►
From YouTube: 6th PAWS Webinar
Description
The sixth webinar from the Paleoclimate Advances Webinar Series (PAWS) which took place on October 13th 2022.
Gabriel Pontes discussed "Towards a unifying theory on the background impact on ENSO variability across climates" and Pedro DiNezio discussed "Paleodata from Glacial Intervals Help Predict Increasing Risk of Extreme El Niño under Greenhouse Warming"
For more information and to signup for the PAWS Google Group visit:
https://www.cesm.ucar.edu/events/webinars/paws/
A
B
Okay,
so
everybody
can
see
my
screen.
Yes,
hey
hello.
Everyone
welcome
to
the
Paleo
climate
advances
webinar
series
I
would
like
to
briefly
remind
people
our
code
of
conduct
and
the
format
of
the
webinar.
By
joining
this
webinar,
you
agree
to
follow
our
code
of
conduct
during
the
webinar.
Please
offer
constructive
feedback.
Consider
new
ideas,
show
appreciation,
encourage
Innovation,
acknowledge
teamwork
and
share
the
air.
The
webinar
has
two
talks.
Each
has
20
minutes
with
additional
5
minutes
for
Q
a
in
the
end.
Hopefully
we
will
have
another
10
minutes
also
for
General
discussion.
B
I
will
give
too
many
reminder
for
each
speaker
just
to
keep
us
on
time
and
I'm,
not
sure
why
the
situation
is
here
today.
He
will
help
us
moderate,
the
Q
and
A.
Hopefully
he
will
draw
us
later,
if
not
right.
Now,
if
you
have
questions
for
the
speakers,
please
either
type
them
in
the
chat
or
use
a
raise
hand
function.
As
always,
we
welcome
speaker
nomination.
You
can
fill
out
the
nomination
form
on
our
web
page
or
contact
anyone
else.
B
Organizing
committee
okay,
it's
great
pleasure
for
me
to
introduce
today's
two
speakers:
doctors,
Gabriel
panties
and
Pedro
Delicia
Dr
Gabriel
pontes
is
a
postdoctoral
researcher
at
the
climate
change
Research
Center
at
the
University
of
New,
South
Wales,
and
also
at
the
Australian
Center
for
Excellency
in
Antarctic
science.
He
has
expertise
in
large-scale
climate
and
ocean
Dynamics.
His
research
increase
include
polar
warming
and
so
support
Dynamics
thermal
Highland,
circulation,
teleconnection
and
feedback
between
high
and
low
latitudes,
as
well
as
a
rough
climate
change.
B
B
C
So
we
have
a
variety
of
of
of
main
features
of
of
the
climate
change
that
have
that
we
have
seen
in
the
past
and
you
you're
going
to
face
in
the
future.
For
example,
we
have
changes
on
the
in
the
ice
sheets,
then
changing
orbital
configuration
even
intimacy
that
I
don't
know
when
tube
affecting
severability
and-
and
we
now
have
a
quite
of
let's
say,
a
large
symbol
of
climate
simulations
from
the
from
the
paleoclimate
modeling
the
comparison
project
and
from
the
in
front
of
coupled
model
in
the
comparison
projects.
C
Just
a
a
brief
overview
of
the
install
Dynamics
for
those
who
are
not
familiar
with
so
insta
cruising
the
equatorial
Pacific
and
under
normal
conditions.
What
we
have
is
the
is
the
West
is
the
Tradewinds
in
the
equatorial
Pacific
pushing
surface
waters
west,
pushing
those
who
are
warm
and
surface
water
westwards,
and
these
increasing
increased
convections
under
the
margin
continent
and
creates
a
walker
circulation
with
a
descending
Branch
and
in
the
Eastern
Pacific,
and
this
is
also
influence.
The
some
Oceanic
features
and
the
main
Ocean
Future.
C
That's
the
fact
that
here
is
the
thermocline,
so
we
can
see
that
the
demo
client
is
lower
in
the
Eastern
Pacific
and
deep
in
the
in
the
Western
Pacific,
and
when
we
have
the
weakening
of
those
trade
winds.
Here
we
have
a
series
of
process
that
are
triggered
and
displace
those
warm
Waters
from
the
east,
from
the
Western
Pacific
to
the
Eastern
Pacific
and
I
change,
all
of
the
climate
setting
in
the
in
the
in
the
equatorial
Pacific,
with
teleconnections
from
many
regions
across
the
globe.
C
We
then
have
for
the
early
pliosine
and
for
the
last
glacial
maximum
study
shows
that
story
shown
that
I
have
shown
that
the
democrine
dev
seems
to
be
more
important
than
the
Arizona
SST
gradient.
We
then
have
the
integral
Convergence
Zone,
which
we
are
going
to,
which
is
going
to
be
the
focus
of
this
presentation,
and
then
we
have
for
future
projections
ocean
stratification,
as
in
as
a
key
process
in
understanding
how
it's
a
variability
will
increase
in
the
future.
C
We
have
also
a
study
that
shows
that
changes
in
the
Sahara
vegetation
could
also
be
linked
to
enso
activity,
and
so,
as
I
told
you
in
the
beginning,
we
have
now
lots
of
simulations
for
key
best
to
warm
climates
that
have
been
under
intense
investigation,
and
we
have
also
the
future
projections.
So
I
just
gave
you
guys
I
just
give
you
guys
a
large
Ensemble
to
work
with
and
under
those
different
background
States.
So
at
the
right
end
of
this
figure,
we
have
the
future
projections.
C
Then
we
have
this
this
historical
period,
the
brain
industrial
at
1850,
and
before
that
we
have
some
key
paleo
climate
periods,
which
are
the
mid
Hollow
thing.
The
last
integration
and
the
meat
placing
so
I
will
start
to
show
you
some
results
from
the
mid
pliosine
and
then
we
we
try
to
explain
that
towards
the
the
other.
The
other
paleo
climates
into
future
projections.
C
So
were
the
models
tell
us
about
the
install
Dynamics
or
about
insular
variability
in
the
mid
pliocene
is
that
there
is
a
pretty
consistent
weakening
in
the
in
the
variability
of
the
equatorial
Pacific.
So
the
top
panel
here
I'm
showing
you
the
change,
the
change
in
the
amplitude
of
the
sea
surface
temperature
variability,
which
is
a
measure
of
how
variable
the
sea
surface
temperature
was
during
the
pliocene,
and
we
can
see
that
the
model
show
a
significant
weakening
the
equatorial
Pacific.
C
And
if
you
take
here
the
red
box,
which
which
is
the
Ninja
3
region,
it's
a
a
key
region
for
for
install
Dynamics.
We
can
see
that
there
is
a
near
and
unanimous
and
so
weakening
across
the
plyomet
models,
which
were
the
models
that
simulated
the
mid
pliocin
climate
and
after
looking
at
a
variety
of
process
that
could
be
affecting.
Could
it
be
affecting
answer
variability
in
those
simulations.
C
C
C
So
there
are
a
number
of
process
that
that
are
that
are
affected
by
the
small
forward
movement
of
the
ATC
set.
So
what
are
we
seeing?
The
mid
prior
thing
is
that
the
its
ad,
which
is
represented
here
by
this
blue
band,
moves
towards
and
of
the
hemisphere,
mainly
because
the
polar
Amplified,
while
in
the
north
of
the
hemisphere
was.
C
A
higher
magnitude
than
the
polar,
not
providing
the
in
the
Southern,
Hemisphere
and
and
and
and
these
results
in
in
a
atmospheric
energy
imbalance
and
and
the
way
that
the
climate
system
solves
these
imbalancing
is
by
intensifying
the
south.
The
southern
hemispheres
had
like
circulation
in
order
to
in
order
to
export
this
excess
of
heat
from
the
south
front,
end
of
the
hemisphere
towards
the
Southern,
Hemisphere
and,
and
so
what
we
have
in
the
southern
hemisphere
is
and
despite
the
hardest
circulation
and
also
intensified
winds
in
the
in
the
subtropical
southern
hemisphere.
C
So
this
creates
the
stable
conditions
and
in
the
tropical
and
southern
hemisphere,
with
varying
things,
the
winds
and
so
the
first
process
that
is
affected
by
there's.
No
water
shift
of
the
tcz
is
that
it
suppress
the
equatorial
convective
feedback
in
the
in
the
in
the
equatorial
Pacific,
because
the
foreign.
C
Normally
that
could
enhance
the
the
convective
feedback.
In
addition,
those
in
things
fired
winds
in
the
southern
hemisphere.
They
surprise
the
variability
of
the
winds
in
the
Western
Pacific,
which
are
known
to
initiate
some
ends
of
events,
so
the
Awakening
of
those
and
they're
not
only
the
weakening
but
also
the
reversal
of
those
winds.
They
are
now
into
to
trigger
the
displacement
of
the
of
of
warm
Waters
from
the
west,
but
from
the
west
from
the
Western
Pacific
to
the
Eastern
Pacific.
C
Decent
things
find
the
chromatological
circulation
in
the
equatorial
Pacific
supplies
all
of
those
all
of
those
events,
and
thus
resulting
in
the
and
a
weaker
variability
in,
not
only
in
the
in
the
equatorial
Pacific,
but
also
in
the
southern
in
the
South
Pacific,
and
if
we
take
into
account
only
the
models
that
are
very
simulated.
These
this
convective
feedback
in
the
equatorial
Pacific,
which
is
key
for
this
mechanism.
C
So
we
have
a
look
at
the
mid
plier
scene
and
we
have
seen
that
the
model
shows
some
very
consistent.
It
shows
a
very
consistent
mechanism
in
the
for
the
mid
pricing
and
a
kind
of
a
straightforward
question
is
if
it
applies
to
all
the
climate
periods
or
projections
or
even
or
even
future,
projections
so
have
a.
Let's
have
a
look
at
what
what
happens
for
all
the
climate
periods
and
for
future
projections.
C
So,
for
example,
here
for
the
mid
bio
scene,
which
we,
which
was
the
the
simulation
that
we
were
looking
at,
we
have
those
intensified
decent
inspired
circulation
in
the
in
the
southern
hemisphere.
So
here
with
SS
with
a
cooler
ssts
than
the
surrounding
than
the
tropical
Pacific
and
regions
where,
with
one
with
Walmart
ssds,
then
the
then
the
main
equator,
then
the
main
SST
in
the
equatorial
Pacific.
C
And
here,
where
I'm
showing
you
is.
That
is
the
is
how
the
tropical
precipitation
is
projected
to
change
in
the
in
the
in
the
future
projections.
So
what
we
have
here
is
Aiden
a
a
trained
that
indicates
a
movement
of
the
igcz
southwards
towards
the
equator.
But
then
we
also
have
a
movement
of
the
South
Pacific
conversions
only
towards
the
equator.
C
And
if
we
take
the
a
similar
metric
that
we
were
using
for
the
mid
pliozene,
where
we
just
tried
to
find
a
kind
of
a
mess
center
of
the
precipitation
in
the
in
the
equatorial
Pacific.
And
then
those
changes
would
basically
cancel
out
because
we
have
a
movement
of
the
ideas
towards
the
towards
the
equator
and
the
movement
of
the
saps
is
actually
towards
the
equator
as
well.
So
that's
a
so
that's
in
a
software
movement
of
the
ATC
set.
C
C
So
so
so
taking
those
those
changes
separated
water,
we
can
see
what
I'm
showing
you
what
I'll
show.
You
is
just
some
results
where
we
can
and
then
we
have
to
change
a
little
bit,
how
we
were
looking
at
the
tropical
Pacific
and
how
it
and
how
answer
responds
to
the
to
those
to
those
precipitation
regions
and
what
we
did
was.
C
C
So-
and
this
results
in
this
graph
here
when
we
take
into
account
all
of
the
payload
and
future
simulations.
So
here
we
have
basically
most
of
the
few
of
the
payload
simulations
with
a
Bollywood
movement
of
the
itcz
into
the
sapc
that
are
resulting
in
reduced
insufferability.
As
we
have
seen,
then
we
have
those
red
dots
here
which
are
the
future
projections
that
tends
to
show
an
equator
or
movement
of
that
species
and
that
the
in
the
in
the
itcz
and
when
we
incorporate
the
abruptive
force
CO2
simulations.
C
Here
we
have
this
kind
of
main
state,
which
is
a
very
strong
mistake,
a
very
strong
circulation
that
tends
to
suppress
and
so
variability
and
another
kind
of
mistake
that
will
tend
to
suppress
and
survivability
would
be
a
static
mean
state
in
the
equatorial
Pacific,
where
we
have
a
flat
demo.
Client
reduce
the
Tradewinds
and
a
the
HSN
and
that's
located
very
closely
to
the
equator
and
then
we'll
have
another
main
stage
that
tends
to
enhance
insular
ability.
C
So
we
would
have
this
kind
of
bi-stable
Enzo
Dynamics,
with
the
two
main
states
that
tend
to
A
reduced
sense
of
variability
in
the
warming
stage.
That
enhances
answer
variability,
and
this
is
important
because
about
10
to
15
years
ago
we
had
some
studies
suggesting
that
ain't
so
could
be
a
potential,
a
potential
in
potential
chipping
element
of
the
climate
system.
But
then
we
just
didn't
know
yet
how
answer
would
Behavior
under
those
different
climate
States?
A
A
B
D
Here
we
go
thanks
everyone
for
joining
and
for
inviting
me
to
present
this
work.
I've
been
presenting
some
of
these
results
since
some
many
AG
years
ago,
but
this
is
the
final
distillation
of
our
work
and
it's
a
people
in
review.
Hopefully
it's
going
to
be
out
soon
like
next
year,
so
I
want
to
acknowledge
my
collaborators
and
the
NSF
for
funding
our
work.
D
D
The
most
common
approach
is
just
running
multiple
models,
I'm
hoping
they
all
agree
and
if
they
all
agree,
we
will
hope
that
there's
some
fundamental
underlining
mechanism
that
makes
those
predictions
and
we
will
trust
the
models
that
we
would
be
happy
and
that
approach
has
made
a
lot
of
progress,
understanding
future
climate
change,
but
not
for
El
Nino.
Most
models
don't
agree
regarding
future
predictions.
Future
changes
in
answer
in
response
to
in
global
warming,
so
we
I
mean
we're
trying
a
different
approach.
It's
not
that
different.
D
Other
people
have
tried
it,
but
it's
for
any
prediction.
We
need
two
things
right:
a
common
mechanism
that
drives
what
we
are
trying
to
predict
in
the
future
and
a
period
for
valuation
and
for
us,
our
period
of
validation,
is
going
to
be
the
past.
The
geological
past
we
don't
know
which
interval
so
we're
going
to
run
a
model
for
different
climate
intervals
and
see
which
one
which
of
these
intervals
is
useful
to
identify
a
common
mechanism.
D
But
the
other
thing
that
we
need
is
an
observational
Target
for
validating
this
mechanism
or
our
model.
We
do
this
a
lot
in
predicting
anything.
It
just
happens
that
for
El
Nino
the
historical
record,
it's
not
that
it's
too
short,
it's
just
answer
is
too
viable
on
its
own,
so
the
historical
record
is
not
adequate
for
detecting
forced
externally
forced
changes
in,
and
so
there's
a
lot
of
research
about
that.
So
this
target
for
model
validation
has
to
have
a
high
signal
to
noise.
D
D
We
perform
them
with
the
community
System
model
version
one
it's
a
model
that
has
highly
realistic,
Enzo,
Dynamics
and
I'll.
Explain
what
I
mean
by
this,
but
I.
Think
ccsm1
was
probably
the
pineapple
of
realistic
simulation
of
Enzo.
Dynamics
I've
learned
a
lot
about
this
model,
because
I
use
it
to
predict
La
Nina
and
the
asymmetric
Dynamics
between
a
linear
and
La
Nina.
This
model
is
great
for
doing
that
and
I'll
show
you
why
that
is
important
to
predicting
changes
in
the
whole
and
so
phenomenon.
D
We
run
the
model
sufficiently
long
more
than
500
years
for
each
of
these
climatic
states
to
isolate
the
force,
changes
again
relative
to
Enzo's,
very
large
internally
generated
variability,
and
then
we
validate
it
against
multiple,
independent
paleoclimate
reconstructions
for
the
intervals
that
we
find
common
mechanisms
and
just
to
anticipate
the
results.
Our
interval
is
going
to
be
the
last
glacial
maximum,
a
case
of
a
cold
climate
that
we
think
it's
very
useful
to
understand,
enter
Dynamics
in
the
future
and
I'll
show
you
why.
D
So
we
run
the
model:
every
3,
000
years
in
a
Time
slice
approach,
changing
boundary
conditions
for
these
climatic
intervals,
so
here
I'm
only
showing
the
Holocene
intervals
all
the
way
to
9000
years.
Before
present,
the
zero
interval
is
for
industrial
conditions
and
what
we
see
in
this
plot,
the
y-axis
shows
the
levels
of
sensor
variability
measured
by
the
standard
deviation
of
the
linear
3.4
index,
but
here
we're
applying
them
as
a
distribution.
D
So
I'll
walk
you
through
that
we
sub-semble
our
runs,
are
very
long
into
30-year
intervals,
which
is
the
typical
length
of
paleo
climate
records
just
to
show
you
the
range
of
levels
of
insulviability,
measured
by
this
metric.
The
standard
deviation
of
the
linear
3.4
SST
index
in
this
30-year
intervals
over
our
simulations,
and
you
can
see
that
there's
this
box
box
and
whisker
plot
shows
a
very
wide
distributions
and
the
medians,
the
centers
of
the
Box,
show
the
actual
level
of
vulnerability.
So
these
are
not
the
changes
you
can
see.
D
So
it
would
be
difficult
to
detect
a
change,
and
this
is
consistent
with
the
literature
from
corals
that
show
that,
and
so
maybe
highly
valuable,
and
we
can
extend
our
view
back
in
time
to
all
these
intervals
every
3000
years,
and
you
can
see
that
enso
and
Nina
is
capable
of
large
externally
Ford
Force
changes,
the
weakest
levels
of
variability
are
in
these
intervals.
Out
of
the
deglassiation.
Just
one
clarification,
we
did
not
run
simulations
with
fresh
water,
forcing
that
was
important
during
this
interval.
D
But
this
is
offering
other
intervals
that
we
could
use
for
validation
like
the
last
glacial
maximum.
We
chose
weaker
variability
than
the
pre-industrial
conditions,
so
we
could
validate
these
responses.
A
lot
of
overlap
and
I'm
going
to
address
that
and
the
good
thing
and
those
results
from
the
lgm
are
consistent
with
previous
work
by
a
header.
D
Four
I
also
buy
work
by
Xiang,
using
the
same
version
of
the
model,
I'm
just
moving
the
screen
just
to
show
that
we
also
run
a
simulation
for
the
future
at
doubling
CO2
concentrations,
just
to
assess
the
mechanisms
that
will
operate
in
the
future
two
times.
Co2
is
about
concentrations
for
2050
halfway
through
the
century,
so
the
first
thing
we
find
is
that
the
changes
in
variability
in
these
climatic
intervals
are
dominated
by
the
extreme
El
Nino.
D
So
you
can
see
that
some
intervals,
the
distribution,
is
nearly
gaussian
like
the
colder
intervals.
Then
you
can
see
that
enso
is
simulated
in
a
skewed
way.
The
Neo
3.4
index
is
skilled,
so
there's
more
variability
positively
skewed
more
by
ability
in
the
warm
Tales,
because
alienu
events
are
stronger,
but
if
you
zoom
in
you'll
see
that
the
biggest
changes
in
these
situations
happen
in
this
very
High
values
of
the
Neo
3.4
SSD
index.
So
it's
showing
that
extremely
linear
events
are
changing.
D
The
frequency
is
changing
by
a
lot,
particularly
for
the
two-time
CO2
simulation,
with
with
a
huge
increase
in
these
extreme
events.
And
then,
as
we
go
into
the
colder
climates,
you
can
see
the
Bluer
curves
here,
those
Extreme
Ways,
those
extreme
events
and
linear
events
become
less
frequent.
So
let's
look
at
this
in
a
different
way.
We
can
find
that
across
these
climatic
States
each
dot
is
a
climatic
state.
The
percentage
of
extreme
El
Nino
is
correlated
with
the
overall
levels
of
financial
liability
for
doubling
CO2.
D
We
have
the
strongest
tensor
viability
and
one
in
two
events
reach
this
extreme
variability,
which
we
Define
as
events
in
which
the
peak
linear,
3.4
SSD
index
exceeds
two
degrees
and
a
very
strong
correlation
with
the
colder
climatic
States,
especially
the
ones
in
the
deglassiation,
with
almost
no
extreme
El
Nino
on
the
lowest,
it's
SST
variability,
so
you'll
think.
Well,
these
two
quantities
are
related,
but
let
me
explain
a
bit
more
if
we
think
on
an
average
simulated
event.
So
this
is
how
well
csm1
simulates
the
evolution
of
extreme
and
the
new
events.
D
The
blue
curve
is
a
composite
of
all
the
events
in
our
simulation
on
the
extreme
events,
and
you
can
see
that
after
the
peak
and
then
you
is
followed
by
La
Nina
conditions
that
last
multiple
years-
and
this
is
the
research
that
I
done
before
and
that
led
me
to
think
to
approach
this
problem.
This
way,
their
green
curves
are
observed.
D
So
how
do
we
validate
our
simulations?
Well,
the
NGM
has
a
lot
of
reconstructions
of
SSD
viability
from
single
Forum
analysis
and
we
focus
on
Surface
and
subsurface
reconstructions.
So
this
map
is
showing
the
surface
and
the
shading
is
the
changes
in
the
sea.
Surface
temperature,
variability
simulated
by
our
model
for
the
lgm,
applying
reduction
in
CO2
changes
in
ice
sheets
and
changes
in
the
orbital
configuration
and
coastlines,
and
you
can
see
that
the
blue
colors
indicate
a
decrease
in
variability,
and
this
is
total
and
so
on.
D
It's
total
sea
surface
temperature
variability
so
includes
changes
in
seasonality
and
Enzo.
Among
other
ways
the
viability
can
change,
but
everything
that
is
not
steepled
in
the
model
is
dominated
by
enso.
So
you
can
see
that
these
two
sides-
these
are
the
sites
that
header
4
use
in
her
science
paper
fall
where
the
model,
the
simulated
changes
are
dominated
by
Enzo.
So
we
can
use
this
data
to
validate
our
model
and
the
color
of
the
circles
is
already
showing
you.
D
The
changes
in
in
Sea
surface
temperature
variability
estimated
from
the
proxies
this
paper
headers
paper,
estimated
SST
directly
from
single
forums
using
the
Magnesium
calcium
technique,
and
you
can
see
a
list
for
site
849,
which
is
the
one
that
shows
the
largest
changes:
the
distributions
of
the
monthly
sea
surface
temperature
from
the
side
from
the
proxies
and
from
the
model.
So
these
are
not
the
the
ranges
of
variability
how
they
conflict.
D
It's
probably
the
hardest
thing
I'm
going
to
show,
and
it's
three
different
distributions
in
yellow
is
the
distribution
of
the
percentage
changes
in
variability
from
our
model
without
any
changes.
So
it's
from
the
pre-industrial
and
you
can
see
this
is
essentially
Andrew.
Wittenberg's
results,
there's
a
lot
of
internally
generated
levels
of
variability
in
the
model
and
and
so
and
then,
as
we
change
different
levels
of
variability
using
the
simulations,
we
can
look
at
how
the
distribution
of
these
levels
of
variability
changes.
So
blue
is
when
we
only
allow
the
seasonal
cycle
to
change.
D
So
essentially,
we
combine
anomalies
from
the
industrial
and
the
lgm
simulation,
which
has
changed
the
seasonal
cycle,
and
you
can
see
that
we're
getting
closer
to
the
green
value,
which
is
the
reduction
in
headers
data
minus
40.
So
it's
a
massive
reduction
that
the
proxies
are
showing
at
the
lgm
at
that
side
in
the
Eastern
equatorial
Pacific.
And
what
we
find
is
that
the
the
best
way
we
explain
the
reduction.
D
What
we
see
in
the
proxies
is
when
we
consider
all
of
the
changes
in
variability
includes,
including
the
reduction
in
and
survivability
that
the
model
simulates,
which
is
the
red
distribution.
So
we
think
that
the
paleoclimate
data
is
best
explained.
At
least
this
massive
reduction
is
best
explained
by
the
reduction
in
and
so
availability
simulated
by
the
model.
D
We
also
look
at
subsurface
data,
but
I
am
dissipating
that
I'm
going
to
run
out
of
time,
so
I'm
going
to
jump
and
I'm
going
to
try
to
explain
the
mechanism
that
we
see
in
the
simulations.
So
the
first
thing
to
think
about
is
how
the
BNS
feedback
works
during
extremely
linear
recall
that
the
extreme
linear
are
the
energizers
of
the
Enzo
cycle
in
these
simulations
and
in
nature.
So
an
extremely
linear
is
triggered
every
time
the
Tradewinds
relax
in
general
by
a
Wesley
wimbers.
D
We
like
to
think
sometimes
we
think
it's
thermal
gland
anomalies
that
communicate
that
relaxation
of
the
Tradewinds,
the
Central
and
Eastern
Pacific,
but
it's
actually
cernal
currents
when
the
sonal
currents,
the
South
equatorial
current
weakens
when
the
trade
winds
relax,
it
will
allow
the
Wormhole
to
move
to
the
Central
Pacific
and
that's
what
you're
seeing
in
this
schematic
that
is
based
on
observations.
These
arrows
are
that
I
know
are
those
anomalies,
surface
currents
that
I
estimated
for
promotional
analysis,
so
you'll
think.
Well,
what's
all
that
warming
in
the
East?
Is
it
not
important?
D
Well,
it's
really
not
that
important,
because
in
the
Eastern
Pacific,
the
atmosphere
and
the
climate
is
very
cold.
So,
even
though
we're
warming
a
lot,
the
Kelvin
waves
triggered
by
these
Wesley
Williams
will
warn
me
by
a
lot.
They
will
not
be
able
to
influence
the
overlying
atmosphere.
So
it's
actually,
this
warming
at
the
edge
of
the
Wormhole
that
kicks
in
to
be
honest
feedback
and
the
worm
pulls
expands
Westward.
D
The
Colton
will
warm
up
but
more
forced
by
the
Kelvin
waves
triggered
by
these
changes
in
the
winds
and
once
the
Central
Pacific
warms
up,
then
the
Cherry
winds
will
further
relax
and
that's
how
we
kick
this
version
of
the
BNS
feedback.
It's
not
how
beer
next
thought,
and
so
we
grow.
That
is
our
contemporary
view
of
the
business
feedback
for
extreme
El,
Nino's,
extremely
new
events.
D
So
let's
go
back
to
the
schematic
and
think
all
the
processes
that
are
involved.
The
response,
optional
currents
to
TradeWinds
depends
on
mechanical
coupling
is
sonal
currents
in
different
climates
will
be
more
responsive
or
less
responsive
to
winds.
Depending
on
the
position
of
the
mixed
layer,
a
thick
mixed
layer
will
be
hard
to
move
by
the
winds.
A
shallow
mix
layer
will
be
easy
to
move.
D
There's
two
more
couplings
the
ocean
thermal
coupling,
which
is
the
one
that
most
people
think
about.
If
you
have
a
strong
temperature
gradient
once
the
currents
weakened,
the
one
would
move
faster
to
the
east
and
so
forth
for
the
opposite,
and
then
we
win
SSD
couplings
wind
will
have
to
response
to
SST
so
also,
and
these
this
coupling
is
controlled
by
the
wormple
extent.
D
So
when
we
have
a
one
pull
in
the
mean
climate
that
is
expanding
further
east
is
wider
in
longitude,
then
we
have
a
bigger
area
in
which
ssts
can
influence
the
wind.
So
all
these
three
processes
can
influence
the
weirdness
feedback
and
the
growth
rates
that
I
showed
you
before.
The
nice
thing
of
using
a
model
is
that
we
can
compute
all
these
couplings.
D
So
the
black
curve
is
the
total
strength
of
the
Viennese
feedback,
the
one
that
I
showed
you
before.
That
correlates
very
well
with
the
overall
levels
offensive
variability
in
Gray
here
with
the
distributions,
and
then
purple
blue
and
gold
show
what
the
BMS
feedback
would
look
like
when
only
one
coupling
changes
in
isolation.
You
can
see
that
all
the
curves
cross
in
the
industrial,
because
that's
the
one
that
we
use
for
reference
so
as
we
go
backwards
in
time.
This
coupling
is
weaken
as
we
go
forward
in
time.
D
So
mixed
layer
is
key
and
I'm
going
to
explain
this
again.
So
these
glacial
climates
have
a
deeper
mixed
layer
across
the
Pacific
and
make
a
deeper
mix.
Layer
is
harder
to
move
so
every
time
the
tray
wins
weakened
the
external
currents
that
will
push
the
wormboard
to
the
East
and
kick
the
behind
its
feedback,
we'll
push
it
in
a
slower
way,
so
the
whirlpool
will
take
longer
to
shift
will
be
slower
to
warm
up
in
the
Central
Pacific
and
it'll
be
harder
to
trigger
an
extremely
linear.
D
The
opposite
happens
for
global
warming.
The
mechanical
coupling
increases
to
be
on
the
feedback,
because
the
mixed
layer
shows
there's
multiple
reasons
why
they
mix
layer
shows
and
a
shallower
mix
here
is
easier
to
move.
Whenever
the
wind,
the
trade
winds
weekend,
the
zonal
currents
will
respond
more
effectively
and
will
more
easily
expand
the
worm,
pull
to
the
east,
triggering
the
BNS
feedback.
C
D
Us
oh
hold
on
this
is
my
yeah.
This
is
my
last
slide,
so
ultimately,
extremely
linear
are
controlled
by
the
Walker
circulation,
because
the
worker
circulation
controls
the
depth
of
the
mix
layer,
and
this
figure
is
just
showing
you
different
features
of
the
mean
state
that
control
these
couplings.
The
mix
layer,
depth
controlling
mechanical
coupling,
the
Tradewind
strength,
which
is
the
the
worker
circulation
and
then
the
Wormhole
Edge,
which
is
what
controls
the
wind
SSD
coupling.
D
So
together,
these
two
couplings,
the
mixed
layer,
depth
and
the
wobble
Edge
are
what
are
controlling
the
be
honest
feedback
across
all
these
climatic
States,
and
that
is
the
common
mechanism
connecting
that's
the
future
and
ultimately,
all
related
to
the
strength
of
the
Walker
circulation.
So
I'm
gonna,
it
says,
there's
a
common
mechanism
linking
past
your
future.
The
level
of
answer
variabilities
are
controlled
by
the
frequency
of
extreme
El
Nino.
The
strength
of
the
BMS
feedback
is
controlled
by
mechanical
coupling,
but
also
by
wind,
assisted
coupling
under
glacial
and
greenhouse
warming.
D
And
ultimately,
all
this
is
tied
to
the
strength
of
the
Walker
circulation,
which
is
how
Global
mean
temperatures
are
linked
to
Enzo.
We
know
that
global
warming
weakens
the
Walker
circulation.
Global
cooling
strengthens
the
worker
circulation,
and
that
allows
us
to
make
this
prediction
that,
because
we
know
that
Enzo
was
weaker
at
the
lgm
from
value
data,
and
these
common
mechanism
links
the
lgm
to
the
future,
we
should
expect
an
increased
frequency
of
extreme
anemia
in
a
warming
climate
I'll
stop
there.
Thank
you
so
much.
A
A
C
Warming,
for
example,
you
say
that
in
cooler,
climates
we
have
in
strengthening
work
circulation
and
in
the
last,
in
the
end,
in
the
future
scenario
we
expected
reduced
or
or
reduce
it.
Well,
we
can
work
with
circulation
and
but
how
exactly
do
you
expect
those
mechanisms
to
be
like
more
consistent
across
a
wider
change?
Our
of
course
a
wider.
Why
the
climate
States.
D
Yeah,
that's
a
great
question.
Gabrielle,
so
I
think
this
mechanism,
so
the
control
of
mix
layer
that
on
and
one
will
extend
an
extremely
new
product.
It
works
for
any
climatic
State,
since
the
overall
levels
of
internal
viability
are
controlled
by
extreme
and
linear.
So
that's
one
result
and
I
will
probably
work
for
orbital
forcing
for
other
forcings,
but
the
ones
that
are
important
and
I'm.
D
So
then,
which
shows
when,
when
moisture
and
precipitation
don't
increase
at
the
same
pace
and
when
the
climate
warms
then
precipitation,
which
increases
more
slowly
than
moisture,
requires
convection
to
weaken
and
that
will
weaken
the
ascending
branch
of
the
water
circulation.
The
opposite.
It
happens
for
the
lgm
in
my
2012
paper.
I
talked
about
about
that
mechanism
in
lgm
simulations.
So
that's
assuming
again
that
models
are
representing
these
changes
in
mean
State
realistically,
which
is
questionable.
D
We
have
evidence
for
the
lgm
having
a
deeper
mix
thermocline,
which
would
be
indicative
of
stronger
Trade,
Winds
I
think
is
one
of
the
most
I
mean
since
I'm,
assuming
everyone
here
is
a
Paleo
lover.
You
know
to
me:
that's
one
of
the
biggest
questions
our
field
is
facing.
What
was
the
state
of
the
Pacific
during
the
lgm
I
think
we
should
be
working
on
that
I'm
sure
a
lot
of
us
are
working
on
that.
D
D
That's
another
massive
question
that
I
think
a
lot
more
people
are
getting
excited
about
now
and
probably
what
we
are
at
right
now
is
a
transient
in
which
other
processes
like
Cooling
in
the
Southern
Ocean
is
an
ozone
depletion
is
and
by
its
influence
in
the
Westerly,
is
affecting
the
tropics
and
driving
this
trend.
Eventually,
when
the
climate
warms
up,
the
water
circulation
would
weaken,
and
again
this
is
a
model
result.
I
mean
probably
our
strongest
evidence
for
this
mechanism.
Plan
comes
from
the
plyosine
just
soon
it
has
a
bunch
of
people
showing
that
apply.
D
Your
data
from
deployosine,
which
is
probably
about
two
times
CO2,
cannot
be
explained
without
a
weakening
of
the
Walker
circulation,
so
I
think
those
are
the
gaps,
I
would
say
or
the
holes.
However,
you
want
to
call
them
in
what
I
just
showed
and
the
mechanism
of
the
mint
State
controls
and
then
so
I
think
work.
D
A
D
Excellent
yeah,
that's
a
great
question.
I'm
happy
about
them.
You
know
there's
so
many.
The
devil
is
in
the
details
when
you
compute
these
growth
rates
and
coupling
strengths,
and
it
has
to
be
done
on
the
new
3.4
index,
because
it's
where
it
will
influence
the
coupling
the
currents,
so
everything
is
done
in
the
Neo
3.4
index.
So
it's
close.
You
know
little
East
from
the
warm
ball
we're
using
a
fix
of
more
clarifications
on
the
technique,
we're
using
a
fixed
box
or
we're
not
moving
this
Center
of
action
of
Enzo.
D
In
these
climate
stage
we
probably
should
be
moving
it,
but
we
didn't.
Our.
We
didn't
want
to
over
complicate
the
analysis
by
moving
the
center
of
action,
although
we
probably
should,
but
the
changes
are
not
that
large-
that
it
really
changes
a
lot,
but
you
know
there's
there's
a
lot
more
to
do.
My
student,
Brandon
Molina,
is
looking
into
into
this
this
details,
so
stay
tuned.
D
Yeah
I
think
I
understand
the
question
excellent
question
too
right.
Everything
is
controlled
by
this
very
extreme
and
linear,
which
trigger
Long,
Live
learning
events.
So
we
we
should
expect
more
frequent,
persistent
and
linear
conditions,
which
is
also
not
good
news.
La
Nina
produces
throughout
our
many
places
throughout
the
world.
D
My
home
country,
in
Argentina,
among
many
and
so
more
frequent
extreme
La,
Nina
and
most
frequent
long-leveling
means
more
more
frequent,
persistent
drought,
brand
new
one
is
working
on
this
he'll
be
presenting
at
AMS
and
you'll
probably
meet
him
soon,
and
we
also
think
it
affects
the
oscillatory
Dynamics
of
fences.
There's
there's
a
lot
more
going
on
than
just
more
frequent,
extremely
linear,
but
that
is
the
first
order
response
in
enso
that
leads
to
this
overall
increases
and
decreases
in
variance.
D
It's
a
good
question:
yeah,
there's
changes
in
the
Spectrum
you
you
know
it's
we're
working
on
that.
So
again,
it's
not
really
a
shift.
It's
changes
in
the
spectrum
of
Enzo,
oh
yeah,
with
clients,
more
sharp,
less
sharp
and
but
the
one
thing
I
think
is
important
to
know.
I
mean
to
me
the
one
thing
that
I
learned
by
doing
this
is
that
it's
not
really-
and
this
this
result
is.
D
Someone
is
consistent
with
work
that,
when
your
child
has
been
presenting,
is
that
it's
not
that
the
the
amplitude
of
a
linear
events
will
increase
it's
just
the
strongest
and
the
new
events
will
become
more
frequent.
It's
really
hard
to
increase
the
amplitude
of
the
extreme,
the
extremist
and
linear,
because
that's
controlled
by
the
wombold
gold
golden
temperature
difference.
So
if,
if
you
change,
if
you
don't
change
the
gradient
I
mean
actually,
you
have
to
enlarge
the
gradient
to
make
stronger
any
new
events
right
like
1997
and
linear.
D
You
reach
the
temperature
of
the
worm
Bowl
everywhere
and
at
that
point,
in
new
events
stop
growing.
So
what
I
like
about
this
result
is
that
it
explains
how
a
weakening
of
the
temperature
gradient
can
still
lead
to
increased,
variance
it's
not
by
making
a
linear
event
stronger.
It
just
makes
them
more
frequent,
and
it
leads
to
this
counter-intuitive
result
that
a
weaker
temperature
gradient
East
West,
can
produce
more
viability,
but
it
produces
it
by
making
this
extreme
and
linear
more
frequent.
B
It's
already
403
and
the
people
feel
free
to
leave
and
thank
you
for
joining.
I
do
have
a
quick
question
for
Gabriel.
If
you
have
time
and
I
could
stick
for
a
few
more
minutes.
So
it's
about
a
comparison
with
proxy
data
on
the
pliers
and
model
simulation,
so,
whether
Google,
how
the
models
capture
the
means
State
climate
or
do
we
have
any
other
other
reconstructions
on
the
answer.
Variability
from
the
pliers.
C
Yeah,
so
we
do
not
have
that
much
of
proxy
data
foreign
so
in
the
mid
plasticine,
as
we
have
available
for
the
last
integration
and
for
the
midi
Holocene,
the
few
that
we
have.
They
indicate
that,
especially
in
the
mid
pliers
in
days,
so
variability
was
kind
of
similar
to
that
of
the
of
the
late
Holocene.
C
Thank
you
just
for
five
minutes,
okay,
so
so
during
the
so,
during
the
mid
blessing
from
reality
to
the
to
the
to
the
to
the
late
spliocene,
we
had
some
changes
in
gateways
that
could
have
affected
the
chain,
the
the
tropical
Pacific
and
Main
State,
and
we
are
not
taking
those
into
account
when
simulating
the
mid-place
in
climate.
So
that
should
be
one
difference
between
what
proxy
say
and
what
we
are
seeing
in
the
middle
class
in
simulations.