PALEOCEANOGRAPHY,
VOL. 14,
14, NO.
NO.3,
PALEOCEANOGRAPHY, VOL.
3, PAGES
PAGES 350-359,
350-359, JUNE
JUNE 1999
1999
Foraminiferal
pa!eotemperature:
Foraminiferal fauna!
faunal estimates
estimatesof
of paleotemperature:
Circumventing
Circumventingthe
theno-ana!og
no-analogprob!em
problemyie!ds
yieldscoo!
cool
ice
age
tropics
ice age tropics
Alan C.
Nicklas G.
G. Pisias
Pisias
Alan
C. Mix,
Mix, Ann
Ann E.
E. Morey,
Morey, and
andNicklas
College
Sciences,
Oregon
Corvallis
Collegeof
of Oceanic
Oceanicand
andAtmospheric
Atmospheric
Sciences,
OregonState
StateUniversity,
University,
Corvallis
Steven
Steven W.
W. Hostetler
Hostetler
U.S.
U.S. Geological
GeologicalSurvey,
Survey,Corvallis,
Corvallis,Oregon
Oregon
Abstract.
sensitivity
ofofthe
tropics
toto
climate
change,
particularly
the
ofofglacial-to-interglacial
changes
inin
Abstract.The
The
sensitivity
the
tropics
climate
change,
particularly
theamplitude
amplitude
glacial-to-interglacial
changes
sea surface
surface temperature
temperature (SST),
(SST), is
is one
one of
of the
the great
great controversies
controversies in
inpaleoclimatology.
paleoclimatology. Here
Here we
we reassess
reassess faunal
faunal estimates
estimates of
of
sea
ice
age
SSTs,
focusing
on
the
problem
of
no-analog
planktonic
foraminiferal
assemblages
in
the
equatorial
oceans
that
ice age SSTs,focusingon the problemof no-analogplanktonicforaminiferalassemblages
in the equatorialoceansthat
confounds both
both classical
classical transfer
transfer function
function and
and modern
modern analog
analog methods.
methods. A
developed here,
here, which
confounds
A new
new calibration
calibrationstrategy
strategydeveloped
which
uses past
past variability
solves the
the no-analog
no-analog problem
problem and
and reveals
reveals ice
ice age
age
uses
variabilityof
of species
speciesto
to define
definerobust
robustfaunal
faunalassemblages,
assemblages,
solves
Classical transfer
cooling of
of 5°
current
systems
of
Pacific
cooling
5øto
to6°C
6øCininthe
theequatorial
equatorial
current
systems
ofthe
theAtlantic
Atlanticand
andeastern
eastern
PacificOceans.
Oceans. Classical
transfer
functions underestimated
underestimatedtemperature
temperaturechanges
changesinin some
some areas
areas of
of the
because
core-top
functions
thetropical
tropicaloceans
oceans
because
core-topassemblages
assemblages
misrepresented
the
ice
age
faunal
assemblages.
Our
finding
is
consistent
with
some
geochemical
estimates
and
misrepresented
the ice agefaunalassemblages.
Our findingis consistent
with somegeochemical
estimates
andmodel
model
predictions
of
inferred by
by Climate:
Climate: Long-Range
Long-Range Investigation,
Investigation, Mapping,
Mapping, and
and
predictions
of greater
greaterice
iceage
agecooling
coolingin
in the
thetropics
tropicsthan
thanwas
wasinferred
Prediction
[19811and
andthus
thus may
may help
help to
to resolve
resolve aa long-standing
long-standingcontroversy.
controversy. Our
Prediction(CL/MAP)
(CLIMAP) [1981]
Our new
new foraminiferal
foraminiferaltransfer
transfer
function
that such
current
however,
CLIMAPs
functionsuggests
suggests
that
suchcooling
coolingwas
waslimited
limitedto
tothe
theequatorial
equatorial
currentsystems,
systems,
however,and
andsupports
supports
CLIMAP's
inference
of
stability
of
the
subtropical
gyre
centers.
inferenceof stabilityof the subtropicalgyrecenters.
1. Introduction
1.
Introduction
The
The Climate:
Climate: Long-Range
Long-RangeInvestigation,
Investigation,Mapping,
Mapping, and
and PrePrediction
(CL/MAP)
[1981]
reconstruction
of
the
ice
age
diction (CLIMAP) [1981] reconstructionof the ice age world
world
suggested
relatively
little change
change in
in the
the low
low latitudes.
latitudes. An
suggested
relativelylittle
Anarray
array
of
of
of newer
newerdata
datafrom
fromlow
low latitudes,
latitudes,including
includingreconstructions
reconstructions
of
mountain
mountain glacier
glacier advances
advancesand
andtropical
tropical ice
ice core
corerecords
records
[Thompson et
etal.,
[Thompson
al., 1995],
1995],Sr/Ca
Sr/Caratios
ratiosin
incorals
coralsat
ataafew
fewlocations
locations
the rare
[Guilderson et
et al.,
[Guilderson
al., 1994],
1994], the
rare gas
gaschemistry
chemistryof
of lowland
lowland
groundwaters
[Stute
et al.,
about
groundwaters
[Stuteet
al., 19951,
1995], and
andinferences
inferences
aboutglacier
glacier
advances from
from atmospheric
circulation
advances
atmospheric
circulationmodels
models[Rind
[Rindand
andPeteet,
Peteet,
1985],
greater tropical
tropical cooling
cooling but
but do
do not
1985], suggest
suggestgreater
not constrain
constrainthe
the
regional
regional distribution
distributionor
or mechanisms
mechanismsof
of such
suchcooling.
cooling. Other
Other
geochemical data
data from
the ocean,
geochemical
from the
ocean,however,
however,such
suchas
asthe
thealkenone
alkenone
index
[Sikes
and
1994;
et
al., 1998],
U
Uk'37
index
[Sikes
andKeigwzn,
Keigwin,
1994;Roselle-Mele
Roselle-Mel•
etal.,
1998],
and Mg/Ca
et al.,
and
Mg/Ca data
data [Hastings
[Hastingset
al., 1998]
1998] are
areto
tofirst
firstapproxiapproximation consistent
[1981] in
mation
consistentwith
with CLIMAP
CLIMAP [1981]
in many
manylocations.
locations.
of the
Point-by-point comparisons
Point-by-point
comparisons
of
the CLIMAP
CLIMAP data
data and
and
atmospheric model
model output
are
atmospheric
outputsuggest
suggestthe
themismatches
mismatches
aresmaller
smaller
than
originally
thought
[Broccoli
and
Marciniak,
1996].
thanoriginallythought[Broccoliand Marciniak,1996].
Four recent
Four
recentocean
oceanmodels,
models,some
somecoupled
coupledto
to the
theatmosphere
atmosphere
with varying
different
with
varyingdegrees
degreesof
of sensitivity,
sensitivity,yield
yieldsignificantly
significantly
different
responses in
in the
responses
the tropical
tropicaloceans,
oceans,perhaps
perhapsbecause
becauseof
of different
different
degrees of
of linkage
the atmosphere
and ocean
degrees
linkage between
betweenthe
atmosphereand
oceanin
in each
each
1998;
model
model [Bigg
[Bigg et
et al.,
al., 1998;
1998; Bush
Bush and
andPhilander,
Philander, 1998;
Ganopoiski
etal.,
etal.,
of
Ganopolskiet
al., 1998;
1998;Weaver
Weaveret
al., 1998].
1998]. Thus
Thusthe
therange
rangeof
Copyright
1999
Geophysical
Union.
Copyright
1999by
bythe
theAmerican
American
Geophysical
Union.
Paper
number 1999PA900012.
Papernumber
1999PA900012.
08
83-8305/99/1 999PA900012$l 2.00
0883-8305/99/1999PA900012512.00
cooling
from
coolingin
in ice
ice age
agereconstructions
reconstructions
from both
bothmodels
modelsand
anddata
datais
is
large,
about
of
large,and
andthe
thecontroversy
controversy
aboutsensitivity
sensitivity
of tropical
tropicalclimate
climateto
to
change
changeremains
remainsunresolved.
unresolved. Progress
Progressrequires
requiresresolving
resolvingthe
the
extent,
and
extent,mechanisms,
mechanisms,
andregional
regionaldistribution
distributionof
ofice
iceage
agecooling.
cooling.
If the
If
the CL/MAP
CLIMAP [1981]
[1981] reconstruction
reconstructionbased
basedon
on faunal
faunal transfer
transfer
functions
functionswas
wasbiased
biasedin
in the
thetropics,
tropics,we
we need
needto
to know
knowwhy.
why.
2. Methods
2.
Methods
2.1. Classical
Transfer Functions
2.1.
Classical Transfer
Functions
In
functions,
common
In the
thenow
nowtraditional
traditionaluse
useofoftransfer
transfer
functions,
common
practice is
is to
to find
of aa fossil
practice
find empirical
empiricalassemblages
assemblages
of
fossilfauna
faunain
in
modem
The
modem(core-top)
(core-top)sediments.
sediments.
Thecore-top
core-topfauna
faunais
isconverted
converted
into
into orthogonal
orthogonalQ-mode
Q-modefactors
factorsrotated
rotatedwith
with aavarimax
varimaxcriterion
criterion
[Klovan and
and Imbrie,
defines
[Klovan
Imbrie,1971].
1971]. Q-mode
Q-modefactor
factoranalysis
analysis
defines
each fossil
as
of
each
fossilassemblage
assemblage
asaa linear
linearcombination
combination
of input
inputspecies.
species.
The
weighting of
of each
each species
in aa factor
is indicated
indicated by
by aa factor
Theweighting
species
in
factoris
factor
score.
The
weighting
of
each
factor
in
describing
a
score. The weightingof eachfactorin describing
asample
sampleis
is
referred to
to as
as aa factor
referred
factorloading.
loading.
The
an
that
aauseful
Thenext
nextstep
stepis
isto
tocalibrate
calibrate
anequation
equation
thatpredicts
predicts
useful
property, such
such as
temperature
(SST),
by regressing
property,
assea
seasurface
surface
temperature
(SST),by
regressing
core-top
faunal
factor
loadings
against
modem
oceanographic
core-topfaunalfactorloadingsagainstmodemoceanographic
properties [Imbrie
[Imbrie and
and Kipp,
Kipp, 1971].
1971]. This
properties
Thisis
isusually
usuallydone
donewith
with
multiple
methods,
allowing
and
multiplelinear
linearregression
regression
methods,
allowingsquared
squared
andcrosscrossproduct
terms of
to
to
productterms
of the
thefactor
factorloadings
loadings
to enter
enterthe
theequation
equation
to
account
for
nonlinearity
in the
to
account
for moderate
moderate
nonlinearity
in
thefaunal
faunalresponse
response
tothe
the
environment.
environment. Finally, the
the modem
modemfactor
factordefinitions
definitionsand
andcalicalibrated
are
census
data
bratedequations
equations
areapplied
appliedto
tospecies
species
census
datafrom
fromancient
ancient
samples
to make
of
properties
in
samples
to
makeestimates
estimates
ofenvironmental
environmental
properties
in the
the
past.
past.
350
350
MIX
MIX ET
ET AL.:
AL.: FORAMINIFERAL
FORAMINIFERAL FAUNAL
FAUNAL ESTIMATES
ESTIMATES
Calibration of
of aa transfer
is meaningful
only ifif an
Calibration
transferfunction
functionis
meaningfulonly
an
oceanographic
property
oceanographic
propertyis
is chosen
chosenthat
thateither
eithercontrols,
controls,or
orisisconconsistently
that
species
dissistentlycorrelated
correlatedto
toother
otherprocesses
processes
thatcontrol,
control,
species
distributions.
A
variety
of
statistical
tests
suggests
that
temperature
tributions.A varietyof statistical
testssuggests
thattemperature
is
not
is indeed
indeedaa dominant
dominantcontrol
controlbut
butperhaps
perhaps
notthe
theonly
onlycontrol
controlon
on
foraniiniferal
species assemblages
assemblages [Parker
[Parker and
and Berger,
foraminiferalspecies
Berger,1971;
1971;
Coulbourn
1989]. Andreasen
Coulbournet
et al.,
al., 1980;
1980;Loubere,
Loubere,1982;
1982;Mix,
Mix, 1989].
Andreasen
and
Ravelo
[1997]
argue
for
upper
ocean
mixed
andRavelo[ 1997]argueforupperoceanmixedlayer
layerdepth
depthas
asaa
primary
although
primary control
control of
of tropical
tropical foraminiferal
foraminiferalfaunas,
faunas, although
Watkinsand
and Mix
Mix [1998]
[1998] note
note that
Watkins
thatmixed
mixedlayer
layerdepth
depthand
and
biological
may
be difficult
from
biologicalproductivity
productivity
maybe
difficultto
to distinguish
distinguish
fromeach
each
other
they are
in the
otherbecause
becausethey
are highly
highly correlated
correlatedin
themodern
modemocean
ocean
[Herbiand
[Herblandand
andVoituriez,
Voituriez,1979].
1979].
Multiple oceanic
Multiple
oceanicproperties
propertiesmay
may influence
influencefaunal
faunalassemassemblages, either
or
It is
blages,
eitherindependently
independently
or in
in combination.
combination.It
ispossible
possible
that transfer
functions may
may still
still work
work in
in such
as
that
transferfunctions
suchsituations
situations
aslong
long
as the
as
thestatistical
statisticalrelationships
relationshipsbetween
betweenthe
the various
variouscontrols
controls
remain
constant through
through time.
time. However,
remainconstant
However,it
it is
is also
alsopossible
possiblefor
for
transfer functions
functions to
to be
be biased
of
transfer
biasedby
by the
theinteractions
interactions
of multiple
multiple
processes.
For
Watkins
that
processes.
Forexample,
example,
Watkinsand
andMix
Mix [1998]
[1998] suggest
suggest
that
pycnocline
depth,
upper
ocean
temperature,
and
biological
pycnoclinedepth, upper ocean temperature,
and biological
productivity
may all
all influence
the living
productivitymay
influencethe
living fauna
faunaand
andshow
show
examples
of bias
on
examplesof
biasinduced
inducedby
byproductivity
productivity
ontemperature
temperature
estimates
(and
samples
estimates
(andvice
viceversa)
versa)in
in an
anarray
arrayof
ofplankton-tow
plankton-tow
samples
from
the
equatorial
Pacific
Ocean.
Thus,
in
any
application
of
fromthe equatorial
PacificOcean. Thus,in anyapplication
of
transfer
transferfunctions,
functions,possible
possiblebias
biaseffects
effectsmust
mustbe
be considered.
considered.
A
in
transfer
function
approach
A key
keyassumption
assumption
in the
theclassical
classical
transfer
function
approach
is
of
is that
thatmodern
modemvariations
variations
of the
thefauna
faunathrough
throughspace
space(i.e.,
(i.e.,in
in an
an
array
of
sediment
core
tops)
simulate
past
variations
through
arrayof sedimentcoretops)simulatepastvariations
through
time.
If processes
that
groupings
of the
time. If
processes
thatmodify
modifythe
theecological
ecological
groupings
of
the
past
past are
are not
not well
well expressed
expressedin
in the
themodern
modemocean,
ocean,then
thenthis
this
assumption would
would break
estimates
assumption
breakdown,
down,and
andpaleotemperature
paleotemperature
estimates
would likely
likely be
be in
in error.
error. Here
and
would
Herewe
wetest
testthis
thisassumption
assumption
andfind
find
that it
it is
that
is sufficiently
sufficientlyin
in error
error to
to bias
biasthe
theCLIMAP
CLIMAP [1981]
[1981] temtemperature estimates
estimates of
of the
the equatorial
equatorial zones.
perature
zones.
It
from
It has
haslong
longbeen
beenknown
knownthat
thatice
iceage
agesamples
samples
fromthe
theeastern
eastern
equatorial
Pacific
and
Atlantic
are
not
well
described
by
equatorialPacificandAtlanticarenot well describedbymodern
modem
core-top assemblages
assemblages [Moore
[Moore et
et al.,
1986].
core-top
al., 1981;
1981; Mix
Mix et
et al.,
al., 1986].
Faunal
Faunalfactors
factorsdefined
definedusing
usingonly
onlytropical
tropicalAtlantic
Atlanticcore
coretops
tops
explain just
just 35%
explain
35% of
of the
thevariance
variancein
inLast
LastGlacial
GlacialMaximum
Maximum
samples from
from the
the same
samples
sameregion
region[Ravelo
[Ravelo et
et al.,
al., 1990].
1990]. This sosocalled no-analog
no-analog problem
problem [Hutson,
called
[Hutson,1977]
1977]either
eithercould
couldreflect
reflectthe
the
response of
of past
past faunas
response
faunasto
to climate
climatechanges
changesoutside
outsidethe
the range
range
experienced
by the
the present
present fauna
fauna or
or could
could be
be an
experiencedby
an artifact
artifact of
of
empirical
core-top
assemblages
that
are
not
true
ecological
empirical core-top assemblages
that are not true ecological
assemblages in
in the
the tropics.
tropics. In
assemblages
In either
eithercase
casethe
thetraditional
traditionaltransfer
transfer
function
yield erroneous
erroneous answers.
answers. Such
functionwould
would yield
Sucherrors
errorswould
wouldnot
not
be
calibrations;
be easily
easilydetectable
detectableinincore-top
core-top
calibrations;that
that is,
is, the
the
calibration statistics
of the
calibration
statisticsof
thetransfer
transferfunction
functionmight
mightappear
appear
precise, but
but the
the transfer
transfer functions
functions might
might still
be inaccurate
when
precise,
still be
inaccuratewhen
applied to
to ancient
applied
ancientsamples.
samples.
2.2. Modern
2.2.
Modern Analog
Analog Technique
Technique
-ñrkers
have attempted
to circumvent
thisthis
problem
by
Son-ieworkers
haveattempted
to circumvent
problem
by
using various
using
variousversions
versionsof
of the
themodern
modemanalog
analogtechnique
technique(MA1')
(MAT)
[Hutson,
1980; Prell,
Prell, 1985;
et al.,
al., 1996;
[Hutson,1980;
1985; Pflaumann
Pfiaumannet
1996; Ortiz
Ortiz and
and
Mix,
et al.,
al., 1998].
Mix, 1997;
1997; Waelbroeck
Waelbroecket
1998]. This
Thismethod
methodfinds
findsaaselecselection
of
modern
(core-top)
samples
that
are
statistically
tion of modem (core-top)samplesthat are statisticallymost
most
similar to
to ancient
ancient samples
and then
the temperature
of
similar
samplesand
thenestimates
estimates
the
temperature
of
351
351
the ancient
as an
(or in
the
ancientsample
sampleas
an average
average(or
in some
somecases
casesan
an average
average
weighted by
by geographic
distance)
weighted
geographic
distance)of
of those
thosemodern
modemsamples.
samples.
The
tropical
The MAT
MAT may
mayalso
alsounderestimate
underestimate
tropicalclimate
climatechanges.
changes.
As with
As
with the
theclassical
classicaltransfer
transferfunction
functionmethod,
method,temperatures
temperatures
calculated with
with the
the MAT
MAT are
are limited
to the
calculated
limited to
the range
rangeof
of modern
modem
variability. Also,
variability.
Also, if
if there
thereis
is no
nogood
goodmodern
modemanalog
analogof
of ancient
ancient
conditions, then
then the
conditions,
the modern
modernanalog
analogcannot
cannotfind
find an
anappropriate
appropriate
modern sample
sample to
to match
Because its
modem
match the
the ancient
ancientsample.
sample. Because
its
estimate
of
precision
is
based
on
variability
within
the array
array of
of
estimateof precisionis basedon variability within the
modern samples
samples chosen
chosen as
as best
best analogs,
modem
analogs,it
it may
mayyield
yield apparently
apparently
precise estimates
that are
Prell [1985]
precise
estimatesthat
are inaccurate.
inaccurate. Prell
[1985] shows
showsthat
that
the MAT
the
MAT and
andclassical
classicaltransfer
transferfunction
functionmethods
methodsyield
yieldroughly
roughly
similar results
results in
in the
the tropics.
similar
tropics.
The
The primary
primary issue
issuefor
for both
bothtransfer
transferfunctions
functionsand
andMATs
MATs is
is
whether the
the modern
represent the
the
whether
modem (core-top)
(core-top)faunas
faunasadequately
adequatelyrepresent
range of
of past
setrange
pastvariations
variationsin
in faunas
faunaswithin
within an
anoceanographic
oceanographic
setting.
If
not,
then
it
is
unlikely
that
either
the
classical
transfer
ting. If not, then it is unlikely that either the classicaltransfer
function or
or the
the MAT
would predict
predict the
the past
past range
range of
of
function
MAT method
methodwould
temperatures correctly.
correctly. The
would be
be to
to
temperatures
The result
resultof
of either
eithermethod
methodwould
underestimatethe
the range
range of
of changes
changes in
in the
the environment.
environment. Such
underestimate
Such
errors would
errors
would not
not be
be detected
detected in
in modern
moderncalibration
calibration statistics,
statistics,
which can
can only
only test
test the
which
the success
successof
of the
the methods
methodsin
in the
the modern
modem
world. In
estimates
must
world.
In short,
short,temperature
temperature
estimates
mustbe
beaccurate
accurateas
aswell
well
as precise.
precise.
as
2.3. A
Transfer Function
Function Method
Method
2.3.
A Revised
Revised Transfer
Our
the
problem.
Our primary
primarypurpose
purposeis
is to
toaddress
address
theno-analog
no-analog
problem.
We
the tropics
tropics
We examine
examinewhether
whetherthe
theforaminiferal
foraminiferalspecies
specieswithin
within the
combine
through
time
to
form
assemblages
in
subtly
different
combinethroughtime to form assemblages
in subtly different
ways
than are
waysthan
areobserved
observedthrough
throughspace
spacein
inmodern
modemcore-top
core-topsedisediments.
in
ments. Instead
Insteadof
of solving
solvingfor
for fossil
fossilassemblages
assemblages
in core-top
core-top
samples,
we define
based on
on Q-mode
samples,we
define assemblages
assemblagesbased
Q-mode factors
factors of
of
planktonic
foraminifera in
in ancient
planktonicforaminifera
ancientsamples
samplesfrom
from the
the equatorial
equatorial
Atlantic
Oceans'.
This procedure
guarantees
that
Atlanticand
andPacific
Pacific
Oceans
1. This
procedure
guarantees
that
the assemblages
are
that
the
assemblages
arebased
basedon
onspecies
species
thatcovary
covaryin
in the
thepast,
past,and
and
samples
a larger
samplesa
largerrange
rangeof
of variation
variationthan
thanis
is observed
observedin
in the
thecore
core
tops.
they
tops.Because
Because
theyare
aredefined
definedentirely
entirelyfrom
fromtropical
tropicalsamples,
samples,
these
assemblages
better
describe
the
total
faunal
these assemblagesbetter describethe total faunalvariations
variations
within
processes.
within the
theregional
regionalcontext
contextof
of tropical
tropicaloceanographic
oceanographic
processes.
Samples
used here
span
Samplesused
hereto
to define
definethe
thefactor
factorassemblages
assemblages
spanthe
the
past
past 300,000
300,000 years,
years, a
a time
time long
long enough
enoughto
to capture
capturethe
thelarge
large
Samples
range
range of
of climate
climatevariations
variationsof
of the
thelate
latePleistocene.
Pleistocene. Samples
come
cores in
in the
the tropical
comefrom
from 10
10 sediment
sedimentcores
tropicalAtlantic
Atlantic and
and Pacific
Pacific
Oceans
are
Oceans(Figure
(Figure 1).
1). Four
Fourof
ofthese
thesecores
cores(1057
(1057samples)
samples)
arefrom
from
the
the tropical
tropicalAtlantic
Atlantic Ocean,
Ocean,and
and six
six of
of the
thecores
cores(715
(715 samples)
samples)
are
Core names
are from
from the
theeastern
easterntropical
tropicalPacific
PacificOcean.
Ocean. Core
namesand
and
locations
and Morey
locationsare
aretabulated
tabulatedby
by Mix
Mix and
Morey [1996].
[ 1996].
Two
Two other
otherfeatures
featuresof
of our
ourfactor
factoranalysis
analysisdiffer
differfrom
fromcommon
common
practice.
practice. First,
First, after
aftersome
someexperimentation
experimentationto
to minimize
minimizespecies
species
percentage
artifacts
with
percentage
artifactsassociated
associated
with selective
selectivedissolution
dissolutionat
at great
great
water
water depths
depthswe
we excluded
excludedthe
thedissolution
dissolutionresistant
resistantspecies
species
Globorotalia
Globorotalia tumida,
turnida, Globorotalia
Globorotalia menardii,
menardii, and
and Globorotalia
Globorotalia
menardii
neoflexuosa. Mix
menardii neofiexuosa.
Mix and
and Morey
Morey [1996]
[1996] test
testand
anddocudocudissolution effects
these assemblages.
ment
ment potential
potential dissolution
effects on
on these
assemblages.
'Data are
electronically
at
for
1Data
areavailable
available
electronically
atWorld
WorldData
DataCenter-A
Center-A
forPaleoPaleoclimatology,
NOAA/NGDC,
325 Broadway,
Broadway, Boulder,
Boulder, Colorado
Colorado (e-mail:
climatology,
NOAA/NGDC, 325
(e-mail:
paleo@mail.ngdc.noaa.gov;
URL:
paleo@mail.ngdc.noaa.gov;
URL:http://www.ngdc.noaa.gov/paleo).
http://www.ngdc.noaa.
gov/paleo).
MIX
MIX ET AL.:
AL.' FORAMINIFERAL
FORAMINIFERAL FAUNAL
FAUNAL ESTIMATES
ESTIMATES
352
352
1300W
130øW
30°N
30øN '"•8 ':•.."
57;"•'".'•J
2
20°E
20øE
':iii•
'-'22
"":;'!"•'
i:•26
•.2 766
..
2 8 '"•,?.'::•
......
30°S
30os
130øW
130°W
30°N
• .'":
.............
'............
':•:"'•'::'
, '30øN
.i::':::
'
•'::..
SI
:
........
0
26
'
26
/26
o
- - -'
0
,'..•.<.
,:•:F:-:.•'i
•:•,:•:.:..::•: ß
20•8,,•,:•,•..,..•.<._•.:.
Is
I' • 30°S
30os
20°E
20øE
Figure 1.
(dots)
containing
downcore
foraminiferal
species
Figure
1. Core
Coresites
sites
(dots)
containing
downcore
foraminiferal
species
Second, our
our analyses
analyses are
are based
based on
on the
the In(species
ln(species percentage
percentage +
+
Second,
1). Lognormal
distributions
of
relative
are
Lognormal
distributions
of species
species
relativeabundances
abundances
are
conunonly
and
commonlyobserved
observedin
in many
manyecosystems,
ecosystems,
andare
areconsistent
consistent
with
with biological
biologicaltheory
theory(MacArthur,
(MacArthur,1960).
1960). In the
thefactor
factor
analysis,
log
amplify
of
analysis,
logtransforms
transforms
amplifythe
theimportance
importance
ofless
lessabundant
abundant
species
(and thus
the dominance
of the
the analysis
by a
a
species
(and
thusminimize
minimize
the
dominance
of
analysis
by
few
species)
and
the
distributions
more
fewabundant
abundant
species)
andmakes
makes
thespecies
species
distributions
more
Gaussian, an
assumption inherent
Gaussian,
an assumption
inherentin
in multiple
multipleregression
regression
techniques. One
One is
is added
value
techniques.
addedto
toeach
eachpercentage
percentage
valueto
to avoid
avoid
taking the
the log
is applied
taking
logof
of zero.
zero. The
Thelog
logtransform
transform
is
appliedto
to all
all
species
percentage
data (downcore
(downcore and
and core-top
samples)
prior
species
percentage
data
core-top
samples)
prior
census data
data used
used here
census
here to
to define
definetropical
tropicalfaunal
faunalfactors
factorsfor
for transfer
transfer
function calibration.
calibration. Details
function
Detailsof
of locations
locationsand
anddata
datasources
sourcesare
areprovided
provided
Contours are
are modem
modem annual
elsewhere [Mix
and Morey,
elsewhere
[Mix and
Morey, 1996].
1996]. Contours
annualsea
sea
surface temperature
temperature (SST)
surface
(SST) [Levihis,
[Levitus,1982].
1982].
to calculation
calculation of
of factor
loadings or
or scores.
to
factorloadings
scores.
Our
analysis
is
closed
(100%)
26
and
morphoOuranalysis
is closed(100%)around
around
26species
species
and
morphotypes,
listed
in
Table
1.
Only
species
without
significant
taxotypes,listedin Table 1. Only specieswithoutsignificant
taxonomic controversies
are
nomic
controversies
areincluded.
included. We
We included
includedspecimens
specimens
referred to
to by
with
referred
byKipp
Kipp[1976]
[ 1976]as
as"P-D"
"P-D"intergrade
intergrade
withNeogloboNeoglobo-
Exclusion of
of these
Exclusion
thesespecies
speciesalso
alsoeliminates
eliminatesaa problem
problemin
in Atlantic
Atlantic
quadrina dutertrei,
as
quadrina
dutertrei,
asthis
thisis
ishow
howmuch
muchof
of the
thePacific
Pacificcore-top
core-top
data
set
was
counted.
Also,
Globorotalia
theyeri
data set was counted. Also, Globorotaliatheyeriis
is grouped
grouped
with Globorotalia
with
Globorotaliascitula
scitulabecause
becausethe
theformer
formerspecies
specieswas
wasnot
not
widely recognized
recognized in
in earlier
Morphotypes of Globiwidely
earlierstudies.
studies. Morphotypes
Globigerinoides sacculifer
saccu1fer with
with and
are
gerinoides
and without
without aa terminal
terminal chamber
chamberare
Ocean reconstructions
reconstructions as
as all
all three
three are
Ocean
are absent
absent from
from the
the Atlantic
Atlantic
for long
and
for
long periods
periodsof
of the
thelate
latePleistocene
Pleistocene
andepisodically
episodicallyrerepopulate the
the Atlantic
are advected
advected around
around South
South
populate
Atlantic only
only when
when they
they are
Africa [Ericson,
[Ericson, 1968].
Africa
1968].
Table
Varimax-Rotated
Factor
Scores
for
ForanMniferal
Faunas and
and for
for the
the Core-Top
Core-Top Climate:
Climate: LongLongTable1.
1.Q-mode,
Q-mode,
Varimax-Rotated
Factor
Scores
forthe
theDowncore
Downcore
ForaminiferalFaunas
Range
Five Factors
Factors Shown
RangeInvestigation,
Investigation,Mapping,
Mapping,and
andPrediction
Prediction(CLIMAP)
(CLIMAP) FA-20
FA-20 Solution,
Solution,Three
Three of
of Five
Shown
Specie?
Species
a
Downcore Factor
Factor Scores
Scores (This
(This Paper)
Paper)
Downcore
Factor
Factor 2
Factor
Factor 11
Factor
Factor 33
Warm
E. Boundary
Boundary
WarmTropical
Tropical Upwelling
Upwellin•
E.
Globigerinoides ruber
ruber (total)
(total)
Globigerinoides
Globigerinoides ruber
ruber (white)
Globigerinoides
(white)
Globigerinoides r.
ruber
Globigerinoides
uber(pink)
(pink)
Globigerinoides
saccuhfer (total)
Globi
gerinoidessacculifer
(total)
Globigerinoides
sacculfer (with
Globigerinoides
sacculifer
(with sac)
sac)
Globigerinoides
Globigerinoidessacculifer
sacculifer(no
(nosac)
sac)
Globigerinifa
glutinafa
Globigerinitaglutinata
Globigerinella
aequilaferalis
Globigerinellaaequilateralis
Pulleniatina
Pulleniatinaobliquiloculata
obliquiloculata
Globigerina
Globigerinarubescens
rubescens
Globigerinoides
tenellus
Globigerinoides
tenellus
Globigerinafalconensis
Globigerinafalconensis
Globigerinoides
conglobatus
Globigerinoidesconglobatus
Globigerinella
calida
Globigerinellacalida
Globorotalia
Globorotaliascitula
scitula+
+ Globorotalia
Globorotaliatheyeri
theyeri
Globigerina
digitata
Globigerinadigitam
Neogloboquadrina
dutertrei
Neogloboquadrina
dutertrei
Globigerina
Globigerinabulloides
bulloides
Orbulina universa
Orbulina
universa
Globorotalia
Globorotalia menardii
menardii
Globorotalia
Globorotalia tumida
tumida
Globorotalia
Globorotalia inflata
infiata
Neogloboquadrina pachyderma
pachyderma (dextral)
Neogloboquadrina
(dextral)
Globorotalia truncatulinoides
truncatulinoides(dextral)
(dextral)
Globorotalia
Globorotaliacrassafornus
crassaformis
Neogloboquadrina pachyderma
pachyderma (sinistral)
Neogloboquadrina
(sinistral)
Globigerina
Globigerina quinquiloba
quinquiloba
Globorotalia
Globorotalia truncatulinoides
truncatulinoides(sinistral)
(sinistral)
Sphaeroidinella dehiscens
Sphaeroidinella
dehiscens
Globoquadrina
glomerata
Globoquadrinacon
conglomerata
Globoquadrina
Globoquadrinahexagona
hexagona
Globorotalia
Globorotalia hirsuta
hirsuta
P-D
P-Dintergradeb
intergrade
b
0.56
0.56
-0.03
-0.03
0.09
0.09
- - - - 0.47
-0.09
CLIMAP
FA-20 Factor
Factor Scores
Scores
CLIMAP FA-20
Factor
Factor 44
Factor 11
Factor
Factor 2
Factor
Tropical
Tropical Gyre
GyreMargin
Mar•in Transitional
Transitional
0.93
0.93
0.11
0.11
-0.12
-0.12
0.07
0.07
0.03
0.03
0.01
0.01
0.10
0.10
0.24
0.24
0.33
0.33
-0.02
-0.02
0.04
0.04
0.26
0.26
-0.02
-0.02
-0.03
-0.03
-0.12
-0.12
0.02
0.02
-0.00
-0.00
-0.02
-0.02
0.02
0.02
0.60
0.60
0.08
0.08
0.04
0.04
0.46
0.46
0.37
0.37
0.06
0.06
-0.06
-0.06
-0.04
-0.04
0.07
0.07
0.00
0.00
-0.05
-0.05
-0.06
-0.06
0.01
0.01
-0.01
-0.01
-0.00
-0.00
0.01
0.01
0.32
0.32
0.01
0.01
0.02
0.02
-0.00
-0.00
0.01
0.01
0.05
0.05
0.03
0.03
0.03
0.03
-0.00
-0.00
0.13
0.13
0.21
0.21
0.38
0.22
0.22
0.23
0.23
0.20
0.20
0.18
O.
18
0.14
O.
14
0.10
O.
10
0.11
O.
11
0.09
0.09
0.03
0.03
0.17
O.
17
-0.01
-0.01
0.05
0.05
0.09
-0.17
-0.17
-0.06
-0.06
-0.02
-0.02
-0.07
-O.O7
-0.06
-0.06
-0.05
-0.05
-0.05
-0.05
0.05
0.05
-0.02
-0.02
0.00
0.00
0.77
0.77
0.47
0.47
0.10
0.10
0.01
0.01
-0.08
-0.08
-0.15
-0.15
-0.14
-0.14
-0.03
-0.03
-O.O6
0.68
O.68
0.46
O.46
0.30
0.30
0.24
0.24
0.09
0.09
0.04
0.04
0.01
0.01
-0.03
-0.03
-0.12
-0.12
-0.11
-0.11
0.02
0.02
0.00
0.00
-0.02
-0.02
0.02
0.02
-0.16
-0.16
0.10
0.10
0.10
0.10
0.20
0.20
0.10
0.10
0.01
0.01
0.03
0.03
0.05
0.05
0.06
0.06
0.02
0.02
0.03
0.03
0.01
0.01
0.01
0.0I
0.00
0.00
-0.03
-0.03
0.02
0.02
0.06
0.06
-0.01
-0.01
0.06
-0.11
-0.11
0.14
0.14
0.12
O.12
-0.01
-0.01
0.00
0.00
0.03
0.03
0.02
0.02
0.02
0.02
0.07
0.07
0.00
0.00
-- -
0.34
O.34
-0.10
-0.10
-0.02
-0.02
0.02
0.02
0.01
0.01
-0.01
-0.01
0.00
0.00
0.04
0.04
0.03
0.03
0.00
0.00
___
0.00
0.00
-0.03
-0.03
-0.01
-0.01
0.05
0.05
0.01
0.01
-0.01
-0.01
-0.00
-0.00
-0.11
-0.11
-0.01
-0.01
-0.01
-0.01
0.90
0.90
0.11
0.11
0.07
0.07
0.01
0.01
0.01
0.01
-0.00
-0.00
-0.02
-0.02
0.04
0.04
-0.06
-0.06
0.13
0.13
0.00
0.00
0.02
0.02
-0.02
-0.02
0.10
0.10
-0.01
-0.01
-0.01
-0.01
0.14
0.14
0.03
0.03
aspecies are
by
factor
in
solution.
Larger
values
of
indicate
greater
weighting
in
aSpecies
aregrouped
grouped
bytheir
theirdominant
dominant
factorscores
scores
in the
thedowncore
downcore
solution.
Largerabsolute
absolute
values
ofscores
scores
indicate
greater
weighting
in
the factor
the
factor index.
index.
hPD intergrade
is
with
dutertrei in
solution.
bP-D
intergrade
iscombined
combined
withN.
N. dutertrei
inthe
thedowncore
downcore
solution.
MIX ET AL.: FORAMINIFERAL
MIX
FORAMINIFERAL FAUNAL
FAUNAL ESTIMATES
grouped together
together as
as are
of
grouped
arepink
pinkand
andwhite
whitevarieties
varieties
ofGlobigeriGlobigerinoides
noides ruber.
ruber.
353
130ow
130°W
35°N
35ON
200E
35°N
3. Results
3.
Results
3.1.
3.1. Faunal
Faunal Factors
Factors
Q-mode factor
factor analysis
analysis of
of the
the downcore
downcore faunas
faunas in
in the
the 10
10 sites
sites
Q-mode
used
usedas
asinput
inputhere
hereresolves
resolvesthree
threesignificant
significantfaunal
faunalfactors
factors[Mix
[Mix
and
Core-top studies
studies typically
typically resolve
resolve five
five or
or
and Morey,
Morey, 1996].
1996]. Core-top
more factors
factors [Imbrie
[Imbrie and
and Kipp,
Kipp, 1971;
more
1971; Kipp,
Kipp, 1976;
1976; Imbrie
Imbrie et
et al.,
al.,
1989].
1989]. Our
Ourresults
resultsindicate
indicatethat
thatjust
just three
threefactors
factorsare
are sufficient
sufficient
to
in the
to describe
describetemporal
temporalvariability
variabilityin
the eastern
easternequatorial
equatorialcurrent
current
systems (Table
(Table 1).
systems
1).
In
In all
all cases
casesour
ourdowncore
downcoresamples
samplesare
arewell
well described
describedby
by our
our
three
fauna!
factors.
Communalities
in
the
downcore
samples
three faunal factors. Communalitiesin the downcoresamples
analyzed
here are
are always
>0.7 and
and average
0.9; that
that is,
analyzed here
always >0.7
average0.9;
is, the
the
0
0
35°S
35os
35°S
35°N
35°N
35øN
,• 8•"•
..........
.'7'..:;•;::;:,;,¾
'- ß • :3'. .•;•'-::"
....
:.,::::.
35øN
. ß
0
o
.7 .........
.•:.•:
<.:..:;•:
.......
..... ,..... :..
0
o
factors
account for
for 90%
90% of
of the
the downcore
downcore fauna.
fauna. This
factors account
This contrasts
contrasts
with
of tropical
factors to
to ice
ice age
with the
the application
application of
tropical core-top
core-top factors
age
samples,
just 35%
samples,which
whichaccounts
accountsfor
for just
35% of
of the
thepopulation
populationvariance
variance
in
from the
the tropics
et al.,
in downcore
downcoresamples
samplesfrom
tropics[Ravelo
[Ravelo et
al., 1990].
1990].
Thus
changes in
in the
Thus oceanographic
oceanographicchanges
the tropical
tropicalwater
water masses
massesare
are
by Prell
by
Prell [1985]
[1985] with
with additional
additionalsamples
samplesfrom
from Atlantic
Atlantic and
and
Pacific core
core tops
tops [CoMbourn
[Coulbourn et
etal.,
1983;
Pacific
al.,1980;
1980;Sverdlove,
Sverdlove,
1983;Mix,
Mix,
1986; Mix
Mix and
and Morey,
Morey, 1996].
1996]. This
1986;
Thisanalysis
analysisyields
yieldsthree
threemaps
mapsof
of
loadings
(strengths)
of
the
three
downcore
factors
(Figure
loadings(strengths)of the threedowncorefactors(Figure2).
2).
When mapping
mapping the
the factor
any core-top
When
factorloadings,
loadings,we
we excluded
excludedany
core-top
samples with
with factor
factor communalities
<0.6 as
samples
communalities<0.6
asthese
thesewere
werenot
notwell
well
described by
by the
the fauna
fauna present
present in
in the
the equatorial
equatorialoceans.
oceans. None
None of
of
described
these
low-communality
samples
come
from
the
equatorial
these low-communality samples come from the equatorial
Pacific or
assemblages
are
Pacific
or Atlantic
AtlanticOceans.
Oceans. The
The Pleistocene
Pleistocene
assemblages
are
thus good
good analogs
for the
thus
analogsfor
the modern
modemfauna
faunain
in much
muchof
of the
thetropical
tropical
oceans. This
oceans.
Thismay
mayseem
seemsurprising
surprisingas
aswe
wepreviously
previouslynoted
notedthat
that
the
modem
assemblages
were
not
good
analogs
for
the
the modemassemblages
were not good analogsfor the ancient
ancient
fauna. The
is
in the
the tropics
tropics
fauna.
Theexplanation
explanation
isthat
thatice
iceage
ageassemblages
assemblages
in
reflect aa larger
reflect
largerrange
rangeof
of conditions
conditionsthan
thanoccur
occurat
atpresent
presentwithin
within
the tropics.
Our downcore
of factors
the
tropics. Our
downcorecalibration
calibrationof
factorscaptures
capturesthis
this
broader dynamic
dynamic range
range and
and thus
in
broader
thussucceeds
succeeds
in simulating
simulatingboth
both
modem and
modem
andancient
ancientsamples
sampleswith
withgood
goodfidelity.
fidelity.
In nearly
core-top
In
nearlyall
all cases
casesthe
thelow-communality
low-communality
core-topsamples
samplesare
are
from
such as
from distant
distant regions
regions such
as the
the NW
NW Pacific
Pacificand
andAntarctic,
Antarctic,
which
little relevance
of
whichhave
havelittle
relevanceto
to interpretation
interpretation
of the
thetropical
tropicaldata.
data.
This
is
important
as
it
reveals
the
likely
reason
for
the
This is importantasit revealsthe likely reasonfor theno-analog
no-analog
problem
problemin
in CLIMAP
CLIMAP studies.
studies. Core-top
Core-topfactors
factorsattempted
attemptedto
to
simulate
the cool
in
simulate the
cool ice
iceage
ageforaminiferal
foraminiferalassemblages
assemblages
in the
the
tropics
with modem
assemblages that
tropics with
modem cool
cool water
water assemblages
that were
were
inappropriate
because
they
inappropriate
because
theywere
werefrom
fromhigh
highlatitudes.
latitudes.
For
Table 11 also
For comparison,
comparison,Table
alsonotes
notesthe
thethree
threemost
mostrelevant
relevant
CLIMAP
Atlantic core-top
core-top factors
factors used
used for
CLIMAP Atlantic
for equation
equationFA-20
FA-20
reported
by Mix
Mix [1989].
[1989]. McIntyre
reportedby
Mcintyre et
et al.
al. [1989]
[1989]document
documentthe
the
-
.•
.•?
' % ' •"•....'A-
ß•
•
. :-.-••
g -'-
'
0 "'.-
_•.,
•
ß'
0
ß '
.....
4 ß ' f%'•'•
. :
•>.....
35°S
• 35os
•'•
35°N
ß •
0
0
•.•
........
• ............ •t
.•
.
C-)
reflected
better by
reflectedbetter
by variations
variationsof
of the
thefactors
factorswe
we present
presenthere
herethan
than
by
of
by the
thecore-top
core-topassemblages
assemblages
ofCLIMAP
CLIMAP [1981]
[ 1981] or
orRavelo
Raveloet
etal.
al.
[1990].
[1990].
To
To gain
gaininsight
insightinto
intothe
theenvironmental
environmentalmeaning
meaningof
of the
the three
three
downcore faunal
fauna! factors,
factors, we
we applied
downcore
appliedthem
them to
to core-top
core-topsamples
samples
between
40°N and
and 40°S.
with
between40øN
40øS.Problems
Problems
withno-analog
no-analogconditions
conditions
are
apparent
as
low
communalities
in
are apparent as low communalitiesin core-top
core-topsamples.
samples.
Because these
these anomalous
anomalous samples
samples can
can be
be examined
in the
Because
examinedin
the concontext of
of modem
modem oceanographic
oceanographic data,
data, they
they can
can be
be understood
understood more
more
text
clearly and
calibration.
clearly
and excluded
excludedfrom
fromthe
thepaleoenvironmental
paleoenvironmental
calibration.
Core-top
samples
used
here
come
from
the
database
assembled
Core-topsamplesusedhere comefrom the database
assembled
/.a
35°S
35os
35°N
35°S
3508
130°W
•30ow
-
:.':•
•
:•.........:...
ß ' ß •-
5' ..6. •[• 3508
35°S
.
20°E
20OE
Figure 2.
faunal
applied
to corn-top
core-top staples.
samples. Values
Figure
2. Downcore
Downcore
faunalfactors
factors
appliedto
Values
contoured •e
are factor
factor loadings
loadings of
of samples
with communality
>0.60: (a)
(a)
contoured
stapleswith
communali•>0.60:
factor 1,
(b) factor
2, the
factor
1, the
thewaim
w• tropical
tropicalassemblage,
•semblage,(b)
factor2,
theupwelling
upwelling
assemblage, •d
and (c)
(c) factor
factor 3,
3, the
the eastern
•semblage,
e•tem boundary
bound• assemblage.
•semblage.
contributions of
of these
Atlantic
contributions
thesefactors
factorsto
totropical
tropical
Atlanticplanktonic
planktonic
foraminiferal assemblages.
In
the
that
foraminiferal
assemblages.
In all
allcases
cases
themajor
majorspecies
species
that
identify
the
CLIMAP
factors
are
also
strong
contributors
to
identifytheCLIMAP factorsarealsostrongcontributors
to our
our
downcore factors.
factors. In
however,
the
of
downcore
Indetail,
detail,
however,
theweighting
weighting
ofspecies
species
in the
the downcore
factors
differ from
from that
that in
in the
factors.
in
downcore
factors
differ
thecore-top
core-top
factors.
It is
that allow
us to
better
It
is these
thesesubtle
subtledifferences
differences
that
allowus
tocapture
capture
betterthe
the
faunal
in
faunalpatterns
patterns
inice
iceage
agesamples.
samples.
Factor
contains seven
species:
G. ruber,
FactorI1 contains
sevenabundant
abundant
species: G.
tuber,G.
G.
saccu1fer,
G!obigerinita
g!utinata,
G!obigerine!la
aequilaterasacculifer,
Globigerinita
glutinata,
Globigerinella
aequilateraus,
ob!iqui!ocu!ata,
G!obigerina
rubescens,
and
lis,Pul!eniatina
Pulleniatina
obliquiloculata,
Globigerina
rubescens,
and
dextral-coiling
G!oborora!ia
truncatulinoides,
in
order
of
dextral-coiling Globorotalia truncatulinoides,in
importance.
All
of
these
species
are
common
in
warm
tropical
importance.
All of thesespecies
arecommon
in warmtropical
and
environments
[Kipp, 1976;
1976; Parker
Parker and
andsubtropical
subtropical
environments
[Kipp,
andBerger,
Berger,
1971].
samples
this
with
1971
]. In
Incore-top
core-top
samples
thisfactor
factoris
isclearly
clearlyassociated
associated
with
warm,
salty
subtropical
and
tropical
water
masses
where
warm, salty subtropicaland tropicalwater masseswherethe
the
thermoclineis
is deep
deep in
in the
the westem
Ocean and
and in
in the
thermocline
westernAtlantic
AtlanticOcean
the
Pacific Ocean
Ocean (Figure
(Figure 2a).
2a). Factor
the
Pacific
FactorI1also
alsodominates
dominates
thePanama
Panama
Basin, where
where SSTs
SSTs are
are warm,
are low,
low, and
Basin,
warm, salinities
salinities are
and the
the
thermocline is
is shallow
shallow [Levitus,
1982]. Warm
isis
thermocline
[Levitus,1982].
Warmtemperature
temperature
the variable
variable common
common to all these
these locations,
locations, so
so we refer
refer to factor
factor
the
I1as
warm
asthe
the"warm
"warmtropical
tropicalfactor".
factor".This
Thisisisthe
therelatively
relatively
warmend
end
member
against
which
with
aspect,
member
against
whichthe
theother
othertwo
twofactors,
factors,
withcooler
cooler
aspect,
contrast.
contrast.
MIX
ESTIMATES
MIX ET
ET AL.:
AL.: FORAMINIFERAL
FORAMINIFERAL FAUNAL
FAUNAL ESTIMATES
354
354
The
The second
secondfactor
factorisisdominated
dominatedby
byN.
N.dutertrei,
dutertrei,with
withseconsecon-
30
dary
dary contributions
contributionsfrom
from Globigerina
Globigerinabulloides
hulloidesand
anddextraldextralcoiling
pachyderina.
These species
coiling Neogloboquadrina
Neogloboquadrina
pachyderma. These
speciesare
are
common
in tropical
and at
common in
tropicalupwelling
upwelling environments
environmentsand
at high
high latilatitudes
tudes[Kipp,
[Kipp, 1976;
1976; Parker
Parkerand
andBerger,
Berger,1971;
1971;Prell
Prelland
andCurry,
Curry,
19811.
with
1981]. The
The core-top
core-topprojection
projectionof
of factor
factor22 is
is associated
associated
with the
the
equatorial
upwellingzone
zone in
in the
the Pacific
and is
equatorial upwelling
Pacific Ocean
Ocean and
is also
also
present
in
the
eastern
tropical
Atlantic
Ocean
and
present in the eastern tropical Atlantic Ocean and in
in the
the
Benguela
We infer
Benguelaupwelling
upwelling system
system(Figure
(Figure2b).
2b). We
infer that
that this
this
assemblage records
records the
the strength
strength of
of cool
systems of
of
assemblage
cool upwelling
upwelling systems
as the
relatively high
high biological
biological productivity
productivity and
and refer
refer to
to itit as
relatively
the
"upwelling factor".
"upwelling
factor".
In
In the
thethird
thirdfactor,
factor,Globorotalia
Globorotaliainflata
infiataisisthe
themost
mostimportant
important
contributor, followed
N. pachyderma
pachyderma (which
contributor,
followed by
by dextral-coiling
dextral-coilingN.
(which
is shared
is
sharedwith
with factor
factor2),
2),dextral-coiling
dextral-coilingG.
G.truncatulinoides
truncatulinoides
(which
is shared
(whichis
sharedwith
with factor
factor1),
1), and
andGloborotalia
Globorotaliacrassaformis.
crassaformis.
The
high-latitude
species
sinistral-coiling
N. pachyderma
pachyderma also
The high-latitudespeciessinistral-coiling
N.
alsois
is
ß
•' 25
ß
II
ß
mm
ß
ß
ß
ß
ß
ß
ß'. ø_%., ',
m'
ß ßmmß
ß
ß
ß
[
mmmm
m
ß mmmlmmm
m:
ßm
mmm
ß
ß
•
mmm
ß
20
ß
•mm
ß
ß
ß
ß
ß
m ß
==,
I I
ß
m
ß
ß
ß
associated
with factor
factor 33 but
but is
associated with
is not
not dominant
dominant because
because it
it is
is
relatively rare
rare in
in the
relatively
the tropical
tropicalsamples
samplesthat
thatdefine
definethe
theassemassemblages. The
N.
in
blages.
Theequatorial
equatorialspecies
species
N. dutertrei,
dutertrei,which
whichis
is common
commonin
factor
2,
has
a
significantly
negative
score
in
factor
3.
This
factor 2, has a significantlynegativescorein factor 3. This
implies
impliesthat
that tropical
tropicalsamples
sampleswith
with high
highloadings
loadingsof
of factor
factor33 have
have
both
species such
such as
both high
high concentrations
concentrationsof
of high-latitude
high-latitudespecies
as G.
G.
inflata
pachyderma and
of
dutertrei.
infiataor
or N.
N. pachyderma
andlow
low concentrations
concentrations
of N.
N. dutertrei.
In
factor
into
In the
thecore-top
core-topsamples,
samples,
factor33 penetrates
penetrates
intothe
thelow
lowlatitudes
latitudes
in
currents (Figure
(Figure 2c).
2c). This
in the
the eastern
easternboundary
boundarycurrents
Thisfactor
factoris
is not
not
strongly associated
associated with
strongly
with productive
productivecoastal
coastalupwelling
upwellingcenters.
centers.
with positive
Where
Where present
present near
near the
the equator
equator(i.e.,
(i.e., with
positive factor
factor
appears
to
loadings),
it
reflect
advection
off
eastern
loadings), it appears to reflect advection off the
the eastern
boundary.
boundary
boundary.Thus
Thuswe
werefer
referto
toititas
asthe
the"eastern
"eastern
boundaryfactor".
factor".
A
isisselective
preservaA potential
potentialworry
worryin
in all
allfaunal
faunalanalyses
analyses
selective
preservation. If
tion.
If dissolution
dissolutionmodifies
modifiesthe
the relative
relativeweighting
weightingof
of faunal
faunal
assemblages,
transfer
function
equations
operating
on
the
assemblages,
transferfunctionequationsoperatingon the assemassemblage weightings
weightings will
will produce
produce biased
biased estimates.
blage
estimates.Mix
Mix and
andMorey
Morey
[19961
consider this
this possibility
possibility in
in detail
[1996] consider
detail and
and demonstrate
demonstrateno
no
significant
significant correlation
correlation in the
the modern
modem ocean
ocean between
between the
the
assemblage
loadings and
and water
assemblageloadings
water depth
depth(related
(relatedto
todissolution
dissolution
through
a
pressure
effect)
or
in
ancient
samples
througha pressureeffect) or in ancientsamplesto
to indices
indicesof
of
shell
shellfragmentation,
fragmentation,an
an empirical
empiricalmeasure
measureof
of dissolution
dissolutionintenintensity
and Shackleton,
sity [Le
[Le and
Shackleton,1992].
1992]. Thus,
Thus,although
althoughsome
someeffects
effectsof
of
dissolution
may remain
remain in
in the
dissolutionmay
the data
dataset,
set,with
with the
thefactors
factorsdefined
defined
here
heredissolution
dissolutionartifacts
artifactsare
arerelatively
relativelyminor.
minor.
3.2. Temperature
3.2.
Temperature Estimates
Estimates
We calibrate
foraminiferal
We
calibratethe
thethree
threedowncore
downcore
foraminiferalassemblages,
assemblages,
as
expressed
in
core
tops,
to
annual
average
asexpressedin coretops,to annualaverageSST
SST[Levitus,
[Levitus,1982]
1982]
following
standard procedures
of multiple
following standard
proceduresof
multiple linear
linear regression
regression
essentially identical
identical to
to those
those of
of CLIMAP
[1981]. Terms
essentially
CLIMAP [1981].
Terms only
only
enter the
the transfer
if they
enter
transferfunction
functionequation
equationif
they are
are above
abovethe
the95%
95%
confidence level
level (based
(based on
confidence
on partial
partialFF statistic).
statistic). The
The resulting
resulting
transfer function,
function, which
which accounts
accounts for
for 72%
72% of
of the
transfer
the variance
variance in
in
modern SST
to 30°C
modem
SST from
from 15°
15 ø to
30øC is
T
++4.81
(F3)2
T ==27.0
27.0
4.81
(F3)2++12.59
12.59(F,)(F2)
(F/)(F2)
-- 5.02
5.02 (F1)(F3)
(F/)(F3)- - 10.50
10.50 (F2)
(F2)-- 8.66
8.66 (F3)
(F•)
15
20
25
20
25
SST
SST Measured
Measured(°C)
(øC)
15
30
30
6
3
0
-3
-6
15
I
20
25
30
20
25
SST Measured
(°C)
SST
Measured
(øC)
0
'
I
'
I
'
I
'
100
200 300
500
100 200
300 400
400 500
Productivity
(g C
C m'2
m2y-l)
y1)
Productivity
(g
6
•,6-I'....I. .
•• 3
=
:-'l.,;I;;m..,m'
,
::i!;i!;!li!j .;;
!
m -3
I!!.'::
3
'
ß
•
0
' ; ' ß
I ' ;
(•:..m:.
50
100 150
I
I
50
100
0
-3
-3
d: .!!.
(o -13
6•..,=;•..
I
I
150
ß
ß ,' 2, .' 4,-.•
6
-13
200
200
Pycnocline
Depth
(m)
Pycnocline
Depth
(m)
0
2
4
6
m,
o--3ggcm
cm-3)
aot (100-0
(100-0
m,110
'3)
Figure
function
calibration
and
of
Figure3.
3. Transfer
Transfer
function
calibration
andanalysis
analysis
ofresiduals.
residuals.(a)
(a)
Annual
Annual average
averageSST
SST (degrees
(degreesCelsius)
Celsius)estimated
estimated(this
(thispaper)
paper)versus
versus
measured
1982].
(b)
(estimated
measured[Levitus,
[Levitus, 1982].
(b) SST
SSTresiduals
residuals
(estimatedminus
minus
measured,
degrees Celsius)
Celsius) versus
versus SST
SST measured.
measured. The
measured,
degrees
The dashed
dashedline
line is
is aa
2
linear
(r2=O.23).
SST residuals
(degrees
Celsius)
versus
linearregression
regression
(r =0.23). cc)
(c)
SST
residuals
(degrees
Celsius)
versus
-2
1
estimated at
at each
each site
site from
primary
productivity (g
(g C
Cm
primaryproductivity
m' y',y', estimated
from satellite
satellite
imagery
[Antoine
et
al.,
1997]).
(d) SST
imagery[Antoineet al., 1997]). (d)
SST residuals
residuals(degrees
(degreesCelsius)
Celsius)
versus
for
versusannual
annualaverage
averagepycnocline
pycnoclinedepth
depth(m,
(m, calculated
calculated
for each
eachcore
coresite
site
from
Levitus [1982]).
[1982]). (e)
the upper
upper ocean
ocean density
fromLevitus
(e) SST
SST residuals
residualsversus
versusthe
density
cm3,
calculatedfor
foreach
each core
core
g
contrast
from
units,
contrast
from00 to
to100
100mm(a1
(c•t
units,10l0'3gcm
'3,
calculated
is aa linear
site
sitefrom
fromLevilus
Levitus[1982]).
[1982]). The
The dashed
dashedline
line is
linearregression
regression
(1-2=0.11). The lack of significant correlation between temperature
(r2=0.11).
Thelackof significant
correlation
between
temperature
residuals and
and productivity
or pycnocline
depth suggests
that these
residuals
productivityor
pycnoclinedepth
suggeststhat
these
variables
do not
not bias
bias the
the temperature
variablesdo
temperatureestimates.
estimates.Significant
Significantcorrelation
correlation
of
temperature residuals
residuals to
to the
suggests
the
of temperature
theupper
upperocean
oceandensity
densitycontrast
contrast
suggests
the
possibility
of bias
with
possibility
of
biasassociated
associated
withthe
theavailability
availabilityof
ofdifferent
differentecological
ecological
niches within
within the
the euphotic
euphotic zone.
zone.
niches
(I)
(1)
where T
T is
is the
where
the SST
SST (in
(in degrees
degreesCelsius),
Celsius),and
andF1,
F•, F2,
F2, and
andF3
F• are
are
the
loadings
of
factors
1,
2,
and
3,
respectively
(Figure
3).
The
the loadingsof factors1, 2, and 3, respectively(Figure 3). The
statistical error
error (1
(1 oa standard
statistical
standarddeviation
deviationof
of residuals)
residuals)of
of estimated
estimated
mean annual
annual SSTs
larger
mean
SSTsin
in this
thisequation
equationisis±+1.7°C,
1.7øC,somewhat
somewhatlarger
than the
the seasonal
precision of
of ±1.2°
to 1.3°C
reported for
for the
the
than
seasonalprecision
+1.2ø to
1.3øCreported
FA-20 equation
equation calibrated
on
FA-20
calibrated
onAtlantic
Atlanticcore
coretops
tops[Molfino
[Molfinoet
etal.,
al.,
1982].
1982].
There
for
difference
Thereare
areseveral
severalreasons
reasons
forthis
thisapparent
apparent
differencein
in precipreci-
MIX
FAUNAL
MIX ET
ET AL.:
AL.' FORAMINIFERAL
FORAMINIFERAL
FAUNAL ESTIMATES
ESTIMATES
sion.
sion. First,
First,because
becauseour
ourdefinitions
definitionsof
of the
thefaunal
faunalassemblages
assemblages
are
our
are based
basedon
onancient
ancientsamples,
samples,
ourfactors
factorsare
arenot
notspecifically
specifically
tuned
used
calitunedto
to fit
fit the
thecore-top
core-topsamples
samples
usedfor
for the
thetemperature
temperature
calibration.
Statistical
errors
that
might
occur
in
applying
bration. Statisticalerrors that might occur in applyingthe
the
assemblages
to unknown
assemblages
to
unknownsamples
samplesare
arethus
thusrealistically
realisticallyrevealed
revealed
within
The traditional
within our
our core-top
core-topcalibration.
calibration. The
traditionalCLIMAP
CLIMAP
approach,
in which
calibraapproach,in
whichthe
thefactor
factoranalysis
analysisand
andtemperature
temperature
calibration
are
done
on
the
same
samples,
cannot
reveal
such
errors.
tion aredoneon the samesamples,cannotrevealsucherrors.
Second, our
our equation
Second,
equationand
andstatistical
statisticalerrors
errorswere
werecalculated
calculated
from aa global
global array
array of
of samples
the latitude
from
samples(within
(withinthe
latituderange
range40°N
40øNto
to
40°S),
statistics
were
for
40øS),while
whilethe
theFA-20
FA-20equation
equation
statistics
werecalculated
calculated
for
Atlantic
samples
only.
When
we
apply
the
FA-20
equations
to
Atlanticsamplesonly. Whenwe applythe FA-20 equations
to
the
and
and cold
theglobal
globalcore-top
core-topdatabase
database
andaverage
averagethe
thewarm
warmand
coldseaseason
son estimates,
estimates, the
the statistical
statistical error
error of
of FA-20
FA-20 for
for same
same set
set of
of corecoretop
we
15°
isis±2.2°C,
signifitopsamples
samples
weused
used(between
(between
15øand
and30°C)
30øC)
+--2.2øC,
significantly
here.
cantlyworse
worsethat
thatthe
thetransfer
transferfunction
functiondeveloped
developed
here. This
This
finding
finding is
is consistent
consistentwith
with Prell's
Prell's[1985]
[1985] demonstration
demonstrationthat
that
transfer
functions calibrated
transferfunctions
calibratedwithin
withinaa single
singleocean
oceandegrade
degradewhen
when
applied
appliedto
to another
anotherocean.
ocean.
Third, we
designed
aarelatively
Third,
we have
havepurposely
purposely
designed
relativelysimple
simpleequaequation,
with
just
three
faunal
factors
and
six
terms
in
the
tion,withjust threefaunalfactorsandsixtermsin theequation.
equation.
In contrast,
In
contrast,foraminiferal
foraminiferalequation
equationFA-20
FA-20 includes
includessix
sixfactors
factors
and
terms in
in each
equation [Imbrie
[Imbrie et
et al.,
al., 1989].
1989]. A
and 28
28 terms
eachseasonal
seasonalequation
A
global
transfer function
function [Ortiz
global foraminiferal
foraminiferaltransfer
[Ortiz and
andMix,
Mix, 1997]
1997]
calculated
with traditional
calculatedwith
traditionalcore-top
core-topcalibration
calibrationof
of seven
sevenfaunal
faunal
355
355
may
the full
may tend
tend to
to underestimate
underestimatethe
full range
rangeof
of SST
SSTchanges
changes
through time.
time. One
through
Onepossible
possiblereason
reasonfor
for this
thisbias
biasis
is that
that at
at colder
colder
locations,
locations, foraminiferal
foraminiferal productivity
productivity is
is biased
biased toward
toward the
the
warmer
summer months
months [Sautter
[Sautter and
and Thunell,
warmer summer
Thunell, 1989],
1989], while
while at
at
lower latitudes,
are more
lower
latitudes,foraminifera
foraminifera are
more abundant
abundantduring
duringcooler
cooler
seasonal
upwelling events
events [Thunell
[Thunelland
and Reynolds,
Reynolds,1984].
1984]. The
seasonalupwelling
The
biased samples
are not,
with areas
biased
samples are
not, however,
however, associated
associatedwith
areas of
of
strongest
strongestseasonal
seasonalupwelling.
upwelling. This
This leads
leadsus
us to
toexamine
examineother
other
oceanographic variables
oceanographic
variablesthat
thatmay
maybias
biasthe
thetransfer
transferfunction.
function.
Comparison of
to modem
Comparison
of the
the temperature
temperatureresiduals
residualsto
modernbiological
biological
productivity (Figure
(Figure 3c)
3c) and
productivity
and to
to pycnocline
pycnoclinedepth
depth(Figure
(Figure3d)
3d) at
at
the core
the
coresites
sitesreveals
revealsno
no significant
significantcorrelations,
correlations,suggesting
suggestingthat
that
these variables
do not
these
variables do
not bias
bias the
the transfer
transfer function
function estimates
estimates of
of
temperature. Primary
temperature.
Primaryproductivity
productivityis
iscalculated
calculatedhere
hereat
at each
eachsite
site
by
by averaging
averaging12
12 monthly
monthlyvalues
valuesof
ofprimary
primaryproductivity
productivityestiestimated
from satellite
satellite color
color data
data and
and binned
binned in
in 1.2°
by
mated from
1.2ø latitude
latitude by
1.2°
longitude
blocks
[Antoine
et
al.,
1997].
Pycnocline
depth
1.2ø longitudeblocks[Antoineet al., 1997]. Pycnoclinedepth
is defined
is
defined here
here as
as the
the depth
depthof
of maximum
maximum rate
rate of
of change
changein
in
density
densityas
asaa function
functionof
of depth
depthfor
forannual
annualaverage
averagewater
watercolumn
column
profiles of
profiles
of temperature
temperatureand
and salinity.
salinity. The
The rate
,•ateof
of change
changein
in
density
at aa resolution
densitywas
was calculated
calculatedat
resolutionof
of ±5
+5 m
m from
from aa spline
splinefit
fit
of
of atlas
atlasdata
data[Levitus,
[Levitus, 1982].
1982].
Although the
between
residuals,
Although
the correlations
correlations
betweentemperature
temperature
residuals,
productivity, and
and pycnocline
pycnocline depth
depth (Figures
(Figures 3c
3c and
and 3d)
3d) are
are not
productivity,
not
are
relationships are
different from
from zero,
significantly
significantly different
zero, some
some relationships
assemblages
and 21
21 equation
equation terms
terms gave
gave aa statistical
error of
of
apparent.
The
total
range
of
residuals
is
higher
in
regions
where
assemblages
and
statisticalerror
apparent.The total rangeof residualsis higherin regionswhere
±1.9°C,
somewhat worse
worse than
than our
productivity
is
is
+1.9øC,somewhat
our result
resultusing
usingdowncore
downcorecalicaliproductivity
is low
low and
andthe
thepycnocline
pycnocline
is shallow.
shallow.This
Thisprovides
provides
bration of
bration
of faunal
faunalassemblages.
assemblages.
an
an important
important hint
hint about
about where
where biases
biasesmay
may exist
exist in
in our
ournew
new
Summarizing, we
we believe
believe our
our method
transfer
are
compared
to
Summarizing,
methodof
of calculating
calculatingfaunal
faunal
transferfunction.
function.These
Thesesamples
samples
areanomalous
anomalous
compared
to the
the
assemblages
from Pleistocene
Pleistocene samples
samples gives
gives aa more
situation
prevalent in
in the
assemblagesfrom
more realistic
realistic
situation prevalent
the tropics,
tropics, where
where areas
areasof
of shallow
shallow
view
view of
of statistical
statistical errors
errors in
in the
the transfer
transfer function
function while
while miniminiare generally
pycnocline
associated with
higher primary
primary
pycnocline are
generally associated
with higher
mizing
[Herbland and
mizingthe
the no-analog
no-analogproblem
probleminherent
inherentin
in CLIMAP's
CLIMAP's approach.
approach. productivity
productivity[Herbland
andVoituriez,
Voituriez,1979].
1979]. The
Theanomalous
anomalous
Biases
in the
in
areas
Biasesand
andmisfits
misfitsthat
thatremain
remainin
thecalibration
calibrationare
are apparent
apparentin
areasin
in which
whichproductivity
productivityis
isrelatively
relativelylow
low in
in spite
spiteof
ofaashallow
shallow
the
where
pycnocline
with
theapplication
applicationof
of downcore
downcorefactors
factorsto
tocore-top
core-topsediments,
sediments,
where
pycnoclineare
are generally
generallyassociated
associated
withlow-salinity
low-salinitysurface
surface
they
they can
canbe
be examined
examinedand
andunderstood.
understood.
waters
(for example
west of
of the
in the
waters(for
examplewest
the Panama
PanamaIsthmus
Isthmusin
the Pacific
Pacific
Ocean
and in
Ocean and
in the
theGulf
Gulf of
ofGuinea
Guineain
inthe
theAtlantic
AtlanticOcean).
Ocean). In
3.3.
Evaluating
Transfer
Function
Bias
3.3. Evaluating Transfer FunctionBias
these
greatly
evaporation. The
theseareas
areaslocal
localprecipitation
precipitation
greatlyexceeds
exceedsevaporation.
The
systematic
nature of
bias
To
potential
function,
which
systematic
nature
of this
thisapparent
apparent
biasis
isrevealed
revealedby
bycomparing
comparing
To assess
assess
potentialbiases
biasesin
inour
ourtransfer
transfer
function,
which
SST
within
zone
might
result,
we
the
of
SST residuals
residualsand
andthe
thedensity
densitycontrast
contrast
withinthe
theeuphotic
euphotic
zone
mightyield
yieldan
aninaccurate
inaccurate
result,
weconsider
consider
therelationship
relationship
of
(calculated
from
the
difference
of
annual
average
densities
at
estimated
temperatures
to
measured
(calibration)
temperatures
the
estimated
temperatures
to measured
(calibration)
temperatures (calculatedfrom the differenceof annualaveragedensitiesatthe
sea
and 100
m, Figure
Figure 3e).
3e). This
and
of
residuals
(the
of
seasurface
surfaceand
100 m,
Thiscorrelation
correlationis
is weak
weakbut
but
andthe
therelationship
relationship
oftemperature
temperature
residuals
(thedifference
difference
of
different
from
(r2=0.
11).
estimated
minus
SST)
temperature
and
significantly
different
fromzero
zero
(r2=0.11).
estimated
minusmeasured
measured
SST)to
tomeasured
measured
temperature
and significantly
other
variables.
We focus
otheroceanographic
oceanographic
variables. We
focus on
on variables
variablesthat
that
previous
work
suggests
might
estimates:
previouswork suggests
mightbias
biasthe
thetemperature
temperature
estimates:
biological
primary
pycnocline depth,
depth, and
biological
primaryproductivity,
productivity,
pycnocline
andupper
upper
ocean
et al.,
Andreasen and
and
oceandensity
densitygradients
gradients[Ravelo
[Raveloet
al., 1990;
1990; Andreasen
Ravelo,
and Mix,
Mb 1998]
Ravelo,1997;
1997; Watkins
Watkinsand
1998]
A significant
correlation
exists
between
temperature
residuals
A
significant
correlation
exists
between
temperature
residuals
and
measured
temperatures
(r2=0.23).
At
temperatures
<18°C,
andmeasured
temperatures
(r2=0.23).
At temperatures
<18øC,
SST
estimates
tend
to
be
too
high
(Figure
3b).
These
samples
in
SSTestimates
tendtobetoohigh(Figure
3b). These
samples
in
the
calibration
are
mostly
at
poleward
thecore-top
core-top
calibration
arelocated
located
mostly
atlatitudes
latitudes
poleward
of
of 35°
35ønorth
northand
andsouth.
south. The faunal
faunal factors
factorsderived
derived from the
the
tropics
do
not
the
faunas
well
Future
tropicsdo notrepresent
represent
thecore-top
core-top
faunas
wellhere.
here. Future
4mprovements
in these
these regions
regions may
may come
come from
from including
including higherhigherimprovements
in
latitude
samples in
in the
the factor
analysis. Communalities
in
latitudesamples
factoranalysis.
Communalities
in these
these
anomalous
samples
are
low,
0.7,
anomalous
samples
arerelatively
relatively
low,averaging
averaging
0.7,compared
compared
to the
atathigher
temperatures
ininwhich
to
thesamples
samples
higher
temperatures
whichcommunalities
communalities
average 0.9.
average
0.9.
At
>28°C,
estimates
appear
to
At temperatures
temperatures
>28øC,temperature
temperature
estimates
appear
tobe
be
too law.
low. This
that
function
developed
here
tee
Thissuggests
suggests
thatthe
thetransfer
transfer
function
developed
here
The
in the
The geographic
geographicdistribution
distributionof
of temperature
temperatureresiduals
residualsin
the
eastern Pacific
Pacific and
and tropical
Oceans supports
supports the
the correeastern
tropicalAtlantic
AtlanticOceans
corre-
lation
transfer
function
temperature
residuals
and
lationbetween
between
transfer
function
temperature
residuals
andupper
upper
ocean
If the
oceandensity
densitycontrast
contrast(Figure
(Figure4).
4). If
the anomalous
anomalousareas
areaswith
with
high
density gradients
gradients(>3
(>3xx l0
such
highvertical
verticaldensity
10-3 gg cm3),
cm-3),
suchas
as
Panama
PanamaBasin
Basin and
andthe
the Gulf
Gulf of
of Guinea
Guineaare
are excluded,
excluded,the
the standard
standard
error
for (1)
error of
of estimate
estimate for
(1) is
is reduced
reducedto
to ±1.5°C
+1.5øC and
and the
the trend
trend of
of
temperature
residuals
compared
to
measured
temperatures
temperatureresidualscomparedto measuredtemperaturesis
is
reduced
reduced (i.e.,
(i.e., within
within the
thestatistical
statisticalerror).
error). Thus,
Thus, in
in areas
areasthat
that
have
havemoderate
moderatedensity
densitygradients
gradientswithin
within the
the euphotic
euphoticzone,
zone,(1)
(1)
estimates
SSTs accurately
that warrants
estimates SSTs
accurately with
with aaprecision
precision that
warrants
application
in
applicationto
to ancient
ancientsamples
samples
inthe
thetropics.
tropics.
Note
that
none
of
the
downcore
included in
in the
the factor
factor
Notethatnoneof the downcoresamples
samplesincluded
analysis
that defined
came
analysisthat
definedthe
thefaunal
faunalassemblages
assemblages
camefrom
fromthe
theareas
areas
of
anomalously high
high vertical
vertical density
density gradients
gradients (Figure
(Figure 1).
1). Given
of anomalously
Given
more
downcore faunal
faunal data
data from
from those
those areas,
areas, itit is
is possible
that
moredowncore
possiblethat
the
faunal
descriptions
and
temperature
estimates
of
such
areas
the faunal descriptionsand temperatureestimatesof suchareas
would
wouldimprove.
improve.
MIX
MIX ET AL:
AL.: FORAMINIFERAL
FORAMINIFERAL FAUNAL
FAUNAL ESTIMATES
ESTIMATES
356
130°W
130øW
25øN
25°N
ß ""
............
•:!•...5:•..
,•:i.' • .'......
....
>.•i;•:•:•::½•:::..:.:i
!
Atlantic [Mix
[Mix et
et al.,
aL, 1986].
1986]. Next
20øE
20°E
Atlantic
Next we
we found
foundthe
thegridded
griddedcore-top
core-top
25°N
estimate (i.e.,
(i.e., the
the average
average of
of core
core tops
tops within
within aa 22°
latitude xx 55°ø
ß0",""" • ."•:•':•
............•'•'"'*'
25øN
estimate
ø latitude
'i;
:i
longitude box)
box) associated
with
longitude
associated
with each
eachglacial
glacialmaximum
maximumsample.
sample.
The difference
between these
these two
two values
values defines
defines the
the extent
extent of
of
The
difference between
ee
0
'• .:'?
ß.........
•'•
.:..•-•
ß
•.
ß
'•.•
eee
eeß ß•'.
.
:•,:?.•..:
.•'•
.... .:•.:.:.....::....
•
•
....::•
.....
•.:::•
ß
.,.
25°S
25øS
•
change
from LGM
LGMto
to modem
modern(Plate
(Platelb).
ib).
0
' .....
===============================================
changefrom
..........
•::?•?•;•5:L
'::•:•-."•'. o
• ß ß ½•:..'.•,
"
130°W
130øW
. •: 25os
25°S
20øE
20°E
20°E
20øE
130°W
130øW
We
Wecalculated
calculatedthe
the
differences
this way
way rather
rather than
than gridding
both the
the LGM
differencesthis
gridding both
LGM and
and
modem
and differencing
the grids
modem estimates
estimatesand
differencingthe
grids because
becausethe
the data
data
density
of the
densityof
theLGM
LGM samples
samplesis
is lower
lowerthan
thanthat
thatof
of the
thecore
coretops.
tops.
By
calculating the
between
and
By calculating
thedifferences
differences
betweenLGM
LGM estimates
estimates
andcorecoretop
estimates
rather
than
between
LGM
estimates
and
top estimatesratherthan betweenLGM estimatesandmodem
modem
atlas
we minimize
the effects
effects of
of bias
bias in
atlas values
values we
minimize the
in the
the transfer
transfer
25°N
functions noted
noted above.
functions
above.
¾""•* •":'"'•" 25øN
...".
.........
i;:;5'i'.,:•:•!i•i•
.......
:.•i• '"'•
' '
. ß "".
The
anomalies
The resulting
resultingmap
mapof
oftemperature
temperature
anomalies(LGM
(LGM minus
minus
modern)
reveals
major
cooling
of
the
tropics,
up
to 6øC
6°C on
on an
an
modem) revealsmajor coolingof the tropics,up to
.......
-::•:•:•:•;;:;-;•::.;:•:•?•5•:•...-::•--::•:::.::.
•:•:" ..•.•
•.........
.
. .::•.'•:•?;•?::•:•..::;:?.
:::.::•...:......
::::::::::::::::::::::
.... ...
annual
average
in
the
equatorial
Atlantic
and
-5°C
in
the
ß
ee
......
:'*
•::'•i•:.•:•-J-:•:::
'"
-"
.
.-.•'.'-----:
;•
.....
:::::...
ß
ß
.•;:.•:•
•yf•i•.•:.:e::..?.
';
.•;:•:•::::::
...:::
annual
average
in
the
equatorial
Atlantic
and
-5øC
in
the
0
0o
o
.. z ... .....
;":.-'-.:::::::.:?•.
4•L.:..:"'.."'
• '"?:?•::%•;•:.•;•-•:::•?'::?•
ß
. ß •...:
:.-.:..:;:•.•:•..
.
ß
......
•:•:•::•:•;•:•:.;::•.
equatorial Pacific
Pacific (Plate
(Plate lb).
ib). Significant
cooling
equatorial
Significant
coolingin
in our
ourreconrecon' "•
.. :':;•:•
".
......
.
2
•
.
............
.;•
?'•:
::':;•'"1
:..
struction extends
extends into
into the
the Caribbean
and in
struction
Caribbean(3-4°C
(3-4øC change)
change)and
in the
the
eastern
boundary
regions
(up
to
6°C
change).
In
the
equatorial
easternboundaryregions(up to 6øC change). In the equatorial
25°S
25°S
ß •D•;;•:'•,•D:•P
TM.
ß . ß
. .•:,.•
25øS
zones our
our new
of
zones
new reconstruction
reconstruction
of LGM
LGM temperature
temperatureanomalies
anomaliesis
is
130°W
20°E
130øW
20øE
significantly different
different than
than that
significantly
thatof
ofCLIMAP
CLIMAP[1981].
[1981]. In
In the
the
(a) SST
(estimated
minus
degrees
Figure 4.
Figure
4. (a)
SSTresiduals
residuals
(estimated
minusmeasured,
measured,
degrees center
center of
of the
with
the subtropical
subtropicalgyres
gyresour
ourfindings
findingsare
areconsistent
consistent
with
Celsius) at
at core-top
core-top locations
locations in
in the
the eastern
Pacific
Celsius)
eastern
Pacificand
andtropical
tropicalAtlantic
Atlantic
previous
inferences
of
little
or
no
change
[CLIMAP,
1981;
Prell
previous
inferences
of
little
or
no
change
[CLIMAP,
1981;
Prell
g
cm3)
Oceans.
(b)
The
upper
ocean
density
contrast
(a1
units,
10
Oceans.
(b)Theupper
ocean
density
contrast
(atunits,10'3 gcm'3) 1985], although the spatial patterns of subtropical change are
1985], althoughthe spatialpatternsof subtropicalchangeare
calculated from
from00 to
to 100
100 m
m (i.e.,
(i.e., across
across the
the euphotic
euphotic zone).
zone). Areas
calculated
Areasof
of
somewhat
different.
somewhat
different.
strongest
density
contrast
in
the
Pacific
Ocean
(west
of
the
Panama
strongest
densitycontrastin the PacificOcean(westof the Panama
25°N
25øN
2
"'
:'•'•?.
' ....•
ß.
,• ' ' '
Isthmus) and
and in
with
Isthmus)
in the
theAtlantic
AtlanticOcean
Ocean(Gulf
(Gulfof
ofCluinea)
Guinea)are
areassociated
associated
with
areas the
In these
anomalously
low surface
water salinities.
anomalouslylow
surface water
salinities. In
these areas
the
foraminiferal
transfer function
function estimates
lower
foraminiferaltransfer
estimatestemperatures
temperatures
lower than
thanthose
those
measured at the
the sea surface.
measured
Summarizing,
the depth
and
Summarizing,
the
depthof
of the
thepycnocline
pycnocline
andbiological
biologicalproproductivity
do
not
appear
to
induce
systematic
bias
ductivitydo not appearto inducesystematic
biasin
in the
thetransfer
transfer
function
There is
is aa weak
functiondeveloped
developedhere.
here. There
weaktendency
tendencyfor
for bias
bias
induced
by the
induced by
the strength
strengthof
of the
thedensity
densitygradient
gradientwithin
within the
the
euphotic
estimates
euphoticzone.
zone.Transfer
Transferfunction
functiontemperature
temperature
estimatestend
tendto
to
be
too
low
in
areas
of
very
strong
vertical
density
gradients.
be too low in areasof very strongverticaldensitygradients.We
We
speculate that
that such
such underestimates
of SST
SST in
in these
speculate
underestimatesof
these areas
areasare
are
associated
availability of
of significantly
different
associatedwith
with the
the availability
significantly different
ecological
niches within
within the
the euphotic
ecologicalniches
euphoticzone,
zone, exploited
exploitedby
by
different
foraminiferal
populations
but
averaged
in
differentforaminiferalpopulations
but averagedinthe
thegeologic
geologic
record
al., 1982].
1982].
Although we
we have
record [Fairbanks
[Fairbanks et
et al.,
Although
have not
not
attempted to
to make
make corrections
corrections for
attempted
for such
suchan
an effect
effect here,
here,additional
additional
information
of
perhaps
based
informationon
on the
thestrength
strength
of the
thepycnocline,
pycnocline,
perhaps
basedon
on
the
oxygen
isotopic
difference
between
shallow-dwelling
and
the oxygenisotopicdifferencebetweenshallow-dwelling
and
deep-dwelling
[MuIi:za et
et aL,
deep-dwellingforaminifera
foraminifera [Mulitza
al., 1997], might
might
improve
improvetransfer
transferfunctions
functionsof
of SST.
SST.
3.4.
3.4. The
The Last
Last Glacial
Glacial Maximum
Maximum
The
here as
The CLIMAP
CLIMAP [1981]
[1981] reconstruction
reconstruction (recalculated
(recalcu!ated here
as
annual temperature
anomalies by
by averaging
annual
temperatureanomalies
averagingthe
the CLIMAP
CLIMAP
August
suggested little
little change
change in
in the
Augustand
andFebruary
Februaryestimates)
estimates)suggested
the
tropics.
For
example,
CLIMAP
[1981]
estimated
Last
Glacial
tropics. For example, CLIMAP [1981] estimatedLast Glacial
Maximum (LGM)
(LGM) cooling
cooling of
of 2øC
2°C in
in the
the equatorial
equatorial Atlantic
Atlantic and
and
Maximum
slight warming
warming relative
relative to
to modem
in the
slight
modem averages
averages in
the eastem
eastern
equatorial Pacific
Pacific (Plate
(Plate 1la).
equatorial
a).
To
we
To compare
compareour
our new
new transfer
transferfunction
functionto
to that
thatof
of CLIMAP,
CLIMAP, we
applied
(1)
to
both
core-top
sediments
and
to
the
CLIMAP
applied (1) to both core-top sedimentsand to the CLIMAP
LGM data
data set
LGM
setaugmented
augmentedby
by newer
newerdata
dataon
onLGM
LGM foramimferal
foraminiferal
assemblages in
in the
the tropical
and Morey,
assemblages
tropicalPacific
Pacific [Mix
[Mix and
Morey, 1996]
1996] and
and
4.
4. Discussion
Discussion
Why
Why are
are our
our estimates
estimatesof
of tropical
tropicalclimate
climatechange
changeso
sodifferent
different
from
from those
thoseof
of CLIMAP
CLIMAP [1981]
[ 1981] when
when they
theyuse
usebasically
basicallysimilar
similar
transfer function
function methods?
methods? We
transfer
We see two
two reasons.
reasons.
First, our
our downcore
factors, all
all generated
from tropical
tropical
First,
downcorefaunal
faunal factors,
generatedfrom
locations, better
better represent
the faunal
locations,
representthe
faunal variations
variationsthat
that actually
actually
occurred in
in the
the tropics.
occurred
tropics.Because
Becausethe
theCLIMAP
CLIMAP factors
factorsgrouped
grouped
some species
species that
that are
some
are not
not correlated
correlatedin
in the
thegeologic
geologicpast,
past,they
they
effectively smoothed
smoothed the
the downcore
effectively
downcorerecord
recordof
of faunal
faunalvariability.
variability.
This smoothing
smoothing resulted
in underestimates
of SST
changes.
This
resultedin
underestimates
of
SST changes.
Second,
Second, because
becauseour
our calibration
calibrationincludes
includesonly
only low-latitude
low-latitude
samples,
it optimizes
samples,it
optimizesthe
the temperature
temperatureestimate
estimateequations
equationsto
to
reconstruct
the tropics.
tropics. In
reconstructthe
In contrast,
contrast,the
theCLIMAP
CLIMAP equations
equationswere
were
heavily
heavily weighted
weightedto
to polar
polarfaunas.
faunas. The
The cold
coldend-member
end-memberthat
that
dominated
the
CLIMAP
temperature
equations
the
dominatedthe CLIMAP temperatureequations(including
(including the
sinistral-coiling
sinistral-coilingmorphotype
morphotypeof
of the
the species
speciesN.
N. pachyderma)
pachyderma)was
was
not
to the
the oceanographic
contextof
ofthe
thetropics.
tropics. Polar
not relevant
relevant to
oceanographiccontext
Polar
conditions
conditionsnever
neveroccurred
occurredthere.
there. Our
Our present
presentresult
resultsuggests
suggests
that
to
that the
the CLIMAP
CLIMAP equations
equationswere
weredesensitized
desensitized
totemperature
temperature
variations
variations at low
low latitudes.
latitudes.
How do
do our
How
our new
new estimates
estimatesof
of LGM
LGM cooling
cooling of
of the
the tropical
tropical
oceans
oceanscompare
compareto
to other
otherestimates?
estimates? Recent
Recent faunal
faunalestimates
estimates
based on
based
on radiolaria
radiolariafrom
from the
theeastern
easternequatorial
equatorialPacific
Pacific [Pisias
[Pisias
and
and Mix,
Mix, 1997]
1997] agree
agreewith
with the
theestimates
estimatesmade
madehere
herebased
basedon
on
foraminifera.
foraminifera. Some
Somegeochemical
geochemicaldata
datafrom
from the
thetropical
tropicalocean
ocean
and
and from
from the
the continents
continentssuggest
suggestlarge
largechanges
changesin
in tropical
tropicalSSTs,
SSTs,
although
the
geochemical
data
are
themselves
in
althoughthe geochemicaldata are themselvesin conflict
conflictwith
with
For example,
each other.
each
other. For
example, Sr/Ca
Sr/Ca ratios
ratios in
in Barbados
Barbadoscorals
corals
suggest
et al.,
suggest44°
ø to
to 5°C
5øC ice
ice age
agecooling
cooling[Guilderson
[Guildersonet
al., 1994],
1994],
somewhat
more than
than the
the 3°C
somewhatmore
3øC change
changewe
we estimate
estimatehere
here near
near
Barbados.
ofofcoral
however,
Barbados.Other
Otherstudies
studies
coralchemistry,
chemistry,
however,question
question
these
these results
resultsbecause
becausethe
the sensitivity
sensitivityof
of Sr/Ca
Sr/Ca to
totemperature
temperature
changes
changesis
is affected
affectedby
by coral
coralgrowth
growthrates
ratesand
andvaries
variesin
in different
different
coral
aL, 1994].
coralspecies
species[de
[de Villiers
Villiers et
et al.,
1994].
357
357
MIX ET AL.: FORAMINIFERAL
MIX
FORAMINIFERAL FAUNAL
FAUNAL ESTIMATES
ESTIMATES
a
a
130°W
130øW
20°E
20øE
25°N
25øN
25°N
25øN
0
00
25°S
25øS
25°S
25øS
b
25°N
25°N
25øN
0
0
25°S
25øS
25°S
25øS
I130øW
30°W
20°E
20øE
Plate 1.
SST
LGM
modem,
based
onon(a)(a)The
CLIMAP [19811
averaging
Plate
1. Annual
Annualaverage
average
SSTanomalies,
anomalies,
LGMminus
minus
modem,
based
TheCLIMAP
[1981] reconstruction,
reconstruction,
averaging
winter
estimates, (b)
(this paper,
winterand
andsummer
summerestimates,
(b)The
TheOregon
OregonState
StateUniversity
University(OSU)
(OSU) reconstruction
reconstruction
(this
paper,LUM
LGM core
corelocations
locationsnoted).
noted).
suggests significantly
significantly greater
greater ice
ice age
age cooling
cooling in
The
The white
white area
areain
in Pacific
Pacificindicates
indicatesno
nodata.
data. Our
Our new
new reconstruction
reconstructionsuggests
in the
the
equatorial band
band associated
associated with
with the
the equatorial
equatorial current
currentsystems.
systems. Although
Although the
the spatial
spatial pattern
pattern of
of change
change within
within the
the subtropics
subtropics is
is
equatorial
somewhat different,
different, our
our reconstruction
reconstruction supports
supportsthe
the inference
inference of
ofCL!M4P
[1981] that
that the
the subtropical
somewhat
CLIMAP [1981]
subtropicalgyre
gyrecenters
centersremained
remained
relatively warm
warm and
relatively
andstable
stableduring
duringthe
theLGM.
Another
index, U/Ca,
U/Ca, in
Another geochemical
geochemical index,
in the
thesame
sameBarbados
Barbados
corals
glacial maximum
maximumcooling
coolingof-3øC
of 3°C relative
corals suggests
suggestsglacial
relativeto
to
modern conditions
[Mm
et al.,
modern
conditions
[Min et
al., 1995].
1995]. AAtemperature
temperatureindex
index
based on
on Mg/Ca
based
Mg/Ca ratios
ratiosin
in foraminifera
foraminiferasuggests
suggestsice
ice age
agecooling
cooling
of 2.6°±1
.3°Crelative
relativetoto modern
modem inin the
the Caribbean
and in the
2.6"+1.3"C
Caribbean and
the
North
Counter Current
Current of
of the
North Equatorial
Equatorial Counter
the Atlantic
Atlantic [Hastings
[Hastingset
et
al.,
are
al., 1998].
1998]. Both
Boththe
theU/Ca
U/Caand
andMg/Ca
Mg/Caestimates
estimates
areconsistent
consistent
with our
our new
new faunal
faunal estimates
with
estimatesin
in the
the region.
region.
Oceanic
data that
little ice
ice age
Oceanicdata
that support
supportrelatively
relatively little
age cooling
coolingin
in
the
include
organic
geochemical
thermometers
Uk37
thesubtropics
subtropics
include
organic
geochemical
thermometers
uk'37
[Sikes
and
Keigwin,
1994;
Roselle-Melé
et
al.,
1998].
Our
[Sikes and Keigwin, 1994; Roselle-Mel• et al., 1998]. Our
estimates
in these
little change
or even
estimatesin
these regions
regionssuggest
suggestlittle
changeor
even slight
slight
warming
during the
however,
warming during
the LCIM.
LGM. Our
Ourcalibration
calibrationscheme,
scheme,
however,
does
gyres,
doesnot
not include
includedowncore
downcorerecords
recordsfrom
fromthe
thesubtropical
subtropical
gyres,
and should
should be
be re-assessed
with
and
re-assessed
with local
localdata
datafrom
fromthose
thoseregions.
regions.
What
Rare gas
gas content
of ice
What about
about the
thecontinents?
continents? Rare
content of
ice age
age
groundwater
in tropical
groundwaterin
tropicalSouth
SouthAmerica
Americaand
andthe
thesouthern
southernUnited
United
States implies
implies significant
significant ice
ice age
age cooling
cooling of
of 33°
to 5øC
5°C in
States
ø to
in these
these
regions
et al.,
al., 1995].
low-latitude
regions[Sture
[Stuteet
1995]. Because
Because
low-latitudeland
landtemperatempera-
tures are
are relatively
closely tied
tied to
to regional
SSTs [Rind
and
tures
relatively closely
regional SSTs
[Rind and
Peteet, 1985],
Peteet,
1985], these
theseestimates
estimateswould
would appear
appearto
to constrain
constrainlower
lower
ocean temperatures
temperatures at
at the
the glacial
glacial maximum.
maximum. At
ocean
At high
high altitudes,
altitudes,
oxygen isotope
isotopedata
data from
from ice
ice cores
cores in
in Peru
ice age
oxygen
Peru suggest
suggestice
age
lowlands
with those
of the
cooling roughly
roughly consistent
cooling
consistentwith
those of
the lowlands
[Thompson
et
al.,
1995].
[Thompson
et al., 1995].
The continental
The
continentaldata,
data, however,
however,do
do not
notnecessarily
necessarilyrequire
requirethat
that
oceanic
cooling occurred
occurred in
in all
all areas
areas of
of the
the tropics.
tropics. The
oceaniccooling
Theeffect
effect
on
limited
ice age
age
on the
the continents
continentsof
of our
ourproposed
proposedgeographically
geographically
limited ice
cooling
by
coolingof
of the
theequatorial
equatorialoceans
oceansis
is addressed
addressed
by using
usingour
ourLGM
LGM
SSTs
circulation
SSTs as
as aaboundary
boundarycondition
conditionto
toan
anatmospheric
atmospheric
circulation
model
(S.W. Hostetler
Hostetler and
and A.C.
A.C. Mix,
Mix, Ice
Ice age
of the
model (S.W.
age cooling
cooling of
the
tropics
reassessed, submitted
submitted to
to Nature,
Nature, 1999).
tropicsreassessed,
1999). This
Thiscomparison
comparison
reveals
between the
the continental
continental and
reveals that most of the mismatches
mismatches between
oceanic
here.
oceanicdata
datacan
canbe
be resolved
resolvedby
by SSTs
SSTsreconstructed
reconstructed
here.
What
oceanic
processes
could
produce
such
cooling,
What oceanicprocesses
couldproducesuchcooling,concenconcen-
trated
associated
with
trated in
in the
thecold
cold"tongues"
"tongues"
associated
withthe
thewestward
westward
flowing
equatorial currents?
currents? We
cooling
flowingequatorial
Wesuggest
suggest
coolingof
ofthe
theice
iceage
age
Mode
thermocline
driven
by
faster
upper
ocean
ventilation.
thermoclinedriven by faster upper ocean ventilation. Mode
waters forming
forming in
in subpolar
waters
subpolarregions,
regions,chilled
chilled by
by cold
coldcontinental
continental
air masses
by polar
air
massesdisplaced
displacedequatorward
equatorwardby
polarglaciation,
glaciation,return
returnto
to
the surface
the
surfacethrough
throughequatorial
equatorialand
andeastern
easternboundary
boundaryupwelling.
upwelling.
Such
Such cold
cold waters
waters are
are then
then advected
advectedinto
into the
the tropical
tropical oceans
oceansby
by
Poleward
strong
westward
flowing
equatorial
currents.
strongwestwardflowing equatorialcurrents. Polewardreturn
return
flow
flow of
of tropical
tropicalsurface
surfacewaters
waterswould
wouldcomplete
completethe
the advective
advective
ioop,
in enhanced
net transport
transportof
of heat
heat out
out of
loop, resulting
resultingin
enhancednet
of the
the
tropics.
tropics.
358
358
MIX
FAUNAL
ESTIMATES
MIX ET
ET AL.:
AL.: FORAMINIFERAL
FORAMINIFERAL
FAUNAL ESTIMATES
This
This view
view of
of greater
greaterthennocline
thermoclineturnover,
turnover,effectively
effectively aa
smaller
smallerversion
versionof
of the
theso-called
so-calledconveyor
conveyorbelt
beltadvective
advectiveheat
heat
transport,
transport, is
is consistent
consistentwith
with benthic
benthicforaminiferal
foraminiferaldata
datafrom
from
shallow
cooler
shallowsites
sitessuggesting
suggesting
coolerand
andbetter
betterventilated
ventilatedthermocline
thermocline
[Slowey
and Curry,
[Sloweyand
Curry, 1992],
1992], and
andwith
with inferences
inferencesof
of aastronger
stronger
east-west
gradient in
in pycnocline
depth at
east-westgradient
pycnoclinedepth
at the
the glacial
glacialmaximum
maximum
[Andreasen
Such aa change
[Andreasen and
and Ravelo,
Ravelo, 1997].
1997]. Such
change is
is also
also
manifested
manifesteddirectly
directlyin
in the
theforaminiferal
foraminiferalspecies
speciesin
in the
theequatorial
equatorial
region.
with
region. The
Thelargest
largestchange
changehere
hereisisassociated
associated
with the
theeastern
eastern
boundary
fauna
(our
factor
3
noted
in
Table
1)
rather
boundaryfauna (our factor 3 notedin Table 1) ratherthan
thanwith
with
the
fauna [Mix
and Morey,
Morey, 1996].
1996]. Thus,
the upwelling
upwellingfauna
[Mix and
Thus,the
thespecies
species
distributions
distributionssupport
supportthe
theidea
ideathat
thatadvection
advectionis
isthe
themajor
majorsource
source
of
tongues. This
of cooling
coolingin
in the
the equatorial
equatorialcold
cold tongues.
Thisinference
inferencebased
based
analysis
by analysis
analysis of
of the
theforaniinifera
foraminifera is
is supported
supported by
analysis of
of
radiolarian
radiolarianfaunas
faunas[Pisias
[Pisiasand
andMix,
Mix, 1997].
1997].
5.
5. Conclusions
Conclusions
Here we
we develop
develop aa new
for calibrating
faunal transfer
transfer
Here
new strategy
strategyfor
calibratingfaunal
functions
to estimate
functions to
estimate tropical
tropical SSTs
SSTs in
in the
thepast.
past. Our
Our key
key
innovation
the tropics
to
innovationis
is the
theuse
useof
of Pleistocene
Pleistocenesamples
samplesfrom
from the
tropicsto
define
that
definerobust
robustfaunal
faunalassemblages
assemblages
thatcovary
covarythrough
throughtime
timerather
rather
than core-top
than
core-top samples
samples to
to define
define assemblages
assemblagesthat
that covary
covary
geographically
in
the
modern
ocean.
Our
approach
circumvents
geographicallyin the modem ocean. Our approachcircumvents
the problem
in the
the
problem of
of no-analog
no-analogfaunas
faunas in
the ice
ice age
ageocean,
ocean,which
which
plagues
plaguespast
pastfaunal
faunalestimates
estimatesusing
usingeither
eithertransfer
transferfunction
functionor
or
modem
modemanalog
analogtechniques.
techniques.
We estimate
LGM SSTs
in the
and eastern
eastern
We
estimateLGM
SSTsin
the equatorial
equatorialAtlantic
Atlanticand
Pacific
significantly
cooler
than
those
of
CLIMAP[1981].
Pacific significantlycoolerthan thoseof CLIMAP [ 1981]. Our
Our
faunal
are more
geochemical
faunal estimates
estimatesare
more in
in line
linewith
withrecent
recent
geochemical
proxies
changes. The
proxiesof
of SST
SST and
andcontinental
continentaltemperature
temperaturechanges.
Thelonglongstanding
in
standingconflict
conflict between
betweenthe
the CLIMAP
CLIMAP reconstruction
reconstruction
in the
the
tropics
tropicsand
andother
otherdata
dataand
andmodels
modelsmay
maythus
thusbe
beat
atleast
leastpartially
partially
resolved.
resolved.
Our
for the
of the
Our findings
findingshave
haveimplications
implicationsfor
the sensitivity
sensitivityof
the
tropics
climate
that
tropicsto
to large-scale
large-scale
climatechange
changeand
andthe
theprocesses
processes
thatdrive
drive
such
that
currents,
espesuchchanges.
changes.We
Wesuggest
suggest
thatthe
theequatorial
equatorial
currents,
especially
boundary
cially where
where they
they interact
interactwith
with the
theeastern
eastern
boundarycurrent
current
systems,
are the
the most
to
and
systems,
are
mostsensitive
sensitive
tochange
change
andthat
thatthese
thesesystems
systems
are
for the
the bulk
are responsible
responsiblefor
bulk of
of the
theLGM
LGM cooling
coolingwithin
withinthe
the
tropics. We
CLIMAP's
of
tropics.
Wesupport
support
CLIMAP'soriginal
originalinference
inference
of stability
stabilityof
of
SSTs
the subtropical
SSTswithin
within the
subtropicalgyres.
gyres.
With our
to
faunal
in
With
ournew
newapproach
approach
tocalibrating
calibrating
faunalassemblages
assemblages
in
transfer
but
transferfunctions
functionswe
we have
havemade
madeprogress,
progress,
butuncertainties
uncertainties
remain. Our
isisfocused
on
equatorial
remain.
Ourreconstruction
reconstruction
focused
onthe
theeastern
eastern
equatorial
Atlantic
Pacific Oceans. Areas
Atlantic and Pacific
Areas distant
distant from
from our
our downcore
downcore
calibration
of faunas
calibrationof
faunasshould
shouldbe
bereanalyzed
reanalyzedwith
withlocal
localcalibration
calibration
using
to resolve
resolve robust
robust faunal
using our
our downcore
downcore method
method to
faunal
assemblages.
We
biases
in
assemblages.
Wehave
havenoted
notedpotential
potential
biases
inour
ourtemperature
temperature
estimates,
perhaps linked
linked to
to the
the intensity
intensity (but
(but not
not the
the depth)
depth) of
of
estimates,
perhaps
the
pycnocline.
For
example,
SSTs
in
areas
with
a
low-salinity
thepycnocline.For example,SSTsin areaswitha low-salinity
surface layer
layer may
may be
be difficult
from
surface
difficult to
toreconstruct
reconstruct
fromforaminiferal
foraminiferal
faunas.
that
ofofmore
faunal
faunas.ItItisispossible
possible
thatthe
theaddition
addition
moredowncore
downcore
faunal
data
would
datain
in such
suchregions
regions
wouldbetter
betterdefine
definethe
thefaunal
faunalassemblages
assemblages
and
Alternatively, tracers
tracers of total
andimprove
improvethe
theSST
SSTestimates.
estimates.Alternatively,
total
density
contrast
within
the
upper
ocean
could
help
densitycontrastwithinthe upperoceancouldhelpto
toidentify
identify
problem
estimates of
of
problemareas
areasand
andimprove
improvefuture
futuretransfer
transferfunction
functionestimates
SST.
SST.
Acknowledgments.
We thank
Acknowledgments. We
thank W.
W. Curry,
Curry, P.
P. Valdez,
Valdez, and
and two
two
anonymous reviewers
reviewersfor
for constructive
constructivecomments.
comments. Discussions
with J.
J.
anonymous
Discussionswith
Imbrie, W.
W. Prell,
Prell, A.
A. Martin,
Martin, U.
U. Pflaumann,
J. Ortiz,
Imbrie,
Pflaumann, J.
Ortiz, D.
D. Lund,
Lund, and
and M.
M.
Feldberg
were also
also helpful.
helpful. L.
provided
the
estimates
Feldberg
were
L.Beaufort
Beaufort
provided
thesatellite
satellite
estimates
of primary
primary productivity.
productivity. This
of
Thiswork,
work,and
andcuration
curationof
ofcores
coresat
atthe
theOSU
OSU
Repository, was
was supported
supported by
by NSF.
NSF.
Repository,
References
References
Andreasen,D.
D. J.,
J., and
Andreasen,
and A.
A. C.
C.Ravelo,
Ravelo,Tropical
Tropical
Pacific Ocean
Pacific
Oceanthermocline
thermoclinedepth
depthreconstrucreconstructions for
tions
for the
the Last
Last Glacial
Glacial Maximum,
Maximum, PaleoPaleo-
ceanography,
12,
ceanography,
12,395-413,
395-413,1997.
1997.
Antoine, D.,
D., J.-M.
J.-M. André,
Antoine,
Andr6, and
andA.
A. Morel,
Morel, Oceanic
Oceanic
primary
primaryproductivity,
productivity,2,
2, Estimation
Estimationat
atglobal
global
scale
scale from
from satellite
satellite (coastal
(coastalzone
zone color
colorscanscanner)
Cycles.
ner) chlorophyll,
chlorophyll,Global
GlobalBiogeochem.
Biogeochem.
Cycles,
of
5,
of the
the North
North Pacific,
Pacific,Mar.
Mar.Micropaleontol..
Micropaleontol.,
5,
329-399,
329-399, 1980.
1980.
de
Villiers,
S.,
G.
T.
Shen,
and
B.
K.
Nelson,
The
de Villiers, S., G. T. Shen, and B. K. Nelson, The
Sr/Ca-temperature
Sr/Ca-temperaturerelationship
relationship in
in coralline
coralline
aragonite:
aragonite:Influence
Influence of
of variability
variability in
in (Sr/Ca)
(Sr/Ca)
seawater
Geoseawaterand
andskeletal
skeletalgrowth
growthparameters,
parameters,
Geochim.
Ada, 58,
chim. Cosmochim.
Cosmochim.Acta,
58, 197-208,
197-208, 1994.
1994.
Pleistocene climates
and
Ericson,
D.B., Pleistocene
Ericson, D.B.,
climates and
10, 57-70, 1997.
1997.
Bigg,
Bigg, G.
G. R.,
R., M.
M. R.
R. Wadley,
Wadley,D.
D. P.
P.Stevens,
Stevens,and
and1.
J.
chronology
in deep-sea
Science.
chronologyin
deep-seasediments.
sediments,
Science,
simulated
simulatedglacial
glacialclimate
climateand
andpaleodata:
paleodata:A
A rereexamination,
examination,Paleoceanography,
Paleoceanography, 11,
11, 3-14,
3-14,
298,
298, 841-844,
841-844, 1982.
1982.
Ganopoiski,
A.. S.
Ganopolski,A.,
S. Rahmstorf.
Rahmstorf,V.
V. Petoukhov,
Petoukhov,and
and
M. Claussen,
Claussen, Simulation
M.
Simulationof
of modem
modemand
andglacial
glacial
climates with
with aa coupled
of interclimates
coupledglobal
globalmodel
model of
inter-
162,
162, 1227-1234,
1227-1234, 1968.
1968.
Fairbanks,
R.G., M.
M. Sverdlove,
R. Free,
A.
Fairbanks, R.G.,
Sverdlove, R.
Free, P.H.
P.H.
A. Johnson,
Johnson, Simulation
Simulation of
of two
two Last
Last Glacial
Glacial
Wiebe, and
and A.W.H.
Maximum
Wiebe,
A.W.H. Be,
B6, Vertical
Vertical distribution
distribution
Maximum ocean
oceanstates,
states,Paleoceanography.
Paleoceanography,
and
living
13, 340-351,
340-35 1, 1998.
13,
1998.
and isotopic
isotopic composition
composition of
of
living
foraminifera
Broccoli,
A., and
foraminifera from
from the
the Panama
Panama Basin,
Basin, Nature,
Nature,
Broccoli, A.,
andE.
E. P.P.Marciniak,
Marciniak,Comparing
Comparing
1996.
1996.
Bush,
Bush, A.
A. B.
B. G.,
G., and
and S.
S. G.
G. H.
H. Philander,
Philander,The
The role
role
of
interactions
of ocean-atmosphere
ocean-atmosphere
interactionsin
in tropical
tropical
cooling
glacial maximum,
cooling during
during the
the last
last glacial
maximum,
Science,
Science, 279,
279, 1341-1344,
1341-1344, 1998.
1998.
Climate:
Investigation,
Climate:Long-Range
Long-Range
Investigation,Mapping,
Mapping,
and
and Prediction
PredictionProject
ProjectMembers
Members(CLIMAP),
(CLIMAP),
Seasonal
Seasonal reconstructions
reconstructions of the earth's
earth's surface
surface
at the
the Last
Last Glacial
Glacial Maximum,
Maximum, GeoL
Geol.Soc.
Soc.Am.
Am.
Map Chart
Map
ChartSer.,
Ser.,MC-36,
MC-36, 1-18,
1-18,1981.
1981.
Coulbourn,
W. T.,
T., F.
Coulboum, W.
F. L.
L. Parker,
Parker, and
and W.
W. H.
H.
Berger,
Berger, Faunal
Faunal and
andsolution
solution patterns
patterns of
of
planktonic
planktonicforaminifera
foraminiferain
in surface
surfacesediments
sediments
mediate
mediate complexity.
complexity, Nature.
Nature, 391,
391, 351-356,
351-356,
1998.
1998.
Guilderson,
P., R.
R. G.
G. Fairbanks,
andJ.J. L.
L
Guilderson, T.T.P.,
Fairbanks, and
Rubenstone,
variations
Rubenstone,Tropical
Tropicaltemperature
temperature
variations
since
since 20,000
20,000 years
yearsago:
ago:Modulating
Modulatinginterinterhemispheric
climate change.
hemisphericclimate
change,Science.
Science,263.
263,
663-665.
663-665, 1994.
1994.
Hastings,
Hastings,D.W.,
D.W., A.D.
A.D. Russell.
Russell,and
andS.R.
S.R.Emerson,
Emerson,
Foraminiferal
magnesium in
in Globigerinoides
Foraminiferalmagnesium
Globigerinoides
sacculifer
as
a
paleotemperature
proxy,
sacculiferas a paleotemperature
proxy,PaleoPaleoceanogr.
ceanogr,13.
13, 161-169,
161-169,1998.
1998.
Herbiand,
A.. and
Herbland, A.,
and B.
B. Voituriez,
Voituriez, Hydrological
Hydrological
structure
structureanalysis
analysisfor
for estimating
estimatingprimary
primaryproproduction
ductionof
of the
thetropical
tropicalAtlantic
AtlanticOcean,
Ocean,1.
J. Mar.
Mar.
Res.. 37,
37, 87-101,
Res.,
87-101, 1979.
1979.
Hutson,
Hutson, W.
W. H.,
H., Transfer
Transfer functions
functionsunder
underno-anano-analog
conditions: Experiments
with Indian
Indian
log conditions:
Experiments with
Ocean
Quat. Res.,
Res.. 8,
Ocean planktonic
planktonicforaminifera,
foraminifera,Quat.
8,
355-367, 1977.
1977.
Hutson,
Hutson,W.
W. H.,
H., The
The Agulhas
AgulhasCurrent
Currentduring
duringthe
the
late
of modem
late Pleistocene:
Pleistocene:Analysis
Analysis of
modemfaunal
faunal
analogs.
analogs,Science.
Science,207,
207, 64-66,
64-66,1980.
1980.
Imbue, J.,
Imbrie,
J., and
andN.
N. G.
G.Kipp,
Kipp,AAnew
newmicropaleonmicropaleontological method
paleoclimatological
methodfor
forquantitative
quantitative
paleoclimatology, Application
Application to
to aa late
tology,
late Pleistocene
PleistoceneCarCaribbean core,
ibbean
core, in
in Late
LateCenozoic
CenozoicGlacial
GlacialAges.
Ages,
edited
editedby
by K.
K. K.
K. Turekian,
Turekian,pp.
pp.71-182,
71-182, Yale
Yale
Univ. Press,
Press,New Haven,
Haven, Conn.,
Conn., 1971.
1971.
Imbrie,
J., A.
Imbrie, J.,
A. McIntyre.
Mcintyre, and
andA.
A. Mix,
Mix, Oceanic
Oceanic
response
responseto
to orbital
orbitalforcing
forcingin
in the
thelate
lateQuaterQuaternary:
nary: Observational
Observationaland
and experimental
experimentalstratestrategies,
edited
gies,in
in Climate
Climateand
andGeosciences,
Geosciences,
editedby
by A.
A.
Berger
1-164, Kluwer
Bergeret
et al.,
al.,pp.
pp.12
121-164,
KluwerAcademic.
Academic,
Boston,
Boston, Mass., 1989.
1989.
Kipp,
Kipp, N.
N. G.,
G., New
New transfer
transferfunction
functionfor
for estimating
estimating
past
past sea
sea surface
surfaceconditions
conditions from
from sea
sea bed
bed
distribution
distributionof
of planktonic
planktonicforaminiferal
foraminiferalassemassemblages in
blages
in the
theNorth
NorthAtlantic,
Atlantic,in
inInvestigation
Investigation
of
Late Quaternary
of Late
QuaternaryPaleoceanography
Paleoceanographyand
and
Paleoclimatology. edited
Paleoclimatology,
editedby
byR.
R. M.
M. Cline
Clineand
and1.
J.
MIX
FAUNAL
MIX ET AL.:
AL.: FORAMINIFERAL
FORAMINIFERAL
FAUNAL ESTIMATES
ESTIMATES
D. Hays,
D.
Hays, Mem.
Mere. Geol.
Geol. Soc.
Soc. Ant,
Am., 145,
145, 3-41,
3-41,
1976.
1976.
Klovan,
J. B.,
Klovan, J.
E., and
and J.
J.Imbrie.
Imbrie, An
An algorithm
algorithmand
and
FORTRAN IV
Q-mode
FORTRAN
IV program
programfor
for large-scale
large-scale
Q-mode
factor analysis,
analysis, Math.
Math. Geol.,
Geol., 3,
3,61-67,
factor
61-67,1971.
1971.
Le, J.,
J. Shackieton,
Le,
J., and
and N.
N.J.
Shackleton, Carbonate
Carbonate dissoludissolu-
tion
in the
equatorial
tion fluctuations
fluctuationsin
thewestem
western
equatorial
Pacific
during
the
Pacific during
the late
late Quatemary,
Quaternary,
Paleoceanography, 7,
Paleoceanography,
7, 21-42,
21-42, 1992.
1992.
Levitus, S.,
S., Climatological
atlas of
of the
Levitus,
Climatologicalatlas
the World
World
Ocean, NOAA
NOAAProf
Prof Pap.,
Pap.. 13,
U.S. Gov.
Gov.
Ocean,
13, 1-173,
1-173, U.S.
Print.
Washington D.C.,
Print. Off.,
Off., Washington
D.C., 1982.
1982.
Loubere, P.,
P., Plankton
and the
Loubere,
Plankton ecology
ecology and
the paleopaleoceanographic-climatic
record, Quar.
ceanographic-climatic
record,
Quat. Res.,
Res.,17,
17,
3
14-324,
1982.
314-324, 1982.
MacArthur,
R. H.,
MacArthur, R.
H., On
On the
the relative
relative abundance
abundanceof
of
species, Arner.
Amer. Natur.
Natur. 94,
94, 25-36,
species,
25-36, 1960.
1960.
Mcintyre,
A., W.
Mcintyre, A.,
W. F.
F. Ruddiman,
Ruddiman,K.
K. Karlin.
Karlin, and
and
A.C. Mix,
of the
A.C.
Mix, Surface
Surface water
water response
responseof
the
equatorial
equatorialAtlantic
Atlantic Ocean
Oceanto
toorbital
orbitalforcing.
forcing
Paleoceanography,
4,
Paleoceanography,
4, 19-55,
19-55,1989.
1989.
Mm,
Min, G.
G. R..
R., R.
R. L.
L. Edwards,
Edwards,F.
F. W.
W. Taylor,
Taylor, I.
J. Recy,
Recy,
C.
C. D.
D. Gallup,
Gallup,and
andJ.
J.W.
W. Beck,
Beck,Annual
Annualcycles
cyclesof
of
U/Ca
in coral
skeletons and U/Ca
U/Ca
U/Ca
in
coral
skeletons
thermometry,
Ada. 59,
thermometry,Geochim.
Geochirn.Cosmochim.
Cosrnochirn.
Acta,
59,
2025-2042,
2025-2042, 1995.
1995.
Mix,
of
Mix, A.
A. C.,
C., Late
LateQuatemary
Quaternarypaleoceanography
paleoceanography
of
the Atlantic
Atlantic Ocean:
Ocean: Foraminiferal
Foraminiferal faunal
faunal and
and
stable
stableisotopic
isotopicevidence,
evidence,Ph.D.
Ph.D.thesis,
thesis,738
738 pp.,
pp.,
Columbia
Columbia Univ.,
Univ., New
New York,
York, 1986.
1986.
Mix,
paleoproductivity:
EviMix, A.
A. C.,
C.,Pleistocene
Pleistocene
paleoproductivity:
Evidence
dencefrom
from organic
organiccarbon
carbonand
andforamimferal
foraminiferal
species,
of the
the Ocean:
species,in
in Productivity
Productivityof
Ocean:Present
Present
and
and Past,
Past, edited
edited by
by W.
W. H.
H. Berger,
Berger,V.
V. S.
S.
Smetacek,
$metacek,and
andG.
G. Wefer,
Wefer,pp.
pp.313-340,
313-340,Wiley,
Wiley,
New York, 1989.
1989.
Mix,
A.
C.,
and
A.
E
Morey,
Climate
feedback
Mix, A. C., and A. E Morey, Climate feedback
and Pleistocene
Pleistocene variations
variations in
in the
the Atlantic
Atlantic south
south
equatorial
in The
equatorial current,
current, in
TheSouth
SouthAtlantic,
Atlantic,
Present
Present and
and Past
PastCirculation,
Circulation, edited
edited by
by G.
G.
Wefer,
Wefer, et
et al.,
al.,pp.
pp.503-525,
503-525,Springer-Verlag,
Springer-Verlag,
New
New York,
York, 1996.
1996.
Mix, A.
Mix,
A. C.,
C., W.
W. F.
F. Ruddiman,
Ruddiman,and
andA.
A. McIntyre,
Mcintyre,
Late
Quaternary paleoceanography
of the
Late Quaternary
paleoceanography
of
the
tropical
Atlantic,
1,
Spatial
variability
tropical Atlantic, 1, Spatial variability of
of
annual
annual mean
mean sea
seasurface
surfacetemperatures,
temperatures,0020,000
1,
20,000 years
yearsB.P.,
B.P., Paleoceanography,
Paleoceanography,
1, 434366,
66, 1986.
1986.
Molfino, B.,
N. G.
5. Morley,
Morley,
Molfino,
B., N.
G. Kipp,
Kipp, and
and5.
J. J.
Comparison
coccolithoComparison of
of foraminiferal,
foraminiferal, coccolithophorid,
paleotemperature
equaphorid,and
andradiolarian
radiolarian
paleotemperature
equations:
tions:Assemblage
Assemblagecoherency
coherencyand
andestimate
estimate
concordancy,
Quat.
concordancy,
Quat.Res.,
Res.,17,
17,279-313,
279-313,1982.
1982.
Moore, T.
T. C.,
C., Jr.,
Jr., W.
Moore,
W. H.
H. Hutson,
Hutson,N.
N. Kipp,
Kipp, J.
J. D.
D.
Hays, W.
Hays,
W. L.
L. Prell,
Prell, P.
P. Thompson,
Thompson,and
and G.
G.
Boden,
record of
of the
Boden,The
The biological
biologicalrecord
theice
iceage
age
359
359
ocean,
Palaeoocean,Palaeogeogr.
Palaeogeogr.Palaeoclimatol.
Palaeoclimatol.
Palaeoecol., 35,
35, 357-370,
357-370, 1981.
ecol.,
1981.
Muhtza,
Mulitza, S..
S., H.
H. S.
S. Niebler,
Niebler,A.
A. Dürkoop,
Di•rkoop,W.
W. Hale,
Hale,
Atlantic
temperature
for
Atlanticsea-surface
sea-surface
temperature
forthe
thelast
last30
30
kyr:
of
and forakyr:A
A comparison
comparison
of Ui7,
Un37,•5180,and
foraminiferal
temperature
estimates,
minifetalassemblage
assemblage
temperature
estimates,
and G.
and
G. Wefer,
Wefer, Planktonic
Planktonic foraminifera
foraminifera as
as
Paleoceanography,
9,
Paleoceanography,
9,31-46,
31-46,1994.
1994.
recorders of
stratification,
N..
and
W.
B.
Curry,
Enhanced
recorders
of past
pastsurface-water
surface-water
stratification, Slowey,
Slowey,N., and W. B. Curry, EnhancedventilaventilaGeology, 25,
25, 335-338,
335-338. 1997.
tion
gyre
Geology,
1997.
tion of
of the
theNorth
NorthAtlantic
Atlanticsubtropical
subtropical
gyre
Ortiz,
J.
D.,
and
A.
C.
Mix,
Comparison
of
thermocline
Ortiz, J. D., and A. C. Mix, Comparisonof
thermoclineduring
duringthe
thelast
lastglaciation,
glaciation,Nature.
Nature,
imbrie-Kipp
transfer
function
and
modem
358,
Imbrie-Kipp transfer function and modem
358, 665-668,
665-668, 1992.
1992.
analog temperature
estimates using
analog
temperatureestimates
usingsediment
sediment Stute,
Stute,M.,
M., M.
M. Forster,
Forster,H.
H. Frischkom,
Frischkom,A.
A. Serejo,
Serejo,1.
J.
trap and
F.
trap
and core-top
core-top foraminiferal
foraminiferal faunas,
faunas,
F. Clark,
Clark, P.
P. Schlosser,
Schlosser,W.
W. S.
S. Broecker,
Broecker, and
and G.
G.
Paleoceanography,
12,
Bonani,
Paleoceanography,
12,175-190,
175-190,1997.
1997.
Bonani, Cooling
Cooling of
of tropical
tropical Brazil
Brazil (5°C)
(5øC)
Parker,
during
Parker,F.,
F., and
andW.
W. H.
H. Berger,
Berger,Faunal
Faunaland
andsolution
solution
during the
theLast
LastGlacial
GlacialMaximum,
Maximum,Science.
Science,
pattems
of
planktonic
foraininifera
in
surface
269,
379-3
83,
1995.
269, 379-383, 1995.
patternsof planktonicforaminiferain surface
sediments
M. S.,
sedimentsof
of the
theSouth
SouthPacific,
Pacific,Deep
DeepSea
SeaRes..
Res., Sverdlove,
Sverdlove, M.
S., Planktonic
Planktonic foraminiferal
foraminiferal
18, 73-107,
73-107, 1971.
ecology
eastern equatorial
equatorial Pacific
18,
1971.
ecology of
of the
the eastern
Pacific
Duprat, C.
Pflaumann,
Ocean:
reconPflaumann,U.,
U., J.
J. Duprat,
C. Pujol.
Pujol, and
and L.
L.
Ocean:Including
Includingaapaleoceanographic
paleoceanographic
recon-
Labracherie, SIMMAX:
SIMMAX: AA modem
Labracherie,
modem analog
analog
struction
of the
struction of
the Panama
Panama Basin
Basin for
for the
the last
last
technique
techniqueto
to deduce
deduceAtlantic
Atlanticsea
seasurface
surfacetemtemfrom planktonic
peratures
peraturesfrom
planktonic foraminifera
foraminiferain
in
11,
deep-sea
sediments,
Paleoceanography.
deep-seasediments,Paleoceanography,11,
320,000
320,000 years.
years,Ph.D.
Ph.D. thesis,
thesis,317
317 pp.,
pp.,Univ.
Univ. of
of
Cincinnati,
Cincinnati, Cincinnati,
Cincinnati, Ohio,
Ohio, 1983.
1983.
Thompson,
L.
G.,
E.
Mosley-Thompson,
M.
E.
Thompson,L. G., E. Mosley-Thompson,
M. E.
Davis,
Davis, P.-N.
P.-N. Lin,
Lin, K.
K. A.
A. Henderson,
Henderson,J.J.ColeColeDai,
Dai, J.
J. F.
F. Bolzan,
Bolzan,and
andK.-B.
K.-B. Liu,
Liu,Late
Lateglacial
glacial
stage
stageand
and Holocene
Holocenetropical
tropical ice
ice core
corerecords
records
15-35, 1996.
15-35,
C. Mix,
Pisias,
Pisias, N.
N. G.,
G., and
and A.
A. C.
Mix, Spatial
Spatial and
and
the
temporal
temporal oceanographic
oceanographicvariability
variability of
of the
Pacific during
eastem
easternequatorial
equatorial Pacific
during the
the late
late
Pleistocene:
Evidence from
from Radiolaria
Pleistocene: Evidence
Radiolaria micromicrofossils, Paleoceanography,
Paleoceanography, 12.
1-393, 1997.
fossils,
12,38
381-393,
1997.
Prell,
W. L.,
Prell, W.
L., The
The stability
stabilityof
of low-latitude
low-latitudeseaseasurface
An evaluation
of the
surface temperatures:
temperatures:An
evaluation of
the
CLIMAP reconstruction
reconstruction with
with emphasis
emphasis on
on the
CLIMAP
the
positive SST
positive
SST anomalies,
anomalies,Tech.
Tech.Rep.
Rep.TRO-25,
TRO-25,
60 pp.,
S. Dep.
Washington,
60
pp., U.
U.S.
Dep.ofofEnergy,
Energy,
Washington,
D.C., 1985.
D.C.,
1985.
Prell,
W. L.,
L., and
Prell, W.
and W.
W. B.
B. Curry,
Curry, Faunal
Faunaland
and isoisotopic
indexes of
of monsoonal
topic indexes
monsoonal upwelling:
upwelling:
Westem
Ada, 4,
1-98,
WesternArabian
Arabian Sea,
Sea, Oceanol.
Oceanol. Acta,
4, 991-98,
1981.
1981.
Ravelo,
A. C.,
C., R.
R. G.
0 Fairbanks,
Ravelo, A.
Fairbanks, and
and S.
S. G.
G. H.
H.
Philander,
Reconstructing tropical
Atlantic
Philander, Reconstructing
tropical Atlantic
hydrography
using planktonic
planktonic foraminifera
hydrography
using
foraminiferaand
and
an
5,
an ocean
oceanmodel,
model,Paleoceanography,
Paleoceanography,
5, 409409431. 1990.
431.
1990.
Rind, D.,
D., and
and D. Peteet,
Peteet,Terrestrial
Terrestrial conditions
conditionsat
at
the Last Glacial
Glacial Maximum
Maximum and
and CLIMAP
CLIMAP seasea-
surface temperature
estimates: Are
surface
temperatureestimates:
Are they
theyconconsistent?, Quat.
sistent?,
Quat. Res.,
Res.,24, 1-22,
1-22, 1985.
1985.
Roselle-Melé, A., E.
B. Bard,
Roselle-Me16,
Bard, K-C.
K.-C. Emeis,
Emeis, P.
P.
Farrimond, J.
5. Grimalt,
Grimalt, P.
P. J.
5. Mailer,
Muller, and
and R.
Farrimond,
R. R.
R.
Schneider, Project
Project takes
takes a
a new
Schneider,
new look
look at
at past
pastsea
sea
surface temperatures,
temperatures, Eos
Eos Trans.
79(33),
surface
Trans.AGU,
AGU, 79(33),
393-394, 1998.
393-394,
1998.
Sautter,
R., and
Sautter, L.
L. R.,
and R.
R. C.
C.Thunell,
Thunell,Seasonal
Seasonal
succession of
succession
of planktonic
planktonicforaminifera:
foraminifera:Results
Results
from
from aa four-year
four-yeartime-series
time-seriessediment
sedimenttrap
trap
experiment
experimentin
in the
thenortheast
northeastPacific,
Pacific,J.
J. ForamiForarniniferal
niferalRes.,
Res.,19,
19, 253-267,
253-267, 1989.
1989.
Sikes,
L., and
Sikes, E.
E. L.,
and L.
L. D.
D.Keigwin,
Keigwin,Equatorial
Equatorial
from
from Huascarán,
Huascar•n, Peru,
Peru, Science,
Science, 269,
269, 46-50,
46-50,
1995.
1995.
Thunell,
R. C.,
C., and
L. Reynolds,
Reynolds, Sedimentation
Sedimentation of
of
Thunell,R.
andL.
planktonic
planktonicforaminifera:
foraminifera:Seasonal
Seasonalchanges
changesin
in
species
Basin,
speciesflux
flux in
in the
thePanama
Panama
Basin,MicropaleMicropaleontology.,
1-260, 1984.
ontology.,30,
30, 24
241-260,
1984.
Waelbroeck,
C., L.
Waelbroeck,C.,
L. Labeyrie,
Labeyrie,5.-C.
J.-C.Duplessy,
Duplessy,J.J.
Guiot,
H. Leclaire,
Guiot, M.
M. Labracherie,
Labracherie, H.
Leclaire, and
and J.
J.
Duprat,
past sea
Duprat, Improving
Improvingpast
seasurface
surfacetemperatempera-
ture
estimates based
ture estimates
based on
on planktonic
planktonic fossil
fossil
faunas,
Paleoceanography. 12,
faunas,Paleoceanography,
12,272-283,
272-283,1998.
1998.
Watkins,
J.
M.,
and
A.
C
Mix,
Testing
the
effects
Watkins,J. M., and A. C Mix, Testingthe effects
of tropical
productivity, and
of
tropical temperature,
temperature, productivity,
and
mixed-layer depth
depth on
mixed-layer
on foraminiferal
foraminiferaltransfer
transfer
functions, Paleoceanography,
Paleoceanography, 13,
96-105,
functions,
13, 96-105,
1998.
1998.
Weaver, A.
A. J.,
J., M.
M. Eby,
Eby, A.
A. F.
F. Fanning,
and E.
E. C.
C.
Weaver,
Fanning,and
Wiebe, Simulated
Wiebe,
Simulated influence
influence of
of carbon
carbondioxide,
dioxide,
orbital forcing,
forcing, and
and ice
ice sheets
sheets on
on the
climate of
of
orbital
theclimate
the Last
Maximum, Nature,
Nature, 394,
394, 847the
Last Glacial
Glacial Maximum,
847853, 1998.
853,
1998.
S. W.
W. Hostetler,
Hostetler, U.S.
Survey,
S.
U.S.Geological
Geological
Survey,
Corvallis, OR 97330.
A.
A. C.
C. Mix,
Mix, A.
A. E.
E.Morey,
Morey,and
andN.
N. G.
G.Pisias,
Pisias,
College of
of Oceanic
Sciences,
College
Oceanicand
andAtmospheric
Atmospheric
Sciences,
Oregon
State
University,
Corvallis,
OR
9733
1OregonStateUniversity,Corvallis,OR 973315503. (mix@oce.orst.
(mix@oce.orst.edu)
5503.
edu)
(Received
February
3,
(Received
February
3, 1999;
1999;
revised
revisedFebruary
February22,
22, 1999;
1999;
accepted
February
25,
1999.)
accepted
February25, 1999.)