The relative abundance of individual archaeal membrane
lipids, namely of glycerol dialkyl glycerol tetraethers (GDGTs) with
different numbers of cyclopentane rings, varies with temperature, which
enables their use as a paleotemperature proxy index. The first
GDGT-based index in marine sediments called TEX86
is believed to reflect mean annual sea surface temperature (maSST). The
TEX86L is an alternative temperature
proxy for “low-temperature” regions (<15∘C), where the original TEX86 proxy
calibration shows a larger scatter. However,
TEX86L-derived temperatures still
display anomalous estimates in polar regions. In order to elucidate the
potential cause of the disagreement between the
TEX86L estimate and SST, we analyzed
GDGT fluxes and TEX86L-derived
temperatures in sinking particles collected with time-series sediment traps
in high-northern- and high-southern-latitude regions. At 1296 m depth in the
eastern Fram Strait (79∘ N), a combination of various transporting
mechanisms for GDGTs might result in seasonally different sinking velocities
for particles carrying these lipids, resulting in strong variability in the
TEX86L signal. The similarity of flux-weighted TEX86L temperatures from
sinking particles and surface sediments implies an export of GDGTs without
alteration in the Fram Strait. The estimated temperatures correspond to
temperatures in water depths of 30–80 m, where nitrification might occur,
indicating the favorable depth habitat of Thaumarchaeota. In the Antarctic
Polar Front of the Atlantic sector (50∘ S),
TEX86L-derived temperatures displayed
warm and cold biases compared to satellite-derived SSTs at 614 m depth, and
its flux-weighted mean signal differs from the deep signal at 3196 m.
TEX86L-derived temperatures at 3196 m
depth and the surface sediment showed up to 7 ∘C warmer
temperatures relative to satellite-derived SST. Such a warm anomaly might be
caused by GDGT contributions from Euryarchaeota, which are known to dominate
archaeal communities in the circumpolar deep water of the Antarctic Polar
Front. The other reason might be that a linear calibration is not
appropriate for this frontal region. Of the newly suggested SST proxies
based on hydroxylated GDGTs (OH-GDGTs), only those with OH-GDGT–0 and
crenarchaeol or the ring index (RI) of OH-GDGTs yield realistic temperature
estimates in our study regions, suggesting that OH-GDGTs could be applied as
a potential temperature proxy in high-latitude oceans.
Introduction
The knowledge that Thaumarchaeota, one phylum of Archaea, regulate the
composition of their membrane lipids according to the surrounding water
temperatures enabled the development of the paleothermometer
TEX86 (Kim
et al., 2010; Schouten et al., 2002). TEX86 is
calculated based on the relative abundance of GDGT containing zero to three
cyclopentane (GDGT–0∼3) or four cyclopentane and one
cyclohexane (crenarchaeol) ring (Fig. 1).
Chemical structures and molecular ion m/z values of the isoprenoid
glycerol dialkyl glycerol tetraethers (GDGTs), branched GDGTs, and
hydroxylated GDGTs.
The ubiquity of Thaumarchaeota, even in the polar oceans where the widely
applied U37k′ proxy for
sea surface temperature (SST) reconstructions is often problematic, supports
the use of TEX86 in high-latitude regions
(Bendle and Rosell-Melé, 2004; Ho et al., 2014).
Nonetheless, the SST dependence of TEX86 in warm
regions is stronger than in colder regions where SSTs are below 5 ∘C (Kim et al., 2010). Thus, a
logarithmic calibration of TEX86L,
excluding the crenarchaeol regio-isomer, was suggested for regions where
maSSTs are below 15 ∘C (see Eq. 2;
Kim et al., 2010). The authors speculated
that the lack of correlation between the crenarchaeol regio-isomer and SST
at low temperatures might be caused by genetically different GDGT producers.
Moreover, all TEX86L calibrations for
temperature include a rather large scatter, resulting in a calibration error
of ±4∘C for the
TEX86L calibration (Kim et al., 2010).
It has been questioned whether temperature is the only factor influencing
the lipid composition (i.e., TEX86), particularly in
the regions where strong cold or warm biases were observed between
TEX86 reconstructed and measured maSSTs. The biases
of TEX86 calibrations have been attributed to
enhanced production of GDGTs during seasons with favorable growth conditions
for Thaumarchaeota (Pitcher et al., 2011; Wuchter
et al., 2006b), a contribution of GDGTs from deep-dwelling communities (Kim
et al., 2015; Lee et al., 2008; Taylor et al., 2013), terrestrial input
(Weijers et al., 2006), influences of different archaeal
communities (Lincoln et al., 2014;
Turich et al., 2007), and/or other environmental factors (Huguet et
al., 2006; Mollenhauer et al., 2015; Park et al., 2018; Turich et al.,
2007).
To circumvent these problems, especially in cold regions, a handful of
studies have developed dedicated calibrations on regional scales
(Meyer et al., 2016; Seki et al.,
2014; Shevenell et al., 2011). For the temperature estimate in the Eocene
Arctic Ocean, a modified version of TEX86, the
index termed TEX′86, was proposed
(Sluijs et al., 2006). It excludes GDGT–3 to eliminated
contribution from terrestrial input, leading to significantly higher
temperature estimates compared to the original
TEX86 calibration. The
TEX′86 values of 104 marine surface
sediments showed a good linear correlation with maSSTs (R2=0.93)
(Sluijs et al., 2006). Following the addition of seven TEX86 values from surface sediments
collected at the continental margin of the western Antarctic Peninsula,
Shevenell et al. (2011) modified the TEX86 calibration of Kim et al. (2008) for this region
(TEX86=0.0125×temp+0.3038, R2=0.82) and estimated water temperatures for the Holocene.
Ho et al. (2014) evaluated
TEX86 and the different calibrations in an extended
set of surface sediments from the polar regions of both hemispheres and
found that the global TEX86 calibration is suitable
for subpolar regions (e.g., the Pacific sector of the Southern Ocean and the
Subarctic Front in the North Pacific). Additionally, the
TEX86L yields reasonable temperature
estimates if regional calibrations are developed for high-latitude regions.
Using a regression of water temperatures (at 20 m depth in August) versus
TEX86L values,
Seki et al. (2014) obtained plausible
paleotemperature estimates for the Sea of Okhotsk and the subpolar North
Pacific region. Meyer et al. (2016) confirmed later that
this regional TEX86L calibration can
also be applied to the subarctic northwest Pacific and the western Bering
Sea.
More recently, hydroxylated GDGTs (OH-GDGTs), which contain an additional
hydroxyl group in one of the biphytanyl moieties of GDGT–0, GDGT–1, and
GDGT–2, have been recognized in marine sediments (Liu
et al., 2012; Fig. 1). Based on the finding that
the abundance of OH-GDGTs relative to the total isoprenoid GDGTs increases
towards cold regions (Huguet et al., 2013), OH-GDGT-based
SST calibrations have been proposed for high-latitude oceans
(Fietz et al., 2013; Huguet et al., 2013).
In addition to the biological, phylogenetic, and statistical studies of the
temperature proxy, sinking particles collected in specific regions can
provide new perspectives on the distribution of GDGTs and
TEX86-based temperature reconstructions. The study
using time-series sediment traps can give insights into the seasonal
variability of lipid flux and temperature signals, and allows the comparison of the
lipid signal to those in underlying sediments. Only one
TEX86 study using a time-series sediment trap in
high-latitude regions has been conducted near Iceland
(Rodrigo-Gámiz et al., 2015), regardless of the
interest in polar regions, where temperature reconstructions with lipid
proxies are troublesome. Here, we examine the seasonal GDGT production and
TEX86L-derived temperatures in
comparison to measured SSTs in order to better understand the drivers
influencing TEX86L values in sinking
particles collected in two high-latitude oceans (eastern Fram Strait,
79∘ N, and Antarctic Polar Front of the Atlantic sector,
50∘ S). The temperatures derived from OH-GDGT proxies were also
calculated to evaluate the applicability of these novel proxies for high-latitude regions.
Study areaFram Strait
The Fram Strait is located west of Spitsbergen, where the heat exchange
occurs between the Arctic Ocean and the North Atlantic
(Fig. 2). Relatively warm and nutrient-rich
Atlantic water (AW) is transported by the West Spitsbergen Current (WSC) to
the central Arctic Ocean, while colder and less saline Arctic water is
transported along the East Greenland Current (EGC) to the Nordic Seas
(Fig. 2). Part of the WSC is separated from the
northward flow and recirculated within the region
(Manley, 1995; Soltwedel et al., 2016). Eddies,
mixing, and recirculating Atlantic water contribute to the hydrographic
complexity observed in the Fram Strait (Walczowski, 2013). The
volume transport of the WSC and AW within the WSC displays strong
seasonality with maxima in March and minima in July for the WSC and maxima
in late autumn and winter and minima in June for the AW
(Beszczynska-Möller et al., 2012b). The occurrence of sea
ice and its variability has an effect on the particle fluxes as well as on
benthic ecosystems (Bauerfeind et al., 2009;
Hebbeln and Wefer, 1991; Soltwedel et al., 2016). Sinking particles in the
Fram Strait, including organic and terrigenous material, are well known to
originate not only from the photic zone in the upper water column, but also
from the Svalbard archipelago and Siberian shelf, from where they are
transported by sea ice (Hebbeln, 2000; Lalande et
al., 2016). Phytoplankton blooms enhance the vertical flux of biogenic
components (organic carbon, carbonate, and opal) in spring–summer, but the
downward flux of particles is likely affected by the environmental
conditions (e.g., hydrographic changes, sea ice extent, and atmospheric
low pressure) on an annual timescale (Wassmann et al., 2006).
Based on oceanographic measurements from mooring arrays at multiple depths
across the Fram Strait (from 7∘ W to 9∘ E,
78.3∘ N), Beszczynska-Möller et al. (2012a)
found two warm anomalies in the Atlantic water through the Fram Strait in
1999–2000 and 2005–2007. During the second warm anomaly, community
structures of phytoplankton and zooplankton were affected by the decreased
sea ice in this region (Lalande et al., 2013;
Nöthig et al., 2015) and had a profound influence on biogenic sediment
fluxes.
Map of sediment trap locations in high northern latitudes (FEVI16)
and in Antarctic Polar Front (PF3). In the Fram Strait, the northward red arrow
represents the West Spitsbergen Current bringing warm and saline Atlantic
waters to the Arctic and the southward blue arrow displays the East
Greenland Current transporting cold and fresh water to the Norwegian Sea.
Around the Antarctic, the digitized blue shadow exhibits the location of the
Antarctic Polar Front for the years 2002–2014 at weekly resolution
(Freeman and Lovenduski, 2016). The blue arrow indicates the
clockwise Antarctic Circumpolar Current. Ocean Data View is used for mapping
(Schlitzer, 2017; available at https://odv.awi.de, last access: 3 May 2018).
Antarctic Polar Front
The Antarctic Polar Front (APF) region is one of the major frontal zones
within the westerly wind-driven Antarctic Circumpolar Current (ACC)
(Fig. 2). Spatial and temporal variability of the
APF is strongly affected by seafloor topography. The APF has an average
width of 43 km (1987–1993), which moves southward during the austral
summer and reaches its northernmost position during the austral winter
(Moore et al., 1999). The ACC is well known for its
meandering jet flow and eddy formation (Moore et al., 1999; Orsi et al.,
1995), which likely stimulate phytoplankton blooms and primary production in
this region by supplying growth-limiting nutrients, and/or bringing deeper-dwelling phytoplankton into the photic zone
(Abbott et al., 2001; Moore and Abbott, 2000,
2002). Thus, the hydrographic structure is an important factor controlling
the distribution of phytoplankton and small zooplankton in this region
(Read et al., 2002). The highest chlorophyll-a levels
of up to 3.5 mg m-3 were recorded in the APF during the early austral
summer of 1990–1991, and it co-occurred with elevated silicate levels as well
as water column stratification (Laubscher et al., 1993). The
current speed close to the PF3 trap was recorded to be up to 8 cm s-1
(Walter et al., 2001). Read et al. (2002) reported the vertical profile of
currents measured between the surface and 400 m depth in the polar frontal
regions in December 1995. The currents recorded at the surface and
subsurface of the Polar Front were much stronger (30–50 cm s-1) than
the ones measured at 700 m depth (1–8 cm s-1) by
Walter et al. (2001).
Materials and methodsSediment traps
A time-series sediment trap system was moored to collect sinking particles
at the eastern Fram Strait (FEVI16) between July 2007 and July 2008 at 1296 m water depth (Table 1,
Fig. 2). Two traps (PF3) were deployed in the
permanent ice-free area of the APF in the Atlantic sector at 614 and 3196 m water depth from November 1989 to December 1990
(Table 1, Fig. 2). The
cone-shaped funnel of the Kiel trap systems had a 0.5 m2 collection
area, and the collection periods of each individual sample cup were
programmed depending on the expected time of ice cover and/or the
seasonality of the production. The sampling cups were filled with filtered
seawater enriched in sodium chloride (NaCl) to achieve a salinity of 40 psu, and they were poisoned with mercury chloride (HgCl2; 0.14 %
final solution) for sample preservation. For FEVI16 samples, zooplankton
“large swimmers” were removed using forceps before splitting each sampling
cup into smaller subsamples (Lalande et al., 2016). For
PF3 samples, large swimmers were removed using forceps and a sieve (mesh
size: 1 mm mesh) in the laboratory (Fischer et al.,
2002). Afterwards, the samples were split for different purposes. A current
meter (RCM 9/11) was attached to both trap moorings, and the datasets are
available on PANGAEA (10.1594/PANGAEA.845610; FEVI16) and in
Walter et al. (2001; PF3).
Information on FEVI16 and PF3 trap.
Trap nameFEVI16PF3RegionEastern Fram StraitAntarctic Polar FrontLocationLatitude (∘ N)79.02-50.13Longitude (∘ E)4.355.83Water depth (m)25803785Trap depth (m)12966143196Deployment periodStart23 Jul 200710 Nov 1989End30 Jun 200823 Dec 1990Sampling interval (d)10–3121, 42*Cruise reportsARK-XXII/1cANT-VIII/3(Klages and Participants, 2007)(Gersonde and Participants, 1990)ARK-XXIII/2ANT-IX/2(Kattner and Participants, 2009)(Fahrbach and Cruise Participants, 1992)
* The exact sampling interval of each sample at FEVI and PF3 can be found on PANGAEA (https://doi.pangaea.de/10.1594/PANGAEA.897268).
Mass flux
Split FEVI16 samples were filtered onto GF/F filters (pre-combusted at 500 ∘C for 4 h) for organic carbon, organic nitrogen (PON), and
carbonate and onto cellulose acetate filters for biogenic opal
quantification. Organic carbon (POC) and organic nitrogen (PON) were
measured with a CHN elemental analyzer after 0.1 N HCl treatment. Carbonate
was calculated by subtracting the weight from total mass after 0.1 N HCl
treatments and recorrected according to the aragonite contents. The
detailed methods used for biogenic silica can be found in
Lalande et al. (2016). Split PF3 samples were
freeze-dried for further processing. Decalcified samples (6 N HCl) were
analyzed for POC using a CHN elemental analyzer. Total nitrogen (TN) was
also determined. Carbonate was calculated by subtracting POC from total
carbon (TC), which was directly measured without decalcification (carbonate=8.33×(TC-POC)). The methodology for the quantification of
biogenic silica is described in Fischer et al. (2002).
GDGT analyses
For GDGT analysis, total lipids were extracted with a solvent mixture (9:1v/v dichloromethane (DCM) : methanol (MeOH)) using an ultrasonic bath for 10 min and a centrifuge for 5 min, after which the supernatant was decanted.
This process was repeated three times and the supernatants were combined.
Before the extraction process, a known amount of C46-GDGT (internal
standard) was added to each sample for GDGT quantification.
Following saponification of total lipids with 1 mL of 0.1 M potassium
hydroxide (KOH) in a mixture of MeOH:purified water 9:1 (v/v) at 80 ∘C for 2 h, neutral lipids (NLs) were recovered with 1 mL
of hexane (three times). NLs were separated into F1 (apolar), F2 (ketone), and F3
(polar) polarity fractions eluted in 2 mL of hexane, 4 mL of DCM:hexane (2:1v/v), and 4 mL of DCM:MeOH (1:1v/v), respectively, using deactivated
silica-gel chromatography (mesh size: 70–230 µm).
The F3 polar fraction containing GDGTs was redissolved in 500 µL
of hexane:isopropanol99:1 (v/v) and filtered through a
polytetrafluoroethylene (PTFE) filter (pore size: 0.45 µm) into a
glass insert of a 2 mL vial according to Hopmans et al. (2000). The filtered polar
fraction was diluted with hexane:isopropanol99:1 (v/v) to a concentration
of approximately 2 mg mL-1 before the instrumental analysis.
GDGTs were analyzed using high-performance liquid chromatography/atmospheric
pressure chemical ionization mass spectrometry (HPLC/APCI-MS) according to
Chen et al. (2014). Molecular ions m/z 1302, 1300,
1298, 1296, and 1292 for isoprenoid GDGTs and 1050, 1036, and 1022 for branched
GDGTs were determined and quantified in relation to the molecular ion m/z 744
of the C46-GDGT. The late eluting peaks of OH-GDGT–0, OH-GDGT–1, and OH-GDGT–2
with m/z 1318, 1316, and 1314 were also determined in the m/z 1300, 1298, and 1296
scan, as described by Fietz et
al. (2013).
A lab-internal standard sediment was repeatedly analyzed along with the
samples to assess the analytical error. The standard deviation of replicate
analyses was 0.1 units of TEX86L and
6 % for isoprenoid GDGT concentrations.
GDGT flux and indices
TEX86L values were calculated according
to Kim et al. (2010).
TEX86L=Log10[GDGT-2][GDGT-1]+[GDGT-2]+[GDGT-2],
where the numbers represent the number of cyclopentane moieties in the
isoprenoid GDGTs.
TEX86L values were converted into
temperatures using the following equation (Kim
et al., 2010):
SST(∘C)=67.5×TEX86L+46.92(R2=0.86,n=396,calibration error:±4∘C).
GDGT flux represents the sum of fluxes of individual GDGTs, which are used
for calculating TEX86L.
GDGTflux=[GDGT-1]+[GDGT-2]+[GDGT-3]
OH-GDGT-based indices and estimated temperatures were calculated using the
following equations (Eq. 4 by Fietz et
al., 2013; Eqs. 5 and 6 by Lü et al., 2015):
[OH-GDGT-0][Cren]=0.25-0.025×SST(∘C)4(R2=0.58,n=13,calibration error:±1.5∘C),5RI-OH′=[OH-GDGT-1]+2×[OH-GDGT-2][OH-GDGT-0]+[OH-GDGT-1]+[OH-GDGT-2],RI-OH′=0.0382×SST(∘C)+0.16(R2=0.75,n=107,calibration error:±6∘C).
BIT, an index of relative contribution of terrestrial versus marine input,
was calculated according to Hopmans et al. (2004).
BIT=[I+II+III][I+II+III+Cren]
The Roman numerals I, II, and III refer to the branched GDGTs with four, five, and
six methyl moieties, and Cren represents crenarchaeol containing four
cyclopentane moieties and one cyclohexane ring (Fig. 1). A terrestrial effect on TEX86 can be
significant when the BIT value is >0.3
(Weijers et al., 2006).
MI, the methane index indicating the relative contribution of GDGTs derived
from methanotrophic Archaea to those from planktonic Thaumarchaeota, was
calculated as follows (Zhang et al., 2011):
MI=[GDGT-1]+[GDGT-2]+[GDGT-3][GDGT-1]+[GDGT-2]+[GDGT-3]+[Cren]+[Cren′].
Cren′ represents the regio-isomer of crenarchaeol.
Contributions from methanotrophic Archaea are considered to be significant
when MI>3.
%GDGT–0, an indicator of a methanogenic source of GDGTs with a %GDGT
value >67 %, was calculated as follows (Inglis
et al., 2015):
%GDGT-0=[GDGT-0][GDGT-0]+[Cren]×100%.
RI, the ring index, is a tool for identifying a potential nonthermal
influence on GDGT distributions. A sample's ring index is
defined as follows (Zhang et al., 2016):
RIsample=0×[GDGT-0]+1×[GDGT-1]+2×[GDGT-2]+3×[GDGT-3]10+4×[Cren]+4×[Cren′].|ΔRI| indicates the residual of a
sample's RI (RIsample) from a calculated RI
(RIcalculated) based on the global TEX86-RI
regression. RIcalculated and |ΔRI|
are defined as follows:
RIcalculated=-0.77(±0.38)×TEX86+3.32(±0.34)×(TEX86)2+1.59(±0.10)11(R2=0.87,n=531),12ΔRI=RIcalculated-RIsample.
Potential nonthermal influences on TEX86 can be
recognized when |ΔRI| is >0.3
(Zhang et al., 2016).
Environmental parameters
TEX86L-derived temperatures were compared
to satellite-derived SSTs obtained by the Advanced Very High Resolution
Radiometer (AVHRR), which has a spatial grid resolution of 0.25∘
and temporal resolution of 1 d (Reynolds et al.,
2007). The satellite-derived SSTs were averaged over the collection period
of each sample cup. The depth profiles of mean annual water temperature and
nitrate concentration were obtained from the World Ocean Atlas 2013 (WOA13)
representing averaged values for the years 1955–2012 (Garcia et
al., 2013; Locarnini et al., 2013).
ResultsEastern Fram Strait: FEVI16Mass and GDGT fluxes
The mass flux data of the FEVI16 trap have been published previously by
Lalande et al. (2016). In summary, the mass flux (opal + POC + PON + lithogenic fluxes) measured at 1296 m water depth varied in between 18.5 and 652.9 mg m-2 d-1, with minima in the winter
season (November 2007–February 2008; Fig. 3a).
Lithogenic flux is presented separately in Fig. 3a
due to its significantly higher contribution to total mass flux. The
composition of sinking particles changed between seasons. Lithogenic
material flux showed sudden pulses in spring (March and April–May 2008).
POC was predominant in mid-July 2007. Carbonate flux was elevated in March,
early May, and mid-June 2008. Opal was dominant in early September 2007 and
mid-May to June 2008.
Changes in mass flux (a), GDGT flux (b), terrestrial biomarker
flux and diatom and coccolithophore abundances (c), fecal pellet flux (d),
sea ice concentration (e), and
TEX86L-derived temperatures and
satellite-derived SSTs obtained at 79.125∘ N, 4.375∘ E
near the trap site (f) at 1296 m water depth at the eastern Fram Strait
site, FEVI16, during the deployment period July 2007–July 2008. Panels (a), (c),
(d), and (e) were previously reported by Lalande et al. (2016).
GDGT fluxes varied between 7.2 and 85.9 ng m-2 d-1
(Fig. 3b). Peaks of GDGT flux occurred in
August–September 2007 and March and May–June 2008, while minima were
observed in the winter season (November 2007–February 2008). The GDGT flux
was strongly correlated with opal flux (R2=0.82, p<0.0001)
when two GDGT maxima in late September 2007 and March 2008 were excluded
and with carbonate flux (R2=0.86, p<0.0001) except for the
last 2 months in May–June 2008 (Fig. 3a and b).
TEX86L values varied between
-0.70 and -0.64 and their TEX86L-derived
temperatures (Kim et al., 2010, calibration) ranged between 0.6 and 3.7 ∘C (Fig. 3b and f). The flux-weighted
mean TEX86L temperature during the trap
deployment period was 2.8 ∘C. Two surface sediment
TEX86L values (-0.66 at PS68-251/2,
79.1∘ N, 4.6∘ E; -0.65 at PS68-271/2,
79.3∘ N, 4.3∘ E) are available near the FEVI16 trap
(Ho et al., 2014). Most index values of BIT, MI,
%GDGT–0, and |ΔRI| for samples from
FEVI16 were below the critical values of each index (>0.3 for
BIT and MI, >67 % for %GDGT–0, >0.3 for
|ΔRI|). One sample with a |ΔRI| value >0.3 was found, indicating a
nonthermal effect on TEX86
(Table A1 in the Appendix).
Fractional abundance of OH-GDGTs
All OH-GDGTs were clearly present in FEVI16 samples. OH-GDGT–0 was
predominant in the OH-GDGT pool, accounting for 87 %–95 %
(Fig. 4a). The proportion of OH-GDGTs in the sum of
iso- and OH-GDGTs ranged between 7 % and 11 % during the trap deployment
period (Fig. 4a). Estimated temperatures calculated
using the calibrations as described in Eqs. (4) and (6)
(Fietz et al., 2013; Lü et
al., 2015) ranged between -0.3 and 2.5 ∘C and between -0.9 and 1.4 ∘C, respectively. The last two data points for the trap time
series were unusually low when using Eq. (4) (Fig. 4b).
Changes in fractional abundance of OH-GDGTs (a) and estimated
temperatures based on OH-related calibrations (b) at site FEVI16. The purple
and orange lines indicate the calculated temperatures using the calibrations
by Fietz et al. (2013) (Eq. 4) and
Lü et al. (2015) (Eq. 6), respectively. The green
line indicates the TEX86L-derived
temperatures.
Antarctic Polar Front: PF3Mass and GDGT fluxes
The mass flux data of the PF3 trap have been published previously by
Fischer et al. (2002). The mass flux at the shallow
trap ranged between 19.7 and 592.1 mg m-2 d-1 with a distinct
seasonal variability (Fig. 5a). Flux maxima
occurred in November 1989 to early March 1990 and mid-October to November 1990, while it stayed relatively low from mid-March to mid-October 1990. At
the deep trap, the mass flux varied between 0 and 516.6 mg m-2 d-1
(Fig. 5d). The mass flux peaked in mid-December 1989 and January and March 1990 but stayed low for the remaining sampling
period except for mid-May 1990.
Changes in mass flux (a, d), GDGT flux (b, e), and
TEX86L-derived temperatures and
satellite-derived SSTs obtained at 50.125∘ S, 5.875∘ E
near the trap site (c, f) at the 614 m (a, b, c) and 3196 m (d, e, f) water depths at the Antarctic Polar Front site (PF3) during the
deployment period (November 1989–December 1990).
There was no clear correlation between organic material and GDGT fluxes at
either depth. The fluxes in GDGTs ranged from 10.5 to 73.9 ng m-2 d-1 at 614 m and from 0.5 to 153.9 ng m-2 d-1 at 3196 m depth
(Fig. 5b and e). GDGT fluxes were mostly enhanced
during the low-mass-flux season at the shallow trap
(Fig. 5b). GDGT flux maxima occurred in November
and December 1989 and late May and early November 1990 at the deep trap
(Fig. 5e). GDGT analysis was not possible between
late July and October 1990 in the deep trap since very little material was
collected during this time period.
TEX86L values varied between
-0.58 and -0.67 and -0.55 and -0.59 at the shallow and deep traps, respectively
(Fig. 5b and e). The TEX86L-derived temperatures ranged
between 1.3 and 7.8 ∘C for the shallow trap and 7.4 and 9.8 ∘C for the deep trap (Fig. 5c and f).
The flux-weighted mean TEX86L
temperatures were 4.6 and 8.5 ∘C at the shallow and
deep traps, respectively. In some samples, GDGT–3 was below the detection
limit. In these cases, TEX86L values
and estimated temperatures were not included in the figures, but GDGT flux
was calculated. A surface sediment
TEX86L value of -0.56 (CHN-115-4-34;
51.00∘ S, 5.33∘ E) in the vicinity of the PF3 trap site
(50.13∘ S, 5.83∘ E) can be found in the
TEX86 global calibration dataset
(Kim et al., 2010), and its
TEX86L-derived temperature estimate is
9.1 ∘C. Most samples did not exceed the index values indicating
nonthermal impacts on TEX86 (see Sect. 3.4). Two samples from the deep trap between early
November and late December 1989, however, showed MI values between 0.3 and
0.5 (Table A1).
Fractional abundance of OH-GDGTs
All OH-GDGTs were clearly present in samples from both depths. OH-GDGT–0
was predominant in the OH-GDGT pool (76 %–89 % for the shallow trap,
77 %–83 % for the deep trap; Fig. 6a and c). The
proportion of OH-GDGTs in the sum of iso- and OH-GDGTs ranged from 4 % to 7 % and 5 % to 6 % at the shallow and deep traps, respectively,
(Fig. 6a and c). Estimated temperatures calculated
according to Eqs. (4) and (6) ranged between 3.0–6.1 and
1.1–5.1 ∘C in the shallow trap (Fig. 6b). With the same calibrations, estimated temperatures varied between
3.6–5.8 and 2.8–5.2 ∘C in the deep trap
(Fig. 6d).
Changes in fractional abundance of OH-GDGTs (a, c) and estimated
temperatures based on OH-related calibrations (b, d) at site PF3. The purple
and orange lines indicate the calculated temperatures using the calibrations
by Fietz et al. (2013) (Eq. 4) and
Lü et al. (2015) (Eq. 6), respectively. The green
lines indicate the TEX86L-derived
temperatures.
DiscussionEastern Fram Strait (79<inline-formula><mml:math id="M207" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N)GDGT flux and particle export
Organic matter (including GDGTs), formed in the upper ocean, is exported to
the bathypelagic mainly as zooplankton fecal pellets or marine snow
aggregates (Fischer and Karakaş, 2009; Wuchter et
al., 2006b). The velocities of the sinking aggregates vary depending on
their composition (Fischer and Karakaş, 2009; Iversen
and Ploug, 2010) and physical characteristics. For instance, aggregates
formed from coccolithophores are ballasted by carbonate and may sink faster
than opal-ballasted diatom aggregates (Iversen and Ploug,
2010). Still, fecal pellets formed from either coccolithophores or diatoms
sink faster than fecal pellets formed from non-ballasted flagellates (Ploug
et al., 2008a, b). GDGTs have been suggested to be preferentially
incorporated into opal-dominated particles
(Mollenhauer et al., 2015). This is in agreement
with observed TEX86-derived temperatures in
sediment trap samples off Cape Blanc being delayed relative to the SST
signal, and this time delay was longer than for
U37K′-derived temperatures
(Mollenhauer et al., 2015).
GDGT fluxes covaried with fluxes of biogenic and non-biogenic components
in the eastern Fram Strait (Fig. 3). GDGT fluxes
were enhanced in summer 2007 and spring 2008 with two clear maxima in late
September 2007 and March 2008 (Fig. 3b). Without
those two distinct GDGT flux maxima, GDGT flux showed a good correlation
with opal (R2=0.82, p<0.0001) throughout the deployment
period. Those two episodic GDGT pulses occurred when carbonate,
coccolithophore, and terrestrial biomarker fluxes were enhanced, potentially
resulting in the enhanced GDGT flux (Chen
et al., 2016; Yamamoto et al., 2012). GDGT fluxes also covaried with
carbonate from the beginning of the deployment until April 2008 (R2=0.8, p<0.0001) (Fig. 3a and b). This
agrees with the concept that GDGTs are transported by particles mainly
containing opal and carbonate as previously shown in sediment trap studies (Chen
et al., 2016; Huguet et al., 2007; Mollenhauer et al., 2015; Park et al.,
2018; Yamamoto et al., 2012). This observation suggests that GDGTs are
exported together with diatoms (opal) and coccolithophores (carbonate).
Supporting this, diatom and coccolithophore fluxes both, previously reported
by Lalande et al. (2016), showed good correlations
with GDGT fluxes (R2=0.64, p<0.001 for diatoms and R2=0.68, p<0.0001 for coccolithophores) when excluding two GDGT
flux maxima in the correlations with opal fluxes. The flux of terrestrial
biomarkers (campesterol +β-sitosterol), previously analyzed by
Lalande et al. (2016), also showed a positive
correlation with GDGT flux (R2=0.64, p<0.001). The sterols
(campesterol and β-sitosterol), used frequently to assess the
plant-derived organic matter input to aquatic systems
(Moreau et al., 2002), reflect the input of terrestrial
matter transported by sea ice in the Fram Strait (Lalande
et al., 2016). We thus assume that terrestrial material is aggregated into
particles which carry GDGTs. However, the lithogenic flux, also representing
terrestrial input, displayed a different trend with maxima only in
mid-April–mid-May 2008, when the sea ice concentration abruptly increased
(Fig. 3e). The lithogenic material seems to be
mainly supplied by downslope export from the nearby Svalbard archipelago
(Lalande et al., 2016), with a significant input when the
sea ice is present (Fig. 3e).
GDGTs might also be exported with fecal pellets after grazing of Archaea by
zooplankton. Indeed, GDGTs have been found in decapod guts and intestines,
and their abundance ratios appear to be unaltered during gut passage
(Huguet et al., 2006). The fecal pellet carbon (FPC) flux
from appendicularians contributed more to the total carbon flux than copepod
FPC flux. Both appendicularian and copepod FPC fluxes were higher during
spring 2008 than during summer 2007 (Fig. 3d;
Lalande et al., 2016). The correlation of GDGT fluxes
with appendicularian FPC fluxes (R2=0.67, p<0.05 in
mid-July to mid-October 2007) implies grazing of Archaea by the
appendicularians. Appendicularians are well known to ingest micro-size
particles like Archaea (<1µm) efficiently through a fine
mucus filter (Conley and Sutherland, 2017).
Overall, it is reasonable to assume that seasonal changes in the relative
proportion of materials that GDGTs can be aggregated with may result in
variable export velocities of particles carrying GDGTs to deeper waters in
the eastern Fram Strait.
Variability of <inline-formula><mml:math id="M227" display="inline"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">TEX</mml:mi><mml:mn mathvariant="normal">86</mml:mn><mml:mi mathvariant="normal">L</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula>-derived
temperature
TEX86L temperatures varied (-0.2 to 3.7 ∘C) within a similar range as the satellite-derived SSTs
(-0.1 to 3.4 ∘C) during the trap deployment period in the eastern
Fram Strait. Although it did not display a clear seasonality, the
TEX86L signal is most likely to reflect
the surface water environments without nonthermal effects influencing the
TEX86L (Fig. 3e). First of all, when the error of the
TEX86L calibration (±4∘C) is considered, the SST estimates are identical to the
satellite-derived SSTs. Secondly, the index values (e.g., BIT, MI,
%GDGT–0, RI), which could indicate potential nonthermal factors
influencing the distribution of GDGTs, suggest that
TEX86L temperatures should reflect the
upper water column temperature (Table A1). The BIT
index defined by Hopmans et al. (2004) as a tracer
for the terrestrial organic matter input was very low (<0.02),
showing that negligible amounts of soil-derived GDGTs are entrained in
sinking particles. MI (Zhang et al., 2011) and
%GDGT–0 (Inglis et al., 2015) have been suggested as
indicators for the impact of methanotrophic Archaea even though it was
questioned if the latter index can be applied in marine settings
(Inglis et al., 2015). Both index values were consistently
lower than the respective critical values of 0.3 for MI and 67 % for
%GDGT–0. |ΔRI| was suggested by
Zhang et al. (2016) as an indicator for the integrated
nonthermal factors on TEX86. Most |ΔRI| values were lower than a value of 0.3, and only
three samples collected in late September 2007 and June 2008 satisfied the
criteria of being influenced by nonthermal factors. Lastly, although
significant portions of particles transported to the mid-depth and deep
water in the eastern Fram Strait originated off Svalbard and the Barents Sea
(Lalande et al., 2016), it is known that GDGTs are most
likely to reflect the local conditions rather than being affected by lateral
transport (Kim et al., 2009; Mollenhauer
et al., 2008). Therefore, we conclude that GDGTs are mainly transported from
upper waters and non-regional sources of particles do not play a significant
role in the TEX86L estimate of sinking
particles of the eastern Fram Strait.
TEX86Ltemperatures varied
strongly between mid-July and October 2007, with the minimum estimated
temperature (0.6 ∘C) when the GDGT and terrestrial biomarker
fluxes were highest (Fig. 3b, c, and f). This is
the most productive period in the Fram Strait and also the period with the
highest export fluxes (e.g., Lalande et al., 2016). The
material collected by deep-ocean sediment traps is a mixture of many types
of aggregates with different composition and settling velocities. Therefore,
fluctuating TEX86L temperatures might
be due to an average of signals from previous (before mid-July, i.e., slowly
sinking particles) and current seasons (mid-July to October, i.e., fast
settling aggregates), which were exported via different aggregation and
sinking mechanisms with different horizontal displacements during particle
descent. Due to the collection of aggregates sinking with different
velocities, one sample cup in a sediment trap may collect GDGTs of different
ages.
During the low flux period between November 2007 and February 2008,
TEX86L temperatures were constantly
lower than the satellite-derived SSTs (Fig. 3a, b,
and f). These signals might be derived from GDGTs synthesized in mid-August
to October, which was a late-bloom period dominated by protists that do not
produce biominerals, such as Phaeocystis sp., dinoflagellates, and nanoflagellates (Nöthig et al., 2015). Aggregates formed without
biominerals have low sinking velocities (Iversen
and Ploug, 2010; Ploug et al., 2008a, b), which may explain why the
satellite-derived SSTs from mid-August to October 2007 were similar to the
TEX86L-derived temperatures in sinking
particles collected during November 2007 and February 2008. In spring 2008,
TEX86L temperatures stayed relatively
warm until mid-April and suddenly dropped in May and June
(Fig. 3f). The relatively warm
TEX86L temperatures in particles
collected in March and April 2008 probably reflect the signal transported by
GDGTs produced in November–December 2007. Warm and cold biases of the
TEX86L-derived temperatures varied
within the calibration error (±4∘C) throughout the trap
deployment period. It shows that the bias of the calibration occurs neither
in one direction only nor to the same extent even at a given location;
instead the temperature estimate is more affected by other processes
discussed above. Like previous seasons, the lack of transporting materials
for GDGTs delayed the TEX86L signal.
The sudden drop in TEX86L-derived
temperatures in mid-May to June 2008 was matched with the season of the
enhanced flux of biogenic materials, GDGTs, phytoplankton, zooplankton fecal
pellets, and terrestrial biomarkers at the trap when the sea ice
concentration was enhanced (Fig. 3). Eddies around
the ice edge create upwelling and downwelling, which breaks the stratified
surface water and supplies nutrients to the upper water column, fostering
phytoplankton blooms (Lalande et al., 2013). The sea
ice-edge bloom probably enhanced the GDGT production followed by zooplankton
grazing. Additionally, the terrestrial materials as ballast were more
available, and were derived from sea ice as it melted during spring 2008.
This process caused the fast export of
TEX86L signal from the surface waters
in mid-May to June 2008. Overall, the particles containing GDGTs captured at
1296 m depth in the eastern Fram Strait were likely delivered by a
combination of packaging mechanisms involving biological and nonbiological
transport materials with different delay times over the season. Moreover,
most GDGTs are exported vertically from the upper waters and reflect a
regional TEX86L signal in the eastern
Fram Strait (Fig. 3f), while significant particle
supply from the south via lateral advection has been reported previously
(Lalande et al., 2016). The reflection of SSTs based on
the TEX86L calibration and the good
correlation of GDGTs with opal and Appendicularian fecal pellet fluxes
agrees with the finding that diatom and fecal pellet fluxes can be traced as
the export of local production regardless of the lateral particle supply in
the Fram Strait (Lalande et al., 2016).
The average export velocity of particles containing GDGTs can be calculated
by dividing the travel distance of particles (i.e., the depth of the
sediment trap) by the temporal offset between
TEX86L-derived temperature and the
corresponding satellite-derived SST, greatly simplifying the complexity of
sinking mechanisms and range of settling velocities throughout the season
(Mollenhauer et al., 2015). When the
TEX86L-derived temperatures are shifted
by 82 d, we found the best fit to the satellite-derived SSTs
(Fig. 7b). Therefore, we see that GDGTs synthesized
in the eastern Fram Strait likely travel to the trap (1296 m) approximately
within 82 d. This translates into an average minimum export velocity of
GDGT-containing particles of approximately 15 m d-1 when assuming
export from 30 to 80 m water depth (see Sect. 5.1.3). We have assumed that the export velocity
includes the time of GDGT synthesis and transport time to the trap. The
estimated sinking velocity of 15 m d-1 is within the range (9–17 m d-1) estimated at a similar water depth (∼1300 m) in the
filamentous upwelling zone off northwest Africa
(Mollenhauer et al., 2015) and only slightly lower
than settling velocities found previously for aggregates from the Fram
Strait (Wekerle et al., 2018). Based on the seasonal
succession of peaks in appendicularian fecal pellets in the same mooring
system, their sinking velocity was estimated to be 5- to 11-fold higher
(15–35 d to 2400 m depth; Lalande et al., 2016)
compared to GDGTs. This is a realistic scenario because of the fast sinking
velocity of a fecal pellet, being rather big and dense, and generally faster
average export velocities to deeper ocean depths
(Fischer and Karakaş, 2009; Iversen et al.,
2017). Moreover, the average export velocity of GDGTs includes the time it
takes to be incorporated into aggregates. A more rapid export of GDGTs to
depth resulting in smaller temporal offsets of
TEX86L-derived temperature to water
temperature changes occurred in mid-May–June 2008 when the FPC flux of
appendicularians was enhanced (Fig. 3d). It has to
be noted that the export velocity we calculated here may represent an
averaged velocity of all the aggregates collected in the trap, as the export
velocity of GDGTs can vary depending on the type of sinking materials GDGTs
are incorporated into.
Changes in TEX86L-derived
temperatures and satellite-derived SSTs (a), time-delayed
TEX86L temperatures (b), and sensor-measured temperatures (c) at approximately 68 m water depth at site FEVI16.
The flux-weighted mean TEX86L
temperature over the mooring period was 2.8 ∘C. In the vicinity
of the site FEVI16 (79.03∘ N, 4.35∘ E), two
TEX86L values from surface sediments
are available in the dataset published by Ho et al. (2014). The estimated temperatures based on
TEX86L are 2.8 and 2.3 ∘C in PS68-251/2 (79.30∘ N, 4.30∘ E) and
PS68-271/2 (79.10∘ N, 4.60∘ E), respectively.
Considering the error of the TEX86L
calibration (Kim et al., 2010),
TEX86L estimates are almost identical
between sinking particles and surface sediments. This suggests that GDGTs
synthesized in the upper waters are propagated through the water column into
the sediment without significant alteration even though the sinking process
of GDGTs in the eastern Fram Strait seems quite complicated as discussed
above. Iversen et al. (2010) and Jackson and Checkley Jr. (2011) also suggested that most
biological activities resulting in aggregate alteration and degradation
occur around the base of the photic zone. Our observations agree with
previous studies, as TEX86 displays consistent
values at multiple depths and/or in the underlying surface sediments in
various environmental regimes such as in the Arabian Sea
(Wuchter et al., 2006b), in the northwestern Pacific
(Yamamoto et al., 2012), off Cape Blanc
(Mollenhauer et al., 2015), near Iceland
(Rodrigo-Gámiz et al., 2015), off southern Java
(Chen et al., 2016), and in the northern Gulf of
Mexico (Richey and Tierney, 2016).
In the eastern Fram Strait, the changes in
TEX86L-derived temperature are largely
controlled by the depth and time of GDGT production and temporally variable
sinking materials aggregated with GDGTs. Additionally, the absolute
estimated temperatures varied within the
TEX86L calibration error (±4∘C). In this case, the temperature error inherited from the
calibration is less important than other relative changes.
Potential depth of <inline-formula><mml:math id="M286" display="inline"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">TEX</mml:mi><mml:mn mathvariant="normal">86</mml:mn><mml:mi mathvariant="normal">L</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> signal origin
To determine the water depth where the
TEX86L signal originated, the
flux-weighted mean TEX86L temperature
(2.8 ∘C) was compared to the depth profile of nutrient
concentrations (NH4+,
NO2-, and
NO3-) measured in late June to early
July 2010, 2011, and 2013 and water temperature extracted from WOA13
(79.125∘ N, 4.375∘ E) in the eastern Fram Strait
(Fig. 8). Unfortunately, ammonia data are
unavailable in 2007 and 2008. The mean estimated temperature (2.8 ∘C) in sinking particles corresponds to the surface water in
July–September or of 30–80 m subsurface waters in April–June
(Fig. 8). Major production of GDGTs in both seasons
is a possible scenario because GDGT flux peaked in both seasons
(Fig. 3b). However, the latter period
(April–June) is the more plausible season dominantly supplying GDGTs to
the sediment if we consider the delay time of the GDGT signal (approximately
82 d; see Sect. 5.1.2), which illustrates the
initial time of GDGT production (Fig. 3b).
Furthermore, Thaumarchaeota, the primary synthesizers of GDGTs in the ocean,
are aerobic ammonia oxidizers (Könneke et al.,
2005) and maximum GDGT concentrations have been found near the
NO2- maximum, where maximum rates of
ammonia oxidation and nitrification occur (Beman et al., 2008; Hurley et al.,
2016). The depths of consumption of ammonia and production of nitrite as
well as nitrate accumulation as a result of the nitrification process in the
subsurface can be deduced from the nutrient profile
(Fig. 8). Assuming that maximum thaumarchaeotal
abundance occurs at the depth of highest substrate availability, we,
therefore, infer that Thaumarchaeota mainly record the subsurface water
temperature (30–80 m) during the warm season when the spring bloom may
occur (April–June) in the eastern Fram Strait. Similar observations were
made in the Sea of Okhotsk and the northwest Pacific
(Seki et al., 2014) and in the western Bering Sea
(Meyer et al., 2016), where the
TEX86L-derived temperature is
attributable to the regional season and water depth of GDGT production. The
temperatures measured by a temperature sensor attached to the mooring array
at approximately 68 m depth (Beszczynska-Möller et al.,
2012a), which is approximately in the middle of the depth interval (30–80 m) corresponding to the TEX86L
temperature (Fig. 8), support our interpretation.
The flux-weighted mean TEX86L
temperature is within the range of measured temperatures at this depth
and it reflects well the temperatures in March-June as deduced from the
nutrient depth profile well (Fig. 7c).
Depth profiles of nutrient concentration
(NH4+,
NO2-,
NO3-) and
temperature in the Fram Strait. The navy, green, and gray lines represent
the central station of the Long-Term Ecological Research (LTER) observatory
HAUSGARTEN (HG-IV), close to the FEVI16 trap location. The purple and brown
lines represent the southern and northern stations of HG-IV. Black dotted
lines for NO3- and
temperature and blue and orange lines for temperature represent the datasets extracted from WOA13. Only two seasonal profiles (April to June and
July to September) from WOA13 are available at the same location
(79.125∘ N, 4.375∘ E) near the site FEVI16. Measured
nutrient concentrations are reported on PANGAEA (Bauerfeind
et al., 2014).
Antarctic Polar Front (50<inline-formula><mml:math id="M307" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> S)GDGT flux
At the shallow trap (614 m water depth) of site PF3, there was no clear
correlation between fluxes of organic matter and GDGTs, which displayed
their respective maxima in different seasons (Fig. 5a and b). Peaks of mass flux occurred in November 1989–early March 1990
and mid-October–December 1990, while GDGT fluxes were highest in
mid-May–June and late July–August 1990 (late austral autumn and winter)
(Fig. 5a and b). Similar observations were made in
the North Sea, where the abundance of Thaumarchaeota (previously known as
marine group I Crenarchaeota) and GDGT concentrations were high in wintertime, supported by the seasonality of Thaumarchaeotal 16S rRNA and amoA gene
abundances in that region (Herfort
et al., 2007; Pitcher et al., 2011; Wuchter et al., 2006a). An austral
“winter bloom” of planktonic Archaea was also found near the Antarctic
Peninsula (Church et al., 2003;
Murray et al., 1999; Tolar et al., 2016). Photoautotrophic phytoplankton,
which use ammonium as a N source, would outcompete Archaea for ammonia in
spring and summer. In contrast, in wintertime when phytoplankton
productivity is limited due to the lack of light, ammonia availability for
Archaea is higher (Pitcher et al., 2011; Wuchter
et al., 2006b). This explains the winter bloom of ammonia-oxidizing Archaea.
Laubscher et al. (1993) found that ammonia was highly
depleted at the chlorophyll-a maximum across the Antarctic Polar Front in
early austral summer, which might create less favorable conditions for
ammonia-oxidizing planktonic Archaea. Additionally, Thaumarchaeota are known
to be sensitive to photoinhibition (Horak et al.,
2017). Therefore, the austral winter maxima in GDGT flux at the shallow trap
of PF3 might be a consequence of the higher production of planktonic Archaea
in surface waters of the APF during the time when ammonia is more available
and photoautotrophs cannot compete due to light limitation.
At the deep trap (3196 m water depth) of site PF3, GDGT flux peaked in
November 1989 while mass flux was most pronounced in March 1990
(Fig. 5d and e). Due to the lack of sinking
particles captured in the deep trap in June–November 1990, it is unclear
if a potential winter bloom of GDGTs was also exported to deeper waters.
The trapping efficiency of PF3 was found to be below 50 % at the deep
trap using 230Th as flux proxy (Walter et al.,
2001). The negligible mass flux could thus be caused by low trapping
efficiency. However, Fischer et al. (2002) observed a
similar seasonal flux pattern in the following years measured almost at the
same depths and at the same location as site PF3. At site BO
(54.50∘ S, 3.33∘ W), which was located further south
than site PF3, the authors also found a period of almost no flux in
July–December for 4 years approximately at 2200 m depth. Thus, it is
assumed that a low trapping efficiency or a failure of the trap system did
not account for the lack of particle samples at the deep trap of PF3.
Variability of <inline-formula><mml:math id="M312" display="inline"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">TEX</mml:mi><mml:mn mathvariant="normal">86</mml:mn><mml:mi mathvariant="normal">L</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula>-derived
temperature
TEX86L-derived temperatures at the
shallow trap did not display a clear seasonal variability. Only one-third of the
data points were similar to the SSTs, while the remaining samples showed
warm- or cold-biased temperatures relative to the satellite-derived SSTs
during the sampling period (Fig. 5c). All samples
in the deep trap displayed warmer
TEX86L temperatures by up to 7 ∘C relative to the satellite-derived SSTs
(Fig. 5f). As discussed above, the mismatch between
lipid proxy-based temperature estimates and satellite-derived temperature
could be explained by the delay time of the proxy signal, due to the time
needed between lipid synthesis and incorporation into sinking aggregates
plus the sinking time (Mollenhauer
et al., 2015; Müller and Fischer, 2003; Park et al., 2018). However,
without a marked seasonality of the estimated temperature, it is difficult
to determine a delay time of the TEX86 signal. The
other explanation for the absence of covariance between the
TEX86 signal and the satellite-derived SST is that
we observe a mixed signal of Thaumarchaeotal GDGTs derived from surface and
deep ocean. The warm biases have a tendency to occur during periods of lower
GDGT flux and thus may include higher proportions of material from different
sources (early December, mid-June–July, September–November), which may
be more dominant in the deep trap (see discussion below).
Temperature residuals (∼7∘C) in the deep trap,
which are larger than the calibration error (±4∘C),
suggest significant nonthermal effects on GDGT compositions or the
unreliability of the global calibration in this region.
Warm-biased TEX86L-derived temperatures
have been attributed to the contribution of different archaeal communities
or GDGT input from terrestrial sources, which may alter the composition of
pelagic GDGTs, leading to unusual
TEX86L-derived temperature estimates
(Hopmans et al., 2004; Inglis et
al., 2015; Zhang et al., 2011, 2016). For example, several studies showed
that anomalous TEX86 based temperature estimates
can be caused by isoprenoid GDGTs produced by Group II Euryarchaeota as
significant contributors to the archaeal tetraether lipid pool and, thus,
TEX86 (Lincoln et
al., 2014; Schouten et al., 2008, 2014; Turich et al., 2007), a suggestion
that is controversial. A prevalence of marine Group II Euryarchaeota has
been reported in Circumpolar Deep Water (Alonso-Sáez
et al., 2011) and in deep waters of the Antarctic Polar Front
(López-García et al.,
2001; Martin-Cuadrado et al., 2008; Moreira et al., 2004; Murray et al.,
1999). Therefore, we would assume that GDGTs produced by deep-dwelling
Euryarchaeota might have caused the warm-biased
TEX86L signal at depth in the region.
Alternatively, advection of particles from warmer ocean regions could
potentially lead to warm biases. However, it is known that the impact of
lateral transport of GDGTs on the local signal is insignificant
(Kim et al., 2009; Mollenhauer et al.,
2008).
BIT, MI, %GDGT–0, and |ΔRI| were
examined to evaluate nonthermal factors on GDGT composition and
TEX86 values at both PF3 traps. However, none of
the values for BIT, %GDGT–0, and |ΔRI|
exceeded the defined critical values (Table A1). MI
values were higher at the deep trap than at the shallow one. The first two
samples at the deep trap reached values of 0.3–0.5 MI, suggesting that
GDGTs are derived from a mixture of non-methanotrophic and methanotrophic
communities (Zhang et al., 2011). However, these two
samples alone cannot fully explain the episodic and continuous warm-biased
TEX86L estimates at the shallow and
deep traps. Therefore, those indices cannot explain the warm anomaly of
TEX86L estimates in the APF. Like in
the Gulf of Mexico (Richey and Tierney, 2016), a longer time
series of samples would be helpful to investigate inter-annual variability,
paired with a direct assessment of the archaeal community in the region.
The flux-weighted mean TEX86L
temperatures averaged over the trap deployment period were 4.6 and 8.5 ∘C at the shallow and deep traps, respectively. The
TEX86L temperature in surface sediment
(CHN 115-4-34; 51.00∘ S, 5.33∘ E) from the vicinity of
the PF3 trap was 9.1 ∘C, which is 4.5 ∘C warmer than
the TEX86L estimate temperature in the
shallow trap (4.6 ∘C) but is similar to the one in the deep trap
(8.5 ∘C). This situation is different from the Fram Strait and
other time-series trap studies which showed that
TEX86 values in different depths are almost
identical (Chen et al., 2016;
Mollenhauer et al., 2015; Wuchter et al., 2006b). This implies that there is
a clearly different origin of GDGTs in particles collected in the upper
(<∼600 m) and deeper ocean, yet the main explanation
of the warm-biased TEX86L is still
speculative.
Re-evaluation of <inline-formula><mml:math id="M345" display="inline"><mml:mrow><mml:msubsup><mml:mi mathvariant="normal">TEX</mml:mi><mml:mn mathvariant="normal">86</mml:mn><mml:mi mathvariant="normal">L</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> calibration in polar oceans
In the two previous sections, the
TEX86L calibration developed by
Kim et al. (2010) for regions where maSSTs
are below 15 ∘C was applied. This calibration is based on data
from 396 surface sediments in the global ocean. Several studies have
expanded the TEX86 and
TEX86L surface sediment dataset. We
revisited the latest global surface sediment dataset containing 1095 surface
sediment measurements (Tierney and Tingley, 2015, and
references therein) to recalculate the
TEX86L calibration and to examine the
agreement of our results with the new calibration. From the dataset, the
data points for which TEX86L values
cannot be calculated or which were pointed out as problematic by
Ho et al. (2014) (see BIT>0.3 or
∼1.0 and GDGT concentration below detection limit) were
excluded to avoid potential biases. With approximately 2 times more data
points for the TEX86L calibration than
the original one (Kim et al., 2010; n=396), the new calibration again shows a linear correlation with maSSTs even
though the correlation coefficient is slightly lower
(Fig. 9;
TEX86L=0.013×SST-0.657; R2=0.80, n=744) and the residual standard error (±5.0∘C) is higher than those of the calibration of
Kim et al. (2010) (R2=0.86, ±4.0∘C). It should be noted that our new linear
calibration has SST on the x axis as the control variable and
the TEX86L value on the y axis as the
dependent variable because the composition of GDGTs and membrane lipids of
Thaumarchaeota (i.e., TEX86/TEX86L value) is affected by water
temperature. This different regression might explain parts of the
discrepancy between our and the original calibrations of Kim et al. (2010).
Larger scatter towards colder temperatures appears in the new calibration as
shown in previous studies (Ho et
al., 2014; Kim et al., 2010) (Fig. 9).
Correlation of TEX86L values
with mean annual SSTs in the global ocean (gray symbols;
Tierney and Tingley (2015) and reference therein). Blue
and black round symbols represent the high-latitude regions higher than
50∘ S and 50∘ N, respectively. In the southern regions
(>50∘ S), TEX86L
values have a polynomial correlation with maSSTs (R2=0.63, n=95). Red and orange diamonds represent the flux-weighted mean
TEX86L of the two traps at the site PF3
and TEX86L on the surface sediment
(CHN-115-4-34), respectively. Yellow and green round symbols represent the
flux-weighted mean TEX86L at the site
FEVI16 and TEX86L on the surface
sediments (PS68-251/2 and PS68-271/2), respectively.
It is obvious that the relationship of maSSTs and
TEX86L values in high-latitude northern
(>50∘ N) and southern (>50∘ S)
oceans is significantly different when both regions are plotted separately
(Fig. 9). The data from the north (>50∘ N) are scattered on both the right- and left-hand sides of the
regression line, resulting in potential warm and cold anomalies,
respectively (Fig. 9). This may reflect various
sources of GDGTs in the high-latitude northern oceans, which have direct
geographical connections to Eurasia and North America. Warm-biased
TEX86L estimates in these regions might
be caused by input of soil-derived GDGTs, which are picked up by the sea ice
in the Arctic marginal seas (Laptev Sea, Kara Sea, eastern Greenland),
and released while the sea ice melts. Alternatively,
TEX86L-derived temperatures might
reflect warmer subsurface temperatures in the warm season as shown in the
subarctic North Pacific region with the regional calibration by
Seki et al. (2014) and Meyer et
al. (2016). Cold-biased TEX86L
estimates are mainly found in the Barents Sea, North Sea, and the Norwegian
Sea. In the North Sea, a significantly enhanced abundance of planktonic
Archaeal cells and high GDGT concentration in the wintertime could account
for colder TEX86L-derived temperature
compared to SST (Herfort et al.,
2007; Pitcher et al., 2011; Wuchter et al., 2006a). Hence, regional
calibrations in the high-latitude northern oceans seem to be a valid
approach.
By contrast, most of the data from the south (>50∘ S)
show higher TEX86L values than
predicted by the regression line (n=95). This explains the observed warm
anomalies of TEX86L-derived temperature
when using the original linear regression calibration. Instead of the global
linear calibration, a polynomial one seems to be a better option in these
regions (Fig. 9). When excluding one data point in
the winter sea-ice-covered Southern Ocean, the correlation coefficient of
the polynomial correlation is encouraging (R2=0.63).
Summary of TEX86L thermometry
in FEVI16 and PF3 site.
Trap nameFEVI16PF3Oceanographic settingSeasonal ice coverWinter ice edgeMain GDGTs producerThaumarchaeotaThaumarchaeota + EuryarchaeotaSurface oceanaSatellite SST: -0.1 to 3.4 ∘CaSatellite-SST: 1.8 to 5.2 ∘CtemperaturebAve. SST: 1.9 ∘CbAve. SST: 3.5 ∘CShallow trapcTEX86LT: 2.8 ∘C (30–80 m depth signal)cTEX86LT: 4.6 ∘C(Thaumarchaeota + Euryarchaeota)Deep trapcTEX86LT: 8.5 ∘C(dominant Euryarchaeota)Surface sedimentTEX86LT: 2.3/2.8 ∘C at 2400 mTEX86LT: 9.1 ∘C at 3800 mRelevant processes for GDGTs– Export of upper ocean signal by fast– Contribution of Euryarchaeota in eCDWsettling particlescausing warm biases– Highly ballasted with opal and carbonateds.v.: 15 m d-1Conclusions– Linear calibration (TEX86L) applicable– Linear calibration (TEX86L) unreliable– Temporal offset due to changing ballast– Nonlinear relationship between TEX86Lmaterials and s.v.and SST (>50∘ N)– OH-GDGT-based calibrations– OH-GDGT-based calibrationsapplicableapplicable
a Satellite-derived sea surface temperature. b Averaged surface temperature over the trap deployment period. c Flux-weighted average temperature over the trap deployment period. d s.v.: sinking velocity. e CDW: Circumpolar Deep Water.
The data points of the flux-weighted mean
TEX86L in the FEVI16 trap and
TEX86L in the underlying surface
sediment against maSSTs are both closely located to the linear regression
line. It illustrates the applicability of the linear
TEX86L calibration in the eastern Fram
Strait (Fig. 9). At the site PF3, the
TEX86L-derived temperatures based on
the polynomial calibration yield -0.1, 1.9, and
2.2 ∘C at the shallow and deep traps and in the underlying
surface sediment, respectively (Fig. 9), which are
colder than the estimates based on the linear calibration. The polynomial
TEX86L temperatures at the deep trap
and on the sediment are slightly colder than maSST (2.4 ∘C; the
data can be found in Tierney and Tingley, 2015), but
less biased compared to the reconstructed temperatures based on the linear
TEX86L calibration (see Sect. 5.2.2). To test this polynomial calibration in the
Southern Ocean (>50∘ S), a further assessment needs
to be made with down-core sediments in similar regions, where the original
TEX86L estimate displays a warm bias.
Applicability of OH-GDGT-related calibrations for SST estimates in the high-latitude Atlantic Ocean
OH-GDGTs were determined in sinking particles of the eastern Fram Strait and
of the APF. The proportions of OH-GDGTs to total GDGTs (sum of OH- and
iso-GDGTs) ranged between 7 % and 14 % in the eastern Fram Strait and 4 % and 7 % in the APF (Figs. 4a,
6a, and c). This is a relatively smaller portion
than in the Nordic Seas (∼16 %;
Fietz et al., 2013).
Several OH-GDGT-based calibrations for SST have been suggested for the
global ocean and the Nordic Seas (Fietz
et al., 2013; Huguet et al., 2013; Lü et al., 2015). We applied those
calibrations suggested by Fietz et al. (2013) and
Lü et al. (2015). Only two calibrations (Eqs. 4
and 6) yield realistic temperature estimates, which varied within a similar
range as satellite-derived SSTs or
TEX86L-derived temperatures
(Figs. 4b, 6b and d). The
former calibration considers relative abundances of OH-GDGT–0 and
crenarchaeol (Eq. 4). The latter is based on the RI-OH-GDGTs (Eqs. 5 and 6).
In the eastern Fram Strait, both OH-GDGT-based temperatures showed similar
changes with an increasing trend until late April 2008 and decreasing
temperatures in May 2008 except for the last two samples
(Fig. 4b). In the APF, both OH-GDGT-based
temperatures were also close to the satellite-derived SSTs and the
TEX86L-derived temperatures at the
shallow trap (Fig. 6b). Warm biases such as the
TEX86L calibration did not occur with
those OH-GDGT calibrations at the deep trap (Fig. 6d). We speculated in Sect. 5.2.2 that the
warm-biased TEX86L temperatures
relative to SSTs may be caused by GDGTs synthesized by Euryarchaeota
dwelling in deep waters of the APF. The methanogenic Euryarchaeota are known
to produce GDGT–1, GDGT–2, and GDGT–3 and may alter the
TEX86 signal derived from pelagic Thaumarchaeota
(Weijers et al., 2011). This might explain why only
calibrations (OH-GDGT/Cren and RI-OH′), which do not contain
GDGTs potentially originating from Euryarchaeota, show a correspondence to
regional SSTs. At this stage, however, this has to be considered
speculative. Moreover, it is not clear yet if OH-GDGTs are exclusively
produced by Thaumarchaeota since OH-GDGTs were also detected in a culture of
Euryarchaeota (Methanothermococcus thermolithotrophicus) (Liu et al., 2012). Therefore, further
research is needed to clarify various aspects of distribution, production,
and modification of OH-GDGTs in response to physicochemical changes in the
global ocean. Nonetheless, OH-GDGTs appear to be a potential temperature
proxy in our two high-latitude regions.
Summary and conclusions
Sinking particles collected using time-series sediment traps allowed us to
determine the variability of the downward GDGT export and the environmental
influence on TEX86L thermometry in
northern and southern high-latitude regions of the Atlantic Ocean. We
observed fundamentally different patterns between the eastern Fram Strait
and the Antarctic Polar Front (Table 2).
In the eastern Fram Strait, the seasonally different composition of sinking
materials resulted in different sinking velocities of GDGTs, and thus the
temporal offsets between TEX86L-derived
temperatures and SSTs may vary by season. Although increased flux of
terrestrial matter transported by sea ice in intermediate and deep waters is
well known in the region (Lalande et al., 2016),
TEX86L thermometry does not seem to be
affected by lateral advection of particles. The flux-weighted mean
TEX86L temperature in sinking particles
was similar to the one in the underlying surface sediment, indicating that
the TEX86L signal did not experience
substantial changes while sinking. The flux-weighted mean
TEX86L temperature corresponded to
temperatures in water depths ranging between 30 and 80 m, where nutrient
profiles suggest favorable conditions for Thaumarchaeota.
In the Antarctic Polar Front, changes in GDGT fluxes and
TEX86L temperatures are different
between the two traps moored at different depths. At the shallow trap,
TEX86L estimates do not covary with
satellite-derived SSTs, and its flux-weighted mean temperature is 4.6 ∘C. The warm-biased TEX86L
at the deep trap and in the underlying surface sediment may be caused by
GDGT contributions from Euryarchaeota, which is dominant in the deeper part
of the water column of CDW at the Antarctic Polar Front. Alternatively, a
systematic warm bias of the linear calibration in the high-latitude Southern
Ocean could explain the discrepancy between
TEX86L reconstructed and observed
temperatures. The discrepancies can be reduced by using a nonlinear
TEX86L calibration developed for high-latitude samples from the Southern Hemisphere.
Our findings offer insights into the potential factors governing
TEX86L thermometry such as nutrient
availability or archaeal community composition in high-latitude regions. In
the high-latitude North Atlantic, regional
TEX86L calibrations can be an
additional option next to the global calibration, while in the high-latitude
Southern Ocean, the benefit of a polynomial
TEX86L calibration needs to be further
tested. Our studies suggest that OH-GDGT-based calibrations are also worth further investigation in the polar oceans. Accordingly, our study
highlights that multiple approaches to global versus regional calibrations,
polynomial relationships, or OH-GDGT-based calibrations are beneficial to
overcome the limitations of a single global
TEX86L calibration in high-latitude
ocean regions.
Data availability
The data presented here are available in the PANGAEA database
(https://doi.pangaea.de/10.1594/PANGAEA.897268; Park et al., 2019).
Index values for assessing the terrestrial and/or methanogen GDGT
inputs at FEVI16 and PF3 sediment traps. The exact sampling interval of each
sample number at FEVI16 and PF3 can be found in PANGAEA
(https://doi.pangaea.de/10.1594/PANGAEA.897268).
Cup no.FEVI16 PF3 (1296 m) (614 m) (3196 m) BITaMIb%GDGT–0c|ΔRI|dBITMI%GDGT–0|ΔRI|BITMI%GDGT–0|ΔRI|10.010.09600.150.000.15580.050.000.34620.2320.010.11600.220.020.14580.080.000.30610.1730.010.11600.210.000.14640.240.000.21580.0940.010.10600.200.000.16610.16––––50.010.11610.300.000.19620.170.000.22580.0660.010.10600.230.000.15630.190.000.21560.0570.010.11600.230.000.20650.240.000.19550.0880.010.11600.090.000.19600.080.000.21560.0190.000.19620.16100.010.13610.140.000.23640.20––––110.000.20590.06––––120.010.15590.190.000.21640.26––––130.010.13590.100.000.21620.19––––140.000.16610.16––––150.020.14590.130.000.15600.15––––160.020.13610.220.000.14600.110.000.21560.04170.010.11610.200.000.16620.16––––180.010.09630.26190.010.10640.3120––––Total 20 samples Total 17 samples
a BIT (Hopmans et al., 2004; Weijers et al., 2006) for terrestrial input when >0.3. b MI (Zhang et al., 2011) for methanotrophic archaeal input when >0.5 and normal oceanic signal when <0.3. c %GDGT–0 (Blaga et al., 2009; Inglis et al., 2015) for methanogenic archaeal input when >67 %. d|ΔRI| (Zhang et al., 2016) for both methanotrophic archaeal and terrestrial input >0.3.
Author contributions
EP and GM designed the study. EP measured the samples with the help of JH,
analyzed the data, prepared figures and tables, and wrote the paper.
All the authors provided feedback on the paper and GM reviewed the
paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge the captain, crew, and scientists who participated in the expeditions for collecting sediment trap samples used in this study. This project is supported by GLOMAR – Bremen International Graduate School for Marine Sciences and the MARUM Centre of Marine Environmental Research at the University of Bremen. The trap samples were supplied by Bremen (PF3) by the MARUM and Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research (FEVI16).
Financial support
This research has been supported by the Deutsche Forschungsgemeinschaft – Clusters of Excellence “The Ocean in the Earth System” at MARUM (grant no. EXC309). Morten Hvitfeldt Iversen received funding for the joint Helmholtz Young Investigators Group SEAPUMP “Seasonal and regional food web interactions with the biological pump” (VH-NG-1000) at the Alfred Wegener Institute.The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.
Review statement
This paper was edited by Markus Kienast and reviewed by two anonymous referees.
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