Flat meridional temperature gradient in the early Eocene ... · The efficiency of downward export...

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Supplementary Information

Flat meridional temperature gradient in the early Eocene in the

subsurface rather than surface ocean

Sze Ling Ho and Thomas Laepple

Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research,

Telegrafenberg A43, D-14473 Potsdam, Germany.

(E-mail: [email protected]; [email protected])

This supplementary file contains:

• Discussion on “Non-thermal effects on TEX86”.

• Discussion on “Implications and mechanisms of mesopelagic GDGT export”.

• Supplementary Figures S1 to S10.

• Supplementary Table S1.

Calibration ensemble derived from variability comparison is available in a separate

Microsoft Excel file. The non-Eocene data compilation used in variability analyses is

available in PANGAEA (https://doi.pangaea.de/10.1594/PANGAEA.149998).

Flat meridional temperature gradient in theearly Eocene in the subsurface rather than

surface ocean

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NGEO2763

NATURE GEOSCIENCE | www.nature.com/naturegeoscience 1

© 2016 Macmillan Publishers Limited. All rights reserved.

http://dx.doi.org/10.1038/ngeo2763

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Non-thermal effects on TEX86

There is a growing body of evidence1–5 that in addition to temperature, non-

thermal secondary effects, including physiology, might have a strong control on

individual TEX86 records. Although unlikely to yield a systematic effect across the

broad temporal and spatial range covered by the proxy records constituting the power

spectra (Fig. 1) and thus our derived calibration equation, these factors likely

contribute to TEX86-based temperature reconstructions from individual sites. They

might therefore also contribute to the model - -derived seawater temperatures

mismatch that remains after our proposed recalibration (Fig. 3).

Recent studies demonstrated that in addition to temperature, values in

archaeal lipids are also sensitive to physiological factors such as growth phase4,

oxygen availability5 and archaeal ecotypes1–3.

In spite of a considerable change throughout archaeal growth, most of

the GDGTs that are incorporated in sediments are plausibly from the stationary

growth phase, regardless of climate states. Thus no systematic effect on the

variability across time-scales from millennia to millions of years is expected. We

further see no direct evidence that growth phase influences should lead to the

relatively high Eocene -temperatures in the Southern Ocean.

An influence of oxygen changes on 5 might lead to non-temperature

variations in records from specific regions, for example at the boundary of oxygen

minimum zones. Given that we find a widespread variability mismatch between

and i.e. at 20 of 22 globally distributed sites, it is likely not the major factor

for the variability mismatch between both proxies. A warm bias due to low

TEX86H

TEX86H

TEX86H

TEX86H

TEX86H

TEX86H

U37K'

TEX86H


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oxygenation might lead to an overestimation of the temperature of warm climate

states as the solubility of oxygen is reduced in warm waters5. However, it does not

explain why the largest mismatch occurs in the Southern Ocean (Fig. 3), as here, signs

of bioturbation and the lack of lamination in sediments6, suggest that the sediments

were oxygenated during early Eocene. Furthermore, given the localities of these sites

in the Southern Ocean (i.e. deepwater formation region), the bottom waters

overlaying the sediments were probably relatively young and therefore should not be

depleted of oxygen.

Changes in archaeal assemblages due to differences in regional oceanography

and/or changes in the habitat depth could provide an explanation for the high -

derived temperature in high southern latitudes. Although the -temperature

relationship in Southern Ocean surface sediments (Pacific sector) agrees well with the

global calibration7, the archaeal assemblage here might be different during the early

Eocene. This is plausible in light of the substantially different temperature response of

two strains of archaea despite originating from the same hydrographic setting1

(Benguela upwelling system). Temporal evolution in archaeal assemblage can

introduce either a warm or cold bias depending on the main constituent of the

assemblage. In this case, neither a global nor regional calibration based on the modern

spatial temperature distribution would be able to account for these effects. Ultimately,

a better mechanistic understanding of the thaumarchaeotal ecophysiology as well as

the continued analysis of multiple independent proxy evidences is needed to reduce

the uncertainty of past climate reconstructions.

Implications and mechanisms of mesopelagic GDGT export

TEX86H

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Numerous studies suggest that mesopelagic (defined here as 200–1000m)

GDGTs are exported to the marine sediments2,8–11. The transport mechanism of

mesopelagic GDGTs to the seafloor is likely via passive pathways, such as marine

snow and faecal pellet formation. Mid-water maxima in faecal pellet flux are

ubiquitous in the global ocean12,13, suggesting an active food-web in dark mesopelagic

ocean (also know as the twilight zone). For instance, GDGTs have been found in the

guts of amphipods collected at depths (540m–1024m)14. As archaea are too small to

be grazed directly by amphipods, the GDGTs might have been ingested accidentally,

or were embedded in larger particles (e.g. faecal pellets of smaller zooplankton)

grazed by the amphipods. The latter is in agreement with a study in the Southwest

Pacific which found picoplankton (of which archaea is a subset) within flagellates and

copepod faecal pellets, both of which were incorporated in organic aggregates15.

Mesopelagic GDGTs may also be incorporated into large sinking particles via

collision, due to differential settling16, or attachment by sticky mucus / exopolymers17.

The efficiency of downward export of the GDGT produced in the mesopelagic

waters is lower than that from the epipelagic zone (<200m), where a substantially

more active food web is present. However, its relative importance to the sedimentary

GDGT pool may be amplified by the extensive loss (>90%) of epipelagic zone

derived particle flux during its descent in the water column, with the majority of

particles lost between 100m and 500m18. In summary, the evidence from water

column studies on suspended and sinking particulate matters, zooplanktons, as well as

surface sediment studies, is consistent with our independent statistical evidence of a

subsurface signal in records.

Some recent studies also suggest that different archaeal communities thriving

at different water depths19,20 may produce GDGTs with different temperature

TEX86H


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relationships21–24. This complicates the interpretation of both values in water

column studies and the depth origin of sedimentary . To investigate whether

such a depth relationship would systematically bias our results or could be

incorporated into the global calibration, we tested the influence of the water depth at

the core-sites for the core-top calibration, as well as for the variability comparison of

vs. , assuming that shallow sites only receive a minimal contribution from

the deepwater GDGTs. Dividing the global calibration dataset25 in two sets of less and

more than 1000m water depth does not result in a significantly different calibration

slope (p=0.56, =a*SST+b); similarly, dividing the downcore records of the

variability comparison into shallow (<1000m) and deep records does not affect the

ratio of to (<5% ratio change).

TEX86H

TEX86H

U37K' TEX86

H

TEX86H

U37K' TEX86

H


6

Figure S1: Locations of proxy records in our three compilations, namely paired -

sites (proxy details in Fig. S2), paired surface-subsurface foraminiferal Mg/Ca sites (proxy details in Fig. S6), and early Eocene sites (proxy details in Tab. S1).

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Figure S2: Paired and records constituting the composite power spectra in Fig. 1. a, Sediment core details and proxy calibrations used in the original publications to convert index values into temperatures.

Site

Latitude (°N)

Longitude (°E)

Time coverage (kyr BP)

#samples (TEX86

H

+UK’37)

Reference for TEX86

H calibration*

Reference for UK’37

calibration*

Reference for the proxy record

MD02-2515 27.48 -112.07 6.9–24.9 482 25 26 27 TTR17_293G 36.17 -2.75 0.3–19.4 180 2 26 2

NIOP905 10.78 51.93 0–22.2 216 28 26 29 SO42-74KL 14.32 57.33 0.4–22.1 146 28 26 29 GeoB7702-3 31.65 34.07 0.4–24.2 100 30 31 32 MD97-2146 20.12 117.38 0.4–28 144 25 33 34,35 MD98-2195 31.64 128.94 0.5–41.9 136 25 33 36,37 GeoB7926-2 20.22 -18.45 0.2–48.3 494 38 26 38 MD03-2607 -36.96 137.41 1.1–134.8 340 25 26 39 ODP977A 36.03 -1.96 147.2–244.2 394 25 26 40,41 ODP1241 5.84 -86.44 1.8–147.9 118 25 33 42

MD97-2151 8.73 109.87 1–153.5 222 43 44 45 GeoB9528-3 9.17 -17.66 6.8–192 386 25 26 46

ODP1146 19.45 116.27 67–350.7 508 25 26 47 ODP1147 18.84 116.55 0.3–356.2 130 48 44 49 ODP1239 -0.67 -82.08 0.4–431 488 25 33 50

MD96-2048 -26.17 34.02 0.4–790.1 354 25 33 51 ODP1143 9.36 113.29 82.1–3987.9 184 25 26 52,53 ODP806 0.319 159.36 100–5260 108 25 26 54 ODP882 50.37 167.60 479.5–5865 106 25 33 55 ODP850 1.30 -111.52 0–11880 160 25 26 54

ODP1085 -29.38 13.99 15–13708 132 25 26 56 * Referring to the proxy calibration used in the original studies where the proxy records were presented.

b. Proxy time-series as published shown as temperature anomalies with a vertical

scaling given by the vertical bar in the third column.

U37K' TEX86

H


8

Figure S3: Single spectral estimates of - and -inferred SST variability at

study sites reported in Fig. S2. Single spectra show that -variability (red) is

larger than -variability (black) at 20 out of 22 sites, and that the discrepancy

cannot be explained by independent noise as artificially adding white noise to

time series (green) does not result in similar spectral shape as . PSD denotes power spectral density.

U37K' TEX86

H

TEX86H

U37K'

U37K'

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9

Figure S4: Comparison of power spectral density (PSD) derived from and TEX86 SST; the latter based on the Bayesian calibration BAYSPAR57. Dividing the TEX86 spectral estimate by a factor of 4 leads to a similar spectra as SST. The

stronger difference (factor of 4 instead of 3) between and TEX86 SST is caused by BAYSPAR having on average a higher temperature sensitivity58 than the classical

calibration from Kim et al25. Paired proxy records constituting the power spectra are as in Fig. 1, with details listed in Fig. S2.

U37K'

U37K'

U37K'

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Figure S5: Ratio of surface and subsurface (0–600 m) seawater temperature change in different climate change scenarios as simulated by the MPI-ESM model in the CMIP5/PMIP3 experiments. Shown are the ratios for a, Last Glacial Maximum (LGM) simulation minus preindustrial; and b, 4×CO2 simulation minus preindustrial. Every black dot is one average ratio calculated from N randomly picked sites, where N varies on the x-axis and the calculation is repeated 100 times for each N. For single sites (N=1), the subsurface and surface change can be substantially different, whereas when averaged over many sites, the ratio converges to one.


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Figure S6: Power spectral density (PSD) of paired Mg/Ca temperature records based on sea surface- and subsurface-dwelling foraminifera, in comparison with the spectra of - and -SST. a, Proxy record details and foraminiferal species in the Mg/Ca records constituting the power spectra in b using the calibrations proposed by the original studies.

Site

Latitude (°N)

Longitude (°E)

Surface dweller Subsurface dweller

Reference

MD01-2390 6.64 113.41 G. ruber P. obliquiloculata 59,60 MD05-2904 19.46 116.25 G. ruber P. obliquiloculata 61 ODP1240 0.02 -86.46 G. ruber N. dutertrei 62

SO213-59-2 -45.83 -116.88 G. bulloides G. truncatulinoides 63 GeoB10038-4 -5.94 103.25 G. ruber N. dutertrei 64

ODP999A 12.74 -78.74 G. sacculifer N. dutertrei 65 ODP1241C 5.84 -86.45 G. sacculifer N. dutertrei 65 MD01-2378 -13.08 121.79 G. ruber P. obliquiloculata 66,67 MD06-3067 6.51 126.50 G. ruber P. obliquiloculata 68 VM12-107 11.33 -66.63 G. ruber G. crassaformis 69

MD01-2461 51.75 -12.92 G. bulloides N. pachyderma 70,71 SO18460 9.09 129.24 G. ruber P. obliquiloculata 72 ODP1146 19.46 116.27 Gs. Subquadratus + Gs. obliquus Gs. Trilobus 73 WIND28K -10.15 51.01 G. ruber N. dutertrei 74,75

64PE-174P13 -29.76 2.40 G. ruber G. truncatulinoides 76 GeoB12610-2 -4.82 39.42 G. ruber N dutertrei 77

b, Raw average power spectra for , and Mg/Ca data of surface- and subsurface-dwelling foraminifera

U37K' TEX86

H

TEX86H U37

K'


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Figure S7: Histogram of to temperature slopes derived from comparing the

and variability. The distribution includes the uncertainty of the to

ratio, as well as the uncertainty of the to SST calibration. The resulting

slopes are significantly smaller than the commonly used calibrations.

TEX86H

U37K' TEX86

H TEX86H

U37K' U37

K'

TEX86H

TEX86H calibration slope

De

nsity

20 30 40 50 60 70 80

0.0

00

.04

0.0

8

Kim

et a

l., 2

010

Kim

et a

l., 2

012

(0-2

00m

)


13

Figure S8: Implications of recalibrated on selected published time series across timescales. For visual purposes, only the mean calibration and not the full calibration ensemble is shown. a, records in our paired - compilation (Fig. S2) consisting of time series spanning at least the Last Glacial Maximum to as far back as the Miocene. All time series are anomalies relative to the time period shown. We note that the same dataset was used in the construction of the calibration but as only a single parameter was estimated from 22 paired time-series, the artificial skill is very small.

b, Implications of recalibrated for transient changes at site ODP1172 during

Middle Eocene Climatic Optimum (MECO) derived from and 78. MECO is as defined in the original publication. The recalibration results in a convergence of the amplitude of MECO warming between proxies. All time series are anomalies relative to the time period shown.

TEX86H

TEX86H U37

K' TEX86H

TEX86H

U37K' TEX86

H


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Figure S9: Comparison of ECHAM5/MPI-OM with other EoMIP models studied by Lunt et al.94. a, Eocene warming.

b, As in Fig. 3a, with the addition of ECHAM5 temperature at sites, and

EoMIP ensemble mean temperature for both the zonal mean and at sites (except West Siberia Sea site because paleolongitude data is not available in the original publication; refer to Supplementary Table S1).

Comparison of EoMIP models

Latitude

Eoce

ne S

ST −

PI S

ST(°

C)

−80 −60 −40 −20 0 20 40 60 80

05

1015

2025

30 EoMIP model ensemble meanEnsemble mean +−2sdECHAM5/MPI−OM Annual meanECHAM5/MPI−OM Monthly mean

TEX86H

TEX86H


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Figure S10: Seasonal range of simulated Eocene zonal mean surface and subsurface (0–600m) temperatures. The seasonal range is negligible for the subsurface case and is thus not considered in the proxy-model comparison shown in Fig. 3b.


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Table S1: Site details for early Eocene temperature data in Fig. 3. The compilation is an extension to the EoMIP proxy compilation (Lunt et al.94), with the addition of two new TEX86

H sites.

Site Present-day

Latitude (°N)

Present-day

Longitude (°E)

Paleo-latitude

during 49-55Ma (°N)

Paleo-latitude during

49–55Ma (lower bound)

Paleo-latitude during

49–55Ma (upper bound)

Proxy Reference

ODP 690 -65.2 1.2 -71.9 -80.1 -64.1 Foraminifera δ18O 79 ODP 738 -62.7 82.8 -62.3 -68.6 -56.2 Foraminifera δ18O 80,81

ODP 1172 -44.0 149.9 -57.8 -62.8 -52.8 TEX86H 82

Waipara River, NZ -43.2 172.8 -47.8 -52.0 -44.1 TEX86H 83

Foraminifera Mg/Ca 84 Tanzania (TDP) -7.8 39.1 -22.4 -24.4 -20.2 Foraminifera δ18O 85

TEX86H 85

Hatchetigbee 32.0 -88.0 29.8 26.4 33.2 Clumped isotopes 86 ACEX (302-4A) 87.9 136.2 77.4 68.6 88.0 TEX86

H 87 Bass River 39.6 -74.4 34.1 31.9 36.2 Foraminifera δ18O 88

TEX86H 89

Wilson Lake 39.7 -75.1 34.2 32.1 36.4 TEX86H 90

ODP 865 18.4 -179.6 11.4 9.8 12.8 Foraminifera Mg/Ca 91 DSDP 527 -28.0 1.8 -41.2 -43.6 -38.4 Foraminifera Mg/Ca 91 ODP 1209 32.7 158.5 29.0 26.6 31.3 Foraminifera Mg/Ca 92

IODP U1356 -63.0 135.0 -57.6 -62.7 -52.6 TEX86H 6

West Siberia Sea 53.5 73.5 51.5 47.9 56.2 TEX86H 93


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Flat meridional temperature gradient in the early Eocene ... · The efficiency of downward export...

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