Geophysical Research Letters,
Год журнала:
2025,
Номер
52(6)
Опубликована: Март 23, 2025
Abstract
Extreme
climate
events
(ECEs)
like
heavy
rainfall
and
heatwaves
significantly
impact
society,
change
is
altering
their
magnitude
frequency.
Generalized
Value
(GEV)
distributions
help
quantify
these
ECEs
guide
human
system
design.
We
train
a
machine
learning
(ML)
model
using
set
of
arbitrary
GEV
to
estimate
the
sample
size
required
determine
return
value
with
specific
uncertainty.
For
negative
shape
parameter
maximum
extreme
temperatures
are
bounded
fewer
samples
needed
given
uncertainty
than
extremes
which
have
positive
unbounded
values.
example,
if
1‐in‐20‐year
heatwave
event
requires
400
1%
uncertainty,
one
would
need
20
different
20‐year
simulations.
Achieving
such
quantities
will
require
extensive
downscaling
simulations,
potentially
aided
by
ML‐based
methods
increase
ensemble
size.
Environmental Research Letters,
Год журнала:
2024,
Номер
19(6), С. 064011 - 064011
Опубликована: Май 1, 2024
Abstract
Recent
years
were
characterized
by
an
increase
in
spatially
co-occurring
hot,
wet
or
dry
extreme
events
around
the
globe.
In
this
study
we
analyze
data
from
multi-model
climate
projections
to
occurrence
of
compounding
and
area
affected
future
climates
under
scenarios
at
+1.5
∘
C,
+2.0
+3.0
C
higher
levels
global
warming
using
Earth
System
Model
simulations
6th
Phase
Coupled
Intercomparison
Project.
Since
can
strongly
amplify
societal
impacts
as
economic
supply
chains
are
increasingly
interdependent,
want
highlight
that
world’s
breadbasket
regions
projected
be
particularly
events,
posing
risks
food
security.
We
show
spatial
extent
top-producing
agricultural
being
potentially
threatened
extremes
will
drastically
if
mean
temperatures
shift
C.
Further
identify
a
large
land
concurrently
with
increased
risk
other
industries
sectors
addition
sector.
Bulletin of the American Meteorological Society,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 13, 2025
Abstract
Disaster
planning
based
on
historical
events
is
like
driving
forward
while
only
looking
in
the
rear-view
mirror.
To
expand
our
field
of
view,
we
use
a
large
ensemble
weather
simulations
to
characterise
current
risk
extreme
case
study
locations
Southeastern
United
States.
We
find
that
temperature
have
become
more
frequent
between
1981
and
2021,
heavy
precipitation
are
also
wettest
months.
Combining
analysis
people’s
recent
experience
with
rate
change
events,
define
four
quadrants
apply
groups
studies:
Sitting
Ducks”
,
“Recent
Rarity”,
“Living
Memory”,
“Fading
Memory”
.
A
critical
storyline
“
ducks
”:
where
high
increase
most
event
memory
(1981-2021)
has
low
return
period
today’s
climate.
these
potential
for
surprise.
For
example,
Montgomery
County,
Alabama,
since
13
years
climate
2021.
In
places,
offer
unprecedented
synthetic
from
disaster
preparedness
help
people
imagine
unprecedented.
Our
results
not
document
substantial
changes
extremes
States
but
propose
generalizable
framework
using
ensembles
changing
PLOS Climate,
Год журнала:
2025,
Номер
4(1), С. e0000466 - e0000466
Опубликована: Янв. 30, 2025
A
growing
number
of
scientists
are
expressing
concerns
about
the
inadequacy
climate
change
policies.
Fewer
questionning
dominant
modelling
paradigm
and
IPCC’s
success
to
prevent
humanity
from
venturing
unprepared
into
hitherto
unknown
territories.
However,
in
view
an
urgent
need
provide
readily
available
data
on
constraining
uncertainty
local
regional
impacts
next
few
years,
there
is
a
debate
most
suitable
path
inform
both
mitigation
adaptation
strategies.
Examples
given
how
common
statistical
methods
emerging
technologies
can
be
used
exploit
wealth
existing
knowledge
drive
policy.
Parsimonious
equitable
approaches
promoted
that
combine
various
lines
evidence,
including
model
diversity,
large
ensembles,
storylines,
novel
applied
well-calibrated,
global
regional,
Earth
System
simulations,
deliver
more
reliable
information.
As
examplified
by
Paris
agreement
desirable
warming
targets,
it
argued
display
unrealistic
ambitions
may
not
best
way
for
modellers
accomplish
their
long-term
objectives,
especially
consensus
emergency
allocated
short
time
delivered
applied.
Geophysical Research Letters,
Год журнала:
2025,
Номер
52(6)
Опубликована: Март 23, 2025
Abstract
Extreme
climate
events
(ECEs)
like
heavy
rainfall
and
heatwaves
significantly
impact
society,
change
is
altering
their
magnitude
frequency.
Generalized
Value
(GEV)
distributions
help
quantify
these
ECEs
guide
human
system
design.
We
train
a
machine
learning
(ML)
model
using
set
of
arbitrary
GEV
to
estimate
the
sample
size
required
determine
return
value
with
specific
uncertainty.
For
negative
shape
parameter
maximum
extreme
temperatures
are
bounded
fewer
samples
needed
given
uncertainty
than
extremes
which
have
positive
unbounded
values.
example,
if
1‐in‐20‐year
heatwave
event
requires
400
1%
uncertainty,
one
would
need
20
different
20‐year
simulations.
Achieving
such
quantities
will
require
extensive
downscaling
simulations,
potentially
aided
by
ML‐based
methods
increase
ensemble
size.