International Journal of Climatology,
Год журнала:
2021,
Номер
41(13), С. 5899 - 5919
Опубликована: Апрель 29, 2021
Abstract
This
study
compared
the
historical
simulations
and
future
projections
of
precipitation
temperature
Coupled
Model
Intercomparison
Project
(CMIP)5
CMIP6
general
circulation
models
(GCMs)
to
quantify
differences
in
due
scenarios.
Five
performance
indicators
were
used
model
reproducibility
observed
levels
at
22
stations
for
period
1970–2005.
The
percentages
change
estimated
near
(2025–2060)
far
(2065–2100)
two
Representative
Concentration
Pathway
(RCP)4.5
RCP8.5
scenarios
CMIP5
Shared
Socioeconomic
(SSP)2–4.5
SSP5‐8.5
CMIP6.
uncertainty
projection
each
case
was
calculated
using
reliability
ensemble
average
(REA)
method.
As
a
result,
GCMs
showed
an
improvement
with
regard
ability
simulate
climate.
higher
SSPs
than
that
RCPs.
With
temperature,
RCPs
SSPs.
means
changes
both
contributes
confidence
bolsters
our
understanding
relative
International Journal of Climatology,
Год журнала:
2021,
Номер
41(9), С. 4743 - 4768
Опубликована: Март 16, 2021
Abstract
This
study
employed
15
CMIP6
GCMs
and
evaluated
their
ability
to
simulate
rainfall
over
Uganda
during
1981–2014.
The
models
the
ensemble
mean
were
assessed
based
on
reproduce
annual
climatology,
seasonal
distribution
trend.
Statistical
metrics
used
include
bias
error,
normalized
root
square
pattern
correlation
coefficient.
Taylor
diagram
skill
score
(TSS)
in
ranking
models.
models'
performance
varies
greatly
from
one
season
other.
reproduced
observed
bimodal
of
March
May
(MAM)
September
November
(SON)
occurring
region.
Some
slightly
overestimated,
while
some
underestimated,
MAM
rainfall.
However,
there
was
a
high
overestimation
SON
by
most
showed
positive
spatial
with
dataset,
whereas
low
shown
inter‐annually.
could
not
capture
patterns
around
local‐scale
features,
for
example,
Lake
Victoria
basin
mountainous
areas.
best
performing
identified
GFDL‐ESM4,
CanESM5,
CESM2‐WACCM,
MRI‐ESM2‐0,
NorESM2‐LM,
UKESM1‐0‐LL,
CNRM‐CM6‐1.
CNRM‐CM6‐1,
CNRM‐ESM2
underestimated
throughout
cycle
climatology.
these
two
better
trends
both
SON.
Caution
should
be
taken
when
employing
climate
change
studies
as
another.
model
spread
area
also
calls
further
investigation
attributions
possible
implementation
robust
approaches
machine
learning
minimize
biases.
Earth Systems and Environment,
Год журнала:
2021,
Номер
5(1), С. 25 - 41
Опубликована: Янв. 1, 2021
Abstract
We
evaluate
the
capability
of
21
models
from
new
state-of-the-art
Coupled
Model
Intercomparison
Project,
Phase
6
(CMIP6)
in
representation
present-day
precipitation
characteristics
and
extremes
along
with
their
statistics
simulating
daily
during
West
African
Monsoon
(WAM)
period
(June–September).
The
study
uses
a
set
standard
extreme
indices
as
defined
by
Expert
Team
on
Climate
Change
Detection
Indices
constructed
using
CMIP6
observational
datasets
for
comparison.
Three
observations;
Global
Precipitation
Climatology
Project
(GPCP),
Hazards
Group
InfraRed
Station
data
(CHIRPS),
Tropical
Applications
Meteorology
SATellite
ground-based
observation
(TAMSAT)
are
used
validation
model
simulations.
results
show
that
observed
present
nearly
same
spatial
pattern
but
discrepancies
magnitude
rainfall
characteristics.
substantial
comparison
observations
among
themselves.
A
number
depict
intensity
some
overestimate
over
coastal
parts
(FGOALS-f3-L
GFDL-ESM4)
western
part
(FGOALS-f3-L)
Africa.
All
simulations
explicitly
wet
days
large
frequencies.
On
rainfall,
half
express
more
intense
95th
percentiles
while
other
simulate
less
extremes.
mean
maximum
spell
length
except
FGOALS-f3-L.
patterns
dry
good
general
agreement
across
different
models,
four
an
overestimation
Sahara
subregion.
INM-CM4-8
INM-CM5-0
display
smaller
long-term
average
characteristics,
terms
estimates
than
datasets.
For
frequency
heavy
TaiESM1
IPSL-CMGA-LR
perform
better
when
compared
MIROC6
GFDL-ESM4
displayed
largest
error
representing
percentile
extremes,
therefore,
cannot
be
reliable.
has
assessed
how
captured
both
models.
Though
there
discrepancies,
it
gives
room
improvement
next
version
CMIP.
International Journal of Climatology,
Год журнала:
2021,
Номер
42(8), С. 4316 - 4332
Опубликована: Ноя. 29, 2021
Abstract
The
global
climate
models
(GCMs)
performances
of
the
recently
released
Coupled
Model
Intercomparison
Project
phase
6
(CMIP6)
compared
to
its
predecessor,
CMIP5,
are
evaluated
anticipate
expected
changes
in
over
Egypt.
Thirteen
GCMs
and
their
multi‐model
ensemble
(MME)
both
CMIPs
were
used
for
this
purpose.
future
projections
two
radiative
concentration
pathways
(RCP
4.5
8.5)
shared
socio‐economic
(SSP
2–4.5
5–8.5).
results
revealed
improvement
most
CMIP6
replicating
historical
rainfall,
maximum
temperature
(Tmax),
minimum
(Tmin)
climatology
MME
that
could
reproduce
Egypt's
spatial
distribution
seasonal
variability.
However,
bias
CMIP5
was
higher
than
CMIP6.
uncertainties
simulating
variability
rainfall
temperatures
lower
CMIP5.
projection
using
a
reduction
precipitation
(10–26
mm)
economically
crucial
northern
region
estimated
(0–17
mm),
133.5
mm
base
period.
also
projected
0.74–1.63°C
more
rise
Tmax
Tmin
by
end
century.
study
indicates
aggravated
scenarios
Egypt
anticipated
earlier,
models.
Therefore,
needs
streamline
existing
adaptation
mitigation
measures
account
projections.
International Journal of Climatology,
Год журнала:
2021,
Номер
41(13), С. 5899 - 5919
Опубликована: Апрель 29, 2021
Abstract
This
study
compared
the
historical
simulations
and
future
projections
of
precipitation
temperature
Coupled
Model
Intercomparison
Project
(CMIP)5
CMIP6
general
circulation
models
(GCMs)
to
quantify
differences
in
due
scenarios.
Five
performance
indicators
were
used
model
reproducibility
observed
levels
at
22
stations
for
period
1970–2005.
The
percentages
change
estimated
near
(2025–2060)
far
(2065–2100)
two
Representative
Concentration
Pathway
(RCP)4.5
RCP8.5
scenarios
CMIP5
Shared
Socioeconomic
(SSP)2–4.5
SSP5‐8.5
CMIP6.
uncertainty
projection
each
case
was
calculated
using
reliability
ensemble
average
(REA)
method.
As
a
result,
GCMs
showed
an
improvement
with
regard
ability
simulate
climate.
higher
SSPs
than
that
RCPs.
With
temperature,
RCPs
SSPs.
means
changes
both
contributes
confidence
bolsters
our
understanding
relative