Introduction
Key to determining the relationship between CO2 and climate in the
geological past is the calculation of reliable estimates of absolute CO2
through time. In recent years the boron isotope composition (δ11B)
of foraminiferal calcite has become a high-profile tool for reconstructing
CO2 beyond the last 800 ky and throughout the Cenozoic Era (Foster,
2008; Hönisch et al., 2009; Pearson et al., 2009; Bartoli et al., 2009;
Foster et al., 2012; Badger et al., 2013; Henehan et al., 2013; Greenop et
al., 2014; Martínez-Botí et al., 2015a). Yet long-term change in
the boron isotope composition of seawater (δ11Bsw) is
currently poorly constrained and represents a major source of the uncertainty
associated with δ11B-determined CO2 estimates (e.g. Pearson et
al., 2009). In the modern ocean, boron is a conservative element with a
spatially invariant isotope ratio (39.61 ‰; Foster et al., 2010),
but this value is subject to change through geological time. The residence
time of boron in the ocean is estimated to lie between 11 and 17 My
(Lemarchand et al., 2000). Therefore, we can expect the uncertainty associated
with δ11Bsw to be an important factor in CO2
estimates beyond the late Pliocene (∼ 4–5 Ma; Palmer et al., 1998;
Lemarchand et al., 2000; Pearson et al., 2009; Foster et al., 2012;
Anagnostou et al. 2016).
The
oceanic boron cycle. Fluxes are from Lemarchand et al. (2000) and Park and
Schlesinger (2002). Isotopic compositions are from Lemarchand et al. (2000),
Foster et al. (2010) and references therein.
The ocean boron budget and its isotopic composition are controlled by a
number of inputs and outputs (Fig. 1). However, because the magnitude of the
boron fluxes between land, the ocean and the atmosphere in modern times are
still poorly understood, the residence time and changes in both concentration
([B]sw) and isotopic composition (δ11Bsw)
through time remain uncertain. The main inputs of B into the ocean are
silicate weathering, and to a lesser extent evaporite and carbonate
weathering, delivered to the ocean by rivers (Lemarchand et al., 2000; Rose
et al., 2000; Lemarchand and Gaillardet, 2006), hydrothermal vents (You et
al., 1993) and fluid expelled from accretionary prisms (Smith et al., 1995).
The major loss terms are low-temperature oceanic crust alteration (Smith et
al., 1995), adsorption onto sediments (Spivack and Edmond, 1987) and
co-precipitation into carbonates (Hemming and Hanson, 1992). In the case of
all three outputs, the light 10B isotope is preferentially removed
relative to 11B, such that the seawater 11B / 10B ratio
(δ11Bsw, 39.61 ‰) is significantly greater
than that of the cumulative inputs (δ11B of
∼ 10.4 ‰; Lemarchand et al., 2000). Our understanding of the
modern boron fluxes outlined above, and illustrated in Fig. 1, implies a
significant imbalance between inputs and outputs. Consequently, the poorly
constrained ocean–atmosphere boron fluxes may also be an important part of
the ocean's modern boron mass balance (Park and Schlesinger, 2002). Here,
however, we follow Lemarchand et al. (2000) in assuming that atmospheric
fluxes are unlikely to have varied significantly on geological timescales and
therefore will not be discussed further in reference to the Neogene
δ11Bsw record we present.
Unlike many other isotope systems (e.g. δ7Lisw, δ26Mgsw, δ44/40Casw,
87Sr / 86Sr) to date, no direct archive has been documented
for δ11Bsw. This is a result of the pH-dependent boron
speciation in seawater upon which the δ11B–pH proxy is based
(Hemming and Hanson, 1992), which imparts a pH dependency on the δ11B
of all marine precipitates so far examined. Empirical reconstructions of
δ11Bsw must therefore use “indirect” approaches. So
far four approaches have been applied to the problem (Fig. 2):
(1) geochemical modelling (Lemarchand et al., 2000), (2) δ11B
analysis of halites (Paris et al., 2010), (3) measurements of benthic
foraminiferal δ11B coupled to various assumptions about past
changes in ocean pH (Raitzsch and Hönisch, 2013), and (4) measurements of
δ11B in surface- and thermocline-dwelling foraminifera coupled with
additional information on the pH gradient of the surface ocean (Palmer et
al., 1998; Pearson and Palmer 1999, 2000; Anagnostou et al., 2016).
A compilation of published δ11Bsw records.
Seawater composition reconstructed from foraminifera depth profiles (light
blue squares and dark blue crosses) from Pearson and Palmer (2000) and Foster
et al. (2012) respectively; numerical modelling (green line), with additional
green lines showing ±1 ‰ confidence interval (Lemarchand et al.,
2000); benthic δ11B (purple diamonds and dark purple line showing
5-point moving average uses the fractionation factor of Klochko et al.,
2006, light purple line showing 5-point moving average uses an empirical
calibration) from Raitzsch and Hönisch (2013); and halites (orange
crosses) from Paris et al. (2010). The orange crosses in brackets were
discarded from the original study.
Geochemical modelling of the changes in the flux of boron into and out of the
ocean through time has been used to suggest that δ11Bsw increased from 37 ‰ at 60 Ma to
40 ‰ ± 1 ‰ today, driven by a combination of
processes including changing boron continental discharge (Lemarchand et al.,
2000). In the case of approach 2, while modern natural halites reflect
δ11Bsw (39.7 ‰) with no apparent
fractionation, measurement of δ11B in ancient halites yields
isotopic ratios that are significantly lower than all other approaches
(Fig. 2; Paris et al., 2010), with implausible variability among samples of
the same age (7 ‰ range), thereby casting doubt over the reliability
of this approach (Raitzsch and Hönisch, 2013). In the case of approach 3,
δ11Bsw is calculated from globally distributed benthic
δ11B data, with an imposed degree of deep-ocean pH change (Fig. 2;
Raitzsch and Hönisch, 2013). This method hinges on two key assumptions:
(a) a near-linear surface water pH increase of 0.39 over the past 50 My
taken from the average pH output from a number of modelling studies (Berner
and Kothavala, 2001; Tyrrell and Zeebe, 2004; Ridgwell, 2005) and (b) a
prescribed constant surface-to-deep ocean pH gradient of 0.3 (Tyrrell and
Zeebe, 2004, and modern observations). The modelled surface pH and estimated
fixed pH gradient are then used to estimate deep ocean pH, and then convert
benthic foraminiferal δ11B measurements into
δ11Bsw. This approach yields broadly similar results to
geochemical modelling (Fig. 2).
Approach 4 exploits the non-linear relationship between δ11B and
pH alongside estimated pH gradients in the ocean to constrain
δ11Bsw (Palmer et al., 1998; Pearson and Palmer 1999,
2000), and this is the basis of the approach used in this study. The
advantage of this method is that δ11Bsw can be
reconstructed empirically without relying on a priori absolute-pH
constraints. The non-linear relationship between δ11B and pH means
that the pH difference between two δ11B data points varies as a
function of δ11Bsw (Fig. 3). Consequently, if the size
of the pH gradient can be estimated, then there is only one
δ11Bsw value that is consistent with the foraminiferal
δ11B measurements and the specified pH gradient, irrespective of
the absolute pH (Fig. 3c). Previously this approach was applied to pH
variations in the surface ocean and used in studies of Cenozoic
pCO2 to account for changes in δ11Bsw
(determined using δ11B in surface- and thermocline-dwelling
foraminifera) (Fig. 2) (Palmer et al., 1998; Pearson and Palmer 1999, 2000;
Anagnostou et al., 2016). This approach uses a constant pH gradient between
the surface and some depth proximal to the oxygen minimum zone and the boron
isotope values of a mixed-layer-dwelling species and thermocline dweller to
calculate a value for δ11Bsw (Pearson and Palmer,
1999). The resulting record suggests that δ11Bsw varies
between 37.7 ‰ and 39.4 ‰
throughout the Neogene (Fig. 2)
(Pearson and Palmer, 2000).
Schematic diagram showing the change in pH gradient with a
3 ‰ change in δ11B for δ11Bsw of
(a) 39.6 and (b) 37.5 ‰. Arrows highlight the
different pH gradients. Note how a δ11B difference of 3 ‰
is translated into different pH gradients depending on the δ11Bsw. Calculated using
BT= 432.6 µmol kg-1 (Lee et al., 2010) and
αB= 1.0272 (Klochko et al., 2006). (c) The pH
change for a δ11B change of 3 ‰ at a range of different
δ11Bsw.
The same method, but using planktic-benthic instead of surface
planktic-thermocline planktic δ11B gradients to calculate
δ11Bsw, was recently applied to the middle Miocene
where it yielded a δ11Bsw of 37.6
+0.4-0.5 ‰ (Foster et al., 2012). A further modification
to the method of Pearson and Palmer (1999) was also proposed in that study
wherein δ13C in foraminiferal calcite was used to estimate the
surface-to-deep pH gradient (Foster et al., 2012). Here, we reconstruct
δ11Bsw for the last 23 My, the Neogene, based on this
modified approach. We undertake extensive sensitivity tests using both the
CYCLOPS carbon cycle box model and the GENIE Earth system model to define the
plausible range in the relationship between surface–deep pH difference and
δ13C difference, which is an essential parameter for this approach.
Finally, we employ a Monte Carlo approach for comprehensive propagation of
uncertainty in all input parameters, and we focus on reconstructing
δ11Bsw. The implications of our work for
understanding the evolution of Neogene ocean pH and atmospheric
pCO2 will be documented elsewhere.
Methods
Site locations and age models
Foraminifera from four sites are used to construct the planktic–benthic
δ11B pairs; Ocean Drilling Program, ODP, Site 758 and ODP Site 999
for the Pleistocene and Pliocene samples and ODP Site 926 and Site 761 for
the Miocene samples (Fig. 4) (this study; Foster et al., 2012;
Martìnez-Botì et al., 2015a, and a follow up study by Sosdian et
al., 2017). We also incorporate the middle-Miocene planktic–benthic pair
from Site 761 in Foster et al. (2012). To place all data from all sites on a
single age model we use the nanno and planktic foraminifera stratigraphy from
sites 999, 926 and 761 (Shipboard Scientific Party, 1995, 1997; Zeeden et
al., 2013; Holbourn et al., 2004) updated to GTS2012 (Gradstein et al.,
2012). At Site 758 the magnetostratigraphy (Shipboard Scientific Party, 1989)
is used and updated to GTS2012 (Gradstein et al., 2012).
Map of study sites and mean annual air–sea disequilibrium with
respect to pCO2. The black dots indicate the location of the
sites used in this study. ODP sites 758, 999, 926 and 761 used in this study
are highlighted with water depth. Data are from Takahashi et al., 2009,
plotted using Ocean Data View (Schlitzer, 2016).
Boron isotope analysis and pH calculation
The boron isotope measurements (expressed in delta notation as δ11B – per mil variation) were made relative to the boric acid standard
standard reference material (SRM)
951; (Catanzaro et al., 1970). Boron was first separated from the Ca matrix
prior to analysis using the boron-specific resin Amberlite IRA743 following
Foster et al. (2013). The boron isotope composition was then determined using
a sample-standard bracketing routine on a Thermo Fisher Scientific Neptune
multicollector inductively coupled plasma mass spectrometer (MC-ICPMS) at the
University of Southampton (following Foster et al., 2013). The relationship
between δ11B of CaCO3 and pH is very closely approximated by
the following equation:
pH=pKB∗-log-δ11BSW-δ11BCaCO3δ11BSW-∝B.δ11BCaCO3-1000∝B-1,
where pKB∗ is the equilibrium constant, dependent on salinity,
temperature, pressure and seawater major ion composition (i.e. [Ca] and
[Mg]); ∝B is the fractionation factor between the two boron
species; and δ11Bsw is the boron isotope composition of
seawater. Here we use the fractionation factor of 1.0272, calculated from
spectrophotometric measurements (Klochko et al., 2006). No temperature
correction was applied because a number of recent studies suggest that it is not
significant over our investigated temperature range (Rae et al. 2011;
Henehan et al., 2013; Martínez-Botí et al., 2015b; Kaczmarek et
al. 2016).
Although the δ11B of foraminifera correlates well with pH and hence
[CO2]aq, the δ11Bcalcite is often not
exactly equal to δ11Bborate (Sanyal et al., 2001; Foster,
2008; Henehan et al., 2013). The planktic species used to construct the
benthic–planktic pairs changes through time since a single species is not
available for the entire Neogene (this study; Foster et al., 2012;
Martìnez-Botì et al., 2015a, and a follow up study by Sosdian et
al., 2017). Here Globigerinoides ruber is used for 0 to 3 Ma,
Trilobatus sacculifer (formally Globigerinoides sacculifer
and including Trilobatus trilobus; Hembleden et al., 1987;
Spezzaferri et al., 2015) for 0 to 20 Ma and Globigerina praebulloides for 22 to 23 Ma. The calibration for G. ruber
(300–355 µm) is derived from culturing data supported by core top
data (Henehan et al., 2013). The T. sacculifer calibration
(300–355 µm) is from a follow up study by Sosdian et al. (2017)
where the T. sacculifer calibration of Sanyal et al. (2001) is used
with a modified intercept so that it passes through the core top value for
T. sacculifer (300–355 µm) from ODP Hole 999A (Seki et
al., 2010). Unlike the nonsymbiotic modern G. bulloides,
G. praebulloides appears to be symbiotic at least in the latest
Oligocene (Pearson and Wade, 2009). Therefore, we apply the T. sacculifer (300–355 µm) calibration to this species. For
T. sacculifer (500–600 µm) between 0 and 1 Ma, we use
the calibration from Martìnez-Botì et al. (2015b), where the
calibration of Sanyal et al. (2001) measured using negative thermal ionization mass spectrometry (NTIMS) is corrected for the offset
between MC-ICPMS and NTIMS using a comparison of core top
T. sacculifer measured by the two different methods from adjacent
sites (Foster, 2008; Sanyal et al., 1995). In order to constrain deep-water
pH, analysis was conducted on benthic foraminifera Cibicidoides wuellerstorfi or Cibicidoides mundulus depending on which species
was most abundant in each sample. The δ11B of both
Cibicidoides species shows no offset from the theoretical δ11B of the borate ion and therefore no calibration is needed to adjust
for species-specific offsets (Rae et al., 2011).
As mentioned above, in addition to δ11Bcalcite,
temperature, salinity, water depth (pressure) and seawater major ion
composition are also needed to calculate pH from δ11B. We use the
MyAMI Specific Ion Interaction Model (Hain et al., 2015) to calculate the
appropriate equilibrium constants based on existing [Ca] and [Mg]
reconstructions (Horita et al., 2002; Brennan et al., 2013). Sea surface
temperature (SST) is calculated from tandem Mg / Ca analyses on an
aliquot of the δ11B sample (with a conservative 2σ
uncertainty of 2 ∘C). Adjustments were made for changes in
Mg / Casw using the records of Horita et al. (2002) and
Brennan et al. (2013). We corrected for changes in dependence on
Mg / Casw following Evans and Müller (2012), using H= 0.41 calculated from T. sacculifer (where H describes the
power relationship between test Mg / Ca incorporation and
Mg / Casw; Delaney et al., 1985; Hasiuk and Lohmann, 2010;
Evans and Müller, 2012) using the equation
Mg/Casw,c=Mg/Casw,a/Mg/Casw,m0.41,
where Mg / Casw,c is the correction factor applied to the
temperature equation for changing Mg / Casw,
Mg / Casw,a is the estimated Mg / Casw for
the age of the sample and Mg / Casw,m is modern
Mg / Casw. Temperature is then calculated using the generic
planktic foraminifera calibration of Anand et al. (2003) and including a
correction factor for Mg / Casw.
Temperature=ln(Mg/Catest/(0.38×Mg/Casw,c))/0.09
Mg / Ca analysis was conducted on a small aliquot of the sample dissolved
for isotope analysis at the University of Southampton using a Thermo Fisher
Scientific Element 2 XR. Al / Ca was also measured to assess the
competency of the sample cleaning. Because of complications with the
Mg / Ca temperature proxy in Cibicidoides species (Elderfield et
al., 2006), bottom-water temperatures (BWTs) are estimated here by taking the
global secular temperature change from the Mg / Ca temperature
compilation of Cramer et al. (2011), using the calibration of Lear et
al. (2010), and applying this change to the modern bottom-water temperature at
each site taken from the nearest Global Ocean Data Analysis Project (GLODAP) site (with a conservative 2σ
uncertainty of 2 ∘C). Salinity is held constant at modern values
determined from the nearest GLODAP site (2σ uncertainty of
2 ‰ uncertainty) for the entire record. Note that temperature and
salinity have little influence on the calculated pH, and the uncertainty in
δ11Bsw is dominated by the uncertainty in the
δ11B measurement and the estimate of the pH gradient.
The majority of the δ13C data were measured at Cardiff University
on a Thermo Finnigan MAT 252 coupled with a Kiel III carbonate device for
automated sample preparation. Additional samples were measured on a gas
source mass spectrometer Europa GEO 20-20, University of Southampton, equipped
with an automated carbonate preparation device and a Finnigan MAT 253 gas
isotope ratio mass spectrometer connected to a Kiel IV automated carbonate
preparation device at the Zentrum für Marine Tropenökologie (ZMT),
Bremen. The Pliocene benthic δ13C from Site 999 were taken from the
nearest sample in Haug and Tiedemann (1998). In almost all cases
δ13C was analysed on the same foraminiferal species as
δ11B and Mg / Ca (38/44 samples). Where this was not possible
another surface dweller or benthic foraminifera was used from the same depth
habitat. C. wuellerstorfi or C. mundulus were measured in
all cases for benthic δ13C. Stable isotope results are reported
relative to the Vienna Pee Dee Belemnite (VPDB) standard. We use a carbon
isotope vital effect for G. ruber (+0.94 ‰; Spero et al.,
2003), T. sacculifer and G. praebulloides (+0.46 ‰;
Spero et al., 2003; Al-Rousan et al., 2004;), C. mundulus
(+0.47 ‰; McCorkle et al., 1997), and C.
wuellerstorfi (+0.1 ‰; McCorkle et al., 1997) to
calculate the δ13C of dissolved inorganic carbon (DIC).
Carbon isotopes as a proxy for vertical ocean pH gradient
The use of δ13C in foraminiferal calcite to estimate the
surface-to-deep pH gradient requires knowledge of the slope of the
pH–δ13C relationship in the past. In this section we briefly
outline the main factors that contribute to the pH–δ13C
relationship in order to underpin our analysis of extensive carbon cycle
model simulations.
The production, sinking and sequestration into the ocean interior of
low-δ13C organic carbon via the soft-tissue component of the
biological pump leads to a broad correlation between δ13C,
[CO32-] and macronutrients in the ocean (e.g. Hain et al., 2014a).
The remineralization of this organic matter decreases δ13C and
titrates [CO32-], thereby reducing pH, while nutrient concentrations
are increased. In waters that have experienced more soft tissue
remineralization, both pH and δ13C will be lower (Fig. 5a, b).
This is the dominant reason for the positive slope between δ13C and
pH in the modern ocean (e.g. Foster et al., 2012; Fig. 5c).
Latitudinal cross section through the Atlantic showing (a)
pH variations and (b) the δ13C composition. Data are plotted
using Ocean Data View (Schlitzer 2016). pH data are from the CARINA data set
(CARINA group, 2009) and the δ13C data are from the GLODAP data
compilation (Key et al., 2004). (c) pH and
δ13CDIC relationships in the modern ocean are adapted from
Foster et al. (2012). Data are from all the ocean basins spanning
approximately 40∘ N to 40∘ S. Because of anthropogenic
acidification and the Suess effect, only data from > 1500 m are
plotted. Also included in the plot are the data from a transect in the North
Atlantic (from 0 to 5000 m) where the effects of anthropogenic perturbation
on both parameters have been corrected (Olsen and Ninneman, 2010).
Another significant factor affecting the spatial distribution of both
δ13C and pH is seawater temperature, which affects both the
equilibrium solubility of DIC and the equilibrium isotopic composition of
DIC. Warmer ocean waters have decreased equilibrium solubility of DIC and so
increased local [CO32-] and pH (Goodwin and Lauderdale, 2013), while
warmer waters have relatively low equilibrium δ13C values
(Lynch-Stieglitz et al., 1995). This means that a spatial gradient in
temperature acts to drive δ13C and pH in opposite directions:
warmer waters tend to have higher pH but lower δ13C. These opposing
temperature effects act to reduce the pH difference between two points with
greatly different temperature to below the value expected based on
δ13C alone. In other words, when using δ13C differences
to estimate the pH gradient between the warm low-latitude surface waters and cold
deep waters, the appropriate ΔpH–Δδ13C gradient will
be less than expected when only considering the effect of organic carbon
production, sinking and sequestration. For this reason, in our modelling
analysis we focus on the warm surface to cold bottom ΔpH–Δδ13C rather than the slope of the overall
pH–δ13C relationship, with the latter expected to be greater than
the former.
In the modern ocean, and for the preceding tens of millions of years, the
two dynamics described above are likely dominant in setting spatial
variation in δ13C and pH (and [CO32-]). However,
other processes will have had a minor effect on either pH or δ13C.
For instance, the dissolution of CaCO3 shells increases
[CO32-] and pH (Broecker and Peng, 1982), but does not
significantly affect δ13C (Zeebe and Wolf-Gladrow, 2001).
Moreover, the long timescale of air–sea isotopic equilibration of CO2
combined with kinetic isotope fractionation during net carbon transfer is an
important factor in setting the distribution of δ13C on a
global ocean scale (Galbraith et al., 2015; Lynch-Stieglitz et al., 1995). Meanwhile, the effect of CO2 disequilibrium on [CO32-] and pH is
modest (Goodwin and Lauderdale, 2013).
Modelling the pH–δ13C relationship
After correcting for the shift in δ13C due to anthropogenic
activity, or Suess effect (Keeling 1979), modern global ocean observations
demonstrate a near-linear relationship between global ocean data of in situ
seawater pH and δ13CDIC, with a slope of
0.201 ± 0.005 (2σ) (Foster et al., 2012; Fig. 5c). This
empirically determined slope might well have been different in past oceans
with very different nutrient cycling, carbon chemistry and circulation
compared to today, and it does not appropriately represent the temperature
effect described above (i.e. warm surface to cold bottom-water ΔpH–Δδ13C). Here we use an ensemble approach with two
independent carbon cycle models to investigate changes in the ΔpH–Δδ13C regression. Below we provide pertinent information
on the GENIE and CYCLOPS model experiments.
We use the Earth system model GENIE-1 (Edwards and Marsh, 2005; Ridgwell et
al. 2007) to assess the robustness of the ΔpH–Δδ13C relationship and its sensitivity to physical and biogeochemical ocean
forcing. The configuration used here is closely related to that of Holden et
al. (2013), in which the controls on oceanic δ13C distribution were
assessed, with an energy and moisture balance in the atmosphere, simple
representations of land vegetation and sea ice, and frictional geostrophic
ocean physics. In each of the 16 vertical levels in the ocean, increasing in
thickness with depth, there are 36 × 36 grid cells (10∘ in
longitude and nominally 5∘ in latitude, with higher resolution at low
latitudes). Modern ocean bathymetry and land topography is applied in all
simulations. The ocean biogeochemical scheme (Ridgwell et al., 2007) is based
on conversion of DIC to organic carbon associated with phosphate uptake with
fixed P : C : O stoichiometry. Organic carbon and nutrients are
remineralized according to a remineralization profile with a predefined
e-folding depth scale. This depth scale, as well as the rain ratio
of inorganic to organic carbon in sinking particulate matter, is among the
parameters examined in the sensitivity study. In these simulations, there is
no interaction with sediments. As a result of this, the steady state
solutions reported here are reached within the 5000-year simulations, but
they are not consistent with being in secular steady state with regard to the
balance of continental weathering and ocean CaCO3 burial.
The sensitivity study consists of seven sets of experiments, each varying a
single model parameter relative to the control simulation with preindustrial
atmospheric pCO2. This enables us to assess which processes, if
any, are capable of altering the oceanic relationship between ΔpH and
Δδ13C and the uncertainty in the predictive skill of this
relationship due to spatial variability. These experiments are therefore
exploratory in nature and intended to study plausible range rather than
determine magnitude of past changes. The seven parameters varied are (1) the
ocean alkalinity reservoir; (2) the ocean's carbon reservoir; (3) the
parameter “S. Lim gas exchange”, which
blocks air–sea gas exchange south of the stated latitude, significant here
because of the dependence of δ13C on surface disequilibrium
(Galbraith et al., 2015); (4) inorganic-to-organic carbon rain ratio,
controlling the relationship between DIC and alkalinity distributions;
(5) “Antarctic shelf FWF”, a freshwater flux adjustment (always switched
off in control experiments with GENIE) facilitating the formation of
brine-rich waters, which produces a high-salinity poorly ventilated deep
ocean at high values; (6) “Atlantic–Pacific FWF”, a freshwater flux
adjustment equivalent to freshwater hosing, leading to a shut down of the
Atlantic meridional overturning circulation at low values; and
(7) remineralization depth scale of sinking organic matter, which affects the
vertical gradient of both pH and δ13C. A wide range of parameter
values is chosen for each parameter in order to exceed any plausible changes
within the Cenozoic.
For the second exploration of the controls on the slope of the ΔpH–Δδ13C relationship we use the CYCLOPS biogeochemical
18-box model that includes a dynamical lysocline, a subantarctic zone surface
box and a polar Antarctic zone box (Sigman et al., 1998; Hain et al., 2010,
2014b). The very large model ensemble with 13 500 individual model scenarios
is designed to capture the full plausible range of (a) glacial–interglacial
carbon cycle states by sampling the full solution space of Hain et al.
(2010) and (b) reconstructed secular changes in seawater [Ca] (calcium
concentration), carbonate compensation depth (CCD), weathering and
atmospheric CO2 (Table 1). The following seven model parameters are
systematically sampled to set the 13 500 model scenarios: (1) shallow- versus
deep-Atlantic meridional overturning circulation represented by modern
reference North Atlantic deep water (NADW) versus peak glacial North Atlantic
intermediate water (GNAIW) circulation; (2) iron-driven changes in nutrient
drawdown in the subantarctic zone of the Southern Ocean; (3) changes in
nutrient drawdown of the polar Antarctic; (4) changes in vertical exchange
between the deep Southern Ocean and the polar Antarctic surface; (5) range in
seawater [Ca] concentration from 1× to 1.5× modern as per
reconstructions (Horita et al., 2002); (6) Pacific CCD is set to the range of
4.4–4.9 km via changes in the weathering flux, as per sedimentological
evidence (Pälike et al., 2012); and (7) atmospheric CO2 is set from 200
to 1000 ppm by changes in the “weatherability” parameter of the silicate
weathering mechanism.
CYCLOPS model parameter values defining the ensemble of 13 500
simulations*.
Parameter
Description
Values assumed
PAZ surface phosphate**
Unutilized polar nutrient
1, 1.25, 1.5, 1.75, 2 µM
PAZ vertical exchange**
Bottom water formation
2, 7.75, 13.5, 19.25, 25 Sv
SAZ surface phosphate**
Unutilized polar nutrient
0.7, 0.825, 0.95, 1.075, 1.2 µM
AMOC circulation scheme**
Deep vs. shallow overturning
NADW, GNAIW
Representative time slice***
Age ([Ca2+] / CCD); calcium set outright; CCD set via riverine CaCO3 flux using inverse scheme
0 My (10.6 mM, 4.65 km),9 My (12.89 mM, 4.4 km),11 My (13.33 mM, 4.9 km),16 My (14.28 mM, 4.7 km),18 My (14.57 mM, 4.25 km),20 My (14.86 mM, 4.7 km)
Atm. CO2****
Set via silicate weatherability
200, 300, 400, 500, 600, 700, 800, 900, 1000 ppm
* The six parameters assume 5, 5, 5, 2, 9 and 6 values,
yielding 13 500 distinct parameter combinations. ** These
parameters are intended to span the full range of ocean carbon cycling over
late Pleistocene glacial–interglacial cycles, as described in more detail in
Hain et al. (2010). *** We selected representative time slices
based on local extrema in the CCD reconstruction of Pälike et al. (2012)
and we combine these with appropriate reconstructed calcium concentrations
based on Horita et al. (2002). These choices are intended to capture the
range of long-term steady state conditions of the open system CaCO3
cycle relevant to our study interval. **** These atmospheric
CO2 levels are chosen to span a range wider than expected for the study
interval. Following the silicate-weathering-feedback paradigm, long-term CO2
is fully determined by the balance of geologic CO2 sources and silicate
weathering, whereby faster acting processes of the open system CaCO3
cycle compensate relative to that CO2 level. All else equal, high
CO2 levels, low calcium concentrations and deep CCD correspond to high
bulk ocean carbon concentrations (Hain et al., 2015), with many of the
individual simulations of this ensemble exceeding 4000 µM DIC.
The ensemble spans predicted bulk ocean DIC between 1500 and
4500 µmol/kg, a wide range of ocean pH and CaCO3 saturation
states consistent with the open system weathering cycle, and widely different
states of the oceanic biological pump. All 13 500 model scenarios are run
for 2 million years after every single weatherability adjustment, part
of the CCD inversion algorithm, guaranteeing the specified CCD depth and
steady state with regard to the balance of continental weathering and ocean
CaCO3 burial for the final solution (unlike the GENIE simulations,
CaCO3 burial was entirely neglected due to computational cost of the
long model integrations it would require). The inverse algorithm typically
takes at least 10 steps to conversion, resulting in ∼ 300 billion
simulated years for this ensemble. This range of modelling parameters was
chosen to exceed the range of carbonate system and ocean circulation changes
that can be expected for the Neogene based on records of [Ca] and [Mg]
(Horita et al., 2002), CCD changes (Pälike et al., 2012), atmospheric
CO2 (Beerling and Royer, 2011) and records of glacial–interglacial
circulation change (Curry and Oppo, 2005).
Assessing uncertainty
δ11Bsw uncertainty was calculated using a Monte Carlo
approach where pH was calculated for deep and surface waters at each time
slice using a random sampling (n=10000) of the various input parameters
within their respective uncertainties as represented by normal distributions.
These uncertainties (2σ uncertainty in parentheses) are temperature
(±2 ∘C), salinity (±2 units on the practical salinity
scale),
[Ca] (±4.5 mmol kg-1), [Mg] (±4.5 mmol kg-1),
δ11Bplanktic (±0.15–0.42 ‰) and
δ11Bbenthic (±0.21–0.61 ‰). For the
estimate of the surface-to-sea floor pH gradient, we use the central value of
the ΔpH–Δδ13C relationship diagnosed from our
CYCLOPS and GENIE sensitivity experiments (i.e. 0.175 ‰-1; see
Sect. 3.2 below) and then we assign a ±0.05 uncertainty range with a
uniform probability (rather than a normal distribution) to the resulting
surface-to-sea floor ΔpH estimate (see also Table 2). Thus, the
magnitude of this nominal uncertainty is equivalent to a 0.14–0.21 ‰-1 ΔpH / Δδ13C uncertainty
range that spans the vast majority of our CYCLOPS and GENIE simulations and
the prediction error (RMSE) of fitting a linear relationship to the GENIE pH
and δ13C output (see Sect. 3.2 below). The uncertainty in the
δ11B measurements is calculated from the long-term reproducibility
of Japanese Geological Survey Porites coral standard (JCP;
δ11B = 24.3 ‰) at the University
of Southampton using the equations
2σ=2.25exp-23.01[11B]+0.28exp-0.64[11B]2σ=33450exp-168.2[11B]+0.311exp-1.477[11B],
where [11B] is the intensity of 11B signal in volts and Eqs. (4)
and (5), used with 1011Ω and 1012Ω resistors
respectively.
Uncertainty inputs into the Monte Carlo simulations to calculate
δ11B. The sources of uncertainty are also added. All uncertainty
estimates are 2σ.
Input parameter
Uncertainty applied
Source of uncertainty estimate
Surface-to-sea floor ΔpH
Uniform ±0.05 pH units
Plausible range of ΔpH / Δδ13C in CYCLOPS and GENIE sensitivity tests; prediction error of linear ΔpH / Δδ13C regression in GENIE
δ11B measurement
0.15–0.61 ‰
Long-term external reproducibility
Temperature
±2 ∘C
Uncertainty in the Mg / Ca measurement and Mg / Ca temperature calibration
Salinity
±2 psu
In the absence of a salinity proxy this uncertainty is applied to cover variations through time.
Seawater [Mg]
±4.5 mmol kg-1
Following Horita et al. (2002)
Seawater [Ca]
±4.5 mmol kg-1
Following Horita et al. (2002)
From the 10 000 Monte Carlo ensemble solutions of our 22 benthic–planktic
pairs, we construct 10 000 randomized records of δ11Bsw
as a function of time. Each of these randomized δ11Bsw
records are subjected to smoothing using the locally weighted scatterplot
smoothing (LOWESS) algorithm with a smoothing parameter (span) of 0.7. The
purpose of the smoothing is to put some controls on the rate at which the
resulting individual Monte Carlo δ11Bsw records are
allowed to change, which in reality is limited by the seawater boron mass
balance (∼ 0.1 ‰ per million years; boron residence time is
11–17 million years; Lemarchand et al., 2000). Our choice of smoothing
parameter allows for some of the individual Monte Carlo records to change as
fast as ∼ 1 ‰ per million years, although in reality the
average rate of change is much smaller than this (see Sect. 3.3).
Consequently, this method removes a significant amount of uncorrelated
stochastic noise (resulting from the uncertainty in our input parameters),
while not smoothing away the underlying signal. As a result of anomalously
low δ11B differences (< 1 ‰) between benthic and
planktic pairs, two pairs at 8.68 and 19 Ma were discarded from the
smoothing. It may be possible that preservation is not so good within these
intervals and the planktic foraminifera are affected by partial dissolution
(Seki et al., 2010). The spread of the ensemble of smoothed
δ11Bsw curves represents the combination of the
compounded, propagated uncertainties of the various inputs (i.e. Monte Carlo
sampling) with the additional constraint of gradual
δ11Bsw change over geological time imposed by the
inputs and outputs of boron to the ocean and the total boron inventory (i.e.
the smoothing of individual Monte Carlo members). Various statistical
properties (i.e. mean, median, standard deviation (σ), various
quantiles) of this δ11Bsw reconstruction were evaluated
from the ensemble of smoothed δ11Bsw records.
Generally, for any given benthic–planktic pair the resulting δ11Bsw estimates are not perfectly normally distributed and
thus we use the median as the metric for the central tendency (i.e.
placement of marker in Fig. 10).
The average δ11B, δ26Mg, δ44/40Ca and δ7Li composition of
major fluxes into and out of the ocean. Colour coding reflects the
relative importance of each of the processes (darker shading reflects greater
importance). The colour coding for boron is based on Lemarchand et al. (2000) and references therein, lithium on Misra and Froelich (2012) and
references therein, magnesium on Tipper et al. (2006b) and calcium on
Fantle and Tipper (2014) and Griffin et al. (2008) and references therein.
Modern δ26Mgsw and δ11Bsw are from Foster et al. (2010) and δ7Lisw is from
Tomascak (2004). The δ44/40Ca presented here was
measured relative to seawater and hence seawater has a δ44/40Casw of zero per mill by definition.
The isotopic ratio of each process is (a) Lemarchand et
al. (2000) and references therein; (b) Misra and Froelich (2012) and
references therein; (c) Burton and Vigier (2012); (d) Tipper et al. (2006b);
(e) Wombacher et al. (2011); (f) includes dolomitization; (g) removal through
hydrothermal activity; (h) Griffith et al. (2008); (i) Fantle and
Tipper (2014) and references therein; (j) dolomitization may be an important
component of the carbonate flux. n/a = not applicable.
δ11Bplanktic, temperature and δ13CDIC estimates for the surface and deep ocean through the
last 23 million years. (a) δ11Bplanktic surface;
(b) δ11Bborate deep from benthic foraminifera
(blue) from this study and (green) Raitzsch and Hönisch (2013). The error
bars show the analytical external reproducibility at 95 % confidence for
this study. For the Raitzch and Hönisch (2013) data the error bars
represent propagated uncertainties of external reproducibilities of time
equivalent benthic foraminifer samples from different core sites in different
ocean basins; (c) Mg / Ca based temperature reconstructions of
surface dwelling planktic foraminifera; (d) deep-water temperature
estimates from Cramer et al. (2011); (e) δ13CDIC
surface record; and (f) δ13CDIC benthic record.
Squares depict ODP Site 999, triangles are ODP Site 758, diamonds are ODP
Site 926 and circles are ODP Site 761. Species are highlighted by colour:
Orange is T. trilobus, purple G. ruber, pink G. praebulloides, dark blue Cibicidoides wuellerstorfi and light blue
Cibicidoides mundulus. The two benthic–planktic pairs that were
removed prior to smoothing are highlighted with arrows.
The output from GENIE sensitivity analysis showing the
warm surface-to-cold deep ΔpH–Δδ13C
relationship. A pre-industrial model set-up was taken and perturbations were
made to alkalinity inventory, carbon inventory, Antarctic shelf freshwater
flux (Sv), Atlantic–Pacific freshwater flux, S. Lim gas exchange (blocks
air–sea gas exchange south of the stated latitude), remineralization depth
scale (m) and rain ratio – as described in the methods section. Blue
circles depict the ΔpH–Δδ13C relationship
(where the colours reflect the CO2 level of each experiment) and red
open circles show the RMSE. The green
stars are the ΔpH–Δδ13C relationship for the
control experiment conducted at 292.67 ppm CO2. The green (open) points
show the RMSE for this control run. Inventories are dimensionless (1 is
control). For the Atlantic–Pacific FWF 1 is equivalent to 0.32 Sv. The
alkalinity and carbon inventory experiments are very extreme and
inconsistent with geologic evidence.
Results and discussion
δ11B benthic and planktic data
Surface and deep-ocean δ11B broadly show a similar but inverse
pattern to δ13C and temperature throughout the Neogene (Fig. 6).
The δ11B benthic record decreases from ∼ 15 ‰ at
24 Ma to a minimum of 13.28 ‰ at 14 Ma before increasing to
∼ 17 ‰ at present day (Fig. 6). This pattern and the range of
values in benthic foraminiferal δ11B are in keeping with previously
published Neogene δ11B benthic records measured using NTIMS
(Raitzsch and Hönisch, 2013). This suggests that our deep-water
δ11B record is representative of large-scale pH changes in the
global ocean. While the surface δ11Bplanktic remained
relatively constant between 24 and 11 Ma at ∼ 16 ‰, there has
been a significant increase in δ11B between the middle Miocene and
present (values increase to ∼ 20 ‰) (Fig. 6b). The
reconstructed surface water temperatures show a long-term decrease through
the Neogene from ∼ 28 to 24 ∘C, aside from during the Miocene
Climatic Optimum (MCO) where maximum Neogene temperatures were reached
(Fig. 6c). Following Cramer et al. (2011), deep-water temperatures decrease
from ∼ 12 to 4 ∘C at present day and similarly show
maximum temperatures in the MCO. Surface and deep-water
δ13CDIC both broadly decrease through the Neogene and
appear to co-vary on shorter timescales (Fig. 6e, f).
The relationship between δ13C and pH gradients
In the global modern ocean data, after accounting for the anthropogenic
carbon, the empirical relationship between in situ pH and DIC
δ13C is well described by a linear function with a slope of
0.201 ± 0.005 (2σ) (Fig. 5; Foster et al., 2012). However, this
slope is only defined by surface waters in the North Atlantic due to a
current lack of modern data where the impact of the Suess effect has been
corrected (Olsen and Ninneman, 2010). Consequently, we are not currently able
to determine the slope between the warm surface and cold deep ocean in the
modern ocean at our sites. Instead, here we use the two modelling experiments
to define this slope. In the control GENIE experiment (green star, Fig. 7),
the central value for the slope of the pH–δ13C relationship is
slightly greater than 0.2 ‰-1 for the full three-dimensional
data regression (not shown) and about 0.175 ‰-1 for the warm
surface–cold deep ΔpH–Δδ13C relationship (Fig. 7);
this is consistent with the theory for the effect of temperature gradients
(see Sect. 2.3). For both ways of analysing the GENIE output, the prediction
uncertainty of the regressions, the RMSE, is ∼ 0.05 ‰-1
under most conditions (open red circles in Fig. 7), with the exception of
cases where large changes in either DIC or alkalinity (ALK) yield somewhat larger
changes in the relationship between pH and δ13C (see below).
The output from sensitivity analysis of the relationship between
pH gradient and δ13C gradient from the 13 500-run CYCLOPS
ensemble (see text for model details). Panel (a) shows the mean gradient
when the results from all 18 ocean boxes are included in the regression.
Panel (b) shows only the boxes from the low-latitude ocean from all basins
and (c) shows the regression from only North Atlantic low-latitude boxes.
Note the lower ΔpH / Δδ11B slope at the lower
latitudes due to the effect of temperature. The 0.201 line in each panel is
the mean gradient when all the ocean boxes are included in the regression.
The pH gradient between surface and deep water through time
calculated from the δ13C gradient and using a flat probability
derived from the low-latitude ensemble regressions from the CYCLOPS model.
The modern pH gradients at each site are also plotted.
In our CYCLOPS model ensemble, the central values of the slopes of the full
three-dimensional pH–δ13C regressions and of the warm
surface-to-cold deep ΔpH–Δδ13C are
0.2047 ‰-1 (1σ of 0.0196 ‰-1; Fig. 8a)
and 0.1797 ‰-1 (1σ of 0.0213 ‰-1;
Fig. 8b) respectively. If we restrict our analysis of the CYCLOPS ensemble to
only the Atlantic basin warm surface-to-cold deep ΔpH–Δδ13C, where most of our samples come from, we find a relationship of
only 0.1655 ‰-1 (1σ of 0.0192 ‰-1;
Fig. 8c). That is, overall, we find near-perfect agreement between modern
empirical data and our GENIE and CYCLOPS experiments. Encouraged by this
agreement we select the warm surface-to-cold deep ΔpH–Δδ13C central value of 0.175 ‰-1 to estimate the
surface–sea floor pH difference from the planktic–benthic foraminifera
δ13C difference. To account for our ignorance as to the accurate
value of ΔpH–Δδ13C in the modern ocean, its temporal
changes over the course of the study interval and the inherent prediction
error from using a linear ΔpH–Δδ13C relationship, we
assign a nominal uniform uncertainty range of ±0.05 around the central
ΔpH estimate for the purpose of Monte Carlo uncertainty propagation.
Our analysis also suggests that where surface-to-thermocline
planktic–planktic gradients are
employed, the plausible ΔpH–Δδ13C range should be
significantly higher than applied here to account for the relatively lower
temperature difference. Based on the appropriate ΔpH–Δδ13C relationship, we reconstruct a time varying surface-to-deep pH
gradient, which ranges between 0.14 and 0.35 pH units over our study
interval (Fig. 9); we also apply a flat uncertainty of ±0.05. The
reconstructed pH gradient remains broadly within the range of the modern
values (0.19 to 0.3), although there is some evidence of multimillion-year
scale variability (Fig. 9).
The calculated δ11Bsw from the
benthic–planktic δ11B pairs using a pH gradient derived from
δ13C . The uncertainty on each data point is determined using a
Monte Carlo approach, including uncertainties in temperature, salinity,
δ11B and pH gradient (see text for details). Data are plotted
as box and whisker diagrams where the median and interquartile ranges as
plotted in the box and whiskers show the maximum and minimum output from the
Monte Carlo simulations. The line of best fit is the probability maximum of a
LOWESS fit given the uncertainty in the calculated
δ11Bsw. The darker shaded area highlights the 68 %
confidence interval and the lighter interval highlights the 95 %
confidence interval. The bottom panel shows box plots of the mean and 2
standard error (SE) of “binning” the individual
δ11Bsw measurements into 8 My intervals. The middle
line is the mean and the box shows the 2 SE of the data points in that bin.
The smoothed record is also plotted for comparison where the line of best fit
is the probability maximum of a LOWESS fit given the uncertainty in the
calculated δ11Bsw. The darker shaded area highlights
the 68 % confidence interval and the lighter interval highlights the
95 % confidence interval. The black dot is the modern value of
39.61 ‰ (Foster et al., 2010).
The δ11Bsw curve calculated using the variable
pH gradient derived from δ13C. The median (red line), 68 %
(dark red band) and 95 % (light red band) confidence intervals are
plotted. Plotted with a compilation of published δ11Bsw
records. Seawater composition reconstructed from foraminifera depth profiles
(light blue squares and dark blue crosses) from Pearson and Palmer (2000) and
Foster et al. (2012) respectively; numerical modelling (green line), with
additional green lines showing ±1 ‰ confidence interval
(Lemarchand et al., 2000); and benthic δ11B (purple diamonds and
dark purple line showing 5-point moving average is using the fractionation
factor of Klochko et al., 2006, light purple line showing 5-point moving
average using an empirical calibration) from Raitzsch and Hönisch (2013).
All the published δ11Bsw curves are adjusted so that at
t=0 and the isotopic composition is equal to the modern (39.61 ‰).
As a caveat to our usage of the ΔpH–Δδ13C
relationship, we point to changes in that relationship that arise in our GENIE
sensitivity experiments where carbon and alkalinity inventories are
manipulated, which can yield values outside of what is plausible. We note
that our CYCLOPS ensemble samples a very much wider range of carbon and
alkalinity inventories, with ΔpH–Δδ13C remaining
inside that range. While CYCLOPS simulates the balance between weathering and
CaCO3 burial, which is known to neutralize sudden carbon or alkalinity
perturbations on timescales much less than 1 million years, the
configuration used for our GENIE simulations does not and is therefore
subject to states of ocean carbon chemistry that can safely be ruled out for
our study interval and likely for most of the Phanerozoic. The differing
outputs from CYCLOPS and GENIE in the DIC and ALK experiments shows that
ΔpH–Δδ13C depends on background seawater
acid–base chemistry in ways that are not yet fully understood. That said,
the generally coherent nature of our results confirms that we likely
constrain the plausible range of ΔpH–Δδ13C for
at least the Neogene, if not the entire Cenozoic, outside of extreme events
such as the Palaeocene–Eocene Thermal Maximum.
δ11Bsw record through the Neogene
Using input parameter uncertainties as described in section 2.5 yields
individual Monte Carlo member δ11Bsw estimates between
30 and 43.5 ‰ at the overall extreme points and typically ranging by
∼ 10 ‰ (dashed in Fig. 10a) for each time point. This suggests
that the uncertainties we assign to the various input parameters are generous
enough not to predetermine the quantitative outcomes. However, for each
planktic–benthic time point most individual Monte Carlo
δ11Bsw estimates fall into a much narrower central
range (∼ 1 to 4 ‰; thick black line showing interquartile
range in Fig. 10a). The δ11Bsw for Plio–Pleistocene
time points cluster around ∼ 40 ‰, while middle–late Miocene
values cluster around ∼ 36.5 ‰. The estimates at individual
time points are completely independent from each other, such that the
observed clustering is strong evidence for an underlying long-term signal in
our data, albeit one that is obscured by the uncertainties involved in our
individual δ11Bsw estimates. The same long-term signal
is also evident when pooling the individual Monte Carlo member δ11Bsw estimates into 8-million-year bins and evaluating the
mean and spread (2σ) in each bin (Fig. 10b). This simple treatment
highlights that there is a significant difference between our
Plio–Pleistocene and middle Miocene data bins at the 95 % confidence
level and that δ11Bsw appears to also have been
significantly lower than present day during the early Miocene.
Data smoothing
The ∼ 1 to 4 ‰ likely ranges for δ11Bsw
would seem to be rather disappointing given the goal to constrain
δ11Bsw for pH reconstructions. However, most of that
uncertainty is stochastic random error that is uncorrelated from time point
to time point. Furthermore, we know from mass balance considerations that
δ11Bsw of seawater should not change by more than
∼ 0.1 ‰ per million years (Lemarchand et al., 2000) because
of the size of the oceanic boron reservoir compared the inputs and outputs
(see Fig. 1). We use this as an additional constraint via the LOWESS
smoothing we apply to each Monte Carlo time series. One consideration is that
every individual Monte Carlo δ11Bsw estimate is equally
likely and the smoothing should therefore target randomly selected individual
Monte Carlo δ11Bsw estimates, as we do here, rather
than smoothing over the likely ranges identified for each time point. In this
way the smoothing becomes an integral part of our Monte Carlo uncertainty
propagation and the spread among the 10 000 individual smoothed
δ11Bsw curves carries the full representation of
propagated input uncertainty conditional on the boron cycle mass balance
constraint. A second consideration is that the smoothing should only remove
noise, not underlying signal. As detailed above, for this reason the
smoothing parameter we chose has enough freedom to allow the
δ11Bsw change to be dictated by the data, with only the
most extreme shifts in δ11Bsw removed. We also tested
the robustness of the smoothing procedure itself (not shown) and found only
marginal changes when changing algorithm (LOESS versus LOWESS, with and
without robust option) or when reducing the amount of smoothing (i.e.
increasing the allowed rate δ11Bsw change). The
robustness of our smoothing is further underscored by the good correspondence
with the results of simple data binning (Fig. 10b).
Comparison to other δ11Bsw records
The comparison of our new δ11Bsw record to those
previously published reveals that despite the differences in methodology, the
general trends in the records show excellent agreement. The most dominant
common feature of all the existing estimates of Neogene δ11Bsw evolution is an increase through time from the middle
Miocene to the Plio–Pleistocene (Fig. 11). While the model-based δ11Bsw record of Lemarchand et al. (2000) is defined by a
monotonous and very steady rise over the entire study interval, all three
measurement-based records, including our own, are characterized by a single
dominant phase of increase between roughly 12 and 5 Ma. Strikingly, the
Pearson and Palmer (2000) record falls almost entirely within our 95 %
likelihood envelope, overall displaying very similar patterns of long-term
change but with a relatively muted amplitude and overall rate of change
relative to our reconstruction. Conversely, some of the second-order
variations in the reconstruction by Raitzsch and Hönisch (2013) are not
well matched by our reconstruction. However, the dominant episode of rapid
δ11Bsw rise following the middle Miocene is in almost
perfect agreement. We are encouraged by these agreements resulting from
approaches based on very different underlying assumptions and techniques,
which we take as indication for an emerging consensus view of δ11Bsw evolution over the last 25 My and as a pathway towards
reconstructing δ11Bsw further back in time.
(a) The δ11Bsw curve from this study
plotted with other trace element isotopic records. On the
δ11Bsw panel the darker shaded area highlights the
68 % confidence interval and the lighter interval highlights the 95 %
confidence interval, (b) δ26Mgsw record from
Pogge von Strandmann et al. (2014) (error bars are ±0.28 ‰ and
include analytical uncertainty and scatter due to the spread in modern
O. universa and the offset between the two analysed species),
(c) δ44/40Casw record from Griffith et
al. (2008) (error bars show 2σ uncertainty) and (d)
δ7Lisw record from Misra and Froelich (2012) (error
bars show 2σ uncertainty). Blue dashed lines show middle Miocene
values and red dashed lines highlight the modern.
Crossplots of the records of δ11Bsw using a
variable pH gradient derived from δ13C (error bars show 2σ uncertainty), with δ44/40Casw from Griffith et
al. (2008) (error bars show 2σ uncertainty), δ7Lisw from Misra and Froelich (2012) (error bars show 2σ
uncertainty) and δ26Mgsw from Pogge von Strandmann et al. (2014) (error bars are ±0.28 ‰ and include
analytical uncertainty and scatter due to the spread in modern O. universa and the offset between the two analysed species). The colour of
the data points highlights the age of the data points where red = modern
and blue = 23 Ma.
The record by Pearson and Palmer (2000) is well correlated to our
reconstruction, but especially during the early Miocene there is a notable
∼ 0.5 ‰ offset (Fig. 11). This discrepancy could be due to a
number of factors. Firstly, the applicability of this
δ11Bsw record (derived from δ11B data
measured using NTIMS) to δ11B records generated using the MC-ICPMS
is uncertain (Foster et al., 2013). In addition, this δ11Bsw record is determined using a fractionation factor of
1.0194 (Kakihana et al., 1977), whereas recent experimental data have shown
the value to be higher (1.0272 ± 0.0006, Klochko et al., 2006),
although foraminiferal vital effects are likely to mute this discrepancy.
Thirdly, given our understanding of the δ11B difference between
species–size fractions (Foster, 2008; Henehan et al., 2013), the mixed
species and size fractions used to make the δ11B measurements in
that study may have introduced some additional uncertainty in the
reconstructed δ11Bsw. Conversely, there is a substantial
spread between our three time points during the earliest Miocene, which
combined with the edge effect of the smoothing, gives rise to a widening
uncertainty envelope during the time of greatest disagreement with Pearson
and Palmer (2000). This could be taken as an indication that our reconstruction,
rather than that of Pearson and Palmer, is biased during the early Miocene.
The δ11Bsw record calculated using benthic δ11B and assumed deep-ocean pH changes (Raitzsch and Hönisch, 2013) is
also rather similar to our δ11Bsw reconstruction. The
discrepancy between the two records in the early Miocene could plausibly be
explained by bias in our record (see above) or may in part be a result of
the treatment of surface water pH in the study of Raitzsch and Hönisch
(2013) and their assumption of a constant surface–deep pH gradient (see
Fig. 9). The combined output from two carbon cycle box models is used to make
the assumption that surface ocean pH near-linearly increased by 0.39 over the
last 50 My. The first source of surface water pH estimates is from the
study of Ridgwell et al. (2005), where CO2 proxy data (including some
derived using the boron isotope–pH proxy) is used, leading to some circularity
in the methodology. The second source of surface water pH estimates is from
Tyrrell and Zeebe (2004) and based on the GEOCARB model, where the circularity problem
does not apply. While this linear pH increase broadly matches the CO2
decline from proxy records between the middle Miocene and present, it is at
odds with the CO2 proxy data during the early Miocene that show that CO2
was lower than the middle Miocene during this interval (Beerling and Royer,
2011). Consequently, the proxy CO2 and surface water pH estimates may not
be well described by the linear change in pH applied by Raitzsch and
Hönisch (2013) across this interval, potentially contributing to the
discrepancy between our respective δ11Bsw
reconstructions.
Our new δ11Bsw record falls within the broad
uncertainty envelope of boron mass balance calculations of Lemarchand et
al. (2000), but those modelled values do not show the same level of
multimillion-year variability of either Raitzsch and Hönisch (2013) or
our new record. This therefore suggests that the model does not fully account
for aspects of the changes in the ocean inputs and outputs of boron through
time on timescales less than ∼ 10 million years.
In line with the conclusions of previous studies (e.g. Raitzsch and
Hönisch, 2013), our data show that the δ11Bsw
signal in the fluid inclusions (Paris et al., 2010) is most likely a
combination of the δ11Bsw and some other factor such as
a poorly constrained fractionation factor between the seawater and the
halite. Brine–halite fractionation offsets of -20 to -30 ‰ and
-5 ‰ are reported from laboratory and natural environments
(Vengosh et al., 1992; Liu et al., 2000). These fractionations and riverine
input during basin isolation will drive the evaporite-hosted boron to
low-δ11B isotope values such that the fluid inclusion record likely
provides a lower limit for the δ11Bsw through time
(i.e. δ11Bsw is heavier than the halite fluid
inclusions of Paris et al., 2010). For this halite record to be interpreted
directly as δ11Bsw, a better understanding of the
factor(s) controlling the fractionation during halite formation and any
appropriate correction need to be better constrained.
Common controls on the seawater isotopic ratios of B, Mg, Ca and
Li
Our new record of δ11Bsw has some substantial
similarities to secular change seen in other marine stable isotope records
(Fig. 12). The lithium isotopic composition of seawater
(δ7Lisw; Misra and Froelich, 2012) and the calcium
isotopic composition of seawater as recorded in marine barites
(δ44/40Casw; Griffith et al., 2008) both increase
through the Neogene, whereas the magnesium isotopic composition of seawater
(δ26Mgsw) decreases (Pogge von Strandmann et al.,
2014),
suggesting a similar control on the isotopic composition of all four elements
across this time interval (Fig. 12). To further evaluate the correlation
between these other marine isotope records and δ11Bsw,
we interpolate and cross plot δ11Bsw and the
δ7Lisw, δ44/40Casw and
δ26Mgsw records. This analysis suggests that the
isotopic composition of δ11Bsw, δ7Lisw, δ26Mgsw and
δ44/40Casw are well correlated through the Neogene,
although there is some scatter in these relationships (Fig. 13). Although the
Sr isotope record shows a similar increase during the Neogene (Hodell et al.,
1991), we focus our discussion on δ11Bsw,
δ7Lisw, δ26Mgsw and
δ44/40Casw, given that the factors fractionating these
stable isotopic systems are similar (see below).
To better constrain the controls on δ11Bsw, δ7Lisw, δ26Mgsw and δ44/40Casw, it is instructive to compare the size and isotopic
composition of the fluxes of boron, lithium, calcium and magnesium to the
ocean (Table 3). The major flux of boron into the ocean is via riverine input
(Lemarchand et al., 2000), although some studies suggest that atmospheric
input may also play an important role (Park and Schlesinger, 2002). The loss
terms are dominated by adsorption onto clays and the alteration of oceanic
crust (Spivack and Edmond, 1987; Smith et al., 1995). Similarly, the primary
inputs of lithium into the ocean come from hydrothermal sources and riverine
input and the main outputs are ocean crust alteration and adsorption onto
sediments (Misra and Froelich, 2012). The three dominant controls on
magnesium concentration and isotope ratio in the oceans are the riverine
input, ocean crust alteration and dolomitization (Table 3) (Tipper et al.,
2006b). The main controls on the amount of calcium in the modern ocean and
its isotopic composition are the balance between riverine and hydrothermal
inputs and removal through CaCO3 deposition and alteration of oceanic
crust (Fantle and Tipper, 2014; Griffith et al., 2008). Dolomitization has
also been cited as playing a potential role in controlling
δ44/40Casw, although the contribution of this process
through time is poorly constrained (Griffith et al., 2008).
Analysis of the oceanic fluxes of all four ions suggests that riverine input
may be an important factor influencing the changing isotopic composition of
B, Li, Ca and Mg over the late Neogene (Table 3). In the case of all four
elements, a combination of the isotopic ratio of the source rock and isotopic
fractionation during weathering processes are typically invoked to explain
the isotopic composition of a particular river system. However, in most cases
the isotopic composition of the source rock is found to be of secondary
importance (Rose et al., 2000; Kisakũrek et al., 2005; Tipper et al.,
2006b; Millot et al., 2010). For instance, the δ11B composition of
rivers is primarily dependent on isotopic fractionation during the reaction
of water with silicate rocks and to a lesser extent the isotopic composition
of the source rock (i.e. the proportion of evaporites and silicate rocks;
Rose et al., 2000). While some studies have suggested that the isotope
composition of rainfall within the catchment area may be an important factor
controlling the δ11B in rivers (Rose-Koga et al., 2006), other
studies have shown atmospheric boron to be a secondary control on riverine
boron isotope composition (Lemarchand and Gaillardet, 2006). The source rock
also appears to have limited influence on the δ7Li composition of
rivers, and riverine δ7Li varies primarily with weathering intensity
(Kisakũrek et al., 2005; Millot et al., 2010). The riverine input of
calcium to the oceans is controlled by the composition of the primary
continental crust (dominated by carbonate weathering) and a recycled
component, although the relative influence of these two processes is not well
understood (Tipper et al., 2006a). In addition, vegetation may also play a
significant role in the δ44/40Ca of rivers (Fantle and Tipper,
2014). For Mg, the isotopic composition of the source rock is important for
small rivers; however, lithology is of limited significance at a global scale
in comparison to fractionation in the weathering environment (Tipper et al.,
2006b). Given the lack of evidence of source rock as a dominant control on
the isotopic composition of rivers, here we focus on some of the possible
causes for changes in the isotopic composition and/or flux of riverine input
over the Neogene.
In this regard, of the four elements discussed here, the Li isotopic system
is the most extensively studied. Indeed, the change in δ7Lisw has already been attributed to an increase in the
δ7Lisw composition of the riverine input (Hathorne and
James, 2006; Misra and Froelich, 2012). The causes of the shift in δ7Li riverine have been variably attributed to (1) an increase in
incongruent weathering of silicate rocks and secondary clay formation as a
consequence of Himalayan uplift (Misra and Froelich, 2012; Li and West,
2014), (2) a reduction in weathering intensity (Hathorne and James, 2006;
Froelich and Misra, 2014; Wanner et al., 2014), (3) an increase in silicate
weathering rate (Liu et al., 2015), (4) an increase in the formation of
floodplains and the increased formation of secondary minerals (Pogge von
Strandmann and Henderson, 2014), and (5) a climatic control on soil production
rates (Vigier and Godderis, 2015). In all five cases the lighter isotope of
Li is retained on land in clay and secondary minerals. A mechanism associated
with either an increase in secondary mineral formation or the retention of
these minerals on land is also consistent across Mg, Ca and B isotope
systems. For instance, clay minerals are preferentially enriched in the light
isotope of B (Spivack and Edmond, 1987; Deyhle and Kopf, 2004; Lemarchand and
Gaillardet, 2006) and Li (Pistiner and Henderson, 2003), and soil carbonates
and clays are preferentially enriched in the light isotope of Ca (Tipper et
al., 2006a; Hindshaw et al., 2013; Ockert et al., 2013). The formation of
secondary silicate minerals, such as clays, is assumed to preferentially take
up the heavy Mg isotope into the solid phase (Tipper et al., 2006a, b; Pogge
von Strandmann et al., 2008; Wimpenny et al., 2014), adequately explaining
the inverse relationship between δ11Bsw and
δ26Mgsw. Consequently, the increased formation or
retention on land of secondary minerals would alter the isotopic composition
of the riverine input to the ocean in the correct direction to explain the
trends in all four isotope systems through the late Neogene (Fig. 13). While
the relationships between the different isotope systems discussed here
suggest a common control, the influence of carbonate and dolomite formation
on Ca and Mg isotopes are also likely to have played a significant role in
the evolution of these isotope systems (Tipper et al., 2006b; Fantle and
Tipper, 2014). Consequently, a future model of seawater chemistry evolution
through the Neogene must also include these additional factors. Further
exploration is also needed to determine the influence of residence time on
the evolution of ocean chemistry. Nonetheless, given the similarities between
the geochemical cycles of B and Li, and despite the large difference in
residence time (Li = 1 million years, B = 11–17 million years), the
correlation between these two records is compelling and would no doubt
benefit from additional study.