Download Global sediment core-top calibration of the TEX86

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Geochimica et Cosmochimica Acta 72 (2008) 1154–1173
www.elsevier.com/locate/gca
Global sediment core-top calibration
of the TEX86 paleothermometer in the ocean
Jung-Hyun Kim a,*, Stefan Schouten a, Ellen C. Hopmans a,
Barbara Donner b, Jaap S. Sinninghe Damsté a
a
Royal Netherlands Institute for Sea Research, Department of Marine Biogeochemistry and Toxicology,
P.O. Box 59, 1790 AB, Den Burg, Texel, The Netherlands
b
Universität Bremen, FB 5 Geowissenschaften, Klagenfurter Straße, D-28359 Bremen, Germany
Received 8 August 2007; accepted in revised form 10 December 2007; available online 25 December 2007
Abstract
The TEX86 (TetraEther indeX of tetraethers consisting of 86 carbon atoms) paleothermometer is based on the relative distribution of archaeal lipids, i.e. isoprenoid glycerol dibiphytanyl glycerol tetraethers (GDGTs), and is increasingly used to
reconstruct past sea water temperatures. To establish a more extensive, global calibration of the TEX86 paleothermometer,
we analyzed GDGTs in 287 (in comparison with 44 in currently used calibration) core-top sediments distributed over the
world oceans and deposited at different depths. Comparisons of TEX86 data with (depth-weighted) annual mean temperatures
of the overlying waters between 0 m and 4000 m as well as with different seasonal mean temperatures at 0 m water depth
showed that the TEX86 proxy reflects mostly annual mean temperatures of the upper mixed layer. The relationship between
TEX86 values and sea-surface temperatures (SSTs) was non-linear mainly because below 5 !C the change in TEX86 values was
minor with temperature. This suggests that the TEX86 proxy might not be directly applicable for the Polar Oceans. Nevertheless, between 5 !C and 30 !C, the TEX86 proxy has a strong linear relationship with SSTs. Here, we, therefore, propose
a new linear calibration model (T = !10.78 + 56.2 * TEX86, r2 = 0.935, n = 223) for past SST reconstructions using the
TEX86 palaeothermometer.
" 2007 Elsevier Ltd. All rights reserved.
1. INTRODUCTION
Several geochemical proxies are currently used to reconstruct past sea water temperatures and they can be broadly
subdivided into proxies based on inorganic and organic fossil remains. Examples of inorganic temperature proxies are
d18O (e.g. Erez and Luz, 1983) and Mg/Ca ratios of foraminifera (e.g. Elderfield and Ganssen, 2000; Lea, 2003 and
references cited therein). The isotopic and trace element
inorganic proxies are based on thermodynamically different
behavior of isotopes and trace elements during the calcification of foraminifera which is primarily a function of temperature. Many approaches are used to investigate the
*
Corresponding author.
E-mail address: [email protected] (J.-H. Kim).
0016-7037/$ - see front matter " 2007 Elsevier Ltd. All rights reserved.
doi:10.1016/j.gca.2007.12.010
impact of factors other than temperature (so-called ‘‘vital
effects”) on the reliability of inorganic sea water temperature proxies such as culture studies, plankton tows, sediment traps and sediment core-top studies (e.g. Spero
et al., 1999). They, generally, show that calibrations of inorganic proxies are different for different species and that
other factors also influence the partitioning of stable isotopes and trace elements such as pH, salinity and the calcifying organism itself. In addition, inorganic proxies require
knowledge on the original composition of the sea water
especially on longer geological time scales (see Lea, 2003
and references cited therein).
Until recently, there was only one proxy based on organic fossil remnants, the alkenone unsaturation index (UK37 or
0
UK37 ). The original UK37 proxy proposed by Brassell et al.
(1986) was based on marine sediment core studies and
calculated from the relative distribution of C37 methyl
Global sediment core-top calibration of the TEX86 paleothermometer
alkenones containing 2–4 double bonds synthesized by a
limited number of haptophyte phytoplankton. This index
was later confirmed as a temperature proxy by culture
studies of Emiliania huxleyi (Prahl and Wakeham, 1987)
and global core-top studies (e.g. Müller et al., 1998). Subse0
quent studies, using the simplified UK37 which excludes the
C37 alkenones with 4 double bonds, have focused on establishing the accuracy and calibration of this proxy using
cultures, particulate organic matter, sediment traps and sediment core-tops (reviewed by Herbert, 2003). Although
these studies are still continuing, the general picture emerg0
ing is that the UK37 proxy is a fairly robust temperature proxy
which does not require knowledge on original sea water
composition, is confounded by only a relatively few factors
(e.g. growth rate; Prahl et al., 2005 and diagenesis; Hoefs
et al., 1998) and can be analyzed with relatively high preci0
sion (Herbert, 2003). Unfortunately, the use of UK37 proxy
is limited in tropical areas as tri-unsaturated alkenones
reach their detection limit complicating the reconstruction
0
of UK37 sea surface temperature (SST) above 28 !C and in
the Polar Oceans where alkenone-producing coccolithophorids do not occur (e.g. Sikes and Volkman, 1993).
Recently, a new organic sea water temperature proxy
was proposed based on archaeal tetraether lipids, the
1155
TEX86 proxy (TetraEther indeX of tetraethers consisting
of 86 carbon atoms; Schouten et al., 2002). These lipids
are biosynthesized by marine Crenarchaeota which occur
ubiquitously in the marine water column and are one of
the dominant prokaryotes in today’s oceans (Karner
et al., 2001). Marine Crenarchaeota biosynthesize different
types of glycerol dibiphytanyl glycerol tetraethers (GDGTs)
containing 0–3 cyclopentane moieties (I–IV; Fig. 1) and
crenarchaeol which, in addition to 4 cyclopentane moieties,
has a cyclohexane ring (V; Schouten et al., 2000; Sinninghe
Damsté et al., 2002). Finally, they also biosynthesize small
quantities of a regio-isomer of crenarchaeol (V0 ). From culture studies of the membrane composition of hyperthermophilic archaea it is known that the relative number of
cyclopentane moieties increases with growth temperature
(Gliozzi et al., 1983; Uda et al., 2001). An initial mesocosm
study showed that marine Crenarchaeota use the same
mechanism, i.e. higher temperatures result in an increase
in the relative amounts of GDGTs with 2 or more cyclopentane moieties (Wuchter et al., 2004). Thus, by measuring
the relative amounts of GDGTs present in marine sediments the temperature can be determined at which Crenarchaeota were living when they produced their
membranes. The TEX86 proxy was proposed as a means
HO
O
GDGT-I
O
O
[I]
O
OH
HO
O
GDGT-II
O
O
[II]
O
OH
HO
O
GDGT-III
O
O
[III]
O
HO
GDGT-IV
OH
O
O
O
[IV]
O
HO
OH
O
Crenarchaeol
O
O
[V]
O
OH
regio-isomer of crenarchaeol
Fig. 1. Structures of GDGTs discussed in the text.
[V’]
1156
J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173
to quantify the relative abundance of GDGTs (Schouten
et al., 2002):
TEX86 ¼ ð½III% þ ½IV% þ ½V0 %Þ=ð½II% þ ½III% þ ½IV% þ ½V0 %Þ
ð1Þ
The numbers in TEX86 refer to structures in Fig. 1. The
TEX86 proxy was calibrated using 44 sediment core-tops
obtained from 15 different locations. The resulting equation
was as follows (Eq. (2), Table 1):
T ¼ ðTEX86 ! 0:28Þ=0:015 ðr2 ¼ 0:92; n ¼ 44Þ
ð2Þ
This equation allowed the conversion of the TEX86 values
obtained into SSTs (see Schouten et al., 2002 for details).
This initial calibration study has been followed by other studies which investigated the validity of TEX86 as an SST proxy.
Mesocosm experiments confirmed that marine Crenarchaeota changed their membrane composition with
growth temperature and showed that changes in salinity
and nutrients did not substantially affect the temperature signal recorded by the TEX86 proxy (Wuchter et al., 2004; Schouten et al., 2007b). However, the correlation of TEX86 values
with temperatures had a similar slope as Eq. (2) but a different intercept (Eq. (3), Table 1). A survey of particulate organic matter showed that TEX86 values correlated well with
in situ temperature at depths <100 m (Wuchter et al., 2005)
with a correlation similar to that of the sediment core-top calibration (Eq. (4); Table 1). A recent sediment trap study in
the Arabian Sea showed that TEX86 temperature estimates
in a sediment trap at 500 m showed a seasonal cycle in agreement with satellite SST measurements but with an offset of
ca. 1–2 !C (Wuchter et al., 2006a,b). The deeper traps did
not show this seasonality and instead reflected annual mean
SSTs. The TEX86 proxy has also been shown to work in a
number of lakes (Powers et al., 2004). However, Powers
(2005) surveyed a larger number of lakes and found the
TEX86 proxy to work primarily in large lakes with relatively
high Crenarchaeota production compared to terrestrial input. The calibration line based on sediment core-tops in lakes
was similar to that reported by Schouten et al. (2002) for marine environments (Eq. (5), Table 1). This indicates, as shown
previously by Wuchter et al. (2004) in their mesocosm study,
that salinity has no large effect on the TEX86 proxy.
Application of the TEX86 proxy in reconstructing glacial
to Holocene SST in the Arabian Sea showed that several
features in the TEX86 temperature record corresponded
with well known global climatic temperature changes such
as Termination I and the Antarctic Cold Reversal (Huguet
et al., 2006). Differences were, however, noted between
0
TEX86 and UK37 SSTs, which was attributed to the different
source organisms which grew in different seasons. Indeed,
Herfort et al. (2006) showed that the TEX86 values in North
Sea sediments primarily reflected winter temperatures as
they bloom in that season of major input. The TEX86 proxy
seems to be unaffected by changes in water redox conditions
but is biased towards lower temperatures during maturation of organic matter (Schouten et al., 2004). Finally, it
was recently shown that terrestrial organic matter often
contain GDGTs I–V0 and, thus, TEX86 values in marine
sediments receiving large amounts of terrestrial organic
matter are biased, usually towards higher temperatures
(Weijers et al., 2006).
Although a number of studies have shown that the
TEX86 proxy is capable of reconstructing past SSTs, a number of issues still have to be resolved. One of the most
important issues is the calibration of TEX86 with temperatures which, until now, is based on relatively few sediment
core-tops (Schouten et al., 2002) and used less sensitive high
performance liquid chromatography/mass spectrometry
(HPLC/MS) techniques (see discussion in Schouten et al.,
2007a). Furthermore, considering the ubiquitous occurrence of Crenarchaeota in the ocean in contrast to the
Haptophytes, the TEX86 proxy has a potential to become
a useful tool for temperature reconstructions in the Polar
0
Oceans where the UK37 proxy cannot be applied. However,
the applicability of the TEX86 proxy to the Polar Oceans
has not been thoroughly tested yet.
Here we substantially improve the global sediment coretop calibration of the TEX86 proxy by reanalyzing previously published sediment core-top TEX86 samples using improved HPLC/MS methodology and adding a substantial
number of new sediment core-top data globally distributed
in the ocean. We also compare the new sediment core-top
calibration dataset with previously published calibration
equations and discuss their implications for past SST
reconstructions.
2. MATERIALS AND METHODS
2.1. Sediment core-tops
Marine sediment core-tops analyzed in this study were
collected mostly using box corer and multicorer from a
variety of regions and represent the uppermost 0–3 cm,
mostly 0–1 cm (see Appendix for detailed information).
Based on the recent analytical improvements in the analysis
of GDGTs (Schouten et al., 2007a), we reanalyzed the 0–
1 cm interval sediment core-top samples from the sample
suite used in the TEX86 temperature calibration of Schouten et al. (2002). In addition, we added TEX86 sediment
core-top data from the southern North Sea (Herfort
et al., 2006) into our new global sediment core-top dataset.
This global dataset is based on, in total, 287 core-top sediments from various oceans with 200 from the Atlantic, 28
from the Pacific, 23 from the Indian Ocean, and 36 from
the Polar Oceans (Table 2, Fig. 2a). Although the global
sediment core-top dataset is biased toward the South Atlantic and dominated by continental margin sediments, it
encompasses the entire range of temperatures and various
geographic provinces. Sediment core tops also include a
large range of water depths: epipelagic (38), mesopelagic
(42), bathypelagic (159), and abyssopelagic (48) zones (Table 2, Fig. 2b). It is assumed that the core-top sediments
represent Late Holocene time spans covering a few hundreds to, at maximum, a few thousands of years (cf. Müller
et al., 1998). Therefore, the comparison of sediment-based
TEX86 values with the temperature data from the instrumental period such as World Ocean Database temperatures
may cause some scatter in calibrations. However, sedimentbased approaches also provide the benefit of temporal and
spatial averaging of other factors, such as GDGT production at all seasons and depths throughout the annual cycle,
Table 1
Correlation of TEX86 of core-top sediments with temperatures obtained from the World Ocean Database 2001 (Conkright et al., 2002) and TEX86 temperature calibration models
Temp.
range
(!C)
TEX86definition
Eq. (#)
Equation
1
TEX86 = ([III] + [IV] + [V0 ])/ ([II] + [III] + [IV] + [V0 ])
Data
points
(n)
Determination
coefficient (r2)
Standard
error of
estimate (!C)
Schouten
et al. (2002)
0 to 30
2
T = (TEX86 ! 0.28)/0.015
43
0.920
2.0
Mesocosom
10 to 40
3
T = (TEX86 ! 0.0640)/0.017
15
0.790
3.9
Marine sediment trap
5 to 29
4
T = (TEX86 ! 0.29)/0.017
65
0.800
3.1
Lake sediment
5 to 27
5
T = (TEX86 ! 0.28)/0.016
10
0.890
Core-top
22 to 36
6
T = (TEX86 + 0.016)/0.027
24
0.780
!2 to 30
!2 to 30
!2 to 25
!2 to 15
0 to 10
0 to 6
!2 to 15
0 to 9
0 to 5
!2 to 30
!2 to 30
!2 to 30
!2 to 30
9
10
11
12
13
14
15
16
17
18
19
20
21
TEX86 = 0.374 ! 0.005 * T + 0.0009 * T2 ! 0.00001 * T3
TEX86 = 0.371 ! 0.005 * T + 0.0010 * T2 ! 0.00002 * T3
TEX86 = 0.377 ! 0.009 * T + 0.0020 * T2 ! 0.00004 * T3
TEX86 = 0.430 ! 0.047 * T + 0.0111 * T2 ! 0.00046 * T3
TEX86 = 0.422 ! 0.073 * T + 0.0241 * T2 ! 0.00143 * T3
TEX86 = 0.481 ! 0.119 * T + 0.0476 * T2 ! 0.00367 * T3
TEX86 = 0.414 ! 0.048 * T + 0.0159 * T2 ! 0.00086 * T3
TEX86 = 0.419 ! 0.096 * T + 0.0393 * T2 ! 0.00294 * T3
TEX86 = 0.498 ! 0.164 * T + 0.0771 * T2 ! 0.00746 * T3
TEX86 = 0.394 ! 0.009 * T + 0.0012 * T2 ! 0.00002 * T3
TEX86 = 0.375 ! 0.003 * T + 0.0004 * T2 + 0.000002 * T3
TEX86 = 0.377 ! 0.006 * T + 0.0009 * T2 ! 0.00001 * T3
TEX86 = 0.401 ! 0.010 * T + 0.0016 * T2 ! 0.00003 * T3
287
286
257
188
129
42
188
129
42
257
282
260
236
0.877
0.873
0.868
0.837
0.829
0.854
0.749
0.784
0.812
0.852
0.813
0.872
0.853
5
5
5
5
5
5
22
23
24
25
26
27
T = !10.78 + 56.2 * TEX86
T = !9.24 + 44.3 * TEX86
T = !10.70 + 56.1 * TEX861780.9
T = !11.3 + 57.03 * TEX86
T = !8.47 + 51.9 * TEX86
T = !11.54 + 57.5 * TEX86
223
209
178
45
33
190
0.935
0.870
0.915
0.941
0.813
0.936
mean,
mean,
mean,
mean,
mean,
mean,
mean,
mean,
0–30 m
0–200 m
0–1000 m
0–2000 m
0–4000 m
200–1000 m
200–2000 m
200-4000 m
New sediment core-top calibration models
Global core-top calibration model (0 m)
Global core-top calibration model (0–200 m)
Atlantic core-top calibration model (0 m)
Pacific-Indian core-top calibration model (0 m)
Epipelagic core-top calibration model (0 m)
Other pelagic core-top calibration model (>200 m)
to 30
to 25
to 30
to 30
to 30
to 30
Schouten
et al. (2002)
Schouten
et al. (2007b)
Wuchter
et al. (2005)
Powers
et al. (2004)
Schouten
et al. (2003)
This study
This study
This study
this study
This study
This study
This study
this study
This study
This study
This study
This study
This study
1.7
1.8
Global sediment core-top calibration of the TEX86 paleothermometer
Selected calibrations from the literature
Core-top
Polynomial models
Annual mean, 0 m
Depth-weighted annual
Depth-weighted annual
Depth-weighted annual
Depth-weighted annual
Depth-weighted annual
Depth-weighted annual
Depth-weighted annual
Depth-weighted annual
Spring, 0 m
Summer, 0 m
Fall, 0 m
Winter, 0 m
References
This study
This study
This study
This study
This study
This study
1157
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J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173
Table 2
Number of core-top sediment studied divided according to
geographical areas and pelagic zones
Sediment core-top
sample source
Geographic areas
Atlantic region
Nordic Sea
Northeast Atlantic
Western equatorial
Atlantic
Southeast Atlantic
Southwest Atlantic
Central South
Atlantic
Mediterranean
Black Sea
Pacific region
Northeast Pacific
Southeast Pacific
Pacific warm pool
East Sea (Japan Sea)
Indian Ocean region
Red Sea
Arabian sea
Equatorial Indian
Ocean
Polar Oceans
Arctic Ocean
Southern Ocean
Total sediment core-top data
Water depths
Epipelagic zone
Mesopelagic zone
Bathypelagic zone
Abyssopelagic zone
Total sediment core-top data
0–200 m
200–1000 m
1000–4000 m
4000–6000 m
Sample
number (n)
200
15
22
15
58
54
28
1
7
28
8
2
15
3
23
3
10
10
36
13
23
287
38
42
159
48
287
which is probably more suitable for temperature reconstruction on geological time scales.
2.2. Sediment extraction
Generally, sediments (1–3 g) were freeze-dried and
homogenized by mortar and pestle. The sediments were extracted by DionexTM accelerated solvent extraction (ASE)
technique using dichloromethane/methanol (9:1, v:v). The
extracts were separated by Al2O3 column chromatography
using hexane/dichloromethane (9:1, v/v), hexane/dichloromethane (1:1, v/v) and dichloromethane/methanol (1:1, v/
v) as subsequent eluents. The polar fraction (dichloromethane/methanol) was concentrated under N2, dissolved in
hexane/isopropanol (99:1, v/v), and filtered using a
0.4 lm PTFE filter prior to injection as described by Hopmans et al. (2000, 2004).
2.3. HPLC/MS analysis
Analyses were performed using an Agilent (Palo-Alto,
CA, USA) 1100 series HPLC/MS equipped with an autoinjector and Chemstation chromatography manager software (see Hopmans et al., 2000 and Schouten et al.,
2007a). Separation was achieved on a Prevail Cyano column (2.1 ( 150 mm, 3 lm; Alltech, Deerfield, IL, USA),
maintained at 30 !C. Injection volumes varied from 1 to
20 ll. GDGTs were eluted isocratically with 99% A and
1% B for 5 min, followed by a linear gradient to 1.8% B
in 45 min, where A = hexane and B = propanol. Flow rate
was 0.2 ml/min. After each analysis the column was cleaned
by back-flushing hexane/propanol (90:10, v/v) at 0.2 ml/
min for 10 min. Detection was achieved using atmospheric
pressure positive ion chemical ionization mass spectrometry
(APCI-MS) of the eluent. Conditions for the HP 1100
APCI-MS were as follows: nebulizer pressure 60 psi, vaporizer temperature 400 !C, drying gas (N2) flow 6 l/min and
temperature 200 !C, capillary voltage !3 kV, corona 5 lA
()3.2 kV). GDGTs were detected by Single Ion Monitoring
(SIM) of their [M+H]+ ions (dwell time = 234 ms) (Schouten et al., 2007a) and quantified by integration of the peak
areas. The TEX86 values were calculated according to Schouten et al. (2002) as mentioned above.
2.4. Statistical analysis
To compare TEX86 values obtained from core-top sediments with annual and seasonal mean temperatures of the
upper water column, temperature data were obtained from
the World Ocean Database (WOD) 2001 (Conkright et al.,
2002). The selected water depths were 0 m, 10 m, 20 m,
30 m, 50 m, 75 m, 100 m, 125 m, 150 m, 200 m, 250 m,
300 m, 600 m, 1000 m, 2000 m, and 4000 m. The WOD
temperatures were chosen in the nearest degree in latitude
and longitude to each core position. Depth-weighted annual mean temperatures from e.g. 0 to 200 m water depths
were calculated from WOD as follows:
Temp:0–200m ¼ ½ð10 * ðTemp:0m þ Temp:10m Þ=2Þ
þ ð10 * ðTemp:10m þ Temp:20m Þ=2Þ
þ ð10 * ðTemp:20m þ Temp:30m Þ=2Þ
þ ð20 * ðTemp:30m þ Temp:50m Þ=2Þ
þ ð25 * ðTemp:50m þ Temp:75m Þ=2Þ
þ ð25 * ðTemp:75m þ Temp:100m Þ=2Þ
þ ð25 * ðTemp:100m þ Temp:125m Þ=2Þ
þ ð25 * ðTemp:125m þ Temp:150m Þ=2Þ
þ ð50 * ðTemp:150m þ Temp:200m Þ=2Þ%=200
ð7Þ
To identify outliers from the dataset prior to further statistical analysis, bivariate scatter plots and dotplots were
applied using the Brodgar v.2.5.2 (www.brodgar.com) software package. To describe relationships between response
(TEX86 values) and explanatory (temperature) variables,
polynomial models and generalized additive models
(GAMs) were carried out using SigmaStat and Brodgar
software packages, respectively. The use of polynomial
models and GAMs in this study allowed the visualization
of the non-linear relationships between TEX86 values and
temperatures. Especially, GAMs (e.g. Hastie and Tibshirani, 1990) represent a method of fitting a smooth relationship between two or more variables through a scatter plot
of data points. GAMs are useful where the relationship be-
Global sediment core-top calibration of the TEX86 paleothermometer
1159
90
60
30
0
-30
-60
Atlantic region
Pacific region
Indian Ocean region
Polar Ocean region
-90
-150
-100
-50
0
50
100
150
-100
-50
0
50
100
150
90
60
30
0
-30
-60
Epipelagic
Mesopelagic
Bathypelagic
Abyssopelagic
-90
-150
Fig. 2. Locations of the core-top sediments analyzed for the TEX86 proxy in this study. They are grouped based on (a) geographical areas and
(b) different pelagic zones.
tween the variables is expected to be of a complex form, not
easily fitted by standard linear or non-linear regression
models. We applied 4 degrees of freedom (df = 4) for the
GAMs. For the bivariate linear regression models, analysis
of variance (ANOVA) tests or F tests were performed using
Brodgar v.2.5.2.
3. RESULTS AND DISCUSSION
3.1. Relation of TEX86 sedimentary signals with
temperatures
Based on the recent analytical improvements in the analysis of GDGTs (Schouten et al., 2007a), we reanalyzed the
0–1 cm interval sediment core-top samples (n = 29) from
the sample suite used in the TEX86 temperature calibration
study by Schouten et al. (2002). No systematic differences
compared to our previous results were obtained, when the
TEX86 values of both studies are cross-plotted, the following equation is obtained:
TEX86ðnewÞ ¼ 0:94 * TEX86ðoldÞ þ 0:02 ðr ¼ 0:975; n ¼ 29Þ
ð8Þ
In addition, we analyzed a wide variety of other sediment core-tops from various parts of the world oceans
and different water depths (Table 2, Fig. 2) and added published TEX86 data from the southern North Sea (Herfort
et al., 2006) into our new global sediment core-top dataset
(see Appendix for detailed information). Although the global sediment core-top data are dominated by continental
margin sediments, they all contained relatively low amounts
of soil-derived branched GDGT lipids, i.e. the Branched
Isoprenoid Tetraether Index (Hopmans et al., 2004) had
all values below 0.1, indicating negligible terrestrial influence on marine TEX86 signals (cf. Weijers et al., 2006).
We first explored from which water depth the temperature signal represented by the global core-top TEX86 data
appears to be derived. To achieve this, we compared the
TEX86 values with the (depth-weighted) annual mean temperatures of the overlying waters between 0 and 4000 m and
compared the degree of correlation (Table 1, Fig. 3). Strong
correlations (determination coefficient r2) ranging from
0.749 to 0.877 and adjusted determination coefficient (r2
(adj) 0.784–0.878) of TEX86 values with temperatures from
different water depths were observed confirming that this
proxy contains a strong temperature signal. However, the
0.3
0-30 m
0.2
Red Sea
15
20
25
30
5
10
15
20
25
−0.2 −0.1
30
0.1
0-2000 m
2
4
6
8
10
12
14
0
2
4
6
8
5
10
15
20
25
0-4000 m
r2 (adj) = 0.842
n = 42
−0.20
10
200-2000 m
1
2
3
4
5
6
200-4000 m
0
2
4
6
8
10
12
−0.10
r2 (adj) = 0.784
n = 129
−0.2
−0.2
r2 (adj) = 0.791
n = 225
14
0
2
4
6
8
r2 (adj) = 0.800
n = 42
−0.20
−0.1
−0.1
0.0
0.0
0.00
0.1
200-1000 m
0
0.10
0
r2 (adj) = 0.837
n = 129
−0.2
−0.2
r2 (adj) = 0.842
n = 188
r2 (adj) = 0.872
n = 257
−0.10
−0.1
−0.1
0.0
0.0
0.1
0-1000 m
0
0.10
10
Red Sea
0.00
5
0-200 m
0.0
0.1
−0.1
0.0
0
r2 (adj) = 0.877
n = 286
−0.2
r2 (adj) = 0.878
n = 287
0.1
s(Annual mean WOD temperature, df = 4)
−0.2 −0.1
0.0
0.1
0.2
Red Sea
0.1
0m
0.2
0.3
J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173
0.3
1160
1
2
3
4
5
Annual mean WOD temperature (°C)
Fig. 3. Generalized additive model (GAM) smoothing curves fitted to effects of temperatures on TEX86 values. TEX86 values are represented
as a function of depth-weighted annual mean temperatures of different water masses (Conkright et al., 2002), i.e. GAM models in this study
are given by: Yi ¼ gðXiÞ þ ei where Yi is the value of the response variable (TEX86) at sample i, g(Xi) is the predictor function, and ei is the
residual. The predictor function g(Xi) is given by: gðX i Þ ¼ a þ sðX i Þ where Xi is the explanatory variable (temperatures), a is the intercept, and
s(Xi) is the smoothing function. The y-axes represent the values of the smoother (s(Xi)). A normal distribution was assumed for the response
(TEX86) variable. The solid lines are the estimated smoother and the dashed lines represent 95% confidence intervals around the main effects.
The dots indicate the residuals. The black lines at the bottom of each plot indicate where the data values lie.
relationships deviated from linearity at high and low temperatures in contrast to the initial core-top study by Schouten et al. (2002). In fact, the third order of polynomial
regression equations showed better fits to the global coretop data rather than linear regressions (Table 1). The highest correlation applying polynomial models was obtained
using temperatures from 0 m water depth (r2 = 0.877). This
was confirmed by GAMs, with also the highest r2 (adj)
(0.878). When temperatures of deeper water depth intervals
were used to cross-correlate, r2 and r2 (adj) decreased but
still remained rather high, e.g. r2 = 0.829 and r2
(adj) = 0.837 when the TEX86 is correlated with temperatures from 0 to 2000 m water depth (Table 1, Fig. 3). However, when temperatures only from below 200 m were used,
the correlation patterns changed compared to those which
included epipelagic temperatures and r2 and r2 (adj)
dropped (Table 1, Fig. 3). It should be noted that for temperatures from mesopelagic to abyssopelagic water depths
available WOD temperature data were scarcer and the temperature ranges smaller which could have biased the correlation patterns.
Our statistical analysis thus suggests that the TEX86 values of sediment core-tops reflect predominantly mixed layer
(i.e. 0–30 m) temperatures. However, Crenarchaeota have
been found throughout the water column (e.g. Karner
et al., 2001) and our results do not exclude that they reflect
subsurface temperatures in certain areas. Menzel et al.
(2006) found lower temperatures during the periods of
sapropel deposition in the Mediterranean and attributed
this to a change in depth habitat of Crenarchaeota, i.e. from
0.3
Spring
0.0
5
10
15
20
25
−0.1
Fall
0
5
10
15
20
25
30
Winter
Red Sea
0
5
10
15
20
25
30
r2 (adj) = 0.857
n = 236
−0.2
−0.1
−0.2
r2 (adj) = 0.872
n = 260
−0.1
0.0
0.1
0.1
0.2
Red Sea
0.2
r2 (adj) = 0.816
n = 282
−0.2
30
0.3
0
0.3
Red Sea
0.1
0.1
0.0
−0.1
−0.2
r2 (adj) = 0.855
n = 257
0.0
s(WOD temperature-0 m, df = 4)
1161
Summer
0.2
Red Sea
0.2
0.3
Global sediment core-top calibration of the TEX86 paleothermometer
0
5
10
15
20
25
30
WOD temperature-0 m (°C)
Fig. 4. Generalized additive model (GAM) smoothing curves fitted to effects of temperatures on TEX86 values. TEX86 values are represented
as a function of seasonal mean temperatures at 0 m water depth (Conkright et al., 2002), i.e. GAM models in this study are given by:
Yi ¼ gðXiÞ þ ei where Yi is the value of the response variable (TEX86) at sample i, g(Xi) is the predictor function, and ei is the residual (see the
caption of Fig. 3 for detailed information).
surface waters to just above the chemocline at ca. 100 m.
Huguet et al. (2007) observed that the TEX86 values in sediment traps and a sediment core in the Santa Barbara basin
reflected subsurface temperatures (100–150 m) rather than
surface temperatures. However, taken together, it appears
that the TEX86 proxy represents more epipelagic temperatures rather than mesopelagic ones. Nevertheless, the varying degree in depth of the TEX86 signal might be one of the
reasons of the scatter observed in our global sediment coretop dataset.
To explore to what extent the global core-top TEX86 signals are biased by seasonal cycles of Crenarchaeota production, we plotted the TEX86 values against the seasonal
mean WOD temperatures (Table 1, Fig. 4). The core-top
TEX86 values were highly correlated to the mean temperatures of all major seasons at 0 m water depth. The r2 values
were similar ranging from 0.813 to 0.872 with the highest
value for fall. The results from the GAMs were in line with
those from the polynomial models. This suggests that the
TEX86 values of core-tops reflect annual mean temperatures and seasonality has a relatively minor effect on the
TEX86 calibration overall. However, it has been frequently
documented that Crenarchaeota are more abundant in certain times of the season (Murray et al., 1998; Wuchter et al.,
2005, 2006a; Herfort et al., 2006). Thus, if the seasonal temperatures at times of growth of Crenarchaeota are substantially different from that of annual mean SST then there will
likely be a bias towards seasonal SST in the TEX86 proxy.
Indeed, Herfort et al. (2006) found TEX86 values in North
Sea surface sediments which reflected winter SST in agreement with the high abundance of Crenarchaeota in the winter (Wuchter et al., 2006a). Seasonality in crenarchaeotal
abundance may account for some of the scatter in the global core-top calibration dataset.
The correlation patterns between TEX86 values and temperatures (Figs. 3 and 4) are different from the initial coretop study (Schouten et al., 2002), the mesocosm study
(Wuchter et al., 2004) and the sediment trap study (Wuchter et al., 2005). Specifically, the correlation based on the
287 core-tops presented here is best described by a non-linear relationship in contrast to the other studies. This phenomenon is mainly due to the large scatter and non-linear
behavior which can be observed at the low (<5 !C) end of
the TEX86-temperature calibrations. The high scatter for
temperatures <5 !C may be partly due to larger analytical
errors of the TEX86 values at low temperatures as a result
of the low abundance of all GDGT isomers used in the
TEX86 ratio versus GDGT-I and crenarchaeol (Schouten
et al., 2007a). Therefore, subtle changes in the composition
of these isomers are more difficult to quantify. Nevertheless,
it seems that the response of TEX86 to temperature is not
linear at these low temperatures and is actually rather
invariant. The sediment locations which showed TEX86 values substantially higher than expected (>0.4) for such cold
temperatures were all derived from the Polar Oceans. This
suggests that the TEX86 proxy does not correspond well to
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J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173
annual or seasonal mean temperatures in the Polar Oceans.
Possibly, growth conditions in this area are completely different for the Crenarchaeota compared to other oceanic
provinces or Crenarchaeota thriving in the Polar Oceans respond differently to temperature in their membrane
composition.
An apparently larger scatter is also visible for the
TEX86-temperature relationships at temperatures >25 !C
(Figs. 3 and 4). Strikingly, all data points that substantially
deviated from the correlation lines were obtained from Red
Sea surface sediments. These have TEX86 values ranging
from 0.75 to 0.85 while core-tops from, for example, the
Indonesian Pacific Warm Pool with similar annual mean
SSTs have TEX86 values ranging from 0.69 to 0.71. This
suggests that Crenarchaeota thriving in the Red Sea may
be a different population with a different temperature response or that they thrive in a different season. Possibly,
Crenarchaeota in the Red Sea are predominantly thriving
in the summer at higher temperatures than the annual mean
SST. Another important difference between the Red Sea
and other oceans is salinity which varies between 37&
and 40&, substantially higher than open ocean salinities
of 35–36&. However, Wuchter et al. (2004) showed, using
mesocosm experiments, that salinity does not have an effect
on the TEX86. In addition, Powers et al. (2004) showed that
the TEX86 calibration line in lakes is similar to that in marine systems. Hence, it seems unlikely that salinity is the sole
cause for the relatively high TEX86 values in the Red Sea
core-tops. Further studies of the TEX86 proxy in the Red
Sea are needed to investigate the causes for the unusually
high TEX86 values.
3.2. Global sediment core-top calibration of the TEX86 proxy
To estimate past temperatures from the TEX86 proxy,
we need to establish a convenient calibration equation so
that we can easily convert TEX86 values into temperatures.
From the above it is clear that the correlation between
TEX86 values and temperatures appears to be non-linear.
There was no significant TEX86-temperature relationship
below 5 !C and thus the TEX86 proxy is not recommended
to reconstruct temperatures in such low temperature environments. In fact, the correlation can be well described with
a linear regression if we only consider TEX86 data above
5 !C (Fig. 5). As the TEX86 values of sediment core-tops reflect predominantly epipelagic water temperatures (see
30
T = -9.24 + 44.3 * TEX86 (Eq. 23)
(n = 209, r2 = 0.870)
Annual mean WOD temperature (°C)
T = -10.78 + 56.2 * TEX86 (Eq. 22)
(n = 223, r2 = 0.935)
25
Pacific warm pool
20
15
10
5
Eq. 2 by Schouten et al. (2002)
T = (TEX86 - 0.28) / 0.015
(n = 43, r2 = 0.92)
a
0.3
0.4
0.5
0.6
0m
c
0-200 m
0.3
0.7
0.4
0.5
0.6
0.7
TEX 86
1
0
1
-1
0
-1
b
-2
-2
Standardised residuals
2
2
TEX 86
0.4
0.5
TEX86
0.6
0.7
d
0.4
0.5
0.6
0.7
TEX86
Fig. 5. Global core-top calibration models. (a) cross plot of annual mean WOD temperature at 0 m water depth with TEX86 values, (b)
standardized residuals obtained by the linear regression model (Eq. (22)) versus TEX86 values, (c) cross plot of depth-averaged annual mean
WOD temperature above 200 m with TEX86 values, and (d) standardized residuals obtained by the linear regression model (Eq. (23)) versus
TEX86 values. The stippled line in (a) indicates the calibration line by Schouten et al. (2002). The data from the Polar Oceans and the Red Sea
were excluded. Cross symbols indicate excluded data points which deviated more than 2 times from the standard deviation of the linear
regression models. Dashed lines represent 95% confidence intervals.
Global sediment core-top calibration of the TEX86 paleothermometer
above discussion), we tested two different linear regression
models: 0 m and 0–200 m. The TEX86 dataset, which excludes all data from the Polar Oceans (<5 !C) and the
Red Sea (see above), was adjusted by removing outliers
(>±1r standardized residuals) in order to remove the unrealistically large influence of a few data points on the whole
calibration dataset (cf. Conte et al., 2006). Based on the
TEX86 data remaining (223 out of 287 for 0 m and 209
out of 287 for 0–200 m), we established linear TEX86 calibration models. The resulting linear calibration models
were as follows (Table 1, Fig. 5):
T ¼ ! 10:78 þ 56:2 * TEX86 ðr2 ¼ 0:935; n ¼ 223; 0 mÞ
2
ð22Þ
T ¼ ! 9:24 þ 44:3 * TEX86 ðr ¼ 0:870; n ¼ 209; 0–200 mÞ
ð23Þ
ANOVA tests confirmed the linear relationships for
both calibration models (p < 0.0001, Table 3). However,
when depth-weighted temperatures from 0–200 m water
depth were used for a regression model (Fig. 5c), standardised residuals showed a sigmoid-like pattern (Fig. 5d),
indicating that additional factors besides temperatures
may influence TEX86 values. Moreover, data from the Pacific warm pool appeared as a deviating cluster from the
general linear trend and r2 decreased significantly compared
to that of the 0 m regression model (Fig. 5a). This suggests
that the TEX86 proxy represents more mixed-layer temperatures (0-30 m water depth) than those from lower parts of
the epipelagic water mass.
Our sediment core-top collection is mostly from the
Atlantic (200 out of 287), especially from the South Atlantic
(150 out of 287) and therefore our global calibration may
be geographically biased. To investigate this, we established
TEX86-temperature calibrations based on geographic distributions and then compared them to the global calibration
(Fig. 6a). Although the sample number from the Pacific
and the Indian Ocean is much less than that of the Atlantic,
the calibration equation based on the data from the Pacific
and the Indian Ocean only is almost identical to the globaland Atlantic-based ones. This implies that there is no significant geographical bias in our calibration and that it is generally applicable. We also established TEX86-temperature
calibrations for sediments deposited at shallow depths (0–
200 m water depth) and deeper depths (below 200 m water
depth) to test the effect of Crenarchaeota production in deeper water depths (Fig. 6b). The TEX86 calibration based on
sediments from shallow depths differs only slightly from
that of the deeper sites, suggesting that there is a negligible
influence of the deeper Crenarchaeota production to the
global calibration dataset. It should also be noted that the
1163
dataset from shallow sites does not cover fully the entire
temperature range, which might have biased the correlation
pattern.
The standard deviation of TEX86 temperature estimates
using our new global linear calibration (Eq. (22)) was 1.7 !C
(Fig. 7a) which is lower than the initial core-top calibration
(2.0 !C) by Schouten et al. (2002) and slightly higher than
0
that of the alkenone unsaturation index UK37 (1.2 !C; Conte
et al., 2006). The estimation error in temperature from
TEX86 analysis is relatively high but it should be noticed
that it comprises several errors. Firstly, it contains the error
introduced by the analytical reproducibility which is currently ±0.3 !C (Schouten et al., 2007a). In this study, the
mean standard deviation of TEX86 analysis was 0.3 !C,
ranging from 0.1 !C to 1 !C. As mentioned above, seasonality and depth of Crenarchaeota production may cause
additional scatter of TEX86 values and thus increase estimation errors. To investigate this, we plotted the relative difference of TEX86–based temperatures with the actual
annual mean WOD temperatures at 0 m water depth
(Fig. 7b). One striking feature is that TEX86 temperatures
were lower relative to real annual mean temperatures off
NW Africa, an upwelling area. This may be due to higher
GDGT fluxes during upwelling seasons which are characterized by cold, nutrient-rich waters (Wuchter et al.,
2006b). However, it is impossible at this stage to estimate
the seasonal bias in our core-tops due to the lack of data
on the seasonal abundance of Crenarchaeota in the different oceanic provinces. In addition to seasonality, the depth
of Crenarchaeota production may also cause lower TEX86
temperatures as has, for example, been observed for the
Santa Barbara basin (Huguet et al., 2007). This may also
be the cause, for example, of the cold-biased TEX86 temperature off NW Africa. One supporting finding is that the differences between modeled and WOD temperatures were
smaller when we used the calibration model of 0–200 m
water depth (Fig. 7c and d). However, it is impossible at
this stage to establish the production depth of the TEX86
signals for all the individual core-tops. Finally, it is well
known that proxy signals in sediments are not always derived only from overlaying water columns but may be derived by lateral sediment advection from completely
different areas with potentially different SSTs. Especially organic compounds, such as alkenones and likely also
GDGTs, which are primarily attached to fine-grained particles can suffer from lateral transport (e.g. Ohkouchi
et al., 2002). Previously, Benthien and Müller (2000)
showed that core-tops in the Argentine basin were affected
0
by lateral sediment transport inducing cold-biased UK37
SST estimates. This lateral transport effect has been confirmed by recent studies by Mollenhauer et al. (2006) and
Table 3
Results of ANOVA tests
df
Sum Sq.
Mean Sq.
F value
p value
0m
TEX86
Residuals
1
221
8957.0
621.5
8957.0
2.8
3184.8
<0.0001
0–200 m
TEX86
Residuals
1
207
4590.7
680.7
4590.7
3.3
1395.9
<0.0001
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J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173
Annual mean WOD temperature (°C)
30
Global
T = -10.78 + 56.2 * TEX86 (Eq. 22)
25
(n = 223, r2 = 0.935)
20
Atlantic region (+)
T = -10.70 + 56.1 * TEX86 (Eq. 24)
(n = 178, r2 = 0.915)
15
Pacific-Indian Ocean regions (O)
T = -11.3 + 57.03 * TEX86 (Eq. 25)
(n = 45, r2 = 0.941)
10
a
Annual mean WOD temperature, 0 m (°C)
5
0.3
0.4
0.5
0.6
0.7
Annual mean WOD temperature (°C)
30
Global
T = -10.78 + 56.2 * TEX86 (Eq. 22)
25
(n = 223, r2 = 0.935)
20
Epipelagic zone (0-200 m, O)
T = -8.47 + 51.9 * TEX86 (Eq. 26)
(n = 33, r2 = 0.813)
15
Other pelagic zones (>200 m, + )
T = -11.54 + 57.5 * TEX86 (Eq. 27)
(n = 190, r2 = 0.936)
10
5
b
Annual mean WOD temperature, 0 m (°C)
0.3
0.4
0.5
0.6
0.7
TEX86
Fig. 6. Comparison of sediment core-top calibration models: (a) Cross plot of annual mean WOD temperatures from the Atlantic region
(cross) and from the Pacific-Indian Ocean region (circle) with TEX86 values and (b) cross plot of annual mean WOD temperatures for the
epipelagic samples (circle) and for those sediment core-tops recovered from deeper than 200 m (cross) with TEX86 values. Note that the annual
mean WOD temperatures are from 0 m water depth.
Rühlemann and Butzin (2006). However, we did not observe cold-biased TEX86 temperatures in the Argentine basin (Fig. 7). This indicates that the TEX86 is less subjected
to lateral sediment transport effect. This idea is primarily
derived from the reported relatively younger 14C ages of
GDGTs compared those of alkenones in the same sediment
layer (Mollenhauer et al., 2007). Therefore, lateral transport probably plays a smaller role in introducing scatter
in global core-top calibrations in case of GDGTs than for
alkenones.
3.3. Implications for past TEX86 temperature reconstructions
Our results above show that there is in general a strong
positive relationship between TEX86 values and annually
averaged temperatures in the global ocean. However, it is
clear that this correlation varies in some parts of the ocean
and some regions (Polar Oceans) are not appropriate at
present for estimation. This will likely depend on the ecology and the seasonality of Crenarchaeota. Relative changes
in SST can be viewed with more confidence provided that
no large changes in the ecology of Crenarchaeota occurred.
In this sense, the TEX86 proxy does not differ much from
0
other proxies such as the UK37 proxy and the Mg/Ca ratio
of planktonic foraminifera which are also strongly influenced by the ecology of their source organisms. Constraints
on past ecological behavior of Crenarchaeota might be obtained by stable carbon isotopic analysis of GDGTs. The
d13C values of crenarchaeotal GDGTs from different regions are relatively similar and thought to reflect mainly
6
Standard dev. of residuals = 1.8°C
Standard dev. of residuals = 1.7°C
4
2
0
-2
-4
a
-6
c
5
10
15
20
25
30
5
10
Annual mean WOD temperature, 0 m (°C)
60
30
30
0
0
-30
-30
-60
-60
Annual mean SST, 0 m
0m
-100
-50
0
50
100
30
5
4
4
3
2
0
-1
-150
25
5
1
b
20
150
Longitude
-2
-3
Residuals (modeled SST - WOD SST, ˚C)
90
60
-90
15
Annual mean WOD temperature, 0-200 m (°C)
90
Latitude
1165
3
2
1
0
-1
-2
-3
d
-4
-90
0-200 m
-5
-150
-100
-50
0
50
100
-4
∆T (modeled T - WOD T, °C)
∆T (modeled T - WOD T, °C)
Global sediment core-top calibration of the TEX86 paleothermometer
-5
150
Longitude
Fig. 7. (a) Temperature differences between modeled temperatures based on the Eq. (22) and annual mean WOD temperatures at 0 m
water depth and (b) their spatial distribution pattern using variogram analysis and ordinary kriging (Davis, 2002), interpolating the data
to a 2.5! ( 2.5! grid with 7! ( 7! searching radius. (c) and (d) are based on the Eq. (23) and averaged annual mean WOD temperatures
above 200 m water depth. There was no significant difference using different data interpolation grids and searching radiuses in latitude
and longitude.
the 13C of dissolved inorganic carbon (e.g. Kuypers et al.,
2001). Similar to the use of 13C of planktonic foraminifera,
we may thus use the d13C of crenarchaeotal GDGTs to infer the ecology of past marine Crenarchaeota. Unfortunately, the d13C analysis of GDGTs requires laborious
chemical degradation techniques (e.g. Schouten et al.,
1998) and is quite insensitive and thus will be difficult to
do in sufficiently high resolution as required for paleoceanographic studies.
In light of our new core-top calibration it may be necessary to re-evaluate previous SST reconstructions which
used Eq. (2). As an example, TEX86 values used to reconstruct changes in SST in the Arabian Sea from the last glacial maximum (LGM) to the Late Holocene by Huguet
et al. (2006) were converted into SSTs using two different
calibrations (Fig. 8a and b). As expected they showed the
same trends and most temperatures were within 1.5 !C of
each other, i.e. within the TEX86 temperature estimation errors (Fig. 8c). However, the range in absolute temperatures
reconstructed with the new calibration was somewhat smaller. The previous reconstruction yielded a SST maximum of
32.3 !C while here the SST maximum of 32.2 !C was found
and previous LGM estimates were ca 22 !C while the new
calibrations generated LGM estimates of ca. 24 !C. These
latter temperatures are likely more realistic (cf. Huguet
et al., 2006). In summary, the new linear calibration yields
slightly warmer temperatures (about 1–1.5 !C) than those
previously determined, especially at relatively lower
temperatures.
For SST reconstructions where TEX86 values exceeds
present day values (>0.72), such as is the case for mid latitude and tropical oceans in the Mesozoic and Cenozoic a
different equation has been used (Eq. (6), Table 1; Schouten
et al., 2003). The reason for using this equation rather than
that of Eq. (2) was that in order to reconstruct SSTs for
these time periods, it was necessary to extrapolate the calibration line given in Eq. (2) as SST was much higher than
present day. However, close examination revealed that for
core-top sediments with annual mean SST >20 !C there
seemed to be a different relation between TEX86 values
and temperatures, i.e. Eq. (6), which has a steeper slope
compared to general calibration line, i.e. Eq. (2) (see discussion in Schouten et al., 2003). Our new calibration line obtained in this study shows that there is no substantial
difference in the TEX86 versus SST calibration line above
or below 20 !C. In agreement with this finding, Schouten
et al. (2007b) recently showed that incubation of Crenarchaeota in mesocosms at temperatures between 30 !C
and 40 !C yielded a similar relationship between TEX86 values and temperatures as observed by Wuchter et al. (2004)
for temperatures between 10 !C and 30 !C. Therefore, it
seems now that instead of Eq. (6) it is better to use Eq.
(22) to convert TEX86 values into SSTs for time periods
where SSTs have been >29 !C (i.e. above present day equatorial SST).
TEX86 values measured for tropical oceans during the
mid-Cretaceous are varying from 0.69 to 0.84 for OAE 1a
to 0.92–0.96 for OAE 2 (Schouten et al., 2003). Using Eq.
1166
J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173
Sediment core 905
0.72
0.68
0.64
TEX 86 SST (°C)
34
b
32
TEX86 index
0.76
a
0.60
30
28
26
24
Eq. 22 (this study, 0 m))
Eq. 2 (Schouten et al., 2002)
22
∆SST (°C)
2
c
1
0
Eq.22-Eq.2
-1
0
1
2
3
4
5
Core depth (m)
Fig. 8. (a) TEX86 record of core NIOP905 in the Somali Basin, Arabian Sea (Huguet et al., 2006), (b) TEX86 temperature records using the
core-top calibration (Eq. (2)) of Schouten et al. (2002) and the new linear global core-top calibrations (Eq. (22)), and (c) temperature
differences (DSST) between two calibrations (Eq. (22)-Eq. (2)).
(22) these TEX86 values would be converted to SSTs of
28–36 !C for OAE 1a and 41–43 !C for OAE 2. This is
3–8 !C higher than the original SST reconstructions using
Eq. (6) and higher than most estimates using d18O of presumed planktonic foraminifera (e.g. Wilson and Norris,
2001; Norris et al., 2002). However, recent estimates
using d18O of planktonic foraminifera also suggest peak
SSTs of up to 41 !C (Bice et al., 2006). Hence, the revised
upper SST estimates may not be inconsistent with those
of planktonic foraminifera especially considering the
number of assumptions involved in calculating SST from
d18O (e.g. salinity, pH, d18O of water, etc.), the uncertainty regarding the ecology of extinct species of foraminifera and seasonal biases in both foraminifera and
crenarchaeotal proxies. Nevertheless, care has to be taken
in interpreting the absolute value of SST reconstructions
using extrapolated calibration lines and studies using
extrapolated TEX86 temperature reconstructions are likely
to be more of use in documenting relative changes in
temperature rather than providing absolute temperature
estimates.
4. CONCLUSIONS
Our newly extended sediment core-top dataset based
on 287 TEX86 measurements obtained from core-top sediments distributed over the world oceans provides new insights for the TEX86 application in the low temperature
regions and the depth and seasonal origin of the sedimen-
tary signal. Our study showed that the TEX86 was
strongly correlated with annual mean temperatures of
the mixed layer but the relationship was non-linear in contrast to the initial core-top calibration by Schouten et al.
(2002). The non-linearity was due to the lack of a
TEX86-temperature relationship below 5 !C, suggesting
that the TEX86 proxy might not be applicable in the Polar
Oceans. However, between 5 !C and 30 !C, a clear linear
relationship of TEX86 with SST was apparent. Therefore,
we established a new calibration model based on a linear
regression between the TEX86 and the temperatures of the
surface mixed layer which can serve for past SST reconstructions. However, our results do not exclude that
reconstructed TEX86 temperatures can be biased due to
the seasonality and deeper water depth of Crenarchaeota
production.
ACKNOWLEDGMENTS
Various people gratefully provided core-top sediments for
this study: A. Jaeschke, E. Epping and T. van Weering (NIOZ),
the WHOI core repository, E. Schefuß (Bremen University), R.
Smittenberg (Washington University), A. Schimmelman (Indiana
University), S. Wakeham (SKIO), T. Eglinton (WHOI), X. Crosta (EPOC), E. Michele and M.-A. Sicre (LSCE), N. Ohkouchi
(JAMSTEC), and G. Mollenhauer (AWI). We thank three anonymous reviewers and R. Harvey for their constructive suggestions which improved the paper. We are also grateful for
discussions on statistical techniques with H. Witte and F.
Lespinas.
Global sediment core-top calibration of the TEX86 paleothermometer
1167
APPENDIX A1
Sediment core
Longitude
Latitude
Water
depth (m)
Coring
device
Sampling
level (cm)
TEX86
index
AI I-107-6-17
AI I-107-6-18
AI I-107-6-20
AI I-107-6-24
AI I-107-6-27
AI I-107-6-70
AI I-107-6-72
AII-GC-9
AII-GGC-22
All-093-19-96
AMR-1
AMR-2
Anker A24
Anker A26
BalsfjordARC14
BOFS 5K 0–1
BOFS 8K 0–1
BS 01E-27
BS 02E-105
BS 04E-114
BS 05E-564
BS 06E-335
BS 07E-1288
BS 09E-2129
CB1 MC15
CB2 MC19
CHN-043-1-4
CHN-043-1-5
CHN-115-4-34
CHN-115-4-35
CHN-115-4-43
Drammensfjord-1
Drammensfjord-2
ENAM9407
Frisian Front
G2-9box0–1
G2-9 trp 0–1
G6-3 0–1
GeoB 8301-5
GeoB 8303-5
GeoB 8305-1
GeoB 8306-1
GeoB 8307-5
GeoB 8308-2
GeoB 8310–1
GeoB 8311-1
GeoB 8313-1
GeoB 8314-1
GeoB 8315-5
GeoB 8316-1
GeoB 8317-1
GeoB 8318-1
GeoB 8319-1
GeoB 8321-1
GeoB 8322-1
GeoB 8323-1
GeoB 8324-1
GeoB 8325-1
GeoB 8327-1
!22.182
!21.975
!17.952
!8.718
!3.518
7.763
8.008
!32.498
!3.330
34.572
140.237
139.978
12.567
12.483
19.117
!21.688
!22.040
29.500
32.000
30.000
30.500
35.000
34.000
34.000
!128.000
!125.833
40.167
40.167
5.330
0.100
0.080
10.396
10.423
!4.032
4.500
123.592
123.592
117.193
17.699
16.785
17.177
16.843
16.500
16.268
16.382
16.310
15.944
15.813
15.695
15.734
15.163
16.805
18.078
18.117
18.118
18.222
18.091
17.279
17.007
!55.278
!55.270
!54.857
!54.857
!54.753
!47.535
!46.822
!56.195
!54.792
28.535
!63.855
!64.658
!6.033
!6.050
69.367
50.865
52.500
45.500
45.000
44.500
44.000
44.500
44.000
45.500
46.750
46.767
17.650
17.650
!51.000
!53.600
!54.592
59.668
59.633
62.956
53.700
!9.343
!9.340
!10.152
!34.775
!34.261
!33.478
!33.741
!33.834
!33.920
!32.909
!32.365
!32.123
!32.499
!32.888
!32.740
!32.329
!32.152
!32.496
!31.864
!31.954
!32.032
!31.747
!30.595
!29.703
!3926
!4119
!4119
!3255
!2921
!2473
!2270
!3223
!2768
!963
!3726
!2948
!5
!6
>!100
!3547
!4045
!27
!105
!114
!564
!335
!1288
!2129
!2700
!2700
!1296
!1470
!3788
!2643
!1260
!100
!110
!2060
!34
!2720
!2720
!1182
!1941
!3447
!712
!1924
!2668
!3162
!1995
!2535
!1090
!1977
!2996
!2667
!2930
!307
!69
!104
!105
!90
!100
!134
!88
GC
GC
PC
GC
GC
GC
PC
GC
GGC
PC
MUC
MUC
Grab
Grab
BC
KC
KC
BC
BC
BC
BC
BC
BC
BC
BC
BC
GC
GC
PC
PC
GC
BC
BC
BC
BC
BC
Triple
PC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–2
0–2
0–1
0–1
0–1
0–1
0–1
0–0.5
0–0.5
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0.427
0.435
0.480
0.338
0.317
0.408
0.408
0.343
0.333
0.755
0.449
0.440
0.625
0.646
0.343
0.482
0.452
0.503
0.450
0.446
0.482
0.486
0.457
0.509
0.477
0.467
0.806
0.838
0.415
0.343
0.458
0.420
0.410
0.363
0.406
0.682
0.685
0.691
0.496
0.497
0.475
0.488
0.501
0.504
0.484
0.499
0.480
0.475
0.504
0.499
0.508
0.419
0.401
0.410
0.399
0.410
0.409
0.402
0.419
STDEV
0.009
0.010
0.006
0.004
0.011
0.009
0.001
0.037
0.002
0.003
0.014
0.007
0.002
0.030
0.008
0.005
0.008
0.004
0.012
0.008
0.006
0.003
0.000
0.002
0.003
0.005
0.008
0.000
0.004
0.003
0.006
0.005
0.003
Temp.
0 m (!C)
Temp.
0–200 m (!C)
0.8
1.0
2.7
0.8
0.4
5.7
6.9
0.6
0.4
25.4
!0.3
0.2
25.4
25.4
6.7
12.9
12.1
21.6
13.7
13.7
13.7
14.5
14.4
13.7
12.3
12.4
29.4
29.4
2.5
0.7
0.1
9.0
9.0
8.0
10.6
28.2
28.2
28.0
17.9
18.4
16.0
18.1
18.1
18.1
17.1
17.1
18.1
18.1
18.1
18.1
18.1
17.1
15.0
14.9
14.9
15.0
14.9
15.8
13.6
(continued
0.3
0.4
2.2
0.0
0.2
4.1
5.4
0.5
0.2
21.8
0.6
0.3
16.5
16.5
5.9
11.0
10.5
16.8
8.3
8.5
8.3
8.4
23.3
23.3
1.8
0.4
!0.1
6.9
6.9
6.4
20.3
20.3
21.0
12.8
14.3
11.3
13.9
13.9
13.9
12.6
12.6
14.3
14.3
14.3
14.3
14.3
12.6
10.2
10.2
10.2
10.2
10.2
11.0
on next page)
1168
J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173
Appendix A1 (continued)
Sediment core
Longitude
Latitude
Water
depth (m)
Coring
device
Sampling
level (cm)
TEX86
index
STDEV
Temp.
0 m (!C)
GeoB 8328-1
GeoB 8329-1
GeoB 8331-2
GeoB 8332-3
GeoB 8333-1
GeoB 8336-5
GeoB 8337-5
GeoB 8338-1
GeoB 8340-1
GeoB 8342-5
GeoB 8343-1
GeoB10016-2
GeoB10026-2
GeoB10032-1
GeoB10034-3
GeoB10038-3
GeoB10040-3
GeoB10042-2
GeoB1501-1
GeoB1503-2
GeoB1504-1
GeoB1506-1
GeoB1515-2
GeoB2022-3
GeoB2102-1
GeoB2104-1
GeoB2105-3
GeoB2106-1
GeoB2107-5
GeoB2108-1
GeoB2110-1
GeoB2111-2
GeoB2112-1
GeoB2124-1
GeoB2125-2
GeoB2126-1
GeoB2130-1
GeoB2201-1
GeoB2202-5
GeoB2205-4
GeoB2206-1
GeoB2207-2
GeoB2208-1
GeoB2212-1
GeoB2213-1
GeoB2215-8
GeoB2216-2
GeoB2701-4
GeoB2703-7
GeoB2705-7
GeoB2706-6
GeoB2707-4
GeoB2708-5
GeoB2712-1
GeoB2714-5
GeoB2715-1
GeoB2717-8
GeoB2718-1
GeoB2719-3
GeoB2722-2
GeoB2723-2
17.058
17.034
16.714
16.660
16.615
12.344
13.178
14.322
14.825
13.001
13.325
96.660
99.521
99.681
101.499
103.246
102.859
104.643
!32.012
!30.648
!31.287
!35.182
!43.665
!20.908
!41.200
!46.378
!46.738
!46.497
!46.457
!46.230
!45.522
!45.223
!43.377
!39.560
!39.857
!38.933
!37.103
!34.463
!34.263
!34.345
!34.480
!34.135
!33.700
!25.623
!24.153
!23.492
!23.102
!55.015
!54.202
!53.363
!55.535
!56.323
!57.300
!59.330
!57.997
!57.662
!56.487
!58.175
!60.093
!58.620
!57.875
!29.939
!29.928
!29.136
!29.127
!29.122
!29.210
!29.408
!29.500
!30.317
!31.501
!30.849
1.597
!0.944
!1.667
!4.165
!5.937
!6.476
!7.113
!3.682
2.310
2.288
2.205
4.238
!34.435
!23.983
!27.290
!26.738
!27.098
!27.180
!27.487
!28.650
!29.112
!29.135
!20.958
!20.820
!21.270
!20.615
!8.168
!8.197
!8.573
!8.558
!8.735
!8.917
!4.032
!1.265
0.008
0.000
!37.807
!38.513
!39.243
!42.368
!41.945
!41.418
!43.675
!43.863
!43.903
!47.162
!47.307
!47.442
!47.327
!48.912
!112
!111
!88
!117
!20
!3626
!3079
!915
!600
!3521
!3089
!1900
!1641
!1758
!995
!1891
!2605
!2457
!4258
!2298
!2980
!4267
!3125
!4016
!1805
!1505
!202
!502
!1052
!1991
!3003
!3498
!4009
!2003
!1542
!2537
!2113
!794
!1128
!1790
!1442
!2585
!3977
!5521
!4323
!3712
!3914
!576
!1162
!4474
!4700
!3167
!392
!1228
!2361
!3280
!4479
!2990
!684
!2351
!569
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–2
0–2
0–2
0–2
0–2
0–2
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0.410
0.418
0.400
0.424
0.415
0.488
0.494
0.463
0.474
0.520
0.508
0.715
0.696
0.694
0.684
0.664
0.659
0.670
0.595
0.595
0.598
0.593
0.613
0.547
0.651
0.656
0.592
0.614
0.638
0.640
0.656
0.639
0.633
0.660
0.647
0.659
0.665
0.681
0.697
0.655
0.672
0.627
0.578
0.574
0.585
0.590
0.590
0.418
0.416
0.450
0.410
0.377
0.411
0.356
0.366
0.398
0.382
0.385
0.344
0.390
0.360
0.006
0.000
0.005
0.007
0.008
0.004
0.004
0.009
0.005
0.004
0.004
0.000
0.002
0.001
0.000
0.007
0.006
0.000
0.002
0.001
0.002
0.007
0.012
0.001
0.001
0.001
0.001
0.004
0.001
0.002
0.002
0.002
0.001
0.006
0.006
0.003
0.003
0.003
0.008
0.005
0.004
0.001
0.005
0.001
0.007
0.002
0.005
0.010
0.003
0.009
0.005
0.005
0.000
0.003
0.002
0.000
0.004
0.004
0.000
0.002
0.001
13.6
13.6
13.6
13.6
13.6
18.7
18.0
17.1
17.7
18.2
18.4
28.8
29.3
29.7
28.4
27.8
28.0
28.0
27.4
27.6
27.4
27.6
27.5
18.6
23.5
23.6
23.8
23.6
23.6
23.6
23.5
22.7
22.3
25.8
25.8
25.5
26.3
27.4
27.4
27.4
27.4
27.4
27.5
26.7
26.6
26.6
26.6
13.2
12.2
14.6
11.6
9.2
9.0
9.6
10.0
10.0
8.5
7.6
8.3
7.6
7.9
Temp.
0–200 m (!C)
15.0
14.2
12.9
13.8
14.9
14.6
21.4
21.6
20.1
20.6
19.6
19.6
20.0
18.7
18.7
18.5
19.0
21.1
15.2
18.9
18.9
18.9
18.9
19.2
19.3
18.2
20.5
20.5
21.7
21.5
22.3
22.3
22.3
22.3
22.3
22.4
18.4
17.8
17.8
17.8
7.6
10.0
6.1
5.3
5.7
5.5
5.5
5.5
5.1
4.8
5.6
4.8
5.1
Global sediment core-top calibration of the TEX86 paleothermometer
1169
Appendix A1 (continued)
Sediment core
Longitude
Latitude
Water
depth (m)
Coring
device
Sampling
level (cm)
TEX86
index
STDEV
Temp.
0 m (!C)
Temp.
0–200 m (!C)
GeoB2724-7
GeoB2727-1
GeoB2730-1
GeoB2731-1
GeoB2734-2
GeoB2802-2
GeoB2803-1
GeoB2804-2
GeoB2805-1
GeoB2806-6
GeoB2809-2
GeoB2810–2
GeoB2811-1
GeoB2812-3
GeoB2813-1
GeoB2817-3
GeoB2818-1
GeoB2819-2
GeoB2820-1
GeoB2821-2
GeoB2824-1
GeoB2825-3
GeoB2826-1
GeoB2827-2
GeoB2828-1
GeoB2829-3
GeoB2830-1
GeoB6402-9
GeoB6404-3
GeoB6405-8
GeoB6406-1
GeoB6407-2
GeoB6408-3
GeoB6409-2
GeoB6409-3
GeoB6410-1
GeoB6411-4
GeoB6412-1
GeoB6413-4
GeoB6414-1
GeoB6416-2
GeoB6417-2
GeoB6418-3
GeoB6419-1
GeoB6420-2
GeoB6421-1
GeoB6422-5
GeoB6423-1
GeoB6424-2
GeoB6425-1
GeoB6426-2
GeoB6427-1
GeoB6428-3
GeoB6429-1
GeoB9501-4
GeoB9506-3
GeoB9508-4
GeoB9510-3
GeoB9512-4
GeoB9513-5
GeoB9520-4
GeoB9521-3
!56.175
!56.538
!53.248
!51.423
!54.342
!53.982
!53.703
!53.537
!53.443
!53.137
!51.522
!51.978
!52.270
!52.387
!52.562
!38.073
!38.170
!38.343
!38.438
!38.817
!42.498
!41.432
!40.970
!40.728
!40.718
!43.430
!44.000
!22.757
!23.465
!21.853
!20.784
!19.500
!20.441
!21.717
!21.717
!20.900
!18.352
!17.647
!17.340
!13.070
!18.163
!21.042
!21.535
!21.864
!22.148
!22.445
!22.733
!22.992
!23.276
!23.588
!24.024
!24.248
!24.248
!24.248
!16.733
!18.350
!17.948
!17.653
!17.485
!17.294
!17.591
!17.490
!47.963
!48.013
!44.475
!44.207
!39.290
!37.207
!37.407
!37.537
!37.605
!37.832
!36.332
!35.980
!35.753
!35.602
!35.532
!30.915
!30.873
!30.848
!30.822
!30.453
!33.498
!32.500
!31.902
!31.478
!31.475
!30.873
!29.020
!39.743
!41.506
!42.000
!42.000
!42.045
!43.614
!44.507
!44.507
!44.517
!44.363
!44.254
!44.207
!43.999
!39.955
!39.093
!38.427
!37.774
!37.158
!36.448
!35.707
!35.253
!34.608
!33.825
!33.499
!33.182
!32.510
!31.950
16.843
15.610
15.498
15.417
15.376
15.318
13.829
13.849
!4799
!2803
!5817
!5691
!2272
!1007
!1162
!1836
!2743
!3542
!3539
!2909
!1789
!1041
!508
!2943
!3110
!3435
!3606
!3936
!4512
!4352
!3959
!3702
!3741
!3523
!3815
!3878
!4223
!3863
!3514
!3384
!3797
!4296
!4296
!4038
!3893
!3475
!3768
!3830
!3525
!4024
!4126
!3568
!3998
!4216
!3972
!3963
!3820
!4352
!4381
!4491
!4022
!4335
!330
!2964
!2385
!1567
!787
!498
!1102
!522
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
GBC
MUC
MUC
MUC
MUC
GBC
GC
GBC
MUC
GBC
GBC
GBC
GBC
GBC
GBC
GBC
GBC
GBC
GBC
GBC
GBC
GBC
GBC
MUC
MUC
GBC
GBC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
MUC
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–2
0–2
0–2
0–2
0–2
0–2
0–2
0–2
0–2
0–2
0–2
0–2
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0.375
0.377
0.412
0.428
0.377
0.420
0.443
0.460
0.468
0.497
0.538
0.555
0.527
0.484
0.459
0.603
0.606
0.606
0.600
0.598
0.539
0.556
0.598
0.609
0.607
0.622
0.634
0.462
0.423
0.444
0.420
0.402
0.400
0.370
0.468
0.381
0.582
0.400
0.388
0.394
0.454
0.461
0.477
0.500
0.496
0.515
0.529
0.543
0.551
0.563
0.564
0.573
0.579
0.614
0.555
0.592
0.588
0.575
0.584
0.579
0.579
0.584
0.002
0.000
0.006
0.008
0.001
0.005
0.002
0.001
0.003
0.001
0.001
0.007
0.002
0.003
0.004
0.003
0.004
0.003
0.006
0.002
0.009
0.001
0.005
0.001
0.006
0.004
0.003
0.007
0.002
0.001
0.007
0.002
0.002
0.003
0.001
0.002
0.003
0.003
0.001
0.003
0.002
0.001
0.005
0.004
0.005
0.000
0.004
0.006
0.003
0.001
0.005
0.001
0.000
0.006
0.006
0.014
0.005
0.007
0.014
0.011
0.008
0.009
8.5
7.5
12.5
13.4
11.4
16.9
16.9
16.9
16.9
16.9
19.7
19.5
17.1
17.1
17.1
22.2
22.2
22.2
22.2
22.2
20.4
20.1
20.6
20.6
20.6
21.2
21.9
16.1
13.4
13.3
11.8
11.8
10.9
10.9
10.0
10.9
10.2
10.4
10.4
11.1
16.0
16.1
16.5
18.5
17.5
17.2
19.9
19.9
18.0
19.8
20.0
20.0
21.4
22.5
23.6
24.2
23.5
23.5
23.5
23.5
24.1
24.1
(continued
5.1
5.0
8.9
10.2
7.1
12.3
12.3
12.3
12.3
12.3
16.7
16.8
14.3
14.3
14.3
18.0
18.0
18.0
18.0
18.0
17.4
16.8
17.2
17.2
17.2
17.5
18.3
12.6
10.7
9.9
11.0
9.0
9.2
7.0
9.4
7.4
8.3
8.3
9.5
13.0
14.0
13.6
14.1
15.1
15.1
14.2
15.7
15.8
15.8
16.8
17.3
15.3
15.3
15.6
15.6
15.6
15.6
on next page)
1170
J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173
Appendix A1 (continued)
Sediment core
Longitude
Latitude
Water
depth (m)
Coring
device
Sampling
level (cm)
TEX86
index
STDEV
Temp.
0 m (!C)
Temp.
0–200 m (!C)
GeoB9525-5
GeoB9526-4
GeoB9528-1
GeoB9529-1
GeoB9534-4
GeoB9535-5
Gullmarsfjord
IS-S1
IS-S2
IS-S3
IS-S4
IS-S5
IS-S6
M2003-01
M2003-06
M2003-08
MC-1
MC-3
MC-6
MD03-2603
MD03-2604
Mokbaai
MW91-9 BC-22
MW91-9 BC-37
MW91-9 BC-51
MW91-9 BC-58
MW91-9 BC-74
MW91-9GGC-12
MW91-9GGC-15
MW91-9GGC-18
MW91-9 GGC-29
MW91-9 GGC-38
MW91-9 GGC-44
MW91-9 GGC-55
MW91-9 GGC-6
MW91-9 GGC-68
MW91-9 GGC-8
NIOP 325
NIOP 902
NIOP 903
NIOP 904
NIOP 905
NIOP 906
NIOP 907
NIOP 908
NIOP 915
NIOP 922
North Frisian Front
North Sea Breeveertien 14
North Sea Central Southern Bight
North Sea Dutch Coast
OMEX 98-5
Oyster grounds
Peru Margin PM1
Peru Margin PM5
PLS-91-1-1
PLS-91-1-10
PLS-91-1-12
PLS-91-1-14
PLS-91-1-17
PLS-91-1-20
!17.880
!18.056
!17.664
!17.369
!14.936
!14.961
11.500
!9.852
!9.707
!9.709
!6.099
!5.593
!5.584
!1.966
!1.315
!1.125
141.546
138.597
139.211
139.375
139.375
4.083
160.400
157.800
161.000
162.200
162.600
157.900
158.900
160.500
158.900
159.400
160.600
161.800
156.990
162.700
156.900
53.550
51.577
51.658
51.771
51.944
52.129
52.249
52.915
53.524
52.530
4.500
3.733
3.000
4.500
9.290
4.500
!77.317
!78.067
29.202
32.377
33.940
40.599
46.722
48.229
12.640
12.435
9.166
8.352
8.901
8.876
58.333
48.062
48.178
48.267
51.218
53.883
54.119
62.001
62.643
62.767
41.300
41.251
39.320
!64.285
!64.285
53.000
0.000
!0.980
0.000
0.000
0.000
!1.000
0.000
0.5000
0.000
0.000
0.000
0.000
!2.300
0.000
!2.200
10.683
10.779
10.783
10.788
10.916
10.812
10.804
10.778
10.690
16.170
54.000
52.908
53.000
53.000
41.380
54.500
!11.983
!11.050
78.308
83.706
84.185
84.807
84.009
83.017
!2648
!3223
!3053
!1234
!493
!666
!110
!2006
!1035
!497
!106
!104
!54
!1602
!1702
!1601
!1002
!1268
!856
!3290
!3290
!5
!2965
!2022
!3397
!4329
!4445
!2018
!2311
!2980
!2322
!2456
!3160
!4025
!1618
!4449
!1625
!4065
!459
!789
!1194
!1567
!2020
!2807
!3572
!4035
!201
!42
>!50
!40
!24
!765
!47
!100
!20
!303
!4004
!4052
!4018
!3960
!2900
MUC
MUC
MUC
MUC
MUC
MUC
BC
MUC
MUC
MUC
MUC
MUC
MUC
BC
BC
BC
MUC
MUC
MUC
PC
GC
BC
BC
BC
BC
BC
BC
GGC
GGC
GGC
GGC
GGC
GGC
GGC
GGC
GGC
GGC
BC
BC
BC
BC
BC
BC
BC
BC
BC
GBC
BC
BC
BC
BC
BC
BC
KC
BC
PC
BC
BC
BC
GC
GC
0–1
0–1
0–1
0–1
0–1
0–1
0–0.5
0–0.5
0–0.5
0–0.5
0–0.5
0–0.5
0–0.5
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–2
0–1
0–1
0–1
0–1
0–1
0–1
0.582
0.594
0.603
0.604
0.612
0.610
0.393
0.560
0.537
0.505
0.364
0.338
0.347
0.380
0.371
0.364
0.409
0.353
0.385
0.013
0.367
0.433
0.710
0.700
0.709
0.699
0.697
0.701
0.695
0.709
0.702
0.698
0.707
0.699
0.700
0.700
0.696
0.660
0.652
0.681
0.662
0.679
0.696
0.680
0.676
0.683
0.703
0.389
0.368
0.412
0.390
0.552
0.372
0.532
0.609
0.325
0.331
0.312
0.337
0.377
0.346
0.015
0.008
0.012
0.007
0.008
0.009
0.005
0.006
0.001
0.009
0.003
0.003
0.000
0.013
0.006
0.002
0.007
24.8
25.4
27.0
27.4
27.4
27.4
9.0
13.5
13.5
13.5
11.9
10.8
10.8
8.7
8.7
8.7
12.1
12.9
15.5
0.2
0.2
10.6
28.9
29.2
29.2
29.2
29.2
29.4
29.4
29.3
29.4
29.3
29.3
29.2
29.4
29.2
29.4
26.6
26.2
26.2
26.2
26.2
26.2
26.2
26.2
26.6
26.5
10.5
11.3
10.6
10.6
17.9
10.5
19.8
19.8
0.9
!1.7
!0.4
!0.8
1.4
0.3
15.9
15.6
16.4
16.4
16.5
16.5
6.9
11.3
11.3
11.3
0.006
0.013
0.002
0.042
0.027
0.012
0.005
0.015
0.013
0.016
0.017
0.019
0.001
0.005
0.033
0.009
0.005
7.0
7.0
7.0
8.9
4.2
8.5
0.3
0.3
24.3
23.9
24.0
24.1
24.1
24.2
24.1
24.3
24.1
24.0
24.3
24.0
24.8
24.1
24.8
20.8
19.7
19.7
19.7
19.7
19.7
19.7
19.7
20.8
18.6
13.7
14.1
14.1
!0.5
0.1
!0.9
!0.5
0.2
0.9
Global sediment core-top calibration of the TEX86 paleothermometer
1171
Appendix A1 (continued)
Sediment core
Longitude
Latitude
Water
depth (m)
Coring
device
Sampling
level (cm)
TEX86
index
PLS-91-1-21
PLS-91-1-23
PLS-91-1-24
PLS-91-1-31
PLS-91-1-5
PLS-91-1-6
Santa Barbara basin
South Frisian Front
ST-32
ST-34
ST-35
ST-45
T89-10
T89-12
T89-13
T89-14
T89-15
T89-16
T89-17
T89-19
T89-20
T89-21
T89-22
T89-23
T89-24
T89-25
T89-28
T89-30
T89-32
T89-33
T89-34
T89-35
T89-36
T89-40
T89-41
T89-47
TSP-1
TSP-2
TSP-3
WM1 MC33
WM2 MC30
WM3 MC28
WM4 MC21
WM5 MC20
47.889
43.432
42.874
21.273
29.021
28.534
!120.017
4.500
143.682
142.002
141.675
140.498
1.322
7.973
9.238
9.690
10.022
11.228
7.805
9.955
11.537
11.993
12.067
12.108
12.058
10.608
5.637
8.220
10.668
11.620
11.967
11.620
13.838
6.782
6.000
4.427
147.490
146.900
146.410
!124.300
!124.383
!124.633
!125.000
!125.200
82.662
81.118
81.021
73.724
80.867
81.487
34.217
53.500
!56.002
!60.045
!62.013
!65.468
!2.077
!5.200
!4.107
!3.509
!4.213
!5.705
!6.818
!6.043
!7.307
!7.275
!7.130
!8.513
!8.908
!9.368
!10.387
!14.877
!14.893
!14.960
!15.127
!17.293
!14.960
!21.617
!20.800
!8.787
!47.572
!48.127
!48.558
46.333
46.500
46.667
46.817
46.750
!1345
!466
!472
!1536
!4018
!628
!530
!24
!3429
!4354
!4265
!1567
!2088
!4068
!3092
!868
!1930
!826
!4253
!3140
!10.80
!490
!200
!796
!2157
!4164
!5307
!4752
!3342
!2027
!999
!802
!131
!3060
!4282
!5362
!1301
!2283
!2897
!70
!90
!150
!600
!1000
BC
PC
PC
BC
BC
PC
BC
BC
MUC
MUC
MUC
MUC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
BC
MUC
MUC
MUC
BC
BC
BC
BC
BC
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–1
0–2
0–2
0–2
0–2
0–2
0.381
0.351
0.355
0.345
0.351
0.350
0.456
0.409
0.448
0.371
0.416
0.373
0.641
0.660
0.695
0.612
0.623
0.651
0.653
0.632
0.669
0.645
0.641
0.631
0.624
0.661
0.621
0.580
0.556
0.610
0.566
0.562
0.535
0.554
0.570
0.603
0.533
0.472
0.467
0.355
0.423
0.465
0.466
0.480
STDEV
0.001
0.007
0.009
0.002
0.033
0.002
0.011
0.026
0.045
0.042
0.027
0.013
0.020
0.004
0.006
0.035
0.022
0.012
0.008
0.018
0.009
0.026
0.053
0.5
0.006
0.001
0.035
0.109
0.020
0.021
0.001
Temp.
0 m (!C)
0.3
!1.2
0.3
5.3
!0.5
!0.5
14.9
10.6
2.4
1.6
0.5
!1.0
25.6
26.1
25.2
25.4
25.5
25.1
25.8
26.2
26.0
26.0
26.0
25.0
25.0
25.8
24.7
22.3
22.5
23.2
21.2
18.6
23.2
19.9
20.2
26.0
10.5
9.8
9.8
11.9
11.9
11.9
12.4
12.4
Temp.
0–200 m (!C)
0.9
0.6
4.2
0.2
0.7
10.1
1.2
0.8
0.5
!1.1
16.2
16.1
16.2
16.8
16.6
16.6
16.1
16.2
16.4
16.4
16.4
16.1
16.1
15.1
15.2
14.5
14.4
14.9
14.5
13.8
14.9
15.6
15.4
15.6
9.5
8.6
8.6
8.1
8.1
8.1
8.4
8.4
BC (Box corer), GBC (Giant box corer), GC (Gravity corer), GGC (Giant gravity corer), KC (Karsten corer), MUC (Multicorer), PC (Piston
corer), SC (Slow Score).
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Associate editor: H. Rodger Harvey