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Available online at www.sciencedirect.com Geochimica et Cosmochimica Acta 72 (2008) 1154–1173 www.elsevier.com/locate/gca Global sediment core-top calibration of the TEX86 paleothermometer in the ocean Jung-Hyun Kim a,*, Stefan Schouten a, Ellen C. Hopmans a, Barbara Donner b, Jaap S. Sinninghe Damsté a a Royal Netherlands Institute for Sea Research, Department of Marine Biogeochemistry and Toxicology, P.O. Box 59, 1790 AB, Den Burg, Texel, The Netherlands b Universität Bremen, FB 5 Geowissenschaften, Klagenfurter Straße, D-28359 Bremen, Germany Received 8 August 2007; accepted in revised form 10 December 2007; available online 25 December 2007 Abstract The TEX86 (TetraEther indeX of tetraethers consisting of 86 carbon atoms) paleothermometer is based on the relative distribution of archaeal lipids, i.e. isoprenoid glycerol dibiphytanyl glycerol tetraethers (GDGTs), and is increasingly used to reconstruct past sea water temperatures. To establish a more extensive, global calibration of the TEX86 paleothermometer, we analyzed GDGTs in 287 (in comparison with 44 in currently used calibration) core-top sediments distributed over the world oceans and deposited at different depths. Comparisons of TEX86 data with (depth-weighted) annual mean temperatures of the overlying waters between 0 m and 4000 m as well as with different seasonal mean temperatures at 0 m water depth showed that the TEX86 proxy reflects mostly annual mean temperatures of the upper mixed layer. The relationship between TEX86 values and sea-surface temperatures (SSTs) was non-linear mainly because below 5 !C the change in TEX86 values was minor with temperature. This suggests that the TEX86 proxy might not be directly applicable for the Polar Oceans. Nevertheless, between 5 !C and 30 !C, the TEX86 proxy has a strong linear relationship with SSTs. Here, we, therefore, propose a new linear calibration model (T = !10.78 + 56.2 * TEX86, r2 = 0.935, n = 223) for past SST reconstructions using the TEX86 palaeothermometer. " 2007 Elsevier Ltd. All rights reserved. 1. INTRODUCTION Several geochemical proxies are currently used to reconstruct past sea water temperatures and they can be broadly subdivided into proxies based on inorganic and organic fossil remains. Examples of inorganic temperature proxies are d18O (e.g. Erez and Luz, 1983) and Mg/Ca ratios of foraminifera (e.g. Elderfield and Ganssen, 2000; Lea, 2003 and references cited therein). The isotopic and trace element inorganic proxies are based on thermodynamically different behavior of isotopes and trace elements during the calcification of foraminifera which is primarily a function of temperature. Many approaches are used to investigate the * Corresponding author. E-mail address: [email protected] (J.-H. Kim). 0016-7037/$ - see front matter " 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.gca.2007.12.010 impact of factors other than temperature (so-called ‘‘vital effects”) on the reliability of inorganic sea water temperature proxies such as culture studies, plankton tows, sediment traps and sediment core-top studies (e.g. Spero et al., 1999). They, generally, show that calibrations of inorganic proxies are different for different species and that other factors also influence the partitioning of stable isotopes and trace elements such as pH, salinity and the calcifying organism itself. In addition, inorganic proxies require knowledge on the original composition of the sea water especially on longer geological time scales (see Lea, 2003 and references cited therein). Until recently, there was only one proxy based on organic fossil remnants, the alkenone unsaturation index (UK37 or 0 UK37 ). The original UK37 proxy proposed by Brassell et al. (1986) was based on marine sediment core studies and calculated from the relative distribution of C37 methyl Global sediment core-top calibration of the TEX86 paleothermometer alkenones containing 2–4 double bonds synthesized by a limited number of haptophyte phytoplankton. This index was later confirmed as a temperature proxy by culture studies of Emiliania huxleyi (Prahl and Wakeham, 1987) and global core-top studies (e.g. Müller et al., 1998). Subse0 quent studies, using the simplified UK37 which excludes the C37 alkenones with 4 double bonds, have focused on establishing the accuracy and calibration of this proxy using cultures, particulate organic matter, sediment traps and sediment core-tops (reviewed by Herbert, 2003). Although these studies are still continuing, the general picture emerg0 ing is that the UK37 proxy is a fairly robust temperature proxy which does not require knowledge on original sea water composition, is confounded by only a relatively few factors (e.g. growth rate; Prahl et al., 2005 and diagenesis; Hoefs et al., 1998) and can be analyzed with relatively high preci0 sion (Herbert, 2003). Unfortunately, the use of UK37 proxy is limited in tropical areas as tri-unsaturated alkenones reach their detection limit complicating the reconstruction 0 of UK37 sea surface temperature (SST) above 28 !C and in the Polar Oceans where alkenone-producing coccolithophorids do not occur (e.g. Sikes and Volkman, 1993). Recently, a new organic sea water temperature proxy was proposed based on archaeal tetraether lipids, the 1155 TEX86 proxy (TetraEther indeX of tetraethers consisting of 86 carbon atoms; Schouten et al., 2002). These lipids are biosynthesized by marine Crenarchaeota which occur ubiquitously in the marine water column and are one of the dominant prokaryotes in today’s oceans (Karner et al., 2001). Marine Crenarchaeota biosynthesize different types of glycerol dibiphytanyl glycerol tetraethers (GDGTs) containing 0–3 cyclopentane moieties (I–IV; Fig. 1) and crenarchaeol which, in addition to 4 cyclopentane moieties, has a cyclohexane ring (V; Schouten et al., 2000; Sinninghe Damsté et al., 2002). Finally, they also biosynthesize small quantities of a regio-isomer of crenarchaeol (V0 ). From culture studies of the membrane composition of hyperthermophilic archaea it is known that the relative number of cyclopentane moieties increases with growth temperature (Gliozzi et al., 1983; Uda et al., 2001). An initial mesocosm study showed that marine Crenarchaeota use the same mechanism, i.e. higher temperatures result in an increase in the relative amounts of GDGTs with 2 or more cyclopentane moieties (Wuchter et al., 2004). Thus, by measuring the relative amounts of GDGTs present in marine sediments the temperature can be determined at which Crenarchaeota were living when they produced their membranes. The TEX86 proxy was proposed as a means HO O GDGT-I O O [I] O OH HO O GDGT-II O O [II] O OH HO O GDGT-III O O [III] O HO GDGT-IV OH O O O [IV] O HO OH O Crenarchaeol O O [V] O OH regio-isomer of crenarchaeol Fig. 1. Structures of GDGTs discussed in the text. [V’] 1156 J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173 to quantify the relative abundance of GDGTs (Schouten et al., 2002): TEX86 ¼ ð½III% þ ½IV% þ ½V0 %Þ=ð½II% þ ½III% þ ½IV% þ ½V0 %Þ ð1Þ The numbers in TEX86 refer to structures in Fig. 1. The TEX86 proxy was calibrated using 44 sediment core-tops obtained from 15 different locations. The resulting equation was as follows (Eq. (2), Table 1): T ¼ ðTEX86 ! 0:28Þ=0:015 ðr2 ¼ 0:92; n ¼ 44Þ ð2Þ This equation allowed the conversion of the TEX86 values obtained into SSTs (see Schouten et al., 2002 for details). This initial calibration study has been followed by other studies which investigated the validity of TEX86 as an SST proxy. Mesocosm experiments confirmed that marine Crenarchaeota changed their membrane composition with growth temperature and showed that changes in salinity and nutrients did not substantially affect the temperature signal recorded by the TEX86 proxy (Wuchter et al., 2004; Schouten et al., 2007b). However, the correlation of TEX86 values with temperatures had a similar slope as Eq. (2) but a different intercept (Eq. (3), Table 1). A survey of particulate organic matter showed that TEX86 values correlated well with in situ temperature at depths <100 m (Wuchter et al., 2005) with a correlation similar to that of the sediment core-top calibration (Eq. (4); Table 1). A recent sediment trap study in the Arabian Sea showed that TEX86 temperature estimates in a sediment trap at 500 m showed a seasonal cycle in agreement with satellite SST measurements but with an offset of ca. 1–2 !C (Wuchter et al., 2006a,b). The deeper traps did not show this seasonality and instead reflected annual mean SSTs. The TEX86 proxy has also been shown to work in a number of lakes (Powers et al., 2004). However, Powers (2005) surveyed a larger number of lakes and found the TEX86 proxy to work primarily in large lakes with relatively high Crenarchaeota production compared to terrestrial input. The calibration line based on sediment core-tops in lakes was similar to that reported by Schouten et al. (2002) for marine environments (Eq. (5), Table 1). This indicates, as shown previously by Wuchter et al. (2004) in their mesocosm study, that salinity has no large effect on the TEX86 proxy. Application of the TEX86 proxy in reconstructing glacial to Holocene SST in the Arabian Sea showed that several features in the TEX86 temperature record corresponded with well known global climatic temperature changes such as Termination I and the Antarctic Cold Reversal (Huguet et al., 2006). Differences were, however, noted between 0 TEX86 and UK37 SSTs, which was attributed to the different source organisms which grew in different seasons. Indeed, Herfort et al. (2006) showed that the TEX86 values in North Sea sediments primarily reflected winter temperatures as they bloom in that season of major input. The TEX86 proxy seems to be unaffected by changes in water redox conditions but is biased towards lower temperatures during maturation of organic matter (Schouten et al., 2004). Finally, it was recently shown that terrestrial organic matter often contain GDGTs I–V0 and, thus, TEX86 values in marine sediments receiving large amounts of terrestrial organic matter are biased, usually towards higher temperatures (Weijers et al., 2006). Although a number of studies have shown that the TEX86 proxy is capable of reconstructing past SSTs, a number of issues still have to be resolved. One of the most important issues is the calibration of TEX86 with temperatures which, until now, is based on relatively few sediment core-tops (Schouten et al., 2002) and used less sensitive high performance liquid chromatography/mass spectrometry (HPLC/MS) techniques (see discussion in Schouten et al., 2007a). Furthermore, considering the ubiquitous occurrence of Crenarchaeota in the ocean in contrast to the Haptophytes, the TEX86 proxy has a potential to become a useful tool for temperature reconstructions in the Polar 0 Oceans where the UK37 proxy cannot be applied. However, the applicability of the TEX86 proxy to the Polar Oceans has not been thoroughly tested yet. Here we substantially improve the global sediment coretop calibration of the TEX86 proxy by reanalyzing previously published sediment core-top TEX86 samples using improved HPLC/MS methodology and adding a substantial number of new sediment core-top data globally distributed in the ocean. We also compare the new sediment core-top calibration dataset with previously published calibration equations and discuss their implications for past SST reconstructions. 2. MATERIALS AND METHODS 2.1. Sediment core-tops Marine sediment core-tops analyzed in this study were collected mostly using box corer and multicorer from a variety of regions and represent the uppermost 0–3 cm, mostly 0–1 cm (see Appendix for detailed information). Based on the recent analytical improvements in the analysis of GDGTs (Schouten et al., 2007a), we reanalyzed the 0– 1 cm interval sediment core-top samples from the sample suite used in the TEX86 temperature calibration of Schouten et al. (2002). In addition, we added TEX86 sediment core-top data from the southern North Sea (Herfort et al., 2006) into our new global sediment core-top dataset. This global dataset is based on, in total, 287 core-top sediments from various oceans with 200 from the Atlantic, 28 from the Pacific, 23 from the Indian Ocean, and 36 from the Polar Oceans (Table 2, Fig. 2a). Although the global sediment core-top dataset is biased toward the South Atlantic and dominated by continental margin sediments, it encompasses the entire range of temperatures and various geographic provinces. Sediment core tops also include a large range of water depths: epipelagic (38), mesopelagic (42), bathypelagic (159), and abyssopelagic (48) zones (Table 2, Fig. 2b). It is assumed that the core-top sediments represent Late Holocene time spans covering a few hundreds to, at maximum, a few thousands of years (cf. Müller et al., 1998). Therefore, the comparison of sediment-based TEX86 values with the temperature data from the instrumental period such as World Ocean Database temperatures may cause some scatter in calibrations. However, sedimentbased approaches also provide the benefit of temporal and spatial averaging of other factors, such as GDGT production at all seasons and depths throughout the annual cycle, Table 1 Correlation of TEX86 of core-top sediments with temperatures obtained from the World Ocean Database 2001 (Conkright et al., 2002) and TEX86 temperature calibration models Temp. range (!C) TEX86definition Eq. (#) Equation 1 TEX86 = ([III] + [IV] + [V0 ])/ ([II] + [III] + [IV] + [V0 ]) Data points (n) Determination coefficient (r2) Standard error of estimate (!C) Schouten et al. (2002) 0 to 30 2 T = (TEX86 ! 0.28)/0.015 43 0.920 2.0 Mesocosom 10 to 40 3 T = (TEX86 ! 0.0640)/0.017 15 0.790 3.9 Marine sediment trap 5 to 29 4 T = (TEX86 ! 0.29)/0.017 65 0.800 3.1 Lake sediment 5 to 27 5 T = (TEX86 ! 0.28)/0.016 10 0.890 Core-top 22 to 36 6 T = (TEX86 + 0.016)/0.027 24 0.780 !2 to 30 !2 to 30 !2 to 25 !2 to 15 0 to 10 0 to 6 !2 to 15 0 to 9 0 to 5 !2 to 30 !2 to 30 !2 to 30 !2 to 30 9 10 11 12 13 14 15 16 17 18 19 20 21 TEX86 = 0.374 ! 0.005 * T + 0.0009 * T2 ! 0.00001 * T3 TEX86 = 0.371 ! 0.005 * T + 0.0010 * T2 ! 0.00002 * T3 TEX86 = 0.377 ! 0.009 * T + 0.0020 * T2 ! 0.00004 * T3 TEX86 = 0.430 ! 0.047 * T + 0.0111 * T2 ! 0.00046 * T3 TEX86 = 0.422 ! 0.073 * T + 0.0241 * T2 ! 0.00143 * T3 TEX86 = 0.481 ! 0.119 * T + 0.0476 * T2 ! 0.00367 * T3 TEX86 = 0.414 ! 0.048 * T + 0.0159 * T2 ! 0.00086 * T3 TEX86 = 0.419 ! 0.096 * T + 0.0393 * T2 ! 0.00294 * T3 TEX86 = 0.498 ! 0.164 * T + 0.0771 * T2 ! 0.00746 * T3 TEX86 = 0.394 ! 0.009 * T + 0.0012 * T2 ! 0.00002 * T3 TEX86 = 0.375 ! 0.003 * T + 0.0004 * T2 + 0.000002 * T3 TEX86 = 0.377 ! 0.006 * T + 0.0009 * T2 ! 0.00001 * T3 TEX86 = 0.401 ! 0.010 * T + 0.0016 * T2 ! 0.00003 * T3 287 286 257 188 129 42 188 129 42 257 282 260 236 0.877 0.873 0.868 0.837 0.829 0.854 0.749 0.784 0.812 0.852 0.813 0.872 0.853 5 5 5 5 5 5 22 23 24 25 26 27 T = !10.78 + 56.2 * TEX86 T = !9.24 + 44.3 * TEX86 T = !10.70 + 56.1 * TEX861780.9 T = !11.3 + 57.03 * TEX86 T = !8.47 + 51.9 * TEX86 T = !11.54 + 57.5 * TEX86 223 209 178 45 33 190 0.935 0.870 0.915 0.941 0.813 0.936 mean, mean, mean, mean, mean, mean, mean, mean, 0–30 m 0–200 m 0–1000 m 0–2000 m 0–4000 m 200–1000 m 200–2000 m 200-4000 m New sediment core-top calibration models Global core-top calibration model (0 m) Global core-top calibration model (0–200 m) Atlantic core-top calibration model (0 m) Pacific-Indian core-top calibration model (0 m) Epipelagic core-top calibration model (0 m) Other pelagic core-top calibration model (>200 m) to 30 to 25 to 30 to 30 to 30 to 30 Schouten et al. (2002) Schouten et al. (2007b) Wuchter et al. (2005) Powers et al. (2004) Schouten et al. (2003) This study This study This study this study This study This study This study this study This study This study This study This study This study 1.7 1.8 Global sediment core-top calibration of the TEX86 paleothermometer Selected calibrations from the literature Core-top Polynomial models Annual mean, 0 m Depth-weighted annual Depth-weighted annual Depth-weighted annual Depth-weighted annual Depth-weighted annual Depth-weighted annual Depth-weighted annual Depth-weighted annual Spring, 0 m Summer, 0 m Fall, 0 m Winter, 0 m References This study This study This study This study This study This study 1157 1158 J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173 Table 2 Number of core-top sediment studied divided according to geographical areas and pelagic zones Sediment core-top sample source Geographic areas Atlantic region Nordic Sea Northeast Atlantic Western equatorial Atlantic Southeast Atlantic Southwest Atlantic Central South Atlantic Mediterranean Black Sea Pacific region Northeast Pacific Southeast Pacific Pacific warm pool East Sea (Japan Sea) Indian Ocean region Red Sea Arabian sea Equatorial Indian Ocean Polar Oceans Arctic Ocean Southern Ocean Total sediment core-top data Water depths Epipelagic zone Mesopelagic zone Bathypelagic zone Abyssopelagic zone Total sediment core-top data 0–200 m 200–1000 m 1000–4000 m 4000–6000 m Sample number (n) 200 15 22 15 58 54 28 1 7 28 8 2 15 3 23 3 10 10 36 13 23 287 38 42 159 48 287 which is probably more suitable for temperature reconstruction on geological time scales. 2.2. Sediment extraction Generally, sediments (1–3 g) were freeze-dried and homogenized by mortar and pestle. The sediments were extracted by DionexTM accelerated solvent extraction (ASE) technique using dichloromethane/methanol (9:1, v:v). The extracts were separated by Al2O3 column chromatography using hexane/dichloromethane (9:1, v/v), hexane/dichloromethane (1:1, v/v) and dichloromethane/methanol (1:1, v/ v) as subsequent eluents. The polar fraction (dichloromethane/methanol) was concentrated under N2, dissolved in hexane/isopropanol (99:1, v/v), and filtered using a 0.4 lm PTFE filter prior to injection as described by Hopmans et al. (2000, 2004). 2.3. HPLC/MS analysis Analyses were performed using an Agilent (Palo-Alto, CA, USA) 1100 series HPLC/MS equipped with an autoinjector and Chemstation chromatography manager software (see Hopmans et al., 2000 and Schouten et al., 2007a). Separation was achieved on a Prevail Cyano column (2.1 ( 150 mm, 3 lm; Alltech, Deerfield, IL, USA), maintained at 30 !C. Injection volumes varied from 1 to 20 ll. GDGTs were eluted isocratically with 99% A and 1% B for 5 min, followed by a linear gradient to 1.8% B in 45 min, where A = hexane and B = propanol. Flow rate was 0.2 ml/min. After each analysis the column was cleaned by back-flushing hexane/propanol (90:10, v/v) at 0.2 ml/ min for 10 min. Detection was achieved using atmospheric pressure positive ion chemical ionization mass spectrometry (APCI-MS) of the eluent. Conditions for the HP 1100 APCI-MS were as follows: nebulizer pressure 60 psi, vaporizer temperature 400 !C, drying gas (N2) flow 6 l/min and temperature 200 !C, capillary voltage !3 kV, corona 5 lA ()3.2 kV). GDGTs were detected by Single Ion Monitoring (SIM) of their [M+H]+ ions (dwell time = 234 ms) (Schouten et al., 2007a) and quantified by integration of the peak areas. The TEX86 values were calculated according to Schouten et al. (2002) as mentioned above. 2.4. Statistical analysis To compare TEX86 values obtained from core-top sediments with annual and seasonal mean temperatures of the upper water column, temperature data were obtained from the World Ocean Database (WOD) 2001 (Conkright et al., 2002). The selected water depths were 0 m, 10 m, 20 m, 30 m, 50 m, 75 m, 100 m, 125 m, 150 m, 200 m, 250 m, 300 m, 600 m, 1000 m, 2000 m, and 4000 m. The WOD temperatures were chosen in the nearest degree in latitude and longitude to each core position. Depth-weighted annual mean temperatures from e.g. 0 to 200 m water depths were calculated from WOD as follows: Temp:0–200m ¼ ½ð10 * ðTemp:0m þ Temp:10m Þ=2Þ þ ð10 * ðTemp:10m þ Temp:20m Þ=2Þ þ ð10 * ðTemp:20m þ Temp:30m Þ=2Þ þ ð20 * ðTemp:30m þ Temp:50m Þ=2Þ þ ð25 * ðTemp:50m þ Temp:75m Þ=2Þ þ ð25 * ðTemp:75m þ Temp:100m Þ=2Þ þ ð25 * ðTemp:100m þ Temp:125m Þ=2Þ þ ð25 * ðTemp:125m þ Temp:150m Þ=2Þ þ ð50 * ðTemp:150m þ Temp:200m Þ=2Þ%=200 ð7Þ To identify outliers from the dataset prior to further statistical analysis, bivariate scatter plots and dotplots were applied using the Brodgar v.2.5.2 (www.brodgar.com) software package. To describe relationships between response (TEX86 values) and explanatory (temperature) variables, polynomial models and generalized additive models (GAMs) were carried out using SigmaStat and Brodgar software packages, respectively. The use of polynomial models and GAMs in this study allowed the visualization of the non-linear relationships between TEX86 values and temperatures. Especially, GAMs (e.g. Hastie and Tibshirani, 1990) represent a method of fitting a smooth relationship between two or more variables through a scatter plot of data points. GAMs are useful where the relationship be- Global sediment core-top calibration of the TEX86 paleothermometer 1159 90 60 30 0 -30 -60 Atlantic region Pacific region Indian Ocean region Polar Ocean region -90 -150 -100 -50 0 50 100 150 -100 -50 0 50 100 150 90 60 30 0 -30 -60 Epipelagic Mesopelagic Bathypelagic Abyssopelagic -90 -150 Fig. 2. Locations of the core-top sediments analyzed for the TEX86 proxy in this study. They are grouped based on (a) geographical areas and (b) different pelagic zones. tween the variables is expected to be of a complex form, not easily fitted by standard linear or non-linear regression models. We applied 4 degrees of freedom (df = 4) for the GAMs. For the bivariate linear regression models, analysis of variance (ANOVA) tests or F tests were performed using Brodgar v.2.5.2. 3. RESULTS AND DISCUSSION 3.1. Relation of TEX86 sedimentary signals with temperatures Based on the recent analytical improvements in the analysis of GDGTs (Schouten et al., 2007a), we reanalyzed the 0–1 cm interval sediment core-top samples (n = 29) from the sample suite used in the TEX86 temperature calibration study by Schouten et al. (2002). No systematic differences compared to our previous results were obtained, when the TEX86 values of both studies are cross-plotted, the following equation is obtained: TEX86ðnewÞ ¼ 0:94 * TEX86ðoldÞ þ 0:02 ðr ¼ 0:975; n ¼ 29Þ ð8Þ In addition, we analyzed a wide variety of other sediment core-tops from various parts of the world oceans and different water depths (Table 2, Fig. 2) and added published TEX86 data from the southern North Sea (Herfort et al., 2006) into our new global sediment core-top dataset (see Appendix for detailed information). Although the global sediment core-top data are dominated by continental margin sediments, they all contained relatively low amounts of soil-derived branched GDGT lipids, i.e. the Branched Isoprenoid Tetraether Index (Hopmans et al., 2004) had all values below 0.1, indicating negligible terrestrial influence on marine TEX86 signals (cf. Weijers et al., 2006). We first explored from which water depth the temperature signal represented by the global core-top TEX86 data appears to be derived. To achieve this, we compared the TEX86 values with the (depth-weighted) annual mean temperatures of the overlying waters between 0 and 4000 m and compared the degree of correlation (Table 1, Fig. 3). Strong correlations (determination coefficient r2) ranging from 0.749 to 0.877 and adjusted determination coefficient (r2 (adj) 0.784–0.878) of TEX86 values with temperatures from different water depths were observed confirming that this proxy contains a strong temperature signal. However, the 0.3 0-30 m 0.2 Red Sea 15 20 25 30 5 10 15 20 25 −0.2 −0.1 30 0.1 0-2000 m 2 4 6 8 10 12 14 0 2 4 6 8 5 10 15 20 25 0-4000 m r2 (adj) = 0.842 n = 42 −0.20 10 200-2000 m 1 2 3 4 5 6 200-4000 m 0 2 4 6 8 10 12 −0.10 r2 (adj) = 0.784 n = 129 −0.2 −0.2 r2 (adj) = 0.791 n = 225 14 0 2 4 6 8 r2 (adj) = 0.800 n = 42 −0.20 −0.1 −0.1 0.0 0.0 0.00 0.1 200-1000 m 0 0.10 0 r2 (adj) = 0.837 n = 129 −0.2 −0.2 r2 (adj) = 0.842 n = 188 r2 (adj) = 0.872 n = 257 −0.10 −0.1 −0.1 0.0 0.0 0.1 0-1000 m 0 0.10 10 Red Sea 0.00 5 0-200 m 0.0 0.1 −0.1 0.0 0 r2 (adj) = 0.877 n = 286 −0.2 r2 (adj) = 0.878 n = 287 0.1 s(Annual mean WOD temperature, df = 4) −0.2 −0.1 0.0 0.1 0.2 Red Sea 0.1 0m 0.2 0.3 J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173 0.3 1160 1 2 3 4 5 Annual mean WOD temperature (°C) Fig. 3. Generalized additive model (GAM) smoothing curves fitted to effects of temperatures on TEX86 values. TEX86 values are represented as a function of depth-weighted annual mean temperatures of different water masses (Conkright et al., 2002), i.e. GAM models in this study are given by: Yi ¼ gðXiÞ þ ei where Yi is the value of the response variable (TEX86) at sample i, g(Xi) is the predictor function, and ei is the residual. The predictor function g(Xi) is given by: gðX i Þ ¼ a þ sðX i Þ where Xi is the explanatory variable (temperatures), a is the intercept, and s(Xi) is the smoothing function. The y-axes represent the values of the smoother (s(Xi)). A normal distribution was assumed for the response (TEX86) variable. The solid lines are the estimated smoother and the dashed lines represent 95% confidence intervals around the main effects. The dots indicate the residuals. The black lines at the bottom of each plot indicate where the data values lie. relationships deviated from linearity at high and low temperatures in contrast to the initial core-top study by Schouten et al. (2002). In fact, the third order of polynomial regression equations showed better fits to the global coretop data rather than linear regressions (Table 1). The highest correlation applying polynomial models was obtained using temperatures from 0 m water depth (r2 = 0.877). This was confirmed by GAMs, with also the highest r2 (adj) (0.878). When temperatures of deeper water depth intervals were used to cross-correlate, r2 and r2 (adj) decreased but still remained rather high, e.g. r2 = 0.829 and r2 (adj) = 0.837 when the TEX86 is correlated with temperatures from 0 to 2000 m water depth (Table 1, Fig. 3). However, when temperatures only from below 200 m were used, the correlation patterns changed compared to those which included epipelagic temperatures and r2 and r2 (adj) dropped (Table 1, Fig. 3). It should be noted that for temperatures from mesopelagic to abyssopelagic water depths available WOD temperature data were scarcer and the temperature ranges smaller which could have biased the correlation patterns. Our statistical analysis thus suggests that the TEX86 values of sediment core-tops reflect predominantly mixed layer (i.e. 0–30 m) temperatures. However, Crenarchaeota have been found throughout the water column (e.g. Karner et al., 2001) and our results do not exclude that they reflect subsurface temperatures in certain areas. Menzel et al. (2006) found lower temperatures during the periods of sapropel deposition in the Mediterranean and attributed this to a change in depth habitat of Crenarchaeota, i.e. from 0.3 Spring 0.0 5 10 15 20 25 −0.1 Fall 0 5 10 15 20 25 30 Winter Red Sea 0 5 10 15 20 25 30 r2 (adj) = 0.857 n = 236 −0.2 −0.1 −0.2 r2 (adj) = 0.872 n = 260 −0.1 0.0 0.1 0.1 0.2 Red Sea 0.2 r2 (adj) = 0.816 n = 282 −0.2 30 0.3 0 0.3 Red Sea 0.1 0.1 0.0 −0.1 −0.2 r2 (adj) = 0.855 n = 257 0.0 s(WOD temperature-0 m, df = 4) 1161 Summer 0.2 Red Sea 0.2 0.3 Global sediment core-top calibration of the TEX86 paleothermometer 0 5 10 15 20 25 30 WOD temperature-0 m (°C) Fig. 4. Generalized additive model (GAM) smoothing curves fitted to effects of temperatures on TEX86 values. TEX86 values are represented as a function of seasonal mean temperatures at 0 m water depth (Conkright et al., 2002), i.e. GAM models in this study are given by: Yi ¼ gðXiÞ þ ei where Yi is the value of the response variable (TEX86) at sample i, g(Xi) is the predictor function, and ei is the residual (see the caption of Fig. 3 for detailed information). surface waters to just above the chemocline at ca. 100 m. Huguet et al. (2007) observed that the TEX86 values in sediment traps and a sediment core in the Santa Barbara basin reflected subsurface temperatures (100–150 m) rather than surface temperatures. However, taken together, it appears that the TEX86 proxy represents more epipelagic temperatures rather than mesopelagic ones. Nevertheless, the varying degree in depth of the TEX86 signal might be one of the reasons of the scatter observed in our global sediment coretop dataset. To explore to what extent the global core-top TEX86 signals are biased by seasonal cycles of Crenarchaeota production, we plotted the TEX86 values against the seasonal mean WOD temperatures (Table 1, Fig. 4). The core-top TEX86 values were highly correlated to the mean temperatures of all major seasons at 0 m water depth. The r2 values were similar ranging from 0.813 to 0.872 with the highest value for fall. The results from the GAMs were in line with those from the polynomial models. This suggests that the TEX86 values of core-tops reflect annual mean temperatures and seasonality has a relatively minor effect on the TEX86 calibration overall. However, it has been frequently documented that Crenarchaeota are more abundant in certain times of the season (Murray et al., 1998; Wuchter et al., 2005, 2006a; Herfort et al., 2006). Thus, if the seasonal temperatures at times of growth of Crenarchaeota are substantially different from that of annual mean SST then there will likely be a bias towards seasonal SST in the TEX86 proxy. Indeed, Herfort et al. (2006) found TEX86 values in North Sea surface sediments which reflected winter SST in agreement with the high abundance of Crenarchaeota in the winter (Wuchter et al., 2006a). Seasonality in crenarchaeotal abundance may account for some of the scatter in the global core-top calibration dataset. The correlation patterns between TEX86 values and temperatures (Figs. 3 and 4) are different from the initial coretop study (Schouten et al., 2002), the mesocosm study (Wuchter et al., 2004) and the sediment trap study (Wuchter et al., 2005). Specifically, the correlation based on the 287 core-tops presented here is best described by a non-linear relationship in contrast to the other studies. This phenomenon is mainly due to the large scatter and non-linear behavior which can be observed at the low (<5 !C) end of the TEX86-temperature calibrations. The high scatter for temperatures <5 !C may be partly due to larger analytical errors of the TEX86 values at low temperatures as a result of the low abundance of all GDGT isomers used in the TEX86 ratio versus GDGT-I and crenarchaeol (Schouten et al., 2007a). Therefore, subtle changes in the composition of these isomers are more difficult to quantify. Nevertheless, it seems that the response of TEX86 to temperature is not linear at these low temperatures and is actually rather invariant. The sediment locations which showed TEX86 values substantially higher than expected (>0.4) for such cold temperatures were all derived from the Polar Oceans. This suggests that the TEX86 proxy does not correspond well to 1162 J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173 annual or seasonal mean temperatures in the Polar Oceans. Possibly, growth conditions in this area are completely different for the Crenarchaeota compared to other oceanic provinces or Crenarchaeota thriving in the Polar Oceans respond differently to temperature in their membrane composition. An apparently larger scatter is also visible for the TEX86-temperature relationships at temperatures >25 !C (Figs. 3 and 4). Strikingly, all data points that substantially deviated from the correlation lines were obtained from Red Sea surface sediments. These have TEX86 values ranging from 0.75 to 0.85 while core-tops from, for example, the Indonesian Pacific Warm Pool with similar annual mean SSTs have TEX86 values ranging from 0.69 to 0.71. This suggests that Crenarchaeota thriving in the Red Sea may be a different population with a different temperature response or that they thrive in a different season. Possibly, Crenarchaeota in the Red Sea are predominantly thriving in the summer at higher temperatures than the annual mean SST. Another important difference between the Red Sea and other oceans is salinity which varies between 37& and 40&, substantially higher than open ocean salinities of 35–36&. However, Wuchter et al. (2004) showed, using mesocosm experiments, that salinity does not have an effect on the TEX86. In addition, Powers et al. (2004) showed that the TEX86 calibration line in lakes is similar to that in marine systems. Hence, it seems unlikely that salinity is the sole cause for the relatively high TEX86 values in the Red Sea core-tops. Further studies of the TEX86 proxy in the Red Sea are needed to investigate the causes for the unusually high TEX86 values. 3.2. Global sediment core-top calibration of the TEX86 proxy To estimate past temperatures from the TEX86 proxy, we need to establish a convenient calibration equation so that we can easily convert TEX86 values into temperatures. From the above it is clear that the correlation between TEX86 values and temperatures appears to be non-linear. There was no significant TEX86-temperature relationship below 5 !C and thus the TEX86 proxy is not recommended to reconstruct temperatures in such low temperature environments. In fact, the correlation can be well described with a linear regression if we only consider TEX86 data above 5 !C (Fig. 5). As the TEX86 values of sediment core-tops reflect predominantly epipelagic water temperatures (see 30 T = -9.24 + 44.3 * TEX86 (Eq. 23) (n = 209, r2 = 0.870) Annual mean WOD temperature (°C) T = -10.78 + 56.2 * TEX86 (Eq. 22) (n = 223, r2 = 0.935) 25 Pacific warm pool 20 15 10 5 Eq. 2 by Schouten et al. (2002) T = (TEX86 - 0.28) / 0.015 (n = 43, r2 = 0.92) a 0.3 0.4 0.5 0.6 0m c 0-200 m 0.3 0.7 0.4 0.5 0.6 0.7 TEX 86 1 0 1 -1 0 -1 b -2 -2 Standardised residuals 2 2 TEX 86 0.4 0.5 TEX86 0.6 0.7 d 0.4 0.5 0.6 0.7 TEX86 Fig. 5. Global core-top calibration models. (a) cross plot of annual mean WOD temperature at 0 m water depth with TEX86 values, (b) standardized residuals obtained by the linear regression model (Eq. (22)) versus TEX86 values, (c) cross plot of depth-averaged annual mean WOD temperature above 200 m with TEX86 values, and (d) standardized residuals obtained by the linear regression model (Eq. (23)) versus TEX86 values. The stippled line in (a) indicates the calibration line by Schouten et al. (2002). The data from the Polar Oceans and the Red Sea were excluded. Cross symbols indicate excluded data points which deviated more than 2 times from the standard deviation of the linear regression models. Dashed lines represent 95% confidence intervals. Global sediment core-top calibration of the TEX86 paleothermometer above discussion), we tested two different linear regression models: 0 m and 0–200 m. The TEX86 dataset, which excludes all data from the Polar Oceans (<5 !C) and the Red Sea (see above), was adjusted by removing outliers (>±1r standardized residuals) in order to remove the unrealistically large influence of a few data points on the whole calibration dataset (cf. Conte et al., 2006). Based on the TEX86 data remaining (223 out of 287 for 0 m and 209 out of 287 for 0–200 m), we established linear TEX86 calibration models. The resulting linear calibration models were as follows (Table 1, Fig. 5): T ¼ ! 10:78 þ 56:2 * TEX86 ðr2 ¼ 0:935; n ¼ 223; 0 mÞ 2 ð22Þ T ¼ ! 9:24 þ 44:3 * TEX86 ðr ¼ 0:870; n ¼ 209; 0–200 mÞ ð23Þ ANOVA tests confirmed the linear relationships for both calibration models (p < 0.0001, Table 3). However, when depth-weighted temperatures from 0–200 m water depth were used for a regression model (Fig. 5c), standardised residuals showed a sigmoid-like pattern (Fig. 5d), indicating that additional factors besides temperatures may influence TEX86 values. Moreover, data from the Pacific warm pool appeared as a deviating cluster from the general linear trend and r2 decreased significantly compared to that of the 0 m regression model (Fig. 5a). This suggests that the TEX86 proxy represents more mixed-layer temperatures (0-30 m water depth) than those from lower parts of the epipelagic water mass. Our sediment core-top collection is mostly from the Atlantic (200 out of 287), especially from the South Atlantic (150 out of 287) and therefore our global calibration may be geographically biased. To investigate this, we established TEX86-temperature calibrations based on geographic distributions and then compared them to the global calibration (Fig. 6a). Although the sample number from the Pacific and the Indian Ocean is much less than that of the Atlantic, the calibration equation based on the data from the Pacific and the Indian Ocean only is almost identical to the globaland Atlantic-based ones. This implies that there is no significant geographical bias in our calibration and that it is generally applicable. We also established TEX86-temperature calibrations for sediments deposited at shallow depths (0– 200 m water depth) and deeper depths (below 200 m water depth) to test the effect of Crenarchaeota production in deeper water depths (Fig. 6b). The TEX86 calibration based on sediments from shallow depths differs only slightly from that of the deeper sites, suggesting that there is a negligible influence of the deeper Crenarchaeota production to the global calibration dataset. It should also be noted that the 1163 dataset from shallow sites does not cover fully the entire temperature range, which might have biased the correlation pattern. The standard deviation of TEX86 temperature estimates using our new global linear calibration (Eq. (22)) was 1.7 !C (Fig. 7a) which is lower than the initial core-top calibration (2.0 !C) by Schouten et al. (2002) and slightly higher than 0 that of the alkenone unsaturation index UK37 (1.2 !C; Conte et al., 2006). The estimation error in temperature from TEX86 analysis is relatively high but it should be noticed that it comprises several errors. Firstly, it contains the error introduced by the analytical reproducibility which is currently ±0.3 !C (Schouten et al., 2007a). In this study, the mean standard deviation of TEX86 analysis was 0.3 !C, ranging from 0.1 !C to 1 !C. As mentioned above, seasonality and depth of Crenarchaeota production may cause additional scatter of TEX86 values and thus increase estimation errors. To investigate this, we plotted the relative difference of TEX86–based temperatures with the actual annual mean WOD temperatures at 0 m water depth (Fig. 7b). One striking feature is that TEX86 temperatures were lower relative to real annual mean temperatures off NW Africa, an upwelling area. This may be due to higher GDGT fluxes during upwelling seasons which are characterized by cold, nutrient-rich waters (Wuchter et al., 2006b). However, it is impossible at this stage to estimate the seasonal bias in our core-tops due to the lack of data on the seasonal abundance of Crenarchaeota in the different oceanic provinces. In addition to seasonality, the depth of Crenarchaeota production may also cause lower TEX86 temperatures as has, for example, been observed for the Santa Barbara basin (Huguet et al., 2007). This may also be the cause, for example, of the cold-biased TEX86 temperature off NW Africa. One supporting finding is that the differences between modeled and WOD temperatures were smaller when we used the calibration model of 0–200 m water depth (Fig. 7c and d). However, it is impossible at this stage to establish the production depth of the TEX86 signals for all the individual core-tops. Finally, it is well known that proxy signals in sediments are not always derived only from overlaying water columns but may be derived by lateral sediment advection from completely different areas with potentially different SSTs. Especially organic compounds, such as alkenones and likely also GDGTs, which are primarily attached to fine-grained particles can suffer from lateral transport (e.g. Ohkouchi et al., 2002). Previously, Benthien and Müller (2000) showed that core-tops in the Argentine basin were affected 0 by lateral sediment transport inducing cold-biased UK37 SST estimates. This lateral transport effect has been confirmed by recent studies by Mollenhauer et al. (2006) and Table 3 Results of ANOVA tests df Sum Sq. Mean Sq. F value p value 0m TEX86 Residuals 1 221 8957.0 621.5 8957.0 2.8 3184.8 <0.0001 0–200 m TEX86 Residuals 1 207 4590.7 680.7 4590.7 3.3 1395.9 <0.0001 1164 J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173 Annual mean WOD temperature (°C) 30 Global T = -10.78 + 56.2 * TEX86 (Eq. 22) 25 (n = 223, r2 = 0.935) 20 Atlantic region (+) T = -10.70 + 56.1 * TEX86 (Eq. 24) (n = 178, r2 = 0.915) 15 Pacific-Indian Ocean regions (O) T = -11.3 + 57.03 * TEX86 (Eq. 25) (n = 45, r2 = 0.941) 10 a Annual mean WOD temperature, 0 m (°C) 5 0.3 0.4 0.5 0.6 0.7 Annual mean WOD temperature (°C) 30 Global T = -10.78 + 56.2 * TEX86 (Eq. 22) 25 (n = 223, r2 = 0.935) 20 Epipelagic zone (0-200 m, O) T = -8.47 + 51.9 * TEX86 (Eq. 26) (n = 33, r2 = 0.813) 15 Other pelagic zones (>200 m, + ) T = -11.54 + 57.5 * TEX86 (Eq. 27) (n = 190, r2 = 0.936) 10 5 b Annual mean WOD temperature, 0 m (°C) 0.3 0.4 0.5 0.6 0.7 TEX86 Fig. 6. Comparison of sediment core-top calibration models: (a) Cross plot of annual mean WOD temperatures from the Atlantic region (cross) and from the Pacific-Indian Ocean region (circle) with TEX86 values and (b) cross plot of annual mean WOD temperatures for the epipelagic samples (circle) and for those sediment core-tops recovered from deeper than 200 m (cross) with TEX86 values. Note that the annual mean WOD temperatures are from 0 m water depth. Rühlemann and Butzin (2006). However, we did not observe cold-biased TEX86 temperatures in the Argentine basin (Fig. 7). This indicates that the TEX86 is less subjected to lateral sediment transport effect. This idea is primarily derived from the reported relatively younger 14C ages of GDGTs compared those of alkenones in the same sediment layer (Mollenhauer et al., 2007). Therefore, lateral transport probably plays a smaller role in introducing scatter in global core-top calibrations in case of GDGTs than for alkenones. 3.3. Implications for past TEX86 temperature reconstructions Our results above show that there is in general a strong positive relationship between TEX86 values and annually averaged temperatures in the global ocean. However, it is clear that this correlation varies in some parts of the ocean and some regions (Polar Oceans) are not appropriate at present for estimation. This will likely depend on the ecology and the seasonality of Crenarchaeota. Relative changes in SST can be viewed with more confidence provided that no large changes in the ecology of Crenarchaeota occurred. In this sense, the TEX86 proxy does not differ much from 0 other proxies such as the UK37 proxy and the Mg/Ca ratio of planktonic foraminifera which are also strongly influenced by the ecology of their source organisms. Constraints on past ecological behavior of Crenarchaeota might be obtained by stable carbon isotopic analysis of GDGTs. The d13C values of crenarchaeotal GDGTs from different regions are relatively similar and thought to reflect mainly 6 Standard dev. of residuals = 1.8°C Standard dev. of residuals = 1.7°C 4 2 0 -2 -4 a -6 c 5 10 15 20 25 30 5 10 Annual mean WOD temperature, 0 m (°C) 60 30 30 0 0 -30 -30 -60 -60 Annual mean SST, 0 m 0m -100 -50 0 50 100 30 5 4 4 3 2 0 -1 -150 25 5 1 b 20 150 Longitude -2 -3 Residuals (modeled SST - WOD SST, ˚C) 90 60 -90 15 Annual mean WOD temperature, 0-200 m (°C) 90 Latitude 1165 3 2 1 0 -1 -2 -3 d -4 -90 0-200 m -5 -150 -100 -50 0 50 100 -4 ∆T (modeled T - WOD T, °C) ∆T (modeled T - WOD T, °C) Global sediment core-top calibration of the TEX86 paleothermometer -5 150 Longitude Fig. 7. (a) Temperature differences between modeled temperatures based on the Eq. (22) and annual mean WOD temperatures at 0 m water depth and (b) their spatial distribution pattern using variogram analysis and ordinary kriging (Davis, 2002), interpolating the data to a 2.5! ( 2.5! grid with 7! ( 7! searching radius. (c) and (d) are based on the Eq. (23) and averaged annual mean WOD temperatures above 200 m water depth. There was no significant difference using different data interpolation grids and searching radiuses in latitude and longitude. the 13C of dissolved inorganic carbon (e.g. Kuypers et al., 2001). Similar to the use of 13C of planktonic foraminifera, we may thus use the d13C of crenarchaeotal GDGTs to infer the ecology of past marine Crenarchaeota. Unfortunately, the d13C analysis of GDGTs requires laborious chemical degradation techniques (e.g. Schouten et al., 1998) and is quite insensitive and thus will be difficult to do in sufficiently high resolution as required for paleoceanographic studies. In light of our new core-top calibration it may be necessary to re-evaluate previous SST reconstructions which used Eq. (2). As an example, TEX86 values used to reconstruct changes in SST in the Arabian Sea from the last glacial maximum (LGM) to the Late Holocene by Huguet et al. (2006) were converted into SSTs using two different calibrations (Fig. 8a and b). As expected they showed the same trends and most temperatures were within 1.5 !C of each other, i.e. within the TEX86 temperature estimation errors (Fig. 8c). However, the range in absolute temperatures reconstructed with the new calibration was somewhat smaller. The previous reconstruction yielded a SST maximum of 32.3 !C while here the SST maximum of 32.2 !C was found and previous LGM estimates were ca 22 !C while the new calibrations generated LGM estimates of ca. 24 !C. These latter temperatures are likely more realistic (cf. Huguet et al., 2006). In summary, the new linear calibration yields slightly warmer temperatures (about 1–1.5 !C) than those previously determined, especially at relatively lower temperatures. For SST reconstructions where TEX86 values exceeds present day values (>0.72), such as is the case for mid latitude and tropical oceans in the Mesozoic and Cenozoic a different equation has been used (Eq. (6), Table 1; Schouten et al., 2003). The reason for using this equation rather than that of Eq. (2) was that in order to reconstruct SSTs for these time periods, it was necessary to extrapolate the calibration line given in Eq. (2) as SST was much higher than present day. However, close examination revealed that for core-top sediments with annual mean SST >20 !C there seemed to be a different relation between TEX86 values and temperatures, i.e. Eq. (6), which has a steeper slope compared to general calibration line, i.e. Eq. (2) (see discussion in Schouten et al., 2003). Our new calibration line obtained in this study shows that there is no substantial difference in the TEX86 versus SST calibration line above or below 20 !C. In agreement with this finding, Schouten et al. (2007b) recently showed that incubation of Crenarchaeota in mesocosms at temperatures between 30 !C and 40 !C yielded a similar relationship between TEX86 values and temperatures as observed by Wuchter et al. (2004) for temperatures between 10 !C and 30 !C. Therefore, it seems now that instead of Eq. (6) it is better to use Eq. (22) to convert TEX86 values into SSTs for time periods where SSTs have been >29 !C (i.e. above present day equatorial SST). TEX86 values measured for tropical oceans during the mid-Cretaceous are varying from 0.69 to 0.84 for OAE 1a to 0.92–0.96 for OAE 2 (Schouten et al., 2003). Using Eq. 1166 J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173 Sediment core 905 0.72 0.68 0.64 TEX 86 SST (°C) 34 b 32 TEX86 index 0.76 a 0.60 30 28 26 24 Eq. 22 (this study, 0 m)) Eq. 2 (Schouten et al., 2002) 22 ∆SST (°C) 2 c 1 0 Eq.22-Eq.2 -1 0 1 2 3 4 5 Core depth (m) Fig. 8. (a) TEX86 record of core NIOP905 in the Somali Basin, Arabian Sea (Huguet et al., 2006), (b) TEX86 temperature records using the core-top calibration (Eq. (2)) of Schouten et al. (2002) and the new linear global core-top calibrations (Eq. (22)), and (c) temperature differences (DSST) between two calibrations (Eq. (22)-Eq. (2)). (22) these TEX86 values would be converted to SSTs of 28–36 !C for OAE 1a and 41–43 !C for OAE 2. This is 3–8 !C higher than the original SST reconstructions using Eq. (6) and higher than most estimates using d18O of presumed planktonic foraminifera (e.g. Wilson and Norris, 2001; Norris et al., 2002). However, recent estimates using d18O of planktonic foraminifera also suggest peak SSTs of up to 41 !C (Bice et al., 2006). Hence, the revised upper SST estimates may not be inconsistent with those of planktonic foraminifera especially considering the number of assumptions involved in calculating SST from d18O (e.g. salinity, pH, d18O of water, etc.), the uncertainty regarding the ecology of extinct species of foraminifera and seasonal biases in both foraminifera and crenarchaeotal proxies. Nevertheless, care has to be taken in interpreting the absolute value of SST reconstructions using extrapolated calibration lines and studies using extrapolated TEX86 temperature reconstructions are likely to be more of use in documenting relative changes in temperature rather than providing absolute temperature estimates. 4. CONCLUSIONS Our newly extended sediment core-top dataset based on 287 TEX86 measurements obtained from core-top sediments distributed over the world oceans provides new insights for the TEX86 application in the low temperature regions and the depth and seasonal origin of the sedimen- tary signal. Our study showed that the TEX86 was strongly correlated with annual mean temperatures of the mixed layer but the relationship was non-linear in contrast to the initial core-top calibration by Schouten et al. (2002). The non-linearity was due to the lack of a TEX86-temperature relationship below 5 !C, suggesting that the TEX86 proxy might not be applicable in the Polar Oceans. However, between 5 !C and 30 !C, a clear linear relationship of TEX86 with SST was apparent. Therefore, we established a new calibration model based on a linear regression between the TEX86 and the temperatures of the surface mixed layer which can serve for past SST reconstructions. However, our results do not exclude that reconstructed TEX86 temperatures can be biased due to the seasonality and deeper water depth of Crenarchaeota production. ACKNOWLEDGMENTS Various people gratefully provided core-top sediments for this study: A. Jaeschke, E. Epping and T. van Weering (NIOZ), the WHOI core repository, E. Schefuß (Bremen University), R. Smittenberg (Washington University), A. Schimmelman (Indiana University), S. Wakeham (SKIO), T. Eglinton (WHOI), X. Crosta (EPOC), E. Michele and M.-A. Sicre (LSCE), N. Ohkouchi (JAMSTEC), and G. Mollenhauer (AWI). We thank three anonymous reviewers and R. Harvey for their constructive suggestions which improved the paper. We are also grateful for discussions on statistical techniques with H. Witte and F. Lespinas. Global sediment core-top calibration of the TEX86 paleothermometer 1167 APPENDIX A1 Sediment core Longitude Latitude Water depth (m) Coring device Sampling level (cm) TEX86 index AI I-107-6-17 AI I-107-6-18 AI I-107-6-20 AI I-107-6-24 AI I-107-6-27 AI I-107-6-70 AI I-107-6-72 AII-GC-9 AII-GGC-22 All-093-19-96 AMR-1 AMR-2 Anker A24 Anker A26 BalsfjordARC14 BOFS 5K 0–1 BOFS 8K 0–1 BS 01E-27 BS 02E-105 BS 04E-114 BS 05E-564 BS 06E-335 BS 07E-1288 BS 09E-2129 CB1 MC15 CB2 MC19 CHN-043-1-4 CHN-043-1-5 CHN-115-4-34 CHN-115-4-35 CHN-115-4-43 Drammensfjord-1 Drammensfjord-2 ENAM9407 Frisian Front G2-9box0–1 G2-9 trp 0–1 G6-3 0–1 GeoB 8301-5 GeoB 8303-5 GeoB 8305-1 GeoB 8306-1 GeoB 8307-5 GeoB 8308-2 GeoB 8310–1 GeoB 8311-1 GeoB 8313-1 GeoB 8314-1 GeoB 8315-5 GeoB 8316-1 GeoB 8317-1 GeoB 8318-1 GeoB 8319-1 GeoB 8321-1 GeoB 8322-1 GeoB 8323-1 GeoB 8324-1 GeoB 8325-1 GeoB 8327-1 !22.182 !21.975 !17.952 !8.718 !3.518 7.763 8.008 !32.498 !3.330 34.572 140.237 139.978 12.567 12.483 19.117 !21.688 !22.040 29.500 32.000 30.000 30.500 35.000 34.000 34.000 !128.000 !125.833 40.167 40.167 5.330 0.100 0.080 10.396 10.423 !4.032 4.500 123.592 123.592 117.193 17.699 16.785 17.177 16.843 16.500 16.268 16.382 16.310 15.944 15.813 15.695 15.734 15.163 16.805 18.078 18.117 18.118 18.222 18.091 17.279 17.007 !55.278 !55.270 !54.857 !54.857 !54.753 !47.535 !46.822 !56.195 !54.792 28.535 !63.855 !64.658 !6.033 !6.050 69.367 50.865 52.500 45.500 45.000 44.500 44.000 44.500 44.000 45.500 46.750 46.767 17.650 17.650 !51.000 !53.600 !54.592 59.668 59.633 62.956 53.700 !9.343 !9.340 !10.152 !34.775 !34.261 !33.478 !33.741 !33.834 !33.920 !32.909 !32.365 !32.123 !32.499 !32.888 !32.740 !32.329 !32.152 !32.496 !31.864 !31.954 !32.032 !31.747 !30.595 !29.703 !3926 !4119 !4119 !3255 !2921 !2473 !2270 !3223 !2768 !963 !3726 !2948 !5 !6 >!100 !3547 !4045 !27 !105 !114 !564 !335 !1288 !2129 !2700 !2700 !1296 !1470 !3788 !2643 !1260 !100 !110 !2060 !34 !2720 !2720 !1182 !1941 !3447 !712 !1924 !2668 !3162 !1995 !2535 !1090 !1977 !2996 !2667 !2930 !307 !69 !104 !105 !90 !100 !134 !88 GC GC PC GC GC GC PC GC GGC PC MUC MUC Grab Grab BC KC KC BC BC BC BC BC BC BC BC BC GC GC PC PC GC BC BC BC BC BC Triple PC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–2 0–2 0–1 0–1 0–1 0–1 0–1 0–0.5 0–0.5 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0.427 0.435 0.480 0.338 0.317 0.408 0.408 0.343 0.333 0.755 0.449 0.440 0.625 0.646 0.343 0.482 0.452 0.503 0.450 0.446 0.482 0.486 0.457 0.509 0.477 0.467 0.806 0.838 0.415 0.343 0.458 0.420 0.410 0.363 0.406 0.682 0.685 0.691 0.496 0.497 0.475 0.488 0.501 0.504 0.484 0.499 0.480 0.475 0.504 0.499 0.508 0.419 0.401 0.410 0.399 0.410 0.409 0.402 0.419 STDEV 0.009 0.010 0.006 0.004 0.011 0.009 0.001 0.037 0.002 0.003 0.014 0.007 0.002 0.030 0.008 0.005 0.008 0.004 0.012 0.008 0.006 0.003 0.000 0.002 0.003 0.005 0.008 0.000 0.004 0.003 0.006 0.005 0.003 Temp. 0 m (!C) Temp. 0–200 m (!C) 0.8 1.0 2.7 0.8 0.4 5.7 6.9 0.6 0.4 25.4 !0.3 0.2 25.4 25.4 6.7 12.9 12.1 21.6 13.7 13.7 13.7 14.5 14.4 13.7 12.3 12.4 29.4 29.4 2.5 0.7 0.1 9.0 9.0 8.0 10.6 28.2 28.2 28.0 17.9 18.4 16.0 18.1 18.1 18.1 17.1 17.1 18.1 18.1 18.1 18.1 18.1 17.1 15.0 14.9 14.9 15.0 14.9 15.8 13.6 (continued 0.3 0.4 2.2 0.0 0.2 4.1 5.4 0.5 0.2 21.8 0.6 0.3 16.5 16.5 5.9 11.0 10.5 16.8 8.3 8.5 8.3 8.4 23.3 23.3 1.8 0.4 !0.1 6.9 6.9 6.4 20.3 20.3 21.0 12.8 14.3 11.3 13.9 13.9 13.9 12.6 12.6 14.3 14.3 14.3 14.3 14.3 12.6 10.2 10.2 10.2 10.2 10.2 11.0 on next page) 1168 J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173 Appendix A1 (continued) Sediment core Longitude Latitude Water depth (m) Coring device Sampling level (cm) TEX86 index STDEV Temp. 0 m (!C) GeoB 8328-1 GeoB 8329-1 GeoB 8331-2 GeoB 8332-3 GeoB 8333-1 GeoB 8336-5 GeoB 8337-5 GeoB 8338-1 GeoB 8340-1 GeoB 8342-5 GeoB 8343-1 GeoB10016-2 GeoB10026-2 GeoB10032-1 GeoB10034-3 GeoB10038-3 GeoB10040-3 GeoB10042-2 GeoB1501-1 GeoB1503-2 GeoB1504-1 GeoB1506-1 GeoB1515-2 GeoB2022-3 GeoB2102-1 GeoB2104-1 GeoB2105-3 GeoB2106-1 GeoB2107-5 GeoB2108-1 GeoB2110-1 GeoB2111-2 GeoB2112-1 GeoB2124-1 GeoB2125-2 GeoB2126-1 GeoB2130-1 GeoB2201-1 GeoB2202-5 GeoB2205-4 GeoB2206-1 GeoB2207-2 GeoB2208-1 GeoB2212-1 GeoB2213-1 GeoB2215-8 GeoB2216-2 GeoB2701-4 GeoB2703-7 GeoB2705-7 GeoB2706-6 GeoB2707-4 GeoB2708-5 GeoB2712-1 GeoB2714-5 GeoB2715-1 GeoB2717-8 GeoB2718-1 GeoB2719-3 GeoB2722-2 GeoB2723-2 17.058 17.034 16.714 16.660 16.615 12.344 13.178 14.322 14.825 13.001 13.325 96.660 99.521 99.681 101.499 103.246 102.859 104.643 !32.012 !30.648 !31.287 !35.182 !43.665 !20.908 !41.200 !46.378 !46.738 !46.497 !46.457 !46.230 !45.522 !45.223 !43.377 !39.560 !39.857 !38.933 !37.103 !34.463 !34.263 !34.345 !34.480 !34.135 !33.700 !25.623 !24.153 !23.492 !23.102 !55.015 !54.202 !53.363 !55.535 !56.323 !57.300 !59.330 !57.997 !57.662 !56.487 !58.175 !60.093 !58.620 !57.875 !29.939 !29.928 !29.136 !29.127 !29.122 !29.210 !29.408 !29.500 !30.317 !31.501 !30.849 1.597 !0.944 !1.667 !4.165 !5.937 !6.476 !7.113 !3.682 2.310 2.288 2.205 4.238 !34.435 !23.983 !27.290 !26.738 !27.098 !27.180 !27.487 !28.650 !29.112 !29.135 !20.958 !20.820 !21.270 !20.615 !8.168 !8.197 !8.573 !8.558 !8.735 !8.917 !4.032 !1.265 0.008 0.000 !37.807 !38.513 !39.243 !42.368 !41.945 !41.418 !43.675 !43.863 !43.903 !47.162 !47.307 !47.442 !47.327 !48.912 !112 !111 !88 !117 !20 !3626 !3079 !915 !600 !3521 !3089 !1900 !1641 !1758 !995 !1891 !2605 !2457 !4258 !2298 !2980 !4267 !3125 !4016 !1805 !1505 !202 !502 !1052 !1991 !3003 !3498 !4009 !2003 !1542 !2537 !2113 !794 !1128 !1790 !1442 !2585 !3977 !5521 !4323 !3712 !3914 !576 !1162 !4474 !4700 !3167 !392 !1228 !2361 !3280 !4479 !2990 !684 !2351 !569 MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–2 0–2 0–2 0–2 0–2 0–2 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0.410 0.418 0.400 0.424 0.415 0.488 0.494 0.463 0.474 0.520 0.508 0.715 0.696 0.694 0.684 0.664 0.659 0.670 0.595 0.595 0.598 0.593 0.613 0.547 0.651 0.656 0.592 0.614 0.638 0.640 0.656 0.639 0.633 0.660 0.647 0.659 0.665 0.681 0.697 0.655 0.672 0.627 0.578 0.574 0.585 0.590 0.590 0.418 0.416 0.450 0.410 0.377 0.411 0.356 0.366 0.398 0.382 0.385 0.344 0.390 0.360 0.006 0.000 0.005 0.007 0.008 0.004 0.004 0.009 0.005 0.004 0.004 0.000 0.002 0.001 0.000 0.007 0.006 0.000 0.002 0.001 0.002 0.007 0.012 0.001 0.001 0.001 0.001 0.004 0.001 0.002 0.002 0.002 0.001 0.006 0.006 0.003 0.003 0.003 0.008 0.005 0.004 0.001 0.005 0.001 0.007 0.002 0.005 0.010 0.003 0.009 0.005 0.005 0.000 0.003 0.002 0.000 0.004 0.004 0.000 0.002 0.001 13.6 13.6 13.6 13.6 13.6 18.7 18.0 17.1 17.7 18.2 18.4 28.8 29.3 29.7 28.4 27.8 28.0 28.0 27.4 27.6 27.4 27.6 27.5 18.6 23.5 23.6 23.8 23.6 23.6 23.6 23.5 22.7 22.3 25.8 25.8 25.5 26.3 27.4 27.4 27.4 27.4 27.4 27.5 26.7 26.6 26.6 26.6 13.2 12.2 14.6 11.6 9.2 9.0 9.6 10.0 10.0 8.5 7.6 8.3 7.6 7.9 Temp. 0–200 m (!C) 15.0 14.2 12.9 13.8 14.9 14.6 21.4 21.6 20.1 20.6 19.6 19.6 20.0 18.7 18.7 18.5 19.0 21.1 15.2 18.9 18.9 18.9 18.9 19.2 19.3 18.2 20.5 20.5 21.7 21.5 22.3 22.3 22.3 22.3 22.3 22.4 18.4 17.8 17.8 17.8 7.6 10.0 6.1 5.3 5.7 5.5 5.5 5.5 5.1 4.8 5.6 4.8 5.1 Global sediment core-top calibration of the TEX86 paleothermometer 1169 Appendix A1 (continued) Sediment core Longitude Latitude Water depth (m) Coring device Sampling level (cm) TEX86 index STDEV Temp. 0 m (!C) Temp. 0–200 m (!C) GeoB2724-7 GeoB2727-1 GeoB2730-1 GeoB2731-1 GeoB2734-2 GeoB2802-2 GeoB2803-1 GeoB2804-2 GeoB2805-1 GeoB2806-6 GeoB2809-2 GeoB2810–2 GeoB2811-1 GeoB2812-3 GeoB2813-1 GeoB2817-3 GeoB2818-1 GeoB2819-2 GeoB2820-1 GeoB2821-2 GeoB2824-1 GeoB2825-3 GeoB2826-1 GeoB2827-2 GeoB2828-1 GeoB2829-3 GeoB2830-1 GeoB6402-9 GeoB6404-3 GeoB6405-8 GeoB6406-1 GeoB6407-2 GeoB6408-3 GeoB6409-2 GeoB6409-3 GeoB6410-1 GeoB6411-4 GeoB6412-1 GeoB6413-4 GeoB6414-1 GeoB6416-2 GeoB6417-2 GeoB6418-3 GeoB6419-1 GeoB6420-2 GeoB6421-1 GeoB6422-5 GeoB6423-1 GeoB6424-2 GeoB6425-1 GeoB6426-2 GeoB6427-1 GeoB6428-3 GeoB6429-1 GeoB9501-4 GeoB9506-3 GeoB9508-4 GeoB9510-3 GeoB9512-4 GeoB9513-5 GeoB9520-4 GeoB9521-3 !56.175 !56.538 !53.248 !51.423 !54.342 !53.982 !53.703 !53.537 !53.443 !53.137 !51.522 !51.978 !52.270 !52.387 !52.562 !38.073 !38.170 !38.343 !38.438 !38.817 !42.498 !41.432 !40.970 !40.728 !40.718 !43.430 !44.000 !22.757 !23.465 !21.853 !20.784 !19.500 !20.441 !21.717 !21.717 !20.900 !18.352 !17.647 !17.340 !13.070 !18.163 !21.042 !21.535 !21.864 !22.148 !22.445 !22.733 !22.992 !23.276 !23.588 !24.024 !24.248 !24.248 !24.248 !16.733 !18.350 !17.948 !17.653 !17.485 !17.294 !17.591 !17.490 !47.963 !48.013 !44.475 !44.207 !39.290 !37.207 !37.407 !37.537 !37.605 !37.832 !36.332 !35.980 !35.753 !35.602 !35.532 !30.915 !30.873 !30.848 !30.822 !30.453 !33.498 !32.500 !31.902 !31.478 !31.475 !30.873 !29.020 !39.743 !41.506 !42.000 !42.000 !42.045 !43.614 !44.507 !44.507 !44.517 !44.363 !44.254 !44.207 !43.999 !39.955 !39.093 !38.427 !37.774 !37.158 !36.448 !35.707 !35.253 !34.608 !33.825 !33.499 !33.182 !32.510 !31.950 16.843 15.610 15.498 15.417 15.376 15.318 13.829 13.849 !4799 !2803 !5817 !5691 !2272 !1007 !1162 !1836 !2743 !3542 !3539 !2909 !1789 !1041 !508 !2943 !3110 !3435 !3606 !3936 !4512 !4352 !3959 !3702 !3741 !3523 !3815 !3878 !4223 !3863 !3514 !3384 !3797 !4296 !4296 !4038 !3893 !3475 !3768 !3830 !3525 !4024 !4126 !3568 !3998 !4216 !3972 !3963 !3820 !4352 !4381 !4491 !4022 !4335 !330 !2964 !2385 !1567 !787 !498 !1102 !522 MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC MUC GBC MUC MUC MUC MUC GBC GC GBC MUC GBC GBC GBC GBC GBC GBC GBC GBC GBC GBC GBC GBC GBC GBC MUC MUC GBC GBC MUC MUC MUC MUC MUC MUC MUC MUC 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–2 0–2 0–2 0–2 0–2 0–2 0–2 0–2 0–2 0–2 0–2 0–2 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0.375 0.377 0.412 0.428 0.377 0.420 0.443 0.460 0.468 0.497 0.538 0.555 0.527 0.484 0.459 0.603 0.606 0.606 0.600 0.598 0.539 0.556 0.598 0.609 0.607 0.622 0.634 0.462 0.423 0.444 0.420 0.402 0.400 0.370 0.468 0.381 0.582 0.400 0.388 0.394 0.454 0.461 0.477 0.500 0.496 0.515 0.529 0.543 0.551 0.563 0.564 0.573 0.579 0.614 0.555 0.592 0.588 0.575 0.584 0.579 0.579 0.584 0.002 0.000 0.006 0.008 0.001 0.005 0.002 0.001 0.003 0.001 0.001 0.007 0.002 0.003 0.004 0.003 0.004 0.003 0.006 0.002 0.009 0.001 0.005 0.001 0.006 0.004 0.003 0.007 0.002 0.001 0.007 0.002 0.002 0.003 0.001 0.002 0.003 0.003 0.001 0.003 0.002 0.001 0.005 0.004 0.005 0.000 0.004 0.006 0.003 0.001 0.005 0.001 0.000 0.006 0.006 0.014 0.005 0.007 0.014 0.011 0.008 0.009 8.5 7.5 12.5 13.4 11.4 16.9 16.9 16.9 16.9 16.9 19.7 19.5 17.1 17.1 17.1 22.2 22.2 22.2 22.2 22.2 20.4 20.1 20.6 20.6 20.6 21.2 21.9 16.1 13.4 13.3 11.8 11.8 10.9 10.9 10.0 10.9 10.2 10.4 10.4 11.1 16.0 16.1 16.5 18.5 17.5 17.2 19.9 19.9 18.0 19.8 20.0 20.0 21.4 22.5 23.6 24.2 23.5 23.5 23.5 23.5 24.1 24.1 (continued 5.1 5.0 8.9 10.2 7.1 12.3 12.3 12.3 12.3 12.3 16.7 16.8 14.3 14.3 14.3 18.0 18.0 18.0 18.0 18.0 17.4 16.8 17.2 17.2 17.2 17.5 18.3 12.6 10.7 9.9 11.0 9.0 9.2 7.0 9.4 7.4 8.3 8.3 9.5 13.0 14.0 13.6 14.1 15.1 15.1 14.2 15.7 15.8 15.8 16.8 17.3 15.3 15.3 15.6 15.6 15.6 15.6 on next page) 1170 J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173 Appendix A1 (continued) Sediment core Longitude Latitude Water depth (m) Coring device Sampling level (cm) TEX86 index STDEV Temp. 0 m (!C) Temp. 0–200 m (!C) GeoB9525-5 GeoB9526-4 GeoB9528-1 GeoB9529-1 GeoB9534-4 GeoB9535-5 Gullmarsfjord IS-S1 IS-S2 IS-S3 IS-S4 IS-S5 IS-S6 M2003-01 M2003-06 M2003-08 MC-1 MC-3 MC-6 MD03-2603 MD03-2604 Mokbaai MW91-9 BC-22 MW91-9 BC-37 MW91-9 BC-51 MW91-9 BC-58 MW91-9 BC-74 MW91-9GGC-12 MW91-9GGC-15 MW91-9GGC-18 MW91-9 GGC-29 MW91-9 GGC-38 MW91-9 GGC-44 MW91-9 GGC-55 MW91-9 GGC-6 MW91-9 GGC-68 MW91-9 GGC-8 NIOP 325 NIOP 902 NIOP 903 NIOP 904 NIOP 905 NIOP 906 NIOP 907 NIOP 908 NIOP 915 NIOP 922 North Frisian Front North Sea Breeveertien 14 North Sea Central Southern Bight North Sea Dutch Coast OMEX 98-5 Oyster grounds Peru Margin PM1 Peru Margin PM5 PLS-91-1-1 PLS-91-1-10 PLS-91-1-12 PLS-91-1-14 PLS-91-1-17 PLS-91-1-20 !17.880 !18.056 !17.664 !17.369 !14.936 !14.961 11.500 !9.852 !9.707 !9.709 !6.099 !5.593 !5.584 !1.966 !1.315 !1.125 141.546 138.597 139.211 139.375 139.375 4.083 160.400 157.800 161.000 162.200 162.600 157.900 158.900 160.500 158.900 159.400 160.600 161.800 156.990 162.700 156.900 53.550 51.577 51.658 51.771 51.944 52.129 52.249 52.915 53.524 52.530 4.500 3.733 3.000 4.500 9.290 4.500 !77.317 !78.067 29.202 32.377 33.940 40.599 46.722 48.229 12.640 12.435 9.166 8.352 8.901 8.876 58.333 48.062 48.178 48.267 51.218 53.883 54.119 62.001 62.643 62.767 41.300 41.251 39.320 !64.285 !64.285 53.000 0.000 !0.980 0.000 0.000 0.000 !1.000 0.000 0.5000 0.000 0.000 0.000 0.000 !2.300 0.000 !2.200 10.683 10.779 10.783 10.788 10.916 10.812 10.804 10.778 10.690 16.170 54.000 52.908 53.000 53.000 41.380 54.500 !11.983 !11.050 78.308 83.706 84.185 84.807 84.009 83.017 !2648 !3223 !3053 !1234 !493 !666 !110 !2006 !1035 !497 !106 !104 !54 !1602 !1702 !1601 !1002 !1268 !856 !3290 !3290 !5 !2965 !2022 !3397 !4329 !4445 !2018 !2311 !2980 !2322 !2456 !3160 !4025 !1618 !4449 !1625 !4065 !459 !789 !1194 !1567 !2020 !2807 !3572 !4035 !201 !42 >!50 !40 !24 !765 !47 !100 !20 !303 !4004 !4052 !4018 !3960 !2900 MUC MUC MUC MUC MUC MUC BC MUC MUC MUC MUC MUC MUC BC BC BC MUC MUC MUC PC GC BC BC BC BC BC BC GGC GGC GGC GGC GGC GGC GGC GGC GGC GGC BC BC BC BC BC BC BC BC BC GBC BC BC BC BC BC BC KC BC PC BC BC BC GC GC 0–1 0–1 0–1 0–1 0–1 0–1 0–0.5 0–0.5 0–0.5 0–0.5 0–0.5 0–0.5 0–0.5 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–2 0–1 0–1 0–1 0–1 0–1 0–1 0.582 0.594 0.603 0.604 0.612 0.610 0.393 0.560 0.537 0.505 0.364 0.338 0.347 0.380 0.371 0.364 0.409 0.353 0.385 0.013 0.367 0.433 0.710 0.700 0.709 0.699 0.697 0.701 0.695 0.709 0.702 0.698 0.707 0.699 0.700 0.700 0.696 0.660 0.652 0.681 0.662 0.679 0.696 0.680 0.676 0.683 0.703 0.389 0.368 0.412 0.390 0.552 0.372 0.532 0.609 0.325 0.331 0.312 0.337 0.377 0.346 0.015 0.008 0.012 0.007 0.008 0.009 0.005 0.006 0.001 0.009 0.003 0.003 0.000 0.013 0.006 0.002 0.007 24.8 25.4 27.0 27.4 27.4 27.4 9.0 13.5 13.5 13.5 11.9 10.8 10.8 8.7 8.7 8.7 12.1 12.9 15.5 0.2 0.2 10.6 28.9 29.2 29.2 29.2 29.2 29.4 29.4 29.3 29.4 29.3 29.3 29.2 29.4 29.2 29.4 26.6 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.6 26.5 10.5 11.3 10.6 10.6 17.9 10.5 19.8 19.8 0.9 !1.7 !0.4 !0.8 1.4 0.3 15.9 15.6 16.4 16.4 16.5 16.5 6.9 11.3 11.3 11.3 0.006 0.013 0.002 0.042 0.027 0.012 0.005 0.015 0.013 0.016 0.017 0.019 0.001 0.005 0.033 0.009 0.005 7.0 7.0 7.0 8.9 4.2 8.5 0.3 0.3 24.3 23.9 24.0 24.1 24.1 24.2 24.1 24.3 24.1 24.0 24.3 24.0 24.8 24.1 24.8 20.8 19.7 19.7 19.7 19.7 19.7 19.7 19.7 20.8 18.6 13.7 14.1 14.1 !0.5 0.1 !0.9 !0.5 0.2 0.9 Global sediment core-top calibration of the TEX86 paleothermometer 1171 Appendix A1 (continued) Sediment core Longitude Latitude Water depth (m) Coring device Sampling level (cm) TEX86 index PLS-91-1-21 PLS-91-1-23 PLS-91-1-24 PLS-91-1-31 PLS-91-1-5 PLS-91-1-6 Santa Barbara basin South Frisian Front ST-32 ST-34 ST-35 ST-45 T89-10 T89-12 T89-13 T89-14 T89-15 T89-16 T89-17 T89-19 T89-20 T89-21 T89-22 T89-23 T89-24 T89-25 T89-28 T89-30 T89-32 T89-33 T89-34 T89-35 T89-36 T89-40 T89-41 T89-47 TSP-1 TSP-2 TSP-3 WM1 MC33 WM2 MC30 WM3 MC28 WM4 MC21 WM5 MC20 47.889 43.432 42.874 21.273 29.021 28.534 !120.017 4.500 143.682 142.002 141.675 140.498 1.322 7.973 9.238 9.690 10.022 11.228 7.805 9.955 11.537 11.993 12.067 12.108 12.058 10.608 5.637 8.220 10.668 11.620 11.967 11.620 13.838 6.782 6.000 4.427 147.490 146.900 146.410 !124.300 !124.383 !124.633 !125.000 !125.200 82.662 81.118 81.021 73.724 80.867 81.487 34.217 53.500 !56.002 !60.045 !62.013 !65.468 !2.077 !5.200 !4.107 !3.509 !4.213 !5.705 !6.818 !6.043 !7.307 !7.275 !7.130 !8.513 !8.908 !9.368 !10.387 !14.877 !14.893 !14.960 !15.127 !17.293 !14.960 !21.617 !20.800 !8.787 !47.572 !48.127 !48.558 46.333 46.500 46.667 46.817 46.750 !1345 !466 !472 !1536 !4018 !628 !530 !24 !3429 !4354 !4265 !1567 !2088 !4068 !3092 !868 !1930 !826 !4253 !3140 !10.80 !490 !200 !796 !2157 !4164 !5307 !4752 !3342 !2027 !999 !802 !131 !3060 !4282 !5362 !1301 !2283 !2897 !70 !90 !150 !600 !1000 BC PC PC BC BC PC BC BC MUC MUC MUC MUC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC BC MUC MUC MUC BC BC BC BC BC 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–1 0–2 0–2 0–2 0–2 0–2 0.381 0.351 0.355 0.345 0.351 0.350 0.456 0.409 0.448 0.371 0.416 0.373 0.641 0.660 0.695 0.612 0.623 0.651 0.653 0.632 0.669 0.645 0.641 0.631 0.624 0.661 0.621 0.580 0.556 0.610 0.566 0.562 0.535 0.554 0.570 0.603 0.533 0.472 0.467 0.355 0.423 0.465 0.466 0.480 STDEV 0.001 0.007 0.009 0.002 0.033 0.002 0.011 0.026 0.045 0.042 0.027 0.013 0.020 0.004 0.006 0.035 0.022 0.012 0.008 0.018 0.009 0.026 0.053 0.5 0.006 0.001 0.035 0.109 0.020 0.021 0.001 Temp. 0 m (!C) 0.3 !1.2 0.3 5.3 !0.5 !0.5 14.9 10.6 2.4 1.6 0.5 !1.0 25.6 26.1 25.2 25.4 25.5 25.1 25.8 26.2 26.0 26.0 26.0 25.0 25.0 25.8 24.7 22.3 22.5 23.2 21.2 18.6 23.2 19.9 20.2 26.0 10.5 9.8 9.8 11.9 11.9 11.9 12.4 12.4 Temp. 0–200 m (!C) 0.9 0.6 4.2 0.2 0.7 10.1 1.2 0.8 0.5 !1.1 16.2 16.1 16.2 16.8 16.6 16.6 16.1 16.2 16.4 16.4 16.4 16.1 16.1 15.1 15.2 14.5 14.4 14.9 14.5 13.8 14.9 15.6 15.4 15.6 9.5 8.6 8.6 8.1 8.1 8.1 8.4 8.4 BC (Box corer), GBC (Giant box corer), GC (Gravity corer), GGC (Giant gravity corer), KC (Karsten corer), MUC (Multicorer), PC (Piston corer), SC (Slow Score). REFERENCES Benthien A. and Müller P. J. (2000) Anomalously low alkenone temperatures caused by lateral particle and sediment transport in the Malvinas Current region, western Argentine Basin. Deep Sea Res. I 47, 2369–2393. Bice K. L., Birgel D., Meyers P. A., Dahl K. A., Hinrichs K. U. and Norris R. D. (2006) A multiple proxy and model study of Cretaceous upper ocean temperatures and atmospheric CO2 concentrations. Paleoceanography 21, PA2002. doi:10.1029/ 2005PA001203. Brassell S. C., Eglinton G., Marlowe I. T., Pflaumann U. and Sarnthein M. (1986) Molecular stratigraphy: a new tool for climatic assessment. Nature 320, 129–133. Conkright M. E., Antonov J. I., Baranova O., Boyer T. P., Garcia H. E., Gelfeld R., Johnson D., Locarnini R. A., Murphy P. P., O’Brien T. D., Smolyar I. and Stephens, C. (2002) World Ocean Database 2001, Vol. 1: Introduction. Ed: Sydney Levitus, NOAA Atlas NESDIS 42, 167 p. U.S. Government Printing Office, Washington, DC. Conte M. H., Sicre M.-A., Rühlemann C., Weber J. C., Schulte S., Schulz-Bull D. and Blanz T. (2006) Global temperature calibration of the alkenone unsaturation index 0 UK37 in surface waters and comparison with surface sediments. Geochem. Geophys. Geosyst. 7, Q02005. doi:10.1029/2005GC001054. Davis J. C. (2002) Statistics and Data Analysis in Geology, 3rd ed. Kansas Geological Survey, John Wiley and Sons, Inc., 638 p. 1172 J.-H. Kim et al. / Geochimica et Cosmochimica Acta 72 (2008) 1154–1173 Elderfield H. and Ganssen G. (2000) Past temperature and d18O of surface ocean waters inferred from foraminiferal Mg/Ca ratios. Nature 405, 442–445. Erez J. and Luz B. (1983) Experimental paleotemperature equation for planktonic foraminifera. Geochim. Cosmochim. Acta 47, 1025–1031. Gliozzi A., Paoli G., DeRosa M. and Gambacorta A. (1983) Effect of isoprenoid cyclization on the transition temperature of lipids in thermophilic archaeabacteria. Biochim. Biophys. Acta 735, 234–242. Hastie T. J. and Tibshirani R. J. (1990) Generalized Additive Models. Chapman and Hall, New York, 352p. Herbert T. D. (2003) Alkenone paleotemperature determinations. In The Ocean and Marine Geochemistry. Treatise on Geochemistry, vol. 6 (eds. H. D. Holland and K. K. Turekian). ElsevierPergamon, Oxford, pp. 365–390. Herfort L., Schouten S., Boon J. P. and Sinninghe Damsté J. S. (2006) Application of the TEX86 temperature proxy in the southern North Sea. Org. Geochem. 37, 1715–1726. Hoefs M. J. L., Versteegh G. J. M., Rijpstra W. I. C., de Leeuw J. W. and Sinninghe Damsté J. S. (1998) Postdepositional oxic degradation of alkenones: implications for the measurements of palaeo sea surface temperatures. Paleoceanography 13, 42–49. Hopmans E. C., Schouten S., Pancost R. D., Van Der Meer M. J. T. and Sinninghe Damsté J. S. (2000) Analysis of intact tetraether lipids in archaeal cell material and sediments using high performance liquid chromatography/atmospheric pressure ionization mass spectrometry. Rap. Comm. Mass. Spectrum 14, 585–589. Hopmans E. C., Weijers J. W. H., Schefuß E., Herfort L., Sinninghe Damsté J. S. and Schouten S. (2004) A novel proxy for terrestrial organic matter in sediments based on branched and isoprenoid tetraether lipids. Earth Plan. Sci. Lett. 224, 107–116. Huguet C., Kim J.-H., Sinninghe Damsté J. S. and Schouten S. (2006) Reconstruction of glacial–interglacial sea surface temperature in the Arabian Sea using organic proxies. Paleoceanography 21, PA3003. doi:10.1029/2005PA001215. Huguet C., Schimmelmann A., Thunell R., Lourens L. J., Sinninghe Damsté J. S. and Schouten S. (2007) A study of the TEX86 paleothermometer in the water column and sediments of the Santa Barbara Basin, California. Paleoceanography. doi:10.1029/2006PA001310. Karner M., DeLong E. F. and Karl D. M. (2001) Archaeal dominance in the mesopelagic zone of the Pacific Ocean. Nature 409, 507–510. Kuypers M. M. M., Blokker P., Erbacher J., Kinkel H., Pancost R. D., Schouten S. and Sinninghe Damsté J. S. (2001) Massive expansion of marine archaea during a mid-Cretaceous oceanic anoxic event. Science 293, 92–95. Lea D. W. (2003) Elemental and isotopic proxies of past ocean temperatures. In The Ocean and Marine Geochemistry. Treatise on Geochemistry, vol. 6 (eds. H. D. Holland and K. K. Turekian). Elsevier-Pergamon, Oxford, pp. 365–390. Menzel D., Schouten S., Hopmans E. C. and Sinninghe Damsté J. S. (2006) Membrane tetraether lipids of planktonic Crenarchaeota in Pliocene sapropels of the eastern Mediterranean Sea. Palaeogeograph. Palaeoclimatol. Palaeoecol. 239, 1–15. Mollenhauer G., MCManus J. F., Benthien A., Müller P. J. and Eglinton T. I. (2006) Rapid lateral particle transport in the Argentine Basin: molecular 14C and 230Thxs evidence. Deep-Sea Res. I 53, 1224–1243. Mollenhauer G., Inthorn M., Vogt T., Zabel M., Sinninghe Damsté J. S. and Eglinton T. I. (2007) Aging of marine organic matter during cross-shelf lateral transport in the Benguela upwelling system revealed by compound-specific radiocarbon dating. Geochem. Geophys. Geosyst.. doi:10.1029/ 2007GC001603. Müller P. J., Kirst G., Ruhland G., Von Storch I. and Rosell-Melé A. (1998) Calibration of the alkenone paleotemperature index 0 UK37 based on core-tops from the eastern South Atlantic and the global ocean (60!N–60!S). Geochim. Cosmochim. Acta 62, 1757–1772. Murray A. E., Blakis A., Massana R., Strawzewski S., Passow U., Alldredge A. and DeLong E. F. (1998) A time series assessment of planktonic archaeal variability in the Santa Barbara Channel. Aquat. Microbiol. Ecol. 20, 129–145. Norris R. D., Bice K. L., Magno E. A. and Wilson P. A. (2002) Jiggling the tropical thermostat in the Cretaceous hothouse. Geology 30, 299–302. Ohkouchi N., Eglinton T. I., Keigwin L. D. and Hayes J. M. (2002) Spatial and temporal offsets between proxy records in a sediment drift. Science 298, 1224–1227. Powers L. A., Werne J. P., Johnson T. C., Hopmans E. C., Sinninghe Damsté J. S. and Schouten S. (2004) Crenarchaeotal lipids in lake sediments: a new paleotemperature proxy for continental paleoclimate reconstruction? Geology 32, 613– 616. Powers L. A. (2005) Calibration and application of a new paleotemperature tool in lacustrine systems: TEX86 for continental paleoclimate reconstruction. Ph.D. Thesis, University of Minnesota, pp. 92. Prahl F. G. and Wakeham S. G. (1987) Calibration of unsaturation patterns in long-chain ketone compositions for paleotemperature assessment. Nature 330, 367–369. Prahl F. G., Popp B. N., Karl D. M. and Sparrow M. A. (2005) Ecology and biogeochemistry of alkenone production at subtropical North Pacific Station ALOHA. Deep Sea Res. I 52, 699–719. Rühlemann C. and Butzin M. (2006) Alkenone temperature anomalies in the Brazil-Malvinas Confluence area caused by lateral advection of suspented particulate material. Geochem. Geophys. Geosyst. 7. doi:10.1029/2006GC001251. Schouten S., Hoefs M. J. L., Koopmans M. P., Bosch H. -J. and Sinninghe Damsté J. S. (1998) Structural identification, occurrence and fate of archaeal ether-bound acyclic and cyclic biphytanes and corresponding diols in sediments. In Advances in Organic Geochemistry 1997. Org. Geochem. (eds. B. Horsfield, M. Radke, R. G. Schaefer and H. Wilkes) 29, pp. 1305– 1319. Schouten S., Hopmans E. C., Pancost R. D. and Sinninghe Damsté J. S. (2000) Widespread occurrence of structurally diverse tetraether membrane lipids: evidence for the ubiquitous presence of low-temperature relatives of hyperthermophiles. Proc. Natl. Acad. Sci. USA 97, 14421–14426. Schouten S., Hopmans E. C., Schefuß E. and Sinninghe Damsté J. S. (2002) Distributional variations in marine crenarchaeotal membrane lipids: a new organic proxy for reconstructing ancient sea water temperatures? Earth Planet. Sci. Lett. 204, 265–274. Schouten S., Hopmans E. C., Kuypers M. M. M., Van Breugel Y., Forster A. and Sinninghe Damsté J. S. (2003) Extremely high sea water temperatures at low latitudes during the middle Cretaceous as revealed by archaeal membrane lipids. Geology 31, 1069–1072. Schouten S., Hopmans E. C. and Sinninghe Damsté J. S. (2004) The effect of maturity and depositional redox conditions on archaeal tetraether lipid palaeothermometry. Org. Geochem. 35, 567–571. Schouten S., Huguet C., Hopmans E. C., Kienhuis M. and Sinninghe Damsté J. S. (2007a) Analytical methodology for TEX86 paleothermometry by high-performance liquid chroma- Global sediment core-top calibration of the TEX86 paleothermometer tography/atmospheric pressure chemical ionization-mass spectrometry. Anal. Chem.. doi:10.1021/ac062339v. Schouten S., Forster A., Panato E. and Sinninghe Damsté J. S. (2007b) Towards the calibration of the TEX86 paleothermometer in ancient green house worlds. Org. Geochem.. doi:10.1016/ j.orggeochem.2007.05.014. Sikes E. L. and Volkman J. K. (1993) Calibration of alkenone 0 unsaturation ratios UK37 for paleotemperature estimation in cold polar waters. Geochim. Cosmochim. Acta 57, 1883–1889. Sinninghe Damsté J. S., Hopmans E. C., Schouten S., Van Duin A. C. T. and Geenevasen J. A. J. (2002) Crenarchaeol: The characteristic core glycerol dibiphytanyl glycerol tetraether membrane lipid of cosmopolitan pelagic crenarchaeota. J. Lipid Res. 43, 1641–1651. Spero H. J., Bijma J., Lea D. W. and Bemis B. E. (1999) Effect of seawater carbonate concentration on foraminiferal carbon and oxygen isotopes. Nature 390, 497–500. Uda I., Sugai A., Itoh Y. H. and Itoh T. (2001) Variation on molecular species of polar lipids from Thermoplasma acidophilum depends on growth temperature. Lipids 36, 103–105. Weijers J. W. H., Schouten S., Spaargaren O. C. and Sinninghe Damsté J. S. (2006) Occurrence and distribution of tetraether membrane in soils: implications for the use of the BIT index and the TEX86 SST proxy. Org. Geochem. 37, 1680–1693. 1173 Wilson P. A. and Norris R. D. (2001) Warm tropical ocean surface and global anoxia during the mid-Cretaceous period. Nature 412, 425–429. Wuchter C., Schouten S., Coolen M. J. L. and Sinninghe Damsté J. S. (2004) Temperature-dependent variation in the distribution of tetraether membrane lipids of marine Crenarchaeota: implications for TEX86 paleothermometry. Paleoceanography 19, PA4028. doi:10.1029/2004PA001041. Wuchter C., Schouten S., Wakeham S. G. and Sinninghe Damsté J. S. (2005) Temporal and spatial variation in tetraether membrane lipids of marine Crenarchaeota in particulate organic matter: implications for TEX86 paleothermometry. Paleoceanography 20, PA3013. doi:10.1029/2004PA001110. Wuchter C., Abbas B., Coolen M. J. L., Herfort L., Timmers P., Strous M., van Bleijswijk J., Teira E., Herndl G. J., Middelburg J. J., Schouten S. and Sinninghe Damsté J. S. (2006a) Archaeal nitrification in the ocean. Proc. Nat. Acad. Sci. USA 103, 12317–12322. Wuchter C., Schouten S., Wakeham S. G. and Sinninghe Damsté J. S. (2006b) Archaeal tetraether membrane lipid fluxes in the northeastern Pacific and the Arabian Sea: implications for TEX86 paleothermometry. Paleoceanography 21, PA4208. doi:10.1029/2006PA001279. Associate editor: H. Rodger Harvey