Isoprenoid and branched GDGTs were measured in soils and lake sediment samples from the Eifel Volcanic field. The modern samples were used to understand sources of GDGTs in sediments, while sediment core samples from Schalkenmehrener Maar, Holzmaar, and Auel Maar were used to reconstruct temperatures during the past 60,000 years. Age model information and additional proxy data from the ELSA-20 stack are found in Sirocko et al., 2021 and Sirocko et al., 2022
This is the archaeal biomarker dataset for global peatlands. The first dataset contains biomarker data for each individual sample as for many peatlands we analysed more than one sample. The second dataset contains the averaged data for isoGDGT isomers in all sites that contains both associated pH and temperature measurements.
Here, we compile an extensive global surface sediment dataset of OH-isoGDGTs as well as regular isoprenoid GDGTs (isoGDGTs), with both data generated at NIOZ and previously published data from other laboratories. We explore recently developed temperature proxies based on hydroxylated isoprenoid Glycerol Dialkyl Glycerol Tetraethers (OH-isoGDGTs), such as %OH, RI-OH, RI-OH' and OHC for their potential for reconstructing past temperature changes. The data contains surface sediment data from the global ocean used in the study 'Evaluating isoprenoidal hydroxylated GDGT-based temperature proxies in surface sediments from the global ocean'. The excel sheet contains information regarding the surface sediments, their location, enivironmental parameters from each location extracted from WOA database, peak area of iso-GDGTs and OH-isoGDGTs, proxy indices discussed in the study and references for previously published data.
Isoprenoid and branched GDGTs were measured in soils and lake sediment samples from the Eifel Volcanic field. The modern samples were used to understand sources of GDGTs in sediments, while sediment core samples from Schalkenmehrener Maar, Holzmaar, and Auel Maar were used to reconstruct temperatures during the past 60,000 years. Age model information and additional proxy data from the ELSA-20 stack are found in Sirocko et al., 2021 and Sirocko et al., 2022
Core MD95-2042 alkenone and GDGT data: This dataset provides the following information for core MD95-2042: depth, age, summed OH-GDGT, iGDGT, and di-unsaturated and tri-unsaturated C37 alkenone concentrations, OH-GDGT-based, iGDGT-based, and alkenone-based paleothermometric indices, GDGT-2/GDGT-3 ratio, and biomarker-based sea surface temperature (SST) and 0‐ to 200‐m sea temperature (subT; gamma function probability distribution for target temperatures with a = 4.5 and b = 15) estimates. Sediment samples were taken every 5 cm from core MD95-2042 and homogenized before lipid extraction. The lipid extracts were splitted into two fractions: one for alkenone analysis by gas chromatography coupled to a flame ionization detector, and the other for GDGT analysis by high-performance liquid chromatography coupled to mass spectrometry. All GDGT analyses were done in duplicate. The 1σ analytical uncertainties from 37 replicate analyses of the core catcher sample from core MD95-2042 are 0.007 (0.4 °C) for RI-OH, 0.008 (0.2 °C) for RI-OH′, 0.003 (0.2 °C) for TEX86, 0.238 for GDGT-2/GDGT-3, and 0.010 (0.26 °C) for UK′37. RI-OH′-SST estimates are from the following global calibration: SST = (RI-OH′ + 0.029)/0.0422 (Fietz et al., 2020). RI-OH-SST estimates are from the following global calibration: SST = (RI-OH − 1.11)/0.018 (Lü et al., 2015). TEX86H-SST estimates are from the following regional paleocalibration: SST = 68.4 × TEX86H + 33.0 (Darfeuil et al., 2016). UK′37-SST estimates are from the following global calibration: SST = 29.876 × UK′37 − 1.334 (Conte et al., 2006). Bayesian calibrations were also used for TEX86-SST and TEX86-subT estimates (BAYSPAR; Tierney & Tingley, 2014, 2015) and for UK′37-SST estimates (BAYSPLINE; Tierney & Tingley, 2018). Alkenone data covering the 160–70 and 70–0 ka BP periods are from Davtian et al. (2021) and Darfeuil et al. (2016), respectively. GDGT data covering the 160–45 ka BP period are from Davtian et al. (2021). The age model of core MD95-2042 for the 160–43 and 43–0 ka BP periods was obtained by tuning to Chinese speleothems (Cheng et al., 2016) and by recalibrating existing 14C ages with the Marine20 calibration curve (Heaton et al., 2020), respectively. MIS, Marine Isotope Stage; GDGT, glycerol dialkyl glycerol tetraether; and N/A, not available. Greenland atmospheric temperature record: This dataset consists in a composite Greenland atmospheric temperature record, which was built with the following records: the GISP2 atmospheric temperature record by Kobashi et al. (2017) for the 10–0 ka BP period, the NGRIP atmospheric temperature record by Kindler et al. (2014) for the 120–10 ka BP period, and the NEEM atmospheric temperature record by NEEM community members (2013) for the 129–120 ka BP period. The NEEM temperature anomalies obtained by NEEM community members (2013) were shifted by –31 °C to obtain absolute air temperatures. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records. Antarctic δ18Oice and atmospheric temperature stacks: This dataset consists in two stacks of three Antarctic records (EDC, EDML, and WD), one for δ18Oice and the other for atmospheric temperature: both stacks are provided with their stacking uncertainties. To build the Antarctic δ18Oice stack, the Antarctic δ18Oice records were resampled every 10 years before centering to zero means and normalization to unit standard deviations over the 140–0 ka BP period (68–0 ka BP for WD). To optimize the continuity between the portions with and without the WD ice core, the Antarctic δ18Oice records were centered to zero means over the 68–67 ka BP period. The resulting Antarctic δ18Oice records were then averaged and stacking uncertainties were calculated as the pooled standard deviation of the stacked Antarctic δ18Oice records divided by the square root of the number of stacked Antarctic δ18Oice records. The final Antarctic δ18Oice stack, expressed in ‰, has the same standard deviation as the δ18Oice record from EDML over the 140–0 ka BP period, and has a zero mean over the 1–0 ka BP. The Antarctic atmospheric temperature stack was built like the Antarctic δ18Oice stack, except that the Antarctic δ18Oice records were corrected for seawater δ18Oice variations before conversion into atmospheric temperature. The employed age model is the one of Davtian and Bard (2023) for Greenland and Antarctic ice-core records.
Cenozoic global TEX86-derived sea surface temperature (SST) compilation from all published marine TEX86 records and new data measured in this study (ODP/DSDP Sites 516, 588, 667, 704, 730, 754, 1146 and 1263). This table contains all GDGT measurements, calculated indices, SSTs and SST gradients.
This dataset provides counts of diatom valves for the Lateglacial sediment sequence retrieved from Lake Hämelsee (Germany) in 2013. Counts per taxon are presented against both depth (m) and age (cal yr. BP). The diatom data provides information on Lateglacial ecosystem dynamics and the dataset was used to interpret changes in aquatic diversity as well as in palaeolimnological conditions. A total of 78 samples were selected for diatom analysis using 2-5 cm sample intervals throughout the Lateglacial section of the core sequence, with a higher sampling resolution (1 cm intervals) around key transitions. Organic matter was removed from the samples (ca. 0.01 grams dried sediment) by oxidation using 5 ml of H2O2 (30%) and heating in a water bath at 70 °C for 24-28 hrs. Subsequently, a few drops of HCl (50%) were added to remove residual H2O2 and carbonates. Samples were washed by adding distilled water, shaking vigorously, centrifuging at 1200 rpm for 4 minutes, and removing the liquid using a pipette. This process was repeated 5 times, and a few drops of ammonia (NH3) were added to the solution prior to the final wash to prevent clumping of diatoms. Diatom slides were mounted using Naphrax and diatoms were identified using Krammer and Lange-Bertalot (1986–1991) and Camburn and Charles (2000). For several samples the target count sum of 300 diatom valves could not be reached due to low concentrations or poor diatom preservation. Prior to analysis and interpretation, and where possible, neighbouring samples with a low count sum were amalgamated until a count of at least 100 valves was reached; if this was not possible the samples were deleted from our dataset prior to subsequent analysis (note that these samples are still included in the dataset provided here). All analyses were performed in the laboratories of University College London, UK.
This dataset provides glycerol dialkyl glycerol tetraethers (GDGTs) concentrations for the Lateglacial sediment sequence retrieved from Lake Hämelsee (Germany) in 2013. GDGTs concentrations (ng/g) are presented against both depth (m) and age (cal yr. BP). The GDGTs dataset was used to calculate the GDGT-0/crenarchaeol ratio, which was interpreted to represent lake water oxygenation, which, given the local settings, was likely driven by changes in windiness. Additionally, the GDGT dataset was used to calculate the degree of methylation of 5-methyl brGDGTs (MBT'5me), which can be used to reconstruct past temperature change through translation MBT'5me into mean temperature of the months above freezing. As such, the GDGT data provides information on LGIT climate dynamics at lake Hämelsee. Of the 167 samples used for lipid extraction (see https://doi.pangaea.de/10.1594/PANGAEA.964524), the alcohol/fatty acid fraction of 94 samples was further processed to analyse glycerol dialkyl glycerol tetraethers (GDGTs), which are membrane lipids of certain archaea and bacteria (Schouten et al., 2013). In short, a known amount of internal standard was added to each fraction, which was then redissolved in hexane:isopropanol 99:1 and passed over a 0.45 µm PTFE filter prior to injection on a Agilent 1260 Infinity ultra-high performance liquid chromatograph coupled to an Agilent 6130 single quadrupole mass spectrometer following the settings and elution protocol of Hopmans et al. (2016). A minimum peak area of 3000 and a signal-to-noise ratio of >3 was maintained as detection limit. Quantification of the GDGTs is based on the assumption that the mass spectrometer equally responds to the GDGTs and the internal standard. All analyses were performed in the laboratories of Utrecht University, the Netherlands.
This dataset provides counts of chironomid head capsules for the Lateglacial sediment sequence retrieved from Lake Hämelsee (Germany) in 2013. Counts per taxon are presented against both depth (m) and age (cal yr. BP), and the total amount of material used for analysis (in g) is provided as well. The chironomid data provides information on Lateglacial ecosystem dynamics and were used to interpret changes in aquatic diversity as well as in local climate conditions. A total of 123 samples from the Lateglacial section of the core were treated with warm KOH (10%) to de-flocculate the material and subsequently rinsed through a sieve with a 100-µm mesh. Chironomid head capsules (HCs) were hand-picked from the residue using a Bogorov sorting tray and mounted on permanent microscope slides using Euparal mounting medium. HCs were identified using Brooks et al. (2007) and the dataset presented here has been matched to the taxonomy of the merged Norwegian/Swiss chironomid-climate calibration dataset. Several samples had low chironomid concentrations and for these we amalgamated adjacent samples (within lithological boundaries) to reach a minimum count sum of 50 head capsules per sample. This process resulted in the final chironomid dataset that is presented here, containing 97 samples. All analyses were performed in the laboratories of the University of Amsterdam, the Netherlands.
This dataset provides concentrations of n-alkanes for the Lateglacial sediment sequence retrieved from Lake Hämelsee (Germany) in 2013. Concentrations of n-alkanes (ug/g), the Carbon Preference Index (CPI) and the Average Chain Length C21-C33 (ACL) are all presented against both depth (m) and age (cal yr. BP). The n-alkane concentration data provides information on the Lateglacial dynamics of local plant productivity, whereas the ACL and CPI of the sediment samples were determined to estimate origin and preservation condition. A total of 167 samples from the Lateglacial section of the core were processed using a Dionex 350 accelerated solvent extraction (ASE) system. Solid phase extraction (SPE) was used to separate the extracts into an aliphatic, aromatic and alcohol/fatty acid fraction. Separation was achieved by loading the extracts on activated silica columns and eluting each fraction with hexane, hexane/DCM (1:1 v/v) and DCM/MeOH (9:1 v/v) successively. The aliphatic fraction, containing the n-alkanes, was analyzed by gas chromatography-mass spectrometry (GC/MS). The peak areas for each n-alkane homologue were compared to the peak areas from an internal standard (5α-androstane) and an external n-alkane standard mixture for absolute quantification. We refer to Rach et al. (2020) for further details on the exact analytical setup. All measurements were performed in the laboratories of GeoForschungsZentrum Potsdam.
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