1

www.sciencemag.org/content/352/6284/444/suppl/DC1

Supplementary Materials for

Continental arc volcanism as the principal driver of icehouse-greenhouse variability

N. Ryan McKenzie,* Brian K. Horton, Shannon E. Loomis, Daniel F. Stockli, Noah J. Planavsky, Cin-Ty A. Lee

*Corresponding author Email: ryan.mckenzie@yale.edu

Published 22 April 2016, Science 352, 444 (2016)

DOI: 10.1126/science.aad5787

This PDF file includes:

Materials and Methods Figs. S1 to S4 Tables S1 and S2 Caption for Data S1 Full Reference List

Other Supplementary Material for this manuscript includes the following: (available at www.sciencemag.org/content/352/6282/444/suppl/DC1)

Data S1 (Excel data tables for zircon U-Pb analyses)

2

Materials and Methods Data Compilation

Data were compiled from publications that reported sample depositional age

constraints to at least the geologic period. In situations where a sample came from a

transitional unit spanning two geologic periods (e.g., Late Permian-Early Triassic) the

data were assigned to the younger age bin. Due to different protocols for data filtering

and reporting from various labs, we accepted all data as reported by the original authors.

In cases where unfiltered data were provided, we used a simple 10% discordance cutoff

for age data (a common filter used by many groups). In rare cases where anomalous

grains far too young for the sedimentary host rock were reported, which can result from

contamination, metamorphic overgrowths, or erroneous measurements, or where grains

with exceedingly large errors were noted, these grains were manually deleted. This

accounted for less than 0.1% of the data compilation, which does not influence the trends

observed or the interpretations made in this study. All references used in our data

compilation are listed below.

Regional bins were defined by prominent terranes/continents that recorded

independent geologic histories or geographically distinct margins on the same

terrane/continent with independent geologic histories (e.g., the Laurentian margins were

partitioned) to increase our spatial resolution of volcanic arc activity (Fig. S1). While the

overarching goal was to define as many regional and spatial bins as possible, we were

ultimately limited by available data. For example, Antarctica (ANT) and Australia (AUS)

have limited overlapping temporal data, which did not allow us to subdivide them

geographically. Nearly all available Phanerozoic age data from South America comes

from the western margin, so we could only divide that region into northern and southern

bins (SAN and SAS). Data from Africa either comes from the northern or southern

regions, and therefore it could only be reasonably divided into northern and southern bins

(AFN and AFS). Avalonia was only treated as a distinct terrane until the Silurian, when it

was amalgamated with eastern Laurentia (40). Modern-day China consists of three

notably distinct cratons: Tarim (TAR), South China Block (SCB), and the North China

Block (NCB). Although these terranes were amalgamated by the Mesozoic (41, 42), we

treated them as distinct throughout to maintain our spatial resolution. Tibet (TIB) consists

3

of various small terranes that were treated as a single region. Southeast (SEA) also

includes a series of what are considered distinct terranes, such as Sibumasu, Burma,

Indochina, etc. (43), but due to limited data, they were lumped into a single region.

Mexico (MEX), consisting of the Oaxaca terrane (44), was treated as a distinct region.

The Alaska–Chukotka terrane (ALK) was treated as distinct terrane throughout. Cadomia

(CDM) and Iberia (IBR), which may have had slightly distinct early Paleozoic tectonic

histories (45), were treated as discrete terranes to increase spatial resolution; the limited

data from other eastern European terranes (e.g., Bohemia and Turkey) were lumped

within Cadomia.

Our normalization process was intended to circumvent biases in available age data

(see Table S1). All age data were divided into 20 Myr age bins and converted to

percentages. The 20 Myr bin size was selected because a median error of ± 21 Ma was

calculated during an early assessment of the compilation (prior to completion) and it is a

convenient round number. When considering cumulative distributions of age data relative

to minimum and maximum ages of the temporal bins (Fig. 3, main text), we must round

to the nearest 20 Myr bin. Minimum ages were as followed (in Ma): Cryogenian=640;

Ediacaran=540; Cambrian=500*; Ordovician=440; Silurian=420; Devonian=360;

Carboniferous=300; Permian=260*; Triassic=200; Jurassic=140; Cretaceous=60;

Paleogene=20; Neogene-Quaternary=0. Maximum ages (in Ma): Cryogenian=720;

Ediacaran=640; Cambrian=540; Ordovician=480; Silurian=440; Devonian=420;

Carboniferous=360; Permian=300; Triassic=240; Jurassic=200; Cretaceous=140;

Paleogene=60; Neogene-Quaternary=20. *The Cambrian–Ordovician boundary was

recently lowered from ~489 to ~485 Ma, however we rounded up to 500 Ma because

most of the Cambrian age data came from deposits older than Cambrian Stage 10 (> 490

Ma), with much of the data coming from Middle Cambrian and older rocks (i.e., rocks

with depositional ages > 500 Ma) (46). Therefore, rounding down to 480 Ma would have

added an inappropriate gap of ~10-20 Myr. We chose to round up for the minimum age

of the Permian as well. Ultimately, using a minimum age of 500 Ma for the Cambrian

had a negligible effect on the trends observed, as it only shifted the mean young age by

~2% from that of the 480 Ma age. In either case the Cambrian dataset clearly contains a

larger abundance of young grains than the all other periods, except the Jurassic and

4

Cretaceous. Further, the maximum ages of both Cambrian and Permian are within < 4

Myr of the dated maximum ages of each bin, and the differentiation in the cumulative

frequency distributions for both minimum and maximum ages show similar spreads.

We backfilled age data for prominent terranes with reasonably well known geologic

histories in order to fill empty temporal bins. For a given empty bin, we took the

normalized frequency distributions from the next youngest bin, removed the grain ages

that were too young for the older bin, re-normalized the data, and used those data to fill

the empty bin. For example, data were generated for the Ordovician of the NCB by

taking the NCB Silurian data, removing all data younger than the terminal age of the

Ordovician (444 Ma), and renormalizing the remaining data so they were equally

weighted to all other Ordovician regional bins. Backfilling allows us to improve the

spatial resolution of global assessment and reduce the potential effects of sampling

biases. We use the next youngest bin to backfill an empty bin because it allows for the

inclusion of any grains that may have existed during the previous interval we aim to fill.

An Ordovician dataset would not have included any grains with magmatic crystallization

ages younger the time of deposition, so using Ordovician data to fill a Carboniferous bin

would instill a potential bias creating an artificial gap that would skew the distribution

towards older grains. For this reason we only backfill and do not attempt to forward-fill.

Here we use India (IND) and western Laurentia (LAW) to illustrate this process.

Backfilled data were generated for the Silurian, Devonian, and Carboniferous of IND by

using Permian data from the region. India was primarily surrounded by passive margins

throughout that interval (that is, there was likely no major zircon-generating magmatism),

and because Permian and Triassic zircon data are remarkably similar to Cambrian and

Ordovician data (Fig. S2), it is reasonable to assume that zircon data from the Silurian,

Devonian, and Carboniferous would be similar to those distributions as well. Only ~3%

of the grains (13 of 452) in the Permian dataset are younger than the terminal Ordovician.

Out of those 13, only 3 grains have Silurian ages, so the other 10 grains that are too

young to have existed in the Silurian were removed from the bin, and the dataset was

renormalized and equally weighted to the other Silurian bins. For the Devonian, only 1

grain in the Permian dataset was too young for that bin, so that single grain was removed

and the dataset was renormalized. All grains were used for the Carboniferous. In each of

5

these cases, all young grains that could have plausibly existed during that interval were

retained.

Our compilation has data gaps for the Ordovician and Silurian of LAW. Ediacaran

and Cambrian deposits show the same prominent trimodal distribution of Proterozoic

grains (~1100, ~1400, and ~1700 Ma), with some older Archean grains. The trimodal

populations are also notable in Devonian and Carboniferous datsets, although these bins

do contain some younger early Paleozoic grains likely transported across the continent

and sourced from the Taconic orogen of Eastern Laurentia (LAE). These early Paleozoic

grains account for ~6% of the Devonian dataset (40 of 691 total grains). Of these, 21

grains (3%) were too young for the Ordovician, so those were removed and the dataset

was renormalized. For the Silurian, 11 grains were too young (~1.6%) and were removed.

Backfilled data were used for all Laurentian bins (LAW, LAN, LAE, GRN), ALK,

ANT, AUS, Baltica (BAL), IND, NCB, SCB, SAN, SAS, SBR, TAR, and TIB (the TIB

record begins during the Carboniferous). Backfilled data were not used for Arabia

(ARB), Iran (IRN), SEA, and MEX due to limited available data and uncertainties on the

geologic histories of those regions. The data record for IBR only spans from the

Ediacaran to Carboniferous, and due to similarities with CDM, data were not generated

for IBR. In cases where major shifts in populations occur across data gaps, backfilled

data were not generated. This is due to uncertainties in the onset of substantial

magmatism, and more importantly, the potential lag time for the surface expression of

this magmatism. The Ediacaran of SAN lacks relatively young zircons whereas the

Ordovician of that region contains abundant young Cambrian-Ediacaran zircons. We

cannot assume that abundant Cambrian zircons were at the surface during the Cambrian;

therefore we did not backfill that particular temporal bin. We also did not backfill any

Neogene-Quaternary bins as they are the youngest possible bins. As discussed above, we

did not forward-fill any bins due to the potential biasing from the inherent lack of young

grains in older bins.

Ultimately, the application of data backfilling should yield more accurate, globally

representative distributions. For the IND and LAW examples, given our knowledge of the

regional geologic histories, it seems unreasonable to ignore those regions and leave the

original empty data bins, as this greatly reduces our spatial resolution and increases the

6

potential for the distributions to be regionally biased. Our objective is to assess

spatiotemporal variation in continental magmatism by way of zircon production, and the

more spatial data we have, the better our assessment will be. It should be noted, however,

that despite some variation between the absolute abundances, the same general trends are

observed when comparing raw data without backfilled data to the trends observed with

the inclusion of backfilled data: that is, the proportions of young grains increase during

greenhouse intervals and decrease during icehouse intervals (Fig. S3).

References for regional data sources are as follows: AFN (47-53); AFS (54-58); ALK (59-65); ANT (57, 66-76); ARB-IRN (77-79); AUS (68, 80-95); AVL (40, 96-99); BAL (100-109); CDM (45, 110-116); GRN (117); IBR (22, 45, 118-123); IND (124-138); LAE (139-148); LAN (63, 149-157); LAW (158-174); MEX (44, 175, 176); NCB (177-190); SCB (125, 191-197); SAN-SAS (198-216); SBR (63, 148, 217-221); SEA (22, 222, 223); TAR (224-232); TIB (131, 233-238)

7

Fig. S1.

Global map of broad regionally partitioned sources of zircon U-Pb data.

8

Fig. S2.

U-Pb age probability density plots for Indian (IND) and western Laurentia (LAW)

as examples of backfilling. Age data for IND show that the general age populations

present in Cambrian and Ordovician datasets (e.g., age populations centered around ~500

Ma (yellow), ~900 Ma (blue), and ~1700 Ma (orange)) match the abundant populations

in Permian and Triassic datasets. Given the known geologic history of IND, it is

9

reasonable to assume comparable detrital age data for Silurian, Devonian, and

Carboniferous deposits; therefore Permian data are used to backfill those empty bins, as

discussed in the text. LAW datasets show that the same general age populations present

in Ediacaran and Cambrian datasets (e.g., populations around ~1100 Ma (yellow), ~1400

Ma (blue), and ~1700 Ma (orange)) are also prominent in Devonian and Carboniferous

datasets, along with subpopulations of younger Taconic age zircons. Therefore, the

Devonian dataset was used to backfill the older empty bins, following the procedure

discussed in the text.

10

Fig. S3.

Comparison of the proportion of young grains for normalized raw and backfilled

datasets. The “raw” curve (blue) does not include backfilled data. The “backfilled” curve

(red) includes both raw and backfilled data. The upper graph compares the mean

proportions of young grains relative to the minimum age of the bin; the lower graph

compares mean proportions of young grains relative to the maximum age of the bin.

Although there is some variation in the absolute proportions of each bin, both raw and

backfilled datasets show the same relative shifts in abundance of young grains: the

proportions are low during the Cryogenian, increase into the early Paleozoic greenhouse,

decrease during the later Ordovician-Silurian icehouse, increase moderately during mid-

Paleozoic time, decease during the late Paleozoic icehouse, increase during the Mesozoic

greenhouse, and decrease during the Cenozoic icehouse.

11

Table S1.

Summary of regional and temporal zircon U-Pb data. Each box indicates the total

number of zircon U-Pb ages for each bin. Yellow boxes highlight bins for which

backfilled data were generated and red boxes indicate bins that were rejected via our

statistical filter, as described in the text. The total number of ages are indicated for each

temporal bin and each regional bin, as well as the percentage each represents of the total

dataset. It is important to note that nearly ~40% of all data come from Laurentian terranes

(21% from LAW alone) and ~50% of the data range from Cretaceous to Quaternary

rocks. This illustrates the importance of the normalization process applied here. If data

were simply pooled, the distribution would be biased towards the Laurentian record and

the 140–0 Ma record.

12

Screening for regional biasing of composite distributions

An outlier test was preformed to identify anomalous datasets that significantly

skewed the composite distributions. First, the mean and standard deviation of the young

zircon populations of each composite distribution were determined by fitting a Gaussian

curve through all ages < 300 Myr older than the youngest depositional age using the

Matlab fit function. All ages outside of this window were converted to zeros to optimize

the similarity to, and fit of, a Gaussian function. Data with a value of zero were also

created for the time period -100 to 0 Ma to optimize the potential for a Gaussian fit for

the Neogene and Paleogene data. To ensure that a single region did not disproportionately

alter the composite distribution, global zircon age probability distributions were

recalculated using a jackknife procedure, leaving out one region at a time, renormalizing

the data, and fitting Gaussian curves to each of these distributions. Regions were

considered outliers if the optimal ages or the error fell outside of 3.5 standard deviations

of the parameter mean. This procedure was employed in an iterative process until no

regions were identified as outliers. Only four outliers were identified (Table S1).

To examine the potential for our choice of window size to significantly alter the

means and standard deviations of the youngest peak, mean fits were recalculated by

increasing the window size by 20 Myr until the widow size reached 400 Myr. Although

this altered the values of the means and standard deviations of the first peak, the temporal

trends remain the same for all periods except the Triassic (Fig. S4). The large difference

in the mean value for the Triassic data occurs because the data have a bimodal

distribution from 700 Ma to present, and increasing the window size results in a Gaussian

fit of both peaks. Given that this method was developed to identify the age of the

youngest population, we ultimately chose to utilize a window size of 300 Myr, as it is

offered the best fit through all geologic periods.

13

Figure S4.

Mean age offset of young populations using a varying time window. Each line

represents the relative age offset (peak age – minimum depositional age) vs. the

minimum depositional age, varying the time window by 20 Myr from 300 Myr to 400

Myr.

14

Detrital Zircon U-Pb Geochronology

New detrital zircon U-Pb age data were generated for Ordovician, Devonian,

Carboniferous, Permian, and Triassic siliciclastic sedimentary rocks from western

Argentina (SAS). These samples consist of: RBL01, Triassic Choiyoi Formation (S

31.91090°; 69.88968°; 2175m); CAR01, Carboniferous Paganzo Group (S 31.51772°; W

68.94825°; 953m); TAL04, Devonian Punta Negra Formation (S 31.00470°; W

68.79044°; 1470m); TAL05, Devonian Punta Negra Formation (S 30.98914°; W

68.80254°; 1512m); ST01, Ordovician Alcaparosa Formation (S 31.25869°; W

69.15356°; 1225m); ST02, Carboniferous Paganzo Group (S 31.24429°; W 69.27795°;

1266m); ST03, Permian Paganzo Group (S 31.24445°; W 69.27845°; 1268m), CRA01S:

Carboniferous El Imperial Formation (S 35.00377°; W 68.64511°; 1254m); CRA02S,

Carboniferous El Imperial Formation (S 34.99136°; W 68.62245°; 1110m); CRA03S,

Devonian (S 34.96428°; W 68.61156°; 1048m); CRA04S, Puesto Viejo Formation

(Triassic) (S 34.81542°; W 68.49364°; 783m).

All samples were processed and analyzed at the UTChron laboratory, Department of

Geological Sciences, University of Texas at Austin. Zircon grains were extracted from

sandstone samples using standard mineral separation techniques, which included

crushing, passing samples over a density-separation water table, magnetic separation, and

heavy liquids. Whole zircon grains were fixed on a ~1 inch resin mount with double-

sided tape for analysis by laser ablation inductively couple mass spectrometry (LA-ICP-

MS). Mounts were placed into a Helex 9 sample cell, volume ca. 30 cm3, and ablated

with a 30µm spot from a Photon Machines Analyte G2 ATLex 300si ArF Excimer laser.

Ablated material was carried by helium gas to a ThermoFisher Element2 double-focusing

magnetic sector ICP-MS for isotopic measurements. Data analyses were accomplished

using Iolite (Igor Pro) (See Table S2 for analytical details). We report the 206Pb/238U ages

for grains < 1000 Ma (< 10 % discordance) and the 207Pb/206Pb ages for grains ≥ 1000 Ma

(< 20% discordance). We allowed the larger discordance window for older grains

because Pb loss is more common in older grains, which produces a greater discordance.

However, there is no fractionation during Pb loss and the 207Pb/206Pb ages are generally

unaffected by this process (18, 239). Therefore, applying the same fixed discordance

filter to old and young grains disproportionately emphasizes younger ages, which is why

15

we prefer a more relaxed filter for older ages. Data tables are available online as

Supplementary Materials (Data S1).

16

Table S2: Laboratory & Sample Preparation

Laboratory name UTChron, Department of Geological Sciences, University of Texas at Austin

Sample type/mineral Detrital zircons Sample preparation Conventional mineral separation, 1 inch resin mount, grain mount on

double-sided tape Laser ablation system Make, Model & type PhotonMachines Analyte.G2 ATLex 300si ArF Excimer laser Ablation cell & volume Helex 9 sample cell, volume c.30cm3 Laser wavelength 193 nm Pulse width ≤ 4ns Energy 6 mJ, laser attenuator 16% Fluence 1.43 J.cm-2 Repetition rate 10 Hz Spot size 30µm Sampling mode 6 preablation shots, 35 sec of baseline data collection, 300 ablation shots,

27 sec washout Sample cell washout time (s)

<0.5 sec

Carrier gas He: 0.5 L/min (ultrapure) Ablation duration/depth 30 secs at ~0.5µm/sec for depth of 15-17 µm ICP-MS Instrument Make, Model & type ThermoFisher Element2 double-focusing magnetic sector ICP-MS Sample introduction Ablation aerosol (dry plasma) RF power 1000-1100 W Cooling gas Ar: 16 L/min Auxiliary gas Ar: 0.79 L/min Sample gas He: 0.93-1.0 L/min Make-up gas flow N2: 2 L/min Detection system Secondary electron multiplier (SEM) Masses measured 202, 204, 206, 207,208, 232, 235, 238, 254 Dwell time 4 ms (238U); 16 ms (207Pb) Sensitivity (238U) 0.4% (238U dry aerosol)

Data Processing Gas blank 35 second on-peak zero subtracted Calibration strategy GJ1 used as primary reference material, Pak1 (internal) used as secondary

reference material Reference Material info GJ1 206Pb/238U 601.7 ± 1.3Ma, 207Pb/206Pb 607 ± 4Ma (Jackson et al, 2004,

Kylander-Clark et al., 2013) Data processing package used

Iolite (Igor Pro)

Mass discrimination 206Pb/238U, 207Pb/235U and 208Pb/232U normalized to reference material Common-Pb correction None applied Uncertainty level & propagation

Ages are quoted at 2 sigma absolute error, propagation is by quadratic addition. Reproducibility and age uncertainty of reference material are propagated.

LA-ICP-MS U-Pb Analytical details.

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