CPClimate of the PastCPClim. Past1814-9332Copernicus PublicationsGöttingen, Germany10.5194/cp-12-1181-2016Palaeogeographic controls on climate and proxy interpretationLuntDaniel J.d.j.lunt@bristol.ac.ukhttps://orcid.org/0000-0003-3585-6928FarnsworthAlexLoptsonClaireFosterGavin L.https://orcid.org/0000-0003-3688-9668MarkwickPaulO'BrienCharlotte L.PancostRichard D.RobinsonStuart A.WrobelNeilSchool of Geographical Sciences, and Cabot Institute, University of
Bristol, Bristol, BS8 1SS, UKOcean and Earth Science, University
of Southampton, and National Oceanography Centre, Southampton,
SO14 3ZH, UKGetech Plc, Leeds, LS8 2LJ, UKDepartment
of Earth Sciences, University of Oxford, Oxford, OX1 3AN, UKDepartment of Geology and Geophysics, Yale University, New Haven,
CT 06511, USASchool of Chemistry, and Cabot Institute, University
of Bristol, Bristol, BS8 1TS, UKDaniel J. Lunt (d.j.lunt@bristol.ac.uk)20May20161251181119816November201514December201526April2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://cp.copernicus.org/articles/12/1181/2016/cp-12-1181-2016.htmlThe full text article is available as a PDF file from https://cp.copernicus.org/articles/12/1181/2016/cp-12-1181-2016.pdf
During the period from approximately 150 to 35 million years ago,
the Cretaceous–Paleocene–Eocene (CPE), the Earth was in a
“greenhouse” state with little or no ice at either pole. It was
also a period of considerable global change, from the warmest
periods of the mid-Cretaceous, to the threshold of icehouse
conditions at the end of the Eocene. However, the relative
contribution of palaeogeographic change, solar change, and carbon
cycle change to these climatic variations is unknown. Here, making
use of recent advances in computing power, and a set of unique
palaeogeographic maps, we carry out an ensemble of 19 General
Circulation Model simulations covering this period, one simulation
per stratigraphic stage. By maintaining atmospheric CO2
concentration constant across the simulations, we are able to
identify the contribution from palaeogeographic and solar forcing to
global change across the CPE, and explore the underlying mechanisms.
We find that global mean surface temperature is remarkably constant
across the simulations, resulting from a cancellation of opposing
trends from solar and palaeogeographic change. However, there are
significant modelled variations on a regional scale. The
stratigraphic stage–stage transitions which exhibit greatest
climatic change are associated with transitions in the mode of ocean
circulation, themselves often associated with changes in ocean
gateways, and amplified by feedbacks related to emissivity and
planetary albedo. We also find some control on global mean
temperature from continental area and global mean orography. Our
results have important implications for the interpretation of
single-site palaeo proxy records. In particular, our results allow
the non-CO2 (i.e. palaeogeographic and solar constant)
components of proxy records to be removed, leaving a more global
component associated with carbon cycle change. This “adjustment
factor” is used to adjust sea surface temperatures, as the deep
ocean is not fully equilibrated in the model. The adjustment factor
is illustrated for seven key sites in the CPE, and applied to proxy data
from Falkland Plateau, and we provide data so that similar
adjustments can be made to any site and for any time period within
the CPE. Ultimately, this will enable isolation of the
CO2-forced climate signal to be extracted from multiple
proxy records from around the globe, allowing an evaluation of the
regional signals and extent of polar amplification in response to
CO2 changes during the CPE. Finally, regions where the
adjustment factor is constant throughout the CPE could indicate
places where future proxies could be targeted in order to
reconstruct the purest CO2-induced temperature change, where
the complicating contributions of other processes are minimised.
Therefore, combined with other considerations, this work could
provide useful information for supporting targets for drilling
localities and outcrop studies.
Introduction
Over the last 150 million years, the climate of the Earth has
experienced change across a broad range of timescales, from geological
(10's of millions of years), to orbital 10's–100's of thousands of
years), to millenial, to decadal.
Variability on timescales from geological to orbital has been
characterised by measurements of the isotopic composition of oxygen in
the calcium carbonate of benthic foraminifera
(δ18Obenthic, e.g. , Fig. ); this indicates, in the broadest
sense, a long-term cooling (and increasing ice volume) trend from the
mid-Cretaceous (∼ 100 million years ago, 100 Ma) to the
modern. Imprinted on this general cooling are several shorter
geological-timescale variations such as cooling through the Paleocene
(65 to 55 Ma), and sustained warmings in the early Eocene (55
to 50 Ma) and middle Miocene (17 to 15 Ma). The
δ18Obenthic record also shows evidence for orbital
scale variability in icehouse periods (for example Quaternary
glacial–interglacial cycles, from around ∼ 2 Ma) and
greenhouse periods (for example Paleogene hyperthermals, ∼ 55 Ma), and other events occurring on sub-geological
timescales, such as the Eocene–Oligocene boundary (34 Ma) and
Cretaceous Oceanic Anoxic Events (OAEs, ∼ 100 Ma).
A methodology for reconstructing the global-mean surface temperature
of the past ∼ 65 million years has been developed by
, making assumptions about the relationship
between δ18Obenthic and deep ocean temperature,
and between deep ocean temperature and surface temperature
(Fig. ). The assumptions mean that the absolute
values of temperature need to be treated with considerable caution,
but the record indicates global mean surface temperatures of ∼ 25 ∘C in the Paleocene, peaking at over
28 ∘C in the early Eocene. From the early Eocene to
the present day, there is a general cooling trend, with temperatures
decreasing to ∼ 24 ∘C degrees by the late Eocene.
Today, global mean surface temperature is close to
15 ∘C.
Although it has long been thought that greenhouse gas concentrations
are the primary cause of Cretaceous and Eocene warmth
(; ), the reasons for variability
within this period are currently largely unknown. Possible candidates
for the forcing on the Earth system on these timescales include
changes in solar forcing, direct tectonic forcing (i.e. changes
in orography and bathymetry and continental position), and greenhouse gas
forcing (most likely CO2) through changes in the carbon cycle,
or some combination of these.
Climate evolution over the last 150 million years, as
expressed by the benthic oxygen isotope records of
and (coloured dots),
and a surface temperature record (H13C09) produced by applying the methodologies
of to the δ18Obenthic data, and applying a 10-point running average (grey line).
The values of solar constant since the earliest Cretaceous,
calculated from and used for the CPE
simulations in this paper (green line), and from
(orange line). Horizontal line shows the
modern value.
The solar forcing is related to an increase in solar constant,
resulting from an increasing luminosity of the sun over 10's of
millions of years. This itself is due to continued nuclear fusion in
the core of the Sun, converting hydrogen into helium, reducing the
core's density
Fig. ,. As a result,
the core contracts and temperature increases. The increase in
luminosity is a consequence of this core temperature increase.
According to the solar model of , the solar
constant increases nearly linearly from ∼ 1348 Wm-2
at 150 Ma to 1365 Wm-2 for the modern.
Also over the timescales of tens of millions of years, plate
tectonics has led to major changes in the position and configuration
of the continents, and bathymetric and topographic depths and heights.
These changes can have a direct effect on climate, for example through
changing the global distribution of low-albedo (ocean) vs. high-albedo
(land) surfaces e.g., and/or changing ocean
gateways leading to modified ocean circulation
e.g., and/or topography modifying the area of
land above the snowline e.g. or atmospheric
circulation e.g.. Palaeogeographical
reconstructions for certain periods in Earth's history exist
e.g., but not always at high temporal
resolution, nor in a form which can readily be implemented into
a climate model. In Sect. we present a new set
of palaeogeographical reconstructions which span the
Cretaceous–Paleocene–Eocene, from approximately 150 to 35 Ma
(Fig. S1 in the Supplement), and provide one of the
reconstructions (Ypresian stage) in digital format in the Supplement.
The greenhouse gas forcing is indirectly related to
tectonic changes, through changes in the balance of sources
(e.g. volcanism) and/or sinks e.g. weathering of silicate rocks,
of CO2, or other greenhouse gases, and/or
changes to the sizes of the relevant reservoirs (e.g. due to
changes in the residence time of carbon in the ocean due to changes in
ocean circulation, , themselves driven ultimately by tectonic
changes). In addition, greenhouse gases will also
likely be modified in response to climate changes caused by the solar
or direct tectonic forcing. Efforts have been made to reconstruct the
history of CO2 over geological timescales e.g. see
compilation in Fig. ,. However,
CO2 proxies are still associated with relatively large
uncertainties, despite currently undergoing a period of rapid
development e.g..
CO2 as compiled by (small dots).
Overlain is the CO2 concentration in each simulation described in
this paper (red dots). The open blue circles show CO2 concentration
in a set of model simulations of the Oligocene and Neogene, which are not
discussed in this paper but are provided for reference. Horizontal black
lines show 280, 560, and 1120 ppmv.
Disentangling these various forcings on long-term climate evolution is
a key challenge. Previous work has often been in a modelling
framework, and has focused on either the role of palaeogeography
across time e.g., or the role of
CO2 for a particular period e.g..
Given the uncertainties in CO2 concentration and the carbon
cycle, and to avoid complications of the feedbacks associated with
continental ice, in this paper we focus on the direct role of
palaeogeography and solar forcing on controlling the greenhouse
climates from the earliest Cretaceous to the end of the Eocene
(Cretaceous–Paleocene–Eocene, CPE). This also allows us to provide
an “adjustment factor” for palaeo proxy records, which accounts for
the non-CO2 component of climate change (see
Sect. ).
The very first attempts to model time periods within the CPE
were carried out in the laboratory, with rotating water tanks
covered with moulded foam representing palaeogeography, and jets
of compressed air simulating wind stress
. Several early numerical modelling
studies also focused on the ocean circulation, and in particular
the flow regime through the Tethys seaway
e.g.. The relative importance of
palaeogeography vs. surface albedo vs. greenhouse gases in warm
palaeoclimates was examined using what would now be considered
low resolution GCMs or
using energy-balance models . These
indicated that CO2 was likely the primary driver of
Cretaceous and Eocene warmth. The majority of modelling work
since then has focused on the periods of maximum warmth,
i.e. during the mid-Cretaceous e.g. or Early
Eocene e.g.; see summary in
, or transient hyperthermals such as the PETM
e.g.. Other work has examined other
relatively warm
periods such as the Late Cretaceous
. Several studies have
addressed issues of model–data comparisons, including the
interpretation of oxygen isotope proxies in both continental and
oceanic proxies e.g., or
uncertainties in Mg/Ca calibrations
e.g.. Recently, it has been
argued that if these uncertainties, and other issues such as
seasonality of the proxies, are taken into account, then some
models can simulate the climate of the early Eocene consistently
with the data .
Although no previous study has explored the role of varying
palaeogeography throughout the CPE as we do here, several previous
modelling studies are worth noting, which carried out sensitivity
studies to palaeogeography with a more limited scope.
carried out model simulations
under two different Cretaceous palaeogeographies, representing
conditions before and after the separation of the African and South
American continents to form the Atlantic. They found that continental
positions strongly influenced ocean circulation, in particular regions
of deep water formation. examined the role of
ocean gateways in the Eocene, and found that the configuration of
polar seaways affected the sensitivity of climate to hydrological
forcing, through changes in ocean overturning.
used three Cretaceous palaeogeographies, and compared a number of
model simulations with data from the Cretaceous Siberian continental
interior, but the sensitivity studies were not consistent across the
time periods. also examined three
palaeogeographies through the Cretaceous, using the FOAM model coupled
to a slab ocean. They focused on the influence of continentality on
seasonality, but noted that changing palaeogeography alone could give
a ∼ 4 ∘C global-mean warming at a constant
CO2 level.
Our work presented here builds on these and other previous studies,
but represents an advance because (a) new palaeogeographic maps of
this time period have become available, which improve on previous
representations in terms of both accuracy and temporal resolution (see
Sect. ) and (b) increases in available computing
power means that for the first time we can (i) spin up a large
number (19) of simulations through this time period towards
equilibrium, allowing unprecedented temporal resolution, and (ii) use
more advanced models than have typically been used previously.
The two key questions which we address are
How and why has palaeogeography and solar output affected global
mean, zonal mean, and local temperatures through the CPE?
What are the implications of our results for interpreting proxy
reconstructions on geological timescales?
Summary of all model simulations. The age column shows the age of
the middle of the respective Stage. The solar constants are calculated using
these ages according to the formula described in . The
smoothing indicates the changes that had to applied to each Stage to ensure
stability. F = fourier filtering at high latitudes, Ar = flat Arctic
ocean, An = flat polar Southern ocean, O1 = polar orographic
smoothing, O2 = polar orographic smoothing and capping of polar
topography, L = minor changes to land–sea mask. P3 and P4 indicate
Phases 3 and 4 of the simulations.
StageAge (Ma)Solar constant (Wm-2)CO2 (ppmv)smoothing P3smoothing P4EocenePriabonian35.71360.861120Bartonian39.01360.481120Lutetian44.71359.831120FFYpresian52.61358.911120F ArArPaleoceneThanetian57.31358.371120FSelandian60.61357.991120FArDanian63.91357.611120ArArCretaceousMaastrichtian68.21357.181120AnCampanian77.11356.161120FF O1 Ar LSantonian84.71355.241120ArArConiacian87.61354.921120F Ar AnF Ar AnTuronian91.41354.491120F AnF ArCenomanian96.41353.901120Ar O1Ar O1Albian105.81352.821120Ar LAr LAptian118.51351.381120ArBarremian127.51350.361120Hauterivian133.21349.721120O1Ar O1Valanginian138.31349.131120ArArBerriasian142.91348.651120O2Ar O2Experimental design
A set of 19 simulations was carried out, one for each stratigraphic
stage (henceforth “Stage”) between the earliest Cretaceous
(Berriasian, 146–140 Ma) and the latest Eocene (Priabonian,
37–34 Ma). This section describes the boundary conditions
implemented, the model used, and the simulation design (see
Table ).
Palaeogeographies
The reconstruction of tectonics, structures, and depositional
environments which underpin this study was created by Getech Plc
using methods based on those of . The palaeo
digital elevation models (henceforth “palaeogeographies”) used as
boundary conditions in the model for each Stage are informed by these
reconstructions, which are in turn constrained by extensive geological
databases. These data include published lithologic, tectonic
and fossil studies, the lithologic databases of the
Paleogeographic Atlas Project (University of Chicago), and deep
sea (DSDP/ODP) data. They are extensively updated from the
series described in ; critically, they include
bathymetric information which is essential for running coupled atmosphere–ocean
climate models, and which is absent from the maps.
These data are also used to develop the plate model on
which the palaeogeographies are built. The palaeogeographies were
produced at an original resolution of
0.5∘×0.5∘, and from these we generated
model-resolution (3.75∘×2.5∘) land–sea mask,
topography and bathymetry, and the sub-gridscale orographic variables
required by the model. In order to maintain stability, the
palaeogeographies were smoothed globally, with additional smoothing
applied in the Arctic. Furthermore, some palaeogeographies required
additional flattening of the Arctic and/or Antarctic bathymetry,
and/or smoothing of the topography, and/or minor changes to the
land–sea mask, at various phases of the spinup of the model
simulations, in order to maintain model stability. Details are given
in Table . The runoff routing is carried out
by assuming that rivers run downhill at the resolution of the model
orography (see Fig. S2).
The palaeogeographies are proprietary and cannot all be distributed
digitally, but figures showing the orography and bathymetry at the
model resolution (of Phase 4, see Sect. ), for each
Stage discussed in this paper, are included in the Supplement,
Fig. S1. In additon, for one stage (Ypresian, early Eocene),
we provide a digital version of the palaeogeography at the model
resolution in the Supplement.
Also provided with the palaeogeographies are
distributions of lakes (shown in the Supplement,
Fig. S3). Finally, for the latest two Stages in the CPE
(Bartonian and Priabonian), there are also very small ice caps prescribed
on Antarctica, which for the purposes of this study we consider part
of the palaeogeography, and which we assume have only a very small
effect on the results (also shown in the Supplement,
Fig. S3).
CO2 forcing
Geological proxy data for atmospheric CO2 on the timescale of
the CPE have large uncertainties, but, in general, indicate a mean of
between 2× and 4× pre-industrial (PI) CO2
concentrations, i.e. 560–1120 ppmv. This paper focuses on the
effects of changing palaeogeography and solar output. As such, we
keep CO2 constant at a prescribed value of 1120 ppm
(4 × PI) for all simulations. Therefore, any changes in
climate between different Stages are due to the palaeogeographical and
solar constant changes alone. The value of 1120 ppmv is
chosen to represent a reasonable estimate of atmospheric CO2
for the duration of the CPE (see Fig. 3), and can be considered as
also incorporating the contribution to radiative forcing form other
greenhouse gases, such as methane, which may also have been elevated
compared to modern . Future work will analyse the
climates of the CPE at lower or higher atmospheric CO2, and
compare the climate sensitivities of these different Stages.
Solar forcing
The insolation at the top of the atmosphere (Total Solar Irradiance,
TSI) for each Stage was calculated following ,
and is shown in Fig. . The evolution of TSI is
very similar to that from , also shown in
Fig. , illustrating that this forcing
likely has relatively small uncertainty.
Model description
The simulations described in this paper are all carried out using the
UK Met Office coupled ocean–atmosphere general circulation model
HadCM3L version 4.5. HadCM3L has been used in several palaeoclimate
studies for different geological periods including the early Eocene
e.g. and the late Miocene
e.g.. The resolution of the model is
3.75∘ in longitude by 2.5∘ in latitude, with 19
vertical levels in the atmosphere and 20 vertical levels the
ocean. The HadCM3L model is very similar to HadCM3, a description of
which can be found in , but HadCM3L has a lower
horizontal resolution in the ocean
(3.75∘× 2.5∘ compared with
1.25∘× 1.25∘).
In addition, HadCM3L is coupled to the dynamic global vegetation model
TRIFFID (Top-down Representation of Interactive Foliage and Flora
Including Dynamics) via the land surface scheme
MOSES 2.1 . TRIFFID calculates the fraction of each
gridcell occupied by each of five plant functional types: broadleaf
trees, needleleaf trees, C3 grasses, C4 grasses and
shrubs. Although TRIFFID simulates modern plant functional types, it
has been argued that such a model can provide the first order signal
from vegetation feedbacks through the last 250 million years
().
The overall model, HadCM3L-MOSES2.1-TRIFFID is identical to that used
in .
Simulation description
All simulations have undergone the same spin-up procedure, totaling
1422 years, with the same initial conditions and boundary
conditions, with the exception of solar constant and palaeogeography
(as discussed in Sects.
and ). The ocean is initialised as stationary,
with a zonal mean temperature structure given by an idealised cosine
function of latitude, (21-z20)( 22cos(ϕ)+10), where ϕ
is latitude and z is the model vertical level from 1 at the ocean
surface to 20 at a depth of ∼ 5200 m, and a constant
salinity of 35 ppt. The atmosphere is initialised from an
arbitrary atmospheric state from a previous preindustrial simulation.
Land-surface initial conditions (e.g. soil moisture, soil temperature)
are globally homogeneous.
The spinup procedure consists of four “Phases”. The first
50 years (Phase 1) of each simulation are run with an
atmospheric CO2 concentration of 1 × PI, and with
global vegetation fixed as bare soil. For the next 319 years
(Phase 2), the atmospheric CO2 is increased to
4 × PI, the TRIFFID vegetation component of the model is
turned on, and the vertical structure of atmospheric ozone is
diagnosed from the modelled troposphere height as opposed to from
a prescribed field (in order to avoid a runaway greenhouse encountered
in previous high-CO2 simulations with fixed ozone
distributions). Phase 3 consists of 53 years of simulation in
which prescribed lakes and glaciers are added to the model (see
Sect. ). In the first three Phases, the ocean
dynamics are simplified to enhance stability, by imposing a purely
baroclinic ocean circulation in which the vertically integrated flow
is zero. The final phase, Phase 4, consists of a final
1000 years in which both the baroclinic and barotropic ocean
dynamics are turned on, giving a total of 1422 years of
simulation. The barotropic streamfunction calculation requires
islands to be defined manually – these are shown in Fig. S4. In addition, to maintain model stability, for some Stages
additional smoothing and/or flattening of the topography was required at the
beginning of Phase 4.
The details of the simulations are summarised in
Table .
ResultsTime series
In order to assess the extent to which the models are spun up, we
first examine the time series of evolution of the global ocean.
Figure shows the temporal evolution of ocean
temperature, at three vertical levels (5, 670, and 2700 m),
for each Stage, over the 1422 years of model simulations. As
can be seen the simulations are not fully in equilibrium at any of the
depths, but are approaching equilibrium in the upper and mid-ocean.
In the deepest ocean, there are still significant trends and little
sign of equilibrium. As such, for the rest of this study we focus on
the surface and upper ocean climatologies. Analysis of the top of the
atmosphere (TOA) energy fluxes indicates that for all Stages the
system is within 1 Wm-2 of radiation balance, but is
losing energy at the end of the simulations, at a rate that varies
from -0.8 (Berriasian) to -0.34 Wm-2 (Campanian).
Modelled evolution of global mean ocean temperature
[∘C] in each CPE simulation at three depths:
(a) surface (5 m), (b) 670 m,
(c) 2.7 km.
There are some discontinuities apparent in the timeseries at
670 m depth, at the beginning of Phase 4 after
422 years. This is because at this time several of the
simulations had flat Arctic bathymetry imposed, to ensure stability as
the barotropic component of circulation was turned on (see
Sect. and Table ). This
results in cold Arctic waters being removed at this depth, resulting
in an apparent sudden warming in the global mean.
The global MAT for each Stage of the CPE at 4 ×CO2
(black filled circles) plotted over a surface temperature record produced by
applying the methodologies of to the
δ18Obenthic data, and applying
a 10-point running average (grey line). The open circles show model results
for the Oligocene and Neogene, with lower CO2, which are not
discussed in this paper but are provided for reference. The modelled trend
(ignoring the outlying Berriasian stage) is shown as a solid line, and the
expected trend assuming solar forcing only is shown as a pair of dashed lines
which start/end at the beginning/end of the modelled trend.
Global annual mean temperatures
Figure shows the global mean annual surface
air temperatures (MATs) for the simulations described in this paper,
superimposed onto a surface temperature record produced by applying
the methodology of to the
δ18Obenthic record of ;
henceforth “H13C09 record”. Model
results from the Oligocene (∼ 34 Ma) through to the
Pleistocene (last 2 million years), in which CO2 varies and
in which large ice sheets are prescribed, are also shown on this
figure. CO2 is
modified to the value shown in Figure at the
beginning of Phase 3, otherwise these additional simulations are identical
to the CPE simulations. These are shown simply to illustrate that the model can reproduce the first-order
response seen in the data during periods when atmospheric CO2
is better constrained – in particular, the temperature in the
Pleistocene model simulation is in good agreement with the Pleistocene
glacials in the H13C09 record. However, these will not be discussed in this
paper; instead, they will be presented in detail in future
publications.
It is clear that for the Paleocene and Eocene, there is much less
variability and trend in the modelled simulations than suggested by
the proxy surface temperature record. Even accounting for the
considerable uncertainties in the proxy record, this implies that the
majority of the variability in the proxy data is caused by forcings
which are not included in the model simulations. The most likely
missing forcing is greenhouse gas forcing, probably primarily
CO2, caused by changes in the carbon cycle on multi-million
year timescales.
The H13C09 record implies warmer temperatures than
given by our results. Our latest Eocene simulation has a similar
temperature to that of the earliest Oligocene in the
H13C09 record. If both the model and proxy record
are correct, then this implies that the earliest Oligocene had an
atmospheric CO2 concentration of about 1120 ppmv.
However this is higher than the values recently reconstructed from
CO2 proxies , which imply
Oligocene CO2 concentrations closer to 600 ppmv.
Indeed, in principle it would be possible to obtain a perfect match
between the modelled and observed global means, by choosing an
appropriate CO2 level in the model, thereby generating
a model-derived CO2 record, for comparison with other proxy
CO2 records such as . However, given the
considerable uncertainties in the H13C09 record,
we consider that this would be of little value. Instead, we await the
development of more long-duration single-site SST proxy records,
across a wide geographical range, and with full consideration of
uncertainties, with which to compare our simulations.
The variability in global mean annual temperature between our
CPE simulations is much less than the available climate data
records imply, and the trends in our simulations do not match
those in the data. However, there is some variability present in
our CPE simulations. In particular there is a long-term warming
trend through the CPE. The trend is 0.0043 ∘C
per million years (correlation coefficient of 0.42). There is
a maximum scatter around this trend of about
±0.5 ∘C, the warmest anomaly being the
Berriasian at +0.74 ∘C, and the coldest
anomaly being the Campanian at -0.52 ∘C.
Excluding the outlying Berriasian stage gives a warming trend of
0.0068 ∘C per million years (correlation
coefficient of 0.64, see Fig. , black
solid line). If the solar forcing was the only forcing acting
on the system, the expected temperature trend,
δTsolar/δt would be
δTsolarδt=δTδF14δS0δt(1-α),
where δT/δF is the climate sensitivity of the model
(KW-1m2), S0 is the solar constant
(Wm-2), and α is the planetary albedo. Under early
Eocene conditions with the HadCM3L model, found
a climate sensitivity to CO2 doubling of
4.8 ∘C, which, assuming a forcing due to CO2
doubling of 3.7 Wm-2, equates to a climate sensitivity,
δT/δF, of 1.3 KW-1m2). Taking
a typical value of α from our simulations of 0.27, and assuming
this does not change greatly through the simulations, gives an
expected warming trend of
δTsolar/δt=0.0272∘C
per million years (see Fig. , black dashed
line), about 5 times greater than that of our simulations. We
interpret this as implying that there is a long-term effect of
palaeogeography which is opposing the trend expected by solar forcing
alone; that is, the changing palaeogeography is resulting in a cooling
trend of 0.0272–0.0068 =0.0204∼ 0.02 ∘Cmillionyears-1. Further sensitivity
studies with constant S0 across the simulations could aid
investigation of this long-term trend, and the component due to
palaeogeography vs. solar constant.
In the following sections we examine the differences between the
simulations in more detail, and investigate the causes of the
similarities and differences between the Stages.
(a–r) Annual mean surface air temperature (at
1.5 m) for each geological Stage, expressed as an anomaly relative to
the previous Stage. (a) Priabonian–Bartonian,
(b) Bartonian–Lutetian, (c) Lutetian–Ypresian,
(d) Ypresian–Thanetian (e) Thanetian–Selandian,
(f) Selandian–Danian, (g) Danian–Maastrichtian,
(h) Maastrichtian–Campanian, (i) Campanian–Santonian,
(j) Santonian–Coniacian, (k) Coniacian–Turonian,
(l) Turonian–Cenomanian, (m) Cenomanian–Albian,
(n) Albian–Aptian, (o) Aptian–Barremian,
(p) Barremian–Hauterivian, (q) Hauterivian–Valanginian,
(r) Valanginian–Berriasian. (s) Annual mean surface air
temperature (at 1.5 m) for the Berriasian stage.
Causes and mechanisms of temperature change
Although the variations in global mean temperature due to changes in
palaeogeography over time are relatively small, there are substantial
variations in regional temperatures. In order to focus on the
influence of palaeogeography, and to minimise the effect of the change
in solar constant through the simulations, this is best expressed in
terms of the temperature anomaly in each Stage, relative to the
previous Stage (Fig. ; see also Fig. S5 for the absolute temperature of each Stage, and
Fig. S6 for the temperature anomaly of each Stage
relative to the mean of all Stages). Looking Stage-to-Stage also
minimises the effects of the changing land–sea contrast due to
continental plate movements.
The response of the system to the palaeogeographic forcing is highly
complex, mediated by positive and negative feedbacks. Here we use
a number of approaches to investigate the causes of the regional and
global differences.
Global annual mean surace air temperature (at 1.5 m) in each
model simulation (black dots). Also shown as vertical lines is the
contribution to temperature difference between each consecutive Stage, from
changes over land (green), over ocean (blue), and over regions which switch
between land and ocean (red). Also shown are the global mean of all
simulations (dotted line), and trend across all simulations except the
Berriasian (dashed line, as in Fig. ).
Plots showing the relationship between Stage–Stage changes
in (a) ocean near-surface (1.5 m) air temperature
(SAT) and continental SAT, (b) continental area and global mean
SAT, (c) global mean orography and continental SAT,
(d) surface temperature due to emissivity change and global mean surface temperature, and
(e) surface temperature due to albedo change and global
mean surface temperature. Note that the continental and ocean SAT
values are the relative contribution to the global mean values.
Figure shows that the largest local changes
are in general over regions which have experienced large local
orographic change (e.g. associated with changes in the Western
Cordillera range in North America), especially where this is also
associated with lateral shifts of the mountains, which is expressed as
regions of localised warming adjacent to localised cooling, for
example in the Aptian–Albian (Fig. n).
However, these local changes are not significant in terms of the
global mean. Figure shows how much of the global
mean temperature change from Stage-to-Stage is due to changes over
land, ocean, or regions which switch between land and ocean. It is
clear that for the largest Stage-to-Stage transitions (for example
Berriasian–Valanginian, ∼ 143–138 Ma;
Campanian–Maastrichtian, ∼ 77–68 Ma), the ocean is the
dominant contributer to the global mean temperature. On average over
all the Stage–Stage transitions, the ocean contributes
0.22 ∘C, the land 0.10 ∘C, and
transitions from land to ocean contribute 0.05 ∘C. In
addition, the temperature change over land correlates well with
the temperature change over ocean (correlation coefficient = 0.74,
see Fig. a). However, it is unclear whether the
ultimate cause of the changes relates to ocean processes (in response
e.g. to bathymetry or gateway changes), or whether the ocean is
amplifying changes that originate over land.
To investigate this further, we explore the relationship between
possible drivers of climate change, and the response.
Figure b shows the modelled global surface
temperature as a function of the change in continental land area.
There is a weak negative correlation (correlation coefficient of
-0.47), implying some influence on global temperature from the
relative albedo of land compared with ocean.
Figure c shows the modelled land surface temperature
as a function of the change in mean orography. There is a weak
negative correlation (correlation coefficient of -0.49),
implying some
influence on land temperature from the local lapse-rate effect. The
relationship between global temperature and orography is weaker (not
shown, correlation coefficient of -0.37), implying that the
lapse-rate effect primarily affects local continental temperatures.
We also carry out an energy balance analysis of the causes of
temperature change between each Stage, following
and . This allows the
global and zonal mean surface temperature change between Stages to be
partitioned into contributions from changes in planetary albedo,
emissivity, solar constant, and heat transport. We do not save
clear-sky flux output from the model, so further partitioning into
cloud albedo vs. surface albedo and cloud emissivity vs. greenhouse
gas emissivity is not possible in this case. The global mean
temperature change correlates very well with the contribution due to
emissivity (Fig. d) and planetary
albedo (Fig. e), implying that on a global scale,
both emissivity feedbacks (due to water vapour and clouds interacting with
long-wave radiation) and planetary albedo feedbacks (due to
clouds and the surface interacting with short-wave radiation) play an
important role
in amplifying the underlying forcing related to the palaeogeographic
changes.
It is instructive to focus on the largest transitions in the modelled
record. From Fig. , we identify 3 such
transitions, which all have a global mean temperature change of over
0.7 ∘C (for comparison with the fourth largest
transition which is 0.57 ∘C): the
Berriasian–Valanginian (∼ 142–138 Ma), the
Aptian–Albian (∼ 119–106 Ma), and the
Campanian–Maastrichtian (∼ 77–68 Ma).
Berriasian–Valanginian (Fig. 6r)
This transition is a cooling of 0.84 ∘C in the global
mean. In the highest Arctic, there is a warming of more than
10 ∘C, due to a transition from a polar Arctic
continent in the Berriasian, to open ocean in the Valanginian. The
opposite effect occurs around the margins of Antarctica around
130–30∘ W, where there is cooling associated with
a transition from open ocean to land. Almost all of the continental
changes can be linked directly to orographic changes, via the
lapse-rate effect (see Fig. S7). An
exception is in the subtropical ocean just south of North America.
Here, there is a cooling whereas from the change in topography
a warming is expected (see Fig. S7r).
There is a substantial cooling in the proto-Arctic Ocean. This can be
linked to the formation of an island chain at the west end of the
Arctic Ocean, which constricts the transport of relatively warm ocean
waters into the Arctic, and therefore cools this region. The cooling
is amplified by an expansion of Arctic sea ice in the Valanginian
(Fig. S8). This cooling effect appears
to extend beyond the Arctic, and into the North Pacific. This is also
related to a decrease in ocean overturning (see Fig. S9) and in the extent and magnitude of regions of deep
water formation (see Fig. S10). As such,
we attribute this global cooling transition primarily to the closure
of the Pacific–Arctic gateway. The timeseries of SST change
indicates that the Arctic itself becomes cooler almost immediately (in
the first year of the simulation) in the Valanginian. This cool
anomaly then spreads southwards, increasing in magnitude over several
hundred years, and is amplified at Phase 4 when the barotropic
circulation is initialised.
The energy balance analysis for the Berriasian–Valanginian transition
(Fig. S11r) shows that, on a global scale,
changes in emissivity contribute about 60 % of the cooling, and
planetary albedo changes 40 %. As CO2 is constant across the
transition, the emissivity change is a longwave water vapour and cloud
feedback effect. The Northern Hemisphere cooling between 50 and
70∘ N is due to a combination of emissivity and heat
transport changes, whereas between 70 and 80∘ N, at the
latitudes of the Arctic Ocean, planetary albedo and emissivity changes dominate.
Aptian–Albian (Fig. 6n)
This transition is a warming of 0.77 ∘C in the global
mean. As for the previous transition, the continental changes are
dominated by a lapse-rate effect, and correlate very closely with
orographic change (Fig. S7). Note that
some of the largest signals, for example in eastern North America, are
essentially artefacts associated with the movement of plates, which
manifest as apparent warm–cold dipoles as a mountain range shifts
horizontally in the model reference frame, as opposed to true tectonic
effects such as uplift. An exception to the strong correlation is in
the region of the Andes in South America, where there is a warming,
whereas the change in topography would be expected to generate
a cooling.
In the ocean, the warmest anomalies are in the northern Pacific, and
in the equatorial region that lies between S.America/N.America and
Africa/Europe. There is also warming over much of the tropical and
subtropical Pacific. However, in the southern Pacific there is
a cooling. This is associated with a significant change in ocean
circulation. In the Aptian stage, there is a region of deep-water
formation off the coast of Antarctica (Fig. S10o), which is associated with a deep overturning cell in the
Pacific sector (Fig. S9o). This is
much weaker or nonexistent in the Albian stage. This reduction in
southern deep water formation reduces surface poleward warm water
transport, leading to a reduction in south Pacific temperatures in the
Albian compared with the Aptian. The opposite signal in the north
Pacific is likely a bipolar seesaw type response, amplified by seaice
feedbacks (Fig. S8n and o).
Again, this is supported by the energy balance analysis
(Fig. S11n), which shows a cooling
contribution due to heat transport changes through most of the region
40 to 80∘ S, and a warming contribution 50 to
75∘N. On a global scale, 10 % of the warming is due to
the solar constant increase directly (the Albian and Aptian are
relatively far apart in time compared to many other consecutive
Stages), with emissivity and planetary albedo feedback contributing roughly
equally.
Campanian–Maastrichtian (Fig. 6h)
This transition is a warming of 0.74 ∘C in the global
mean. In the ocean, there is warming globally, with the exceptions of
the NE Pacific and the southern Atlantic. The largest ocean warmings
are in the Pacific sector of the Southern Ocean (associated with
a transition from land to ocean) and in the Indian sector of the
Southern Ocean. The continental temperatures largely follow
topographic change (Fig. S7h), although
there is significant warming in northern Africa and western Eurasia
which does not appear to be associated with topography, and may
instead be related to the adjacent ocean warming.
This Indian sector warming appears to be associated with an increase
in deep water formation off the Antarctic coast in this sector
(Fig. S10h and i), likely leading to an
increase in poleward heat transport from equatorial regions. Although
there is some change in the overturning associated with this, it is
relatively muted on the global scale (Fig. S9h and i). The reason for the change in ocean circulation is
not clear, but it may be due to the northward migration of India,
allowing greater transport towards Antarctica in the Maastrichtian
stage.
The important role of ocean circulation changes in the Southern
Hemisphere is highlighted in the energy balance analysis
(Fig. S11h), which shows a significant
contribution to the warming polewards of 50∘ S due to heat
transport change. Globally, planetary albedo changes contribute 60 % of the
signal, emissivity changes 30 % and solar constant change less
than 10 %.
(a) The palaeolocation of seven key sites used for
reconstructing climate of the CPE. Large black dots represent the modern
location. Small black dots represent the location at each Stage post the CPE,
and coloured dots represent the location at each Stage during the CPE, for
those Stages that the modern ocean crust was present. (b) Climate
evolution across the CPE, simulated by the model, as experienced at the same
seven sites. Filled (open) symbols indicate that a particular site is marine
(terrestrial) at a particular Stage.
Summary of mechanisms
It appears that the three largest climate transitions are associated
with changes in ocean circulation, and driven by quite subtle changes
in palaeogeography. Whether climate is ultimately driving ocean
circulation, or vice versa, remains difficult to assess without
further sensitivity studies. However, ocean circulation changes do
seem to be key (for example, the fourth largest transition
(Selandian–Thanetian), is also associated with a change in
mixed-layer depth (Fig. S10e and f), and
overturning stream function (Fig. S9e and f). It should be noted that the length of the model
simulations means that the deep ocean is not in equilibrium. As
such, the associated mechanisms should be regarded as hypotheses
at this stage (see Sect. ). All changes are ultimately tectonically driven, but
strong planetary albedo and emissivity feedbacks amplify the initial forcing.
Implications for interpretation of palaeo proxy records
Many records of CPE climate change have been developed, using
a variety of proxies, e.g. using planktic δ18Oe.g., or GDGTs/TEX86e.g.. Often the variations seen in a long-duration proxy
record from a single site are interpreted as being related to global
phenomena, and are often linked to hypothesised atmospheric greenhouse
gas and carbon cycle change. However, a component of the variations
will be a local signal due to palaeogeographic change, either directly
(e.g. due to lapse rate changes for terrestrial sites) or indirectly
(e.g. due to ocean or atmospheric circulation change related to
palaeogeographic change). Similarly, some of the change in very long
records will be due to solar constant change. In addition, any proxy
record will experience a change due to the horizontal movement of
a site due to the movement of the underlying plate, even if the
background climate is constant. This component will be particularly
large if the location moves significantly latitudinally.
In many cases, it would be of interest to “adjust” a record for the
temperature changes associated with the local palaeogeographic
components, solar components, and plate movement components, in order
to leave a component which is likely to have a more global
significance, likely related to greenhouse gas changes through changes
in the carbon cycle. The model simulations presented here can aid in
this process, by generating an “adjustment factor” which can be
applied to long-term proxy records through the CPE. We illustrate this
process below.
The work presented so far has been in a fixed Eulerian
longitude–latitude reference frame (which resulted in the artefacts
discussed in Sect. ), but in order to generate such an
adjustment factor it is necessary to use a Lagrangian frame which can
take into account the effects of rotating plates. Getech Plc have
provided us with palaeolongitudes and palaeolatitudes for each model
gridcell and each Stage, which allows us to ascertain the
palaeolocation of any modern location, consistent with the
palaeogeographies used in the climate model simulations. The
palaeolocations of seven key sites (Blake Nose
e.g., Demerara Rise e.g.,
Falkland Plateau e.g., Walvis Ridge
e.g., Maud Rise e.g., Tanzania e.g., and the Saxony
Basin e.g.), which have previously been used to reconstruct CPE SSTs from
planktic δ18O, is shown in Fig. a.
This shows that all the sites move a significant distance over the
course of the CPE. Note that the “oldest” location is different for
each site, as some sites exist on ocean crust which was not formed
until after the earliest Cretaceous (e.g. Walvis Ridge).
Figure b shows the modelled annual mean surface air
temperature at each of these seven locations, as climate changes
through the CPE and as the plates underlying each site move. These
variations are large; the maximum temperature in the CPE minus minimum
temperature in the CPE varies from 1.2 ∘C at Demerara
Rise, to 9.3 ∘C at Saxony Basin. This is in the context
of a global mean modelled climate which is only varying by a fraction
of a degree over this interval (Fig. ). These
modelled temperature records are the “adjustment factor” we describe
above.
Some of the temporal variations in the adjustment factor, in
particular at Saxony Basin, Tanzania, and Falkland Plateau, are
partly due to
transitions from the site being oceanic to continental
(Fig. b; filled circles compared with open triangles).
For coastal sites, such as Tanzania, the transitions may be an
artefact of the coarse resolution of the model palaeolatitudes and
longitudes, which are 3.75∘× 2.5∘, and which
cannot therefore distinguish correctly between land and ocean near the
coast. Some sites are characterised by relatively stable modelled
temperatures (and therefore small adjustment factors) over 10's
of millions of years, for example Demerara Rise and Blake Nose during
the late Cretaceous.
It is not possible with our current experimental design to partition
the component of the adjustment factor due to solar constant from that
due to palaeogeography; however, it is possible to partition the
effect due to plate movements. Figure S12 shows the modelled temperature evolution over the CPE at
each site in Fig. a, assuming either that the climate
stays constant through the CPE while the site location moves
(Fig. S12a and b), or that the site
location stays constant while climate varies
(Fig. S12c and d), with the constant
being either that of the late Eocene (Fig. S12a and c) or early Cretaceous (Fig. S12b and d). Comparison of Fig. b or
Fig. S12e with
Fig. S12a and Fig. S12c shows that for
many sites the majority of the SST change is due to
changing palaeogeography. However, in the Early Cretaceous, site
movement appears to be playing a role for Falkland Plateau and
Saxony Basin.
(a) Proxy temperature data (black asterixes) at
Falkland plateau (Site 511) from TEX86. Eocene data
are from Liu et al. (2009), and Cretaceous data from Jenkyns
et al. (2012). Black line shows the line of best fit. Also shown
is the modelled temperature evolution at Falkland plateau (green
line), i.e. the adjustment factor. In (b), the proxy
data have been adjusted using the model output, relative to either
the earliest (light blue) or latest (red) proxy data point. Blue
and red lines show the respective lines of best fit. TEX86 temperatures are calculated
using the TEX86H-SST calibration of Kim et al. (2010).
Here we illustrate the adjustment process in the context of the
Falkland plateau (Site 511) site. Temperature data for this site have
been reconstructed using TEX86 by Liu et al. (2009) for
the latest Eocene, and by Jenkyns et al. (2012) for the middle of the
early Cretaceous (Fig. a). For the purposes of this
example, we do not also include δ18O data to avoid
complications from comparison of different proxies. This indicates
significantly warmer temperatures in the Cretaceous than in the
Eocene, and could be interpreted as indicating a global cooling over
this period. However, our model output over this timescale, in which
the global mean temperature is almost constant, also indicates
a significant cooling at this site (Fig. a). If this
cooling related to local palaeogeography is used to adjust the proxy
data, then the apparent cooling in the proxy record is greatly reduced
(Fig. b). As such, any inferred atmospheric CO2
changes implied by the adjusted proxy temperature record would be of
lesser magnitude than that implied by the unadjusted record. It is
clear that it is crucial to take into account the magnitude of this
non-CO2 component of local climate change, before proxies from
single sites are interpreted in a global context.
(a) Total temperature change across the CPE (maximum
temperature in the CPE minus minimum temperature in the CPE,
∘C) for all modern locations, due to changes in
palaeogeography and solar constant. Missing data (white) is where the Getech
Plc plate model indicates that the modern location was not present across all
the CPE. Also shown as black dots are the seven locations in
Fig. . CPE is defined as from the early Cretaceous (Berriasian)
to the late Eocene (Priabonian). (b–d) are similar, but show the
change (b) from the late Cretaceous (Cenomanian) to the late Eocene
(Priabonian), (c) from the early Paleocene (Danian) to the late
Eocene (Priabonian), (d) from the early Eocene (Ypresian) to the
late Eocene (Priabonian).
This analysis can be summarised on a global scale, indicating regions
where this adjustment process is small.
Figure a shows the total change in temperature
(maximum temperature through the CPE minus minimum temperature through
the CPE), for all modern locations. Small values are where the
adjustment factor is constant (note that some of the locations with large
values are associated with transitions between continental and marine
settings). This can be interpreted as a map indicating places where
future proxies could be targeted in order to reconstruct the purest
CO2-induced temperature change, where the complicating
contributions of other (palaeogeographical, solar, and plate movement)
processes are minimised (low values in Fig.
represent the “best” regions in this context). White regions
indicate that the modern crust was not present at the beginning of the
CPE. Figure b–d are similar, but show the total
change in temperature across the Eocene (d), the
Eocene–Palaeocene (c), and the Eocene–Palaeocene-early
Cretaceous (b). Not all of the “best” regions will have suitable
sedimentary material, obviously, but, combined with all other
considerations, this work could provide useful information for
supporting targets for drilling localities and outcrop studies.
Discussion
It is anticipated that the results from these simulations will be of
interest to the palaeoclimate data community; as such, we make the
results available on our website:
http://www.bridge.bris.ac.uk/resources/simulations, including
variables not discussed in this paper. In addition, in the Supplement
we provide the raw data which underlies Figs.
and , in netcdf (.nc) and Excel (.xlsx) format, which will allow
others to develop their own adjustments, over any period in the CPE,
for any site in the world.
The Eocene simulations (Ypresian, Lutetian, Bartonian, and Priabonian)
described in this paper have been discussed in a previous publication
, as has a lower CO2 simulation of the
Priabonian simulation , and a less spun-up version
of the Maastrichtian simulation . In addition, in
future studies we expect to investigate many aspects of the
simulations which have not been possible in the scope of this study,
including the evolution of monsoon systems, ENSO, vegetation, and
atmospheric circulation. Furthermore, we intend to carry out
sensitivity studies, especially to CO2 in order to investigate
the evolution of climate sensitivity through geological time.
However, there are some aspects of the simulations which could be
modified and improved, although we do not think that they will have
a first-order effect on our results. The following is not a complete
list, but includes the main aspects that we intend to explore as we
commence Phase 5 of the ensemble and beyond.
The version of the model used in this study has received little or no
tuning. The internal model parameters in the atmosphere are identical
to those in HadCM3 , which did receive some
(largely undocumented) tuning at the UK Met Office. However, compared
with HadCM3, our model has a lower resolution ocean and a different
land-surface scheme. In addition, the ozone correction discussed in
Sect. cools the climate somewhat. Furthermore,
the subgridscale parameters derived from the Getech
0.5∘× 0.5∘resolution palaeogeographies are not
necessarily consistent with those derived from higher resolution
observational data sets. As such, the modern climate for this version
of the model has greater biases than the HadCM3 model from which it is
derived. Future work will involve tuning the model, using techniques
such as those developed by .
In order to maintain stability in the atmosphere and ocean, some
Stages received more or less smoothing of topography or bathymetry
than others (see Table ). In Phase 5, we will
be more consistent, and apply the same amount of smoothing to all
Stages.
There are still trends in the ocean temperatures, at all depths (see
Fig. ). Although computational constraints mean that
no GCM of this complexity could currently be run to full equilibrium,
and we argue that the main findings presented here will not be
affected significantly by further spinup, we do aim to run Phase 5 for
a further 1000 years in order to further approach
equilibrium. As such, at present the adjustment factors are
presented only for the sea surface temperatures, and the exact
mechanisms associated with ocean overturning changes should be
regarded as hypotheses at this stage.
Getech Plc provide maps of runoff basins and nodes, but these are not
currently used. Instead, as discussed in Sect. ,
water is routed downhill according to the model resolution topography.
In Phase 5 we will make use of the observational constraints on river
basins and river mouths by using the Getech maps.
We have not been consistent in our definition of islands for the
purposes of the barotropic circulation calculation. For example, in
some Stages single gridcell islands are defined as such, and in others
they are not (see Fig. S4).
Furthermore, we have not defined the continent of Antarctica and
Australasia as an island in the mid-Cretaceous simulations, which
could affect the flow through the Tethys–Pacific seaway and Drake
Passage (see discussion in ).
The model does not rigorously conserve water, due to the build-up of
snow on polar continents, the loss of water in inland endorheic regions, and
a salinity cap which affects inland basins. In the modern,
a prescribed freshwater flux is applied in polar regions, in an
attempt to mitigate against salinity drift in long simulations.
However, in these simulations we do not apply such a correction. In
Phase 5 we will diagnose a freshwater flux in order to maintain
constant ocean mean salinity.
The palaeogeographies themselves have associated uncertainties;
for example, the timing of the evolution of key ocean gateways,
and uplift of mountain ranges, is not always well constrained.
As such, it will be important to investigate the sensitivity of
our results to these palaeogeographic uncertainties, in
particular as it is known that they can
potentially have a significant effect on surface temperatures e.g..
All our simulations are carried out at 4× pre-industrial
CO2 values. It has been shown that the ocean
circulation is a function of CO2 value
e.g.; this, coupled with likely thresholds
in the system means that it is possible that the adjustment
factor we present is dependent on the background CO2
concentration. As discussed above, this is currently being
explored by carrying out additional simulations at 2×
pre-industrial CO2 values.
There is undoubtedly some degree of model dependency to our
results, although the extent of this is somewhat uncertain.
Previous work has shown that different models can give variable
results for the Eocene time period ,
although that was a study in which the different models were
constrained by different boundary conditions. The extent of
model dependency in the simulation of CPE climates is currently
being explored in a consistent fashion in the framework of the
“DeepMIP” intercomparison project (see
https://wiki.lsce.ipsl.fr/pmip3/doku.php/pmip3:wg:ppc:index).
Conclusions
We have carried out a set of 19 GCM simulations covering
115 million years, one for each Stage, from the earliest Cretaceous
to the latest Eocene, with constant CO2 but varying
palaeogeography and solar constant (Table ).
All simulations are within 1 Wm-2 of equilibrium after
more than 1400 years of simulation.
The global mean temperatures across the simulations are
remarkably constant, with a trend of only
0.004 ∘Cmillionyears-1
(Fig. ). The lack of trend results from
a cancelling of effects due to changing solar constant with effects
due to changing palaeogeography.
There is also little scatter around the trend,
∼±0.5 ∘C (Fig. ). The
scatter correlates weakly with changing land area, indicating the
albedo contrast between land and ocean may play a role; continental
temperatures correlate weakly with mean orography, indicating lapse
rate and area above snowline also may be playing a role
(Fig. ). Energy balance analysis indicates that the
solar and palaeogeographic forcing is amplified by planetary albedo and
emissivity feedbacks.
The largest Stage–Stage transitions through the CPE are
associated with changes in the mode of ocean circulation. For
example, the largest transition, Berriasian–Valanginian, is
associated with a reduction in deepwater formation in the North
Pacific, and a reduction in the meridional positively overturning
cell; the second largest transition, Aptian–Albian, is associated
with a reduction in deepwater formation off the coast of Antarctica,
and a reduction in the negatively overturning cell. In some cases,
these ocean circulation changes can be directly related to
palaeogeographic change associated with gateway opening or closing,
for example the isolation of the Arctic at the Berriasian–Valanginian
transition.
Although the global mean changes are relatively small across the
CPE, local temperature changes are much larger
(Fig. ). This has implications for interpretations
of proxy records. In particular, our results allow the
non-CO2 (i.e. palaeogeographic and solar constant) components
of proxy records to be removed, through the application of an
adjustment factor, leaving a global component associated with carbon
cycle change. This adjustment factor is illustrated for seven key sites
in the CPE (Fig. ), and applied to proxy data from
Falkland plateau, and data provided so that similar adjustments can be
made to any site and for any time period within the CPE.
Regions where the adjustment factor is constant throughout the CPE
could indicate places where
future proxies could be targeted in order to reconstruct the purest
CO2-induced temperature change, where the complicating
contributions of other processes are minimised. Therefore, combined with other
considerations, this work could provide useful information for
supporting targets for drilling localities and outcrop studies.
Data availability
In the supplement to this paper we provide the following: the file values.nc
is a netcdf file which contains a 96 × 73 × 19 level field.
The 96 × 73 are the longitudes × latitudes of the model,
which have a 3.75∘× 2.5∘ resolution. The
19 represents the 19 stratigraphic stages of the CPE. The field itself
represents the modelled annual mean 1.5 m near-surface air temperature of
each modern grid cell, for that particular stratigraphic stage. The plots in
Figs. 9b and 10 in the main paper can be generated from the data in this
file. The file masks.nc is very similar, but instead of temperature it has 0
for ocean and 1 for land, to indicate whether a particular site in the modern
was marine or terrestrial at a certain period in the past. The file
values.xlsx is an Excel version of values.nc. Each time period is a separate
sheet, and the overview sheet allows you to extract the values and make plots
of the adjustment factor very quickly for any location. The default is for
Falkland plateau, which reproduces the data in Figs. 9b and 10. To choose a
different site, just edit cell C4. The files land_ypr.nc, orog_ypr.nc, and
bath_ypr.nc contain the model-resolution land–sea mask, orography, and
bathymetry respectively, in digital form, for the Ypresian stage (early
Eocene). If you use these as boundary conditions for model simulations, then
please cite this paper, and acknowledge Getech PlC, who produced the original
palaeogeographies. In addition, those interested may explore our results in
further detail here:
http://www.bridge.bris.ac.uk/resources/simulations, following the link
to “Simulations featured in papers”.
The Supplement related to this article is available online at doi:10.5194/cp-12-1181-2016-supplement.
Acknowledgements
Work carried out in the framework of NERC grants Descent into the
Icehouse (NE/I005714/1), Paleopolar (NE/I005722/1,NE/I005501/2), and
CPE (NE/K014757/1,NE/K012479/1) contributed to this paper. We
thank Getech Plc for providing the palaeogeographies and plate
rotations. This work was carried out using the computational
facilities of the Advanced Computing Research Centre, University of
Bristol – http://www.bris.ac.uk/acrc/.
Edited by: Y. Godderis
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