina bulloides and Globorotalia inflata A and B plotted against MFI and ΔCO2−
Panel C:
3
from GLODAP bottle data. Panel D. Fragmentation data from this
Loubere and Chellappa's (2008) fraction undamaged data for G. truncatulinoides plotted against MFI and ΔCO2−
3
study and fraction undamaged data from Loubere and Chellappa (2008) for Globorotalia truncatulinoides plotted against Mg/Ca from G. truncatulinoides tests. MFI values shown are
an average of Loubere and Chellappa (2008) MFI data and those presented herein where core top samples overlap.
Please cite this article as: Mekik, F., et al., Progress toward a multi-basin calibration for quantifying deep sea calcite preservation in the
tropical/subtropical world ocean, Earth Planet. Sci. Lett. (2010), doi:10.1016/j.epsl.2010.08.024
F. Mekik et al. / Earth and Planetary Science Letters xxx (2010) xxx–xxx
13
calibration into oceanic regions of high surface ocean productivity
where the rain ratios at the seabed are important drivers of calcite
dissolution in the sediments, in addition to ΔCO2−
3 . Also, the influence
of [CO2−
3 ] of foraminifer habitat waters on G. menardii shell thickness
needs to be quantified to better understand the effects of surface
ocean parameters on MFI.
Acknowledgments
Fig. 10. Size-normalized shell weight (SNSW) for Neogloboquadrina dutertrei and
Pulleniatina obliquiloculata from Broecker and Clark (2001a) and MFI plotted against
2−
2−
[CO2−
3 ]*. [CO3 ]* for our samples is based on GLODAP bottle data; and [CO3 ]* for
Broecker and Clark's (2001a) core tops is from their work. [CO2−
3 ]* is in μmol/kg. For
samples for which we have MFI data with repeated counts, an average value of the
multiple counts are shown.
(2007) and Mekik and Raterink (2008), among others. This could
potentially explain the scatter in Broecker and Clark's (2001a) SNSW
data. And although Mekik and Raterink (2008) showed that MFI is
unaffected by surface ocean parameters like temperature and/or
[CO2−
3 ], evidence from G. menardii SNSW data to support this point is
currently lacking. Nevertheless, the corroboration of MFI's global
dissolution trend by Broecker and Clark (2001a) global SNSW data is
good news for both proxies.
We are grateful to the curators and repositories who provided
sediment samples without which our work would have been
impossible (June Padman and Bobbi Conard, Oregon State University;
Larry Peterson, RSMAS; Rusty Lotti-Bond, Lamont Doherty Earth
Observatory; Warren Smith, Scripps Institution of Oceanography;
Ellen Roosen, Woods Hole Oceanographic Institution; and curators at
the University of Hawaii). This study was supported by grants to
Mekik (OCE0326686 and OCE0825280) from the National Science
Foundation. We extend many thanks to Paul Loubere, Bob Anderson,
and Peter deMenocal for valuable discussions. We owe special thanks
to Richard Vallery for many thought-provoking discussions and ideas
about the reproducibility of our data. Thanks also to three anonymous
reviewers for their constructive and thoughtful comments which
improved our manuscript. And finally, we are grateful to Brian Adkins
for counting fragments of P.obliquiloculata and G. truncatulinoides (for
some samples), to Mathieu Richaud for sending us his raw MFI counts
for MW91-9-38BC and to Roger François and the staff at the Pacific
Center for Isotopic and Geochemical Research at the University of
British Columbia for their help with the ICP-MS.
Appendix A. Supplementary data
Supplementary data to this article can be found online at
doi:10.1016/j.epsl.2010.08.024.
6. Conclusions
We present data from 89 core top samples in tropical and
subtropical areas of three main ocean basins indicating that MFI and
the MFI transfer function have the potential to trace deep sea CaCO3
preservation as a quantitative bulk sediment dissolution indicator
across the tropical/subtropical globe. The % calcite dissolved that we
infer from MFI is consistent with calcite dissolution rates derived from
benthic flux chambers and other pore water work when combined
with calcite rain rates estimated from sediment accumulation rates
and deep sea sediment trap data.
We demonstrated corroboration for MFI's applicability in the
subtropics by multiple, independent CaCO3 dissolution indicators
including SNSW of G. truncatulinoides, shell fragmentation of multiple
species and decrease of Mg/Ca in tests of foraminifers with increasing
dissolution. In particular, the fragmentation trend of G. truncatulinoides correlates well with MFI's dissolution trend. Stronger evidence
to support MFI's calibration in subtropical core tops comes from Mg/
Ca data from three foraminifer species: G. truncatulinoides, G.
conglobatus and Globorotalia hirsuta. This is significant because surface
ocean temperature is not variable over RIO core top locations and the
dominant factor controlling shell Mg/Ca is dissolution of foraminifer
tests in these sediments. And lastly, MFI's core top calibration
correlates well with the fragmentation trend of foraminifers that
prefer higher latitudes like G. bulloides, G. truncatulinoides and N.
pachyderma.
These are important observations because a quantitative CaCO3
dissolution proxy, or a series of proxies well correlated with one
another, with world-wide applicability will yield a better understanding of the marine carbonate system. MFI is coming closer to
meeting the criteria for an ideal calcite dissolution proxy, but much
work remains to be done. For example, it is necessary to expand MFI's
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