In eastern Mediterranean sediments, the titanium-to-aluminum ratio
(Ti/Al) captures relative variability in eolian to river-derived
material and predominantly integrates climate signals over the Saharan and
Sahel regions. Long Ti/Al time series can, therefore, provide valuable
records of North African humidity and aridity changes. X-ray fluorescence core
scanning (XRF-CS) can generate near-continuous Ti/Al records with relatively
modest effort and in an acceptable amount of time, provided that accurate
Ti/Al values are acquired. Calibration of raw XRF-CS data to those of
established analytical methods is an important pathway for obtaining the
required accuracy. We assess how to obtain reliable XRF-CS Ti/Al calibration
by using different calibration reference sample sets for a long sediment
record from ODP Site 967 (eastern Mediterranean Sea). The accuracy of
reference concentrations and the number of reference samples are important
for reliable calibration. Our continuous Ti/Al record allows detailed
time series analysis over the past 3 Myr. Near-direct control of low-latitude
insolation on the timing and amplitude of North African aridity and humidity is
observed from 3 to ∼ 1.2 Ma. In our Ti/Al record, most arid
North African intervals (i.e., with the longest period and highest
amplitude) occur after the mid-Pleistocene transition (MPT; ∼ 1.2–0.7 Ma), when ice ages intensified. We also observe a subdued
relationship between low-latitude insolation and North African climate after
the MPT. These findings support the growing consensus that African climate
became more sensitive to remote high-latitude climate when a threshold ice
volume was reached during the MPT.
Introduction
Continuous Pliocene–Pleistocene records that capture North African aridity
and humidity variability are sparse. Yet, such records are crucial for
understanding links between insolation, high-latitude climate, and low-latitude climate in Africa during the Plio-Pleistocene (deMenocal, 1995;
Trauth et al., 2009, 2021), when Northern Hemisphere glaciation intensified
(e.g., Shackleton et al., 1984; Mudelsee and Raymo, 2005; Rohling et al., 2021). Moreover, such records are essential for providing climatic context
to contemporaneous hominin evolutionary events and out-of-Africa dispersals
(Blanchet et al., 2021; deMenocal, 2011; Donges et al., 2011; Groucutt et
al., 2015; Kaboth-Bahr et al., 2021; Larrasoaña et al., 2013;
Larrasoaña, 2021; Maslin et al., 2014; Potts et al., 2020; Rohling et
al., 2013; Trauth et al., 2021). Until now, long (> Myr) and
continuous North African records have mainly focused on dust fluxes from
subtropical West Africa (Ocean Drilling Program, ODP, Site 659; Tiedemann et
al., 1994), the Arabian Peninsula and Horn of Africa (ODP Sites 721/722;
Bloemendal and deMenocal, 1989; deMenocal, 1995, 2004), and (sub-)Saharan
North Africa (ODP Site 967; Larrasoaña et al., 2003; Grant et al., 2022a)
(Fig. 1). Although dust flux reconstructions tend to track large-scale
continental aridity changes, they specifically relate to dust source extent,
ablation potential, and eolian transportation. Hence, they do not
necessarily track the intensity and extent of wet episodes. To date, the
only continuous high-resolution records that capture relative
humidity and aridity changes in North Africa throughout the Pleistocene are from
ODP Site 967: (i) a “wet–dry index” (Grant et al., 2017), which combines a
monsoon runoff proxy with an existing eolian record, and (ii) a recently
derived record of the ratio of titanium (Ti) to aluminum (Al) spanning the last 5 Myr (Grant et al., 2022a).
Location of ODP Site 967 in the eastern Mediterranean Sea, ODP
Site 659 in the eastern Atlantic Ocean, and ODP Sites 721 and 722 in the
Arabian Sea. The transparent black arrows indicate the main winds that
transport dust to these sites (after Trauth et al., 2009).
The Ti/Al ratio in bulk sediments from the eastern Mediterranean Sea has
been found to track aridity versus humidity variability in the Sahel and
Saharan regions (Lourens et al., 2001; Wehausen and Brumsack, 2000). This
proxy relies on the assumption that the eolian fraction that reaches the
Levantine basin is enriched in Ti over Al, while the fluvial fraction
comprises mainly clays, such as smectites, that are relatively enriched in
Al over Ti (Lourens et al., 2001; Wehausen and Brumsack, 2000). Dust fluxes
from North Africa toward the eastern Mediterranean Sea were generally
enhanced during arid episodes in the source regions (Larrasoaña et al., 2003; Trauth et al., 2009), while riverine sediment fluxes increase during
intervals with enhanced humidity and related African continental runoff
(Williams et al., 2006). The ODP Site 967 Ti/Al record is, thus, interpreted
as a North African aridity and humidity indicator, with high values representing
arid intervals and low values corresponding to more humid periods (Lourens
et al., 2001).
Ti/Al records from ODP Site 967 and neighboring Site 968 have been used to
deduce monsoon variability over North Africa at 0–1.2 and 2.3–3.2 Ma (De
Boer et al., 2021; Konijnendijk et al., 2014a, 2015; Lourens et al., 2001).
These studies revealed cyclic North African climate variability in tune with
insolation during the 2.3–3.2 Ma interval (De Boer et al., 2021; Lourens et
al., 2001), which persisted during the 0–1.2 Ma interval, albeit with
considerably more lag (up to 3–4 kyr) between precession minima/insolation
maxima and North African climate (Konijnendijk et al., 2014a, 2015) depending
on sea-level and monsoon changes (Grant et al., 2016). These records were
produced using wavelength dispersive X-ray fluorescence (WD-XRF) analyses on
molten glass beads (i.e., glass-bead WD-XRF), which is an established
accurate and precise geochemical approach (Wehausen and Brumsack, 2000), yet
also relatively costly, destructive, and time consuming if a high sampling
resolution is required (Wilhelms-Dick et al., 2012). Future work would
benefit from relatively rapid, cost-efficient, and non-destructive
approaches to produce reliable Ti/Al records, such as XRF core scanning
(XRF-CS) (Croudace and Rothwell, 2015; Jansen et al., 1998). Grant et al. (2017) generated geochemical records using XRF-CS on ODP Site 967 sediments,
which were calibrated using samples measured by energy-dispersive XRF
(ED-XRF) on sediment powders. This resulted in XRF-CS-based elemental
concentration and ratio profiles that generally agree well with existing
quantitative geochemical data from this core. However, the XRF-CS Ti/Al ratio revealed offsets with conventional measurements (Grant et al., 2017),
which prevented its further use for paleoenvironmental purposes. So far, the
cause of these offsets remained unexplained. Here, we show how to obtain
reliable calibrated Ti/Al XRF-CS profiles using glass-bead WD-XRF-derived
calibration samples with relevance to wider use of XRF-CS Ti/Al and
potentially other element ratios. We show that appropriate calibration can
significantly improve (i.e., making it consistent with other established
geochemical methods) the down-core geochemical variability from XRF-CS. This
approach allows us to investigate in detail North African climate dynamics
from 2.3–1.2 Ma, including the interval prior to the mid-Pleistocene
transition (MPT; ∼ 1.2–0.7 Ma; Clark et al., 2006; Chalk et
al., 2017; Ford and Raymo, 2020; Berends et al., 2021), when glacial cycles
changed fundamentally.
The methodological part of our study focuses on calibration sample
selection and the accuracy of calibration concentrations, which are used to
convert qualitative Ti/Al values (counts/counts) from XRF-CS into
quantitative Ti/Al values (ppm ppm-1) (e.g., Weltje et al., 2015). We use the
extensive (N=1060) set of glass-bead WD-XRF measurements of Konijnendijk
et al. (2014a, 2015) instead of those produced with polarized ED-XRF on
sediment powder samples (N=40) of Grant et al. (2017) because the
glass-bead WD-XRF technique is regarded to be more precise and accurate than
ED-XRF on sediment powders (Zhan, 2005). The large sample set of
Konijnendijk et al. (2014a, 2015) also enables investigation of the number of
samples required for accurate XRF-CS calibration. Ultimately, we use the
obtained Ti/Al record to study the relationship of North African climate to
insolation and latitudinal forcing (low versus high latitude) – with a
focus on the 2.3–1.2 Ma interval – which has not yet been investigated in
detail. This work is complementary to the concurrently conducted study of
Grant et al. (2022a), who also calibrated the ODP Site 967 XRF-CS records
with glass-bead WD-XRF samples to obtain a 5 Myr geochemical record.
However, Grant et al. (2022a) did not highlight the methodological
implications of this calibration; they focused on the relationship between a
pronounced geochemical shift at 3.2 Ma and long-term North African climate
evolution over the last 5 Myr.
Materials and methodsSetting and chronology
ODP Site 967 is located on the northern flank of Eratosthenes seamount in
the Levantine Basin at 34∘04′ N and 32∘43′ E (Fig. 1). Sediments from this site were recovered from a water
depth of 2554 m during ODP Leg 160. Dust fluxes to ODP Site 967 are sourced
from Algeria, Libya, and western Egypt from 21–30∘ N;
this dust is mainly transported during boreal late winter and spring
(Larrasoaña et al., 2003; Trauth et al., 2009). Fluvial sediment fluxes
from the Nile river into the eastern Mediterranean peak from June to October,
when discharge is high under the influence of monsoon-related Nile catchment
precipitation (Williams et al., 2006).
We use the composite depth splice and chronology described by Grant et al. (2017, 2022a). The ODP Site 967 chronology is based on peak alignment in
principal component 2 (PC2; a proxy associated with monsoon runoff and
Mediterranean sapropel deposition, based on principal component analysis of
XRF-CS data) to precession minima with zero phase lag, from 0.161 to 3.09 Ma, while ages from 0 to 0.161 Ma are constrained by radiometrically based
ages of sapropels and tephra layers (Grant et al., 2016, 2017). Little or no
lag to precession minima seems a suitable tuning condition for monsoon
maxima not following glacial terminations and before the MPT (Grant et al., 2016, 2017). Exact phasing remains unknown, but the maximum uncertainty is
likely on the order of ±3 kyr. This is based on data from the last
glacial cycle, which can be radiocarbon-dated, showing an average 3 kyr lag
between precession minima and monsoon maxima (e.g., Konijnendijk et al., 2014a).
XRF-CS and calibration assessment
The equipment settings used for XRF-CS measurements are described by Grant
et al. (2017). In short, an Avaatech XRF core scanner was used to measure 90 m of core material at 1 cm intervals with energy settings of 10 and 50 kV,
which produced intensity data (counts) for 11 target elements, i.e., Al, Si,
S, K, Ca, Ti, Mn, Fe, Sr, Zr, and Ba. These intensity data were then
converted into concentrations using the multivariate log-ratio calibration
(MLC) approach of Weltje et al. (2015).
The calibration dataset of Grant et al. (2017) was based on 40 ED-XRF
analyses of bulk sediment powder samples using a PANalytical Epsilon3 XL
instrument. Instead, we use here the 1060 discrete samples from ODP Site 967
measured by Konijnendijk et al. (2014a, 2015) as calibration samples. These
glass-bead samples were prepared by melting 600 mg of sediment with 6000 mg
of lithium tetraborate, after which WD-XRF was performed with a Philips PW
2400 X-ray spectrometer. Both Grant et al. (2017) and Konijnendijk et al. (2014a, 2015) reported analytical precisions of the ED-XRF and WD-XRF
analyses better than 2 % for Al and Ti, but comparison is ambiguous
because different standard samples were used (MAG-1 and ISE 921,
respectively). Yet, as noted before, glass-bead WD-XRF is established as a
more accurate and precise method than sediment powder ED-XRF (Zhan, 2005).
The MLC conversion was implemented using the AvaaXelerate software
(Bloemsma, 2015; Weltje et al., 2015) that minimizes the impact of down-core
physical property changes and parameterizes non-linear matrix effects.
Within the software, we set the sample tolerance (i.e., the allowed maximum
distance between a calibration sample and the XRF-CS data) to ±15 mm
to allow for minor depth mismatches between calibration samples and XRF-CS
measurement depths. Subsequent calibration was performed with all 1060
samples of Konijnendijk et al. (2014a) and subsets thereof: (1) three evenly
spaced subsets of 10 %, 5 %, and 2 % of the samples (i.e., 106, 53,
and 22 samples, respectively) and (2) two subsets (i.e., 53 and 22 samples)
obtained using statistically selected sample positions with the automated
calibration sample selection option in the AvaaXelerate software. This
sample selection is based on the multivariate geometry of the XRF-CS
intensities (Weltje et al., 2005) and provides the minimum of calibration
samples based on the variance and number of elements in the XRF-CS dataset
(Bloemsma, 2015). Conversion of intensities into concentrations was possible
for all target elements except for sulfur, which is known to be
semi-quantitative for measurements performed on glass beads due to its
partial loss during sample preparation. We calibrated 10 elements
simultaneously (Al, Ba, Ca, Fe, K, Mn, Si, Sr, Ti, and Zr) for each
calibration approach (see Fig. S1 to S6 in the Supplement for AvaaXelerate results).
The calibrated XRF-CS records were compared with the glass-bead WD-XRF data
of Konijnendijk et al. (2014a, 2015) to statistically test the similarity
between the (un)calibrated XRF-CS data to this reference record. We tested
for equality of variance (F test; α=0.05) and mean (two-tailed
tests: one-way analysis of variance, (ANOVA), Student t test, and
non-parametric Mann–Whitney test, all at the α=0.05 level). To
correct for multiple comparisons, we used the Bonferroni method to adjust
the obtained p values. Moreover, we performed an ordinary least squares
regression and calculated the Pearson product-moment correlation coefficient
(r) to measure linear correlation between the XRF-CS and reference
glass-bead WD-XRF data. To do so, the depths of the XRF-CS data were
resampled to the same sample intervals of the WD-XRF dataset through linear
interpolation using Analyseries v1.1.1 (Paillard et al., 1996).
Calibration of the XRF-CS data, as performed here, corresponds closely to
that performed by Grant et al. (2022a), both using glass-bead WD-XRF
calibration samples. These studies were conducted in parallel and with close
collaboration; thus, knowledge transfer on appropriate calibration occurred
at an early stage. Considering the match between the calibrated XRF-CS data
from our study and those of Grant et al. (2022a) – as shown below – we
recommend the Grant et al. (2022a) data for other paleoenvironmental studies
(see “Data availability” section).
Results and discussionAccuracy of Ti/Al time series from XRF-CS
XRF-CS Ti/Al records are shown in Fig. 2 prior to calibration (intensities
in Fig. 2a and log ratios of these intensities in Fig. 2b) and after
calibration (Fig. 2c–i) versus the reference Ti/Al data obtained using
WD-XRF on glass beads (Konijnendijk et al., 2014a). Statistical test results
(Table 1) for comparisons between XRF-CS data and reference data indicate
that uncalibrated Ti/Al (counts/counts; Fig. 2a) ratios have no similarity
at the α=0.05 level to the WD-XRF results and relatively low
correlation to these reference data. Likewise, statistical results for
log ratios of Ti/Al intensities also have no similarity and low correlation
to the reference data (Table 1). The previous Grant et al. (2017)
calibration is marginally better, with a slightly higher correlation but
still with significantly (α=0.05) different values from the
reference data (Table 1). The new calibrations have much higher linear
correlation to the reference data (correlation coefficients of 0.60–0.74,
depending on how many samples are used; Table 1), while the means of the
XRF-CS data become statistically similar to the reference data for
calibrations with 5 % (n=53), 10 % (n=106), and all (n=1060)
calibration samples (Table 1). The calibration samples that were
automatically selected using a clustering algorithm (Bloemsma, 2015) produce
only somewhat better results when 22 calibration samples are considered
(Fig. 2i versus g, Table 1) and not for 53 samples (Fig. 2h versus f, Table 1). This may be due to the fact that the calibration samples
are selected automatically based on the whole XRF-CS dataset (i.e., all
calibrated elements) and not specifically Ti and Al data. For other
elements, predicted concentrations from the XRF-CS data may better match the
calibration sample concentrations (Figs. S1 to S6).
Uncalibrated and calibrated Ti/Al ratios (blue) for different
XRF-CS calibration strategies versus reference values obtained using
glass-bead WD-XRF (orange; Konijnendijk et al., 2014a, 2015). (a)Ti/Al from
XRF-CS intensities. (b) Ln(Ti/Al) from XRF-CS intensities. (c)Ti/Al using the
calibration of Grant et al. (2017). (d)Ti/Al using all 1060 calibration
samples from glass-bead WD-XRF (Konijnendijk et al., 2014a, 2015). (e)Ti/Al using 10 % of the samples from glass-bead WD-XRF. (f)Ti/Al using 5 % of
the samples from glass-bead WD-XRF. (g)Ti/Al using 2 % of the samples from
glass-bead WD-XRF. (h)Ti/Al using the 53 samples selected by AvaaXelerate
(Bloemsma, 2015). (i)Ti/Al using the 22 samples selected by AvaaXelerate
(Bloemsma, 2015). Gray circles represent data points used as calibration
samples. Two high-Ti/Al data points (0.13–0.14) were omitted from the
glass-bead WD-XRF record at 26 m for clarity but are incorporated in the
statistical calculations. Statistical test results associated with these
data are presented in Table 1.
Statistical test results between XRF-CS data (uncalibrated and calibrated) and reference data. These data are also shown in Fig. 2. The second column indicates the correlation coefficient r calculated between the datasets. The Y (yes) and N (no) markers in the third to sixth columns indicate whether the null hypotheses of equality of variance (third column) and means (fourth to sixth column) could not (Y) or could (N) be rejected at the α= 0.05 significance level. Hence, a Y indicates that the data are statistically similar. The associated p values are shown between brackets. To correct p values for multiple comparisons, we used the Bonferroni method (i.e., multiply the raw p values, as shown here, by the number of tests).
Our results demonstrate that XRF-CS can be used to acquire accurate Ti/Al data, as long as an appropriate number of calibration samples is used (i.e.,
at least 53 samples here for Ti/Al; Table 1). Ti and Al are challenging to
measure with XRF-CS because they are relatively light elements and hence
prone to sedimentary inhomogeneities due to the fact that the XRF signal
originates from only the upper few micrometers of sediment (Potts et al., 1997). This may also explain the significant (α=0.05) difference
in variance observed between all XRF-CS data and the reference data (Table 1). The reference data have the highest amplitude variability (to more
positive values) at turbidite intervals (Konijnendijk et al., 2014a), which
typically contain larger grain sizes that may result in ambiguous data for
Ti and Al because of their shallow XRF signals and associated grain-size
effect (i.e., larger grains are coated in smaller grains, and thus shallow
XRF signals from light elements preferentially record the small grain
geochemistry). Importantly, all of our new XRF-CS calibrations (Fig. 2d–i)
have similar cyclic variations to the WD-XRF reference data, which would
likely lead to similar paleoenvironmental interpretations. Uncalibrated
XRF-CS Ti/Al ratios and log ratios (Fig. 2a, b), on the other hand, are
seemingly unusable for paleoenvironmental purposes.
Mismatch between uncalibrated and reference Ti/Al values is likely due to
differences in sample preparation, analytical sensitivity, and matrix
effects for the methods used. Matrix effects result from influences of the
fluorescence of other elements of interest in the sediment matrix by
absorption or enhancement. For instance, Ca likely has a large impact on
both Ti and Al because it is an effective absorber of Ti fluorescence and
enhances Al fluorescence (Potts and Webb, 1992); it also has large down-core
variations at ODP Site 967 (∼ 2–33 wt %; Grant et al., 2022a). Matrix effects and associated analytical sensitivity (i.e.,
sensitivity to measurement of an element of interest by the XRF scanner, being
mainly dependent on X-ray source, measurement geometry, instrument settings,
and sample matrix) are approximately constant for WD-XRF analyses performed
on well-homogenized glass beads due to the 1:10 dilution and the low atomic
weight of the lithium tetraborate flux (e.g., Konijnendijk et al., 2014a;
2015). On the other hand, these parameters vary for XRF-CS measurements,
which are performed directly on the split-core surface without sample
preparation. Similarly, the quantitative ED-XRF used to analyze sediment
powders may have been less accurate for the same reason (Fig. 2c) because
these powders have a more variable matrix than fused glass beads. Hence, the
substantial improvement of calibrated XRF-CS records (Fig. 2d–i, Table 1) is
likely due to the use of WD-XRF calibration data from glass beads. The
matrix effects and associated variable element sensitivities that clearly
impact the uncalibrated Ti/Al XRF-CS results (Fig. 2a, b) are appropriately
accommodated by using these accurately constrained calibration samples with
the MLC method of Weltje et al. (2015). This has important implications because there is a general misconception that calibration of XRF-CS data is
only necessary to quantify geochemical data, for instance, for flux analysis
or mass-balance calculation (see, e.g., p. 529 in Weltje et al., 2015).
However, we show that the Weltje et al. (2015) calibration is more powerful
than that, rendering elemental variability usable (in this case for Ti/Al),
while (log) ratios of intensity data do not allow a paleo-interpretation.
The high-resolution Ti/Al record measured by XRF-CS, calibrated using all
1060 samples of Konijnendijk et al. (2014a, 2015), was used to reconstruct
past aridity and humidity variations over North Africa (see Sect. 3.2). We
used the 1060-sample calibration because it has the highest resemblance (i.e.,
highest p values and highest correlation r) to other analytical techniques
(Table 1). The Ti/Al records of Lourens et al. (2001) and the similar
calibration of Grant et al. (2022a) independently validate our chosen
calibration (Fig. 3a).
Calibrated Ti/Al record from XRF-CS (this study; blue) and
companion records (orange and black). (a) XRF-CS Ti/Al (blue; this study)
versus the calibrated XRF-CS data of Grant et al. (2022a) (orange; plotted
behind the overlapping blue line) and the WD-XRF values obtained by Lourens
et al. (2001) (black). (b) XRF-CS Ti/Al versus the wet–dry index of Grant et al. (2017). (c) XRF-CS Ti/Al versus Saharan dust supply (Larrasoaña et
al., 2003). (d) XRF-CS Ti/Al versus ODP Site 659 dust flux (Tiedemann et al., 1994). (e) XRF-CS Ti/Al versus ODP Site 721/722 dust flux (DeMenocal, 1995,
2004). (f) XRF-CS Ti/Al versus calibrated XRF-CS Ba/Al. Ti/Al and dust
records in (b–f) are normalized (i.e., presented as z scores by subtracting the
average and dividing by the standard deviation over the same 3-million-year
interval) to facilitate comparison.
Aridity and humidity variability in North Africa over the last 3 Myr
The ODP Site 967 Ti/Al record (this study; Grant et al., 2022a) and wet–dry
index of Grant et al. (2017) provide the longest continuous, detailed
representations of past North African climate variability (Fig. 3b),
including information on humid period intensity compared to the dust proxy
records. Ti/Al and the wet–dry index have mostly similar variability
throughout the past 3 Myr, and the Ti/Al record confirms the timing and
extent of “Green Sahara Periods” reported by Grant et al. (2017).
Time series analyses of the ODP Site 967 Ti/Al record. (a) Wavelet
analysis (Torrence and Compo, 1998) performed with a Morlet wavelet. The
record was resampled to 1 kyr resolution before analysis. The cone of
influence is indicated by the black line. Blue contour lines indicate the p= 0.05 significance level. The cumulative signal strength is shown on the
right. (b) Filtered XRF-CS Ti/Al (blue; 21 % kyr ±20 % kyr) and precession
(orange). (c) Filtered XRF-CS Ti/Al (blue; 41 % kyr ±20 % kyr) and
obliquity. (d) Filtered XRF-CS Ti/Al (blue; 100%±20 % plus 400 % kyr ±20 % kyr) and eccentricity. (e) XRF-CS Ti/Al versus the summer
inter-tropical insolation gradient (SITIG). (f) Filtered XRF-CS Ti/Al (precession plus obliquity bands) versus SITIG. (g) Running correlation (401 kyr window) for profiles in (e) (blue) and (f) (orange); shadings indicate
95 % confidence intervals. The 401 kyr window was chosen to obtain a
relatively smooth running correlation that focuses on long-term changes in
variability. Change points in (g) are based on changes in the mean (black
diamonds) and standard deviation (blue triangles) of these correlations and
were estimated using the MATLAB built-in function “findchangepts” (Killick
et al., 2012). Wavelet analysis was done using the Past program (Hammer et
al., 2001), while band-pass filtering was performed with Analyseries
(Paillard et al., 1996).
The Ti/Al record indicates a clear increase in length and amplitude of arid
intervals since ∼ 1 Ma, with the highest Ti/Al values recorded
from then on (Fig. 3a). This interval of enhanced intermittent aridity is
coeval with the MPT (∼ 1.2–0.7 Ma; Clark et al., 2006; Chalk
et al., 2017; Ford and Raymo, 2020; Berends et al., 2021) when ice ages
intensified. Similar aridity increases are observed in the Saharan dust
supply (Larrasoaña et al., 2003) (Fig. 3c) and in records that capture
dust inputs from subtropical West Africa (ODP 659; Tiedemann et al., 1994)
(Fig. 3d) and the Arabian Peninsula and Horn of Africa (ODP 721/722;
Bloemendal and deMenocal, 1989; deMenocal, 1995, 2004) (Fig. 3e). However,
we also observe clear differences between ODP Site 967 Ti/Al and dust
records from ODP Sites 659 and 721/722, which may be a result of the lower
resolution of those records and chronological inconsistencies with the ODP
Site 967 record. In addition, transportation effects by monsoon dynamics
might play a role; the dust time series of ODP Sites 659 and 721/722 might record increased dust fluxes not only due to enhanced continental droughts but
also due to stronger monsoon winds, which makes their interpretation more
ambiguous (Trauth et al., 2009). Transportation impact by monsoon dynamics
was likely minimal for the ODP Site 967 dust record (Trauth et al., 2009)
and, thus, by extension, also for the ODP Site 967 Ti/Al record. Our Ti/Al
record delivers an additional reliable and independent line of evidence that
the most arid North African intervals after the MPT were unmatched in the
preceding 2 million years.
The ODP Site 967 sapropel stratigraphy provides a relatively precise
chronology throughout the Ti/Al record by tuning of sapropel geochemistry
to precession minima (Grant et al., 2017, 2022a). Organic-rich sapropel
intervals in eastern Mediterranean sediments resulted from reduced
deep-water ventilation and increased productivity during humid intervals in
North Africa at precession minima (Rohling and Gieskes, 1989; Rohling et
al., 2015). Regular Ba/Al peaks (“export productivity”; e.g., De Lange et
al., 2008) (Fig. 3f), thus, offer robust chronological constraints, paving
the way for detailed time series analysis of the Ti/Al record. Importantly,
the Ti/Al record was not used to tune the ODP Site 967 age model, and no lag
was applied when tuning sapropel mid-points to precession minima (Grant et
al., 2017). Previous studies indicate that precession minima can lead
African monsoon maxima by about 3 kyr (Lourens et al., 1996; Ziegler et al., 2010; Konijnendijk et al., 2014a). However, more recent work has shown that
such a lag of the African monsoon to insolation only occurred after glacial
terminations and principally after the MPT (Grant et al., 2016, 2017). Thus,
before the MPT, and especially during the Late Pliocene and Early
Pleistocene, any lag was likely smaller or absent (Lourens et al., 2001;
Grant et al., 2016, 2017).
ODP Site 967 Ti/Al record and global ice-volume changes. (a) ODP
Site 967 Ti/Al (blue) and sea level relative to present (Rohling et al., 2021; black) reconstructed from deep-sea carbonate microfossil-based δ18O of Westerhold et al. (2020). The red line marks -65 m sea level.
(b) Cross plot between sea-level (Rohling et al., 2021) and ODP Site 967
Ti/Al data for intervals from 3 to 1.2 Ma (black) and 1.2 to 0 Ma (orange).
(c) Box-whisker plots of ODP Site 967 Ti/Al (blue) and sea-level (gray) data
(Rohling et al., 2021). Box edges represent the interquartile range; the
whiskers represent the 5th and 95th percentiles; data exceeding this range
are represented as circles. Tests for equality of variance and means (see
“Methods” section) all indicate a significance (all p values < 0.0001)
difference between the 0–1.2 and 1.2–3 Ma values for both Ti/Al and sea
level.
Wavelet analysis and band-pass filtering highlight strong cyclic Ti/Al variability over the last 3 million years (Fig. 4a–d). In general, we
observe the expected enhanced North African humidity coupled to precession
minima, obliquity maxima, and eccentricity maxima, and vice versa, for North
African aridity (Fig. 4b–d). The 100 and 400 kyr eccentricity bands are
stronger after the MPT, in line with other proxy records (e.g., deMenocal,
1995; Kaboth-Bahr et al., 2021). The eccentricity signal is much more
apparent in Ti/Al than in the dust record from ODP Site 967 (see
Larrasoaña et al., 2003). Eccentricity modulation of precession forcing
strongly affects northward penetration of the African rain belt into
Saharan and Sahel watersheds, and thus the humidity recorded by Ti/Al. Moreover,
eccentricity also impacts the El Niño–Southern Oscillation, which
includes the Walker circulation and thus affects pan-African climate
(Kaboth-Bahr et al., 2021), offering another mechanism for eccentricity
imprint on Ti/Al. The eccentricity modulation of precession forcing is in
phase with Ti/Al before the MPT, while the later phase relationship is more
variable (Fig. 4d). This may be associated with a substantial climate system
change at the MPT that produced a different North African aridity and humidity
response on such long timescales. For instance, Trauth et al. (2009)
suggested that the African climate response became more susceptible to
remote high-latitude climate influences due to the crossing of a threshold
ice volume during and after the MPT.
Forcing of ODP Site 967 Ti/Al variability
The lag between obliquity forcing and the obliquity signal in the Ti/Al record holds information on low- versus high-latitude controls on North
African aridity and humidity. The high-latitude ice sheet response time to
obliquity has been approximated to be ∼ 6.5 kyr (Lisiecki and
Raymo, 2005; Lourens et al., 2010). Hence, a similar lag in North African
aridity and humidity would provide evidence for high-latitude control, while a
low-latitude control would instigate a much smaller lag. The relatively
small lead (∼ 2 kyr) of obliquity relative to Ti/Al over the
3–1 Ma interval, thus, suggests that high-latitude climate had a relatively
limited impact on monsoon activity in North Africa into the MPT (Fig. 4c).
This holds true even if a relatively large (and unlikely; see discussion
above) lag of ∼ 3 kyr is assumed for most of the Pleistocene
because Ti/Al then still lags obliquity considerably less than the
∼ 6.5 kyr ice-volume response to obliquity (Lisiecki and
Raymo, 2005; Lourens et al., 2010). These results, therefore, point to an
obliquity control on the low latitudes that affected North African
aridity and humidity to at least the MPT, which is consistent with data from a
high-resolution coupled general circulation model (Bosmans et al., 2015a).
Climate model results produced using a fully coupled ocean–atmosphere
general circulation model (Bosmans et al., 2015a, b) also indicate that
the cross-equatorial insolation gradient provides a mechanism for the
relatively large obliquity signal originating from low latitudes, which
influences North African monsoon variability. The 21 June insolation
at 65∘ N is often used to tune North African climate variability
(Lourens et al., 1996; Ziegler et al., 2010; Konijnendijk et al., 2014a),
which presumes a high-latitude control of North African climate. Our results
and those of Bosmans et al. (2015a, b) suggest that variability in the
summer inter-tropical insolation gradient (SITIG), i.e., the difference in
21 June insolation between 23∘ N and 23∘ S
(Lourens and Reichart, 1996), in principle provides a better tuning target
curve for North African climate records (Fig. 4e, f). This is in line with
plant-wax isotope data, which indicate that monsoon rainfall variability in
northwestern Africa is principally controlled by low-latitude insolation
gradients (O'Mara et al., 2022).
SITIG and African humidity and aridity (Fig. 4e, g) have a high
cross-correlation, which indicates similar amplitude variability (and
similar timing, which is inherent to the age model construction). This high
correlation is especially visible for insolation (SITIG) and the combined
precession–obliquity signals in African humidity and aridity (Fig. 4f, g)
from ∼ 3 to 1.2 Ma. This is consistent with earlier research
on the 3.2–2.3 Ma interval (De Boer et al., 2021; Lourens et al., 2001), and
we show here that the African climate system operated in a similar manner
until at least 1.2 Ma (Fig. 4g). During and after the MPT this linear
correlation persists, but it is clearly diminished (i.e., with considerably
lower correlation r, shifting from -0.9 to about -0.45; Fig. 4g). Moreover,
change-point analysis also indicates significant changes in the relationship
between SITIG and Ti/Al just before and during the MPT (Fig. 4g). This all
suggests that changes in the Ti/Al amplitude during and after the MPT
responded more variably (“noisy”) to insolation changes, with a larger lag
(∼ 3 kyr; Konijnendijk et al., 2014a). This implies a
substantial global climate system change at the MPT that caused a different
North African aridity and humidity response to insolation.
We observe North African aridity and humidity changes in parallel with ice age
cycle changes at the MPT (i.e., ice ages intensified, lengthened from ∼40 to ∼100 kyr, and became distinctly asymmetrical; Clark et al., 2006). Such large cryosphere changes are represented by sea-level records
that reflect global-scale ice sheet melt and growth during (de)glaciation.
Comparison of the ODP Site 967 Ti/Al record with sea-level records (e.g.,
Rohling et al., 2021) indicates that there is no obvious linear correlation
between these two records before and after the MPT (Fig. 5a, b). However,
the significant change to more intense and asymmetric glacial cycles and
more prolonged and arid North African climate variability occurred
concurrently, around the MPT (Fig. 5b, c), when glacial sea level started to
dip frequently below ∼ 65 m (Fig. 5a). Together with (i) the
statistical differences in the relationship between Ti/Al and SITIG before
and after the MPT and (ii) the strengthening of wavelengths > 100 kyr in the Ti/Al record at around the same time, this suggests an
increased, although non-linear, high-latitude climate impact on North
African climate at the MPT. Meltwater discharge events at glacial
terminations led to a weakened monsoon system by reducing the Atlantic
meridional overturning circulation (e.g., Böhm et al., 2015; Häuselmann
et al., 2015; Marino et al., 2015; Menviel et al., 2021; Ziegler et al., 2010) and were probably important for driving the lagged African climate
response to insolation. Such large-scale meltwater events did not occur
until just after the MPT (Hodell et al., 2008), explaining – at least
partially – the more in-phase relationship between North African climate
and (low-latitude) insolation before the MPT and the more non-linear
response after.
Conclusions
We investigate here XRF-CS calibration of bulk sediment Ti/Al from ODP Site
967 and use a well-calibrated Ti/Al record to analyze responses and forcing
of North African aridity and humidity in detail over the past 3 Myr, with a focus
on the 2.3–1.2 Ma time interval.
Uncalibrated intensity ratios from XRF-CS can deviate significantly from
ratios of the same elements measured by established geochemical methods
using discrete samples, due to matrix effects and associated variable
element measurement sensitivities. Calibration using the multivariate
log-ratio approach of Weltje et al. (2015), which estimates relative matrix
effects and element sensitivity with selected calibration samples,
efficiently corrects for this. We show that highly accurate calibration
measurements are essential for proper calibration of these XRF-CS data and
that calibration improves with the number of calibration samples. Here, at
least 53 samples were necessary for proper calibration of the Ti/Al ratio,
but this number may vary per site depending on sediment matrix and its
variability. Ti/Al is a suitable proxy for tracing sedimentation processes
in many other environments. Hence, our results are also relevant to studies
elsewhere that focus on Ti/Al from XRF-CS. Extending our results to other
elements indicates that calibration may result in useful XRF-CS data for
paleoenvironmental purposes, even if the initial intensity data for elements
did not correspond to reference data.
Our ODP Site 967 Ti/Al record reveals a striking similarity in timing and
amplitude between North African aridity and humidity and low-latitude
insolation, especially during the 3 to 1.2 Ma interval. A small lead in
obliquity to similar frequencies in Ti/Al over that interval points to a
low-latitude origin of this signal, which is consistent with climate model
simulations. Our analyses imply that African climate became more sensitive
to remote high-latitude climate when a threshold ice volume (glacial sea-level equivalent of below ∼ 65 m) was reached around the MPT.
Data availability
The glass-bead WD-XRF elemental concentration data of Konijnendijk et al. (2014a) can be found in the PANGAEA repository as Konijnendijk et al. (2014b) (10.1594/PANGAEA.831712). The calibrated XRF-scanning record of Grant et al. (2022a) is similar to
the final calibrated XRF-scanning record presented here, as
shown above, and is available in the PANGAEA repository as Grant et al. (2022b) (10.1594/PANGAEA.939929). Raw XRF-scanning data and results of the different calibration approaches used
here are included in the Supplement.
The supplement related to this article is available online at: https://doi.org/10.5194/cp-18-2509-2022-supplement.
Author contributions
RH and KMG conceptualized the project. RH conducted XRF-scanning
calibration and data analysis, with the consultation of all co-authors, and
wrote the manuscript. All co-authors contributed to editing of the
manuscript.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We acknowledge the two anonymous reviewers whose constructive comments improved the paper.
Financial support
Rick Hennekam was supported by the Netherlands Organisation for Scientific Research (NWO), with funding for the SCANALOGUE project (grant no. ALWOP.2015.113) awarded to Gert-Jan Reichart. This study was also carried out as part of the Netherlands Earth System Science Centre (NESSC; grant no. 024.002.001), supported by the Dutch Ministry of Education, Culture and Science (OCW). This work was also supported by the Australian Research Council (ARC) through grant nos. DE1900100042 (Katharine M. Grant), LE160100067 (ANZIC Legacy Grant; Katharine M. Grant and Andrew P. Roberts), Australian Laureate Fellowship FL1201000050, grant no. DP200101157 (Eelco J. Rohling), and grant no. DP200100765 (Andrew P. Roberts and David Heslop).
Review statement
This paper was edited by Dominik Fleitmann and reviewed by two anonymous referees.
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