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Regional Climate Mod
eling
Fed
or M
esinger
NCEP Environmental M
odeling Center, Cam
p Springs, MD
Earth System Science Interdisciplinary Center, Univ. M
aryland, College
Park, M
D
fedor.mesinge
fedor.mesinge
Entrenamiento en M
odelad
o Num
érico de
Escenarios de Cam
bios Climáticos
CPT
EC, 13-18 July 2008
Contents
• W
hat is it; some history
• Issues
Added
value?
Lateral bound
ary cond
itions: the prob
lem, schem
es, “spe
ctral nudging”
More on added
value: can one (should one try to) add value at large
scales?
Resolution vs dom
ain size
Resolution vs dom
ain size
. . . . .
• Applications
. . . . .
Exam
ple: Climate ch
ange
in Med
iterranean (A1B
Scenario)
What is it ?
Effortto ach
ieve “ad
ded
value” by running a regional
mod
el (“RCM”) driven by lateral bound
ary cond
itions
(LBCs) of a glob
al mod
el
(A variation: in ad
dition, nud
ge large
scales inside the
RCM)
Also: ach
ieve added
value for a reg
ion of interest by
Also: ach
ieve added
value for a reg
ion of interest by
statistical means
Som
e history
• Dickinson et al. (1989): Enh
anced M
M4 (radiation, land
surface),
but ran a sequence of short (few
days) integrations;
• Giorgi and Bates (1989): “perfect” bound
ary cond
itions, month long
integrations;
• Giorgi (1990): GCM-driven ex
periments
. . . multi-year, decad
al, . . .
Resolution: 50-125 km initially, nowad
ays ~50 and
~25 km, and less . .
Coupled
RCMs (regional ocean, ocean/ice, chem
istry, . . .)
More detail and m
ore references: review
paper by Giorgi (2006)
Issues: ad
ded
value
How
/ for what features can one “add value”?
(Higher resolution ! Additional processes also…)
• Fine scale topographic features
(e.g., precipitation over Great Britain, GCM vs RCM)
• Extrem
e events;
• Com
plex
coastlines;
• M
esoscale circulations driven by surface heterogeneity
• M
esoscale circulations driven by surface heterogeneity
• . . . . .
Exam
ple:
Fro
m:
Gio
rgi (2
00
6)
Issues: ad
ded
value
How
/ for what features can one “add value”?
(Higher resolution ! Additional processes also…)
• Fine scale topographic features
(e.g., precipitation over Great Britain, GCM vs RCM)
• Extrem
e events;
• Com
plex
coastlines;
• M
esoscale circulations driven by surface heterogeneity
• M
esoscale circulations driven by surface heterogeneity
• . . . . .
The prob
lem:
Considered
already in
Charney (1962):
Linearize
d shallow-w
ater
eqs., one space dimension,
characteristics;
“at least tw
o cond
itions have
•Lateral bound
ary
cond
ition schem
e(s)
“at least tw
o cond
itions have
to be specified at inflow
points and
one
cond
ition at
outflow”.
Sub
sequently:
Sundström (1973)
How
ever:
Davies (1976): “bound
ary
relaxation schem
e”
Almost all LA
mod
els:
Davies (“relaxation LB
Cs”):
Outside row: specify allvariab
les
Row
1 grid line inside: specify, e.g.,
0.875
*YDM+ 0.125
*YLA
M
Row
2 grid lines inside:
0.750
*YDM+ 0.250
*YLA
M
. . .
(as required
by the mathem
atical nature of the
initial-bound
ary value prob
lem we are solving)
The schem
e (M
esinge
r 1977)
• At the inflow
bound
ary points, all variab
les prescribed
;
• At the outflowbound
ary points, tangential velocity
extrapolated
from the inside(characteristics!);
• The row of grid points next to the bound
ary row,
“buffer row”; variables four-point average
d(this couples
the gravity waves on tw
o C-sub
grids of the E-grid, will be
explained in the Eta presentation)
the gravity waves on tw
o C-sub
grids of the E-grid, will be
explained in the Eta presentation)
Thus: N
o “bound
ary relaxation” !
“limitation”:
Near inflow
bound
aries, LA mod
el cannot do better -
it can only do worse -that its driver mod
el
Thus: have bound
aries as far as affordab
le !
The
Eta sch
.one
Work in progress:
Com
pare the Eta LBC sch
eme, against Davies’:
Use GCM (ECMWF) LB
Cs and drive the Eta using one
and the other, look at the difference
(Katarina Veljovi
ć, Un. Belgrad
e)
Verification effort in progress in the style of the precip verification:
area of wind speed
s > so and
so, observed
, forecast, hits (O
,F,H)
Another BC (BC related
?) issue: “spe
ctral nudging”inside the dom
ain !
Motivation:
“An
fu
nd
am
en
tal a
ssu
mp
tio
n in
usin
g R
CM
sta
tes th
at th
e la
rge
-sca
le
atm
osp
he
ric c
ircu
latio
n in
th
e d
rivin
g d
ata
an
d in
th
e R
CM
sh
ou
ld r
em
ain
the
sa
me
at a
ll tim
e”
(Lu
ca
s-P
ich
er
et a
l., 2
00
4)
De
nis
et a
l. (
20
02
): “t
he
in
effe
ctive
ne
ss o
f th
e n
estin
g fo
r co
ntr
olli
ng
th
e
larg
e s
ca
les o
ve
r th
e w
ho
le d
om
ain
”.
Thus,“s
pectr
al nudgin
g”
(Kid
a e
t a
l., 1
99
1, W
ald
ron
et a
l. 1
99
6; vo
n
Sto
rch
et a
l. 2
00
0):
pro
vid
e larg
e s
cale
forc
ing t
o the m
odel
field
s t
hro
ughout
the e
ntire
model dom
ain
A lot
of dis
cussio
n a
t:http://cires.colorado.edu/science/groups/pielke/links/Downscale/
Ca
str
o, C
. L
., R
. A
. P
ielk
e, S
r., a
nd
G. L
eo
ncin
i: 2
00
5: D
yn
am
ica
l
do
wn
sca
ling
: A
sse
ssm
en
t o
f va
lue
re
tain
ed
an
d a
dd
ed
usin
g th
e R
eg
ion
al
Atm
osp
he
ric M
od
elin
g S
yste
m (
RA
MS
). J
. Ge
op
hys
. Re
s., 11
0, D
05
10
8,
do
i:1
0.1
02
9/2
00
4JD
00
47
21
Castro et al., 4 types of dow
nscaling:
Type 1: NWP (results dep
ends on initial condition);
Type 2: “Perfect” LBCs (=reanalysis)
Type 3: GCM (=predicted) LBCs, but still specified SSTs inside
Type 4: Fully predicted, both LBCs and inside the RCM dom
ain
*
Type 4: Fully predicted, both LBCs and inside the RCM dom
ain
* In the pape
r as pub
lished
, GCM also
includ
ed within Type 2
Ca
str
o, C
. L
., R
. A
. P
ielk
e, S
r., a
nd
G. L
eo
ncin
i: 2
00
5: D
yn
am
ica
l
do
wn
sca
ling
: A
sse
ssm
en
t o
f va
lue
re
tain
ed
an
d a
dd
ed
usin
g th
e R
eg
ion
al
Atm
osp
he
ric M
od
elin
g S
yste
m (
RA
MS
). J
. Ge
op
hys
. Re
s., 11
0, D
05
10
8,
do
i:1
0.1
02
9/2
00
4JD
00
47
21
Castro et al., 4 types of dow
nscaling:
Type 1: NWP (results dep
ends on initial condition);
Type 2: “Perfect” LBCs (=reanalysis)
Type 3: GCM (=predicted) LBCs, but still specified SSTs inside
Type 4: Fully predicted, both LBCs and inside the RCM dom
ain
Type 4: Fully predicted, both LBCs and inside the RCM dom
ain
Castro et al.: Type 2, conclusions:
“Absent interior nudging . . . . failure of the RCM to correctly retain value of
the large scale . . .”
“. . . underestimation of kinetic energy …” “The results here and past stud
ies
suggest the only solution to alle
viate this problem is to constrain the RCM with
the large-scale mod
el (or reanalysis) values.”
The discussion: 35 ve
ry small fontpages of e-m
ails …
One e-m
ail:
Hi Barry
I do not see how
a regional mod
el can reproduce realistic
long wave patterns, as these are hem
isph
eric features.Roger
fm:
• W
e are solving our RCM mod
el equations as an initial-bound
ary value
prob
lem. Doing things inside the dom
ain beyond what RCM equations tell
us is in conflict with our science.
Alternative formulation of the same idea: an air parcel inside the RCM
know
s ab
out forces acting on it, heating it undergoes, etc. It has no
allegiance to a given scale !! (It has no idea what goes on on the opposite
side of the glob
e!)
• If the RCM is not doing well the large scales inside the dom
ain, there
• If the RCM is not doing well the large scales inside the dom
ain, there
must be a reason for it;
fm:
• W
e are solving our RCM mod
el equations as an initial-bound
ary value
prob
lem. Doing things inside the dom
ain beyond what RCM equations tell
us is in conflict with our science.
Alternative formulation of the same idea: an air parcel inside the RCM
know
s ab
out forces acting on it, heating it undergoes, etc. It has no
allegiance to a given scale !! (It has no idea what goes on on the opposite
side of the glob
e!)
• If the RCM is not doing well the large scales inside the dom
ain, there
• If the RCM is not doing well the large scales inside the dom
ain, there
must be a reason for it;
fm, cont’d:
• Type 2 experiments in which reanalysis is declared truth and
an RCM’s
performance is assessed
according to how
close to the reanalysis it gets
are not appropriate to answer this question. The purpose of an RCM is
to im
proveupon what we have !
Note that in a “though
t ex
periment” a perfect RCM, one that by
definition would beh
ave ex
actly as the real atm
osph
ere, in a Type 2
experiment would depart from
reanalysis more and m
ore as the dom
ain
gets bigger! (LBCs are not perfect !!)
• There areresults, Type 3, in which improvem
ent in large scales can
hardly be questioned
!
fm, cont’d:
• Type 2 experiments in which reanalysis is declared truth and
an RCM’s
performance is assessed
according to how
close to the reanalysis it gets
are not appropriate to answer this question. The purpose of an RCM is
to im
proveupon what we have !
Note that in a “though
t ex
periment” a perfect RCM, one that by
definition would beh
ave ex
actly as the real atm
osph
ere, in a Type 2
experiment would depart from
reanalysis more and m
ore as the dom
ain
gets bigger! (LBCs are not perfect !!)
• There areresults, Type 3, in which improvem
ent in large scales can
hardly be questioned
!
Fennessy and
Altshuler,
2002:
9 ensem
ble
mem
bers
precip
difference
1993-1988
Obs.:
COLA
GCM:
difference
1993-1988
Obs.:
GCM+
Eta:
Dom
ain size
?
Many pe
ople: th
ings get worse as the dom
ain size
gets bigge
r
One cannot improve on large
scales if the dom
ain size
is
small !
small !
ME
ĐU
NA
RO
DN
I S
IMP
OZ
IJU
M
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A
EX
AM
PL
E F
RO
M T
HE
“S
INTA
” P
RO
JE
CT: IP
CC
A1
B
CH
AN
GE
SC
EN
AR
IO D
YN
AM
ICA
L D
OW
NS
CA
LIN
G
FO
R T
HE
ME
DIT
ER
RA
NE
AN
RE
GIO
N
Exam
ple:
Borivo
j Rajk
ovi
ćV
ladim
ir Đ
urđ
evi
ćM
inor
changes:
fm
Inst
itute
of M
ete
oro
logy,
Facu
lty o
f P
hys
ics,
Belg
rade U
niv
ers
ity
Dalj,
maj 2
008.
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
••The first com
ple
teT
he first com
ple
te
math
em
atical th
eory
of ic
e a
ges
math
em
atical th
eory
of ic
e a
ges
* C
limate
variabili
ty a
s c
onsequence o
f
* C
limate
variabili
ty a
s c
onsequence o
f
astr
onom
ical para
mete
rsastr
onom
ical para
mete
rs
* * T
1 1
9,2
2,2
4 k
yr
T1 1
9,2
2,2
4 k
yr
* * T
2 4
1 k
yr
T2 4
1 k
yr
* * T
3 9
5,1
25,4
00 k
yr
T3 9
5,1
25,4
00 k
yr
++R
esults fro
m S
INTA
pro
ject
Results fro
m S
INTA
pro
ject
((SI
SIm
ula
tions o
f clim
ate
cha
mula
tions o
f clim
ate
chaNN
ge in t
he m
edi
ge in t
he m
ediTT
err
anean
err
anean
AAre
a)
rea)
Pro
ject part
ners
:P
roje
ct part
ners
:
+N
atio
na
l In
stitu
te o
f G
eo
ph
ysic
s a
nd
Vo
lca
no
log
y
(IN
GV
), B
olo
gn
a,
Ita
ly.
pro
vid
e g
lob
al c
lima
te c
ha
ng
e e
xpe
rim
en
ts in
teg
ratio
ns.
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
+In
stitu
te o
f M
ete
oro
logy,
Belg
rade U
niv
ers
ity,
Belg
rade, S
erb
ia.
+R
epublic
Hid
rom
ete
oro
logic
al S
erv
ice o
f S
erb
ia, B
elg
rade, S
erb
ia.
dyn
am
ica
l do
wn
sca
ling
with
a r
eg
ion
al m
od
el,
usi
ng
initi
al a
nd
bo
un
da
ry
con
diti
on
fro
m a
glo
ba
l mo
de
l.
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
The m
ost probable estimates and their ranges of global surface
The m
ost probable estimates and their ranges of global surface
worm
ing for the period 2080
worm
ing for the period 2080--2099 relative to 1980
2099 relative to 1980--1999
1999
SC
EN
AR
IO M
OS
T P
RO
B.
R
AN
GE
SC
EN
AR
IO M
OS
T P
RO
B.
R
AN
GE
B1
1
.8
1
.1
B1
1
.8
1
.1 --
2.9
2.9
A1
T
2
.4
1
.4
A1
T
2
.4
1
.4 --
3.8
3.8
B2
2
.4
1
.4
B2
2
.4
1
.4 --
3.8
3.8
A1
B
A1
B
2.8
1.7
2
.8
1
.7 --
4.4
4.4
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
A1
B
A1
B
2.8
1.7
2
.8
1
.7 --
4.4
4.4
A2
3
.4
2
.0
A2
3
.4
2
.0 --
5.4
5.4
A1
FI
4
.0
2
.4
A1
FI
4
.0
2
.4 --
6.4
6.4
#
0
.6
0
.3#
0.6
0.3
--0.9
0
.9
Ide
a o
f d
yn
am
ica
l Id
ea
of
dyn
am
ica
l downscaling
downscaling
: b
ett
er
rep
rese
nta
tio
n o
f lo
ca
l :
be
tte
r re
pre
se
nta
tio
n o
f lo
ca
l
fea
ture
s:
oro
gra
ph
y, s
oil,
ve
ge
tatio
n .
..fe
atu
res:
oro
gra
ph
y, s
oil,
ve
ge
tatio
n .
..
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
‣CRCM: ClimEta/E
BU-POM
‣T
wo
-wa
y r
eg
ion
al co
up
led
mo
de
l, g
rid
po
int,
pri
mitiv
e e
qu
atio
n,
an
d h
yd
rosta
tic.
‣A
tmo
sp
he
ric c
om
po
ne
nt
is E
ta m
od
el (EBU
=E
ta
Be
lgra
de
Un
ive
rsity)
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
Be
lgra
de
Un
ive
rsity)
‣O
ce
an
co
mp
on
en
t is
PO
M
‣M
od
els
exch
an
ge
atm
osp
he
ric s
urf
ace
flu
xe
s
an
d S
ST
eve
ry p
hysic
al tim
e s
tep
of
atm
osp
he
ric m
od
el (~
18
0 s
)
‣C
limate
change
experim
entsetup
‣P
rese
nt clim
ate
in
teg
ratio
n: 1961-1990
,
‣
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08 ‣
Pre
se
nt clim
ate
in
teg
ratio
n: 1961-1990
,
‣F
utu
re c
lima
te in
teg
ratio
n: 2071-2100
(A1
B
Sce
na
rio
)
‣Atm
ospheric m
odel:
‣0
.25
ho
rizo
nta
l re
so
lutio
n (
25
-30
km
) / 3
2 la
ye
rs;
‣6
h la
tera
l b
ou
nd
ary
co
nd
itio
n fro
m S
INT
EX
in
teg
ratio
ns;
‣A
nn
ua
l cycle
of ve
ge
tatio
n fra
ctio
n;
‣U
pg
rad
ed
ra
dia
tio
n (
va
ria
ble
GH
Gs)
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
‣U
pg
rad
ed
ra
dia
tio
n (
va
ria
ble
GH
Gs)
‣S
ST
bo
tto
m b
ou
nd
ary
co
nd
itio
n fro
m S
INT
EX
ove
r u
nco
up
led
se
as.
‣Ocean m
odel:
‣0
.2 h
ori
zo
nta
l re
so
lutio
n / 2
1 v
ert
ica
l le
ve
ls (
Me
dite
rra
ne
an
Se
a),
‣In
itia
l co
nd
itio
n: M
OD
B fo
r 1
96
1 / S
INT
EX
fo
r 2
07
1.
Acro
nym
s:
POM:
Princeton Ocean M
odel
SIN
TEX:
Istituto Nazionale di Geofisica e Vulcanologia (INGV, Bologna) Global
GCM: http://www.cmcc.it/w
eb/pub
lic/ANS/m
odels/ingv-sxg
MODB:
Med
iterranean O
ceanic Data Base
•M
odel dom
ain
s
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
‣Verification of present climate (1961-1990)
integration
‣T
his
verificatio
n s
how
s c
apabili
ty o
f m
odel to
repro
duce p
resent
clim
ate
(Manuscript in press in 'A
nnales Geoph
ysicae’)
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
(Manuscript in press in 'A
nnales Geoph
ysicae’)
‣S
easonal m
eans o
f 2 m
tem
pera
ture
‣W
inte
r se
aso
n: D
ec/J
an
/Fe
b
‣S
um
me
r se
aso
n: J
un
/Ju
l/A
ug
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
bia
sm
ae
rmse
EP
/CR
U-0
.21
1.8
82
.15
CRU: Climate Research Unit, Un. East Anglia
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
bia
sm
ae
rmse
EP
/CR
U2
.63
2.9
63
.42
Diffe
rences b
etw
een
Diffe
rences b
etw
een 2071
2071--2100
2100
and
and 1961
1961--1990
1990
::
‣‣S
easonal m
ean tem
pera
ture
, S
easonal m
ean tem
pera
ture
,
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
‣‣pre
cip
itation a
nd 1
0 m
win
d s
peed
pre
cip
itation a
nd 1
0 m
win
d s
peed
diffe
rences.
diffe
rences.
SE
AS
ON
: D
ecem
ber
January
Febru
ary
SE
AS
ON
: D
ecem
ber
January
Febru
ary
SE
AS
ON
: Jun J
uly
August
SE
AS
ON
: Jun J
uly
August
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
Se
rbia
: te
mp
. in
cre
ase
2.0
Se
rbia
: te
mp
. in
cre
ase
2.0
--2.4
2
.4
de
gd
eg
Ita
ly:
tem
p.
incre
ase
1.8
Ita
ly:
tem
p.
incre
ase
1.8
--2.4
2
.4
de
gd
eg
Alp
s: 2.6
Alp
s: 2.6
--3.2
3.2
deg.
deg.
Se
rbia
: te
mp
. in
cre
ase
3.4
Se
rbia
: te
mp
. in
cre
ase
3.4
--3.8
3
.8
de
gd
eg
Ita
ly:
tem
p.
incre
ase
3.2
Ita
ly:
tem
p.
incre
ase
3.2
--4.0
4
.0
de
gd
eg
SE
AS
ON
: D
ecem
ber
January
Febru
ary
SE
AS
ON
: D
ecem
ber
January
Febru
ary
SE
AS
ON
: Jun J
uly
August
SE
AS
ON
: Jun J
uly
August
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
Italy
costa
l are
a: over
70%
It
aly
costa
l are
a: over
70%
less
less
Se
rbia
: p
rec.
de
cre
ase
10
Se
rbia
: p
rec.
de
cre
ase
10
--
30
%3
0%
Ita
ly:
pre
c.
de
cre
ase
10
Ita
ly:
pre
c.
de
cre
ase
10
--40
%4
0%
Se
rbia
: p
rec.
de
cre
ase
10
Se
rbia
: p
rec.
de
cre
ase
10
--
30
%3
0%
Ita
ly:
pre
c.
de
cre
ase
10
Ita
ly:
pre
c.
de
cre
ase
10
--50
%5
0%
SE
AS
ON
: D
ecem
ber
January
Febru
ary
SE
AS
ON
: D
ecem
ber
January
Febru
ary
SE
AS
ON
: Jun J
uly
S
EA
SO
N: Jun J
uly
August
August
ST
VA
RA
LA
ŠT
VO
MIL
UT
INA
MIL
AN
KO
VIĆ
A _
__
__
____
____
____
____
Da
lj/m
aj,
20
08
Bipolar changes
Serbia:
Serbia:
--No
rth
No
rth
--we
st
pa
rt d
ecre
ase
we
st
pa
rt d
ecre
ase
+S
ou
th+
So
uth
--ea
st
pa
rt in
cre
ase
ea
st
pa
rt in
cre
ase
Italy:
Italy:
Ma
x in
cre
ase
ove
r A
dri
atic
Ma
x in
cre
ase
ove
r A
dri
atic
Serbia:
Serbia:
--South
South
--west part
w
est part
decre
ase
decre
ase
+N
ort
h+
Nort
h--e
ast part
east part
incre
ase
incre
ase
Italy:
Italy:
This was done using:
PC
pro
cessor
: In
tel D
ual-C
ore
Xeon
5160 @
3.0
0G
Hz
4 G
B r
am
Inte
l F
ort
ran C
om
pile
r V
ers
ion 9
.1
Model, a
tmosphere
: 7
9 x
105 p
oin
ts, 3
2 layers
dlm
d =
dphd =
0.2
5, d
t b=
90 s
Model, M
editerr
anean:
193 x
153 p
oin
ts,
21 levels
dx =
dy =
~20 k
m
exchange o
f fluxes a
nd S
ST
every
180 s
(every
atm
ospheric p
hysic
s t
ime s
tep)
30 y
ears
done in ≤
2 m
onth
s
References (more in Giorgi 2006)
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ca
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ich
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P.,
D.
Ca
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04
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CM
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ain
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atic
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an
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, 15
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so
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