GDGT THERMOMETRY:
LIPID TOOLS FOR RECONSTRUCTING PALEOTEMPERATURES
JESSICA E. TIERNEY
Woods Hole Oceanographic Institution, Woods Hole, MA USA 02543
tierney@whoi.edu
ABSTRACT.—Microbial communities adjust the chemical structure of their cell membranes in response to
environmental temperature. This enables the development of lipid-based paleothermometers such as the
glycerol dialkyl glycerol tetraether (GDGT) proxies described here. Surface-sediment calibrations establish a strong empirical relationship between the relative distribution of GDGTs and temperature. GDGT
proxies can be used in marine, lacustrine, and paleosol sequences as long as the organic material is not
thermally mature. Thus far, GDGT proxies have been applied to sediments dating back to the middle Jurassic. Many of the key uncertainties of these proxies are related to our emerging understanding of archaeal (and for the branched GDGTs, bacterial) ecology.
INTRODUCTION
THE LAST twenty-five years have witnessed the
growth and expansion of a new technique for estimating paleotemperatures based on the use of
biomarkers: organism-specific, fossil lipids (fats)
found in sedimentary organic matter. Biomarkers
enhance our ability to reconstruct paleoenvironments in geologic history, in part because they are
subject to a different set of diagenetic alterations
than hard parts of fossils. They may be well preserved in sedimentary environments where traditional skeletal temperature proxies (e.g., calcareous foraminifera) typically are not, thereby enhancing the range of depositional conditions from
which paleotemperature information can be extracted.
Biomarker-based paleothermometers are built
upon the biochemical principle that microorganisms adjust the rigidity of their cell membrane
structures by altering the number of double bonds,
rings or branches in response to environmental
temperature (e.g., Ray et al., 1971; Kita et al.,
1973; Nozawa et al., 1974). The first biomarkerbased paleotemperature proxy was based on the
relative distribution of unique long-chain (C37) triand di-unsaturated ketones (alkenones) produced
by marine haptophyte algae, such as the ubiquitous coccolithophorid Emiliania huxleyi (Volkman
et al., 1980; Marlowe et al., 1984). The alkenone
paleo sea-surface temperature (SST) proxy (UK
37) can be applied widely in marine environments
that are mid-Paleogene in age or younger, and has
been a mainstay of paleoclimate reconstruction
since its development in the late 1980s (Herbert,
2003). There are, however, some limitations to the
alkenone proxy. Extant haptophyte species that
the modern calibrations of the alkenone proxy are
based on evolved relatively recently (e.g., 0.28
Ma for Emiliania huxleyi, Thierstein et al., 1977),
so alkenones found in older sedimentary sequences can only be presumed to be produced by
ancient relatives with a similar temperature sensitivity. Also, the alkenone calibrations to SST are
limited to ~28°C, such that the proxy cannot be
used in some areas of the tropical oceans, nor in
ancient hothouse climates.
In the last decade, a new biomarker paleotemperature approach based on the relative abundances of glycerol dialkyl glycerol tetraethers
(GDGTs) has been developed that complements
and extends the capabilities of the alkenone proxy.
GDGTs are relatively large (up to 86 carbon atoms) membrane lipids produced by Archaea and
some Bacteria. They are common, typically abundant, easy-to-analyze lipids found in a variety of
environments (lakes, soils, oceans). Such attributes make GDGTs a promising universal tool for
estimating temperatures in the geologic past, especially in deep geologic time.
′
In Reconstructing Earth’s Deep-Time Climate—The State of the Art in 2012, Paleontological Society Short Course,
November 3, 2012. The Paleontological Society Papers, Volume 18, Linda C. Ivany and Brian T. Huber (eds.),
pp. 115–131. Copyright © 2012 The Paleontological Society.
THE PALEONTOLOGICAL SOCIETY PAPERS, VOL. 18
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FIGURE 1.—A) Core structures of the isoprenoidal glycerol dialkyl glycerol tetraethers (isoGDGTs), with mass-tocharge ratios (m/z) on the right associated with analysis via HPLC/positive ion APCI. B) Common polar headgroups of intact isoGDGTs, which would replace one or both of the terminal hydroxyl groups indicated in (A). C)
Core structures of branched glycerol dialkyl glycerol tetraethers (brGDGTs).
WHAT ARE GDGTs?
GDGT structures remain in ancient sediments.
IsoGDGTs are characteristic lipids of Archaea, and are produced by a number of methanogenic, hyperthermophilic, and mesophilic species,
although not all species produce each kind of
isoGDGT shown in Figure 1. The isoGDGT paleothermometer rests on the assumption that the
dominant producers of isoGDGTs are members of
the recently renamed phylum Thaumarchaeota
(formerly a division of the Crenarchaeota;
Brochier-Armanet et al., 2008; Spang et al.,
2010). In many environments, this is a reasonable
assumption because other Archaea that produce
GDGTs (e.g., thermophile Archaea) are restricted
to distinct environmental niches. In contrast,
Thaumarchaeota are a ubiquitous mesophilic
group in the global oceans, possibly representing
up to 20% of the ocean’s picoplankton (Karner et
al., 2001). Known species of Thaumarchaeota are
nitrifiers: they convert ammonium to nitrate and
fix bicarbonate autotrophically to form their biomass (Könneke et al., 2005). Nitrifying Thaumar-
There are two main types of GDGTs: the isoprenoidal GDGTs (isoGDGTs) and the nonisoprenoidal, branched GDGTs (brGDGTs). The
isoGDGTs consist of two head-to-head C40 isoprenoid (i.e., made of isoprene units) chains with
a varying number of cyclopentane (five-sided)
and cyclohexane (six-sided) rings, connected by
ether bonds to two terminal glycerol groups (Figure 1). The brGDGTs are structurally similar, but
have branched C30 alkyl chains containing 4–6
methyl groups instead of the C40 isoprenoidal
chains (Figure 1). Additional polar headgroups,
including hexose and phosphate moieties, are attached to the core GDGT structure when the
membrane lipid is intact (Sturt et al., 2004;
Schouten et al., 2008a; Pitcher et al., 2010; and
see Fig. 1). Similar polar headgroups accompany
the brGDGTs (Liu et al., 2010; Peterse et al.,
2011). These polar headgroups are typically
quickly lost during diagenesis; only the “core”
116
TIERNEY: RECONSTRUCTING PALEOTEMPERATURES USING GDGT THERMOMETRY
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chaeota and their GDGTs are also found in soils
and lakes (Ochsenreiter et al., 2003; Leininger et
al., 2006; Llirós et al., 2010; Schouten et al.,
2012). Concentrations of isoGDGTs in soils are
low relative to marine levels (Weijers et al.,
2006b), but concentrations in lakes are often quite
high (Tierney et al., 2010b) and have enabled the
application of the isoGDGT paleothermometer to
certain lake environments (see below).
Thaumarchaeota are the only Archaea known
to make the distinctive GDGT crenarchaeol,
which has a cyclohexyl ring in addition to the cyclopentyl rings (Figure 1), and is a diagnostic
biomarker for these species in both moderate- and
high-temperature environments (Sinninghe Damsté et al., 2002b; Pearson et al., 2004; de la Torre
et al., 2008). Thaumarchaeota also produce
smaller amounts of a crenarchaeol regioisomer
(Figure 1; oft-abbreviated as cren’) that plays a
role in the temperature predictability of the
isoGDGT paleothermometer: relatively more of
the regioisomer is observed at higher temperatures.
BrGDGTs share many structural characteristics with the isoGDGTs, including the ether bonds
to terminal glycerol groups, a feature in membrane lipids typically seen only in Archaea (with
some exceptions, e.g., see Weijers et al., 2006a,
and references therein). Yet both the chain architecture (branched vs. isoprenoidal) and the stereochemistry of the glycerol groups on the brGDGTs
are diagnostic of Bacteria (Weijers et al., 2006a).
BrGDGTs are commonly found in soils, peats,
lakes, and marginal/deltaic environments, and are
not present in pelagic marine environments
(Hopmans et al., 2004), suggesting that they are
associated with terrestrial organisms. However, in
spite of their ubiquity, efforts to identify the organism(s) responsible for these compounds have
not been highly successful (e.g., Weijers et al.,
2009). Recently, Sinninghe Damsté et al. (2011)
demonstrated that a strain of Acidobacteria produces one type of brGDGT (brGDGT-I in Figure
1), confirming suspicions that this phylum of Bacteria, which is diverse, commonly found in peats
in soils, but not well characterized, may be a
source of brGDGTs (Weijers et al., 2006a, 2009).
However, the other eight brGDGTs utilized in
brGDGT-based temperature proxies are, at present, “orphan” compounds.
GDGTs—or chemical remnants of them—
have been observed in both modern and ancient
sedimentary deposits and oils for some time (e.g.,
Chappe et al., 1980; Brassell et al., 1981; Hoefs et
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FIGURE 2.—A) HPLC trace of isoGDGTs from a
tropical marine sediment. TEX86 value is calibrated to
SST using the Kim, et al. (2010) TEXH86 calibration.
(B) HPLC trace of brGDGTs from a freshwater lake.
MBT and CBT values are calibrated to MAAT using
the Tierney et al. (2010b) lakes calibration. All numbers correspond to the structures in Figure 1.
al., 1997), but were difficult to analyze because
intact GDGTs are too large for gas chromatography. It was not until the relatively recent development of a high-performance liquidchromatography mass-spectrometry method
(Hopmans et al., 2000) that GDGTs became a focus of both organic geochemical and paleoclimatological research. Figure 2 illustrates some typical chromatograms of both iso- and br-GDGTs
using high-performance liquid chromatographyatmospheric pressure chemical ionization-mass
spectrometry (HPLC-APCI-MS), which separates
compounds by mass and polarity. The run time for
a single LC/MS analysis is 60–70 minutes, and
preparative procedures are relatively minimal (extraction followed by column chromatography for
117
THE PALEONTOLOGICAL SOCIETY PAPERS, VOL. 18
purification), making GDGT analysis sufficiently
rapid for use in paleoclimate studies. Details of
the GDGT LC/MS methodology may be found in
Schouten et al. (2007b).
tion given above, efforts have been made to expand the collection of modern core-top sediments
available to constrain the TEX86 proxy and better
understand its predictive ability (Kim et al., 2008;
Liu et al., 2009; Kim et al., 2010). The current
calibration dataset includes 350+ core tops from
around the global ocean, though the distribution
of those core tops is not uniform (Figure 3). The
relationship between TEX86 and SST remains
largely linear (Figure 4), although non-linear relationships to SST have been proposed (Liu et al.,
2009; Kim et al., 2010) based on observations that
TEX86 is more sensitive to SST changes at the
high end of the temperature range and less sensitive at the lower end of the calibration range (e.g.,
Schouten et al., 2003). Both linear and non-linear
regressions through the modern core-top dataset
perform comparably (Figure 4).
The core-top studies also indicate that there
are certain areas in the marine realm where TEX86
is unreliable, or necessitates a different calibration. These areas include the polar oceans, where
the relationship between TEX86 and SST is poor
(Kim et al., 2008, 2010). This could reflect a reduced sensitivity of TEX86 in cold waters, or conversely, inaccurate mean annual SST estimates at
high latitudes. Kim et al. (2010) proposed an alternate functional form of TEX86, called “TEXL86”
(L for low-temperature) that has better predictability at low temperatures, but is less precise
given the entire global SST range (Table 1). Interestingly, TEXL86 excludes the regioisomer:
HOW DO GDGTS RECORD
PALEOTEMPERATURES?
isoGDGTs: the TEX86 paleothermometer
In response to temperature variations, the microbial producers of GDGTs produce compounds
with different numbers of rings in their isoprenoid
chains: a GDGT with more rings has a higher
melting point and is more stable at warm temperatures (Gliozzi et al., 1983). Although mesophilic
Thaumarchaeota have not yet been cultured in
different temperature environments, thermophile
relatives of Thaumarchaeota that are more readily
manipulated in culture produce more cyclic
isoGDGTs at higher temperatures (de Rosa et al.,
1980; Uda et al., 2001). Similarly, mesocosm
studies (experiments in which natural seawater is
manipulated in the laboratory) show that more of
the cyclic isoGDGTs are present in seawater at
higher temperatures (Wuchter et al., 2004;
Schouten et al., 2007a). Schouten et al. (2002)
developed an index to mathematically represent
this degree of cyclization called TEX86 (the TetraEther indeX of 86 carbons; numbers correspond
to compounds labeled in Figure 1; cren′ stands for
the crenarchaeol regioisomer):
Schouten et al. (2002) empirically calibrated
TEX86 to SST using a set of 43 modern surface
sediments, yielding a strong linear relationship:
IsoGDGT distributions are also quite different in
the semi-enclosed Red Sea, necessitating a different calibration (Trommer et al., 2009, Table 1)
and perhaps indicating the contribution of unique
archaeal species (Eder et al., 2002; Ionescu et al.,
2009) to the sedimentary isoGDGT pool.
TEX86 may be used in some (typically large)
lakes to infer lake-surface temperature (LST)
(Powers et al., 2004, 2005; Tierney et al., 2008;
Blaga et al., 2009; Powers et al., 2010; Tierney et
al., 2010a); core tops used to constrain the lake
calibrations are shown in Figure 3. Unfortunately,
TEX86 does not appear to be universally applicable in lakes. Reasons for this are not well understood, but it may be that smaller lakes do not have
the chemistry or limnology that supports large
populations of lacustrine Thaumarchaeota. In anoxic lakes or in anoxic sediment columns, Ar-
TEX86 = 0.015 × SST + 0.27 (r2 = 0.92, n = 43)
The paucity of pure Thaumarchaeotal cultures,
along with the surprising observations that Thaumarchaeota seem to produce less of the crenarchaeol isomer in mesocosms than in the open
ocean (Wuchter et al., 2004; Schouten et al.,
2007a) and that the isomer may have a different
source (Shah et al., 2008), have thwarted attempts
to calibrate TEX86 to SST experimentally. This
leaves the TEX86 proxy reliant on empirical coretop calibration, a practice that is commonly
adopted for other paleo-SST proxies (e.g., alkenone UK′37 and foraminiferal Mg/Ca). Since
Schouten et al. (2002) developed the linear equa118
TIERNEY: RECONSTRUCTING PALEOTEMPERATURES USING GDGT THERMOMETRY
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FIGURE 3.—A) Locations of sediment core-top calibration points for TEX86, including data from Kim et al. (2010),
Ho et al. (2011), Trommer et al. (2009), Chazen (2011), Leider et al. (2010), Seki et al. (2009), Castañeda et al.
(2010), Shintani et al. (2010), Powers et al. (2010), Blaga et al. (2009) and Tierney et al. (2010a) and excluding
duplicates. (B) Locations of sediment core-top calibration points for MBT/CBT, including data from Tierney et al.
(2010b), Blaga et al. (2010), Zink et al. (2010), Sun et al. (2011), and Pearson et al. (2011).
chaea performing methanogenesis or anaerobic
oxidation of methane (AOM) may produce
isoGDGTs (see below), compromising the Thaumarchaeotal TEX86 signature (Blaga et al., 2009;
Powers et al., 2010). In small lakes receiving
large amounts of soil organic matter, some of the
isoGDGTs may derive from soil Thaumarchaeota,
again complicating the sedimentary signal (Blaga
et al., 2009). As with the marine TEX86 calibration, the relationship between TEX86 and LST is
generally linear, although a non-linear form is
also possible. Tierney et al. (2010a) proposed a
“warm lakes” calibration with a shallower linear
slope (see Table 1) to account for the apparent
increased sensitivity of lacustrine TEX86 at higher
LSTs, indicating that a nonlinear model could be
appropriate (Figure 4).
Presently, there are a number of calibrations
for TEX86, reflecting ongoing attempts to refine
the relationship between TEX86 and temperature
(Table 1). The most common calibrations used to
date are the Kim et al. (2010) “TEXH86” calibration (where TEXH86 is simply the base-10 loga-
rithimic transform of TEX86) for marine SST, and
the Powers et al. (2010) calibration for lake LST.
brGDGTs: the MBT/CBT paleothermometer
Because the producers of most of the
brGDGTs have not been identified, and no species
that produce any of these lipids other than
brGDGT-I have been identified in culture, the
physiological relationship between brGDGT cyclization and/or methylation to temperature is not
understood. We might expect that such a relationship exists, but to date, it relies solely on empirical evidence in the form of core-top/topsoil studies. Weijers et al. (2007c) studied a global collection of topsoils and identified two major environmental parameters that had a statistical relationship to brGDGT distributions: soil temperature
(approximated by mean annual air temperature;
MAAT) and soil pH. The degree of cyclization of
the brGDGTs seemed to be related only to soil pH
in a non-linear fashion, implying a power law relationship. Thus, Weijers et al. (2007c) devised an
index representing the cyclization of the
119
THE PALEONTOLOGICAL SOCIETY PAPERS, VOL. 18
TABLE 1.—Sediment core top TEX86 calibrations. *= calibration applies just to the Red Sea, where TEX86 distributions are different from the open ocean, ‡=TEXL86 is an alternate functional form of TEX86 arguably better for prediction at low SSTs, see main text and Kim et al. (2010) for more details, †=lake calibrations.
Range
Equation
n
r2
RMSE
Reference
0–30
T E X86 = 0.015 × T + 0.27
43
0.92
N/A
Schouten et al. (2002)
22–30
T E X86 = 0.027 × T + 0.016
24
0.78
N/A
Schouten et al. (2003)
5–30
T = −10.78 + 56.2 × T E X86
223
0.94
1.7
Kim et al. (2008)
25–28*
T E X86 = 0.035 × T − 0.09
11
0.90
N/A
Trommer et al. (2009)
T = 50.475 − 16.332 × (1/T E X86)
287
0.82
3.7
Liu et al. (2009)
396
0.86
4.0
Kim et al. (2010)
-3–30
-3–30‡
T = 49.9 + 67.5 × T E X L86
5–30
T = 38.6 + 68.4 × log(T E X86)
255
0.86
2.5
Kim et al. (2010)
4–30†
T = −14.0 + 55.2 × T E X86
12
0.86
3.6
Powers et al. (2010)
10–30†
T = 3.50 + 38.9 × T E X86
13
0.92
2.1
Tierney et al. (2010a)
brGDGTs called the CBT (Cyclization of
Branched Tetraethers) index:
and CBT (as a surrogate for the pH influence) to
predict MBT, which could be inverted to solve for
MAAT:
MBT = 0.122 + 0.187 × CBT + 0.020 × MAAT
that could be linearly related to pH:
This relation has a slightly higher r2 of 0.77.
However, when the regression is recalibrated with
MAAT as the dependent variable (necessary for
prediction of MAAT), the r2 statistic drops to 0.62
(Figure 5).
The fact that brGDGTs are abundant in a wide
variety of lake basins (Tierney and Russell, 2009;
Sinninghe Damsté et al., 2009; Blaga et al., 2010;
Bechtel et al., 2010; Tierney et al., 2010b; Tyler et
al., 2010; Zink et al., 2010; Sun et al., 2011) offers the tantalizing possibility that these compounds could be used to develop a universal lacustrine temperature proxy. Early applications of
the Weijers et al. (2007c) equation to lacustrine
brGDGTs (driven by the assumption that all
brGDGTs are soil-derived) revealed that the resulting temperatures were 10°C or greater different from observed MAAT (Tierney and Russell,
CBT = 3.33 − 0.38 × pH
The degree of methylation of soil brGDGTs was
found to relate to both temperature and pH.
Weijers et al. (2007c) devised the MBT (Methylation of Branched Tetraethers) index to represent
the degree of methylation (numbers refer to structures in Figure 1; denominator indicates the sum
of all nine brGDGT compounds):
MBT is reasonably correlated with MAAT alone
(r2 =0.62), but is better constrained when both pH
and MAAT are used as predictors (r2 = 0.82),
therefore, Weijers et al. (2007c) used both MAAT
120
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FIGURE 4.—TEX86 regressions to SST and LST respectively, with best-fit linear and non-linear models. The marine
TEX86 data shown exclude data from the Red Sea (Trommer et al., 2009) and the polar oceans (SST<2, Kim et al.,
2010) where the relationship between TEX86 and SST is different and/or poor. RMSE = root mean square error.
EXAMPLE APPLICATIONS OF GDGT
PROXIES
2009; Zink et al., 2010; Tyler et al., 2010); thus,
the soil calibration was not applicable. This may
suggest that lacustrine brGDGTs are produced in
situ with a different sensitivity to lake temperature—a hypothesis also supported by the presence
of intact polar brGDGT in lakes (Tierney et al.,
2012). However, recent attempts to develop lakespecific brGDGT distributions are promising and
demonstrate that lake sediments perform similar
to or better than soils in predicting MAAT (Figure
5).
For both soils and lakes, the MBT/CBT temperature proxy is considerably less accurate in
predicting temperature than TEX86, with a root
mean square error near 5°C. Alternate functional
forms representing the distribution of brGDGTs
may improve predictive performance and are beginning to be explored. Tierney et al. (2010b)
proposed a calibration based on the fractional
abundance (relative to the sum of all nine
GDGTs) of the three major non-cyclic brGDGTs
that provides more precise prediction of MAAT in
tropical East African lakes (Figure 5, Table 2).
Similarly, Pearson et al. (2011) proposed a calibration based on the fractional abundances of
brGDGT-III, brGDGT-II, and brGDGT-Ib for a
more globally distributed set of lakes (Table 2). A
list of brGDGT-based calibrations is provided in
Table 2.
GDGTs have been used to reconstruct both air
and water temperatures on a variety of geologic
timescales, but have been especially useful for
estimating temperatures in the Mesozoic and early
Cenozoic, where there are few applicable methods
for estimating paleotemperature. In one of the
earliest applications of the TEX86 proxy, Schouten
et al. (2003) reconstructed low-latitude SSTs during select intervals in the middle Cretaceous (Albian–Cenomanian–Turonian), a hothouse period
in Earth history where temperatures are relatively
poorly constrained. They estimated low-latitude
SSTs of 32–36°C, in general agreement with
δ18O-based estimates from well-preserved Cenomanian foraminifera (Schouten et al., 2003).
GDGTs also have been used to provide both
low- and high-latitude constraints on temperature
excursions during the Paleocene–Eocene Thermal
Maximum (PETM), a brief interval when massive
amounts of greenhouse gases were injected into
the atmosphere ~55 million years ago, causing
rapid and extreme warming. Zachos et al. (2006)
applied TEX86 to a section from Wilson Lake,
New Jersey (paleolatitude of 36°N), and found
that SSTs were ~25–30°C prior to and after the
PETM, but reached a maximum of ~35°C during
the event (Figure 6). Sluijs et al. (2006) and
Weijers et al. (2007b) applied TEX86 and the
121
THE PALEONTOLOGICAL SOCIETY PAPERS, VOL. 18
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FIGURE 5.—brGDGT regressions to MAAT for (A) soils (data from Weijers et al., 2007c), (B) lakes (data from
Tierney et al., 2010b; Blaga et al., 2010; Zink et al., 2010; Sun et al., 2011), and (C) East African lakes, using an
alternative functional form (Tierney et al., 2010b, see Table 2 for the equation).
MBT/CBT proxy, respectively, to the PETM section of a drill core taken from the Lomonosov
Ridge (paleolatitude of 85°N), and reconstructed
surprisingly warm temperatures for the high Arctic (Figure 6). Taken at face value, these PETM
GDGT data suggest an extremely warm world
with a surprisingly minimal latitudinal gradient in
temperature (~10°C) compared to present-day
gradients (20°C). However, it should be noted that
because the TEX86 values at Wilson Lake exceed
those observed in the modern ocean, the inferred
temperatures rely on an extrapolation of the calibration regression lines; thus, the absolute values
are highly uncertain. Nevertheless, such data provide a hypothesis of how Earth systems responded
to what were likely very high levels of atmospheric carbon dioxide during the PETM.
GDGT proxies also have utility on more recent timescales, either as a complement to other
paleotemperature proxies or (as is sometimes the
case with deep-time studies) because it is the only
proxy method available in a given region or depositional setting. For example, Tierney et al.
(2010a) used TEX86 in Lake Tanganyika to reconstruct temperatures for the past 1500 years in East
Africa, a region where there are few options for
recent temperature reconstruction (there are no
developed tree-ring chronologies, and stable isotopes generally reflect hydroclimatic variability).
TEX86 shows evidence for warm conditions during the Medieval Warm Period, followed by cool/
variable temperatures during the Little Ice Age
(Figure 7). In the last 150 years, TEX86 indicates a
warming of ~2°C, an event unprecedented in the
last 1500 years (Figure 7). These data suggest that
anthropogenic climate change has had a strong
impact on the thermal structure of Lake Tanganyika.
BrGDGTs have not been used as widely as
TEX86 for paleoclimate reconstruction because
their dependence on temperature was only described in 2007 and, to date, is not well constrained. In addition to the PETM study, Weijers
et al. (2007a) used MBT/CBT to reconstruct
MAAT in the Congo Basin, using deltaic sediments from the Congo Fan. Their data indicate a
LGM–present change of ~3.5–4°C, a reasonable
finding for the deep tropics, and in agreement
with independent proxy data from Africa. Fawcett
et al. (2011) used MBT/CBT to reconstruct temperature within a Pleistocene lacustrine deposit
from the Valles Caldera, and estimated glacial/
interglacial thermal variability on the order of
~8°C, which is in general agreement with pollenbased estimates. Neither brGDGTs nor isoGDGTs
have been used, as of yet, to reconstruct temperatures in ancient lake basins.
LIMITATIONS ON THE USE OF GDGT
PALEOTHERMOMETERS
Preservation
GDGTs are relatively refractory compounds,
but they do degrade over time, with isoGDGTs
apparently degrading faster than brGDGTs (Huguet et al., 2008). However, diagenetic degradation does not affect the TEX86 index within analytical error (Sinninghe Damsté et al., 2002a;
Schouten et al., 2004; Kim et al., 2009; Huguet et
al., 2009). When sedimentary sequences undergo
122
TIERNEY: RECONSTRUCTING PALEOTEMPERATURES USING GDGT THERMOMETRY
TABLE 2.—Topsoil (Weijers et al., 2007c) or lacustrine (all others) core top brGDGT calibrations. *=pH calibration
from CBT, †=uses African lakes data only, ‡=uses New Zealand lakes only, §=This calibration predicts mean summer temperature (MST) not MAAT.
Range
Equation
n
r2
RMSE
-5–30
MBT = 0.122 + 0.187 × CBT + 0.020 × MAAT
90
0.77
N/A
Weijers et al. (2007c)
3–8*
CBT = 3.33 − 0.38 × pH
90
0.70
N/A
Weijers et al. (2007c)
0–26†
MAAT = 11.84 + 32.54 × MBT − 9.32 × CBT
39
0.89
3.0
Tierney et al. (2010b)
0–26†
MAAT = 50.47 − 74.18 × fbrGDGT III −
31.60 × f brGDGT II − 34.69 × f brGDGT I
39
0.94
2.2
Tierney et al. (2010b)
6–15‡
MAAT = 55.01 × MBT − 6.055
10
0.74
N/P
Zink et al. (2010)
-5–30
MAAT = 3.949 − 5.593 × CBT + 38.213 × MBT
100
0.73
4.3
Sun et al. (2011)
5–30§
MST = 20.9 − 20.5 × f brGDGT III − 12 ×
fbrGDGT II + 98.1 × f brGDGT Ib
85
0.88
2.0
Pearson et al. (2011)
catagenesis, GDGTs break down more rapidly and
do so preferentially: hydrous pyrolysis experiments suggest that they degrade between 240–
300°C, and that TEX86 values decline, indicating
preferential destruction of the more cyclic compounds (Schouten et al., 2004). This experimental
evidence suggests that TEX86 is generally not applicable in more mature sediments (i.e., those
with high amounts of 22S hopanes; 17α,21β (H)hopane 22S/(22S+22R) ratios > 0.1). Thus, while
isoGDGT likely have an ancient biosynthetic origin (Thaumarchaeota have been extant for billions
of years, Spang et al., 2010, although this may or
may not apply to the lipids), and theoretically
TEX86 could be used throughout much of geologic history, the application of the proxy is limited to rock and sediment sequences that have
been minimally thermally altered.
Diagenetic/catagenic effects on the brGDGT
distributions (MBT/CBT) are not as well constrained due to the novelty of the proxy. In a study
of brGDGTs in a suburban lake that became seasonally anoxic and eutrophic as a result of human
disturbance, Tierney et al. (2012) found that there
were more “Type III” (see Figure 1) brGDGTs in
lake sediments that were recently deposited in the
anoxic environment, and less in deeper sediments
that were deposited in the pre-anthropogenic oxygenated environment. Similarly, Bechtel et al.
Reference
(2010) observed that anoxic lakes yield a “colder”
MBT/CBT signature than oxic lakes, indicating
more methylated brGDGTs in the sediments.
These observations may suggest preferential diagenetic loss of Type III brGDGTs upon exposure
to oxygen, but alternatively, could reflect a sensitivity of the microbial producers to anoxia
(Tierney et al., 2012). Further research is needed
to assess the relative diagenetic stability of the
nine brGDGTs.
Ecological constraints
As mentioned previously, the TEX86 proxy is
predicated on members of the phylum Thaumarchaeota acting as the primary producers of
isoGDGTs. This is a reasonable assumption in the
global ocean where Thaumarchaeota dominate,
although the contribution of isoGDGTs from
mesophilic Euryarchaeota, also abundant in marine environments, remains a matter of some uncertainty and debate (Turich et al., 2007; Schouten
et al., 2008b). Even if the source of isoGDGTs in
the modern ocean can be reasonably constrained
to Thaumarchaeota, it is unlikely that isoGDGTs
are predominantly produced by a single species
(unlike alkenones that select marine species produce). The lack of regioisomer in cultures of a
dominant marine archaeon, Nitrosopumilus maritimus (Schouten et al., 2008a), and the observa123
THE PALEONTOLOGICAL SOCIETY PAPERS, VOL. 18
Paleocene-Eocene
Thermal Maximum
such cases, TEX86 cannot be used reliably. Methanogenic Archaea and Archaea performing anaerobic oxidation of methane (AOM) produce
isoGDGTs 0–3 (especially GDGT-2, Blumenberg
et al., 2004; Zhang et al., 2011), and are common
in organic-rich, anoxic environments, such as
stratified freshwater lakes or marine environments
(e.g., the Black Sea, Wakeham et al., 2003). Diagnostic signs of GDGTs originating from
methanogenic/AOM archaeal species include high
concentrations of isoGDGTs 0–3 relative to
crenarchaeol and its regioisomer, as well as characteristically depleted stable carbon isotope signatures for biphytanes originating from GDGTs 0–3
(Wakeham et al., 2003).
The nitrifying metabolism of Thaumarchaeota
also introduces some ambiguity regarding the interpretation of TEX86-based SSTs. Because they
do not require sunlight and may be outcompeted
by faster-growing photosynthetic algae and bacteria, marine Thaumarchaeota tend to reside in the
mesopelagic zone (Massana et al., 1997; Karner et
al., 2001; Herndl et al., 2005; Wuchter et al.,
2005; Turich et al., 2007). It follows that Thaumarchaeota are more likely to be most active and
to synthesize most of their biomass in the subsurface. Several lines of evidence suggest that this is
the case, including the transcription of genes involved in ammonia oxidation (Church et al.,
2010) and the natural radiocarbon (14C) content of
isoGDGTs in both the water column and sediments (Pearson et al., 2001; Ingalls et al., 2006;
Shah et al., 2008), which is more similar to thermocline dissolved inorganic carbon (DIC; 14Cdepleted) than surface water DIC (14C-enriched,
due to the presence of “bomb” 14C).
However, despite these physiological observations, TEX86 clearly has a robust correlation
with SST. Several explanations have been proposed to account for this apparent discrepancy.
One theory argues that GDGTs from the surface
ocean are more effectively grazed and exported to
the deep ocean via fecal pellets, thus sedimentary
TEX86 inordinately represents SST (Wakeham et
al., 2003; Wuchter et al., 2006). This argument is
inconsistent with the existing (quite limited)
sedimentary 14C data (Pearson et al., 2001), although grazing almost certainly plays an important role in the export of GDGTs because archaeal
cells are small and do not sink quickly on their
own (Wakeham et al., 2003). An alternative explanation is correlation: if TEX86 actually is recording subsurface ocean temperatures, this may
be difficult to statistically distinguish from SST;
30 TEX86
25
20
15
IODP 302
Lomonosov Ridge
MBT/CBT Paleolatitude: 85˚N
10
380
40
382
384
386
Core Depth (m)
388
390
TEX86
35
30
Wilson Lake, NJ
Paleolatitude: 36˚N
25
95
100
105
110
Core Depth (m)
FIGURE 6.—TEX86 and MBT/CBT-inferred temperatures during the Paleocene-Eocene Thermal Maximum
(PETM) from the high arctic (Lomonosov Ridge, at
top, Sluijs et al., 2006;Weijers et al., 2007b) and the
mid-low latitudes (Wilson Lake, Zachos et al., 2006).
TEX86 data are re-calibrated with the Kim et al. (2010)
TEXH86 calibration. Error bars represent the calibration root mean square error.
tion that the genetic sequences of active Archaea
change as seawater temperatures are manipulated
in mesocosm experiments (Schouten et al.,
2007b) suggest that the empirical relationship between TEX86 and temperature represents a community effect rather than a single-species adaptation.
In some environments, non-Thaumarchaeotal
archaeal species may contribute a sizable, if not
dominant amount of sedimentary isoGDGTs. In
124
TIERNEY: RECONSTRUCTING PALEOTEMPERATURES USING GDGT THERMOMETRY
LAKE SURFACE TEMPERATURE
26
!-#"*4(
)&*+$43
25.5
!"#$"%&'(
)&*+(,"*$-#
25
24.5
.$//'"(01"(23"
24
23.5
23
22.5
22
500
1000
YEAR AD
1500
2000
FIGURE 7.—TEX86-inferred Lake Tanganyika lake
surface temperatures for the past 1500 years (Tierney
et al., 2010a). Error bars represent the 95% confidence intervals based on a leave-one-out estimation of
the error in the calibration slope.
temperatures at 100–200 m are highly correlated
with SSTs spatially across the global ocean (Huber, 2010). Everywhere the correlation is robust,
TEX86 will predict SST accurately.
In certain oceanographic environments, such
as near-shore and upwelling areas, TEX86 seems
to be overtly biased towards subsurface temperatures (Huguet et al., 2007; Lee et al., 2008; Lopes
Dos Santos et al., 2010; Chazen, 2011). For example, Lopes Dos Santos et al. (2010) found that
the time evolution of TEX86-SST was substantially different from that of alkenone-SST along
the Guinea Margin in the Late Quaternary, and
argued that TEX86 reflected subsurface variability.
Uncertainties surrounding where and why TEX86
reflects subsurface temperatures certainly have
relevance for temperature estimates in deep geological time, where the paleoceanography often is
not well constrained.
Seasonal changes in Thaumarchaeotal abundance and/or production of isoGDGTs may also
influence sedimentary TEX86. Water-column studies indicate that Thaumarchaeota and their
isoGDGTs exhibit pronounced seasonal variations, although the timing of peak abundance varies substantially by environment and geographic
location (Massana et al., 1997; Wuchter et al.,
2006; Galand et al., 2010; Hollibaugh et al., 2010;
Pitcher et al., 2011). Based on limited studies it is
not yet clear how or whether these seasonal cycles
125
are reflected in the GDGT export to, and preservation in, the sedimentary column. For example,
Wuchter et al. (2006) found that the seasonal cycle in TEX86 observed in the Arabian Sea homogenizes in a deep sediment trap, and Rueda et
al. (2009) found that in spite of a highly seasonal
GDGT production in the water column (Pitcher et
al., 2011), sedimentary TEX86 in the North Sea
best reflects mean annual SST. Some paleoclimatic studies have argued in favor of a seasonality
bias to explain unreasonable TEX86 temperatures,
such as the very warm temperatures derived for
high latitudes during the Early Eocene (e.g., Fig.
6 and Sluijs et al., 2006, 2011). However, this is
largely a post-hoc interpretation. It may be that
such warm temperatures reflect the inherent difficulties in calibrating TEX86 at high latitudes during a hothouse interval, for which we have no
modern analog (Pancost et al., 2011). Furthermore, as TEX86 is explicitly calibrated to mean
annual SST, invocations of a seasonal bias need to
be accompanied by an appropriate seasonal calibration (e.g., Shevenell et al., 2011). Further studies aimed at constraining possible seasonal biases
in GDGT export and sedimentary TEX86 expression will be critical towards resolving this issue.
The lack of an identifiable producer fundamentally limits the application of brGDGT proxies. Until the producer(s) is/are better characterized, it is difficult to identify ecological constraints that may interfere with the fidelity of
brGDGTs as temperature recorders (e.g., nutrients, oxygen, community effects). Currently, we
are limited to empirical observations from coretop studies. Weijers et al. (2007c) detected a significant relationship between the MBT index and
precipitation amount in soils, but because precipitation co-varied with temperature in their dataset,
they were not able to determine whether this relationship was meaningful. In lakes, although temperature is the dominant environmental variable
affecting brGDGT distributions there is some influence of pH, lake depth (Tierney et al., 2010b;
Pearson et al., 2011), and possibly degree of
bottom-water anoxia (see discussion above;
Tierney et al., 2012). Interestingly, brGDGTs
seem to be absent or have unusual distributions in
soda lakes with a high pH (Tierney et al., 2010b;
Sun et al., 2011), and brGDGT concentrations are
low in high-pH soils (Weijers et al., 2007c). This
is circumstantial evidence that the producers prefer neutral and lower pH environments, and is
supportive of the proposed association with Acidobacteria.
THE PALEONTOLOGICAL SOCIETY PAPERS, VOL. 18
A potentially severe complicating factor for
application of brGDGT paleothermometers in
lakes is the likelihood that brGDGTs are associated with both allochthonous soil OM and in-situ
production, each of which appear to have a different relationship to temperature (Tierney and Russell, 2009). However, brGDGT concentrations
(per gram of organic matter) tend to be much
higher in lakes than in soils—sometimes by as
much as an order of magnitude (Tierney and Russell, 2009; Tierney et al., 2010b)—and the newest
lake calibrations based on alternate functional
forms (i.e., those that are not based on the MBT
or CBT indices) have remarkably good predictability (Tierney et al., 2010b; Pearson et al., 2011,
and see Table 2). Detailed down-core studies will
help address these issues.
straining absolute temperature error is less important than relative changes, error can be estimated
on the calibration slope (e.g., Tierney et al.,
2010a). In sum, the RMSE values shown in Tables 1 and 2 should not be viewed as immutable
estimates of the error of GDGT-based temperature
inferences. As with any proxy, error needs to be
rigorously assessed within the geological context
at hand, and in light of the climatological hypotheses being investigated.
CONCLUSIONS
Although new to the paleotemperature proxy
scene, GDGTs significantly expand the toolkit
available to geologists for reconstructing past
temperatures. Since GDGTs are exceptionally
ubiquitous compounds found in marine sediments, ancient sedimentary sequences, lakes,
soils, hot springs and even speleothems (Yang et
al., 2011), they offer opportunities to reconstruct
temperatures in geographic places and geological
time intervals where other proxies may not be
available. Many uncertainties, especially ecological uncertainties, need to be better constrained so
that GDGT proxies can be applied with more confidence. Nevertheless, as the data in Figure 6 illustrate, GDGTs have already fundamentally influenced our thinking about climate sensitivity
during ancient hothouse worlds, highlighting their
utility as paleothermometers.
A note on calibration uncertainty
Both TEX86 and brGDGT paleothermometers
rely on surface-sediment calibrations to convert
relative abundances to temperature. Global surface calibrations have an advantage in that they
cover a wide diversity of natural environments
and thus incorporate a large range of sources of
variability leading to error. However, the implicit
assumption of this approach is that the Earth system is ergodic, having the same type of variability
in modern geographical space as it does in geological time. Depending on the paleoenvironment,
this assumption may or may not always be valid.
For example, the TEX86 temperatures estimated
for the Cretaceous and for the mid to low latitudes
during the PETM (discussed above) rely on an
extrapolation of the modern calibration equations
to temperatures higher than observed in the modern oceans. They are also contingent on assuming
that Thaumarchaeota living in (e.g.) the Cretaceous oceans experienced a similar sensitivity to
temperature and non-temperature effects as in the
modern oceans. As the latter is very hard to constrain with confidence, the error associated with
the absolute temperature estimates in ancient hothouse environments is almost certainly quite a bit
larger than the root mean square error (RMSE) of
the modern core-top calibrations. Conversely, a
Late Quaternary TEX86 application in a single
tropical locale is unlikely to incorporate sources
of error associated with the polar oceans (and vice
versa), in which case the global core-top calibration error may be an overestimate. In this situation, local calibrations can be devised to better
constrain sources of error relevant to the region of
interest (e.g., Shevenell et al., 2011) or, if con-
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