Impact des changements des processus de surface sur la variabilité de la mousson ouest africaine...

1. Souleymane SY (1,2)*, Benjamin SULTAN (2), Juan Pablo Boisier (3), Nathalie de NOBLET-DUCOUDRE (3)
Malick WADE (1), Amadou T. GAYE (1), Ousmane NDIAYE (4), Yohann FARE (5)
(1)
-­‐Laboratoire
de
Physique
de
l’Atmosphère
et
de
l’Océan
-­‐Siméon
Fongang,
Ecole
Supérieure
Polytechnique
de
l’Université
Cheikh
Anta
Diop
(UCAD)
Dakar,
Senegal
(2)
-­‐Laboratoire
d’Océanographie
et
du
Climat:
ExpérimentaLon
et
Approches
Numériques,
Université
Pierre
et
Marie
Curie,
Paris
France
(3)-­‐
Laboratoire
de
Science
du
Climat
et
de
l’Environnement
de
l’InsLtut
Pierre
Simon
Laplace,
CEA-­‐CNRS-­‐UVSQ.
Gif
sur
YveXe,
France
(4)-­‐
Agence
NaLonale
de
l'AviaLon
Civile
et
de
la
Météorologie
du
Sénégal
(ANACIM),
Sénégal
(5)-­‐
KINOME,
Montreuil
sous
Bois,
France
Impacts of changes in land surface processes on the West African Monsoon variability :
Results from LUCID Intercomparison project
Our Common Future Under Climate Change
International Scientific Conference
7-10 July 2015 Paris,France
P-­‐2217-­‐28
Human
have
radically
modified
the
land
cover
distribuLon
to
the
profit
of
agricultural
acLviLes.
Nearly
35%
of
the
land
surface
was
directly
converted
into
anthropic
systems
[Ramanku(y
and
Foley1999].
In
the
next
decades,
million
hectares
of
forest
fracLon
could
conLnue
to
disappear,
parLcularly
in
the
tropical
areas
(Davin,
de
Noblet-­‐Ducoudré,
and
Friedlingstein
2007).
At
global
scale,
Land
Use-­‐Land
Cover
Change
(LULCC)
have
direct
consequences
on
animal
and
vegetable
biodiversity,
in
parLcular
on
biophysical
properLes
of
land
surface.
LULCC
is
in
parLcular
related
to
the
reducLon
of
forest
fracLons
and
of
natural
savannas,
to
the
profit
of
sedngs
crops
and
pastures,
as
it
was
observed
on
globale
scale
during
the
last
decades
[Scanlon
and
al.,
2007a,Warburton
and
al.,
2012].
If
this
current
trend
conLnues,
the
culLvated
surface
could
increase
by
20%
during
next
50
years
[Tilman
and
al.,
2001].
Anthropogenic
changes
of
LULCC
affect
climate
through
two
different
pathways.
The
first
one
is
the
biogeophysical
pathway.
It
considers
alteraLon
of
the
physical
characterisLcs
of
the
land
surface
such
as
albedo,
soil
moisture
and
roughness.
The
biogeochemical
pathway,
on
the
other
hand,
takes
into
account
alteraLons
of
the
atmospheric
concentraLons
of
greenhouse
gases
(GHGs),
in
response
to
changes
in
the
land-­‐atmosphere
fluxes
of
these
trace
gases
[Arora
and
Boer
2010].
They
also
affect
the
emissions
and
the
deposiLon
of
carbon,
nitrogen
and
other
chemically
acLve
species,
that
may
have
a
global-­‐scale
impact
on
climate
and
ecosystem
funcLoning,
inducing
potenLally
relevant
feedback
mechanisms.
The
climate
modelling
communiLes
have
demonstrated
impacts
on
surface
temperature,
rainfall
and
turbulent
energy
fluxes
when
land
cover
is
perturbed
[Henderson-­‐Sellers
et
al.,
1993;
Chase
et
al.,
2000;
Werth
and
Avissar,
2002;
Findell
et
al.,
2006].
This
is
consistent
with
the
strong
impact
of
land
surface
processes
on
the
atmosphere
in
some
regions
(Koster
et
al.
2004;
Seneviratne
et
al.
2006).
West
Africa
has
been
highlighted
as
a
hot
spot
of
land
surface–atmosphere
interacLons
(Koster
et
al.
2004)
(Figure
1).
The
West
African
Monsoon
(WAM)
flow
is
driven
by
land–sea
thermal
contrast
and
the
atmosphere–land
surface
interacLons
are
modulated
by
the
magnitude
of
the
associated
north–south
gradient
of
heat
and
moisture
in
the
lower
atmosphere
(Eltahir
and
Gong
1996).
The
links
between
land
surface
processes
and
the
WAM
have
been
demonstrated
in
numerous
numerical
studies
using
global
climate
models
(GCMs)
and
regional-­‐scale
atmospheric
climate
models
(RCMs)
over
the
last
decades.
Three
main
objecLves
of
this
study
are:
-­‐
To
highlights
the
biogeophysical
impacts
of
Land
use
–
land
cover
change
on
surface
climate
over
West
African
region;
-­‐
To
isolate
the
direct
effects
of
LULCC
on
WAM
from
the
indirect
effects
caused
by
interacLons
with
the
other
components
of
climate
system
(e.g.,
elevated
greenhouse
gases
resulLng
changes
in
sea
surface
temperatures
and
sea
ice
extent);
-­‐ To
increase
our
understanding
of
the
land–atmosphere
feedback
mechanisms
in
West
Africa
in
order
to
beXer
understand
the
effect
of
land
surface
processes
on
the
WAM
variability.
Fig 1: Regions of Strong Coupling Between Soil Moisture and Precipitation
= West Africa (Koster et al. 2004)
IntroducLon
Data
and
Methods
Importance
of
land-­‐use
change
for
future
climate
in
the
TROPICS
Past
land-­‐use
change
(1992-­‐1870)
Future
land-­‐use
change
(2100-­‐1992)
A2 socio-economic scenario
Davin et al. 2007•
Projected
future
land-­‐use
change
=
Tropics
LUCID
Simula_on
The
LUCID
simulaLons
analyzed
here
are
same
as
those
described
in
Pitman
et
al.
[2009],
De
Noblet-­‐Ducoudré
et
al.
[2012]
and
Boisier
and
al.
[2012].
All
simulaLons
have
been
run
in
an
ensemble
mode
to
include
more
robustness
in
the
results
reported
herein.
The
seven
GCMs
involved
in
LUCID
and
the
land
surface
models
(LSMs)
embedded
in
each
GCM
(herearer
GCM/LSMs),
are
ARPEGE/ISBA
[Salas-­‐Mélia
et
al.,
2005;
Voldoire,
2006],
CCAM/CABLE
[McGregor
and
Dix,
2008;
Abramowitz
et
al.,
2008],
CCSM/CLM
[Collins
et
al.,
2006;
Oleson
et
al.,
2008],
ECEARTH/TESSEL
[van
den
Hurk
et
al.,
2000],
ECHAM5/JSBACH
[Roeckner
et
al.,
2006;
Raddatz
et
al.,
2007],
IPSL/ORCHIDEE
[MarL
et
al.,
2010;
Krinner
et
al.,
2005]
and
SPEEDY/LPJmL
[Strengers
et
al.,
2010;
Bondeau
et
al.,
2007].
All
seven
climate
models
used
the
same
forcing
:
Ø SST/CO2
and
SIC
from
HadISST
(See
Met
Office
Hadley
Center
Sea
Ice
and
SST)
data
set
of
Rayner
et
al.
2003
Ø
Crops/pastures
fracLon
is
prescribed
at
a
resoluLon
of
0.5
from
RamankuXy
and
Foley
(1999)
and
Goldewijk
(2001).
Observa_on-­‐based
data
sets:
To
evaluate
the
performance
of
the
seven
climate
models
used
in
LUCID
simulaLons
to
reproduce
the
interanual
variability
of
the
WAM,
several
climate
observaLon
data
sets
were
examined:
-­‐ PrecipitaLon
and
surface
temperature
simulated
by
climate
models
were
compared
with
observaLonal
data
CRU
(ClimaLc
Research
Unit)
available
at
spaLal
resoluLon
of
0.5°
laLtude-­‐
longitude
and
from
1901
to
2000
(hXp:
//www.cru.uea.ac.uk,
Mitchell
et
al
2003).
-­‐ LAI
satellite
data
set
form
the
Geoland2
project
(hXp:
/
/
www.geoland2.eu,
Verger
et
al
2012)
were
used
to
evaluate
the
bias
(not
shown)
and
the
seasonal
cycle
of
LAI
simulate
by
the
Land
Surface
Model
(LSM).
The
LAI
dataset
is
available
at
spaLal
resoluLon
of
0.05°
laLtude-­‐longitude
and
with
a
temporal
resoluLon
of
10
days
during
the
period
1982
to
2000.
Ensemble simulations (with & without land-use changes).
SST/GHG (ppm)
Exp. design
Land Cover Year
PDPDv1970-1999/375
PIvPI1870-1899/280
19921870
Results
and
Discussion
Summary
and
Conclusion
LUCID Climate/Vegetation models
MeanRainfall(mm/day)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
ARP CCA CCS ECH IPS SPE ECE ENS OBS
a. Precipitation Sahel
Guinea
LUCID Climate/Vegetation models
Meantemperature(°C)
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
b. 2−m Temperature
ARP CCA CCS ECH IPS SPE ECE ENS OBS
Figure
5:
Each
bar
illustrates
the
simulated
seasonal
average
of
rainfall
(a)
and
temperature
(b)
for
each
of
the
seven
GCM/LSMs,
the
Ensemble-­‐Mean
experiment,
and
observaLon
data
set
in
both
the
region
defined
in
the
Sahel
(light
gray)
and
the
Guinea
area
(dark
gray)
in
the
1970
to1999
period.
Errors
bars
(represenLng
the
mean
value
(±1)
the
standard
deviaLon)
illustrate
the
rainfall
and
temperature
variability
simulated
by
GCM/LSMs
compared
with
observaLons.
DoXed
lines
indicate
the
seasonal
average
from
CRU
observaLon
data
set.
Model
acronyms
are
the
same
as
in
Figure
2
0.0
0.5
1.0
1.5
2.0
2.5
FEV
MAY
AUG
NOV
Monthly mean LAI [m2/m2]
ARPEGE−ISBA
FEV
MAY
AUG
NOV
CCAM−CABLE
FEV
MAY
AUG
NOV
CCSM−CLM
FEV
MAY
AUG
NOV
EC−EARTH−TESSEL
FEV
MAY
AUG
NOV
ECHAM5−JSBACH
FEV
MAY
AUG
NOV
IPSL−ORCHIDEE
FEV
MAY
AUG
NOV
SPEEDY−LPJmL
FEV
MAY
AUG
NOV
Model−MEAN
FEV
MAY
AUG
NOV
Sahel
OBSERVATION
0
1
2
3
4
5
FEV
MAY
AUG
NOV
ARPEGE−ISBA
FEV
MAY
AUG
NOV
CCAM−CABLE
FEV
MAY
AUG
NOV
CCSM−CLM
FEV
MAY
AUG
NOV
EC−EARTH−TESSEL
FEV
MAY
AUG
NOV
ECHAM5−JSBACH
FEV
MAY
AUG
NOV
IPSL−ORCHIDEE
FEV
MAY
AUG
NOV
SPEEDY−LPJmL
FEV
MAY
AUG
NOV
Model−MEAN
FEV
MAY
AUG
NOV
Guinea
OBSERVATION
Model
Evalua_ons
*Contact:
Souleymane
SY,
PhD
Student
at
LOCEAN/IPSL
University
Pierre
et
Marie
Curie
and
LPAOSF/ESP/UCAD
Sénégal
Email:
souleymane.sy@locean-­‐ipsl.upmc.fr
,Tel.
+33652190745,
45B,
boulevard
Jourdan
75014
Paris,
France
Figure
6:
Monthly
mean
of
leaf
area
index
(LAI)
simulated
by
the
models
listed
at
the
top
of
each
panel,
by
Ensemble-­‐Mean
Experiment
and
ObservaLon
data
set.
Monthly
mean
LAI
averaged
over
the
Sahel
(top)
and
the
Guinea
area
(boXom).
ObservaLon
data
set
is
illustrated
as
solid
red
line
in
right
panels
Effect
of
changes
in
land
surface
processes
on
WAM
variability
How
the
seasonal
cycle
of
Leaf
Area
Index
is
simulated
by
the
LSMs?
Changes
in
land
cover
frac_on
between
1870
and
1992
Figure
3:
Changes
in
the
extent
of
crops
and
pastures
cover
between
PI
(1870)
and
the
PD
(1992).
(a)
Crops
fracLon
in
1992,
(b)
pastures
fracLons
in
1992
and
the
difference
in
crops
and
pastures
fracLons
between
1992
and
1870
(c,d).
Combined
changes
of
crop
and
pasture
fracLons
between
PI
and
PD
(e).
The
red
and
orange
color
represent
the
extension
of
culLvated
areas
and
the
blue
color
shows
the
abandoned
areas.
The
crops
and
pastures
data
have
been
reconstructed
by
RamankuXy
and
Foley
(1999)
and
combined
with
a
pastures
area
Goldewijk
Klein
(2001)
on
a
0.50°x
0.50°
resoluLon.
DoXed
contours
illustrate
areas
with
changes
larger
than
5%
in
crop
or
pasture
fracLons
confined
to
Sahel
and
the
South
of
Guinea.
Figure
4:
Differences
(in
fracLon
of
total
area)
in
each
of
those
types
of
vegetaLon
between
PD
and
PI
Lmes
in
the
Sahel
and
the
Gulf
of
Guinea.
The
color
bars
show
the
fracLons
of
the
land
area
occupied
by
crops
(light
gray),
pasture
(dark
gray),
the
evergreen
trees
(light
green),
the
deciduous
trees
(dark
green)
and
deserts
(white).
The
doXed
line
shows
the
changes
in
the
extent
covered
of
crops
fracLons
observed
between
1870
and
1992
from
the
SAGE
data-­‐set.
ARP,
CCA,
CCS,
ECE,
ECH,
IPS
and
SPE
are
the
GCM/LSMs
acronyms
for
respecLvely
ARPEGE/ISBA,
CCAM/CABLE,
CCSM/CLM,
ECEARTH/
TESSEL,
ECHAM5/JSBACH,
IPSL/ORCHIDEE
and
SPEEDY/LPJmL
DJF MAM JJA SON
∆α(X100)
−0.4
−0.2
0.0
0.2
0.4
0.6
0.8
Surface Albedo Anomaly
ARPEGE / ISBA
DJF MAM JJA SON
CCAM / CABLE
DJF MAM JJA SON
CCSM / CLM
DJF MAM JJA SON
EC−EARTH / TESSEL
DJF MAM JJA SON
ECHAM5 / JSBACH
DJF MAM JJA SON
IPSL / ORCHIDEE
DJF MAM JJA SON
SPEEDY / LPJmL
DJF MAM JJA SON
Model−MEAN
Sahel
DJF MAM JJA SON
∆α(X100)
0
1
2
3
4
5
ARPEGE / ISBA
DJF MAM JJA SON
CCAM / CABLE
DJF MAM JJA SON
CCSM / CLM
DJF MAM JJA SON
EC−EARTH / TESSEL
DJF MAM JJA SON
ECHAM5 / JSBACH
DJF MAM JJA SON
IPSL / ORCHIDEE
DJF MAM JJA SON
SPEEDY/ LPJmL
DJF MAM JJA SON
Model−MEAN
Guinea
DJF MAM JJA SON
∆LAI(m2
m−2
)
−6
−4
−2
0
2
4
6
Leaf Area Index Anomaly
ARPEGE / ISBA
DJF MAM JJA SON
CCAM / CABLE
DJF MAM JJA SON
CCSM / CLM
DJF MAM JJA SON
EC−EARTH / TESSEL
DJF MAM JJA SON
ECHAM5 / JSBACH
DJF MAM JJA SON
IPSL / ORCHIDEE
DJF MAM JJA SON
SPEEDY / LPJmL
DJF MAM JJA SON
Model−MEAN
Sahel
DJF MAM JJA SON
∆LAI(m2
m−2
)
−20
−10
0
10
20
ARPEGE / ISBA
DJF MAM JJA SON
CCAM / CABLE
DJF MAM JJA SON
CCSM / CLM
DJF MAM JJA SON
EC−EARTH / TESSEL
DJF MAM JJA SON
ECHAM5 / JSBACH
DJF MAM JJA SON
IPSL / ORCHIDEE
DJF MAM JJA SON
SPEEDY/ LPJmL
DJF MAM JJA SON
Model−MEAN
Guinea
−6 −4 −2 0
−3
−2
−1
0
1
∆QA(Wm−2
)
∆QT(Wm−2
)
ARPEGE
CCAM
CCSM
EC−EARTH
ECHAM5
IPSL
SPEEDY
ENS−MEAN
a.
−6 −4 −2 0
−4
−2
0
2
4
∆QA(Wm−2
)
∆QT(Wm−2
)
ARPEGE
CCAM
CCSM
EC−EARTH
ECHAM5
IPSL
SPEEDY
ENS−MEAN
b.
−8 −6 −4 −2 0 2
−3
−2
−1
0
1
2
∆QA − ∆QT(Wm−2
)
∆QLU(Wm−2
)
c.
−10 −8 −6 −4 −2 0 2
−2.0
−1.5
−1.0
−0.5
0.0
0.5
∆QA − ∆QT(Wm−2
)
∆QLU(Wm−2
)
ARPEGE
CCAM
CCSM
EC−EARTH
ECHAM5
IPSL
SPEEDY
ENS−MEAN
d.
−1.0
−0.5
0.0
0.5
1.0
1.5
2.0
−1.0
−0.5
0.0
0.5
1.0
1.5
2.0
∆T(C)
DJF MAM JJA SON
a.Sahel LULCC
CO2SST
−1.0
−0.5
0.0
0.5
1.0
1.5
2.0
−1.0
−0.5
0.0
0.5
1.0
1.5
2.0
∆T(C)
DJF MAM JJA SON
b.Guinea LULCC
CO2SST
−5
0
5
10
15
20
25
−5
0
5
10
15
20
25
∆QA(Wm−2
)
DJF MAM JJA SON
c.Sahel LULCC
CO2SST
−5
0
5
10
15
20
25
−5
0
5
10
15
20
25
∆QA(Wm−2
)
DJF MAM JJA SON
d.Guinea LULCC
CO2SST
F i g u r e 1 0 :
C h a n g e s
i n
2 -­‐ m
temperature
(a,b)
and
in
available
energy
(sum
of
downward
longwave
and
net
shortwave
radiaLon)
induced
by
LULCC
(light
gray)
and
by
changes
in
CO2SST
(dark
gray
boxes)
between
preindustrial
period
and
the
present
day.
The
anomalies
are
calculated
for
each
grid
cell
in
the
two
regions
defined
in
Sahel
(a,
c)
and
Guinea
(b,
d).
Box-­‐whisker
plots
indicate
the
extremes,
the
inter-­‐quarLle
range
and
the
median
of
the
mean
ensemble
values
of
each
individual
model
and
each
set
of
experiment
(PD-­‐PDv
for
the
LULCC
impacts,
PD-­‐PIv
for
CO2SST
impacts).
Figure
9:
Mean
summer
LULCC-­‐
induced
changes
(Wm-­‐2)
in
(a)
and
(b)
QT
ploXed
against
the
mean
summer
changes
in
QA;
(c),
(d)
longwave
radiaLon
emiXed
by
the
surface
(QLU)
ploXed
Against
the
changes
in
the
difference
between
QA
and
QT.
(a),
(c)
The
Sahel
area;
(b),
(d)
Guinea
Area.
Symbols
refer
to
individual
models
and
the
MulL-­‐
Model
Mean.
DoXed
line
in
all
Panels
represents
the
y
=
x
relaLon
(e,
g
the
ΔQT
=ΔQA
or
the
ΔQA-­‐ΔQT=ΔQLU
curve)
Figure
7:
Seasonal
LULCC-­‐induced
changes
in
the
simulated
of
surface
albedo
(%)
between
PD
and
PI
Lmes
by
the
models
listed
at
the
top
of
each
panel.
The
shading
refer
to
the
differences
calculated
between
the
simulaLons
that
are
forced
with
Present
Day
SSTs,
CO2,
and
GHG
(black,
simulaLon
PD
minus
simulaLon
PDv)
and
Pre-­‐Industrial
SSTs,
CO2,
and
GHG
(gray,
simulaLon
PIv
minus
simulaLon
PI).
PresenLng
both
black
and
gray
bars
demonstrates
the
robustness
of
the
impacts
of
LULCC
changes,
largely
independent
of
the
state
of
the
background
GHG
and
surface
ocean’s
temperatures
on
the
Sahel
(top)
and
Guinea
area
(boXom).
All
seasons
are
ploXed
from
(ler)
winter
(DJF)
to
(right)
fall.
Changes
in
surface
proper_es,
energy
fluxes
and
temperature
Discussions
u
The
figure
3
shows
the
change
in
cropland
and
pastures
paXerns
due
to
LULCC.
The
changes
in
the
extent
of
crops
and
pastures
cover
between
PI
(1870)
and
the
PD
(1992)
are
not
homogeneous
on
the
West
Africa
regions.
The
areas
where
land
cover
change
is
above
5%
are
noted
in
Sahel
and
Guinea.
In
the
Sahel,
LULCC
concerns
the
northwest
of
Senegal,
Mauritania,
part
of
Mali,
central
Burkina
Faso,
the
north
of
Nigeria,
southern
Niger,
Chad
and
Sudan.
In
the
Guinea
area,
the
extension
of
cropland
and
pasture
is
located
in
the
South
zone
of
Cote
d'Ivoire,
in
Ghana,
in
Guinea
Conakry,
in
Sierra
Leone
and
in
Liberia.
u
The
figure
4
shows
the
mean
change
of
fracLon
in
each
of
these
types
of
vegetaLon
between
1870
and
1992
in
the
boxes
selected
for
Sahel
and
Gulf
of
Guinea.
The
color
bars
show
the
fracLon
of
land
area
occupied
by
the
different
land
cover
types.
The
different
land
cover
distribuLons
of
LSMs
resulted
from
three
main
reasons:
(1)
the
way
the
LULCC
informaLon
is
represented
in
models;
(2)
the
strategy
used
by
each
modelling
group
to
implement
LULCC
in
their
background
land
cover
;
and
(3)
the
modelling
groups
use
different
sources
of
informaLon
to
describe
present-­‐day
or
potenLal
vegetaLon.
u
The
figure
5
show
the
various
LUCID
GCM/LMSs
and
observed
mean
seasonal
rainfall
(figure
3a)
and
temperature
(figure
3b)
in
both
region
defined
in
Sahel
and
Guinea.
Error
bars
show
the
±1.0
standard
deviaLon
illustraLng
the
rainfall
and
temperature
variability
simulated
by
each
GCM/LSMs.
In
the
Sahel
and
Guinea
region,
the
observaLons-­‐based
seasonal
(June
to
September
(JJAS))
average
rainfall
values
are
2.7
mmday-­‐1
and
about
4.4
mmday-­‐1
respecLvely.
The
seasonal
(June
to
September
(JJAS))
average
temperature
values
are
about
of
30°C
in
the
Sahel
and
about
25°C
in
the
Guinea
area
according
to
the
observaLons.
u
The
climate
models
show,
however,
significant
differences
in
the
magnitude
of
the
rainfall
and
temperature
variability.
The
magnitude
of
that
variability
varies
significantly
from
model
to
model
resulLng
on
how
different
climate
models
responses
to
LULCC.
Two
major
‘features’
varying
from
one
model
to
another
explain
differences:
the
land-­‐cover
distribuLon
and
the
simulated
sensiLvity
to
LULCC.
The
way
to
explain
the
LULCC
vary
greatly
between
models
depending
on
the
magnitude
and
the
sign
of
LULCC
and
how
the
land-­‐surface
funcLoning
is
parameterized
in
the
LSM
model,
in
parLcular
regarding
the
evapotranspiraLon
parLLoning
within
the
different
land-­‐cover
types,
as
well
as
the
role
of
leaf
area
index
in
the
flux
calculaLons
(Boisier
et
al
2012)
and
how
strongly
the
surface
is
coupled
to
the
atmosphere
(Koster
and
al.
2004;
Seneviratne
et
al.
2006).
u
The
figure
6
illustrates
the
monthly
mean
of
LAI
shown
by
the
each
of
seven
LUCID
LSMs,
the
mulL-­‐model
mean
LAI
and
the
observed
in
the
Sahel
and
the
Guinea
zone.
In
the
Sahel,
four
of
the
seven
LSM
(ISBA
(ARPEGE),
CABLE
(CCAM),
CLM
(CCSM)
and
ORCHIDEE
(IPSL))
reproduce
the
observed
growing
season
of
crops
centred
in
the
late
summer,
TESSEL
(SPEEDY)
and
LPJmL
(EC-­‐EARTH)
LSMs
simulate
fixed
values
of
LAI
during
the
year
in
both
areas.
Other
model
(JSBACH
(ECHAM5)
for
example)
simulates
the
growing
season
of
crops
and
pastures
in
the
fall.
The
model
differences
depicted
in
Figure
5
reflects
the
various
characterizaLon
of
LAI
within
the
LUCID
LSMs.
For
instance,
LAI
in
JSBACH,
LPJmL
and
ORCHIDEE
is
explicitly
simulated
based
on
the
seasonal
carbon
allocaLon
and
the
local
climate.
The
other
LSMs
prescribe
a
LAI
cycle
based
on
satellite
observaLons
(ISBA,
CABLE,
CLM),
or
use
a
fixed
value
year-­‐round
(TESSEL).
u
The
seasonal
LULCC
induced
changes
in
the
simulated
of
surface
albedo
between
the
PD
and
PI
in
the
two
regions
defined
in
Sahel
and
Guinea
are
shown
in
the
Figure
7.
In
most
models
and
seasons,
the
mean
land
surface
albedo
is
higher
under
modern
land
cover
than
in
preindustrial
Lmes.
The
decrease
of
land
surface
albedo
in
ARPEGE-­‐ISBA
model
is
due
to
an
extent
of
crops
and
pastures
fracLon
which
are
occurred
to
the
detriment
of
bar
soil
in
the
Sahel.
The
CABLE
(CCAM)
LSM
shows
no
change
in
albedo
despite
the
prescribed
changes
of
LULCC.
In
this
version
of
the
model,
the
parameters
used
in
calculaLng
canopy
albedo
do
not
vary
as
a
funcLon
of
plant
funcLonal
type
(Sellers
et
al.
1992),
making
the
model
albedo
insensiLve
to
changes
in
the
vegetaLon
structure
(this
has
been
revised
in
more
recent
versions
of
the
model).
In
the
Sahel,
a
few
changes
of
surface
albedo
are
simulated
by
LSM
variant
of
around
0
to
0.6
%
due
to
a
very
few
changes
of
LULCC
imposed
in
LSM.
In
Guinea
area,
except
CABLE
(CCAM)
model,
the
albedo
changes
are
roughly
proporLonal
to
the
deforestaLon
scale
with
a
parLcular
average
albedo
increase
of
4%
for
the
JSBACH
(ECHAM5)
model
and
about
0
to
1%
for
six
other
models.
The
amplitude
of
changes
in
surface
albedo
varies
in
magnitude
from
model
to
model
following
at
first
order
the
intensity
of
forest
fracLon
changes
prescribed.
u The
figure
8
illustrate
the
seasonal
mean
LULCC-­‐induced
changes
in
leaf
area
index
(LAI)
over
Sahel
and
Guinea.
In
both
regions,
the
seasonal
paXerns
of
LAI
changes
in
the
Sahel
and
the
Guinea
area
are
not
homogeneous
among
the
models.
The
most
part
of
LSMs
show
LAI
decreases
during
the
most
of
the
year
(figure
8).
In
Guinea,
all
LSMs
except
CABLE
and
JSBACH
show
decreased
foliage
development
during
all
year
because
forests
fracLon
have
commonly
been
replaced
by
crops
and
grasslands,
which
have
negligible
foliage
development.
Others
LSMs
show
an
increased
LAI
during
all
year
(ISBA,
CABLE)
and
others
during
the
season
(JSBACH,
ORCHIDEE)
in
the
Sahel
region.
Discussion
1
Discussion
2
Discussion
3
Discussion
4
Figure
8:
As
in
Fig.
7,
but
for
changes
in
the
simulated
Leaf
Area
Index
(LAI)
(%)
u The
mean
summer
LULCC-­‐induced
changes
in
QT
ploXed
against
the
mean
summer
changes
in
QA
in
both
region
are
shown
in
the
figure
9a
and
b,
the
longwave
radiaLon
emiXed
by
the
surface
(QLU)
ploXed
against
the
changes
in
the
difference
between
QA
and
QT
are
also
illustrates
in
the
figure
9c,d.
In
the
Sahel,
the
simulated
decrease
of
QA
in
ARPEGE,
CCAM,
ECHAM5
and
SPEEDY
(as
well
as
CCSM,
IPSL,
SPEEDY
in
Guinea
area)
is
accompanied
in
summer
by
a
decrease
in
turbulent
fluxes
(QT)
(figs
9a,b).
In
contrast,
in
the
case
of
CCSM
in
Sahel
(as
well
as
ARPEGE
and
ECEARTH
in
Guinea),
the
slight
simulated
increase
of
available
energy
is
accompanied
by
a
slight
increase
in
turbulent
fluxes.
In
Sahel,
in
others
models
the
simulated
increase
of
QA
in
the
summer
suggests
that
deforestaLon
leads
to
an
increased
porLon
of
QA
that
is
used
to
warm
up
the
land
surface
(reduced
longwave
cooling),
while
the
turbulent
fluxes
decrease.
u For
most
models
in
the
Sahel
(CCAM
parLcularly)
the
summerLme
change
in
turbulent
fluxes
is
smaller
than
the
change
in
QA.
This
suggests
that
the
remaining
energy
decrease
has
been
used
to
cool
down
the
land
surface,
resulLng
in
reduced
emiXed
thermal
radiaLon
as
illustrated
by
Figs.
9c,d.
In
Guinea
(ECHAM5-­‐JSBACH
parLcularly),
the
simulated
parLcular
decrease
of
available
energy
is
compensated
by
increased
turbulent
fluxes
and
a
slight
increase
in
emiXed
thermal
radiaLon
that
is
used
in
surface
warming.
Four
of
the
seven
models
in
Sahel
(and
all
in
Guinea)
the
amplitude
of
QT
change
is
not
similar
than
QA,
therefore
the
relaLve
change
(ΔQT/ΔQA)
varies
from
one
model
to
the
other,
depending
on
how
land
cover
perturbaLon
and
associated
characterisLcs
that
have
led
to
a
change
in
the
funcLoning
of
the
Soil-­‐VegetaLon-­‐Atmosphere.
u The
comparison
between
the
simulated
regional
climate
changes
induced
by
LULCC
and
the
ones
induced
by
CO2SST
is
illustrated
in
Fig
10.
This
figure
shows
the
seasonal
changes
in
available
energy
QA
and
T2m
averaged
over
two
regions
defined
in
the
Sahel
and
the
Guinea
region
resulLng
from
both
driver.
The
signal
of
LULCC
induced
QA
and
T2m
changes
in
both
regions
shown
by
the
ensemble
mean
of
LUCID
simulaLons,
are
very
small
and
opposite
in
sign
to
the
esLmated
responses
of
increasing
GHG
concentraLon
over
the
regions.
The
changes
of
CO2SST
lead
to
an
increase
in
QA
at
the
surface
[2
-­‐
5Wm-­‐2]
in
the
Sahel
[5
-­‐
10Wm-­‐2
in
Guinea]
with
larger
values
during
summerLme
when
incoming
radiaLon
is
the
highest
(Figs
10c,d).
This
increase
is
caused
mainly
by
increased
incoming
infrared
radiaLon
(QLD)
associated
with
the
higher
atmospheric
CO2.
This
increased
QA
is
associated
with
a
surface
warming
[0.2°C
-­‐
0.6°C]
in
Sahel
and
[0.6°C
-­‐
0.7°C
in
Guinea]
(Figs.
10c,d)
over
all
seasons
with
slightly
larger
values
during
summerLme
parLcularly
in
the
Sahel.
In
Guinea
region
there
is
a
no
seasonal
cycle
in
the
increased
in
QA
and
the
surface
warming
and
the
spread
among
the
models
is
larger
in
this
region
compared
to
Sahel
region.
This
posters
extends
the
studies
of
Pitman
et
al.
(2009),
de
Noblet-­‐Ducoudré
et
al.
(2012)
and
Boisier
et
al.
(2012),
that
invesLgated
the
robust
responses
of
the
surface
climate
to
the
land-­‐use
induced
land-­‐cover
change
(LULCC)
since
pre-­‐
industrial
Lmes
in
the
temperate
regions.
This
study
describes
the
biogeophysical
effect
on
the
surface
climate
of
land-­‐use
changes
over
two
areas
defined
in
West
African
Monsoon
regions
(fig3).
One
of
the
important
conclusion
of
this
study
lies
to
the
low
impact
simulated
by
seven
climate
models
due
to
a
low
LULCC
forcing
imposed
in
the
West
African
region.
Therefore,
the
quesLons
being
asked
in
this
study
is
that
the
historical
LULCC
forcing
imposed
in
these
regions
are
they
realisLc?
Within
the
LUCID
models,
most
of
the
seven
models
simulate
a
small
change
during
a
most
part
of
the
year
in
both
regions
due
to
small
land
cover
change
imposed.
The
amplitude
and
the
sign
of
the
responses
of
land
use
change
vary
among
the
models
depending
on
different
land
surface
perturbaLon.
The
small
changes
of
temperature
and
Net
shortwave
radiaLon
simulated
by
the
seven
climate
models
are
due
to
the
insignificant
past
land
use
change
imposed
in
Sahel
and
Guinea
region,
except
CCAM
in
the
Sahel
and
ECAHM5
Guinea
area
that
simulates
parLcularly
changes.
The
cooling
simulated
by
the
majority
of
seven
climate
models
throughout
the
year
is
dominated
by
a
consistent
decrease
in
available
energy
at
the
surface.
The
simulated
decrease
of
available
energy
in
the
major
part
of
seven
climate
models
is
accompanied
during
the
summer
by
a
decrease
in
turbulent
fluxes
(Figs
9a,b),
but
a
different
amplitude.
Another
important
conclusion
of
this
study
results
from
the
spread
among
the
models
that
is
larger
in
Guinea
region.
In
this
region,
the
spread
result
of
the
absence
of
consistent
change
among
the
various
models
regarding
the
impact
of
land
cover
type
on
the
parLLoning
of
QA
between
QT
and
QLU
on
the
surface
energy
balance
(Fig
10).
Nevertheless,
our
results
suggest
that
the
cooling
surface
(and
decrease
in
QA)
induced
by
the
biogeophysical
effects
of
LULCC
are
insignificant
compared
to
surface
warming
(and
increase
in
QA)
induced
by
the
regional
significance
effect
of
CO2SST
due
to
a
small
LULCC
imposed.
In
contrast,
our
results
suggest
that
the
decrease
of
surface
water
balance
resulLng
from
LULCC
effect
are
a
similar
sign
to
those
resulLng
from
CO2SST
during
the
most
part
of
the
year,
but
the
signal
resulLng
from
the
biogeophysical
effects
of
LULCC
is
stronger
than
the
regional
CO2SST
effect
(Not
shown).
In
addiLon
to
several
limitaLons
listed
in
Pitman
et
al
2009,
the
small
LULCC
imposed
in
tropical
region
give
the
necessity
to
revalue
the
LULCC
forcing
used
in
LUCID
projet
for
more
confidence
on
the
land
use
forcings
use
was
good
in
the
this
region.