Remarkably extensive glaciation and fast deglaciation and climate
change in Turkey near the Pleistocene-Holocene boundary
Marek Zreda1, Attila Çiner2, Mehmet Akif Sarıkaya1,3*, Chris Zweck1, and Serdar Bayarı2
1
Department of Hydrology and Water Resources, University of Arizona, Tucson, Arizona 85721, USA
Department of Geological Engineering, Hacettepe University, 06800 Beytepe, Ankara, Turkey
3
Department of Geography, Fatih University, 34500 Büyükçekmece, Istanbul, Turkey
2
ABSTRACT
Moraines in the Taurus Mountains of south-central Turkey, dated to latest Pleistocene or
earliest Holocene, show that glaciers were extraordinarily large, typical of the Last Glacial
Maximum (21 ka), and that rates of glacier retreat and temperature rise exceeded those of
the past century. Surface exposure ages of 7 moraines in a valley at altitudes between 1100 m
and 3100 m above sea level range from 10.2 ± 0.2 ka to 8.6 ± 0.3 ka, computed using our
own production rates and spatiotemporal scaling factors. Hitherto unresolved differences
in cosmogenic 36Cl production-rate estimates can make these ages significantly older, and
therefore the analysis presented here focuses on the rate of change and not on the absolute
chronology. During deglaciation, the equilibrium line altitude ascended 1430 m and the air
temperature rose by 9 °C. Deglaciation occurred in two phases. During the second, faster
phase, which lasted 500 yr, the glacier length decreased at an average rate of 1700 m/100 yr,
implying a warming rate of 1.44 °C/100 yr, indicating a rapid climate shift marking the
onset of the Holocene in Turkey.
INTRODUCTION
After millennia of generally cold but variable climate, the warming trend at the end of
the Pleistocene epoch led to the establishment
of a warm climate of the Holocene epoch (the
past 11.6 k.y.). Ice-core data suggest that the
Holocene was climatically stable (Dansgaard
et al., 1993), but other paleoclimate proxy
data (e.g., Bond et al., 1997; de Menocal et al.,
2000) show clear variations. Understanding
climatic changes during the Holocene provides
long-term context for the assessment of the
nature of the climate change today. The global
temperature rise of the past century (Folland et
al., 2001) could be considered unique within
the Holocene under the assumption of a relatively stable climate of the Holocene (Dansgaard et al., 1993), but unexceptional under
the assumption of large climatic variations (de
Menocal et al., 2000). In this paper we present new results that indicate an extensive late
Pleistocene−early Holocene glaciation from
south-central Turkey, from which we infer the
magnitude and pace of glacial and climatic
changes.
Glaciers are not among the first things usually associated with Turkey, but glaciers exist
in Turkey today (Çiner, 2004; Sarıkaya et al.,
2011), and, as noted first by Palgrave (1872),
glacial geological evidence shows that much
bigger glaciers existed in Turkey in the past,
providing information on former climate
changes (Erinç, 1952). Temperate mountain
glaciers are sensitive to changes in climate
(Oerlemans, 2001), mainly temperature and
*E-mail: [email protected].
precipitation, because their temperature is
close to the melting point of ice (Nesje, 2005;
Ohmura et al., 1992). Variations in glacier size
provide some of the clearest natural signals of
climate change today (Nesje, 2005). By analogy, dating of moraines provides information
on past climates.
GEOLOGIC SETTING
We dated moraines in the Aladağlar (in
Turkish, “ala” means speckled, “dağlar” means
mountains) (37°8′N, 35°2′E) of the Central
Taurus Mountains of Turkey. The highest part
of the mountain range consists of Mesozoic
carbonates with extensive karst that limits surface drainage (Tekeli et al., 1984; Klimchouk
et al., 2006). The modern climate is a mixture
of Mediterranean and continental type, with
hot and dry summers and wet and cold winters. The Aladağlar bear conspicuous evidence
of former glaciers (Klaer, 1962; Klimchouk
et al., 2006; Tekeli et al., 1984). Former glaciers developed in cirques above 3000 m and
flowed down deeply incised valleys. Numerous
morphological features record former glaciations in the Yedigöller (Seven Lakes) Plateau,
a large depression just below the summits of
the Aladağlar, and in the Hacer (Rock) Valley, a deep, U-shaped glacial valley, the largest
in the Aladağlar (14 km long), located on the
east side of the mountains (Fig. 1). Features of
glacial erosion, i.e., cirques, glacially scoured
bedrock, striations, trim lines, aretes, and
horns, are common in the Yedigöller area and
in the upper valley, above 2000 m (Klimchouk
et al., 2006). Features of glacial deposition
(moraines, glacial lakes, and outwash deposits)
are present at all elevations. In the Hacer Valley and in the Yedigöller Plateau, we mapped
seven moraines at elevations from ~3100 m
to ~1100 m (A−G in Fig. 1; Table DR1 in the
Figure 1. A: Location map of Aladağlar. B: View of Hacer Valley (looking to northwest).
C: Moraine and sample locations in Yedigöller Plateau and Hacer Valley. Separate moraines
are labeled from A (highest elevation) to G (lowest). Central flow line is shown in 1-km-long
segments. Asterisks indicate bedrock samples.
© 2011 Geological Society of America. For permission to copy, contact Copyright Permissions, GSA, or [email protected].
GEOLOGY,
November
2011
Geology,
November
2011;
v. 39; no. 11; p. 1051–1054; doi:10.1130/G32097.1; 3 figures; Data Repository item 2011308.
1051
GSA Data Repository1). In the lower valley, the
moraines are large, well preserved, and bouldery, with limestone boulders as large as 15 m
in diameter (Table DR2). In the upper valley
and on the plateau, the moraines are smaller,
and the boulders are less numerous and smaller
than those in the lower valley. Glacial outwash
deposits dominate the landscape near the mouth
of the valley and merge with the fluvial system
below. Today, a few ice patches covered by
debris have been observed in cast shadows of
the Yedigöller Plateau (Klimchouk et al., 2006).
METHODS
We collected and analyzed 22 samples from
seven moraines (A−G in Fig. 1; Tables DR1,
DR2, and DR3); 20 of the samples were boulders and two were glacially scoured bedrock
outcrops. For each analyzed sample we calculated (Appendix DR1 in the Data Repository)
a cosmogenic 36Cl exposure age (Table DR1)
and then averaged the individual ages to obtain
moraine ages (Fig. 2; Table DR1). The averaging of individual cosmogenic ages is justified if
their variance is small, which indicates prede-
Figure 2. A: Cosmogenic 36Cl ages of moraines, terminus altitude, and length of former glacier, measured from headwall. Symbol
size is inversely proportional to uncertainty
of 36Cl age (largest symbol represents 1.6%
error, smallest represents 15% error). B:
Glacier length and equilibrium line altitude
(ELA) from inverse modeling of Hacer Valley
glaciers. Length function (line) matches moraine positions (circles). C: Temperature and
precipitation changes that produced best fit
of ice flowline model to glacier length data.
1
GSA Data Repository item 2011308, methods,
Figure DR1, and Tables DR1−DR4, is available online
at www.geosociety.org/pubs/ft2011.htm, or on request
from [email protected] or Documents Secretary,
GSA, P.O. Box 9140, Boulder, CO 80301, USA.
1052
positional uniformity of clasts and postdepositional stability of the surface (Zreda et al., 1999;
Dzierzek and Zreda, 2007), and thus assures
that all samples come from the same statistical
distribution. We show in Figure 2 and report in
Table DR1 the larger of the two calculated errors
of the mean: the internal error (based on the individual analytical errors; moraines A, D, E, F, and
G) and the external error (based on the boulderto-boulder spread; moraines B and C); this is the
precision of the calculated moraine ages.
The accuracy of 36Cl ages depends on the
accuracy of the cosmogenic production rate
estimates, which has two components: the variability among the samples in the calibration
data set, and the choice of a calibration data set
if more than one exists. The random uncertainty
of the production rates is added to the precision
estimates, using the square rule for variances,
and is reported (in brackets) in Table DR1. This
uncertainty should be used when comparing
36
Cl ages to those obtained using independent
dating methods.
Combinations of temperature and precipitation that could yield the glaciers in the Aladağlar
were calculated with an ice flowline model
(Appendix DR1). This model was driven by
mass balance changes computed from climate
variations using differences from the presentday precipitation and temperature data. The
model simulates the flow of ice at any point of a
topographic flow line of a glacier. The mass balance is calculated by the difference of net accumulation and ablation of snow obtained from
temperature and precipitation reconstructions
(Appendix DR1).
RESULTS AND DISCUSSION
Cosmogenic 36Cl ages of the moraines, calculated with the production rates of Phillips et
al. (1996), range from 10210 ± 160 yr at the
bottom of the valley to 8560 ± 270 yr on the
high plateau (Fig. 2; Table DR1). Their age
trend with altitude and distance from the summit is clear (Fig. 2A). The ages of five moraines
have precisions between 1.6% and 3.5%. The
poorer precision obtained for moraines B (15%)
and C (6.6%) is not critical because they are
between well-dated moraines A (3.2%) and D
(3.5%), and the ages of moraines B and C plot
on the trend line defined by moraines A and D
(Fig. 2A). Possible reasons for the observed
large spread in individual boulder ages, particularly in moraine B, include inheritance of 36Cl
from previous exposure episodes, making sample ages too old (possibly sample AL01–114),
and erosion, boulder rolling, and cover on boulder tops (Ivy-Ochs et al., 2007), making ages
too young (possibly sample AL01–116).
Other available production rates make the
estimates 13% younger (following Swanson and
Caffee, 2001) and 30%−40% older (following
Stone et al., 1996). Using the younger production rate estimates, cosmogenic 36Cl ages of the
moraines range from 8850 ± 140 yr at the bottom of the valley to 7420 ± 240 yr on the plateau. Using the older estimates they range from
13260 ± 210 yr and 12130 ± 390 yr (Table DR4).
The older ages correlate with the Younger Dryas
stadial elsewhere (Kerschner et al., 2000). Here,
for the computations of rates of deglaciation and
of climate change, we use the ages based on the
production rates of Phillips et al. (1996) and the
scaling factors of Desilets et al. (2006).
Deglaciation occurred in two phases
(Fig. 2A). During the first phase, the glacier was
retreating at the average rate of 0.56 m/yr vertically and 4.25 m/yr horizontally. In the second
phase, the deglaciation rates increased fourfold,
to 2.65 m/yr and 17.1 m/yr, respectively. These
rates resemble modern short-term horizontal retreat rates of glaciers (Oerlemans, 2001;
Fig. 3A), but are much higher than the modern
rates when lengths of records are considered.
Longer records have lower average deglaciation
rates because periods of glacier readvances and/
or inactivity are more likely included in longer
records. The longest historical record (450 yr,
Unterer Grindelwaldgletscher, Bernese Alps,
Switzerland; Oerlemans, 2001) has an average
horizontal retreat rate of only 2 m/yr (Fig. 3A),
<1⁄8% of that from Hacer Valley calculated for
Figure 3. A: Average rate of change of glacier length decreases with length of record.
Open triangles are observed rates (Oerlemans, 2001); filled triangles and fitted line
define maximum historical average retreat
rates (from Oerlemans, 2001). Circles represent rates of shrinking with one standard
deviation. Letters indicate moraines as in
Figure 1C. B: Rate of change of temperature from modern measurements (Folland
et al., 2001) and from ice-flow modeling of
paleoglaciers in Aladağlar. Open triangles
are modern temperature data; filled triangles
and fitted line define upper limit. Filled circles are rates of paleotemperature changes
with one standard deviation.
GEOLOGY, November 2011
the time span of 500 yr in the second phase.
The average deglaciation rate for the entire time
interval is 8.4 m/yr, ~25 times higher than the
rate inferred by extrapolating historical records
(Fig. 3A).
Based on the observed retreat pattern, we calculated rates of change of the equilibrium line
altitude (ELA; Fig. 2B) and of climate (Fig. 2C)
that would result in the observed deglaciation
rates. The ELA trend mimics that for the altitude of the terminal moraines, and changes from
~2080 m to 3510 m. The change of the ELA of
~1430 m is typical of the difference between
the Last Glacial Maximum (LGM) and today
(Mark et al., 2005), but is surprisingly high for
the Younger Dryas or the early Holocene.
The large changes of the ELA (Fig. 2B)
imply correspondingly large changes of temperature and/or precipitation (Fig. 2C). Based on a
climate reconstruction parameter space search
(Appendix DR1), modeled glacier retreat from
full extent (moraine G) to smallest size (A) best
matched the moraine locations for a temperature
increase of 9 °C combined with a precipitation
decrease of 960 mm/yr. Because the reconstruction of climatically forced glacier retreat is more
sensitive to temperature changes than to precipitation changes (1 °C is equivalent to 600 mm/
yr water equivalent [w.e.]; Appendix DR1),
the temperature result is robust. Whereas such
a large cooling was common during the LGM,
a cooling of <3.5 °C can be inferred for the
Younger Dryas (Kerschner et al., 2000) and the
early Holocene (Kerschner et al., 2003; Kelly et
al., 2004; Hughes, 2007).
The average rate of change of temperature
is 0.55 °C/100 yr for the entire duration of
1650 yr, and 1.44 °C/100 yr for the last 500 yr of
the record. The first value is similar to the global
average rate observed in the past century (Folland et al., 2001), but represents an average over
1650 yr. The second value is much higher than
the rate of temperature change observed today,
and it is integrated over five times longer.
Two factors must be considered to compare
the long-term warming rates in the Aladağlar to
the shorter-term global warming rate observed
today. First, a part of the calculated long-term
value may be due to amplification of the global
climate signal in high mountains that are in the
zone of influence of the North Atlantic Oscillation (NAO; Beniston, 2005). To account for
this, the calculated value should be divided by
a factor greater than one. In the European Alps
today, this factor is three (Beniston, 2005), and
it may be applicable to the Aladağlar because the
two areas have similar responses to NAO forcing (Hurrell, 1995). Second, long-term rates are
always lower than shorter-term rates (Fig. 3B)
because long-term rates include possible cooling
episodes. Thus, the rates calculated here should
be multiplied by a factor greater than one. We
GEOLOGY, November 2011
calculated a factor of 2−3 by extrapolating the
149-yr-long global temperature trend to 500 yr
(Fig. 3B). Because these two corrections cancel
each other, the high rate of temperature increase
calculated for the Hacer Valley deglaciation,
1.44 °C/100 yr, is probably correct, and can be
used to compare with modern global warming
trends. This rate exceeds the global warming
trend of the past century (0.6 ± 0.2 °C), showing that natural causes can lead to fast and large
climate changes, and that the magnitude and the
rate of climate change observed in the past century are not unprecedented within the past 13 k.y.
What environmental conditions caused the
glaciation of the Aladağlar in the late Pleistocene or early Holocene? Changes in paleoclimate were probably due to shifts in the position
of westerly storm tracks, the extension of the
tropical low-pressure system and of the Siberian high (Wick et al., 2003), possibly linked
to the patterns of the Arctic Oscillation (Arz et
al., 2003) (essentially the same as the NAO).
Regional paleoclimatic reconstructions converge on a climate that was wetter (Arz et al.,
2003; Bar-Matthews et al., 1997; Collins et al.,
2005; Jones et al., 2007; Wick et al., 2003) and
colder (Aksu et al., 2002; Bar-Matthews et al.,
1997; Sarıkaya et al., 2009) than today.
We hypothesize that enhanced moisture delivery and reduced temperature were due to variations in the dominant westerly wind flow, driven
by the pressure difference between the Icelandic Low and the Azores High, the measure of
which is the NAO index (Hurrell, 1995). A positive NAO index represents pressure higher than
normal over the central North Atlantic, and low
pressure across the high latitudes of the North
Atlantic. A high NAO index for winter is associated with storm tracks in the North Atlantic,
leading to strong westerly flow and increased
precipitation over Scandinavia. In contrast, a
low NAO index causes a more southerly storm
track, increasing winter precipitation and lowering temperature in the Eastern Mediterranean
(Beniston, 2005; Cullen et al., 2002; Hurrell,
1995). In addition to short-term variations, the
NAO index displays variations on a century
scale, showing that the NAO can operate on geological time scales (Beniston, 2005). Approximately doubled precipitation (Arz et al., 2003),
increased surface runoff (Collins et al., 2005),
high lake levels (Wick et al., 2003), decreased
sea salinity (Rossignol-Strick, 1999), and sealevel temperature lower than today (Aksu et al.,
2002) suggest a possibility of prolonged negative NAO conditions (Cullen et al., 2002) that
might have led to glaciation of the Aladağlar.
While a negative NAO phase makes the
eastern Mediterranean climate cooler and wetter today, it is unclear whether NAO-induced
changes were big enough to generate glaciers
consistent with the glacial record from the
Aladağlar. The generally cold climate at the
time is supported by geological evidence elsewhere in Turkey. Moraines on Mount Erciyes
volcano, ~80 km north of Aladağlar, were dated
with 36Cl to 9.3 ± 0.5 ka (Sarıkaya et al., 2009),
and those on Mount Uludağ, ~600 km northwest of Aladağlar, were dated with 10Be to 11.5
± 1.0 ka (Zahno et al., 2010).
Other glacial records show much smaller
glaciers, and less cooling, during the Younger
Dryas or early Holocene. The Younger Dryas
ELAs in the Alps were as much as 500 m lower
than today, indicating a cooling of <3.5 °C (Kerschner et al., 2000). In the early Holocene, the
ELAs were only 100 m lower than today in the
Alps (Kerschner et al., 2003; Kelly et al., 2004)
and in mountains of Montenegro (Hughes,
2007), suggesting a 1 °C cooling. Records farther away show ~500 m and up to 300 m ELA
decreases during Younger Dryas and early Holocene, respectively, in Iceland (Wastl et al., 2001)
and Norway (Nesje, 1992). However, ELA
decrease during the LGM was ~900 m (Broecker
and Denton, 1989), and as much as 1500 m in
the Americas (Stansell et al., 2007; Munroe et
al., 2006) and the tropics (Porter, 2001). Our
results, combined with other glacial and climate
records, lead to the conclusion that the glaciation in the Hacer Valley was remarkable in two
ways: it was unusually extensive for its age, and
the deglaciation rates and the inferred climate
changes were faster than modern rates.
ACKNOWLEDGMENTS
This work was supported by the U.S. National Science Foundation and TÜBİTAK (The Scientific and
Technological Research Council of Turkey).
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530, doi:10.1016/S0277-3791(98)00093-6.
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Erciyes, central Turkey, since Last Glacial
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.quascirev.2009.04.015.
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.2001.2278.
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.2010.01.012.
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Strait by Greenland and Ellesmere ice-sheet retreat 10,000 years ago: Nature, v. 398, p. 139–
142, doi:10.1038/18197.
Manuscript received 21 January 2011
Revised manuscript received 14 June 2011
Manuscript accepted 21 June 2011
Printed in USA
GEOLOGY, November 2011
Sarikaya et al.
GSA DATA REPOSITORY 2011308
DATA REPOSITORY APPENDIX DR1: METHODS
Cosmogenic dating
Sample collection, preparation and analysis
Rock samples were collected from top surfaces using hammer and chisel. They were
cleaned of carbonate crusts, ground to size fraction 0.25-1.00 mm, leached overnight in
deionized water, and dried. Samples were mixed with a 35Cl-enriched carrier, dissolved in nitric
acid in a high-pressure reaction vessel at 25°C, and AgCl containing Cl from the sample and
from the carrier was precipitated (Desilets et al., 2006a) and then purified of sulfur using Ba
precipitation (Zreda et al., 1991). 36Cl/Cl was measured using accelerator mass spectrometry and
35
Cl/37Cl immediately following accelerator, both on the same AgCl target, at PRIME Lab, Purdue
University. Powdered aliquots of rocks were analyzed for major elements using X-ray fluorescence spectrometry, for U and Th using neutron activation analysis, and for B and Gd using
neutron activation prompt gamma analysis, all at Activation Laboratories, Ontario, Canada. Total
Cl was calculated from the 35Cl/37Cl values.
Calculation of surface exposure ages
Cosmogenic 36Cl surface exposure ages were calculated using the accumulation equation
(Phillips et al., 1986) dN36/dt=P36-l36N36, as implemented in the ACE cosmogenic dating
software (Anderson et al., 2007), where N36 is the number of atoms of 36Cl, t is the time, P36 is
the production rate (atoms of 36Cl (g of rock)-1 y-1; varies with sample and location), and l36 is the
36
Cl decay constant (2.303x10-6 y-1). The following production rates were used: 71.6±3.7 atoms
36
Cl (g Ca)-1 yr-1, 155.1±9.6 atoms 36Cl (g K)-1 yr-1, and 676±40 fast neutrons (g air)-1 yr-1. These
rates, called reference production rates, are based on the calibration data set of Phillips et al.
(1996), augmented by high-potassium samples from three additional sources: Ivy-Ochs et al.
(1996), Zreda et al. (1999) and Phillips et al. (2009). They have been scaled to sea level and high
geomagnetic latitude following Desilets and Zreda (2003), Desilets et al., (2006b), and to
modern geomagnetic field conditions (referenced to the 1945.0 Definitive Geomagnetic
Reference Field) following Pigati and Lifton (2004). The main target element for 36Cl production
in the Aladağlar limestones is Ca, accounting for 95%-99% of the total production.
Other available production rates from Ca, recomputed using the ACE software, are 13%
higher Swanson and Caffee, (2001) and 30-40% lower Stone et al. (1996) than our production
rates. Application of these rates gives ages that are 13% younger following Swanson and Caffee
1
(2001) and 30-40% older following Stone et al. (1996).
The reference production rates are valid for sea level (atmospheric depth 1033 g cm-2)
and high geomagnetic latitudes (geomagnetic cutoff rigidity <2 GV), and include the necessary
(universal) corrections for secular changes in paleomagnetic intensity, changes in the position of
the geomagnetic pole, and eustatic changes in sea level. Temporal variations in the Earth’s
geomagnetic field intensity were reconstructed using archeomagnetic data (Yang et al., 2000)
and stacked marine cores (Guyodo and Valet, 1999), and the position of the geomagnetic dipole
axis using terrestrial sediments (Ohno and Hamano, 1992; Ohno and Hamano, 1993).
The impact of sea-level changes on cosmogenic production was calculated using global
sea level data (Fairbanks, 1989; Shackleton, 2000). However, following the recent suggestion
Osmaston (2006) that Pleistocene sea-level changes should not be used to correct atmospheric
pressure, we also report ages without the eustatic correction (Table DR4). Uncorrected ages are
150 years to 200 years younger than corrected ages.
The reference production rates were scaled to the sample sites using refs. (Lal, 1991;
Desilets and Zreda, 2003; Desilets et al., 2006b) and include additional corrections for
environmental factors: temperature, pressure, and lapse rate (Zreda et al., 2005). Corrections
were also made for topographic shielding, which we determined by measuring the inclination to
the horizon at 30° azimuthal increments using a hand-held clinometer; the lowering of
production rates due to topographic shielding was between 0.5% and 14.1%. Snow cover, which
is progressively thicker with increasing elevation, was found to reduce cosmogenic production
rates by up to 9%. Corrections for snow cover were calculated by estimating the average annual
snow thickness on boulder tops using the long-term precipitation and temperature data from
nearby six weather stations (Global Historical Climatology Network, version 2,
http://www.ncdc.noaa.gov/oa/climate/ghcn-monthly/index.php, accessed in May 2007) and
interpolating them through Aladağlar by the method described by Özyurt (2005).
Calculation of equilibrium line altitudes, temperature and precipitation
Extents of former glaciers were determined from the positions of moraines A to G.
Former equilibrium line altitudes (ELAs, an elevation separating the accumulation zone above
from the ablation zone below), and temperature and precipitation changes were calculated using
an ice flowline model.
2
Ice flowline model
The ice flowline model was driven by mass balance changes computed from climate
variations using differences from present day precipitation and temperature while assuming
present day lapse rates. In forward mode, we input climatic (monthly temperature, precipitation,
lapse rates), topographic (valley elevation), and model (positive degree day coefficients for ice
and snow, standard deviation of monthly temperatures, deformational ice flow coefficient)
information, and the model calculates climatic and glaciological states (altitude of ice margin,
ELA, mass balance, and ice thickness). In inverse mode, we calculate all possible combinations of
temperature and precipitation that would yield the position of the ice margin at a given time, and
then determine the most likely (optimum) combination.
For each month snowfall and snowmelt as a function of elevation were computed and
water equivalent used as mass balance. Snow and ice melt rates were computed using a positive
degree day model (degree days for snow and ice 4 and 8 mm day-1 C-1 water equivalent,
respectively, standard deviation of monthly temperatures 3C). The mass balance along an
assumed central glacier flowline (Fig. 1 in main text) was then used in the continuity equation
for ice dynamics assuming that glacier velocities are proportional to local shear stresses. Time
integrated mass flux changes then determined ice thickness. Sliding was not implemented into
the ice flowline model as it was considered to be of secondary importance during the significant
retreat simulated by the model. This model has been applied recently in Turkey (Sarıkaya et al.,
2008; 2009) and in Hawaii (Pigati et al., 2008).
Sensitivity of the Hacer glacier to temperature and precipitation
Precipitation increase and temperature decrease result in expansion of glaciers. To
determine the change in climate capable of producing the glacier retreat indicated by the
moraines and their ages, we assumed a range of climate scenarios with temperature and
precipitation changing independently. We then used these as forcing in the ice flowline model,
and compared moraine location with modeled glacier length to determine the most realistic
climate scenarios. Figure DR1 shows the root mean square error (RMSE) between modeled
glacier length and observed moraine location as a function of the change in July temperature and
monthly precipitation between 10.2 and 8.6 ka. As we assumed no change in seasonality over
this period, the change in July temperature is equal to the change in summer and annual
temperature, and the change in monthly precipitation equals the change in yearly precipitation
divided by 12.
The climate reconstruction that generates a glacier retreat most closely in accord with the
moraine locations has an increase in July temperature of 9°C combined with a precipitation
3
decrease of 960 mm/y. However, the near vertical contours of Fig. DR1 suggest that near the
best fit reconstruction the precipitation decrease could be changed by a factor of two with little
change in the results (RMSE < 1 km). However, small changes in the temperature increase (1-2
°C) are sufficient to generate a reconstructed glacial retreat which is not in accord with
observations. This suggests that when modelling glacier retreat in the Hacer Valley, changes in
temperature play a much stronger role in the rate of retreat than changes in precipitation. This
would indicate that despite the uncertainty regarding past climates in the Hacer Valley, the
temperature reconstruction is well determined by the available observational data.
Data Repository References:
Anderson, K.M., Bradley, E., Zreda, M., Rassbach, L., Zweck, C., Sheehan, E., 2007. ACE: Age
Calculation Engine – a design environment for cosmogenic dating techniques. In: Proceedings of
the International Conference on Advanced Engineering Computing and Applications in Sciences,
Papeete, Tahiti, pp. 39–48.
Desilets, D., and Zreda, M., 2003, Spatial and temporal distribution of secondary cosmic-ray
nucleon intensities and applications to in-situ cosmogenic dating: Earth and Planetary Science
Letters, v. 206, p. 21-42.
Desilets, D., Zreda, M., Almasi, P.F., and Elmore, D., 2006a, Determination of cosmogenic 36Cl
in rocks by isotope dilution: innovations, validation and error propagation: Chemical Geology, v.
233, p. 185-195.
Desilets, D., Zreda, M., and Prabu, T., 2006b, Extended scaling factors for in situ cosmogenic
nuclides: New measurements at low latitude: Earth and Planetary Science Letters, v. 246, p. 265276.
Fairbanks, R., 1989, A 17,000-year glacio-eustatic sea level record: influence of glacial melting
rates on the Younger Dryas event and deep-ocean circulation: Nature, v. 342, p. 637-642.
Guyodo, Y., and Valet, J.-P., 1999, Global changes in intensity of the Earth's magnetic field
during the past 800 kyr: Nature, v. 399, p. 249-252.
Ivy-Ochs, S., Schlüchter, C., Kubik, P.W., Synal, H.A., Beer, J., and Kerschner, H., 1996, The
exposure age of an Egesen moraine at Julier Pass, Switzerland measured with the cosmogenic
radionuclides: Eclogae Geologicae Helvetiae, v. 89, p. 1049-1063.
Lal, D., 1991, Cosmic ray labeling of erosion surfaces: in situ nuclide production rates and
erosion models: Earth and Planetary Science Letters, v. 104, 424-439.
4
Ohno, M., and Hamano, Y., 1992, Geomagnetic poles over the past 10,000 years: Geophysical
Research Letters, v. 19, p. 1715-1718.
Ohno, M., and Hamano, Y., 1993, Global analysis of the geomagnetic-field: time variation of the
dipole moment and the geomagnetic pole in the Holocene: Journal of Geomagnetism and
Geoelectricity, v. 45, p. 1455-1466.
Osmaston, H.A., 2006, Should Quaternary sea-level changes be used to correct glacier ELAs,
vegetation belt altitudes and sea level temperatures for inferring climate changes?: Quaternary
Research, v. 65, p. 244-251.
Özyurt, N.N., 2005, Investigation of the groundwater residence time distribution in the Aladağ
(Kayseri-Adana, Turkey) karstic aquifer [PhD thesis]: Ankara (Turkey), Hacettepe University.
Phillips, F.M., Leavy, B.D., Jannik, N.O., Elmore, D., and Kubik, P.W., 1986, The accumulation
of cosmogenic chlorine-36 in rocks: a method for surface exposure dating: Science, v. 231, p.
41-43.
Phillips, F.M., Zreda, M.G., Flinsch, M.R., Elmore, D., and Sharma, P., 1996, A reevaluation of
cosmogenic 36Cl production rates in terrestrial rocks: Geophysical Research Letters, v. 23, p.
949-952.
Phillips, F.M., Zreda, M.G., Plummer, M.A., Elmore, D., and Clark, D.H., 2009, Glacial geology
and chronology of Bishop Creek and vicinity, eastern Sierra Nevada, California: Geological
Society of America Bulletin, v. 121, no. 7-8, p. 1013-1033.
Pigati, J.S., and Lifton, N.A., 2004, Geomagnetic effects on time-integrated cosmogenic nuclide
production rates with emphasis on 14C and 10Be: Earth and Planetary Science Letters, v. 226, p.
193-205.
Pigati, J.S., M. Zreda, C. Zweck, P.F. Almasi, D. Elmore and W. Sharp, 2008, Ages and inferred
causes of Late Pleistocene glaciations on Mauna Kea, Hawaii: Journal of Quaternary Science, v.
23, p. 683-702
Sarıkaya, M. A., Zreda, M., Çiner, A. and Zweck, C., 2008. Cold and wet Last Glacial Maximum
on Mount Sandıras, SW Turkey, inferred from cosmogenic dating and glacier modeling:
Quaternary Science Reviews, 27 (7-8), p. 769-780.
Sarıkaya, M.A., Zreda, M., Çiner, A., 2009. Glaciations and paleoclimatic variations on Mount
Erciyes, central Turkey, since Last Glacial Maximum, inferred from 36Cl cosmogenic dating and
glacier modeling: Quaternary Science Reviews, 28 (23-24), p. 2326-2341.
5
Shackleton, N.J., 2000, The 100,000-year ice-age cycle identified and found to lag temperature,
carbon dioxide, and orbital eccentricity: Science, v. 289, p. 1897-1902.
Stone, J.O., Allan, G.L., Fifield, L.K., and Cresswell, R.G., 1996, Cosmogenic chlorine-36 from
calcium spallation: Geochimica et Cosmochimica Acta, v. 60, p. 679-692.
Swanson, T.W., and Caffee, M.L., 2001, Determination of 36Cl production rates derived from
the well-dated deglaciation surfaces of Whidbey and Fidalgo Islands, Washington: Quaternary
Research, v. 56, p. 366-382.
Yang, S., Odah, H., and Shaw, J., 2000, Variations in the geomagnetic dipole moment over the
last 12 000 years: Geophysical Journal International, v. 140, p. 158-162.
Zreda, M., Desilets, D., Li, Y., Bradley, E., and Anderson, K.M., 2005, iCRONUS meets
CRONUS-Earth: Improved calculations for cosmogenic dating methods-from neutron intensity
to previously ignored correction factors, Geochimica et Cosmochimica Acta, Volume 69: 15th
Goldschmidt Conference, p. A168-A168 (Suppl. S).
Zreda, M., England, J., Phillips, F., Elmore, D., and Sharma, P., 1999, Unblocking of the Nares
Strait by Greenland and Ellesmere ice-sheet retreat 10,000 years ago: Nature, v. 398, p. 139-142.
Zreda, M.G., Phillips, F.M., Elmore, D., Kubik, P.W., Sharma, P., and Dorn, R.I., 1991,
Cosmogenic chlorine-36 production rates in terrestrial rocks: Earth and Planetary Science
Letters, v. 105, p. 94-109.
6
Figure and Table Captions for the Data Repository
Figure DR1. RMS error between moraine location and ice flowline modeled glacier length for
the Hacer Valley as a function of increase in July temperature combined with decrease in
monthly precipitation between 10.2 and 8.6 ka. The red circle (9 C increase in July
temperature, 80 mm/month decrease in precipitation) shows the climate reconstruction where the
modeled glacier retreat best matched the observed retreat.
Table DR1. Cosmogenic 36Cl ages (rounded to the nearest 10 years) of boulders from moraines
A through G in the Yedigöller Plateau and in the Hacer Valley, and length and terminus
elevation for the former glaciers. Moraines are sorted by elevation (age), from highest (youngest)
to lowest (oldest). Uncertainties of moraine ages are based on analytical uncertainties and on
boulder-to-boulder variability, and total uncertainties (in brackets) also include uncertainties on
production rates of 36Cl. Glacier length is measured along the flow line from the western wall in
the Yedigöller Plateau (Fig. 1.c in the main text).
Table DR2. Sample attributes and local corrections to production rates.
Table DR3. Geochemical and isotopic analytical data.
Table DR4. Cosmogenic 36Cl ages of boulders and mean ages of moraines. Also shown are
alternative chronologies calculated with the use of other published production rates, and an
alternative chronology calculated without sea-level corrections.
7
Figure DR1
Zreda et al.
Data Repository Table DR1: Cosmogenic 36Cl ages (rounded to the nearest 10 years) of
boulders from moraines A through G in the Yedigöller Plateau and in the Hacer Valley, and
length and terminus elevation for the former glaciers. Moraines are sorted by elevation
(age), from highest (youngest) to lowest (oldest). Uncertainties of moraine ages are based
on analytical uncertainties and on boulder-to-boulder variability, and total uncertainties (in
brackets) also include uncertainties on production rates of 36Cl. Glacier length is measured
along the flow line from the western wall in the Yedigöller Plateau (Fig. 1 in main text).
Boulder ID
Boulder age(1) Moraine
(y)
Moraine elevation
(m)
Moraine age(2)
(y)
Length
(km)
ELA
(m)
∆T
(°C)
AL01-101
AL01-102
AL01-103
8650 ± 420
8250 ± 510
8740 ± 490
A
3080 ± 17
8560 ± 270
(± 520)
3.3
3510
0
AL01-113
AL01-114
AL01-116
8190 ± 420
11340 ± 580
6930 ± 320
B
2578 ± 6
8750 ± 1.310
(± 1,390)
6.0
3030
-2.7
AL01-118
AL01-119
AL01-120
8290 ± 410
8070 ± 410
9880 ± 460
C
2345 ± 77
8770 ± 580
(± 730)
6.6
3000
-3.0
AL01-127
AL01-128
9270 ± 520
8900 ± 360
D
1745 ± 0
9060 ± 320
(± 560)
11.8
2240
-7.2
AL01-107
AL01-121
AL01-122
AL01-124
AL01-125
9240
9330
9610
9280
8600
460
560
360
540
510
E
1643 ± 9
9250 ± 220
(± 520)
14.1
2210
-7.5
AL01-108
AL01-110
AL01-111
9320 ± 340
10130 ± 540
9270 ± 530
F
1501 ± 18
9540 ± 280
(± 560)
15.4
2170
-8.0
AL05-172
AL05-173
AL05-174
10010 ± 320
10220 ± 240
10360 ± 250
G
1097 ± 10
10210 ± 160
(± 550)
17.2
2080
-9.0
±
±
±
±
±
Notes:
(1) Boulder age ± 1 standard deviation based on uncertainties in chemical and isotopic analyses.
(2) Weighted mean of boulder ages ± 1 standard error of the mean, calculated as the larger of the internal
error based on analytical uncertainties (moraines A, D, E, F and G) and the external error based on boulder-toboulder variability (moraines B and C), excluding the uncertainties on the production rates; the figures in
brackets are standard error of the mean calculated including the uncertainty on the production rates of 36Cl.
Zreda et al.
Data Repository Table DR2. Sample attributes and local corrections to production rates.
(a, b)
Sample ID
Thickness
(c)
Latitude
(d)
Longitude
(d)
Elevation
(e)
Sea-level
pressure
-2
Sea-level
temperature
Lapse
rate
Boulder
(f)
height
Topography
correction
(g)
factor
(-)
Snow
correction
(h)
factor
(-)
(m)
(g cm )
(°C)
35.187
35.189
35.195
3075
3099
3065
1031.72
1031.72
1031.72
21.25
21.25
21.25
6.38
6.38
6.38
0.4
0.5
bedrock
0.995
0.995
0.972
0.9306
0.9431
0.9106
37.812
37.812
37.811
35.219
35.219
35.219
2585
2580
2575
1031.72
1031.72
1031.72
21.25
21.25
21.25
6.38
6.38
6.38
1.2
bedrock
0.6
0.92
0.859
0.92
0.9816
0.9233
0.9568
3
2
3
37.811
37.812
37.813
35.226
35.227
35.227
2340
2291
2293
1031.72
1031.72
1031.72
21.25
21.25
21.25
6.38
6.38
6.38
3
4
5
0.859
0.912
0.938
1
1
0.96
AL01-127
AL01-128
2.5
3
37.807
37.807
35.282
35.282
1745
1745
1031.72
1031.72
21.25
21.25
6.38
6.38
2
3
0.984
0.93
1
1
AL01-107
AL01-121
AL01-122
AL01-124
AL01-125
3
3
2
2.75
3
37.800
37.811
37.811
37.812
37.811
35.304
35.293
35.292
35.287
35.286
1636
1941
1938
1904
1905
1031.72
1031.72
1031.72
1031.72
1031.72
21.25
21.25
21.25
21.25
21.25
6.38
6.38
6.38
6.38
6.38
1.5
2.5
7
6
8
0.967
0.93
0.919
0.956
0.937
0.9983
1
1
1
1
AL01-108
AL01-110
AL01-111
2.5
3
1
37.802
37.804
37.806
35.317
35.318
35.318
1520
1520
1485
1031.72
1031.72
1031.72
21.25
21.25
21.25
6.38
6.38
6.38
2
2
2
0.974
0.982
0.982
1
1
1
AL05-172
AL05-173
AL05-174
5
4
3
37.806
37.802
37.802
35.339
35.341
35.341
1109
1091
1092
1031.72
1031.72
1031.72
21.25
21.25
21.25
6.38
6.38
6.38
1
8
15
0.985
0.983
0.984
1
1
1
(cm)
(°N)
AL01-101
AL01-102
AL01-103
1
1
2
37.806
37.807
37.804
AL01-113
AL01-114
AL01-116
3
2
2
AL01-118
AL01-119
AL01-120
(°E)
Notes:
(a) Water content of 0.5% was assumed.
-3
(b) Density of 2.6 g cm was assumed.
(c) Average sampled depth; measured.
(d) From handheld GPS, nominal accuracy ±5 m.
(e) From handheld GPS, nominal accuracy ±15 m.
(f) Measured; when boulder irregular - averaged or estimated.
(g) Calculated from measurements of angle to topographic features and of surface slope (dip).
(h) Calculated using positive-degree day factors, and with climate data averaged over the past 30 years.
(-°C/km)
(m)
Zreda et al.
Data Repository Table DR3. Geochemical and isotopic analytical data.
Sample ID(a, b)
CO2(c)
Na2O
MgO
Al2O3
SiO2
P2O5
K2O
CaO
TiO
MnO
Fe2O3
(wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %) (wt. %)
Cl(d)
B(e)
(ppm)
Sm
Gd
U
Th
(ppm) (ppm) (ppm) (ppm) (ppm)
36
Cl/Cl(f)
(10-15)
AL01-101
AL01-102
AL01-103
44.65
43.89
44
0
0
0.02
0.4
0.36
0.33
0.06
0.06
0.02
0.26
0.15
0.09
0.01
0.02
0
0.02
0
0.04
54.58
55.51
55.03
0
0
0
0.002
0.002
0.002
0.03
0.09
0.04
36.4 ± 0.3
23.1 ± 0.5
30.0 ± 0.1
0.8
0
0
0
0
0.01
0
0
0.01
0.3
0.6
0.41
0.1
1.2
0
3336 ± 159
5109 ± 295
3868 ± 215
AL01-113
AL01-114
AL01-116
44
43.66
43.69
0.03
0
0
0.47
0.93
0.42
0.05
0.02
0.04
0.25
0.09
0.11
0.01
0
0
0.07
0
0
54.84
55.24
55.76
0
0
0
0
0
0.002
0
0.1
0.02
26.5 ± 0.8
21.0 ± 0.6
25.8 ± 0.4
1.4
0
0
0.02
0
0.02
0.01
0
0.01
0.59
2.1
0.4
0
0.3
0.2
2996 ± 129
4657 ± 203
2536 ± 109
AL01-118
AL01-119
AL01-120
43.44
43.74
43.65
0
0
0
0.34
1.51
0.32
0.02
0.06
0.03
0.08
0.19
0.08
0
0.01
0
0.03
0.01
0
56.08
54.39
55.9
0
0
0
0
0.002
0
0.02
0.04
0.01
12.4 ± 0.2
20.3 ± 0.1
11.2 ± 0.1
0
0
0
0
0
0
0
0
0
0.2
0.8
0.5
0.1
0.2
0
5209 ± 246
3127 ± 159
6948 ± 323
AL01-127
AL01-128
43.32
43.37
0
0
0.34
0.38
0.03
0.04
0.09
0.1
0
0
0
0
55.76
56.1
0
0
0
0
0.03
0.04
17.1 ± 0.4
20.6 ± 0.2
0
0
0
0
0
0
1
0.4
0
0
3195 ± 158
2425 ± 97
AL01-107
AL01-121
AL01-122
AL01-124
AL01-125
43.54
43.61
43.57
43.32
43.36
0
0
0
0
0
0.32
0.35
0.35
0.34
0.38
0.02
0.03
0.03
0.04
0.02
0.06
0.13
0.09
0.07
0.07
0
0
0
0
0.03
0
0
0
0.01
0.01
56.09
55.9
55.87
56.12
55.83
0
0
0
0
0
0.003
0.002
0
0.002
0
0.02
0.04
0.16
0.16
0.15
6.6
11.6
13.4
25.2
14.5
0.1
0.4
0.1
0.5
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0.1
0.6
0.3
0.7
0.7
0.5
0
0
0
0
7349
5108
4522
2410
3718
AL01-108
AL01-110
AL01-111
44
43.52
44
0.04
0
0
0.32
0.3
0.29
0.02
0.05
0.02
0.04
0.12
0.05
0
0
0
0.04
0
0.03
56.36
55.76
55.29
0
0
0
0.002
0.002
0.002
0
0.08
0
22.8 ± 0.1
19.6 ± 0.4
9.8 ± 0.2
0
0
2.2
0.02
0
0
0.02
0
0
1.24
2.1
5.52
0
0.3
0
2061 ± 74
2583 ± 127
4550 ± 241
AL05-172
AL05-173
AL05-174
43.56
43.51
43.65
0.04
0.04
0.06
0.33
0.34
0.31
0.01
0.01
0.02
0.08
0.07
0.05
0
0
0
0.07
0
0.03
55.91
56.04
55.81
0
0
0
0
0.002
0.002
0.01
0.02
0.02
25.3 ± 0.1
22.8 ± 0.2
26.3 ± 0.1
0
0
0
0
0
0
0
0
0
4.4
4.2
3.7
0
0
0
±
±
±
±
±
Notes:
(a) Water content of 0.5% was assumed.
(b) Density of 2.6 g cm-3 was assumed.
(c) Major element concentrations are reported as oxides in weight percent (wt. %). The detection limits are 0.01%.
(d) Total Cl calculated from measurement of 35Cl/37Cl on spiked samples, de-spiked (i.e., converted to value in the rock).
(e) Trace element concentrations are in parts per million (ppm). The detection limits are 0.1 ppm.
(f) The ratio 36Cl/Cl measured with accelerator mass spectrometry on spiked samples, de-spiked (i.e., converted to value in the original rock sample).
±
±
±
±
±
357
247
162
131
218
1469 ± 45
1636 ± 36
1451 ± 34
Zreda et al.
Data Repository Table DR4. Cosmogenic 36Cl ages of boulders and mean ages of moraines. Also shown are alternative chronologies
calculated with the use of other published production rates, and an alternative chronology calculated without sea-level corrections.
36
Cl age, y(a,b)
Surface
Sample
A
AL01-101
AL01-102
AL01-103
Average
8.650
8.250
8.740
8.560
±
±
±
±
420
510
490
270
520
AL01-113
AL01-114
AL01-116
Average
8.190
11.340
6.930
8.750
±
±
±
±
420
580
320
1.310
1.390
AL01-118
AL01-119
AL01-120
Average
8.290
8.070
9.880
8.770
±
±
±
±
406
412
463
580
730
AL01-127
AL01-128
Average
9.270
8.900
9.060
± 520
± 370
± 320
560
AL01-107
AL01-121
AL01-122
AL01-124
AL01-125
Average
9.240
9.330
9.610
9.280
8.600
9.250
±
±
±
±
±
±
450
550
360
540
510
220
520
AL01-108
AL01-110
AL01-111
Average
9.320
10.130
9.270
9.550
±
±
±
±
340
540
530
270
560
AL05-172
AL05-173
AL05-174
Average
10.010
10.220
10.360
10.210
±
±
±
±
320
240
250
160
550
B
C
D
E
F
G
Uncertainty
(c)
36
36
Cl age, y(d)
Cl age, y(e)
no sea-level correction Stone + D&Z scaling
internal
8.460
8.080
8.540
8.370
±
±
±
±
410
500
480
270
510
external
8.030
11.160
6.760
8.580
±
±
±
±
external
8.120
7.910
9.680
8.600
±
±
±
±
36
Cl age, y(f)
Stone + Lal scaling
36
Cl age, y(g)
Swanson + D&Z scaling
9.680
9.230
9.780
9.580
±
±
±
±
470
570
550
310
580
12.230
11.720
12.390
12.130
±
±
±
±
600
740
700
390
620
7.500
7.150
7.580
7.420
±
±
±
±
362
450
420
240
450
420
570
310
1.310
1.380
9.160
12.690
7.750
9.780
±
±
±
±
480
650
350
1.470
1.550
11.240
15.950
9.420
12.090
±
±
±
±
590
830
430
1.950
2.010
7.100
9.830
6.010
7.580
±
±
±
±
370
500
270
1.140
1.200
400
400
450
560
720
9.270
9.030
11.050
9.810
±
±
±
±
450
460
520
640
820
11.470
10.970
13.640
12.060
±
±
±
±
570
570
650
830
960
7.190
7.000
8.560
7.600
±
±
±
±
350
360
400
500
630
internal
9.070
8.700
8.860
± 500
± 360
± 310
550
10.370
9.960
10.130
± 580
± 410
± 350
630
12.430
11.930
12.150
± 700
± 500
± 430
650
8.040
7.710
7.850
± 450
± 320
± 270
490
internal
9.070
9.120
9.400
9.080
8.420
9.060
±
±
±
±
±
±
440
540
350
520
500
210
510
10.340
10.440
10.750
10.380
9.620
10.350
±
±
±
±
±
±
510
620
400
600
570
240
590
12.480
12.700
13.050
12.450
11.670
12.530
±
±
±
±
±
±
620
770
490
730
700
300
580
8.010
8.090
8.330
8.040
7.450
8.020
±
±
±
±
±
±
390
480
310
460
440
190
460
internal
9.110
9.950
9.040
9.340
±
±
±
±
330
530
520
280
560
10.430
11.330
10.370
10.680
±
±
±
±
380
600
590
310
630
12.300
13.440
12.370
12.650
±
±
±
±
450
720
720
370
630
8.080
8.780
8.040
8.270
±
±
±
±
290
460
460
240
490
internal
9.830
10.040
10.170
10.030
±
±
±
±
310
240
240
150
540
11.200
11.430
11.590
11.430
±
±
±
±
360
270
280
180
620
12.950
13.310
13.440
13.260
±
±
±
±
420
320
320
210
570
8.680
8.860
8.980
8.850
±
±
±
±
280
210
210
140
480
Notes:
(a) Red figures are uncertainties based on sample-to-sample variability only
(b) Blue figures are total uncertainties (red + production rate uncertainties)
(c) Uncertainty (red) is external (spread) or internal (analytical). The larger of the two is reported.
(d) Correction of production rates for secular variations in sea level was neglected.
40
(e) Ages calculated with Ca production rate of Stone et al. (1996), recalculated using Desilets and Zreda (2006) scaling factors.
(f) Ages calculated by John Stone using Lal scaling, production rates that account for contributions from Fe and Ti, and his new age calculator.
40
(g) Ages calculated with Ca production rate of Swanson and Caffee (2001), recalculated using Desilets and Zreda (2006) scaling factors.
References cited:
Desilets, D., Zreda, M., and Prabu, T., 2006, Extended scaling factors for in situ cosmogenic nuclides: New measurements at low latitude: Earth
and Planetary Science Letters, v. 246, p. 265-276.
Stone, J.O., Allan, G.L., Fifield, L.K., and Cresswell, R.G., 1996, Cosmogenic chlorine-36 from calcium spallation: Geochimica et
Cosmochimica Acta, v. 60, p. 679-692.
Swanson, T.W., and Caffee, M.L., 2001, Determination of 36Cl production rates derived from the well-dated deglaciation surfaces of Whidbey
and Fidalgo Islands, Washington: Quaternary Research, v. 56, p. 366-382.