Modeling of Precipitation over Africa: Progress, Challenges, and Prospects
Advances in Atmospheric Sciences,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 9, 2025
Abstract
In
recent
years,
there
has
been
an
increasing
need
for
climate
information
across
diverse
sectors
of
society.
This
demand
arisen
from
the
necessity
to
adapt
and
mitigate
impacts
variability
change.
Likewise,
this
period
seen
a
significant
increase
in
our
understanding
physical
processes
mechanisms
that
drive
precipitation
its
different
regions
Africa.
By
leveraging
large
volume
model
outputs,
numerous
studies
have
investigated
representation
African
as
well
underlying
processes.
These
assessed
whether
are
depicted
models
fit
informing
mitigation
adaptation
strategies.
paper
provides
review
progress
simulation
over
Africa
state-of-the-science
discusses
major
issues
challenges
remain.
Language: Английский
Improving Daily CMIP6 Precipitation in Southern Africa Through Bias Correction—Part 1: Spatiotemporal Characteristics
Climate,
Journal Year:
2025,
Volume and Issue:
13(5), P. 95 - 95
Published: May 4, 2025
Impact
models
used
in
water,
ecology,
and
agriculture
require
accurate
climatic
data
to
simulate
observed
impacts.
Some
of
these
emphasize
the
distribution
precipitation
within
a
month
or
season
rather
than
overall
amount.
To
meet
this
requirement,
study
applied
three
bias
correction
techniques—scaled
mapping
(SDM),
quantile
(QDM),
QDM
with
separate
treatment
for
below
above
95th
percentile
threshold
(QDM95)—to
daily
from
eleven
Coupled
Model
Intercomparison
Project
Phase
6
(CMIP6)
models,
using
Climate
Hazards
Group
Infrared
Precipitation
Station
version
2
(CHIRPS)
as
reference.
This
evaluated
performance
all
bias-corrected
CMIP6
over
Southern
Africa
1982
2014
replicating
spatial
temporal
patterns
across
region
against
observational
datasets,
CHIRPS,
Climatic
Research
Unit
(CRU),
Global
Climatology
Centre
(GPCC),
standard
statistical
metrics.
The
results
indicate
that
generally
performs
better
native
model
December–February
(DJF)
mean
seasonal
cycle.
probability
density
function
(PDF)
regional
indicates
enhances
performance,
particularly
range
3–35
mm/day.
However,
both
corrected
uncorrected
underestimate
higher
extremes.
pattern
correlations
GPCC,
CRU,
compared
have
improved
0.76–0.89
0.97–0.99,
0.73–0.87
0.94–0.97,
0.74–0.89
respectively.
Additionally,
Taylor
skill
scores
CRU
0.57–0.80
0.79–0.95,
0.55–0.76
0.80–0.91,
0.54–0.75
0.81–0.91,
Overall,
among
techniques,
consistently
demonstrated
QDM95
SDM
various
implementation
distribution-based
resulted
significant
reduction
consistency
between
observations
region.
Language: Английский
Improving Daily CMIP6 Precipitation in Southern Africa Through Bias Correction— Part 2: Representation of Extreme Precipitation
Climate,
Journal Year:
2025,
Volume and Issue:
13(5), P. 93 - 93
Published: May 2, 2025
Accurate
simulation
of
extreme
precipitation
events
is
crucial
for
managing
climate-vulnerable
sectors
in
Southern
Africa,
as
such
directly
impact
agriculture,
water
resources,
and
disaster
preparedness.
However,
global
climate
models
frequently
struggle
to
capture
these
phenomena,
which
limits
their
practical
applicability.
This
study
investigates
the
effectiveness
three
bias
correction
techniques—scaled
distribution
mapping
(SDM),
quantile
(QDM),
QDM
with
a
focus
on
above
below
95th
percentile
(QDM95)—and
daily
outputs
from
11
Coupled
Model
Intercomparison
Project
Phase
6
(CMIP6)
models.
The
Climate
Hazards
Group
Infrared
Precipitation
Stations
(CHIRPS)
dataset
was
served
reference.
bias-corrected
native
were
evaluated
against
observational
datasets—the
CHIRPS,
Multi-Source
Weighted
Ensemble
(MSWEP),
Global
Climatology
Center
(GPCC)
datasets—for
period
1982–2014,
focusing
December-January-February
season.
ability
generate
eight
indices
developed
by
Expert
Team
Change
Detection
Indices
(ETCCDI)
evaluated.
results
show
that
captured
similar
spatial
patterns
precipitation,
but
there
significant
changes
amount
episodes.
While
generally
improved
representation
its
varied
depending
reference
used,
particularly
maximum
one-day
(Rx1day),
consecutive
wet
days
(CWD),
dry
(CDD),
extremely
(R95p),
simple
intensity
index
(SDII).
In
contrast,
total
rain
(RR1),
heavy
(R10mm),
(R20mm)
showed
consistent
improvement
across
all
observations.
All
techniques
enhanced
accuracy
indices,
demonstrated
higher
pattern
correlation
coefficients,
Taylor
skill
scores
(TSSs),
reduced
root
mean
square
errors,
fewer
biases.
ranking
using
comprehensive
rating
(CRI)
indicates
no
single
model
consistently
outperformed
others
relative
GPCC,
MSWEP
datasets.
Among
methods,
SDM
QDM95
variety
criteria.
strategies,
best-performing
EC-Earth3-Veg,
EC-Earth3,
MRI-ESM2,
multi-model
ensemble
(MME).
These
findings
demonstrate
efficiency
improving
modeling
extremes
ultimately
boosting
assessments.
Language: Английский
Assessment of Historical and Future Mean and Extreme Precipitation Over Sub‐Saharan Africa Using NEX‐GDDP‐CMIP6: Part I—Evaluation of Historical Simulation
International Journal of Climatology,
Journal Year:
2024,
Volume and Issue:
45(2)
Published: Dec. 5, 2024
ABSTRACT
This
study
assesses
the
performance
of
28
NASA
Earth
Exchange
Global
Daily
Downscaled
Climate
Projections
(NEX‐GDDP‐CMIP6)
models
and
their
multi‐model
ensemble
(MME)
in
simulating
mean
extreme
precipitation
across
sub‐Saharan
Africa
from
1985
to
2014.
The
Multi‐Source
Weighted‐Ensemble
Precipitation
(MSWEP)
Hazards
Group
InfraRed
with
Station
Data
(CHIRPS)
are
used
as
reference
datasets.
Various
statistical
metrics
such
bias
(MB),
spatial
correlation
coefficients
(SCCs),
Taylor
skill
scores
(TSS)
comprehensive
ranking
index
(CRI)
employed
evaluate
NEX‐GDDP‐CMIP6
at
both
annual
seasonal
scales.
Results
show
that
can
reproduce
observed
cycle
all
subregions,
model
spread
within
observational
uncertainties.
MME
also
successfully
reproduces
distribution
precipitation,
achieving
SCCs
TSSs
greater
than
0.8
subregions.
biases
consistent
different
However,
most
trends
opposite
observations.
While
generally
its
varies
dataset,
particularly
for
number
rainy
days
(RR1)
maximum
consecutive
dry
(CDD).
TSS
values
indices
differ
significantly
by
region,
data
index,
lowest
over
South
Central
highest
West
Southern
Africa.
CRI
indicates
no
single
consistently
outperforms
others
even
same
when
compared
MSWEP
CHIRPS.
These
results
may
be
helpful
using
future
projections
impact
assessment
studies
Language: Английский