International Journal of Climatology,
Год журнала:
2017,
Номер
39(9), С. 3786 - 3818
Опубликована: Авг. 18, 2017
Temporal
variability
is
an
important
feature
of
climate,
comprising
systematic
variations
such
as
the
annual
cycle,
well
residual
temporal
short‐term
variations,
spells
and
from
interannual
to
long‐term
trends.
The
EU‐COST
Action
VALUE
developed
a
comprehensive
framework
evaluate
downscaling
methods.
Here
we
present
evaluation
perfect
predictor
experiment
for
variability.
Overall,
behaviour
different
approaches
turned
out
be
expected
their
structure
implementation.
chosen
regional
climate
model
adds
value
reanalysis
data
most
considered
aspects,
all
seasons
both
temperature
precipitation.
Bias
correction
methods
do
not
directly
modify
apart
cycle.
However,
wet
day
corrections
substantially
improve
transition
probabilities
spell
length
distributions,
whereas
in
some
cases
deteriorated
by
quantile
mapping.
performance
prognosis
(PP)
statistical
varies
strongly
aspect
method
method,
depends
on
choice.
Unconditional
weather
generators
tend
perform
aspects
they
have
been
calibrated
for,
but
underrepresent
long
Long‐term
trends
driving
are
essentially
unchanged
bias
If
precipitation
simulated
model,
further
deteriorates
these
PP
simulate
predictors.
One Earth,
Год журнала:
2024,
Номер
7(1), С. 72 - 87
Опубликована: Янв. 1, 2024
Global
water
scarcity
threatens
agriculture,
food
security,
and
human
sustainability.
Hence,
understanding
changes
in
terrestrial
storage
(WS)
is
crucial.
By
utilizing
climate
models,
reanalysis,
satellite
data,
we
demonstrate
the
effectiveness
of
multivariate
bias
correction
technique
facilitating
precise
WS
representation
while
ensuring
robust
budget
closure.
Historical
data
indicate
seasonal
changes,
where
forested
basins
exhibit
a
surplus
December-January-February
season,
with
reversal
June-July-August-September
season.
Non-forested
display
varied
patterns
influenced
by
geographical
location
land
use
type.
Future
projections
increased
deficits
most
Southern
Hemisphere
under
middle-road
(SSP
245)
scenario
wetter
conditions
regional
rivalry
370)
scenario.
Weather
systems
governing
vary
season
basin,
resulting
inconsistent
moisture
intake
into
basins.
These
findings
underscore
intricate
interplay
between
transport,
characteristics,
WS,
highlighting
need
to
understand
these
complex
interactions
for
effective
resource
management
strategies
changing
climates.
International Journal of Climatology,
Год журнала:
2018,
Номер
39(9), С. 3692 - 3703
Опубликована: Окт. 5, 2018
VALUE
is
a
network
that
developed
framework
to
evaluate
statistical
downscaling
methods
including
model
output
statistics
such
as
simple
bias
correction
and
quantile
mapping;
perfect
prognosis
regression
models
analog
methods;
weather
generators.
The
first
experiment
addresses
the
performance
in
present
climate
with
predictors.
This
paper
presents
synthesis
of
special
issue,
focus
on
results
this
experiment.
results.
Model
performs
mostly
well,
but
requires
predictors
at
resolution
close
target
one.
Perfect
prog
depends
crucially
structure
predictor
choice.
Weather
generators
perform
principle
well
for
all
aspects
can
be
expressed
by
available
structure.
Inter‐annual
variability
underrepresented
both
generator
approaches.
Spatial
poorly
represented
almost
participating
(inherited
from
driving
model,
not
methods).
Further
studies
are
required
systematically
assess
(a)
role
choice
prog;
(b)
spatial
generators,
study
based
GCM
predictors;
(c)
skill
simulated
future
climates;
(d)
credibility
climate.
International Journal of Climatology,
Год журнала:
2017,
Номер
39(9), С. 3786 - 3818
Опубликована: Авг. 18, 2017
Temporal
variability
is
an
important
feature
of
climate,
comprising
systematic
variations
such
as
the
annual
cycle,
well
residual
temporal
short‐term
variations,
spells
and
from
interannual
to
long‐term
trends.
The
EU‐COST
Action
VALUE
developed
a
comprehensive
framework
evaluate
downscaling
methods.
Here
we
present
evaluation
perfect
predictor
experiment
for
variability.
Overall,
behaviour
different
approaches
turned
out
be
expected
their
structure
implementation.
chosen
regional
climate
model
adds
value
reanalysis
data
most
considered
aspects,
all
seasons
both
temperature
precipitation.
Bias
correction
methods
do
not
directly
modify
apart
cycle.
However,
wet
day
corrections
substantially
improve
transition
probabilities
spell
length
distributions,
whereas
in
some
cases
deteriorated
by
quantile
mapping.
performance
prognosis
(PP)
statistical
varies
strongly
aspect
method
method,
depends
on
choice.
Unconditional
weather
generators
tend
perform
aspects
they
have
been
calibrated
for,
but
underrepresent
long
Long‐term
trends
driving
are
essentially
unchanged
bias
If
precipitation
simulated
model,
further
deteriorates
these
PP
simulate
predictors.
